diff --git a/.gitignore b/.gitignore index d383f3ec8..9dcb6623d 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,12 @@ build *.sln *.ncb *.vcproj +*.vcxproj.user +*.VC.db +*.VC.db-shm +*.VC.db-wal +*.VC.opendb +*.ipch # Output bin/ @@ -17,6 +23,7 @@ CMakeLists.txt.user # Generated assimp.pc +assimp.aps revision.h contrib/zlib/zconf.h contrib/zlib/zlib.pc @@ -31,6 +38,7 @@ cmake_uninstall.cmake *.dir/ assimp-config.cmake assimp-config-version.cmake +assimpTargets*.cmake # MakeFile Makefile diff --git a/.travis.yml b/.travis.yml index 8b00b4cc7..8fe39d59c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -70,6 +70,6 @@ addons: project: name: "assimp/assimp" notification_email: kim.kulling@googlemail.com - build_command_prepend: "cmake . -DASSIMP_ENABLE_BOOST_WORKAROUND=YES" + build_command_prepend: "cmake ./" build_command: "make -j4" branch_pattern: coverity_scan diff --git a/Build.md b/Build.md index d5b443a79..7c908606d 100644 --- a/Build.md +++ b/Build.md @@ -1,32 +1,32 @@ -# Install CMake +# Build Instructions +## Install CMake Asset-Importer-Lib can be build for a lot of different platforms. We are using cmake to generate the build environment for these via cmake. So you have to make sure that you have a working cmake-installation on your system. You can download it at https://cmake.org/ or for linux install it via -``` +```bash sudo apt-get install cmake ``` -# Get the source +## Get the source Make sure you have a working git-installation. Open a command prompt and clone the Asset-Importer-Lib via: -``` +```bash git clone https://github.com/assimp/assimp.git ``` -# Build instructions for Windows with Visual-Studio +## Build instructions for Windows with Visual-Studio First you have to install Visual-Studio on your windows-system. You can get the Community-Version for free here: https://visualstudio.microsoft.com/de/downloads/ To generate the build environment for your IDE open a command prompt, navigate to your repo and type: -``` -> cmake CMakeLists.txt +```bash +cmake CMakeLists.txt ``` This will generate the project files for the visual studio. All dependencies used to build Asset-IMporter-Lib shall be part of the repo. If you want to use you own zlib.installation this is possible as well. Check the options for it. -# Build instructions for Windows with UWP -See https://stackoverflow.com/questions/40803170/cmake-uwp-using-cmake-to-build-universal-windows-app +## Build instructions for Windows with UWP +See - -# Build instrcutions for Linux / Unix +## Build instructions for Linux / Unix Open a terminal and got to your repository. You can generate the makefiles and build the library via: -``` +```bash cmake CMakeLists.txt make -j4 ``` @@ -34,7 +34,23 @@ The option -j descripes the number of parallel processes for the build. In this If you want to use a IDE for linux you can try QTCreator for instance. -# CMake build options +## Build instructions for MinGW + Older versions of MinGW's compiler (e.g. 5.1.0) do not support the -mbig_obj flag +required to compile some of assimp's files, especially for debug builds. +Version 7.3.0 of g++-mingw-w64 & gcc-mingw-w64 appears to work. + +Please see [CMake Cross Compiling](https://cmake.org/cmake/help/latest/manual/cmake-toolchains.7.html#cross-compiling) for general information on CMake Toolchains. + +Some users have had success building assimp using MinGW on Linux using [polly](https://github.com/ruslo/polly/). + +The following toolchain, which is not maintained by assimp, seems to work on Linux: [linux-mingw-w64-gnuxx11.cmake](https://github.com/ruslo/polly/blob/master/linux-mingw-w64-gnuxx11.cmake) + +The following toolchain may or may not be helpful for building assimp using MinGW on Windows (untested): + [mingw-cxx17.cmake](https://github.com/ruslo/polly/blob/master/mingw-cxx17.cmake) + +Besides the toolchain, compilation should be the same as for Linux / Unix. + +## CMake build options The cmake-build-environment provides options to configure the build. The following options can be used: - **BUILD_SHARED_LIBS ( default ON )**: Generation of shared libs ( dll for windows, so for Linux ). Set this to OFF to get a static lib. - **BUILD_FRAMEWORK ( default OFF, MacOnly)**: Build package as Mac OS X Framework bundle @@ -55,4 +71,3 @@ The cmake-build-environment provides options to configure the build. The followi - **INJECT_DEBUG_POSTFIX( default ON )**: Inject debug postfix in .a/.so lib names - **IGNORE_GIT_HASH ( default OFF )**: Don't call git to get the hash. - **ASSIMP_INSTALL_PDB ( default ON )**: Install MSVC debug files. - diff --git a/CMakeLists.txt b/CMakeLists.txt index 14c65c188..520dd68d7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -67,7 +67,7 @@ OPTION( ASSIMP_NO_EXPORT ) OPTION( ASSIMP_BUILD_ZLIB "Build your own zlib" - OFF + OFF ) OPTION( ASSIMP_BUILD_ASSIMP_TOOLS "If the supplementary tools for Assimp are built in addition to the library." @@ -106,7 +106,7 @@ OPTION ( BUILD_DOCS OFF ) OPTION( INJECT_DEBUG_POSTFIX - "Inject debug postfix in .a/.so lib names" + "Inject debug postfix in .a/.so/.dll lib names" ON ) @@ -127,12 +127,15 @@ if (WIN32) ADD_DEFINITIONS( -DWIN32_LEAN_AND_MEAN ) endif() - IF(MSVC) OPTION( ASSIMP_INSTALL_PDB "Install MSVC debug files." ON ) + IF(NOT (MSVC_VERSION LESS 1900)) + # Multibyte character set is deprecated since at least MSVC2015 (possibly earlier) + ADD_DEFINITIONS( -DUNICODE -D_UNICODE ) + ENDIF() ENDIF(MSVC) IF (BUILD_FRAMEWORK) @@ -148,8 +151,8 @@ ELSE() ENDIF(NOT BUILD_SHARED_LIBS) # Define here the needed parameters -SET (ASSIMP_VERSION_MAJOR 4) -SET (ASSIMP_VERSION_MINOR 1) +SET (ASSIMP_VERSION_MAJOR 5) +SET (ASSIMP_VERSION_MINOR 0) SET (ASSIMP_VERSION_PATCH 0) SET (ASSIMP_VERSION ${ASSIMP_VERSION_MAJOR}.${ASSIMP_VERSION_MINOR}.${ASSIMP_VERSION_PATCH}) SET (ASSIMP_SOVERSION 4) @@ -157,7 +160,7 @@ SET (PROJECT_VERSION "${ASSIMP_VERSION}") SET( ASSIMP_PACKAGE_VERSION "0" CACHE STRING "the package-specific version used for uploading the sources" ) -# Enable C++1 globally +# Enable C++11 support globally set_property( GLOBAL PROPERTY CXX_STANDARD 11 ) IF(NOT IGNORE_GIT_HASH) @@ -198,7 +201,7 @@ CONFIGURE_FILE( ${CMAKE_CURRENT_BINARY_DIR}/include/assimp/config.h ) -INCLUDE_DIRECTORIES( +INCLUDE_DIRECTORIES( BEFORE ./ include ${CMAKE_CURRENT_BINARY_DIR} @@ -234,6 +237,11 @@ ELSEIF ( "${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang" ) SET(CMAKE_CXX_FLAGS "-g -fvisibility=hidden -fPIC -fno-strict-aliasing -Wall -Wno-long-long -std=c++11 ${CMAKE_CXX_FLAGS}" ) SET(CMAKE_C_FLAGS "-fPIC -fno-strict-aliasing ${CMAKE_C_FLAGS}") ELSEIF( CMAKE_COMPILER_IS_MINGW ) + IF (CMAKE_CXX_COMPILER_VERSION VERSION_LESS 7.0) + message(FATAL_ERROR "MinGW is too old to be supported. Please update MinGW and try again.") + ELSEIF(CMAKE_CXX_COMPILER_VERSION VERSION_LESS 7.3) + message(WARNING "MinGW is old, if you experience errors, update MinGW.") + ENDIF() SET( CMAKE_CXX_FLAGS "-fvisibility=hidden -fno-strict-aliasing -Wall -Wno-long-long -std=c++11 -Wa,-mbig-obj ${CMAKE_CXX_FLAGS}" ) SET(CMAKE_C_FLAGS "-fPIC -fno-strict-aliasing ${CMAKE_C_FLAGS} ") ADD_DEFINITIONS( -U__STRICT_ANSI__ ) @@ -247,6 +255,7 @@ IF (CMAKE_BUILD_TYPE STREQUAL "Debug") ELSE() SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fembed-bitcode -O3") SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fembed-bitcode -O3") + # Experimental for pdb generation ENDIF() ENDIF( IOS ) @@ -301,7 +310,9 @@ SET( ASSIMP_INCLUDE_INSTALL_DIR "include" CACHE STRING SET( ASSIMP_BIN_INSTALL_DIR "bin" CACHE STRING "Path the tool executables are installed to." ) -IF (CMAKE_BUILD_TYPE STREQUAL "Debug") +get_cmake_property(is_multi_config GENERATOR_IS_MULTI_CONFIG) + +IF (is_multi_config OR (CMAKE_BUILD_TYPE STREQUAL "Debug")) SET(CMAKE_DEBUG_POSTFIX "d" CACHE STRING "Debug Postfix for lib, samples and tools") ELSE() SET(CMAKE_DEBUG_POSTFIX "" CACHE STRING "Debug Postfix for lib, samples and tools") @@ -317,16 +328,24 @@ ENDIF() # cmake configuration files CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/assimp-config.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/assimp-config.cmake" @ONLY IMMEDIATE) CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/assimpTargets.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets.cmake" @ONLY IMMEDIATE) -CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/assimpTargets-debug.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets-debug.cmake" @ONLY IMMEDIATE) -CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/assimpTargets-release.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets-release.cmake" @ONLY IMMEDIATE) +IF (is_multi_config) + CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/assimpTargets-debug.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets-debug.cmake" @ONLY IMMEDIATE) + CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/assimpTargets-release.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets-release.cmake" @ONLY IMMEDIATE) + SET(PACKAGE_TARGETS_FILE "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets-debug.cmake" "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets-release.cmake") +ELSEIF (CMAKE_BUILD_TYPE STREQUAL Debug) + CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/assimpTargets-debug.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets-debug.cmake" @ONLY IMMEDIATE) + SET(PACKAGE_TARGETS_FILE "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets-debug.cmake") +ELSE() + CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/assimpTargets-release.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets-release.cmake" @ONLY IMMEDIATE) + SET(PACKAGE_TARGETS_FILE "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets-release.cmake") +ENDIF() CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/assimp-config-version.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/assimp-config-version.cmake" @ONLY IMMEDIATE) #we should generated these scripts after CMake VERSION 3.0.2 using export(EXPORT ...) and write_basic_package_version_file(...) INSTALL(FILES "${CMAKE_CURRENT_BINARY_DIR}/assimp-config.cmake" "${CMAKE_CURRENT_BINARY_DIR}/assimp-config-version.cmake" "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets.cmake" - "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets-debug.cmake" - "${CMAKE_CURRENT_BINARY_DIR}/assimpTargets-release.cmake" + ${PACKAGE_TARGETS_FILE} DESTINATION "${ASSIMP_LIB_INSTALL_DIR}/cmake/assimp-${ASSIMP_VERSION_MAJOR}.${ASSIMP_VERSION_MINOR}" COMPONENT ${LIBASSIMP-DEV_COMPONENT}) FIND_PACKAGE( DirectX ) @@ -351,6 +370,15 @@ IF( NOT ZLIB_FOUND ) INCLUDE(CheckIncludeFile) INCLUDE(CheckTypeSize) INCLUDE(CheckFunctionExists) + + # Explicitly turn off ASM686 and AMD64 cmake options. + # The AMD64 option causes a build failure on MSVC and the ASM builds seem to have problems: + # https://github.com/madler/zlib/issues/41#issuecomment-125848075 + # Also prevents these options from "polluting" the cmake options if assimp is being + # included as a submodule. + set( ASM686 FALSE CACHE INTERNAL "Override ZLIB flag to turn off assembly" FORCE ) + set( AMD64 FALSE CACHE INTERNAL "Override ZLIB flag to turn off assembly" FORCE ) + # compile from sources ADD_SUBDIRECTORY(contrib/zlib) SET(ZLIB_FOUND 1) @@ -468,6 +496,7 @@ ENDIF ( ASSIMP_BUILD_ASSIMP_TOOLS ) IF ( ASSIMP_BUILD_SAMPLES) IF ( WIN32 ) ADD_SUBDIRECTORY( samples/SimpleTexturedOpenGL/ ) + ADD_SUBDIRECTORY( samples/SimpleTexturedDirectx11 ) ENDIF ( WIN32 ) ADD_SUBDIRECTORY( samples/SimpleOpenGL/ ) ENDIF ( ASSIMP_BUILD_SAMPLES ) @@ -533,18 +562,22 @@ if(WIN32) if (CMAKE_SIZEOF_VOID_P EQUAL 8) SET(BIN_DIR "${PROJECT_SOURCE_DIR}/bin64/") SET(LIB_DIR "${PROJECT_SOURCE_DIR}/lib64/") - elseif() + else() SET(BIN_DIR "${PROJECT_SOURCE_DIR}/bin32/") SET(LIB_DIR "${PROJECT_SOURCE_DIR}/lib32/") ENDIF() - IF(MSVC12) - SET(ASSIMP_MSVC_VERSION "vc120") - ELSEIF(MSVC14) - SET(ASSIMP_MSVC_VERSION "vc140") - ELSEIF(MSVC15) - SET(ASSIMP_MSVC_VERSION "vc141") - ENDIF(MSVC12) + IF(MSVC_TOOLSET_VERSION) + set(MSVC_PREFIX "vc${MSVC_TOOLSET_VERSION}") + ELSE() + IF(MSVC12) + SET(ASSIMP_MSVC_VERSION "vc120") + ELSEIF(MSVC14) + SET(ASSIMP_MSVC_VERSION "vc140") + ELSEIF(MSVC15) + SET(ASSIMP_MSVC_VERSION "vc141") + ENDIF(MSVC12) + ENDIF() IF(MSVC12 OR MSVC14 OR MSVC15 ) ADD_CUSTOM_TARGET(UpdateAssimpLibsDebugSymbolsAndDLLs COMMENT "Copying Assimp Libraries ..." VERBATIM) diff --git a/INSTALL b/INSTALL index 357918d6b..350a5f109 100644 --- a/INSTALL +++ b/INSTALL @@ -35,13 +35,16 @@ http://www.cmake.org/. For Unix: -1. cmake CMakeLists.txt -G 'Unix Makefiles' -2. make +1. mkdir build && cd build +2. cmake .. -G 'Unix Makefiles' +3. make -j4 For Windows: 1. Open a command prompt -2. cmake CMakeLists.txt -2. Open your default IDE and build it +2. mkdir build +3. cd build +4. cmake .. +5. cmake --build . For iOS: Just check the following project, which deploys a compiler toolchain for different iOS-versions: https://github.com/assimp/assimp/tree/master/port/iOS diff --git a/Readme.md b/Readme.md index efb8ecc2b..19e43a3cb 100644 --- a/Readme.md +++ b/Readme.md @@ -17,6 +17,8 @@ A library to import and export various 3d-model-formats including scene-post-pro APIs are provided for C and C++. There are various bindings to other languages (C#, Java, Python, Delphi, D). Assimp also runs on Android and iOS. +[Check the latest doc](https://assimp-docs.readthedocs.io/en/latest/). + Additionally, assimp features various __mesh post processing tools__: normals and tangent space generation, triangulation, vertex cache locality optimization, removal of degenerate primitives and duplicate vertices, sorting by primitive type, merging of redundant materials and many more. This is the development repo containing the latest features and bugfixes. For productive use though, we recommend one of the stable releases available from [Github Assimp Releases](https://github.com/assimp/assimp/releases). @@ -99,7 +101,7 @@ __Importers__: Additionally, some formats are supported by dependency on non-free code or external SDKs (not built by default): -- [C4D](https://en.wikipedia.org/wiki/Cinema_4D) (https://github.com/assimp/assimp/wiki/Cinema4D-&-Melange) +- [C4D](https://en.wikipedia.org/wiki/Cinema_4D) (https://github.com/assimp/assimp/wiki/Cinema4D-&-Melange) IMporting geometry + node hierarchy are currently supported __Exporters__: @@ -128,6 +130,7 @@ Take a look into the https://github.com/assimp/assimp/blob/master/Build.md file. * [Javascript (Alpha)](https://github.com/makc/assimp2json) * [Unity 3d Plugin](https://www.assetstore.unity3d.com/en/#!/content/91777) * [JVM](https://github.com/kotlin-graphics/assimp) Full jvm port (current [status](https://github.com/kotlin-graphics/assimp/wiki/Status)) +* [HAXE-Port](https://github.com/longde123/assimp-haxe) The Assimp-HAXE-port. ### Other tools ### [open3mod](https://github.com/acgessler/open3mod) is a powerful 3D model viewer based on Assimp's import and export abilities. diff --git a/appveyor.yml b/appveyor.yml index 2b5f212f9..851d9c096 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -31,7 +31,7 @@ install: - if "%APPVEYOR_BUILD_WORKER_IMAGE%"=="Visual Studio 2015" set CMAKE_GENERATOR_NAME=Visual Studio 14 2015 - if "%APPVEYOR_BUILD_WORKER_IMAGE%"=="Visual Studio 2017" set CMAKE_GENERATOR_NAME=Visual Studio 15 2017 - if "%platform%"=="x64" set CMAKE_GENERATOR_NAME=%CMAKE_GENERATOR_NAME% Win64 - - cmake %CMAKE_DEFINES% -G "%CMAKE_GENERATOR_NAME%" + - cmake %CMAKE_DEFINES% -G "%CMAKE_GENERATOR_NAME%" . - set PATH=%PATH%;"C:\\Program Files (x86)\\Inno Setup 5" - ps: Invoke-WebRequest -Uri https://download.microsoft.com/download/5/7/b/57b2947c-7221-4f33-b35e-2fc78cb10df4/vc_redist.x64.exe -OutFile .\packaging\windows-innosetup\vc_redist.x64.exe - ps: Invoke-WebRequest -Uri https://download.microsoft.com/download/1/d/8/1d8137db-b5bb-4925-8c5d-927424a2e4de/vc_redist.x86.exe -OutFile .\packaging\windows-innosetup\vc_redist.x86.exe @@ -53,7 +53,13 @@ build: project: Assimp.sln after_build: - - if "%APPVEYOR_BUILD_WORKER_IMAGE%"=="Visual Studio 2017" iscc packaging\windows-innosetup\script.iss + - if "%APPVEYOR_BUILD_WORKER_IMAGE%"=="Visual Studio 2017" ( + if "%platform%"=="x64" ( + iscc packaging\windows-innosetup\script_x64.iss + ) else ( + iscc packaging\windows-innosetup\script_x86.iss + ) + ) - 7z a assimp.7z bin\%CONFIGURATION%\* lib\%CONFIGURATION%\* test_script: diff --git a/assimpTargets-debug.cmake.in b/assimpTargets-debug.cmake.in index a83e6c22d..6ee6b2886 100644 --- a/assimpTargets-debug.cmake.in +++ b/assimpTargets-debug.cmake.in @@ -5,48 +5,75 @@ # Commands may need to know the format version. set(CMAKE_IMPORT_FILE_VERSION 1) +set(ASSIMP_BUILD_SHARED_LIBS @BUILD_SHARED_LIBS@) + if(MSVC) - if( MSVC70 OR MSVC71 ) - set(MSVC_PREFIX "vc70") - elseif( MSVC80 ) - set(MSVC_PREFIX "vc80") - elseif( MSVC90 ) - set(MSVC_PREFIX "vc90") - elseif( MSVC10 ) - set(MSVC_PREFIX "vc100") - elseif( MSVC11 ) - set(MSVC_PREFIX "vc110") - elseif( MSVC12 ) - set(MSVC_PREFIX "vc120") - elseif( MSVC14 ) - set(MSVC_PREFIX "vc140") + if(MSVC_TOOLSET_VERSION) + set(MSVC_PREFIX "vc${MSVC_TOOLSET_VERSION}") else() - set(MSVC_PREFIX "vc150") + if( MSVC70 OR MSVC71 ) + set(MSVC_PREFIX "vc70") + elseif( MSVC80 ) + set(MSVC_PREFIX "vc80") + elseif( MSVC90 ) + set(MSVC_PREFIX "vc90") + elseif( MSVC10 ) + set(MSVC_PREFIX "vc100") + elseif( MSVC11 ) + set(MSVC_PREFIX "vc110") + elseif( MSVC12 ) + set(MSVC_PREFIX "vc120") + elseif( MSVC14 ) + set(MSVC_PREFIX "vc140") + else() + set(MSVC_PREFIX "vc150") + endif() endif() set(ASSIMP_LIBRARY_SUFFIX "@ASSIMP_LIBRARY_SUFFIX@-${MSVC_PREFIX}-mt" CACHE STRING "the suffix for the assimp windows library" ) - set(sharedLibraryName "assimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_DEBUG_POSTFIX@@CMAKE_SHARED_LIBRARY_SUFFIX@") - set(importLibraryName "assimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_DEBUG_POSTFIX@@CMAKE_IMPORT_LIBRARY_SUFFIX@") + if(ASSIMP_BUILD_SHARED_LIBS) + set(sharedLibraryName "assimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_DEBUG_POSTFIX@@CMAKE_SHARED_LIBRARY_SUFFIX@") + set(importLibraryName "assimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_DEBUG_POSTFIX@@CMAKE_IMPORT_LIBRARY_SUFFIX@") - # Import target "assimp::assimp" for configuration "Debug" - set_property(TARGET assimp::assimp APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG) - set_target_properties(assimp::assimp PROPERTIES - IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/lib/${importLibraryName}" - IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/${sharedLibraryName}" - ) - list(APPEND _IMPORT_CHECK_TARGETS assimp::assimp ) - list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/lib/${importLibraryName}") - list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/bin/${sharedLibraryName}" ) + # Import target "assimp::assimp" for configuration "Debug" + set_property(TARGET assimp::assimp APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG) + set_target_properties(assimp::assimp PROPERTIES + IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/lib/${importLibraryName}" + IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/${sharedLibraryName}" + ) + list(APPEND _IMPORT_CHECK_TARGETS assimp::assimp ) + list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/lib/${importLibraryName}") + list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/bin/${sharedLibraryName}" ) + else() + set(staticLibraryName "assimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_DEBUG_POSTFIX@@CMAKE_STATIC_LIBRARY_SUFFIX@") + + # Import target "assimp::assimp" for configuration "Debug" + set_property(TARGET assimp::assimp APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG) + set_target_properties(assimp::assimp PROPERTIES + IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/lib/${staticLibraryName}" + ) + list(APPEND _IMPORT_CHECK_TARGETS assimp::assimp ) + list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/lib/${staticLibraryName}") + endif() else() - set(ASSIMP_LIBRARY_SUFFIX "@ASSIMP_LIBRARY_SUFFIX@" CACHE STRING "the suffix for the openrave libraries" ) - set(sharedLibraryName "libassimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_DEBUG_POSTFIX@@CMAKE_SHARED_LIBRARY_SUFFIX@.@ASSIMP_VERSION_MAJOR@") - set_target_properties(assimp::assimp PROPERTIES - IMPORTED_SONAME_DEBUG "${sharedLibraryName}" - IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/lib/${sharedLibraryName}" + set(ASSIMP_LIBRARY_SUFFIX "@ASSIMP_LIBRARY_SUFFIX@" CACHE STRING "the suffix for the assimp libraries" ) + if(ASSIMP_BUILD_SHARED_LIBS) + set(sharedLibraryName "libassimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_DEBUG_POSTFIX@@CMAKE_SHARED_LIBRARY_SUFFIX@.@ASSIMP_VERSION_MAJOR@") + set_target_properties(assimp::assimp PROPERTIES + IMPORTED_SONAME_DEBUG "${sharedLibraryName}" + IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/lib/${sharedLibraryName}" ) - list(APPEND _IMPORT_CHECK_TARGETS assimp::assimp ) - list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/lib/${sharedLibraryName}" ) + list(APPEND _IMPORT_CHECK_TARGETS assimp::assimp ) + list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/lib/${sharedLibraryName}" ) + else() + set(staticLibraryName "libassimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_DEBUG_POSTFIX@@CMAKE_STATIC_LIBRARY_SUFFIX@") + set_target_properties(assimp::assimp PROPERTIES + IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/lib/${staticLibraryName}" + ) + list(APPEND _IMPORT_CHECK_TARGETS assimp::assimp ) + list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/lib/${staticLibraryName}" ) + endif() endif() @@ -60,7 +87,11 @@ set( ASSIMP_CXX_FLAGS ) # dynamically linked library set( ASSIMP_LINK_FLAGS "" ) set( ASSIMP_LIBRARY_DIRS "${ASSIMP_ROOT_DIR}/@ASSIMP_LIB_INSTALL_DIR@") set( ASSIMP_INCLUDE_DIRS "${ASSIMP_ROOT_DIR}/@ASSIMP_INCLUDE_INSTALL_DIR@") -set( ASSIMP_LIBRARIES ${sharedLibraryName}) +if(ASSIMP_BUILD_SHARED_LIBS) + set( ASSIMP_LIBRARIES ${sharedLibraryName}) +else() + set( ASSIMP_LIBRARIES ${staticLibraryName}) +endif() # for compatibility with pkg-config set(ASSIMP_CFLAGS_OTHER "${ASSIMP_CXX_FLAGS}") @@ -75,4 +106,5 @@ MARK_AS_ADVANCED( ASSIMP_CFLAGS_OTHER ASSIMP_LDFLAGS_OTHER ASSIMP_LIBRARY_SUFFIX + ASSIMP_BUILD_SHARED_LIBS ) diff --git a/assimpTargets-release.cmake.in b/assimpTargets-release.cmake.in index 0b38e4e92..56bfc3602 100644 --- a/assimpTargets-release.cmake.in +++ b/assimpTargets-release.cmake.in @@ -5,59 +5,91 @@ # Commands may need to know the format version. set(CMAKE_IMPORT_FILE_VERSION 1) +set(ASSIMP_BUILD_SHARED_LIBS @BUILD_SHARED_LIBS@) + if(MSVC) - if( MSVC70 OR MSVC71 ) - set(MSVC_PREFIX "vc70") - elseif( MSVC80 ) - set(MSVC_PREFIX "vc80") - elseif( MSVC90 ) - set(MSVC_PREFIX "vc90") - elseif( MSVC10 ) - set(MSVC_PREFIX "vc100") - elseif( MSVC11 ) - set(MSVC_PREFIX "vc110") - elseif( MSVC12 ) - set(MSVC_PREFIX "vc120") - elseif( MSVC14 ) - set(MSVC_PREFIX "vc140") + if(MSVC_TOOLSET_VERSION) + set(MSVC_PREFIX "vc${MSVC_TOOLSET_VERSION}") else() - set(MSVC_PREFIX "vc150") + if( MSVC70 OR MSVC71 ) + set(MSVC_PREFIX "vc70") + elseif( MSVC80 ) + set(MSVC_PREFIX "vc80") + elseif( MSVC90 ) + set(MSVC_PREFIX "vc90") + elseif( MSVC10 ) + set(MSVC_PREFIX "vc100") + elseif( MSVC11 ) + set(MSVC_PREFIX "vc110") + elseif( MSVC12 ) + set(MSVC_PREFIX "vc120") + elseif( MSVC14 ) + set(MSVC_PREFIX "vc140") + else() + set(MSVC_PREFIX "vc150") + endif() endif() set(ASSIMP_LIBRARY_SUFFIX "@ASSIMP_LIBRARY_SUFFIX@-${MSVC_PREFIX}-mt" CACHE STRING "the suffix for the assimp windows library" ) - set(sharedLibraryName "assimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_SHARED_LIBRARY_SUFFIX@") - set(importLibraryName "assimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_IMPORT_LIBRARY_SUFFIX@") + if(ASSIMP_BUILD_SHARED_LIBS) + set(sharedLibraryName "assimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_SHARED_LIBRARY_SUFFIX@") + set(importLibraryName "assimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_IMPORT_LIBRARY_SUFFIX@") - # Import target "assimp::assimp" for configuration "Release" - set_property(TARGET assimp::assimp APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) - set_target_properties(assimp::assimp PROPERTIES - IMPORTED_IMPLIB_RELEASE "${_IMPORT_PREFIX}/lib/${importLibraryName}" - IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/bin/${sharedLibraryName}" + # Import target "assimp::assimp" for configuration "Release" + set_property(TARGET assimp::assimp APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) + set_target_properties(assimp::assimp PROPERTIES + IMPORTED_IMPLIB_RELEASE "${_IMPORT_PREFIX}/lib/${importLibraryName}" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/bin/${sharedLibraryName}" ) - list(APPEND _IMPORT_CHECK_TARGETS assimp::assimp ) - list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/lib/${importLibraryName}") - list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/bin/${sharedLibraryName}" ) + list(APPEND _IMPORT_CHECK_TARGETS assimp::assimp ) + list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/lib/${importLibraryName}") + list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/bin/${sharedLibraryName}" ) + else() + set(staticLibraryName "assimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_STATIC_LIBRARY_SUFFIX@") + + # Import target "assimp::assimp" for configuration "Release" + set_property(TARGET assimp::assimp APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) + set_target_properties(assimp::assimp PROPERTIES + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/${staticLibraryName}" + ) + list(APPEND _IMPORT_CHECK_TARGETS assimp::assimp ) + list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/lib/${staticLibraryName}") + endif() else() - set(ASSIMP_LIBRARY_SUFFIX "@ASSIMP_LIBRARY_SUFFIX@" CACHE STRING "the suffix for the openrave libraries" ) - set(sharedLibraryName "libassimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_SHARED_LIBRARY_SUFFIX@.@ASSIMP_VERSION_MAJOR@") - set_target_properties(assimp::assimp PROPERTIES - IMPORTED_SONAME_RELEASE "${sharedLibraryName}" - IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/${sharedLibraryName}" + set(ASSIMP_LIBRARY_SUFFIX "@ASSIMP_LIBRARY_SUFFIX@" CACHE STRING "the suffix for the assimp libraries" ) + if(ASSIMP_BUILD_SHARED_LIBS) + set(sharedLibraryName "libassimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_SHARED_LIBRARY_SUFFIX@.@ASSIMP_VERSION_MAJOR@") + set_target_properties(assimp::assimp PROPERTIES + IMPORTED_SONAME_RELEASE "${sharedLibraryName}" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/${sharedLibraryName}" ) - list(APPEND _IMPORT_CHECK_TARGETS assimp::assimp ) - list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/lib/${sharedLibraryName}" ) + list(APPEND _IMPORT_CHECK_TARGETS assimp::assimp ) + list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/lib/${sharedLibraryName}" ) + else() + set(staticLibraryName "libassimp${ASSIMP_LIBRARY_SUFFIX}@CMAKE_STATIC_LIBRARY_SUFFIX@") + set_target_properties(assimp::assimp PROPERTIES + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/${staticLibraryName}" + ) + list(APPEND _IMPORT_CHECK_TARGETS assimp::assimp ) + list(APPEND _IMPORT_CHECK_FILES_FOR_assimp::assimp "${_IMPORT_PREFIX}/lib/${staticLibraryName}" ) + endif() endif() # Commands beyond this point should not need to know the version. set(CMAKE_IMPORT_FILE_VERSION) get_filename_component(ASSIMP_ROOT_DIR "@CMAKE_INSTALL_PREFIX@" REALPATH) + set( ASSIMP_CXX_FLAGS ) # dynamically linked library set( ASSIMP_LINK_FLAGS "" ) set( ASSIMP_LIBRARY_DIRS "${ASSIMP_ROOT_DIR}/@ASSIMP_LIB_INSTALL_DIR@") set( ASSIMP_INCLUDE_DIRS "${ASSIMP_ROOT_DIR}/@ASSIMP_INCLUDE_INSTALL_DIR@") -set( ASSIMP_LIBRARIES ${sharedLibraryName}) +if(ASSIMP_BUILD_SHARED_LIBS) + set( ASSIMP_LIBRARIES ${sharedLibraryName}) +else() + set( ASSIMP_LIBRARIES ${staticLibraryName}) +endif() # for compatibility with pkg-config set(ASSIMP_CFLAGS_OTHER "${ASSIMP_CXX_FLAGS}") @@ -72,4 +104,5 @@ MARK_AS_ADVANCED( ASSIMP_CFLAGS_OTHER ASSIMP_LDFLAGS_OTHER ASSIMP_LIBRARY_SUFFIX + ASSIMP_BUILD_SHARED_LIBS ) diff --git a/assimpTargets.cmake.in b/assimpTargets.cmake.in index 68e2c0dec..ab1a8d2c7 100644 --- a/assimpTargets.cmake.in +++ b/assimpTargets.cmake.in @@ -51,7 +51,11 @@ if(_IMPORT_PREFIX STREQUAL "/") endif() # Create imported target assimp::assimp -add_library(assimp::assimp SHARED IMPORTED) +if(@BUILD_SHARED_LIBS@) + add_library(assimp::assimp SHARED IMPORTED) +else() + add_library(assimp::assimp STATIC IMPORTED) +endif() set_target_properties(assimp::assimp PROPERTIES COMPATIBLE_INTERFACE_STRING "assimp_MAJOR_VERSION" diff --git a/code/3DSLoader.cpp b/code/3DSLoader.cpp index 24626d936..96b80c962 100644 --- a/code/3DSLoader.cpp +++ b/code/3DSLoader.cpp @@ -249,13 +249,14 @@ void Discreet3DSImporter::ApplyMasterScale(aiScene* pScene) // Reads a new chunk from the file void Discreet3DSImporter::ReadChunk(Discreet3DS::Chunk* pcOut) { - ai_assert(pcOut != NULL); + ai_assert(pcOut != nullptr); pcOut->Flag = stream->GetI2(); pcOut->Size = stream->GetI4(); - if (pcOut->Size - sizeof(Discreet3DS::Chunk) > stream->GetRemainingSize()) + if (pcOut->Size - sizeof(Discreet3DS::Chunk) > stream->GetRemainingSize()) { throw DeadlyImportError("Chunk is too large"); + } if (pcOut->Size - sizeof(Discreet3DS::Chunk) > stream->GetRemainingSizeToLimit()) { ASSIMP_LOG_ERROR("3DS: Chunk overflow"); @@ -1343,15 +1344,16 @@ void Discreet3DSImporter::ParseTextureChunk(D3DS::Texture* pcOut) // ------------------------------------------------------------------------------------------------ // Read a percentage chunk -ai_real Discreet3DSImporter::ParsePercentageChunk() -{ +ai_real Discreet3DSImporter::ParsePercentageChunk() { Discreet3DS::Chunk chunk; ReadChunk(&chunk); - if (Discreet3DS::CHUNK_PERCENTF == chunk.Flag) - return stream->GetF4(); - else if (Discreet3DS::CHUNK_PERCENTW == chunk.Flag) + if (Discreet3DS::CHUNK_PERCENTF == chunk.Flag) { + return stream->GetF4() * ai_real(100) / ai_real(0xFFFF); + } else if (Discreet3DS::CHUNK_PERCENTW == chunk.Flag) { return (ai_real)((uint16_t)stream->GetI2()) / (ai_real)0xFFFF; + } + return get_qnan(); } diff --git a/code/ASELoader.cpp b/code/ASELoader.cpp index 321e8548a..556e8ce66 100644 --- a/code/ASELoader.cpp +++ b/code/ASELoader.cpp @@ -1299,7 +1299,7 @@ void ASEImporter::BuildMaterialIndices() } } - // Dekete our temporary array + // Delete our temporary array delete[] pcIntMaterials; } diff --git a/code/ASEParser.cpp b/code/ASEParser.cpp index e8d6febc2..14b962320 100644 --- a/code/ASEParser.cpp +++ b/code/ASEParser.cpp @@ -45,7 +45,6 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * @brief Implementation of the ASE parser class */ - #ifndef ASSIMP_BUILD_NO_ASE_IMPORTER #ifndef ASSIMP_BUILD_NO_3DS_IMPORTER diff --git a/code/ASEParser.h b/code/ASEParser.h index b8c820632..0cfd5313c 100644 --- a/code/ASEParser.h +++ b/code/ASEParser.h @@ -188,10 +188,11 @@ struct Animation { } mRotationType, mScalingType, mPositionType; Animation() AI_NO_EXCEPT - : mRotationType (TRACK) - , mScalingType (TRACK) - , mPositionType (TRACK) - {} + : mRotationType (TRACK) + , mScalingType (TRACK) + , mPositionType (TRACK) { + // empty + } //! List of track rotation keyframes std::vector< aiQuatKey > akeyRotations; @@ -243,7 +244,6 @@ struct BaseNode { mTargetPosition.x = qnan; } - //! Name of the mesh std::string mName; diff --git a/code/AssbinLoader.cpp b/code/AssbinLoader.cpp index 7adb8db6f..249e29863 100644 --- a/code/AssbinLoader.cpp +++ b/code/AssbinLoader.cpp @@ -68,7 +68,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. using namespace Assimp; static const aiImporterDesc desc = { - ".assbin Importer", + "Assimp Binary Importer", "Gargaj / Conspiracy", "", "", @@ -708,7 +708,7 @@ void AssbinImporter::InternReadFile( const std::string& pFile, aiScene* pScene, unsigned char * uncompressedData = new unsigned char[ uncompressedSize ]; - int res = uncompress( uncompressedData, &uncompressedSize, compressedData, len ); + int res = uncompress( uncompressedData, &uncompressedSize, compressedData, (uLong) len ); if(res != Z_OK) { delete [] uncompressedData; diff --git a/code/AssxmlExporter.cpp b/code/AssxmlExporter.cpp index fc9a6bae5..937eebe92 100644 --- a/code/AssxmlExporter.cpp +++ b/code/AssxmlExporter.cpp @@ -555,8 +555,6 @@ void WriteDump(const aiScene* scene, IOStream* io, bool shortened) { mesh->mNormals[n].z); } } - else { - } ioprintf(io,"\t\t\n"); } diff --git a/code/BlenderDNA.h b/code/BlenderDNA.h index 5d3a4f6ea..375d0c4bf 100644 --- a/code/BlenderDNA.h +++ b/code/BlenderDNA.h @@ -416,7 +416,7 @@ template <> struct Structure :: _defaultInitializer { void operator ()(T& /*out*/,const char* = "") { // obviously, it is crucial that _DefaultInitializer is used // only from within a catch clause. - throw; + throw DeadlyImportError("Constructing BlenderDNA Structure encountered an error"); } }; diff --git a/code/BlenderLoader.cpp b/code/BlenderLoader.cpp index 90065ceee..d39cb9699 100644 --- a/code/BlenderLoader.cpp +++ b/code/BlenderLoader.cpp @@ -1225,6 +1225,16 @@ aiLight* BlenderImporter::ConvertLight(const Scene& /*in*/, const Object* obj, c case Lamp::Type_Local: out->mType = aiLightSource_POINT; break; + case Lamp::Type_Spot: + out->mType = aiLightSource_SPOT; + + // blender orients directional lights as facing toward -z + out->mDirection = aiVector3D(0.f, 0.f, -1.f); + out->mUp = aiVector3D(0.f, 1.f, 0.f); + + out->mAngleInnerCone = lamp->spotsize * (1.0f - lamp->spotblend); + out->mAngleOuterCone = lamp->spotsize; + break; case Lamp::Type_Sun: out->mType = aiLightSource_DIRECTIONAL; @@ -1255,6 +1265,23 @@ aiLight* BlenderImporter::ConvertLight(const Scene& /*in*/, const Object* obj, c out->mColorAmbient = aiColor3D(lamp->r, lamp->g, lamp->b) * lamp->energy; out->mColorSpecular = aiColor3D(lamp->r, lamp->g, lamp->b) * lamp->energy; out->mColorDiffuse = aiColor3D(lamp->r, lamp->g, lamp->b) * lamp->energy; + + // If default values are supplied, compute the coefficients from light's max distance + // Read this: https://imdoingitwrong.wordpress.com/2011/01/31/light-attenuation/ + // + if (lamp->constant_coefficient == 1.0f && lamp->linear_coefficient == 0.0f && lamp->quadratic_coefficient == 0.0f && lamp->dist > 0.0f) + { + out->mAttenuationConstant = 1.0f; + out->mAttenuationLinear = 2.0f / lamp->dist; + out->mAttenuationQuadratic = 1.0f / (lamp->dist * lamp->dist); + } + else + { + out->mAttenuationConstant = lamp->constant_coefficient; + out->mAttenuationLinear = lamp->linear_coefficient; + out->mAttenuationQuadratic = lamp->quadratic_coefficient; + } + return out.release(); } diff --git a/code/BlenderScene.cpp b/code/BlenderScene.cpp index 4fc353b15..39c2793d5 100644 --- a/code/BlenderScene.cpp +++ b/code/BlenderScene.cpp @@ -211,9 +211,12 @@ template <> void Structure :: Convert ( ReadField(dest.b,"b",db); ReadField(dest.k,"k",db); ReadField(dest.energy,"energy",db); - ReadField(dest.dist,"dist",db); + ReadField(dest.dist,"dist",db); ReadField(dest.spotsize,"spotsize",db); ReadField(dest.spotblend,"spotblend",db); + ReadField(dest.constant_coefficient, "coeff_const", db); + ReadField(dest.linear_coefficient, "coeff_lin", db); + ReadField(dest.quadratic_coefficient, "coeff_quad", db); ReadField(dest.att1,"att1",db); ReadField(dest.att2,"att2",db); ReadField(temp,"falloff_type",db); diff --git a/code/BlenderScene.h b/code/BlenderScene.h index 8e4223eb1..dd3f1444c 100644 --- a/code/BlenderScene.h +++ b/code/BlenderScene.h @@ -538,6 +538,10 @@ struct Lamp : ElemBase { float energy, dist, spotsize, spotblend; //float haint; + float constant_coefficient; + float linear_coefficient; + float quadratic_coefficient; + float att1, att2; //struct CurveMapping *curfalloff; FalloffType falloff_type; diff --git a/code/C4DImporter.cpp b/code/C4DImporter.cpp index 665d74633..6e5b7d39b 100644 --- a/code/C4DImporter.cpp +++ b/code/C4DImporter.cpp @@ -2,7 +2,7 @@ Open Asset Import Library (assimp) ---------------------------------------------------------------------- -Copyright (c) 2006-2012, assimp team +Copyright (c) 2006-2019, assimp team All rights reserved. Redistribution and use of this software in source and binary forms, @@ -68,8 +68,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. using namespace melange; // overload this function and fill in your own unique data -void GetWriterInfo(int &id, String &appname) -{ +void GetWriterInfo(int &id, String &appname) { id = 2424226; appname = "Open Asset Import Library"; } @@ -78,7 +77,10 @@ using namespace Assimp; using namespace Assimp::Formatter; namespace Assimp { - template<> const std::string LogFunctions::log_prefix = "C4D: "; + template<> const char* LogFunctions::Prefix() { + static auto prefix = "C4D: "; + return prefix; + } } static const aiImporterDesc desc = { @@ -97,47 +99,44 @@ static const aiImporterDesc desc = { // ------------------------------------------------------------------------------------------------ C4DImporter::C4DImporter() -{} +: BaseImporter() { + // empty +} // ------------------------------------------------------------------------------------------------ -C4DImporter::~C4DImporter() -{} +C4DImporter::~C4DImporter() { + // empty +} // ------------------------------------------------------------------------------------------------ -bool C4DImporter::CanRead( const std::string& pFile, IOSystem* pIOHandler, bool checkSig) const -{ +bool C4DImporter::CanRead( const std::string& pFile, IOSystem* pIOHandler, bool checkSig) const { const std::string& extension = GetExtension(pFile); if (extension == "c4d") { return true; - } - - else if ((!extension.length() || checkSig) && pIOHandler) { + } else if ((!extension.length() || checkSig) && pIOHandler) { // TODO } + return false; } // ------------------------------------------------------------------------------------------------ -const aiImporterDesc* C4DImporter::GetInfo () const -{ +const aiImporterDesc* C4DImporter::GetInfo () const { return &desc; } // ------------------------------------------------------------------------------------------------ -void C4DImporter::SetupProperties(const Importer* /*pImp*/) -{ +void C4DImporter::SetupProperties(const Importer* /*pImp*/) { // nothing to be done for the moment } // ------------------------------------------------------------------------------------------------ // Imports the given file into the given scene structure. -void C4DImporter::InternReadFile( const std::string& pFile, - aiScene* pScene, IOSystem* pIOHandler) -{ +void C4DImporter::InternReadFile( const std::string& pFile, aiScene* pScene, IOSystem* pIOHandler) { std::unique_ptr file( pIOHandler->Open( pFile)); - if( file.get() == NULL) { + if( file.get() == nullptr ) { ThrowException("failed to open file " + pFile); } @@ -151,7 +150,7 @@ void C4DImporter::InternReadFile( const std::string& pFile, // open document first BaseDocument* doc = LoadDocument(f, SCENEFILTER_OBJECTS | SCENEFILTER_MATERIALS); - if(doc == NULL) { + if(doc == nullptr ) { ThrowException("failed to read document " + pFile); } @@ -160,11 +159,10 @@ void C4DImporter::InternReadFile( const std::string& pFile, // first convert all materials ReadMaterials(doc->GetFirstMaterial()); - // process C4D scenegraph recursively + // process C4D scene-graph recursively try { RecurseHierarchy(doc->GetFirstObject(), pScene->mRootNode); - } - catch(...) { + } catch(...) { for(aiMesh* mesh : meshes) { delete mesh; } @@ -201,8 +199,7 @@ void C4DImporter::InternReadFile( const std::string& pFile, // ------------------------------------------------------------------------------------------------ -bool C4DImporter::ReadShader(aiMaterial* out, melange::BaseShader* shader) -{ +bool C4DImporter::ReadShader(aiMaterial* out, melange::BaseShader* shader) { // based on Melange sample code (C4DImportExport.cpp) while(shader) { if(shader->GetType() == Xlayer) { @@ -220,15 +217,12 @@ bool C4DImporter::ReadShader(aiMaterial* out, melange::BaseShader* shader) // Ignore the actual layer blending - models for real-time rendering should not // use them in a non-trivial way. Just try to find textures that we can apply // to the model. - while (lsl) - { - if (lsl->GetType() == TypeFolder) - { + while (lsl) { + if (lsl->GetType() == TypeFolder) { BlendFolder* const folder = dynamic_cast(lsl); LayerShaderLayer *subLsl = dynamic_cast(folder->m_Children.GetObject(0)); - while (subLsl) - { + while (subLsl) { if (subLsl->GetType() == TypeShader) { BlendShader* const shader = dynamic_cast(subLsl); if(ReadShader(out, static_cast(shader->m_pLink->GetLink()))) { @@ -238,8 +232,7 @@ bool C4DImporter::ReadShader(aiMaterial* out, melange::BaseShader* shader) subLsl = subLsl->GetNext(); } - } - else if (lsl->GetType() == TypeShader) { + } else if (lsl->GetType() == TypeShader) { BlendShader* const shader = dynamic_cast(lsl); if(ReadShader(out, static_cast(shader->m_pLink->GetLink()))) { return true; @@ -248,33 +241,27 @@ bool C4DImporter::ReadShader(aiMaterial* out, melange::BaseShader* shader) lsl = lsl->GetNext(); } - } - else if ( shader->GetType() == Xbitmap ) - { + } else if ( shader->GetType() == Xbitmap ) { aiString path; shader->GetFileName().GetString().GetCString(path.data, MAXLEN-1); path.length = ::strlen(path.data); out->AddProperty(&path, AI_MATKEY_TEXTURE_DIFFUSE(0)); return true; - } - else { + } else { LogWarn("ignoring shader type: " + std::string(GetObjectTypeName(shader->GetType()))); } shader = shader->GetNext(); } + return false; } - // ------------------------------------------------------------------------------------------------ -void C4DImporter::ReadMaterials(melange::BaseMaterial* mat) -{ +void C4DImporter::ReadMaterials(melange::BaseMaterial* mat) { // based on Melange sample code - while (mat) - { + while (mat) { const String& name = mat->GetName(); - if (mat->GetType() == Mmaterial) - { + if (mat->GetType() == Mmaterial) { aiMaterial* out = new aiMaterial(); material_mapping[mat] = static_cast(materials.size()); materials.push_back(out); @@ -286,8 +273,7 @@ void C4DImporter::ReadMaterials(melange::BaseMaterial* mat) Material& m = dynamic_cast(*mat); - if (m.GetChannelState(CHANNEL_COLOR)) - { + if (m.GetChannelState(CHANNEL_COLOR)) { GeData data; mat->GetParameter(MATERIAL_COLOR_COLOR, data); Vector color = data.GetVector(); @@ -307,9 +293,7 @@ void C4DImporter::ReadMaterials(melange::BaseMaterial* mat) if(shader) { ReadShader(out, shader); } - } - else - { + } else { LogWarn("ignoring plugin material: " + std::string(GetObjectTypeName(mat->GetType()))); } mat = mat->GetNext(); @@ -317,14 +301,12 @@ void C4DImporter::ReadMaterials(melange::BaseMaterial* mat) } // ------------------------------------------------------------------------------------------------ -void C4DImporter::RecurseHierarchy(BaseObject* object, aiNode* parent) -{ - ai_assert(parent != NULL); +void C4DImporter::RecurseHierarchy(BaseObject* object, aiNode* parent) { + ai_assert(parent != nullptr ); std::vector nodes; // based on Melange sample code - while (object) - { + while (object) { const String& name = object->GetName(); const LONG type = object->GetType(); const Matrix& ml = object->GetMl(); @@ -356,26 +338,20 @@ void C4DImporter::RecurseHierarchy(BaseObject* object, aiNode* parent) nodes.push_back(nd); GeData data; - if (type == Ocamera) - { + if (type == Ocamera) { object->GetParameter(CAMERAOBJECT_FOV, data); // TODO: read camera - } - else if (type == Olight) - { + } else if (type == Olight) { // TODO: read light - } - else if (type == Opolygon) - { + } else if (type == Opolygon) { aiMesh* const mesh = ReadMesh(object); - if(mesh != NULL) { + if(mesh != nullptr) { nd->mNumMeshes = 1; nd->mMeshes = new unsigned int[1]; nd->mMeshes[0] = static_cast(meshes.size()); meshes.push_back(mesh); } - } - else { + } else { LogWarn("ignoring object: " + std::string(GetObjectTypeName(type))); } @@ -389,28 +365,27 @@ void C4DImporter::RecurseHierarchy(BaseObject* object, aiNode* parent) std::copy(nodes.begin(), nodes.end(), parent->mChildren); } - // ------------------------------------------------------------------------------------------------ -aiMesh* C4DImporter::ReadMesh(BaseObject* object) -{ - ai_assert(object != NULL && object->GetType() == Opolygon); +aiMesh* C4DImporter::ReadMesh(BaseObject* object) { + ai_assert(object != nullptr); + ai_assert( object->GetType() == Opolygon ); // based on Melange sample code PolygonObject* const polyObject = dynamic_cast(object); - ai_assert(polyObject != NULL); + ai_assert(polyObject != nullptr); const LONG pointCount = polyObject->GetPointCount(); const LONG polyCount = polyObject->GetPolygonCount(); if(!polyObject || !pointCount) { LogWarn("ignoring mesh with zero vertices or faces"); - return NULL; + return nullptr; } const Vector* points = polyObject->GetPointR(); - ai_assert(points != NULL); + ai_assert(points != nullptr); const CPolygon* polys = polyObject->GetPolygonR(); - ai_assert(polys != NULL); + ai_assert(polys != nullptr); std::unique_ptr mesh(new aiMesh()); mesh->mNumFaces = static_cast(polyCount); @@ -443,14 +418,14 @@ aiMesh* C4DImporter::ReadMesh(BaseObject* object) // check if there are normals, tangents or UVW coordinates BaseTag* tag = object->GetTag(Tnormal); - NormalTag* normals_src = NULL; + NormalTag* normals_src = nullptr; if(tag) { normals_src = dynamic_cast(tag); normals = mesh->mNormals = new aiVector3D[mesh->mNumVertices](); } tag = object->GetTag(Ttangent); - TangentTag* tangents_src = NULL; + TangentTag* tangents_src = nullptr; if(tag) { tangents_src = dynamic_cast(tag); tangents = mesh->mTangents = new aiVector3D[mesh->mNumVertices](); @@ -458,15 +433,14 @@ aiMesh* C4DImporter::ReadMesh(BaseObject* object) } tag = object->GetTag(Tuvw); - UVWTag* uvs_src = NULL; + UVWTag* uvs_src = nullptr; if(tag) { uvs_src = dynamic_cast(tag); uvs = mesh->mTextureCoords[0] = new aiVector3D[mesh->mNumVertices](); } // copy vertices and extra channels over and populate faces - for (LONG i = 0; i < polyCount; ++i, ++face) - { + for (LONG i = 0; i < polyCount; ++i, ++face) { ai_assert(polys[i].a < pointCount && polys[i].a >= 0); const Vector& pointA = points[polys[i].a]; verts->x = pointA.x; @@ -489,8 +463,7 @@ aiMesh* C4DImporter::ReadMesh(BaseObject* object) ++verts; // TODO: do we also need to handle lines or points with similar checks? - if (polys[i].c != polys[i].d) - { + if (polys[i].c != polys[i].d) { ai_assert(polys[i].d < pointCount && polys[i].d >= 0); face->mNumIndices = 4; @@ -500,8 +473,7 @@ aiMesh* C4DImporter::ReadMesh(BaseObject* object) verts->y = pointD.y; verts->z = pointD.z; ++verts; - } - else { + } else { face->mNumIndices = 3; } face->mIndices = new unsigned int[face->mNumIndices]; @@ -513,8 +485,7 @@ aiMesh* C4DImporter::ReadMesh(BaseObject* object) if (normals_src) { if(i >= normals_src->GetDataCount()) { LogError("unexpected number of normals, ignoring"); - } - else { + } else { ConstNormalHandle normal_handle = normals_src->GetDataAddressR(); NormalStruct nor; NormalTag::Get(normal_handle, i, nor); @@ -616,26 +587,25 @@ aiMesh* C4DImporter::ReadMesh(BaseObject* object) } mesh->mMaterialIndex = ResolveMaterial(polyObject); + return mesh.release(); } - // ------------------------------------------------------------------------------------------------ -unsigned int C4DImporter::ResolveMaterial(PolygonObject* obj) -{ - ai_assert(obj != NULL); +unsigned int C4DImporter::ResolveMaterial(PolygonObject* obj) { + ai_assert(obj != nullptr); const unsigned int mat_count = static_cast(materials.size()); BaseTag* tag = obj->GetTag(Ttexture); - if(tag == NULL) { + if(tag == nullptr) { return mat_count; } TextureTag& ttag = dynamic_cast(*tag); BaseMaterial* const mat = ttag.GetMaterial(); - ai_assert(mat != NULL); + ai_assert(mat != nullptr); const MaterialMap::const_iterator it = material_mapping.find(mat); if(it == material_mapping.end()) { @@ -643,6 +613,7 @@ unsigned int C4DImporter::ResolveMaterial(PolygonObject* obj) } ai_assert((*it).second < mat_count); + return (*it).second; } diff --git a/code/C4DImporter.h b/code/C4DImporter.h index 2f67d90f3..f3b1351f6 100644 --- a/code/C4DImporter.h +++ b/code/C4DImporter.h @@ -2,7 +2,7 @@ Open Asset Import Library (assimp) ---------------------------------------------------------------------- -Copyright (c) 2006-2012, assimp team +Copyright (c) 2006-2019, assimp team All rights reserved. Redistribution and use of this software in source and binary forms, @@ -48,6 +48,8 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include #include + +// Forward declarations struct aiNode; struct aiMesh; struct aiMaterial; @@ -61,8 +63,7 @@ namespace melange { class BaseShader; } -namespace Assimp { - +namespace Assimp { // TinyFormatter.h namespace Formatter { template class basic_formatter; @@ -75,17 +76,10 @@ namespace Assimp { * * Note that Melange is not free software. */ // ------------------------------------------------------------------------------------------- -class C4DImporter : public BaseImporter, public LogFunctions -{ +class C4DImporter : public BaseImporter, public LogFunctions { public: - C4DImporter(); ~C4DImporter(); - - -public: - - // -------------------- bool CanRead( const std::string& pFile, IOSystem* pIOHandler, bool checkSig) const; @@ -119,5 +113,5 @@ private: }; // !class C4DImporter } // end of namespace Assimp -#endif // INCLUDED_AI_CINEMA_4D_LOADER_H +#endif // INCLUDED_AI_CINEMA_4D_LOADER_H diff --git a/code/CMakeLists.txt b/code/CMakeLists.txt index 1726fda72..83209ca03 100644 --- a/code/CMakeLists.txt +++ b/code/CMakeLists.txt @@ -47,6 +47,11 @@ cmake_minimum_required( VERSION 2.6 ) SET( HEADER_PATH ../include/assimp ) +if(NOT ANDROID AND ASSIMP_ANDROID_JNIIOSYSTEM) + message(WARNING "Requesting Android JNI I/O-System in non-Android toolchain. Resetting ASSIMP_ANDROID_JNIIOSYSTEM to OFF.") + set(ASSIMP_ANDROID_JNIIOSYSTEM OFF) +endif(NOT ANDROID AND ASSIMP_ANDROID_JNIIOSYSTEM) + SET( COMPILER_HEADERS ${HEADER_PATH}/Compiler/pushpack1.h ${HEADER_PATH}/Compiler/poppack1.h @@ -140,6 +145,10 @@ SET( Core_SRCS Assimp.cpp ) +IF(MSVC) + list(APPEND Core_SRCS "res/assimp.rc") +ENDIF(MSVC) + SET( Logging_SRCS ${HEADER_PATH}/DefaultLogger.hpp ${HEADER_PATH}/LogStream.hpp @@ -211,7 +220,7 @@ ENDIF ( ASSIMP_BUILD_NONFREE_C4D_IMPORTER ) # ASSIMP_BUILD_XXX_IMPORTER to FALSE for each importer # if this variable is set to FALSE, the user can manually enable importers by setting # ASSIMP_BUILD_XXX_IMPORTER to TRUE for each importer -OPTION(ASSIMP_BUILD_ALL_IMPORTERS_BY_DEFAULT "default value of all ASSIMP_BUILD_XXX_IMPORTER value" TRUE) +OPTION(ASSIMP_BUILD_ALL_IMPORTERS_BY_DEFAULT "default value of all ASSIMP_BUILD_XXX_IMPORTER values" TRUE) # macro to add the CMake Option ADD_ASSIMP_IMPORTER_ which enables compile of loader # this way selective loaders can be compiled (reduces filesize + compile time) @@ -227,19 +236,51 @@ MACRO(ADD_ASSIMP_IMPORTER name) IF (ASSIMP_IMPORTER_ENABLED) LIST(APPEND ASSIMP_LOADER_SRCS ${ARGN}) SET(ASSIMP_IMPORTERS_ENABLED "${ASSIMP_IMPORTERS_ENABLED} ${name}") - SET(${name}_SRCS ${ARGN}) SOURCE_GROUP(${name} FILES ${ARGN}) ELSE() SET(${name}_SRC "") SET(ASSIMP_IMPORTERS_DISABLED "${ASSIMP_IMPORTERS_DISABLED} ${name}") add_definitions(-DASSIMP_BUILD_NO_${name}_IMPORTER) + ENDIF() +ENDMACRO() + +# if this variable is set to TRUE, the user can manually disable exporters by setting +# ASSIMP_BUILD_XXX_EXPORTER to FALSE for each exporter +# if this variable is set to FALSE, the user can manually enable exporters by setting +# ASSIMP_BUILD_XXX_EXPORTER to TRUE for each exporter +OPTION(ASSIMP_BUILD_ALL_EXPORTERS_BY_DEFAULT "default value of all ASSIMP_BUILD_XXX_EXPORTER values" TRUE) + +# macro to add the CMake Option ADD_ASSIMP_IMPORTER_ which enables compile of loader +# this way selective loaders can be compiled (reduces filesize + compile time) +MACRO(ADD_ASSIMP_EXPORTER name) + IF (ASSIMP_NO_EXPORT) + set(ASSIMP_EXPORTER_ENABLED FALSE) + ELSEIF (ASSIMP_BUILD_ALL_EXPORTERS_BY_DEFAULT) + set(ASSIMP_EXPORTER_ENABLED TRUE) + IF (DEFINED ASSIMP_BUILD_${name}_EXPORTER AND NOT ASSIMP_BUILD_${name}_EXPORTER) + set(ASSIMP_EXPORTER_ENABLED FALSE) + ENDIF () + ELSE () + set(ASSIMP_EXPORTER_ENABLED ${ASSIMP_BUILD_${name}_EXPORTER}) + ENDIF () + + IF (ASSIMP_EXPORTER_ENABLED) + SET(ASSIMP_EXPORTERS_ENABLED "${ASSIMP_EXPORTERS_ENABLED} ${name}") + LIST(APPEND ASSIMP_EXPORTER_SRCS ${ARGN}) + SOURCE_GROUP(${name}_EXPORTER FILES ${ARGN}) + ELSE() + SET(ASSIMP_EXPORTERS_DISABLED "${ASSIMP_EXPORTERS_DISABLED} ${name}") add_definitions(-DASSIMP_BUILD_NO_${name}_EXPORTER) ENDIF() ENDMACRO() SET(ASSIMP_LOADER_SRCS "") SET(ASSIMP_IMPORTERS_ENABLED "") # list of enabled importers -SET(ASSIMP_IMPORTERS_DISABLED "") # disabled list (used to print) +SET(ASSIMP_IMPORTERS_DISABLED "") # disabled importers list (used to print) + +SET(ASSIMP_EXPORTER_SRCS "") +SET(ASSIMP_EXPORTERS_ENABLED "") # list of enabled exporters +SET(ASSIMP_EXPORTERS_DISABLED "") # disabled exporters list (used to print) ADD_ASSIMP_IMPORTER( AMF AMFImporter.hpp @@ -256,6 +297,9 @@ ADD_ASSIMP_IMPORTER( 3DS 3DSHelper.h 3DSLoader.cpp 3DSLoader.h +) + +ADD_ASSIMP_EXPORTER( 3DS 3DSExporter.h 3DSExporter.cpp ) @@ -273,12 +317,15 @@ ADD_ASSIMP_IMPORTER( ASE ) ADD_ASSIMP_IMPORTER( ASSBIN - AssbinExporter.h - AssbinExporter.cpp AssbinLoader.h AssbinLoader.cpp ) +ADD_ASSIMP_EXPORTER( ASSBIN + AssbinExporter.h + AssbinExporter.cpp +) + ADD_ASSIMP_IMPORTER( ASSXML AssxmlExporter.h AssxmlExporter.cpp @@ -300,6 +347,9 @@ ADD_ASSIMP_IMPORTER( COLLADA ColladaLoader.h ColladaParser.cpp ColladaParser.h +) + +ADD_ASSIMP_EXPORTER( COLLADA ColladaExporter.h ColladaExporter.cpp ) @@ -416,6 +466,9 @@ ADD_ASSIMP_IMPORTER( OBJ ObjFileParser.cpp ObjFileParser.h ObjTools.h +) + +ADD_ASSIMP_EXPORTER( OBJ ObjExporter.h ObjExporter.cpp ) @@ -434,18 +487,24 @@ ADD_ASSIMP_IMPORTER( OGRE ) ADD_ASSIMP_IMPORTER( OPENGEX - OpenGEXExporter.cpp - OpenGEXExporter.h OpenGEXImporter.cpp OpenGEXImporter.h OpenGEXStructs.h ) +ADD_ASSIMP_EXPORTER( OPENGEX + OpenGEXExporter.cpp + OpenGEXExporter.h +) + ADD_ASSIMP_IMPORTER( PLY PlyLoader.cpp PlyLoader.h PlyParser.cpp PlyParser.h +) + +ADD_ASSIMP_EXPORTER( PLY PlyExporter.cpp PlyExporter.h ) @@ -536,13 +595,16 @@ ADD_ASSIMP_IMPORTER( FBX FBXDeformer.cpp FBXBinaryTokenizer.cpp FBXDocumentUtil.cpp + FBXCommon.h +) + +ADD_ASSIMP_EXPORTER( FBX FBXExporter.h FBXExporter.cpp FBXExportNode.h FBXExportNode.cpp FBXExportProperty.h FBXExportProperty.cpp - FBXCommon.h ) SET( PostProcessing_SRCS @@ -642,6 +704,9 @@ ADD_ASSIMP_IMPORTER( SMD ADD_ASSIMP_IMPORTER( STL STLLoader.cpp STLLoader.h +) + +ADD_ASSIMP_EXPORTER( STL STLExporter.h STLExporter.cpp ) @@ -662,13 +727,14 @@ ADD_ASSIMP_IMPORTER( X XFileImporter.h XFileParser.cpp XFileParser.h +) + +ADD_ASSIMP_EXPORTER( X XFileExporter.h XFileExporter.cpp ) ADD_ASSIMP_IMPORTER( X3D - X3DExporter.cpp - X3DExporter.hpp X3DImporter.cpp X3DImporter.hpp X3DImporter_Geometry2D.cpp @@ -688,6 +754,11 @@ ADD_ASSIMP_IMPORTER( X3D X3DVocabulary.cpp ) +ADD_ASSIMP_EXPORTER( X3D + X3DExporter.cpp + X3DExporter.hpp +) + ADD_ASSIMP_IMPORTER( GLTF glTFAsset.h glTFAsset.inl @@ -695,28 +766,34 @@ ADD_ASSIMP_IMPORTER( GLTF glTFAssetWriter.inl glTFImporter.cpp glTFImporter.h - glTFExporter.h - glTFExporter.cpp glTF2Asset.h glTF2Asset.inl glTF2AssetWriter.h glTF2AssetWriter.inl glTF2Importer.cpp glTF2Importer.h +) + +ADD_ASSIMP_EXPORTER( GLTF + glTFExporter.h + glTFExporter.cpp glTF2Exporter.h glTF2Exporter.cpp ) ADD_ASSIMP_IMPORTER( 3MF - D3MFImporter.h + D3MFImporter.h D3MFImporter.cpp - D3MFExporter.h - D3MFExporter.cpp - D3MFOpcPackage.h + D3MFOpcPackage.h D3MFOpcPackage.cpp 3MFXmlTags.h ) +ADD_ASSIMP_EXPORTER( 3MF + D3MFExporter.h + D3MFExporter.cpp +) + ADD_ASSIMP_IMPORTER( MMD MMDCpp14.h MMDImporter.cpp @@ -727,6 +804,18 @@ ADD_ASSIMP_IMPORTER( MMD MMDVmdParser.h ) +# Workaround for issue #2406 - force problematic large file to be optimized to prevent string table overflow error +# Used -Os instead of -O2 as previous issues had mentioned, since -Os is roughly speaking -O2, excluding any +# optimizations that take up extra space. Given that the issue is a string table overflowing, -Os seemed appropriate +# Also, I'm not positive if both link & compile flags are needed, but this hopefully ensures that the issue should not +# recur for edge cases such as static builds. +if ((CMAKE_COMPILER_IS_MINGW) AND (CMAKE_BUILD_TYPE MATCHES Debug)) + message("-- Applying MinGW StepFileGen1.cpp Debug Workaround") + SET_SOURCE_FILES_PROPERTIES(Importer/StepFile/StepFileGen1.cpp PROPERTIES COMPILE_FLAGS -Os ) + SET_SOURCE_FILES_PROPERTIES(Importer/StepFile/StepFileGen1.cpp PROPERTIES LINK_FLAGS -Os ) + SET_SOURCE_FILES_PROPERTIES(Importer/StepFile/StepFileGen1.cpp PROPERTIES STATIC_LIBRARY_FLAGS -Os ) +endif() + ADD_ASSIMP_IMPORTER( STEP STEPFile.h Importer/StepFile/StepFileImporter.h @@ -735,28 +824,32 @@ ADD_ASSIMP_IMPORTER( STEP Importer/StepFile/StepFileGen2.cpp Importer/StepFile/StepFileGen3.cpp Importer/StepFile/StepReaderGen.h +) + +ADD_ASSIMP_EXPORTER( STEP StepExporter.h StepExporter.cpp ) -SET( Exporter_SRCS - Exporter.cpp - AssimpCExport.cpp - ${HEADER_PATH}/BlobIOSystem.h -) -SOURCE_GROUP( Exporter FILES ${Exporter_SRCS}) +if ((NOT ASSIMP_NO_EXPORT) OR (NOT ASSIMP_EXPORTERS_ENABLED STREQUAL "")) + SET( Exporter_SRCS + Exporter.cpp + AssimpCExport.cpp + ${HEADER_PATH}/BlobIOSystem.h + ) + SOURCE_GROUP( Exporter FILES ${Exporter_SRCS}) +endif() SET( Extra_SRCS MD4FileData.h ) SOURCE_GROUP( Extra FILES ${Extra_SRCS}) - SET( Clipper_SRCS ../contrib/clipper/clipper.hpp ../contrib/clipper/clipper.cpp ) -SOURCE_GROUP( Clipper FILES ${Clipper_SRCS}) +SOURCE_GROUP( Contrib\\Clipper FILES ${Clipper_SRCS}) SET( Poly2Tri_SRCS ../contrib/poly2tri/poly2tri/common/shapes.cc @@ -771,7 +864,7 @@ SET( Poly2Tri_SRCS ../contrib/poly2tri/poly2tri/sweep/sweep_context.cc ../contrib/poly2tri/poly2tri/sweep/sweep_context.h ) -SOURCE_GROUP( Poly2Tri FILES ${Poly2Tri_SRCS}) +SOURCE_GROUP( Contrib\\Poly2Tri FILES ${Poly2Tri_SRCS}) SET( unzip_SRCS ../contrib/unzip/crypt.h @@ -780,7 +873,7 @@ SET( unzip_SRCS ../contrib/unzip/unzip.c ../contrib/unzip/unzip.h ) -SOURCE_GROUP( unzip FILES ${unzip_SRCS}) +SOURCE_GROUP(Contrib\\unzip FILES ${unzip_SRCS}) SET( ziplib_SRCS ../contrib/zip/src/miniz.h @@ -788,6 +881,13 @@ SET( ziplib_SRCS ../contrib/zip/src/zip.h ) +# TODO if cmake required version has been updated to >3.12.0, collapse this to the second case only +if(${CMAKE_VERSION} VERSION_LESS "3.12.0") + add_definitions(-DMINIZ_USE_UNALIGNED_LOADS_AND_STORES=0) +else() + add_compile_definitions(MINIZ_USE_UNALIGNED_LOADS_AND_STORES=0) +endif() + SOURCE_GROUP( ziplib FILES ${ziplib_SRCS} ) SET ( openddl_parser_SRCS @@ -805,7 +905,7 @@ SET ( openddl_parser_SRCS ../contrib/openddlparser/include/openddlparser/DDLNode.h ../contrib/openddlparser/include/openddlparser/Value.h ) -SOURCE_GROUP( openddl_parser FILES ${openddl_parser_SRCS}) +SOURCE_GROUP( Contrib\\openddl_parser FILES ${openddl_parser_SRCS}) SET ( open3dgc_SRCS ../contrib/Open3DGC/o3dgcAdjacencyInfo.h @@ -838,7 +938,7 @@ SET ( open3dgc_SRCS ../contrib/Open3DGC/o3dgcVector.h ../contrib/Open3DGC/o3dgcVector.inl ) -SOURCE_GROUP( open3dgc FILES ${open3dgc_SRCS}) +SOURCE_GROUP( Contrib\\open3dgc FILES ${open3dgc_SRCS}) # Check dependencies for glTF importer with Open3DGC-compression. # RT-extensions is used in "contrib/Open3DGC/o3dgcTimer.h" for collecting statistics. Pointed file @@ -878,8 +978,11 @@ else (UNZIP_FOUND) INCLUDE_DIRECTORIES( "../contrib/unzip/" ) endif (UNZIP_FOUND) -MESSAGE(STATUS "Enabled formats:${ASSIMP_IMPORTERS_ENABLED}") -MESSAGE(STATUS "Disabled formats:${ASSIMP_IMPORTERS_DISABLED}") +MESSAGE(STATUS "Enabled importer formats:${ASSIMP_IMPORTERS_ENABLED}") +MESSAGE(STATUS "Disabled importer formats:${ASSIMP_IMPORTERS_DISABLED}") + +MESSAGE(STATUS "Enabled exporter formats:${ASSIMP_EXPORTERS_ENABLED}") +MESSAGE(STATUS "Disabled exporter formats:${ASSIMP_EXPORTERS_DISABLED}") SET( assimp_src # Assimp Files @@ -894,6 +997,7 @@ SET( assimp_src # Model Support ${ASSIMP_LOADER_SRCS} + ${ASSIMP_EXPORTER_SRCS} # Third-party libraries ${IrrXML_SRCS} @@ -907,7 +1011,6 @@ SET( assimp_src ${PUBLIC_HEADERS} ${COMPILER_HEADERS} - ) ADD_DEFINITIONS( -DOPENDDLPARSER_BUILD ) @@ -932,11 +1035,11 @@ TARGET_INCLUDE_DIRECTORIES ( assimp PUBLIC TARGET_LINK_LIBRARIES(assimp ${ZLIB_LIBRARIES} ${OPENDDL_PARSER_LIBRARIES} ${IRRXML_LIBRARY} ) -if(ANDROID AND ASSIMP_ANDROID_JNIIOSYSTEM) +if(ASSIMP_ANDROID_JNIIOSYSTEM) set(ASSIMP_ANDROID_JNIIOSYSTEM_PATH port/AndroidJNI) add_subdirectory(../${ASSIMP_ANDROID_JNIIOSYSTEM_PATH}/ ../${ASSIMP_ANDROID_JNIIOSYSTEM_PATH}/) target_link_libraries(assimp android_jniiosystem) -endif(ANDROID AND ASSIMP_ANDROID_JNIIOSYSTEM) +endif(ASSIMP_ANDROID_JNIIOSYSTEM) IF (ASSIMP_BUILD_NONFREE_C4D_IMPORTER) TARGET_LINK_LIBRARIES(assimp optimized ${C4D_RELEASE_LIBRARIES}) @@ -946,6 +1049,10 @@ ENDIF (ASSIMP_BUILD_NONFREE_C4D_IMPORTER) if( MSVC ) # in order to prevent DLL hell, each of the DLLs have to be suffixed with the major version and msvc prefix + # CMake 3.12 added a variable for this + if(MSVC_TOOLSET_VERSION) + set(MSVC_PREFIX "vc${MSVC_TOOLSET_VERSION}") + else() if( MSVC70 OR MSVC71 ) set(MSVC_PREFIX "vc70") elseif( MSVC80 ) @@ -960,14 +1067,19 @@ if( MSVC ) set(MSVC_PREFIX "vc120") elseif( MSVC14 ) set(MSVC_PREFIX "vc140") + elseif( MSVC15 ) + set(MSVC_PREFIX "vc141") else() set(MSVC_PREFIX "vc150") endif() + endif() set(LIBRARY_SUFFIX "${ASSIMP_LIBRARY_SUFFIX}-${MSVC_PREFIX}-mt" CACHE STRING "the suffix for the assimp windows library") endif() if (${CMAKE_SYSTEM_NAME} MATCHES "WindowsStore") - set(WindowsStore TRUE) + target_compile_definitions(assimp PUBLIC WindowsStore) + TARGET_LINK_LIBRARIES(assimp advapi32) + #set(WindowsStore TRUE) endif() SET_TARGET_PROPERTIES( assimp PROPERTIES VERSION ${ASSIMP_VERSION} @@ -1028,6 +1140,16 @@ if (ASSIMP_ANDROID_JNIIOSYSTEM) ENDIF(ASSIMP_ANDROID_JNIIOSYSTEM) if(MSVC AND ASSIMP_INSTALL_PDB) + # When only the static library is built, these properties must + # be set to ensure the static lib .pdb is staged for installation. + IF(NOT BUILD_SHARED_LIBS) + SET_TARGET_PROPERTIES( assimp PROPERTIES + COMPILE_PDB_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + COMPILE_PDB_NAME assimp${LIBRARY_SUFFIX} + COMPILE_PDB_NAME_DEBUG assimp${LIBRARY_SUFFIX}${CMAKE_DEBUG_POSTFIX} + ) + ENDIF() + IF(CMAKE_GENERATOR MATCHES "^Visual Studio") install(FILES ${Assimp_BINARY_DIR}/code/Debug/assimp${LIBRARY_SUFFIX}${CMAKE_DEBUG_POSTFIX}.pdb DESTINATION ${ASSIMP_LIB_INSTALL_DIR} diff --git a/code/COBLoader.cpp b/code/COBLoader.cpp index efb22e08b..b7e0f9728 100644 --- a/code/COBLoader.cpp +++ b/code/COBLoader.cpp @@ -144,7 +144,7 @@ void COBImporter::InternReadFile( const std::string& pFile, aiScene* pScene, IOS // check header char head[32]; stream->CopyAndAdvance(head,32); - if (strncmp(head,"Caligari ",9)) { + if (strncmp(head,"Caligari ",9) != 0) { ThrowException("Could not found magic id: `Caligari`"); } @@ -656,14 +656,14 @@ void COBImporter::ReadLght_Ascii(Scene& out, LineSplitter& splitter, const Chunk ReadFloat3Tuple_Ascii(msh.color ,&rgb); SkipSpaces(&rgb); - if (strncmp(rgb,"cone angle",10)) { + if (strncmp(rgb,"cone angle",10) != 0) { ASSIMP_LOG_WARN_F( "Expected `cone angle` entity in `color` line in `Lght` chunk ", nfo.id ); } SkipSpaces(rgb+10,&rgb); msh.angle = fast_atof(&rgb); SkipSpaces(&rgb); - if (strncmp(rgb,"inner angle",11)) { + if (strncmp(rgb,"inner angle",11) != 0) { ASSIMP_LOG_WARN_F( "Expected `inner angle` entity in `color` line in `Lght` chunk ", nfo.id); } SkipSpaces(rgb+11,&rgb); @@ -903,7 +903,7 @@ public: if(nfo.size != static_cast(-1)) { try { reader.IncPtr( static_cast< int >( nfo.size ) - reader.GetCurrentPos() + cur ); - } catch ( DeadlyImportError e ) { + } catch (const DeadlyImportError& e ) { // out of limit so correct the value reader.IncPtr( reader.GetReadLimit() ); } @@ -1214,7 +1214,7 @@ void COBImporter::ReadGrou_Binary(COB::Scene& out, StreamReaderLE& reader, const const chunk_guard cn(nfo,reader); - out.nodes.push_back(std::shared_ptr(new Group())); + out.nodes.push_back(std::make_shared()); Group& msh = (Group&)(*out.nodes.back().get()); msh = nfo; diff --git a/code/ColladaExporter.cpp b/code/ColladaExporter.cpp index 37a6ba4e0..7c21dde43 100644 --- a/code/ColladaExporter.cpp +++ b/code/ColladaExporter.cpp @@ -64,13 +64,11 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. using namespace Assimp; -namespace Assimp -{ +namespace Assimp { // ------------------------------------------------------------------------------------------------ // Worker function for exporting a scene to Collada. Prototyped and registered in Exporter.cpp -void ExportSceneCollada(const char* pFile, IOSystem* pIOSystem, const aiScene* pScene, const ExportProperties* /*pProperties*/) -{ +void ExportSceneCollada(const char* pFile, IOSystem* pIOSystem, const aiScene* pScene, const ExportProperties* /*pProperties*/) { std::string path = DefaultIOSystem::absolutePath(std::string(pFile)); std::string file = DefaultIOSystem::completeBaseName(std::string(pFile)); @@ -93,12 +91,12 @@ void ExportSceneCollada(const char* pFile, IOSystem* pIOSystem, const aiScene* p } // end of namespace Assimp - - // ------------------------------------------------------------------------------------------------ // Constructor for a specific scene to export -ColladaExporter::ColladaExporter( const aiScene* pScene, IOSystem* pIOSystem, const std::string& path, const std::string& file) : mIOSystem(pIOSystem), mPath(path), mFile(file) -{ +ColladaExporter::ColladaExporter( const aiScene* pScene, IOSystem* pIOSystem, const std::string& path, const std::string& file) +: mIOSystem(pIOSystem) +, mPath(path) +, mFile(file) { // make sure that all formatting happens using the standard, C locale and not the user's current locale mOutput.imbue( std::locale("C") ); mOutput.precision(16); @@ -115,17 +113,15 @@ ColladaExporter::ColladaExporter( const aiScene* pScene, IOSystem* pIOSystem, co // ------------------------------------------------------------------------------------------------ // Destructor -ColladaExporter::~ColladaExporter() -{ - if(mSceneOwned) { +ColladaExporter::~ColladaExporter() { + if ( mSceneOwned ) { delete mScene; } } // ------------------------------------------------------------------------------------------------ // Starts writing the contents -void ColladaExporter::WriteFile() -{ +void ColladaExporter::WriteFile() { // write the DTD mOutput << "" << endstr; // COLLADA element start @@ -158,8 +154,7 @@ void ColladaExporter::WriteFile() // ------------------------------------------------------------------------------------------------ // Writes the asset header -void ColladaExporter::WriteHeader() -{ +void ColladaExporter::WriteHeader() { static const ai_real epsilon = ai_real( 0.00001 ); static const aiQuaternion x_rot(aiMatrix3x3( 0, -1, 0, @@ -238,25 +233,64 @@ void ColladaExporter::WriteHeader() mOutput << startstr << "" << endstr; PushTag(); - aiMetadata* meta = mScene->mRootNode->mMetaData; + // If no Scene metadata, use root node metadata + aiMetadata* meta = mScene->mMetaData; + if (nullptr == meta) { + meta = mScene->mRootNode->mMetaData; + } + aiString value; - if (!meta || !meta->Get("Author", value)) + if (!meta || !meta->Get("Author", value)) { mOutput << startstr << "" << "Assimp" << "" << endstr; - else + } else { mOutput << startstr << "" << XMLEscape(value.C_Str()) << "" << endstr; + } - if (!meta || !meta->Get("AuthoringTool", value)) + if (nullptr == meta || !meta->Get("AuthoringTool", value)) { mOutput << startstr << "" << "Assimp Exporter" << "" << endstr; - else + } else { mOutput << startstr << "" << XMLEscape(value.C_Str()) << "" << endstr; + } - //mOutput << startstr << "" << mScene->author.C_Str() << "" << endstr; - //mOutput << startstr << "" << mScene->authoringTool.C_Str() << "" << endstr; + if (meta) { + if (meta->Get("Comments", value)) { + mOutput << startstr << "" << XMLEscape(value.C_Str()) << "" << endstr; + } + if (meta->Get("Copyright", value)) { + mOutput << startstr << "" << XMLEscape(value.C_Str()) << "" << endstr; + } + if (meta->Get("SourceData", value)) { + mOutput << startstr << "" << XMLEscape(value.C_Str()) << "" << endstr; + } + } PopTag(); mOutput << startstr << "" << endstr; - mOutput << startstr << "" << date_str << "" << endstr; + + if (nullptr == meta || !meta->Get("Created", value)) { + mOutput << startstr << "" << date_str << "" << endstr; + } else { + mOutput << startstr << "" << XMLEscape(value.C_Str()) << "" << endstr; + } + + // Modified date is always the date saved mOutput << startstr << "" << date_str << "" << endstr; + + if (meta) { + if (meta->Get("Keywords", value)) { + mOutput << startstr << "" << XMLEscape(value.C_Str()) << "" << endstr; + } + if (meta->Get("Revision", value)) { + mOutput << startstr << "" << XMLEscape(value.C_Str()) << "" << endstr; + } + if (meta->Get("Subject", value)) { + mOutput << startstr << "" << XMLEscape(value.C_Str()) << "" << endstr; + } + if (meta->Get("Title", value)) { + mOutput << startstr << "" << XMLEscape(value.C_Str()) << "" << endstr; + } + } + mOutput << startstr << "" << endstr; mOutput << startstr << "" << up_axis << "" << endstr; PopTag(); @@ -269,12 +303,15 @@ void ColladaExporter::WriteTextures() { static const unsigned int buffer_size = 1024; char str[buffer_size]; - if(mScene->HasTextures()) { + if (mScene->HasTextures()) { for(unsigned int i = 0; i < mScene->mNumTextures; i++) { // It would be great to be able to create a directory in portable standard C++, but it's not the case, // so we just write the textures in the current directory. aiTexture* texture = mScene->mTextures[i]; + if ( nullptr == texture ) { + continue; + } ASSIMP_itoa10(str, buffer_size, i + 1); @@ -428,6 +465,7 @@ void ColladaExporter::WritePointLight(const aiLight *const light){ mOutput << startstr << "" << endstr; } + void ColladaExporter::WriteDirectionalLight(const aiLight *const light){ const aiColor3D &color= light->mColorDiffuse; mOutput << startstr << "" << endstr; @@ -440,6 +478,7 @@ void ColladaExporter::WriteDirectionalLight(const aiLight *const light){ mOutput << startstr << "" << endstr; } + void ColladaExporter::WriteSpotLight(const aiLight *const light){ const aiColor3D &color= light->mColorDiffuse; @@ -496,18 +535,16 @@ void ColladaExporter::WriteAmbienttLight(const aiLight *const light){ // ------------------------------------------------------------------------------------------------ // Reads a single surface entry from the given material keys -void ColladaExporter::ReadMaterialSurface( Surface& poSurface, const aiMaterial* pSrcMat, aiTextureType pTexture, const char* pKey, size_t pType, size_t pIndex) -{ - if( pSrcMat->GetTextureCount( pTexture) > 0 ) - { +void ColladaExporter::ReadMaterialSurface( Surface& poSurface, const aiMaterial* pSrcMat, + aiTextureType pTexture, const char* pKey, size_t pType, size_t pIndex) { + if( pSrcMat->GetTextureCount( pTexture) > 0 ) { aiString texfile; unsigned int uvChannel = 0; pSrcMat->GetTexture( pTexture, 0, &texfile, NULL, &uvChannel); std::string index_str(texfile.C_Str()); - if(index_str.size() != 0 && index_str[0] == '*') - { + if(index_str.size() != 0 && index_str[0] == '*') { unsigned int index; index_str = index_str.substr(1, std::string::npos); @@ -525,15 +562,13 @@ void ColladaExporter::ReadMaterialSurface( Surface& poSurface, const aiMaterial* } else { throw DeadlyExportError("could not find embedded texture at index " + index_str); } - } else - { + } else { poSurface.texture = texfile.C_Str(); } poSurface.channel = uvChannel; poSurface.exist = true; - } else - { + } else { if( pKey ) poSurface.exist = pSrcMat->Get( pKey, static_cast(pType), static_cast(pIndex), poSurface.color) == aiReturn_SUCCESS; } @@ -541,15 +576,13 @@ void ColladaExporter::ReadMaterialSurface( Surface& poSurface, const aiMaterial* // ------------------------------------------------------------------------------------------------ // Reimplementation of isalnum(,C locale), because AppVeyor does not see standard version. -static bool isalnum_C(char c) -{ +static bool isalnum_C(char c) { return ( nullptr != strchr("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz",c) ); } // ------------------------------------------------------------------------------------------------ // Writes an image entry for the given surface -void ColladaExporter::WriteImageEntry( const Surface& pSurface, const std::string& pNameAdd) -{ +void ColladaExporter::WriteImageEntry( const Surface& pSurface, const std::string& pNameAdd) { if( !pSurface.texture.empty() ) { mOutput << startstr << "" << endstr; @@ -803,8 +836,9 @@ void ColladaExporter::WriteControllerLibrary() mOutput << startstr << "" << endstr; PushTag(); - for( size_t a = 0; a < mScene->mNumMeshes; ++a) + for( size_t a = 0; a < mScene->mNumMeshes; ++a) { WriteController( a); + } PopTag(); mOutput << startstr << "" << endstr; diff --git a/code/ColladaLoader.cpp b/code/ColladaLoader.cpp index 0c87330a9..cf548e890 100644 --- a/code/ColladaLoader.cpp +++ b/code/ColladaLoader.cpp @@ -109,13 +109,13 @@ ColladaLoader::~ColladaLoader() { // ------------------------------------------------------------------------------------------------ // Returns whether the class can handle the format of the given file. -bool ColladaLoader::CanRead( const std::string& pFile, IOSystem* pIOHandler, bool checkSig) const -{ +bool ColladaLoader::CanRead( const std::string& pFile, IOSystem* pIOHandler, bool checkSig) const { // check file extension std::string extension = GetExtension(pFile); - if( extension == "dae") + if (extension == "dae") { return true; + } // XML - too generic, we need to open the file and search for typical keywords if( extension == "xml" || !extension.length() || checkSig) { @@ -123,10 +123,13 @@ bool ColladaLoader::CanRead( const std::string& pFile, IOSystem* pIOHandler, boo * support a specific file extension in general pIOHandler * might be NULL and it's our duty to return true here. */ - if (!pIOHandler)return true; + if (!pIOHandler) { + return true; + } const char* tokens[] = {"mRootNode->mTransformation *= aiMatrix4x4(parser.mUnitSize, 0, 0, 0, 0, parser.mUnitSize, 0, 0, 0, 0, parser.mUnitSize, 0, @@ -205,6 +207,17 @@ void ColladaLoader::InternReadFile( const std::string& pFile, aiScene* pScene, I 0, 0, 0, 1); } + // Store scene metadata + if (!parser.mAssetMetaData.empty()) { + const size_t numMeta(parser.mAssetMetaData.size()); + pScene->mMetaData = aiMetadata::Alloc(static_cast(numMeta)); + size_t i = 0; + for (auto it = parser.mAssetMetaData.cbegin(); it != parser.mAssetMetaData.cend(); ++it, ++i) + { + pScene->mMetaData->Set(static_cast(i), (*it).first, (*it).second); + } + } + // store all meshes StoreSceneMeshes( pScene); @@ -1924,21 +1937,28 @@ const Collada::Node* ColladaLoader::FindNodeBySID( const Collada::Node* pNode, c std::string ColladaLoader::FindNameForNode( const Collada::Node* pNode) { // If explicitly requested, just use the collada name. - if (useColladaName) { - return pNode->mName; - } - - // Now setup the name of the assimp node. The collada name might not be - // unique, so we use the collada ID. - if (!pNode->mID.empty()) - return pNode->mID; - else if (!pNode->mSID.empty()) - return pNode->mSID; - else + if (useColladaName) { - // No need to worry. Unnamed nodes are no problem at all, except - // if cameras or lights need to be assigned to them. - return format() << "$ColladaAutoName$_" << mNodeNameCounter++; + if (!pNode->mName.empty()) { + return pNode->mName; + } else { + return format() << "$ColladaAutoName$_" << mNodeNameCounter++; + } + } + else + { + // Now setup the name of the assimp node. The collada name might not be + // unique, so we use the collada ID. + if (!pNode->mID.empty()) + return pNode->mID; + else if (!pNode->mSID.empty()) + return pNode->mSID; + else + { + // No need to worry. Unnamed nodes are no problem at all, except + // if cameras or lights need to be assigned to them. + return format() << "$ColladaAutoName$_" << mNodeNameCounter++; + } } } diff --git a/code/ColladaParser.cpp b/code/ColladaParser.cpp index 0fa59362b..2106bf01c 100644 --- a/code/ColladaParser.cpp +++ b/code/ColladaParser.cpp @@ -264,14 +264,19 @@ void ColladaParser::ReadAssetInfo() // check element end TestClosing( "up_axis"); - } else + } + else if(IsElement("contributor")) { - SkipElement(); + ReadContributorInfo(); + } + else + { + ReadMetaDataItem(mAssetMetaData); } } else if( mReader->getNodeType() == irr::io::EXN_ELEMENT_END) { - if( strcmp( mReader->getNodeName(), "asset") != 0) + if (strcmp( mReader->getNodeName(), "asset") != 0) ThrowException( "Expected end of element."); break; @@ -279,6 +284,75 @@ void ColladaParser::ReadAssetInfo() } } +// ------------------------------------------------------------------------------------------------ +// Reads the contributor info +void ColladaParser::ReadContributorInfo() +{ + if (mReader->isEmptyElement()) + return; + + while (mReader->read()) + { + if (mReader->getNodeType() == irr::io::EXN_ELEMENT) + { + ReadMetaDataItem(mAssetMetaData); + } + else if (mReader->getNodeType() == irr::io::EXN_ELEMENT_END) + { + if (strcmp(mReader->getNodeName(), "contributor") != 0) + ThrowException("Expected end of element."); + break; + } + } +} + +// ------------------------------------------------------------------------------------------------ +// Reads a single string metadata item +void ColladaParser::ReadMetaDataItem(StringMetaData &metadata) +{ + // Metadata such as created, keywords, subject etc + const char* key_char = mReader->getNodeName(); + if (key_char != nullptr) + { + const std::string key_str(key_char); + const char* value_char = TestTextContent(); + if (value_char != nullptr) + { + std::string camel_key_str = key_str; + ToCamelCase(camel_key_str); + aiString aistr; + aistr.Set(value_char); + metadata.emplace(camel_key_str, aistr); + TestClosing(key_str.c_str()); + } + else + SkipElement(); + } + else + SkipElement(); +} + +// ------------------------------------------------------------------------------------------------ +// Convert underscore_seperated to CamelCase: "authoring_tool" becomes "AuthoringTool" +void ColladaParser::ToCamelCase(std::string &text) +{ + if (text.empty()) + return; + // Capitalise first character + text[0] = ToUpper(text[0]); + for (auto it = text.begin(); it != text.end(); /*iterated below*/) + { + if ((*it) == '_') + { + it = text.erase(it); + if (it != text.end()) + (*it) = ToUpper(*it); + } + else + ++it; + } +} + // ------------------------------------------------------------------------------------------------ // Reads the animation clips void ColladaParser::ReadAnimationClipLibrary() diff --git a/code/ColladaParser.h b/code/ColladaParser.h index 232d85654..22b96c5e0 100644 --- a/code/ColladaParser.h +++ b/code/ColladaParser.h @@ -66,6 +66,9 @@ namespace Assimp friend class ColladaLoader; protected: + /** Map for generic metadata as aiString */ + typedef std::map StringMetaData; + /** Constructor from XML file */ ColladaParser( IOSystem* pIOHandler, const std::string& pFile); @@ -81,6 +84,15 @@ namespace Assimp /** Reads asset information such as coordinate system information and legal blah */ void ReadAssetInfo(); + /** Reads contributor information such as author and legal blah */ + void ReadContributorInfo(); + + /** Reads generic metadata into provided map */ + void ReadMetaDataItem(StringMetaData &metadata); + + /** Convert underscore_seperated to CamelCase "authoring_tool" becomes "AuthoringTool" */ + static void ToCamelCase(std::string &text); + /** Reads the animation library */ void ReadAnimationLibrary(); @@ -343,6 +355,9 @@ namespace Assimp /** Which is the up vector */ enum { UP_X, UP_Y, UP_Z } mUpDirection; + /** Asset metadata (global for scene) */ + StringMetaData mAssetMetaData; + /** Collada file format version */ Collada::FormatVersion mFormat; }; diff --git a/code/D3MFOpcPackage.cpp b/code/D3MFOpcPackage.cpp index 2545a2750..aeadba5cc 100644 --- a/code/D3MFOpcPackage.cpp +++ b/code/D3MFOpcPackage.cpp @@ -476,8 +476,11 @@ D3MFOpcPackage::D3MFOpcPackage(IOSystem* pIOHandler, const std::string& rFile) mZipArchive->Close( fileStream ); } else if( file == D3MF::XmlTag::CONTENT_TYPES_ARCHIVE) { - + ASSIMP_LOG_WARN_F("Ignored file of unsupported type CONTENT_TYPES_ARCHIVES",file); + } else { + ASSIMP_LOG_WARN_F("Ignored file of unknown type: ",file); } + } } diff --git a/code/Exporter.cpp b/code/Exporter.cpp index 8848e87f5..4ecb07081 100644 --- a/code/Exporter.cpp +++ b/code/Exporter.cpp @@ -288,7 +288,7 @@ void Exporter::SetProgressHandler(ProgressHandler* pHandler) { // ------------------------------------------------------------------------------------------------ const aiExportDataBlob* Exporter::ExportToBlob( const aiScene* pScene, const char* pFormatId, - unsigned int, const ExportProperties* /*pProperties*/ ) { + unsigned int pPreprocessing, const ExportProperties* pProperties) { if (pimpl->blob) { delete pimpl->blob; pimpl->blob = nullptr; @@ -298,7 +298,7 @@ const aiExportDataBlob* Exporter::ExportToBlob( const aiScene* pScene, const cha BlobIOSystem* blobio = new BlobIOSystem(); pimpl->mIOSystem = std::shared_ptr( blobio ); - if (AI_SUCCESS != Export(pScene,pFormatId,blobio->GetMagicFileName())) { + if (AI_SUCCESS != Export(pScene,pFormatId,blobio->GetMagicFileName(), pPreprocessing, pProperties)) { pimpl->mIOSystem = old; return nullptr; } diff --git a/code/FBXBinaryTokenizer.cpp b/code/FBXBinaryTokenizer.cpp index f12a5c5b2..a4a2bc8e7 100644 --- a/code/FBXBinaryTokenizer.cpp +++ b/code/FBXBinaryTokenizer.cpp @@ -83,7 +83,7 @@ namespace FBX { // e_unknown_21 = 1 << 21, // e_unknown_22 = 1 << 22, // e_unknown_23 = 1 << 23, -// e_flag_field_size_64_bit = 1 << 24, // Not sure what is +// e_flag_field_size_64_bit = 1 << 24, // Not sure what is // e_unknown_25 = 1 << 25, // e_unknown_26 = 1 << 26, // e_unknown_27 = 1 << 27, @@ -98,7 +98,7 @@ namespace FBX { // return (flags & to_check) != 0; //} // ------------------------------------------------------------------------------------------------ -Token::Token(const char* sbegin, const char* send, TokenType type, unsigned int offset) +Token::Token(const char* sbegin, const char* send, TokenType type, size_t offset) : #ifdef DEBUG contents(sbegin, static_cast(send-sbegin)), @@ -122,18 +122,18 @@ namespace { // ------------------------------------------------------------------------------------------------ // signal tokenization error, this is always unrecoverable. Throws DeadlyImportError. -AI_WONT_RETURN void TokenizeError(const std::string& message, unsigned int offset) AI_WONT_RETURN_SUFFIX; -AI_WONT_RETURN void TokenizeError(const std::string& message, unsigned int offset) +AI_WONT_RETURN void TokenizeError(const std::string& message, size_t offset) AI_WONT_RETURN_SUFFIX; +AI_WONT_RETURN void TokenizeError(const std::string& message, size_t offset) { throw DeadlyImportError(Util::AddOffset("FBX-Tokenize",message,offset)); } // ------------------------------------------------------------------------------------------------ -uint32_t Offset(const char* begin, const char* cursor) { +size_t Offset(const char* begin, const char* cursor) { ai_assert(begin <= cursor); - return static_cast(cursor - begin); + return cursor - begin; } // ------------------------------------------------------------------------------------------------ @@ -276,8 +276,8 @@ void ReadData(const char*& sbegin_out, const char*& send_out, const char* input, case 'f': case 'd': case 'l': - case 'i': { - + case 'i': + case 'c': { const uint32_t length = ReadWord(input, cursor, end); const uint32_t encoding = ReadWord(input, cursor, end); @@ -298,6 +298,10 @@ void ReadData(const char*& sbegin_out, const char*& send_out, const char* input, stride = 8; break; + case 'c': + stride = 1; + break; + default: ai_assert(false); }; @@ -420,7 +424,7 @@ bool ReadScope(TokenList& output_tokens, const char* input, const char*& cursor, // ------------------------------------------------------------------------------------------------ // TODO: Test FBX Binary files newer than the 7500 version to check if the 64 bits address behaviour is consistent -void TokenizeBinary(TokenList& output_tokens, const char* input, unsigned int length) +void TokenizeBinary(TokenList& output_tokens, const char* input, size_t length) { ai_assert(input); diff --git a/code/FBXCommon.h b/code/FBXCommon.h index fcb20a5ca..e51644913 100644 --- a/code/FBXCommon.h +++ b/code/FBXCommon.h @@ -47,7 +47,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #ifndef ASSIMP_BUILD_NO_FBX_EXPORTER - +namespace Assimp { namespace FBX { const std::string NULL_RECORD = { // 13 null bytes @@ -80,7 +80,7 @@ namespace FBX TransformInheritance_MAX // end-of-enum sentinel }; } - +} #endif // ASSIMP_BUILD_NO_FBX_EXPORTER #endif // AI_FBXCOMMON_H_INC diff --git a/code/FBXConverter.cpp b/code/FBXConverter.cpp index d88a3cacd..5031665a6 100644 --- a/code/FBXConverter.cpp +++ b/code/FBXConverter.cpp @@ -67,6 +67,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include #include + namespace Assimp { namespace FBX { @@ -76,10 +77,21 @@ namespace Assimp { #define CONVERT_FBX_TIME(time) static_cast(time) / 46186158000L - FBXConverter::FBXConverter(aiScene* out, const Document& doc) - : defaultMaterialIndex() - , out(out) - , doc(doc) { + FBXConverter::FBXConverter(aiScene* out, const Document& doc, bool removeEmptyBones, FbxUnit unit ) + : defaultMaterialIndex() + , lights() + , cameras() + , textures() + , materials_converted() + , textures_converted() + , meshes_converted() + , node_anim_chain_bits() + , mNodeNames() + , anim_fps() + , out(out) + , doc(doc) + , mRemoveEmptyBones( removeEmptyBones ) + , mCurrentUnit(FbxUnit::cm) { // animations need to be converted first since this will // populate the node_anim_chain_bits map, which is needed // to determine which nodes need to be generated. @@ -107,6 +119,7 @@ namespace Assimp { ConvertGlobalSettings(); TransferDataToScene(); + ConvertToUnitScale(unit); // if we didn't read any meshes set the AI_SCENE_FLAGS_INCOMPLETE // to make sure the scene passes assimp's validation. FBX files @@ -128,12 +141,46 @@ namespace Assimp { void FBXConverter::ConvertRootNode() { out->mRootNode = new aiNode(); - out->mRootNode->mName.Set("RootNode"); + std::string unique_name; + GetUniqueName("RootNode", unique_name); + out->mRootNode->mName.Set(unique_name); // root has ID 0 ConvertNodes(0L, *out->mRootNode); } + static std::string getAncestorBaseName(const aiNode* node) + { + const char* nodeName = nullptr; + size_t length = 0; + while (node && (!nodeName || length == 0)) + { + nodeName = node->mName.C_Str(); + length = node->mName.length; + node = node->mParent; + } + + if (!nodeName || length == 0) + { + return {}; + } + // could be std::string_view if c++17 available + return std::string(nodeName, length); + } + + // Make unique name + std::string FBXConverter::MakeUniqueNodeName(const Model* const model, const aiNode& parent) + { + std::string original_name = FixNodeName(model->Name()); + if (original_name.empty()) + { + original_name = getAncestorBaseName(&parent); + } + std::string unique_name; + GetUniqueName(original_name, unique_name); + return unique_name; + } + void FBXConverter::ConvertNodes(uint64_t id, aiNode& parent, const aiMatrix4x4& parent_transform) { const std::vector& conns = doc.GetConnectionsByDestinationSequenced(id, "Model"); @@ -165,35 +212,18 @@ namespace Assimp { aiMatrix4x4 new_abs_transform = parent_transform; + std::string unique_name = MakeUniqueNodeName(model, parent); + // even though there is only a single input node, the design of // assimp (or rather: the complicated transformation chain that // is employed by fbx) means that we may need multiple aiNode's // to represent a fbx node's transformation. - GenerateTransformationNodeChain(*model, nodes_chain, post_nodes_chain); + const bool need_additional_node = GenerateTransformationNodeChain(*model, unique_name, nodes_chain, post_nodes_chain); ai_assert(nodes_chain.size()); - std::string original_name = FixNodeName(model->Name()); - - // check if any of the nodes in the chain has the name the fbx node - // is supposed to have. If there is none, add another node to - // preserve the name - people might have scripts etc. that rely - // on specific node names. - aiNode* name_carrier = NULL; - for (aiNode* prenode : nodes_chain) { - if (!strcmp(prenode->mName.C_Str(), original_name.c_str())) { - name_carrier = prenode; - break; - } - } - - if (!name_carrier) { - std::string old_original_name = original_name; - GetUniqueName(old_original_name, original_name); - nodes_chain.push_back(new aiNode(original_name)); - } - else { - original_name = nodes_chain.back()->mName.C_Str(); + if (need_additional_node) { + nodes_chain.push_back(new aiNode(unique_name)); } //setup metadata on newest node @@ -255,11 +285,11 @@ namespace Assimp { ConvertNodes(model->ID(), *last_parent, new_abs_transform); if (doc.Settings().readLights) { - ConvertLights(*model, original_name); + ConvertLights(*model, unique_name); } if (doc.Settings().readCameras) { - ConvertCameras(*model, original_name); + ConvertCameras(*model, unique_name); } nodes.push_back(nodes_chain.front()); @@ -377,6 +407,7 @@ namespace Assimp { break; default: ai_assert(false); + break; } } @@ -390,9 +421,18 @@ namespace Assimp { out_camera->mAspect = cam.AspectWidth() / cam.AspectHeight(); //cameras are defined along positive x direction - out_camera->mPosition = cam.Position(); + /*out_camera->mPosition = cam.Position(); out_camera->mLookAt = (cam.InterestPosition() - out_camera->mPosition).Normalize(); - out_camera->mUp = cam.UpVector(); + out_camera->mUp = cam.UpVector();*/ + + out_camera->mPosition = aiVector3D(0.0f); + out_camera->mLookAt = aiVector3D(1.0f, 0.0f, 0.0f); + out_camera->mUp = aiVector3D(0.0f, 1.0f, 0.0f); + + out_camera->mHorizontalFOV = AI_DEG_TO_RAD(cam.FieldOfView()); + + out_camera->mClipPlaneNear = cam.NearPlane(); + out_camera->mClipPlaneFar = cam.FarPlane(); out_camera->mHorizontalFOV = AI_DEG_TO_RAD(cam.FieldOfView()); out_camera->mClipPlaneNear = cam.NearPlane(); @@ -401,19 +441,19 @@ namespace Assimp { void FBXConverter::GetUniqueName(const std::string &name, std::string &uniqueName) { - int i = 0; uniqueName = name; - while (mNodeNames.find(uniqueName) != mNodeNames.end()) + auto it_pair = mNodeNames.insert({ name, 0 }); // duplicate node name instance count + unsigned int& i = it_pair.first->second; + while (!it_pair.second) { - ++i; - std::stringstream ext; + i++; + std::ostringstream ext; ext << name << std::setfill('0') << std::setw(3) << i; uniqueName = ext.str(); + it_pair = mNodeNames.insert({ uniqueName, 0 }); } - mNodeNames.insert(uniqueName); } - const char* FBXConverter::NameTransformationComp(TransformationComp comp) { switch (comp) { case TransformationComp_Translation: @@ -643,7 +683,7 @@ namespace Assimp { return name + std::string(MAGIC_NODE_TAG) + "_" + NameTransformationComp(comp); } - void FBXConverter::GenerateTransformationNodeChain(const Model& model, std::vector& output_nodes, + bool FBXConverter::GenerateTransformationNodeChain(const Model& model, const std::string& name, std::vector& output_nodes, std::vector& post_output_nodes) { const PropertyTable& props = model.Props(); const Model::RotOrder rot = model.RotationOrder(); @@ -758,8 +798,6 @@ namespace Assimp { // not be guaranteed. ai_assert(NeedsComplexTransformationChain(model) == is_complex); - std::string name = FixNodeName(model.Name()); - // now, if we have more than just Translation, Scaling and Rotation, // we need to generate a full node chain to accommodate for assimp's // lack to express pivots and offsets. @@ -801,20 +839,20 @@ namespace Assimp { } ai_assert(output_nodes.size()); - return; + return true; } // else, we can just multiply the matrices together aiNode* nd = new aiNode(); output_nodes.push_back(nd); - std::string uniqueName; - GetUniqueName(name, uniqueName); - nd->mName.Set(uniqueName); + // name passed to the method is already unique + nd->mName.Set(name); for (const auto &transform : chain) { nd->mTransformation = nd->mTransformation * transform; } + return false; } void FBXConverter::SetupNodeMetadata(const Model& model, aiNode& nd) @@ -953,9 +991,11 @@ namespace Assimp { unsigned int epcount = 0; for (unsigned i = 0; i < indices.size(); i++) { - if (indices[i] < 0) epcount++; + if (indices[i] < 0) { + epcount++; + } } - unsigned int pcount = indices.size(); + unsigned int pcount = static_cast( indices.size() ); unsigned int scount = out_mesh->mNumFaces = pcount - epcount; aiFace* fac = out_mesh->mFaces = new aiFace[scount](); @@ -1151,7 +1191,7 @@ namespace Assimp { } } } - size_t numAnimMeshes = animMeshes.size(); + const size_t numAnimMeshes = animMeshes.size(); if (numAnimMeshes > 0) { out_mesh->mNumAnimMeshes = static_cast(numAnimMeshes); out_mesh->mAnimMeshes = new aiAnimMesh*[numAnimMeshes]; @@ -1291,8 +1331,7 @@ namespace Assimp { unsigned int cursor = 0, in_cursor = 0; itf = faces.begin(); - for (MatIndexArray::const_iterator it = mindices.begin(), - end = mindices.end(); it != end; ++it, ++itf) + for (MatIndexArray::const_iterator it = mindices.begin(), end = mindices.end(); it != end; ++it, ++itf) { const unsigned int pcount = *itf; if ((*it) != index) { @@ -1384,7 +1423,7 @@ namespace Assimp { const WeightIndexArray& indices = cluster->GetIndices(); - if (indices.empty()) { + if (indices.empty() && mRemoveEmptyBones ) { continue; } @@ -1416,13 +1455,11 @@ namespace Assimp { if (index_out_indices.back() == no_index_sentinel) { index_out_indices.back() = out_indices.size(); - } if (no_mat_check) { out_indices.push_back(out_idx[i]); - } - else { + } else { // this extra lookup is in O(logn), so the entire algorithm becomes O(nlogn) const std::vector::iterator it = std::lower_bound( outputVertStartIndices->begin(), @@ -1438,11 +1475,11 @@ namespace Assimp { } } } - + // if we found at least one, generate the output bones // XXX this could be heavily simplified by collecting the bone // data in a single step. - if (ok) { + if (ok && mRemoveEmptyBones) { ConvertCluster(bones, model, *cluster, out_indices, index_out_indices, count_out_indices, node_global_transform); } @@ -1573,6 +1610,13 @@ namespace Assimp { out_mat->AddProperty(&str, AI_MATKEY_NAME); } + // Set the shading mode as best we can: The FBX specification only mentions Lambert and Phong, and only Phong is mentioned in Assimp's aiShadingMode enum. + if (material.GetShadingModel() == "phong") + { + aiShadingMode shadingMode = aiShadingMode_Phong; + out_mat->AddProperty(&shadingMode, 1, AI_MATKEY_SHADING_MODEL); + } + // shading stuff and colors SetShadingPropertiesCommon(out_mat, props); SetShadingPropertiesRaw( out_mat, props, material.Textures(), mesh ); @@ -1704,22 +1748,22 @@ namespace Assimp { if (!mesh) { for (const MeshMap::value_type& v : meshes_converted) { - const MeshGeometry* const mesh = dynamic_cast (v.first); - if (!mesh) { + const MeshGeometry* const meshGeom = dynamic_cast (v.first); + if (!meshGeom) { continue; } - const MatIndexArray& mats = mesh->GetMaterialIndices(); + const MatIndexArray& mats = meshGeom->GetMaterialIndices(); if (std::find(mats.begin(), mats.end(), matIndex) == mats.end()) { continue; } int index = -1; for (unsigned int i = 0; i < AI_MAX_NUMBER_OF_TEXTURECOORDS; ++i) { - if (mesh->GetTextureCoords(i).empty()) { + if (meshGeom->GetTextureCoords(i).empty()) { break; } - const std::string& name = mesh->GetTextureCoordChannelName(i); + const std::string& name = meshGeom->GetTextureCoordChannelName(i); if (name == uvSet) { index = static_cast(i); break; @@ -1827,22 +1871,22 @@ namespace Assimp { if (!mesh) { for (const MeshMap::value_type& v : meshes_converted) { - const MeshGeometry* const mesh = dynamic_cast (v.first); - if (!mesh) { + const MeshGeometry* const meshGeom = dynamic_cast (v.first); + if (!meshGeom) { continue; } - const MatIndexArray& mats = mesh->GetMaterialIndices(); + const MatIndexArray& mats = meshGeom->GetMaterialIndices(); if (std::find(mats.begin(), mats.end(), matIndex) == mats.end()) { continue; } int index = -1; for (unsigned int i = 0; i < AI_MAX_NUMBER_OF_TEXTURECOORDS; ++i) { - if (mesh->GetTextureCoords(i).empty()) { + if (meshGeom->GetTextureCoords(i).empty()) { break; } - const std::string& name = mesh->GetTextureCoordChannelName(i); + const std::string& name = meshGeom->GetTextureCoordChannelName(i); if (name == uvSet) { index = static_cast(i); break; @@ -2033,6 +2077,12 @@ namespace Assimp { CalculatedOpacity = 1.0f - ((Transparent.r + Transparent.g + Transparent.b) / 3.0f); } + // try to get the transparency factor + const float TransparencyFactor = PropertyGet(props, "TransparencyFactor", ok); + if (ok) { + out_mat->AddProperty(&TransparencyFactor, 1, AI_MATKEY_TRANSPARENCYFACTOR); + } + // use of TransparencyFactor is inconsistent. // Maya always stores it as 1.0, // so we can't use it to set AI_MATKEY_OPACITY. @@ -2183,22 +2233,22 @@ void FBXConverter::SetShadingPropertiesRaw(aiMaterial* out_mat, const PropertyTa if (!mesh) { for (const MeshMap::value_type& v : meshes_converted) { - const MeshGeometry* const mesh = dynamic_cast(v.first); - if (!mesh) { + const MeshGeometry* const meshGeom = dynamic_cast(v.first); + if (!meshGeom) { continue; } - const MatIndexArray& mats = mesh->GetMaterialIndices(); + const MatIndexArray& mats = meshGeom->GetMaterialIndices(); if (std::find(mats.begin(), mats.end(), matIndex) == mats.end()) { continue; } int index = -1; for (unsigned int i = 0; i < AI_MAX_NUMBER_OF_TEXTURECOORDS; ++i) { - if (mesh->GetTextureCoords(i).empty()) { + if (meshGeom->GetTextureCoords(i).empty()) { break; } - const std::string& name = mesh->GetTextureCoordChannelName(i); + const std::string& name = meshGeom->GetTextureCoordChannelName(i); if (name == uvSet) { index = static_cast(i); break; @@ -3378,8 +3428,9 @@ void FBXConverter::SetShadingPropertiesRaw(aiMaterial* out_mat, const PropertyTa na->mNumScalingKeys = static_cast(keys.size()); na->mScalingKeys = new aiVectorKey[keys.size()]; - if (keys.size() > 0) + if (keys.size() > 0) { InterpolateKeys(na->mScalingKeys, keys, inputs, aiVector3D(1.0f, 1.0f, 1.0f), maxTime, minTime); + } } void FBXConverter::ConvertTranslationKeys(aiNodeAnim* na, const std::vector& nodes, @@ -3443,6 +3494,46 @@ void FBXConverter::SetShadingPropertiesRaw(aiMaterial* out_mat, const PropertyTa out->mMetaData->Set(14, "CustomFrameRate", doc.GlobalSettings().CustomFrameRate()); } + void FBXConverter::ConvertToUnitScale( FbxUnit unit ) { + if (mCurrentUnit == unit) { + return; + } + + ai_real scale = 1.0; + if (mCurrentUnit == FbxUnit::cm) { + if (unit == FbxUnit::m) { + scale = (ai_real)0.01; + } else if (unit == FbxUnit::km) { + scale = (ai_real)0.00001; + } + } else if (mCurrentUnit == FbxUnit::m) { + if (unit == FbxUnit::cm) { + scale = (ai_real)100.0; + } else if (unit == FbxUnit::km) { + scale = (ai_real)0.001; + } + } else if (mCurrentUnit == FbxUnit::km) { + if (unit == FbxUnit::cm) { + scale = (ai_real)100000.0; + } else if (unit == FbxUnit::m) { + scale = (ai_real)1000.0; + } + } + + for (auto mesh : meshes) { + if (nullptr == mesh) { + continue; + } + + if (mesh->HasPositions()) { + for (unsigned int i = 0; i < mesh->mNumVertices; ++i) { + aiVector3D &pos = mesh->mVertices[i]; + pos *= scale; + } + } + } + } + void FBXConverter::TransferDataToScene() { ai_assert(!out->mMeshes); @@ -3496,9 +3587,9 @@ void FBXConverter::SetShadingPropertiesRaw(aiMaterial* out_mat, const PropertyTa } // ------------------------------------------------------------------------------------------------ - void ConvertToAssimpScene(aiScene* out, const Document& doc) + void ConvertToAssimpScene(aiScene* out, const Document& doc, bool removeEmptyBones, FbxUnit unit) { - FBXConverter converter(out, doc); + FBXConverter converter(out, doc, removeEmptyBones, unit); } } // !FBX diff --git a/code/FBXConverter.h b/code/FBXConverter.h index 398baa445..dba7c0a05 100644 --- a/code/FBXConverter.h +++ b/code/FBXConverter.h @@ -58,6 +58,8 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include #include #include +#include +#include struct aiScene; struct aiNode; @@ -74,14 +76,22 @@ namespace FBX { class Document; -using NodeNameCache = std::set; +enum class FbxUnit { + cm = 0, + m, + km, + NumUnits, + + Undefined +}; /** * Convert a FBX #Document to #aiScene * @param out Empty scene to be populated - * @param doc Parsed FBX document + * @param doc Parsed FBX document + * @param removeEmptyBones Will remove bones, which do not have any references to vertices. */ -void ConvertToAssimpScene(aiScene* out, const Document& doc); +void ConvertToAssimpScene(aiScene* out, const Document& doc, bool removeEmptyBones, FbxUnit unit); /** Dummy class to encapsulate the conversion process */ class FBXConverter { @@ -112,7 +122,7 @@ public: }; public: - FBXConverter(aiScene* out, const Document& doc); + FBXConverter(aiScene* out, const Document& doc, bool removeEmptyBones, FbxUnit unit); ~FBXConverter(); private: @@ -144,6 +154,11 @@ private: // while these would be allowed, they are a potential trouble spot so better not use them). const char* NameTransformationComp(TransformationComp comp); + // ------------------------------------------------------------------------------------------------ + // Returns an unique name for a node or traverses up a hierarchy until a non-empty name is found and + // then makes this name unique + std::string MakeUniqueNodeName(const Model* const model, const aiNode& parent); + // ------------------------------------------------------------------------------------------------ // note: this returns the REAL fbx property names const char* NameTransformationCompProperty(TransformationComp comp); @@ -167,7 +182,7 @@ private: /** * note: memory for output_nodes will be managed by the caller */ - void GenerateTransformationNodeChain(const Model& model, std::vector& output_nodes, std::vector& post_output_nodes); + bool GenerateTransformationNodeChain(const Model& model, const std::string& name, std::vector& output_nodes, std::vector& post_output_nodes); // ------------------------------------------------------------------------------------------------ void SetupNodeMetadata(const Model& model, aiNode& nd); @@ -414,12 +429,15 @@ private: void ConvertGlobalSettings(); + // ------------------------------------------------------------------------------------------------ + // Will perform the conversion from a given unit to the requested unit. + void ConvertToUnitScale(FbxUnit unit); + // ------------------------------------------------------------------------------------------------ // copy generated meshes, animations, lights, cameras and textures to the output scene void TransferDataToScene(); private: - // 0: not assigned yet, others: index is value - 1 unsigned int defaultMaterialIndex; @@ -429,26 +447,32 @@ private: std::vector lights; std::vector cameras; std::vector textures; - - typedef std::map MaterialMap; + using MaterialMap = std::map; MaterialMap materials_converted; - typedef std::map VideoMap; + using VideoMap = std::map; VideoMap textures_converted; - typedef std::map > MeshMap; + using MeshMap = std::map >; MeshMap meshes_converted; // fixed node name -> which trafo chain components have animations? - typedef std::map NodeAnimBitMap; + using NodeAnimBitMap = std::map ; NodeAnimBitMap node_anim_chain_bits; + // number of nodes with the same name + using NodeNameCache = std::unordered_map; NodeNameCache mNodeNames; + double anim_fps; aiScene* const out; const FBX::Document& doc; + + bool mRemoveEmptyBones; + + FbxUnit mCurrentUnit; }; } diff --git a/code/FBXDocument.cpp b/code/FBXDocument.cpp index 2e0d00a79..1af08fe6d 100644 --- a/code/FBXDocument.cpp +++ b/code/FBXDocument.cpp @@ -69,8 +69,7 @@ LazyObject::LazyObject(uint64_t id, const Element& element, const Document& doc) : doc(doc) , element(element) , id(id) -, flags() -{ +, flags() { // empty } @@ -84,7 +83,7 @@ LazyObject::~LazyObject() const Object* LazyObject::Get(bool dieOnError) { if(IsBeingConstructed() || FailedToConstruct()) { - return NULL; + return nullptr; } if (object.get()) { @@ -553,7 +552,7 @@ const std::vector& Document::AnimationStacks() const LazyObject* Document::GetObject(uint64_t id) const { ObjectMap::const_iterator it = objects.find(id); - return it == objects.end() ? NULL : (*it).second; + return it == objects.end() ? nullptr : (*it).second; } #define MAX_CLASSNAMES 6 @@ -610,7 +609,7 @@ std::vector Document::GetConnectionsSequenced(uint64_t id, bo for (size_t i = 0; i < c; ++i) { ai_assert(classnames[i]); if(static_cast(std::distance(key.begin(),key.end())) == lengths[i] && !strncmp(classnames[i],obtype,lengths[i])) { - obtype = NULL; + obtype = nullptr; break; } } diff --git a/code/FBXDocument.h b/code/FBXDocument.h index cbc6d60cd..53bd65878 100644 --- a/code/FBXDocument.h +++ b/code/FBXDocument.h @@ -85,13 +85,11 @@ class Cluster; /** Represents a delay-parsed FBX objects. Many objects in the scene * are not needed by assimp, so it makes no sense to parse them * upfront. */ -class LazyObject -{ +class LazyObject { public: LazyObject(uint64_t id, const Element& element, const Document& doc); - ~LazyObject(); -public: + ~LazyObject(); const Object* Get(bool dieOnError = false); @@ -136,11 +134,8 @@ private: unsigned int flags; }; - - /** Base class for in-memory (DOM) representations of FBX objects */ -class Object -{ +class Object { public: Object(uint64_t id, const Element& element, const std::string& name); @@ -164,14 +159,12 @@ protected: const uint64_t id; }; - - /** DOM class for generic FBX NoteAttribute blocks. NoteAttribute's just hold a property table, * fixed members are added by deriving classes. */ -class NodeAttribute : public Object -{ +class NodeAttribute : public Object { public: NodeAttribute(uint64_t id, const Element& element, const Document& doc, const std::string& name); + virtual ~NodeAttribute(); const PropertyTable& Props() const { @@ -183,12 +176,11 @@ private: std::shared_ptr props; }; - /** DOM base class for FBX camera settings attached to a node */ -class CameraSwitcher : public NodeAttribute -{ +class CameraSwitcher : public NodeAttribute { public: CameraSwitcher(uint64_t id, const Element& element, const Document& doc, const std::string& name); + virtual ~CameraSwitcher(); int CameraID() const { @@ -209,7 +201,6 @@ private: std::string cameraIndexName; }; - #define fbx_stringize(a) #a #define fbx_simple_property(name, type, default_value) \ @@ -230,13 +221,12 @@ private: /** DOM base class for FBX cameras attached to a node */ -class Camera : public NodeAttribute -{ +class Camera : public NodeAttribute { public: Camera(uint64_t id, const Element& element, const Document& doc, const std::string& name); + virtual ~Camera(); -public: fbx_simple_property(Position, aiVector3D, aiVector3D(0,0,0)) fbx_simple_property(UpVector, aiVector3D, aiVector3D(0,1,0)) fbx_simple_property(InterestPosition, aiVector3D, aiVector3D(0,0,0)) @@ -256,33 +246,26 @@ public: fbx_simple_property(FocalLength, float, 1.0f) }; - /** DOM base class for FBX null markers attached to a node */ -class Null : public NodeAttribute -{ +class Null : public NodeAttribute { public: Null(uint64_t id, const Element& element, const Document& doc, const std::string& name); virtual ~Null(); }; - /** DOM base class for FBX limb node markers attached to a node */ -class LimbNode : public NodeAttribute -{ +class LimbNode : public NodeAttribute { public: LimbNode(uint64_t id, const Element& element, const Document& doc, const std::string& name); virtual ~LimbNode(); }; - /** DOM base class for FBX lights attached to a node */ -class Light : public NodeAttribute -{ +class Light : public NodeAttribute { public: Light(uint64_t id, const Element& element, const Document& doc, const std::string& name); virtual ~Light(); -public: enum Type { Type_Point, @@ -304,7 +287,6 @@ public: Decay_MAX // end-of-enum sentinel }; -public: fbx_simple_property(Color, aiVector3D, aiVector3D(1,1,1)) fbx_simple_enum_property(LightType, Type, 0) fbx_simple_property(CastLightOnObject, bool, false) @@ -338,10 +320,8 @@ public: fbx_simple_property(EnableBarnDoor, bool, true) }; - /** DOM base class for FBX models (even though its semantics are more "node" than "model" */ -class Model : public Object -{ +class Model : public Object { public: enum RotOrder { RotOrder_EulerXYZ = 0, @@ -356,7 +336,6 @@ public: RotOrder_MAX // end-of-enum sentinel }; - enum TransformInheritance { TransformInheritance_RrSs = 0, TransformInheritance_RSrs, @@ -490,13 +469,12 @@ private: }; /** DOM class for generic FBX textures */ -class Texture : public Object -{ +class Texture : public Object { public: Texture(uint64_t id, const Element& element, const Document& doc, const std::string& name); + virtual ~Texture(); -public: const std::string& Type() const { return type; } @@ -551,17 +529,15 @@ private: }; /** DOM class for layered FBX textures */ -class LayeredTexture : public Object -{ +class LayeredTexture : public Object { public: LayeredTexture(uint64_t id, const Element& element, const Document& doc, const std::string& name); virtual ~LayeredTexture(); - //Can only be called after construction of the layered texture object due to construction flag. + // Can only be called after construction of the layered texture object due to construction flag. void fillTexture(const Document& doc); - enum BlendMode - { + enum BlendMode { BlendMode_Translucent, BlendMode_Additive, BlendMode_Modulate, @@ -623,13 +599,12 @@ typedef std::fbx_unordered_map LayeredTextur /** DOM class for generic FBX videos */ -class Video : public Object -{ +class Video : public Object { public: Video(uint64_t id, const Element& element, const Document& doc, const std::string& name); + virtual ~Video(); -public: const std::string& Type() const { return type; } @@ -668,15 +643,15 @@ private: std::string fileName; std::shared_ptr props; - uint32_t contentLength; + uint64_t contentLength; uint8_t* content; }; /** DOM class for generic FBX materials */ -class Material : public Object -{ +class Material : public Object { public: Material(uint64_t id, const Element& element, const Document& doc, const std::string& name); + virtual ~Material(); const std::string& GetShadingModel() const { @@ -713,8 +688,7 @@ typedef std::vector KeyTimeList; typedef std::vector KeyValueList; /** Represents a FBX animation curve (i.e. a 1-dimensional set of keyframes and values therefor) */ -class AnimationCurve : public Object -{ +class AnimationCurve : public Object { public: AnimationCurve(uint64_t id, const Element& element, const std::string& name, const Document& doc); virtual ~AnimationCurve(); @@ -725,14 +699,12 @@ public: return keys; } - /** get list of keyframe values. * Invariant: |GetKeys()| == |GetValues()| && |GetKeys()| > 0*/ const KeyValueList& GetValues() const { return values; } - const std::vector& GetAttributes() const { return attributes; } @@ -751,10 +723,8 @@ private: // property-name -> animation curve typedef std::map AnimationCurveMap; - /** Represents a FBX animation curve (i.e. a mapping from single animation curves to nodes) */ -class AnimationCurveNode : public Object -{ +class AnimationCurveNode : public Object { public: /* the optional white list specifies a list of property names for which the caller wants animations for. If the curve node does not match one of these, std::range_error @@ -804,8 +774,7 @@ private: typedef std::vector AnimationCurveNodeList; /** Represents a FBX animation layer (i.e. a list of node animations) */ -class AnimationLayer : public Object -{ +class AnimationLayer : public Object { public: AnimationLayer(uint64_t id, const Element& element, const std::string& name, const Document& doc); virtual ~AnimationLayer(); @@ -818,7 +787,7 @@ public: /* the optional white list specifies a list of property names for which the caller wants animations for. Curves not matching this list will not be added to the animation layer. */ - AnimationCurveNodeList Nodes(const char* const * target_prop_whitelist = NULL, size_t whitelist_size = 0) const; + AnimationCurveNodeList Nodes(const char* const * target_prop_whitelist = nullptr, size_t whitelist_size = 0) const; private: std::shared_ptr props; @@ -828,8 +797,7 @@ private: typedef std::vector AnimationLayerList; /** Represents a FBX animation stack (i.e. a list of animation layers) */ -class AnimationStack : public Object -{ +class AnimationStack : public Object { public: AnimationStack(uint64_t id, const Element& element, const std::string& name, const Document& doc); virtual ~AnimationStack(); @@ -855,8 +823,7 @@ private: /** DOM class for deformers */ -class Deformer : public Object -{ +class Deformer : public Object { public: Deformer(uint64_t id, const Element& element, const Document& doc, const std::string& name); virtual ~Deformer(); @@ -875,10 +842,10 @@ typedef std::vector WeightIndexArray; /** DOM class for BlendShapeChannel deformers */ -class BlendShapeChannel : public Deformer -{ +class BlendShapeChannel : public Deformer { public: BlendShapeChannel(uint64_t id, const Element& element, const Document& doc, const std::string& name); + virtual ~BlendShapeChannel(); float DeformPercent() const { @@ -892,6 +859,7 @@ public: const std::vector& GetShapeGeometries() const { return shapeGeometries; } + private: float percent; WeightArray fullWeights; @@ -899,10 +867,10 @@ private: }; /** DOM class for BlendShape deformers */ -class BlendShape : public Deformer -{ +class BlendShape : public Deformer { public: BlendShape(uint64_t id, const Element& element, const Document& doc, const std::string& name); + virtual ~BlendShape(); const std::vector& BlendShapeChannels() const { @@ -913,11 +881,11 @@ private: std::vector blendShapeChannels; }; -/** DOM class for skin deformer clusters (aka subdeformers) */ -class Cluster : public Deformer -{ +/** DOM class for skin deformer clusters (aka sub-deformers) */ +class Cluster : public Deformer { public: Cluster(uint64_t id, const Element& element, const Document& doc, const std::string& name); + virtual ~Cluster(); /** get the list of deformer weights associated with this cluster. @@ -958,10 +926,10 @@ private: }; /** DOM class for skin deformers */ -class Skin : public Deformer -{ +class Skin : public Deformer { public: Skin(uint64_t id, const Element& element, const Document& doc, const std::string& name); + virtual ~Skin(); float DeformAccuracy() const { @@ -978,10 +946,10 @@ private: }; /** Represents a link between two FBX objects. */ -class Connection -{ +class Connection { public: Connection(uint64_t insertionOrder, uint64_t src, uint64_t dest, const std::string& prop, const Document& doc); + ~Connection(); // note: a connection ensures that the source and dest objects exist, but @@ -1006,7 +974,7 @@ public: } int CompareTo(const Connection* c) const { - ai_assert( NULL != c ); + ai_assert( nullptr != c ); // note: can't subtract because this would overflow uint64_t if(InsertionOrder() > c->InsertionOrder()) { @@ -1019,7 +987,7 @@ public: } bool Compare(const Connection* c) const { - ai_assert( NULL != c ); + ai_assert( nullptr != c ); return InsertionOrder() < c->InsertionOrder(); } @@ -1047,6 +1015,7 @@ typedef std::multimap ConnectionMap; class FileGlobalSettings { public: FileGlobalSettings(const Document& doc, std::shared_ptr props); + ~FileGlobalSettings(); const PropertyTable& Props() const { @@ -1103,10 +1072,10 @@ private: }; /** DOM root for a FBX file */ -class Document -{ +class Document { public: Document(const Parser& parser, const ImportSettings& settings); + ~Document(); LazyObject* GetObject(uint64_t id) const; diff --git a/code/FBXDocumentUtil.h b/code/FBXDocumentUtil.h index c0435a663..2450109e5 100644 --- a/code/FBXDocumentUtil.h +++ b/code/FBXDocumentUtil.h @@ -56,7 +56,6 @@ namespace Assimp { namespace FBX { namespace Util { - /* DOM/Parse error reporting - does not return */ AI_WONT_RETURN void DOMError(const std::string& message, const Token& token) AI_WONT_RETURN_SUFFIX; AI_WONT_RETURN void DOMError(const std::string& message, const Element* element = NULL) AI_WONT_RETURN_SUFFIX; @@ -73,28 +72,28 @@ std::shared_ptr GetPropertyTable(const Document& doc, const Scope& sc, bool no_warn = false); - // ------------------------------------------------------------------------------------------------ template -inline const T* ProcessSimpleConnection(const Connection& con, +inline +const T* ProcessSimpleConnection(const Connection& con, bool is_object_property_conn, const char* name, const Element& element, - const char** propNameOut = NULL) + const char** propNameOut = nullptr) { if (is_object_property_conn && !con.PropertyName().length()) { DOMWarning("expected incoming " + std::string(name) + " link to be an object-object connection, ignoring", &element ); - return NULL; + return nullptr; } else if (!is_object_property_conn && con.PropertyName().length()) { DOMWarning("expected incoming " + std::string(name) + " link to be an object-property connection, ignoring", &element ); - return NULL; + return nullptr; } if(is_object_property_conn && propNameOut) { @@ -108,13 +107,12 @@ inline const T* ProcessSimpleConnection(const Connection& con, DOMWarning("failed to read source object for incoming " + std::string(name) + " link, ignoring", &element); - return NULL; + return nullptr; } return dynamic_cast(ob); } - } //!Util } //!FBX } //!Assimp diff --git a/code/FBXExportNode.cpp b/code/FBXExportNode.cpp index ace6a6ac2..06c89cee4 100644 --- a/code/FBXExportNode.cpp +++ b/code/FBXExportNode.cpp @@ -54,6 +54,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include // ostringstream #include // shared_ptr +namespace Assimp { // AddP70 helpers... there's no usable pattern here, // so all are defined as separate functions. // Even "animatable" properties are often completely different @@ -252,7 +253,8 @@ void FBX::Node::DumpChildren( } else { std::ostringstream ss; DumpChildrenAscii(ss, indent); - s.PutString(ss.str()); + if (ss.tellp() > 0) + s.PutString(ss.str()); } } @@ -266,7 +268,8 @@ void FBX::Node::End( } else { std::ostringstream ss; EndAscii(ss, indent, has_children); - s.PutString(ss.str()); + if (ss.tellp() > 0) + s.PutString(ss.str()); } } @@ -367,7 +370,7 @@ void FBX::Node::EndBinary( bool has_children ) { // if there were children, add a null record - if (has_children) { s.PutString(FBX::NULL_RECORD); } + if (has_children) { s.PutString(Assimp::FBX::NULL_RECORD); } // now go back and write initial pos this->end_pos = s.Tell(); @@ -432,7 +435,7 @@ void FBX::Node::WritePropertyNodeAscii( char buffer[32]; FBX::Node node(name); node.Begin(s, false, indent); - std::string vsize = std::to_string(v.size()); + std::string vsize = to_string(v.size()); // * { s.PutChar('*'); s.PutString(vsize); s.PutString(" {\n"); // indent + 1 @@ -468,7 +471,7 @@ void FBX::Node::WritePropertyNodeAscii( char buffer[32]; FBX::Node node(name); node.Begin(s, false, indent); - std::string vsize = std::to_string(v.size()); + std::string vsize = to_string(v.size()); // * { s.PutChar('*'); s.PutString(vsize); s.PutString(" {\n"); // indent + 1 @@ -563,6 +566,6 @@ void FBX::Node::WritePropertyNode( FBX::Node::WritePropertyNodeAscii(name, v, s, indent); } } - +} #endif // ASSIMP_BUILD_NO_FBX_EXPORTER #endif // ASSIMP_BUILD_NO_EXPORT diff --git a/code/FBXExportNode.h b/code/FBXExportNode.h index e1ebc3696..ef3bc781a 100644 --- a/code/FBXExportNode.h +++ b/code/FBXExportNode.h @@ -54,16 +54,16 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include #include +namespace Assimp { namespace FBX { class Node; } -class FBX::Node -{ -public: // public data members +class FBX::Node { +public: // TODO: accessors std::string name; // node name - std::vector properties; // node properties + std::vector properties; // node properties std::vector children; // child nodes // some nodes always pretend they have children... @@ -214,7 +214,7 @@ public: // static member functions Assimp::StreamWriterLE& s, bool binary, int indent ) { - FBX::Property p(value); + FBX::FBXExportProperty p(value); FBX::Node node(name, p); node.Dump(s, binary, indent); } @@ -264,7 +264,7 @@ private: // static helper functions ); }; - +} #endif // ASSIMP_BUILD_NO_FBX_EXPORTER diff --git a/code/FBXExportProperty.cpp b/code/FBXExportProperty.cpp index 9981d6b1c..f8593e629 100644 --- a/code/FBXExportProperty.cpp +++ b/code/FBXExportProperty.cpp @@ -52,187 +52,210 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include #include // ostringstream +namespace Assimp { +namespace FBX { // constructors for single element properties -FBX::Property::Property(bool v) - : type('C'), data(1) -{ - data = {uint8_t(v)}; +FBXExportProperty::FBXExportProperty(bool v) +: type('C') +, data(1) { + data = { + uint8_t(v) + }; } -FBX::Property::Property(int16_t v) : type('Y'), data(2) -{ +FBXExportProperty::FBXExportProperty(int16_t v) +: type('Y') +, data(2) { uint8_t* d = data.data(); (reinterpret_cast(d))[0] = v; } -FBX::Property::Property(int32_t v) : type('I'), data(4) -{ +FBXExportProperty::FBXExportProperty(int32_t v) +: type('I') +, data(4) { uint8_t* d = data.data(); (reinterpret_cast(d))[0] = v; } -FBX::Property::Property(float v) : type('F'), data(4) -{ +FBXExportProperty::FBXExportProperty(float v) +: type('F') +, data(4) { uint8_t* d = data.data(); (reinterpret_cast(d))[0] = v; } -FBX::Property::Property(double v) : type('D'), data(8) -{ +FBXExportProperty::FBXExportProperty(double v) +: type('D') +, data(8) { uint8_t* d = data.data(); (reinterpret_cast(d))[0] = v; } -FBX::Property::Property(int64_t v) : type('L'), data(8) -{ +FBXExportProperty::FBXExportProperty(int64_t v) +: type('L') +, data(8) { uint8_t* d = data.data(); (reinterpret_cast(d))[0] = v; } - // constructors for array-type properties -FBX::Property::Property(const char* c, bool raw) - : Property(std::string(c), raw) -{} +FBXExportProperty::FBXExportProperty(const char* c, bool raw) +: FBXExportProperty(std::string(c), raw) { + // empty +} // strings can either be saved as "raw" (R) data, or "string" (S) data -FBX::Property::Property(const std::string& s, bool raw) - : type(raw ? 'R' : 'S'), data(s.size()) -{ +FBXExportProperty::FBXExportProperty(const std::string& s, bool raw) +: type(raw ? 'R' : 'S') +, data(s.size()) { for (size_t i = 0; i < s.size(); ++i) { data[i] = uint8_t(s[i]); } } -FBX::Property::Property(const std::vector& r) - : type('R'), data(r) -{} +FBXExportProperty::FBXExportProperty(const std::vector& r) +: type('R') +, data(r) { + // empty +} -FBX::Property::Property(const std::vector& va) - : type('i'), data(4*va.size()) -{ +FBXExportProperty::FBXExportProperty(const std::vector& va) +: type('i') +, data(4 * va.size() ) { int32_t* d = reinterpret_cast(data.data()); - for (size_t i = 0; i < va.size(); ++i) { d[i] = va[i]; } + for (size_t i = 0; i < va.size(); ++i) { + d[i] = va[i]; + } } -FBX::Property::Property(const std::vector& va) - : type('l'), data(8*va.size()) -{ +FBXExportProperty::FBXExportProperty(const std::vector& va) +: type('l') +, data(8 * va.size()) { int64_t* d = reinterpret_cast(data.data()); - for (size_t i = 0; i < va.size(); ++i) { d[i] = va[i]; } + for (size_t i = 0; i < va.size(); ++i) { + d[i] = va[i]; + } } -FBX::Property::Property(const std::vector& va) - : type('f'), data(4*va.size()) -{ +FBXExportProperty::FBXExportProperty(const std::vector& va) +: type('f') +, data(4 * va.size()) { float* d = reinterpret_cast(data.data()); - for (size_t i = 0; i < va.size(); ++i) { d[i] = va[i]; } + for (size_t i = 0; i < va.size(); ++i) { + d[i] = va[i]; + } } -FBX::Property::Property(const std::vector& va) - : type('d'), data(8*va.size()) -{ +FBXExportProperty::FBXExportProperty(const std::vector& va) +: type('d') +, data(8 * va.size()) { double* d = reinterpret_cast(data.data()); - for (size_t i = 0; i < va.size(); ++i) { d[i] = va[i]; } + for (size_t i = 0; i < va.size(); ++i) { + d[i] = va[i]; + } } -FBX::Property::Property(const aiMatrix4x4& vm) - : type('d'), data(8*16) -{ +FBXExportProperty::FBXExportProperty(const aiMatrix4x4& vm) +: type('d') +, data(8 * 16) { double* d = reinterpret_cast(data.data()); for (unsigned int c = 0; c < 4; ++c) { for (unsigned int r = 0; r < 4; ++r) { - d[4*c+r] = vm[r][c]; + d[4 * c + r] = vm[r][c]; } } } // public member functions -size_t FBX::Property::size() -{ +size_t FBXExportProperty::size() { switch (type) { - case 'C': case 'Y': case 'I': case 'F': case 'D': case 'L': - return data.size() + 1; - case 'S': case 'R': - return data.size() + 5; - case 'i': case 'd': - return data.size() + 13; - default: - throw DeadlyExportError("Requested size on property of unknown type"); + case 'C': + case 'Y': + case 'I': + case 'F': + case 'D': + case 'L': + return data.size() + 1; + case 'S': + case 'R': + return data.size() + 5; + case 'i': + case 'd': + return data.size() + 13; + default: + throw DeadlyExportError("Requested size on property of unknown type"); } } -void FBX::Property::DumpBinary(Assimp::StreamWriterLE &s) -{ +void FBXExportProperty::DumpBinary(Assimp::StreamWriterLE& s) { s.PutU1(type); uint8_t* d = data.data(); size_t N; switch (type) { - case 'C': s.PutU1(*(reinterpret_cast(d))); return; - case 'Y': s.PutI2(*(reinterpret_cast(d))); return; - case 'I': s.PutI4(*(reinterpret_cast(d))); return; - case 'F': s.PutF4(*(reinterpret_cast(d))); return; - case 'D': s.PutF8(*(reinterpret_cast(d))); return; - case 'L': s.PutI8(*(reinterpret_cast(d))); return; - case 'S': - case 'R': - s.PutU4(uint32_t(data.size())); - for (size_t i = 0; i < data.size(); ++i) { s.PutU1(data[i]); } - return; - case 'i': - N = data.size() / 4; - s.PutU4(uint32_t(N)); // number of elements - s.PutU4(0); // no encoding (1 would be zip-compressed) - // TODO: compress if large? - s.PutU4(uint32_t(data.size())); // data size - for (size_t i = 0; i < N; ++i) { - s.PutI4((reinterpret_cast(d))[i]); - } - return; - case 'l': - N = data.size() / 8; - s.PutU4(uint32_t(N)); // number of elements - s.PutU4(0); // no encoding (1 would be zip-compressed) - // TODO: compress if large? - s.PutU4(uint32_t(data.size())); // data size - for (size_t i = 0; i < N; ++i) { - s.PutI8((reinterpret_cast(d))[i]); - } - return; - case 'f': - N = data.size() / 4; - s.PutU4(uint32_t(N)); // number of elements - s.PutU4(0); // no encoding (1 would be zip-compressed) - // TODO: compress if large? - s.PutU4(uint32_t(data.size())); // data size - for (size_t i = 0; i < N; ++i) { - s.PutF4((reinterpret_cast(d))[i]); - } - return; - case 'd': - N = data.size() / 8; - s.PutU4(uint32_t(N)); // number of elements - s.PutU4(0); // no encoding (1 would be zip-compressed) - // TODO: compress if large? - s.PutU4(uint32_t(data.size())); // data size - for (size_t i = 0; i < N; ++i) { - s.PutF8((reinterpret_cast(d))[i]); - } - return; - default: - std::ostringstream err; - err << "Tried to dump property with invalid type '"; - err << type << "'!"; - throw DeadlyExportError(err.str()); + case 'C': s.PutU1(*(reinterpret_cast(d))); return; + case 'Y': s.PutI2(*(reinterpret_cast(d))); return; + case 'I': s.PutI4(*(reinterpret_cast(d))); return; + case 'F': s.PutF4(*(reinterpret_cast(d))); return; + case 'D': s.PutF8(*(reinterpret_cast(d))); return; + case 'L': s.PutI8(*(reinterpret_cast(d))); return; + case 'S': + case 'R': + s.PutU4(uint32_t(data.size())); + for (size_t i = 0; i < data.size(); ++i) { s.PutU1(data[i]); } + return; + case 'i': + N = data.size() / 4; + s.PutU4(uint32_t(N)); // number of elements + s.PutU4(0); // no encoding (1 would be zip-compressed) + // TODO: compress if large? + s.PutU4(uint32_t(data.size())); // data size + for (size_t i = 0; i < N; ++i) { + s.PutI4((reinterpret_cast(d))[i]); + } + return; + case 'l': + N = data.size() / 8; + s.PutU4(uint32_t(N)); // number of elements + s.PutU4(0); // no encoding (1 would be zip-compressed) + // TODO: compress if large? + s.PutU4(uint32_t(data.size())); // data size + for (size_t i = 0; i < N; ++i) { + s.PutI8((reinterpret_cast(d))[i]); + } + return; + case 'f': + N = data.size() / 4; + s.PutU4(uint32_t(N)); // number of elements + s.PutU4(0); // no encoding (1 would be zip-compressed) + // TODO: compress if large? + s.PutU4(uint32_t(data.size())); // data size + for (size_t i = 0; i < N; ++i) { + s.PutF4((reinterpret_cast(d))[i]); + } + return; + case 'd': + N = data.size() / 8; + s.PutU4(uint32_t(N)); // number of elements + s.PutU4(0); // no encoding (1 would be zip-compressed) + // TODO: compress if large? + s.PutU4(uint32_t(data.size())); // data size + for (size_t i = 0; i < N; ++i) { + s.PutF8((reinterpret_cast(d))[i]); + } + return; + default: + std::ostringstream err; + err << "Tried to dump property with invalid type '"; + err << type << "'!"; + throw DeadlyExportError(err.str()); } } -void FBX::Property::DumpAscii(Assimp::StreamWriterLE &outstream, int indent) -{ +void FBXExportProperty::DumpAscii(Assimp::StreamWriterLE& outstream, int indent) { std::ostringstream ss; ss.imbue(std::locale::classic()); ss.precision(15); // this seems to match official FBX SDK exports @@ -240,8 +263,7 @@ void FBX::Property::DumpAscii(Assimp::StreamWriterLE &outstream, int indent) outstream.PutString(ss.str()); } -void FBX::Property::DumpAscii(std::ostream& s, int indent) -{ +void FBXExportProperty::DumpAscii(std::ostream& s, int indent) { // no writing type... or anything. just shove it into the stream. uint8_t* d = data.data(); size_t N; @@ -360,5 +382,8 @@ void FBX::Property::DumpAscii(std::ostream& s, int indent) } } +} // Namespace FBX +} // Namespace Assimp + #endif // ASSIMP_BUILD_NO_FBX_EXPORTER #endif // ASSIMP_BUILD_NO_EXPORT diff --git a/code/FBXExportProperty.h b/code/FBXExportProperty.h index 9c9d37c36..d692fe6ee 100644 --- a/code/FBXExportProperty.h +++ b/code/FBXExportProperty.h @@ -47,7 +47,6 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #ifndef ASSIMP_BUILD_NO_FBX_EXPORTER - #include // aiMatrix4x4 #include // StreamWriterLE @@ -56,11 +55,10 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include #include // is_void +namespace Assimp { namespace FBX { - class Property; -} -/** FBX::Property +/** @brief FBX::Property * * Holds a value of any of FBX's recognized types, * each represented by a particular one-character code. @@ -78,35 +76,34 @@ namespace FBX { * S : string (array of 1-byte char) * R : raw data (array of bytes) */ -class FBX::Property -{ +class FBXExportProperty { public: // constructors for basic types. // all explicit to avoid accidental typecasting - explicit Property(bool v); + explicit FBXExportProperty(bool v); // TODO: determine if there is actually a byte type, // or if this always means . 'C' seems to imply , // so possibly the above was intended to represent both. - explicit Property(int16_t v); - explicit Property(int32_t v); - explicit Property(float v); - explicit Property(double v); - explicit Property(int64_t v); + explicit FBXExportProperty(int16_t v); + explicit FBXExportProperty(int32_t v); + explicit FBXExportProperty(float v); + explicit FBXExportProperty(double v); + explicit FBXExportProperty(int64_t v); // strings can either be stored as 'R' (raw) or 'S' (string) type - explicit Property(const char* c, bool raw=false); - explicit Property(const std::string& s, bool raw=false); - explicit Property(const std::vector& r); - explicit Property(const std::vector& va); - explicit Property(const std::vector& va); - explicit Property(const std::vector& va); - explicit Property(const std::vector& va); - explicit Property(const aiMatrix4x4& vm); + explicit FBXExportProperty(const char* c, bool raw = false); + explicit FBXExportProperty(const std::string& s, bool raw = false); + explicit FBXExportProperty(const std::vector& r); + explicit FBXExportProperty(const std::vector& va); + explicit FBXExportProperty(const std::vector& va); + explicit FBXExportProperty(const std::vector& va); + explicit FBXExportProperty(const std::vector& va); + explicit FBXExportProperty(const aiMatrix4x4& vm); // this will catch any type not defined above, // so that we don't accidentally convert something we don't want. // for example (const char*) --> (bool)... seriously wtf C++ template - explicit Property(T v) : type('X') { + explicit FBXExportProperty(T v) : type('X') { static_assert(std::is_void::value, "TRIED TO CREATE FBX PROPERTY WITH UNSUPPORTED TYPE, CHECK YOUR PROPERTY INSTANTIATION"); } // note: no line wrap so it appears verbatim on the compiler error @@ -114,9 +111,9 @@ public: size_t size(); // write this property node as binary data to the given stream - void DumpBinary(Assimp::StreamWriterLE &s); - void DumpAscii(Assimp::StreamWriterLE &s, int indent=0); - void DumpAscii(std::ostream &s, int indent=0); + void DumpBinary(Assimp::StreamWriterLE& s); + void DumpAscii(Assimp::StreamWriterLE& s, int indent = 0); + void DumpAscii(std::ostream& s, int indent = 0); // note: make sure the ostream is in classic "C" locale private: @@ -124,6 +121,9 @@ private: std::vector data; }; +} // Namespace FBX +} // Namespace Assimp + #endif // ASSIMP_BUILD_NO_FBX_EXPORTER #endif // AI_FBXEXPORTPROPERTY_H_INC diff --git a/code/FBXExporter.cpp b/code/FBXExporter.cpp index acb122714..dd34e135f 100644 --- a/code/FBXExporter.cpp +++ b/code/FBXExporter.cpp @@ -45,6 +45,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include "FBXExportNode.h" #include "FBXExportProperty.h" #include "FBXCommon.h" +#include "FBXUtil.h" #include // aiGetVersion #include @@ -73,7 +74,11 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. const ai_real DEG = ai_real( 57.29577951308232087679815481 ); // degrees per radian +using namespace Assimp; +using namespace Assimp::FBX; + // some constants that we'll use for writing metadata +namespace Assimp { namespace FBX { const std::string EXPORT_VERSION_STR = "7.4.0"; const uint32_t EXPORT_VERSION_INT = 7400; // 7.4 == 2014/2015 @@ -92,11 +97,6 @@ namespace FBX { ";------------------------------------------------------------------"; } -using namespace Assimp; -using namespace FBX; - -namespace Assimp { - // --------------------------------------------------------------------- // Worker function for exporting a scene to binary FBX. // Prototyped and registered in Exporter.cpp @@ -121,6 +121,7 @@ namespace Assimp { IOSystem* pIOSystem, const aiScene* pScene, const ExportProperties* pProperties + ){ // initialize the exporter FBXExporter exporter(pScene, pProperties); @@ -1393,10 +1394,6 @@ void FBXExporter::WriteObjects () // FbxVideo - stores images used by textures. for (const auto &it : uid_by_image) { - if (it.first.compare(0, 1, "*") == 0) { - // TODO: embedded textures - continue; - } FBX::Node n("Video"); const int64_t& uid = it.second; const std::string name = ""; // TODO: ... name??? @@ -1406,7 +1403,33 @@ void FBXExporter::WriteObjects () // TODO: get full path... relative path... etc... ugh... // for now just use the same path for everything, // and hopefully one of them will work out. - const std::string& path = it.first; + std::string path = it.first; + // try get embedded texture + const aiTexture* embedded_texture = mScene->GetEmbeddedTexture(it.first.c_str()); + if (embedded_texture != nullptr) { + // change the path (use original filename, if available. If name is empty, concatenate texture index with file extension) + std::stringstream newPath; + if (embedded_texture->mFilename.length > 0) { + newPath << embedded_texture->mFilename.C_Str(); + } else if (embedded_texture->achFormatHint[0]) { + int texture_index = std::stoi(path.substr(1, path.size() - 1)); + newPath << texture_index << "." << embedded_texture->achFormatHint; + } + path = newPath.str(); + // embed the texture + size_t texture_size = static_cast(embedded_texture->mWidth * std::max(embedded_texture->mHeight, 1u)); + if (binary) { + // embed texture as binary data + std::vector tex_data; + tex_data.resize(texture_size); + memcpy(&tex_data[0], (char*)embedded_texture->pcData, texture_size); + n.AddChild("Content", tex_data); + } else { + // embed texture in base64 encoding + std::string encoded_texture = FBX::Util::EncodeBase64((char*)embedded_texture->pcData, texture_size); + n.AddChild("Content", encoded_texture); + } + } p.AddP70("Path", "KString", "XRefUrl", "", path); n.AddChild(p); n.AddChild("UseMipMap", int32_t(0)); @@ -1419,17 +1442,17 @@ void FBXExporter::WriteObjects () // referenced by material_index/texture_type pairs. std::map,int64_t> texture_uids; const std::map prop_name_by_tt = { - {aiTextureType_DIFFUSE, "DiffuseColor"}, - {aiTextureType_SPECULAR, "SpecularColor"}, - {aiTextureType_AMBIENT, "AmbientColor"}, - {aiTextureType_EMISSIVE, "EmissiveColor"}, - {aiTextureType_HEIGHT, "Bump"}, - {aiTextureType_NORMALS, "NormalMap"}, - {aiTextureType_SHININESS, "ShininessExponent"}, - {aiTextureType_OPACITY, "TransparentColor"}, + {aiTextureType_DIFFUSE, "DiffuseColor"}, + {aiTextureType_SPECULAR, "SpecularColor"}, + {aiTextureType_AMBIENT, "AmbientColor"}, + {aiTextureType_EMISSIVE, "EmissiveColor"}, + {aiTextureType_HEIGHT, "Bump"}, + {aiTextureType_NORMALS, "NormalMap"}, + {aiTextureType_SHININESS, "ShininessExponent"}, + {aiTextureType_OPACITY, "TransparentColor"}, {aiTextureType_DISPLACEMENT, "DisplacementColor"}, //{aiTextureType_LIGHTMAP, "???"}, - {aiTextureType_REFLECTION, "ReflectionColor"} + {aiTextureType_REFLECTION, "ReflectionColor"} //{aiTextureType_UNKNOWN, ""} }; for (size_t i = 0; i < mScene->mNumMaterials; ++i) { @@ -1575,11 +1598,22 @@ void FBXExporter::WriteObjects () // one sticky point is that the number of vertices may not match, // because assimp splits vertices by normal, uv, etc. + // functor for aiNode sorting + struct SortNodeByName + { + bool operator()(const aiNode *lhs, const aiNode *rhs) const + { + return strcmp(lhs->mName.C_Str(), rhs->mName.C_Str()) < 0; + } + }; + // first we should mark the skeleton for each mesh. // the skeleton must include not only the aiBones, // but also all their parent nodes. // anything that affects the position of any bone node must be included. - std::vector> skeleton_by_mesh(mScene->mNumMeshes); + // Use SorNodeByName to make sure the exported result will be the same across all systems + // Otherwise the aiNodes of the skeleton would be sorted based on the pointer address, which isn't consistent + std::vector> skeleton_by_mesh(mScene->mNumMeshes); // at the same time we can build a list of all the skeleton nodes, // which will be used later to mark them as type "limbNode". std::unordered_set limbnodes; @@ -1587,7 +1621,7 @@ void FBXExporter::WriteObjects () std::map node_by_bone; for (size_t mi = 0; mi < mScene->mNumMeshes; ++mi) { const aiMesh* m = mScene->mMeshes[mi]; - std::set skeleton; + std::set skeleton; for (size_t bi =0; bi < m->mNumBones; ++bi) { const aiBone* b = m->mBones[bi]; const std::string name(b->mName.C_Str()); @@ -1728,7 +1762,7 @@ void FBXExporter::WriteObjects () aiMatrix4x4 mesh_xform = get_world_transform(mesh_node, mScene); // now make a subdeformer for each bone in the skeleton - const std::set &skeleton = skeleton_by_mesh[mi]; + const std::set skeleton= skeleton_by_mesh[mi]; for (const aiNode* bone_node : skeleton) { // if there's a bone for this node, find it const aiBone* b = nullptr; @@ -2237,8 +2271,8 @@ void FBXExporter::WriteModelNode( // not sure what these are for, // but they seem to be omnipresent - m.AddChild("Shading", Property(true)); - m.AddChild("Culling", Property("CullingOff")); + m.AddChild("Shading", FBXExportProperty(true)); + m.AddChild("Culling", FBXExportProperty("CullingOff")); m.Dump(outstream, binary, 1); } @@ -2351,7 +2385,7 @@ void FBXExporter::WriteModelNodes( na.AddProperties( node_attribute_uid, FBX::SEPARATOR + "NodeAttribute", "LimbNode" ); - na.AddChild("TypeFlags", Property("Skeleton")); + na.AddChild("TypeFlags", FBXExportProperty("Skeleton")); na.Dump(outstream, binary, 1); // and connect them connections.emplace_back("C", "OO", node_attribute_uid, node_uid); diff --git a/code/FBXImportSettings.h b/code/FBXImportSettings.h index d5e1c2060..1a4c80f8b 100644 --- a/code/FBXImportSettings.h +++ b/code/FBXImportSettings.h @@ -53,19 +53,22 @@ namespace FBX { struct ImportSettings { ImportSettings() - : strictMode(true) - , readAllLayers(true) - , readAllMaterials(false) - , readMaterials(true) - , readTextures(true) - , readCameras(true) - , readLights(true) - , readAnimations(true) - , readWeights(true) - , preservePivots(true) - , optimizeEmptyAnimationCurves(true) - , useLegacyEmbeddedTextureNaming(false) - {} + : strictMode(true) + , readAllLayers(true) + , readAllMaterials(false) + , readMaterials(true) + , readTextures(true) + , readCameras(true) + , readLights(true) + , readAnimations(true) + , readWeights(true) + , preservePivots(true) + , optimizeEmptyAnimationCurves(true) + , useLegacyEmbeddedTextureNaming(false) + , removeEmptyBones( true ) + , convertToMeters( false ) { + // empty + } /** enable strict mode: @@ -141,8 +144,16 @@ struct ImportSettings bool optimizeEmptyAnimationCurves; /** use legacy naming for embedded textures eg: (*0, *1, *2) - **/ + */ bool useLegacyEmbeddedTextureNaming; + + /** Empty bones shall be removed + */ + bool removeEmptyBones; + + /** Set to true to perform a conversion from cm to meter after the import + */ + bool convertToMeters; }; diff --git a/code/FBXImporter.cpp b/code/FBXImporter.cpp index 72f8eea8e..ec8bbd2b4 100644 --- a/code/FBXImporter.cpp +++ b/code/FBXImporter.cpp @@ -60,11 +60,13 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include namespace Assimp { - template<> const char* LogFunctions::Prefix() - { - static auto prefix = "FBX: "; - return prefix; - } + +template<> +const char* LogFunctions::Prefix() { + static auto prefix = "FBX: "; + return prefix; +} + } using namespace Assimp; @@ -72,6 +74,7 @@ using namespace Assimp::Formatter; using namespace Assimp::FBX; namespace { + static const aiImporterDesc desc = { "Autodesk FBX Importer", "", @@ -137,6 +140,8 @@ void FBXImporter::SetupProperties(const Importer* pImp) settings.preservePivots = pImp->GetPropertyBool(AI_CONFIG_IMPORT_FBX_PRESERVE_PIVOTS, true); settings.optimizeEmptyAnimationCurves = pImp->GetPropertyBool(AI_CONFIG_IMPORT_FBX_OPTIMIZE_EMPTY_ANIMATION_CURVES, true); settings.useLegacyEmbeddedTextureNaming = pImp->GetPropertyBool(AI_CONFIG_IMPORT_FBX_EMBEDDED_TEXTURES_LEGACY_NAMING, false); + settings.removeEmptyBones = pImp->GetPropertyBool(AI_CONFIG_IMPORT_REMOVE_EMPTY_BONES, true); + settings.convertToMeters = pImp->GetPropertyBool(AI_CONFIG_FBX_CONVERT_TO_M, false); } // ------------------------------------------------------------------------------------------------ @@ -167,7 +172,7 @@ void FBXImporter::InternReadFile( const std::string& pFile, aiScene* pScene, IOS bool is_binary = false; if (!strncmp(begin,"Kaydara FBX Binary",18)) { is_binary = true; - TokenizeBinary(tokens,begin,static_cast(contents.size())); + TokenizeBinary(tokens,begin,contents.size()); } else { Tokenize(tokens,begin); @@ -180,8 +185,12 @@ void FBXImporter::InternReadFile( const std::string& pFile, aiScene* pScene, IOS // take the raw parse-tree and convert it to a FBX DOM Document doc(parser,settings); + FbxUnit unit(FbxUnit::cm); + if (settings.convertToMeters) { + unit = FbxUnit::m; + } // convert the FBX DOM to aiScene - ConvertToAssimpScene(pScene,doc); + ConvertToAssimpScene(pScene,doc, settings.removeEmptyBones, unit); std::for_each(tokens.begin(),tokens.end(),Util::delete_fun()); } diff --git a/code/FBXMaterial.cpp b/code/FBXMaterial.cpp index f5f6fda03..f43a8b84b 100644 --- a/code/FBXMaterial.cpp +++ b/code/FBXMaterial.cpp @@ -55,6 +55,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include #include // std::transform +#include "FBXUtil.h" namespace Assimp { namespace FBX { @@ -206,6 +207,20 @@ Texture::Texture(uint64_t id, const Element& element, const Document& doc, const props = GetPropertyTable(doc,"Texture.FbxFileTexture",element,sc); + // 3DS Max and FBX SDK use "Scaling" and "Translation" instead of "ModelUVScaling" and "ModelUVTranslation". Use these properties if available. + bool ok; + const aiVector3D& scaling = PropertyGet(*props, "Scaling", ok); + if (ok) { + uvScaling.x = scaling.x; + uvScaling.y = scaling.y; + } + + const aiVector3D& trans = PropertyGet(*props, "Translation", ok); + if (ok) { + uvTrans.x = trans.x; + uvTrans.y = trans.y; + } + // resolve video links if(doc.Settings().readTextures) { const std::vector& conns = doc.GetConnectionsByDestinationSequenced(ID()); @@ -301,13 +316,52 @@ Video::Video(uint64_t id, const Element& element, const Document& doc, const std relativeFileName = ParseTokenAsString(GetRequiredToken(*RelativeFilename,0)); } - if(Content) { + if(Content && !Content->Tokens().empty()) { //this field is omitted when the embedded texture is already loaded, let's ignore if it's not found try { const Token& token = GetRequiredToken(*Content, 0); const char* data = token.begin(); if (!token.IsBinary()) { - DOMWarning("video content is not binary data, ignoring", &element); + if (*data != '"') { + DOMError("embedded content is not surrounded by quotation marks", &element); + } + else { + size_t targetLength = 0; + auto numTokens = Content->Tokens().size(); + // First time compute size (it could be large like 64Gb and it is good to allocate it once) + for (uint32_t tokenIdx = 0; tokenIdx < numTokens; ++tokenIdx) + { + const Token& dataToken = GetRequiredToken(*Content, tokenIdx); + size_t tokenLength = dataToken.end() - dataToken.begin() - 2; // ignore double quotes + const char* base64data = dataToken.begin() + 1; + const size_t outLength = Util::ComputeDecodedSizeBase64(base64data, tokenLength); + if (outLength == 0) + { + DOMError("Corrupted embedded content found", &element); + } + targetLength += outLength; + } + if (targetLength == 0) + { + DOMError("Corrupted embedded content found", &element); + } + content = new uint8_t[targetLength]; + contentLength = static_cast(targetLength); + size_t dst_offset = 0; + for (uint32_t tokenIdx = 0; tokenIdx < numTokens; ++tokenIdx) + { + const Token& dataToken = GetRequiredToken(*Content, tokenIdx); + size_t tokenLength = dataToken.end() - dataToken.begin() - 2; // ignore double quotes + const char* base64data = dataToken.begin() + 1; + dst_offset += Util::DecodeBase64(base64data, tokenLength, content + dst_offset, targetLength - dst_offset); + } + if (targetLength != dst_offset) + { + delete[] content; + contentLength = 0; + DOMError("Corrupted embedded content found", &element); + } + } } else if (static_cast(token.end() - data) < 5) { DOMError("binary data array is too short, need five (5) bytes for type signature and element count", &element); @@ -326,8 +380,11 @@ Video::Video(uint64_t id, const Element& element, const Document& doc, const std content = new uint8_t[len]; ::memcpy(content, data + 5, len); } - } catch (runtime_error runtimeError) { + } catch (const runtime_error& runtimeError) + { //we don't need the content data for contents that has already been loaded + ASSIMP_LOG_DEBUG_F("Caught exception in FBXMaterial (likely because content was already loaded): ", + runtimeError.what()); } } diff --git a/code/FBXMeshGeometry.cpp b/code/FBXMeshGeometry.cpp index d75476b82..44a0264ca 100644 --- a/code/FBXMeshGeometry.cpp +++ b/code/FBXMeshGeometry.cpp @@ -568,15 +568,15 @@ void MeshGeometry::ReadVertexDataColors(std::vector& colors_out, cons } // ------------------------------------------------------------------------------------------------ -static const std::string TangentIndexToken = "TangentIndex"; -static const std::string TangentsIndexToken = "TangentsIndex"; +static const char *TangentIndexToken = "TangentIndex"; +static const char *TangentsIndexToken = "TangentsIndex"; void MeshGeometry::ReadVertexDataTangents(std::vector& tangents_out, const Scope& source, const std::string& MappingInformationType, const std::string& ReferenceInformationType) { const char * str = source.Elements().count( "Tangents" ) > 0 ? "Tangents" : "Tangent"; - const char * strIdx = source.Elements().count( "Tangents" ) > 0 ? TangentsIndexToken.c_str() : TangentIndexToken.c_str(); + const char * strIdx = source.Elements().count( "Tangents" ) > 0 ? TangentsIndexToken : TangentIndexToken; ResolveVertexDataArray(tangents_out,source,MappingInformationType,ReferenceInformationType, str, strIdx, @@ -630,10 +630,11 @@ void MeshGeometry::ReadVertexDataMaterials(std::vector& materials_out, cons materials_out.clear(); } - m_materials.assign(m_vertices.size(),materials_out[0]); + materials_out.resize(m_vertices.size()); + std::fill(materials_out.begin(), materials_out.end(), materials_out.at(0)); } else if (MappingInformationType == "ByPolygon" && ReferenceInformationType == "IndexToDirect") { - m_materials.resize(face_count); + materials_out.resize(face_count); if(materials_out.size() != face_count) { FBXImporter::LogError(Formatter::format("length of input data unexpected for ByPolygon mapping: ") diff --git a/code/FBXParser.cpp b/code/FBXParser.cpp index b255c4734..5d5c2c6c1 100644 --- a/code/FBXParser.cpp +++ b/code/FBXParser.cpp @@ -117,7 +117,7 @@ namespace FBX { Element::Element(const Token& key_token, Parser& parser) : key_token(key_token) { - TokenPtr n = NULL; + TokenPtr n = nullptr; do { n = parser.AdvanceToNextToken(); if(!n) { @@ -643,9 +643,9 @@ void ParseVectorDataArray(std::vector& out, const Element& el) if (type == 'd') { const double* d = reinterpret_cast(&buff[0]); for (unsigned int i = 0; i < count3; ++i, d += 3) { - out.push_back(aiVector3D(static_cast(d[0]), - static_cast(d[1]), - static_cast(d[2]))); + out.push_back(aiVector3D(static_cast(d[0]), + static_cast(d[1]), + static_cast(d[2]))); } // for debugging /*for ( size_t i = 0; i < out.size(); i++ ) { diff --git a/code/FBXTokenizer.h b/code/FBXTokenizer.h index 2af29743f..afa588a47 100644 --- a/code/FBXTokenizer.h +++ b/code/FBXTokenizer.h @@ -93,7 +93,7 @@ public: Token(const char* sbegin, const char* send, TokenType type, unsigned int line, unsigned int column); /** construct a binary token */ - Token(const char* sbegin, const char* send, TokenType type, unsigned int offset); + Token(const char* sbegin, const char* send, TokenType type, size_t offset); ~Token(); @@ -118,14 +118,14 @@ public: return type; } - unsigned int Offset() const { + size_t Offset() const { ai_assert(IsBinary()); return offset; } unsigned int Line() const { ai_assert(!IsBinary()); - return line; + return static_cast(line); } unsigned int Column() const { @@ -147,8 +147,8 @@ private: const TokenType type; union { - const unsigned int line; - unsigned int offset; + size_t line; + size_t offset; }; const unsigned int column; }; @@ -178,7 +178,7 @@ void Tokenize(TokenList& output_tokens, const char* input); * @param input_buffer Binary input buffer to be processed. * @param length Length of input buffer, in bytes. There is no 0-terminal. * @throw DeadlyImportError if something goes wrong */ -void TokenizeBinary(TokenList& output_tokens, const char* input, unsigned int length); +void TokenizeBinary(TokenList& output_tokens, const char* input, size_t length); } // ! FBX diff --git a/code/FBXUtil.cpp b/code/FBXUtil.cpp index c184c4a00..c10e057c8 100644 --- a/code/FBXUtil.cpp +++ b/code/FBXUtil.cpp @@ -49,6 +49,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include #include +#include #ifndef ASSIMP_BUILD_NO_FBX_IMPORTER @@ -85,7 +86,7 @@ const char* TokenTypeString(TokenType t) // ------------------------------------------------------------------------------------------------ -std::string AddOffset(const std::string& prefix, const std::string& text, unsigned int offset) +std::string AddOffset(const std::string& prefix, const std::string& text, size_t offset) { return static_cast( (Formatter::format() << prefix << " (offset 0x" << std::hex << offset << ") " << text) ); } @@ -113,6 +114,128 @@ std::string AddTokenText(const std::string& prefix, const std::string& text, con text) ); } +// Generated by this formula: T["ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"[i]] = i; +static const uint8_t base64DecodeTable[128] = { + 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, + 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, + 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 62, 255, 255, 255, 63, + 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 255, 255, 255, 255, 255, 255, + 255, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, + 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 255, 255, 255, 255, 255, + 255, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, + 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 255, 255, 255, 255, 255 +}; + +uint8_t DecodeBase64(char ch) +{ + const auto idx = static_cast(ch); + if (idx > 127) + return 255; + return base64DecodeTable[idx]; +} + +size_t ComputeDecodedSizeBase64(const char* in, size_t inLength) +{ + if (inLength < 2) + { + return 0; + } + const size_t equals = size_t(in[inLength - 1] == '=') + size_t(in[inLength - 2] == '='); + const size_t full_length = (inLength * 3) >> 2; // div by 4 + if (full_length < equals) + { + return 0; + } + return full_length - equals; +} + +size_t DecodeBase64(const char* in, size_t inLength, uint8_t* out, size_t maxOutLength) +{ + if (maxOutLength == 0 || inLength < 2) { + return 0; + } + const size_t realLength = inLength - size_t(in[inLength - 1] == '=') - size_t(in[inLength - 2] == '='); + size_t dst_offset = 0; + int val = 0, valb = -8; + for (size_t src_offset = 0; src_offset < realLength; ++src_offset) + { + const uint8_t table_value = Util::DecodeBase64(in[src_offset]); + if (table_value == 255) + { + return 0; + } + val = (val << 6) + table_value; + valb += 6; + if (valb >= 0) + { + out[dst_offset++] = static_cast((val >> valb) & 0xFF); + valb -= 8; + val &= 0xFFF; + } + } + return dst_offset; +} + +static const char to_base64_string[] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"; +char EncodeBase64(char byte) +{ + return to_base64_string[(size_t)byte]; +} + +/** Encodes a block of 4 bytes to base64 encoding +* +* @param bytes Bytes to encode. +* @param out_string String to write encoded values to. +* @param string_pos Position in out_string.*/ +void EncodeByteBlock(const char* bytes, std::string& out_string, size_t string_pos) +{ + char b0 = (bytes[0] & 0xFC) >> 2; + char b1 = (bytes[0] & 0x03) << 4 | ((bytes[1] & 0xF0) >> 4); + char b2 = (bytes[1] & 0x0F) << 2 | ((bytes[2] & 0xC0) >> 6); + char b3 = (bytes[2] & 0x3F); + + out_string[string_pos + 0] = EncodeBase64(b0); + out_string[string_pos + 1] = EncodeBase64(b1); + out_string[string_pos + 2] = EncodeBase64(b2); + out_string[string_pos + 3] = EncodeBase64(b3); +} + +std::string EncodeBase64(const char* data, size_t length) +{ + // calculate extra bytes needed to get a multiple of 3 + size_t extraBytes = 3 - length % 3; + + // number of base64 bytes + size_t encodedBytes = 4 * (length + extraBytes) / 3; + + std::string encoded_string(encodedBytes, '='); + + // read blocks of 3 bytes + for (size_t ib3 = 0; ib3 < length / 3; ib3++) + { + const size_t iByte = ib3 * 3; + const size_t iEncodedByte = ib3 * 4; + const char* currData = &data[iByte]; + + EncodeByteBlock(currData, encoded_string, iEncodedByte); + } + + // if size of data is not a multiple of 3, also encode the final bytes (and add zeros where needed) + if (extraBytes > 0) + { + char finalBytes[4] = { 0,0,0,0 }; + memcpy(&finalBytes[0], &data[length - length % 3], length % 3); + + const size_t iEncodedByte = encodedBytes - 4; + EncodeByteBlock(&finalBytes[0], encoded_string, iEncodedByte); + + // add '=' at the end + for (size_t i = 0; i < 4 * extraBytes / 3; i++) + encoded_string[encodedBytes - i - 1] = '='; + } + return encoded_string; +} + } // !Util } // !FBX } // !Assimp diff --git a/code/FBXUtil.h b/code/FBXUtil.h index 1a37d346b..b63441885 100644 --- a/code/FBXUtil.h +++ b/code/FBXUtil.h @@ -48,6 +48,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include "FBXCompileConfig.h" #include "FBXTokenizer.h" +#include namespace Assimp { namespace FBX { @@ -77,7 +78,7 @@ const char* TokenTypeString(TokenType t); * @param line Line index, 1-based * @param column Column index, 1-based * @return A string of the following format: {prefix} (offset 0x{offset}) {text}*/ -std::string AddOffset(const std::string& prefix, const std::string& text, unsigned int offset); +std::string AddOffset(const std::string& prefix, const std::string& text, size_t offset); /** Format log/error messages using a given line location in the source file. @@ -98,6 +99,37 @@ std::string AddLineAndColumn(const std::string& prefix, const std::string& text, * @return A string of the following format: {prefix} ({token-type}, line {line}, col {column}) {text}*/ std::string AddTokenText(const std::string& prefix, const std::string& text, const Token* tok); +/** Decode a single Base64-encoded character. +* +* @param ch Character to decode (from base64 to binary). +* @return decoded byte value*/ +uint8_t DecodeBase64(char ch); + +/** Compute decoded size of a Base64-encoded string +* +* @param in Characters to decode. +* @param inLength Number of characters to decode. +* @return size of the decoded data (number of bytes)*/ +size_t ComputeDecodedSizeBase64(const char* in, size_t inLength); + +/** Decode a Base64-encoded string +* +* @param in Characters to decode. +* @param inLength Number of characters to decode. +* @param out Pointer where we will store the decoded data. +* @param maxOutLength Size of output buffer. +* @return size of the decoded data (number of bytes)*/ +size_t DecodeBase64(const char* in, size_t inLength, uint8_t* out, size_t maxOutLength); + +char EncodeBase64(char byte); + +/** Encode bytes in base64-encoding +* +* @param data Binary data to encode. +* @param inLength Number of bytes to encode. +* @return base64-encoded string*/ +std::string EncodeBase64(const char* data, size_t length); + } } } diff --git a/code/FIReader.cpp b/code/FIReader.cpp old mode 100755 new mode 100644 diff --git a/code/FindDegenerates.cpp b/code/FindDegenerates.cpp index 365f5d744..50fac46db 100644 --- a/code/FindDegenerates.cpp +++ b/code/FindDegenerates.cpp @@ -228,6 +228,7 @@ bool FindDegeneratesProcess::ExecuteOnMesh( aiMesh* mesh) { if ( area < 1e-6 ) { if ( mConfigRemoveDegenerates ) { remove_me[ a ] = true; + ++deg; goto evil_jump_outside; } diff --git a/code/FindInstancesProcess.cpp b/code/FindInstancesProcess.cpp index 23f9d1581..64907458a 100644 --- a/code/FindInstancesProcess.cpp +++ b/code/FindInstancesProcess.cpp @@ -137,6 +137,11 @@ void FindInstancesProcess::Execute( aiScene* pScene) aiMesh* inst = pScene->mMeshes[i]; hashes[i] = GetMeshHash(inst); + // Find an appropriate epsilon + // to compare position differences against + float epsilon = ComputePositionEpsilon(inst); + epsilon *= epsilon; + for (int a = i-1; a >= 0; --a) { if (hashes[i] == hashes[a]) { @@ -154,12 +159,7 @@ void FindInstancesProcess::Execute( aiScene* pScene) orig->mPrimitiveTypes != inst->mPrimitiveTypes) continue; - // up to now the meshes are equal. find an appropriate - // epsilon to compare position differences against - float epsilon = ComputePositionEpsilon(inst); - epsilon *= epsilon; - - // now compare vertex positions, normals, + // up to now the meshes are equal. Now compare vertex positions, normals, // tangents and bitangents using this epsilon. if (orig->HasPositions()) { if(!CompareArrays(orig->mVertices,inst->mVertices,orig->mNumVertices,epsilon)) @@ -178,30 +178,30 @@ void FindInstancesProcess::Execute( aiScene* pScene) // use a constant epsilon for colors and UV coordinates static const float uvEpsilon = 10e-4f; { - unsigned int i, end = orig->GetNumUVChannels(); - for(i = 0; i < end; ++i) { - if (!orig->mTextureCoords[i]) { + unsigned int j, end = orig->GetNumUVChannels(); + for(j = 0; j < end; ++j) { + if (!orig->mTextureCoords[j]) { continue; } - if(!CompareArrays(orig->mTextureCoords[i],inst->mTextureCoords[i],orig->mNumVertices,uvEpsilon)) { + if(!CompareArrays(orig->mTextureCoords[j],inst->mTextureCoords[j],orig->mNumVertices,uvEpsilon)) { break; } } - if (i != end) { + if (j != end) { continue; } } { - unsigned int i, end = orig->GetNumColorChannels(); - for(i = 0; i < end; ++i) { - if (!orig->mColors[i]) { + unsigned int j, end = orig->GetNumColorChannels(); + for(j = 0; j < end; ++j) { + if (!orig->mColors[j]) { continue; } - if(!CompareArrays(orig->mColors[i],inst->mColors[i],orig->mNumVertices,uvEpsilon)) { + if(!CompareArrays(orig->mColors[j],inst->mColors[j],orig->mNumVertices,uvEpsilon)) { break; } } - if (i != end) { + if (j != end) { continue; } } diff --git a/code/FindInvalidDataProcess.cpp b/code/FindInvalidDataProcess.cpp index c99a98865..433f04244 100644 --- a/code/FindInvalidDataProcess.cpp +++ b/code/FindInvalidDataProcess.cpp @@ -282,9 +282,11 @@ void FindInvalidDataProcess::ProcessAnimation (aiAnimation* anim) { // ------------------------------------------------------------------------------------------------ void FindInvalidDataProcess::ProcessAnimationChannel (aiNodeAnim* anim) { - ai_assert( 0 != anim->mPositionKeys ); - ai_assert( 0 != anim->mRotationKeys ); - ai_assert( 0 != anim->mScalingKeys ); + ai_assert( nullptr != anim ); + if (anim->mNumPositionKeys == 0 && anim->mNumRotationKeys == 0 && anim->mNumScalingKeys == 0) { + ai_assert_entry(); + return; + } // Check whether all values in a tracks are identical - in this case // we can remove al keys except one. @@ -328,7 +330,7 @@ void FindInvalidDataProcess::ProcessAnimationChannel (aiNodeAnim* anim) { // ------------------------------------------------------------------------------------------------ // Search a mesh for invalid contents -int FindInvalidDataProcess::ProcessMesh (aiMesh* pMesh) +int FindInvalidDataProcess::ProcessMesh(aiMesh* pMesh) { bool ret = false; std::vector dirtyMask(pMesh->mNumVertices, pMesh->mNumFaces != 0); diff --git a/code/IRRLoader.cpp b/code/IRRLoader.cpp index 6bc4b8ce4..796b73164 100644 --- a/code/IRRLoader.cpp +++ b/code/IRRLoader.cpp @@ -300,13 +300,10 @@ int ClampSpline(int idx, int size) { // ------------------------------------------------------------------------------------------------ inline void FindSuitableMultiple(int& angle) { - if (angle < 3)angle = 3; + if (angle < 3) angle = 3; else if (angle < 10) angle = 10; else if (angle < 20) angle = 20; else if (angle < 30) angle = 30; - else - { - } } // ------------------------------------------------------------------------------------------------ @@ -317,6 +314,8 @@ void IRRImporter::ComputeAnimations(Node* root, aiNode* real, std::vectoranimators.empty()) { return; @@ -674,38 +673,38 @@ void IRRImporter::GenerateGraph(Node* root,aiNode* rootOut ,aiScene* scene, // Get the loaded mesh from the scene and add it to // the list of all scenes to be attached to the // graph we're currently building - aiScene* scene = batch.GetImport(root->id); - if (!scene) { + aiScene* localScene = batch.GetImport(root->id); + if (!localScene) { ASSIMP_LOG_ERROR("IRR: Unable to load external file: " + root->meshPath); break; } - attach.push_back(AttachmentInfo(scene,rootOut)); + attach.push_back(AttachmentInfo(localScene,rootOut)); // Now combine the material we've loaded for this mesh // with the real materials we got from the file. As we // don't execute any pp-steps on the file, the numbers // should be equal. If they are not, we can impossibly // do this ... - if (root->materials.size() != (unsigned int)scene->mNumMaterials) { + if (root->materials.size() != (unsigned int)localScene->mNumMaterials) { ASSIMP_LOG_WARN("IRR: Failed to match imported materials " "with the materials found in the IRR scene file"); break; } - for (unsigned int i = 0; i < scene->mNumMaterials;++i) { + for (unsigned int i = 0; i < localScene->mNumMaterials;++i) { // Delete the old material, we don't need it anymore - delete scene->mMaterials[i]; + delete localScene->mMaterials[i]; std::pair& src = root->materials[i]; - scene->mMaterials[i] = src.first; + localScene->mMaterials[i] = src.first; } // NOTE: Each mesh should have exactly one material assigned, // but we do it in a separate loop if this behaviour changes // in future. - for (unsigned int i = 0; i < scene->mNumMeshes;++i) { + for (unsigned int i = 0; i < localScene->mNumMeshes;++i) { // Process material flags - aiMesh* mesh = scene->mMeshes[i]; + aiMesh* mesh = localScene->mMeshes[i]; // If "trans_vertex_alpha" mode is enabled, search all vertex colors diff --git a/code/Importer/IFC/IFCBoolean.cpp b/code/Importer/IFC/IFCBoolean.cpp index 337e1d40b..d30a87f62 100644 --- a/code/Importer/IFC/IFCBoolean.cpp +++ b/code/Importer/IFC/IFCBoolean.cpp @@ -256,7 +256,7 @@ bool IntersectsBoundaryProfile(const IfcVector3& e0, const IfcVector3& e1, const for( size_t i = 0, bcount = boundary.size(); i < bcount; ++i ) { IfcVector3 b01 = boundary[(i + 1) % bcount] - boundary[i]; IfcVector3 b12 = boundary[(i + 2) % bcount] - boundary[(i + 1) % bcount]; - IfcVector3 b1_side = IfcVector3(b01.y, -b01.x, 0.0); // rotated 90° clockwise in Z plane + IfcVector3 b1_side = IfcVector3(b01.y, -b01.x, 0.0); // rotated 90° clockwise in Z plane // Warning: rough estimate only. A concave poly with lots of small segments each featuring a small counter rotation // could fool the accumulation. Correct implementation would be sum( acos( b01 * b2) * sign( b12 * b1_side)) windingOrder += (b1_side.x*b12.x + b1_side.y*b12.y); diff --git a/code/Importer/STEPParser/STEPFileReader.cpp b/code/Importer/STEPParser/STEPFileReader.cpp index 1707899a1..f099d2be7 100644 --- a/code/Importer/STEPParser/STEPFileReader.cpp +++ b/code/Importer/STEPParser/STEPFileReader.cpp @@ -278,10 +278,10 @@ void STEP::ReadFile(DB& db,const EXPRESS::ConversionSchema& scheme, std::transform( type.begin(), type.end(), type.begin(), &Assimp::ToLower ); const char* sz = scheme.GetStaticStringForToken(type); if(sz) { - const std::string::size_type len = n2-n1+1; - char* const copysz = new char[len+1]; + const std::string::size_type szLen = n2-n1+1; + char* const copysz = new char[szLen+1]; std::copy(s.c_str()+n1,s.c_str()+n2+1,copysz); - copysz[len] = '\0'; + copysz[szLen] = '\0'; db.InternInsert(new LazyObject(db,id,line,sz,copysz)); } if(!has_next) { diff --git a/code/ImproveCacheLocality.cpp b/code/ImproveCacheLocality.cpp index be6452dda..ace9d95ff 100644 --- a/code/ImproveCacheLocality.cpp +++ b/code/ImproveCacheLocality.cpp @@ -112,7 +112,9 @@ void ImproveCacheLocalityProcess::Execute( aiScene* pScene) } } if (!DefaultLogger::isNullLogger()) { - ASSIMP_LOG_INFO_F("Cache relevant are ", numm, " meshes (", numf," faces). Average output ACMR is ", out / numf ); + if (numf > 0) { + ASSIMP_LOG_INFO_F("Cache relevant are ", numm, " meshes (", numf, " faces). Average output ACMR is ", out / numf); + } ASSIMP_LOG_DEBUG("ImproveCacheLocalityProcess finished. "); } } diff --git a/code/LWOMaterial.cpp b/code/LWOMaterial.cpp index 55d0e23f1..b54c21c26 100644 --- a/code/LWOMaterial.cpp +++ b/code/LWOMaterial.cpp @@ -320,13 +320,10 @@ void LWOImporter::ConvertMaterial(const LWO::Surface& surf,aiMaterial* pcMat) // opacity ... either additive or default-blended, please if (0.0 != surf.mAdditiveTransparency) { - const int add = aiBlendMode_Additive; pcMat->AddProperty(&surf.mAdditiveTransparency,1,AI_MATKEY_OPACITY); pcMat->AddProperty(&add,1,AI_MATKEY_BLEND_FUNC); - } - - else if (10e10f != surf.mTransparency) { + } else if (10e10f != surf.mTransparency) { const int def = aiBlendMode_Default; const float f = 1.0f-surf.mTransparency; pcMat->AddProperty(&f,1,AI_MATKEY_OPACITY); diff --git a/code/MD3FileData.h b/code/MD3FileData.h index 0978e548d..2acd6631f 100644 --- a/code/MD3FileData.h +++ b/code/MD3FileData.h @@ -59,7 +59,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include -namespace Assimp { +namespace Assimp { namespace MD3 { // to make it easier for us, we test the magic word against both "endianesses" @@ -303,12 +303,12 @@ inline void Vec3NormalToLatLng( const aiVector3D& p_vIn, uint16_t& p_iOut ) b = int(57.2957795f * ( std::acos( p_vIn[2] ) ) * ( 255.0f / 360.0f )); b &= 0xff; - ((unsigned char*)&p_iOut)[0] = b; // longitude - ((unsigned char*)&p_iOut)[1] = a; // latitude + ((unsigned char*)&p_iOut)[0] = (unsigned char) b; // longitude + ((unsigned char*)&p_iOut)[1] = (unsigned char) a; // latitude } } -} -} +} // Namespace MD3 +} // Namespace Assimp #endif // !! AI_MD3FILEHELPER_H_INC diff --git a/code/MD3Loader.cpp b/code/MD3Loader.cpp index 0faffd5f9..cd58931f8 100644 --- a/code/MD3Loader.cpp +++ b/code/MD3Loader.cpp @@ -258,10 +258,10 @@ bool Q3Shader::LoadSkin(SkinData& fill, const std::string& pFile,IOSystem* io) continue; fill.textures.push_back(SkinData::TextureEntry()); - SkinData::TextureEntry& s = fill.textures.back(); + SkinData::TextureEntry &entry = fill.textures.back(); - s.first = ss; - s.second = GetNextToken(buff); + entry.first = ss; + entry.second = GetNextToken(buff); } return true; } @@ -718,9 +718,7 @@ void MD3Importer::ConvertPath(const char* texture_name, const char* header_name, // ------------------------------------------------------------------------------------------------ // Imports the given file into the given scene structure. -void MD3Importer::InternReadFile( const std::string& pFile, - aiScene* pScene, IOSystem* pIOHandler) -{ +void MD3Importer::InternReadFile( const std::string& pFile, aiScene* pScene, IOSystem* pIOHandler) { mFile = pFile; mScene = pScene; mIOHandler = pIOHandler; @@ -730,11 +728,13 @@ void MD3Importer::InternReadFile( const std::string& pFile, std::string::size_type s = mFile.find_last_of("/\\"); if (s == std::string::npos) { s = 0; + } else { + ++s; + } + filename = mFile.substr(s), path = mFile.substr(0, s); + for (std::string::iterator it = filename.begin(); it != filename.end(); ++it) { + *it = static_cast( tolower(*it) ); } - else ++s; - filename = mFile.substr(s), path = mFile.substr(0,s); - for( std::string::iterator it = filename .begin(); it != filename.end(); ++it) - *it = tolower( *it); // Load multi-part model file, if necessary if (configHandleMP) { @@ -905,15 +905,15 @@ void MD3Importer::InternReadFile( const std::string& pFile, // Now search the current shader for a record with this name ( // excluding texture file extension) if (!shaders.blocks.empty()) { + std::string::size_type sh = convertedPath.find_last_of('.'); + if (sh == std::string::npos) { + sh = convertedPath.length(); + } - std::string::size_type s = convertedPath.find_last_of('.'); - if (s == std::string::npos) - s = convertedPath.length(); - - const std::string without_ext = convertedPath.substr(0,s); + const std::string without_ext = convertedPath.substr(0,sh); std::list< Q3Shader::ShaderDataBlock >::const_iterator dit = std::find(shaders.blocks.begin(),shaders.blocks.end(),without_ext); if (dit != shaders.blocks.end()) { - // Hurra, wir haben einen. Tolle Sache. + // We made it! shader = &*dit; ASSIMP_LOG_INFO("Found shader record for " +without_ext ); } else { @@ -945,8 +945,7 @@ void MD3Importer::InternReadFile( const std::string& pFile, aiString szString; if (convertedPath.length()) { szString.Set(convertedPath); - } - else { + } else { ASSIMP_LOG_WARN("Texture file name has zero length. Using default name"); szString.Set("dummy_texture.bmp"); } @@ -955,8 +954,7 @@ void MD3Importer::InternReadFile( const std::string& pFile, // prevent transparency by default int no_alpha = aiTextureFlags_IgnoreAlpha; pcHelper->AddProperty(&no_alpha,1,AI_MATKEY_TEXFLAGS_DIFFUSE(0)); - } - else { + } else { Q3Shader::ConvertShaderToMaterial(pcHelper,*shader); } @@ -1026,7 +1024,7 @@ void MD3Importer::InternReadFile( const std::string& pFile, if (!shader || shader->cull == Q3Shader::CULL_CW) { std::swap(pcMesh->mFaces[i].mIndices[2],pcMesh->mFaces[i].mIndices[1]); } - pcTriangles++; + ++pcTriangles; } // Go to the next surface @@ -1042,8 +1040,9 @@ void MD3Importer::InternReadFile( const std::string& pFile, } } - if (!pScene->mNumMeshes) + if (!pScene->mNumMeshes) { throw DeadlyImportError( "MD3: File contains no valid mesh"); + } pScene->mNumMaterials = iNumMaterials; // Now we need to generate an empty node graph @@ -1057,7 +1056,6 @@ void MD3Importer::InternReadFile( const std::string& pFile, pScene->mRootNode->mChildren = new aiNode*[pcHeader->NUM_TAGS]; for (unsigned int i = 0; i < pcHeader->NUM_TAGS; ++i, ++pcTags) { - aiNode* nd = pScene->mRootNode->mChildren[i] = new aiNode(); nd->mName.Set((const char*)pcTags->NAME); nd->mParent = pScene->mRootNode; @@ -1085,8 +1083,12 @@ void MD3Importer::InternReadFile( const std::string& pFile, pScene->mRootNode->mMeshes[i] = i; // Now rotate the whole scene 90 degrees around the x axis to convert to internal coordinate system - pScene->mRootNode->mTransformation = aiMatrix4x4(1.f,0.f,0.f,0.f, - 0.f,0.f,1.f,0.f,0.f,-1.f,0.f,0.f,0.f,0.f,0.f,1.f); + pScene->mRootNode->mTransformation = aiMatrix4x4( + 1.f,0.f,0.f,0.f, + 0.f,0.f,1.f,0.f, + 0.f,-1.f,0.f,0.f, + 0.f,0.f,0.f,1.f + ); } #endif // !! ASSIMP_BUILD_NO_MD3_IMPORTER diff --git a/code/MD5Loader.cpp b/code/MD5Loader.cpp index 346b59504..38c44b515 100644 --- a/code/MD5Loader.cpp +++ b/code/MD5Loader.cpp @@ -443,10 +443,10 @@ void MD5Importer::LoadMD5MeshFile () for (MD5::VertexList::const_iterator iter = meshSrc.mVertices.begin();iter != meshSrc.mVertices.end();++iter,++pv) { for (unsigned int jub = (*iter).mFirstWeight, w = jub; w < jub + (*iter).mNumWeights;++w) { - MD5::WeightDesc& desc = meshSrc.mWeights[w]; + MD5::WeightDesc& weightDesc = meshSrc.mWeights[w]; /* FIX for some invalid exporters */ - if (!(desc.mWeight < AI_MD5_WEIGHT_EPSILON && desc.mWeight >= -AI_MD5_WEIGHT_EPSILON )) - ++piCount[desc.mBone]; + if (!(weightDesc.mWeight < AI_MD5_WEIGHT_EPSILON && weightDesc.mWeight >= -AI_MD5_WEIGHT_EPSILON )) + ++piCount[weightDesc.mBone]; } } @@ -493,20 +493,20 @@ void MD5Importer::LoadMD5MeshFile () if (w >= meshSrc.mWeights.size()) throw DeadlyImportError("MD5MESH: Invalid weight index"); - MD5::WeightDesc& desc = meshSrc.mWeights[w]; - if ( desc.mWeight < AI_MD5_WEIGHT_EPSILON && desc.mWeight >= -AI_MD5_WEIGHT_EPSILON) { + MD5::WeightDesc& weightDesc = meshSrc.mWeights[w]; + if ( weightDesc.mWeight < AI_MD5_WEIGHT_EPSILON && weightDesc.mWeight >= -AI_MD5_WEIGHT_EPSILON) { continue; } - const ai_real fNewWeight = desc.mWeight / fSum; + const ai_real fNewWeight = weightDesc.mWeight / fSum; // transform the local position into worldspace - MD5::BoneDesc& boneSrc = meshParser.mJoints[desc.mBone]; - const aiVector3D v = boneSrc.mRotationQuatConverted.Rotate (desc.vOffsetPosition); + MD5::BoneDesc& boneSrc = meshParser.mJoints[weightDesc.mBone]; + const aiVector3D v = boneSrc.mRotationQuatConverted.Rotate (weightDesc.vOffsetPosition); // use the original weight to compute the vertex position // (some MD5s seem to depend on the invalid weight values ...) - *pv += ((boneSrc.mPositionXYZ+v)* (ai_real)desc.mWeight); + *pv += ((boneSrc.mPositionXYZ+v)* (ai_real)weightDesc.mWeight); aiBone* bone = mesh->mBones[boneSrc.mMap]; *bone->mWeights++ = aiVertexWeight((unsigned int)(pv-mesh->mVertices),fNewWeight); diff --git a/code/ObjFileData.h b/code/ObjFileData.h index 5ff9db68c..a2d9f2cc7 100644 --- a/code/ObjFileData.h +++ b/code/ObjFileData.h @@ -281,6 +281,8 @@ struct Model { std::string m_strActiveGroup; //! Vector with generated texture coordinates std::vector m_TextureCoord; + //! Maximum dimension of texture coordinates + unsigned int m_TextureCoordDim; //! Current mesh instance Mesh *m_pCurrentMesh; //! Vector with stored meshes @@ -296,6 +298,7 @@ struct Model { m_pDefaultMaterial(NULL), m_pGroupFaceIDs(NULL), m_strActiveGroup(""), + m_TextureCoordDim(0), m_pCurrentMesh(NULL) { // empty diff --git a/code/ObjFileImporter.cpp b/code/ObjFileImporter.cpp index 782b62a73..549956474 100644 --- a/code/ObjFileImporter.cpp +++ b/code/ObjFileImporter.cpp @@ -442,11 +442,12 @@ void ObjFileImporter::createVertexArray(const ObjFile::Model* pModel, // Allocate buffer for texture coordinates if ( !pModel->m_TextureCoord.empty() && pObjMesh->m_uiUVCoordinates[0] ) { - pMesh->mNumUVComponents[ 0 ] = 2; + pMesh->mNumUVComponents[ 0 ] = pModel->m_TextureCoordDim; pMesh->mTextureCoords[ 0 ] = new aiVector3D[ pMesh->mNumVertices ]; } // Copy vertices, normals and textures into aiMesh instance + bool normalsok = true, uvok = true; unsigned int newIndex = 0, outIndex = 0; for ( size_t index=0; index < pObjMesh->m_Faces.size(); index++ ) { // Get source face @@ -466,12 +467,16 @@ void ObjFileImporter::createVertexArray(const ObjFile::Model* pModel, pMesh->mVertices[ newIndex ] = pModel->m_Vertices[ vertex ]; // Copy all normals - if ( !pModel->m_Normals.empty() && vertexIndex < pSourceFace->m_normals.size()) { + if ( normalsok && !pModel->m_Normals.empty() && vertexIndex < pSourceFace->m_normals.size()) { const unsigned int normal = pSourceFace->m_normals.at( vertexIndex ); - if ( normal >= pModel->m_Normals.size() ) { - throw DeadlyImportError( "OBJ: vertex normal index out of range" ); + if ( normal >= pModel->m_Normals.size() ) + { + normalsok = false; + } + else + { + pMesh->mNormals[ newIndex ] = pModel->m_Normals[ normal ]; } - pMesh->mNormals[ newIndex ] = pModel->m_Normals[ normal ]; } // Copy all vertex colors @@ -482,15 +487,19 @@ void ObjFileImporter::createVertexArray(const ObjFile::Model* pModel, } // Copy all texture coordinates - if ( !pModel->m_TextureCoord.empty() && vertexIndex < pSourceFace->m_texturCoords.size()) + if ( uvok && !pModel->m_TextureCoord.empty() && vertexIndex < pSourceFace->m_texturCoords.size()) { const unsigned int tex = pSourceFace->m_texturCoords.at( vertexIndex ); if ( tex >= pModel->m_TextureCoord.size() ) - throw DeadlyImportError("OBJ: texture coordinate index out of range"); - - const aiVector3D &coord3d = pModel->m_TextureCoord[ tex ]; - pMesh->mTextureCoords[ 0 ][ newIndex ] = aiVector3D( coord3d.x, coord3d.y, coord3d.z ); + { + uvok = false; + } + else + { + const aiVector3D &coord3d = pModel->m_TextureCoord[ tex ]; + pMesh->mTextureCoords[ 0 ][ newIndex ] = aiVector3D( coord3d.x, coord3d.y, coord3d.z ); + } } // Get destination face @@ -534,6 +543,18 @@ void ObjFileImporter::createVertexArray(const ObjFile::Model* pModel, ++newIndex; } } + + if (!normalsok) + { + delete [] pMesh->mNormals; + pMesh->mNormals = nullptr; + } + + if (!uvok) + { + delete [] pMesh->mTextureCoords[0]; + pMesh->mTextureCoords[0] = nullptr; + } } // ------------------------------------------------------------------------------------------------ diff --git a/code/ObjFileParser.cpp b/code/ObjFileParser.cpp index d303cb8b9..d1603c6f0 100644 --- a/code/ObjFileParser.cpp +++ b/code/ObjFileParser.cpp @@ -151,7 +151,8 @@ void ObjFileParser::parseFile( IOStreamBuffer &streamBuffer ) { } else if (*m_DataIt == 't') { // read in texture coordinate ( 2D or 3D ) ++m_DataIt; - getVector( m_pModel->m_TextureCoord ); + size_t dim = getTexCoordVector(m_pModel->m_TextureCoord); + m_pModel->m_TextureCoordDim = std::max(m_pModel->m_TextureCoordDim, (unsigned int)dim); } else if (*m_DataIt == 'n') { // Read in normal vector definition ++m_DataIt; @@ -271,6 +272,17 @@ static bool isDataDefinitionEnd( const char *tmp ) { return false; } +static bool isNanOrInf(const char * in) { + // Look for "nan" or "inf", case insensitive + if ((in[0] == 'N' || in[0] == 'n') && ASSIMP_strincmp(in, "nan", 3) == 0) { + return true; + } + else if ((in[0] == 'I' || in[0] == 'i') && ASSIMP_strincmp(in, "inf", 3) == 0) { + return true; + } + return false; +} + size_t ObjFileParser::getNumComponentsInDataDefinition() { size_t numComponents( 0 ); const char* tmp( &m_DataIt[0] ); @@ -284,7 +296,7 @@ size_t ObjFileParser::getNumComponentsInDataDefinition() { if ( !SkipSpaces( &tmp ) ) { break; } - const bool isNum( IsNumeric( *tmp ) ); + const bool isNum( IsNumeric( *tmp ) || isNanOrInf(tmp)); SkipToken( tmp ); if ( isNum ) { ++numComponents; @@ -296,7 +308,7 @@ size_t ObjFileParser::getNumComponentsInDataDefinition() { return numComponents; } -void ObjFileParser::getVector( std::vector &point3d_array ) { +size_t ObjFileParser::getTexCoordVector( std::vector &point3d_array ) { size_t numComponents = getNumComponentsInDataDefinition(); ai_real x, y, z; if( 2 == numComponents ) { @@ -318,8 +330,20 @@ void ObjFileParser::getVector( std::vector &point3d_array ) { } else { throw DeadlyImportError( "OBJ: Invalid number of components" ); } + + // Coerce nan and inf to 0 as is the OBJ default value + if (!std::isfinite(x)) + x = 0; + + if (!std::isfinite(y)) + y = 0; + + if (!std::isfinite(z)) + z = 0; + point3d_array.push_back( aiVector3D( x, y, z ) ); m_DataIt = skipLine( m_DataIt, m_DataItEnd, m_uiLine ); + return numComponents; } void ObjFileParser::getVector3( std::vector &point3d_array ) { @@ -427,13 +451,6 @@ void ObjFileParser::getFace( aiPrimitiveType type ) { if (type == aiPrimitiveType_POINT) { ASSIMP_LOG_ERROR("Obj: Separator unexpected in point statement"); } - if (iPos == 0) { - //if there are no texture coordinates in the file, but normals - if (!vt && vn) { - iPos = 1; - iStep++; - } - } iPos++; } else if( IsSpaceOrNewLine( *m_DataIt ) ) { iPos = 0; @@ -450,6 +467,9 @@ void ObjFileParser::getFace( aiPrimitiveType type ) { ++iStep; } + if (iPos == 1 && !vt && vn) + iPos = 2; // skip texture coords for normals if there are no tex coords + if ( iVal > 0 ) { // Store parsed index if ( 0 == iPos ) { diff --git a/code/ObjFileParser.h b/code/ObjFileParser.h index e00382f4c..7d1b806ce 100644 --- a/code/ObjFileParser.h +++ b/code/ObjFileParser.h @@ -96,7 +96,7 @@ protected: /// Get the number of components in a line. size_t getNumComponentsInDataDefinition(); /// Stores the vector - void getVector( std::vector &point3d_array ); + size_t getTexCoordVector( std::vector &point3d_array ); /// Stores the following 3d vector. void getVector3( std::vector &point3d_array ); /// Stores the following homogeneous vector as a 3D vector diff --git a/code/PostStepRegistry.cpp b/code/PostStepRegistry.cpp index 469a8ef39..15b4a2884 100644 --- a/code/PostStepRegistry.cpp +++ b/code/PostStepRegistry.cpp @@ -173,6 +173,9 @@ void GetPostProcessingStepInstanceList(std::vector< BaseProcess* >& out) #ifndef ASSIMP_BUILD_NO_TRANSFORMTEXCOORDS_PROCESS out.push_back( new TextureTransformStep()); #endif +#if (!defined ASSIMP_BUILD_NO_GLOBALSCALE_PROCESS) + out.push_back( new ScaleProcess()); +#endif #if (!defined ASSIMP_BUILD_NO_PRETRANSFORMVERTICES_PROCESS) out.push_back( new PretransformVertices()); #endif @@ -208,9 +211,6 @@ void GetPostProcessingStepInstanceList(std::vector< BaseProcess* >& out) #endif #if (!defined ASSIMP_BUILD_NO_GENFACENORMALS_PROCESS) out.push_back( new GenFaceNormalsProcess()); -#endif -#if (!defined ASSIMP_BUILD_NO_GLOBALSCALE_PROCESS) - out.push_back( new ScaleProcess()); #endif // ......................................................................... // DON'T change the order of these five .. diff --git a/code/SMDLoader.cpp b/code/SMDLoader.cpp index 00d92e0b4..4288bf9c3 100644 --- a/code/SMDLoader.cpp +++ b/code/SMDLoader.cpp @@ -486,7 +486,7 @@ void SMDImporter::CreateOutputAnimations(const std::string &pFile, IOSystem* pIO if (bLoadAnimationList) { GetAnimationFileList(pFile, pIOHandler, animFileList); } - int animCount = animFileList.size() + 1; + int animCount = static_cast( animFileList.size() + 1u ); pScene->mNumAnimations = 1; pScene->mAnimations = new aiAnimation*[animCount]; memset(pScene->mAnimations, 0, sizeof(aiAnimation*)*animCount); @@ -510,7 +510,7 @@ void SMDImporter::CreateOutputAnimation(int index, const std::string &name) { anim->mName.Set(name.c_str()); } anim->mDuration = dLengthOfAnim; - anim->mNumChannels = asBones.size(); + anim->mNumChannels = static_cast( asBones.size() ); anim->mTicksPerSecond = 25.0; // FIXME: is this correct? aiNodeAnim** pp = anim->mChannels = new aiNodeAnim*[anim->mNumChannels]; diff --git a/code/STLExporter.cpp b/code/STLExporter.cpp index 00fced6ff..d56c42835 100644 --- a/code/STLExporter.cpp +++ b/code/STLExporter.cpp @@ -127,7 +127,7 @@ STLExporter::STLExporter(const char* _filename, const aiScene* pScene, bool expo mOutput.write((char *)&meshnum, 4); if (exportPointClouds) { - + throw DeadlyExportError("This functionality is not yet implemented for binary output."); } for(unsigned int i = 0; i < pScene->mNumMeshes; ++i) { diff --git a/code/STLLoader.cpp b/code/STLLoader.cpp index c615561a8..c7144e444 100644 --- a/code/STLLoader.cpp +++ b/code/STLLoader.cpp @@ -278,6 +278,7 @@ void STLImporter::LoadASCIIFile( aiNode *root ) { } std::string name( szMe, temp ); node->mName.Set( name.c_str() ); + pMesh->mName.Set( name.c_str() ); //pScene->mRootNode->mName.length = temp; //memcpy(pScene->mRootNode->mName.data,szMe,temp); //pScene->mRootNode->mName.data[temp] = '\0'; diff --git a/code/SortByPTypeProcess.cpp b/code/SortByPTypeProcess.cpp index 87bbe5257..2e0cc5400 100644 --- a/code/SortByPTypeProcess.cpp +++ b/code/SortByPTypeProcess.cpp @@ -228,36 +228,37 @@ void SortByPTypeProcess::Execute( aiScene* pScene) { out->mNumVertices = (3 == real ? numPolyVerts : out->mNumFaces * (real+1)); - aiVector3D *vert(NULL), *nor(NULL), *tan(NULL), *bit(NULL); + aiVector3D *vert(nullptr), *nor(nullptr), *tan(nullptr), *bit(nullptr); aiVector3D *uv [AI_MAX_NUMBER_OF_TEXTURECOORDS]; aiColor4D *cols [AI_MAX_NUMBER_OF_COLOR_SETS]; - if (mesh->mVertices) + if (mesh->mVertices) { vert = out->mVertices = new aiVector3D[out->mNumVertices]; + } - if (mesh->mNormals) - nor = out->mNormals = new aiVector3D[out->mNumVertices]; + if (mesh->mNormals) { + nor = out->mNormals = new aiVector3D[out->mNumVertices]; + } - if (mesh->mTangents) - { + if (mesh->mTangents) { tan = out->mTangents = new aiVector3D[out->mNumVertices]; bit = out->mBitangents = new aiVector3D[out->mNumVertices]; } - for (unsigned int i = 0; i < AI_MAX_NUMBER_OF_TEXTURECOORDS;++i) - { - if (mesh->mTextureCoords[i]) - uv[i] = out->mTextureCoords[i] = new aiVector3D[out->mNumVertices]; - else uv[i] = NULL; + for (unsigned int j = 0; j < AI_MAX_NUMBER_OF_TEXTURECOORDS;++j) { + uv[j] = nullptr; + if (mesh->mTextureCoords[j]) { + uv[j] = out->mTextureCoords[j] = new aiVector3D[out->mNumVertices]; + } - out->mNumUVComponents[i] = mesh->mNumUVComponents[i]; + out->mNumUVComponents[j] = mesh->mNumUVComponents[j]; } - for (unsigned int i = 0; i < AI_MAX_NUMBER_OF_COLOR_SETS;++i) - { - if (mesh->mColors[i]) - cols[i] = out->mColors[i] = new aiColor4D[out->mNumVertices]; - else cols[i] = NULL; + for (unsigned int j = 0; j < AI_MAX_NUMBER_OF_COLOR_SETS;++j) { + cols[j] = nullptr; + if (mesh->mColors[j]) { + cols[j] = out->mColors[j] = new aiColor4D[out->mNumVertices]; + } } typedef std::vector< aiVertexWeight > TempBoneInfo; @@ -323,7 +324,7 @@ void SortByPTypeProcess::Execute( aiScene* pScene) { in.mIndices[q] = outIdx++; } - in.mIndices = NULL; + in.mIndices = nullptr; ++outFaces; } ai_assert(outFaces == out->mFaces + out->mNumFaces); diff --git a/code/StandardShapes.cpp b/code/StandardShapes.cpp index f262b6bac..2e5100130 100644 --- a/code/StandardShapes.cpp +++ b/code/StandardShapes.cpp @@ -127,35 +127,35 @@ aiMesh* StandardShapes::MakeMesh(const std::vector& positions, // Determine which kinds of primitives the mesh consists of aiMesh* out = new aiMesh(); - switch (numIndices) - { - case 1: - out->mPrimitiveTypes = aiPrimitiveType_POINT; - break; - case 2: - out->mPrimitiveTypes = aiPrimitiveType_LINE; - break; - case 3: - out->mPrimitiveTypes = aiPrimitiveType_TRIANGLE; - break; - default: - out->mPrimitiveTypes = aiPrimitiveType_POLYGON; - break; + switch (numIndices) { + case 1: + out->mPrimitiveTypes = aiPrimitiveType_POINT; + break; + case 2: + out->mPrimitiveTypes = aiPrimitiveType_LINE; + break; + case 3: + out->mPrimitiveTypes = aiPrimitiveType_TRIANGLE; + break; + default: + out->mPrimitiveTypes = aiPrimitiveType_POLYGON; + break; }; out->mNumFaces = (unsigned int)positions.size() / numIndices; out->mFaces = new aiFace[out->mNumFaces]; - for (unsigned int i = 0, a = 0; i < out->mNumFaces;++i) - { + for (unsigned int i = 0, a = 0; i < out->mNumFaces;++i) { aiFace& f = out->mFaces[i]; f.mNumIndices = numIndices; f.mIndices = new unsigned int[numIndices]; - for (unsigned int i = 0; i < numIndices;++i,++a) - f.mIndices[i] = a; + for (unsigned int j = 0; i < numIndices; ++i, ++a) { + f.mIndices[j] = a; + } } out->mNumVertices = (unsigned int)positions.size(); out->mVertices = new aiVector3D[out->mNumVertices]; ::memcpy(out->mVertices,&positions[0],out->mNumVertices*sizeof(aiVector3D)); + return out; } @@ -466,8 +466,8 @@ void StandardShapes::MakeCone(ai_real height,ai_real radius1, // Need to flip face order? if ( SIZE_MAX != old ) { - for (size_t s = old; s < positions.size();s += 3) { - std::swap(positions[s],positions[s+1]); + for (size_t p = old; p < positions.size();p += 3) { + std::swap(positions[p],positions[p+1]); } } } diff --git a/code/ValidateDataStructure.cpp b/code/ValidateDataStructure.cpp index 405670bdd..712fd6943 100644 --- a/code/ValidateDataStructure.cpp +++ b/code/ValidateDataStructure.cpp @@ -46,8 +46,6 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * the data structure returned by Assimp. */ - - // internal headers #include "ValidateDataStructure.h" #include @@ -110,8 +108,8 @@ void ValidateDSProcess::ReportWarning(const char* msg,...) } // ------------------------------------------------------------------------------------------------ -inline int HasNameMatch(const aiString& in, aiNode* node) -{ +inline +int HasNameMatch(const aiString& in, aiNode* node) { int result = (node->mName == in ? 1 : 0 ); for (unsigned int i = 0; i < node->mNumChildren;++i) { result += HasNameMatch(in,node->mChildren[i]); @@ -121,9 +119,8 @@ inline int HasNameMatch(const aiString& in, aiNode* node) // ------------------------------------------------------------------------------------------------ template -inline void ValidateDSProcess::DoValidation(T** parray, unsigned int size, - const char* firstName, const char* secondName) -{ +inline +void ValidateDSProcess::DoValidation(T** parray, unsigned int size, const char* firstName, const char* secondName) { // validate all entries if (size) { @@ -160,7 +157,7 @@ inline void ValidateDSProcess::DoValidationEx(T** parray, unsigned int size, { if (!parray[i]) { - ReportError("aiScene::%s[%i] is NULL (aiScene::%s is %i)", + ReportError("aiScene::%s[%u] is NULL (aiScene::%s is %u)", firstName,i,secondName,size); } Validate(parray[i]); @@ -170,8 +167,8 @@ inline void ValidateDSProcess::DoValidationEx(T** parray, unsigned int size, { if (parray[i]->mName == parray[a]->mName) { - this->ReportError("aiScene::%s[%i] has the same name as " - "aiScene::%s[%i]",firstName, i,secondName, a); + ReportError("aiScene::%s[%u] has the same name as " + "aiScene::%s[%u]",firstName, i,secondName, a); } } } @@ -181,7 +178,8 @@ inline void ValidateDSProcess::DoValidationEx(T** parray, unsigned int size, // ------------------------------------------------------------------------------------------------ template inline -void ValidateDSProcess::DoValidationWithNameCheck(T** array, unsigned int size, const char* firstName, const char* secondName) { +void ValidateDSProcess::DoValidationWithNameCheck(T** array, unsigned int size, const char* firstName, + const char* secondName) { // validate all entries DoValidationEx(array,size,firstName,secondName); @@ -201,9 +199,8 @@ void ValidateDSProcess::DoValidationWithNameCheck(T** array, unsigned int size, // ------------------------------------------------------------------------------------------------ // Executes the post processing step on the given imported data. -void ValidateDSProcess::Execute( aiScene* pScene) -{ - this->mScene = pScene; +void ValidateDSProcess::Execute( aiScene* pScene) { + mScene = pScene; ASSIMP_LOG_DEBUG("ValidateDataStructureProcess begin"); // validate the node graph of the scene @@ -330,6 +327,7 @@ void ValidateDSProcess::Validate( const aiMesh* pMesh) { case 0: ReportError("aiMesh::mFaces[%i].mNumIndices is 0",i); + break; case 1: if (0 == (pMesh->mPrimitiveTypes & aiPrimitiveType_POINT)) { @@ -422,7 +420,9 @@ void ValidateDSProcess::Validate( const aiMesh* pMesh) if (!abRefList[i])b = true; } abRefList.clear(); - if (b)ReportWarning("There are unreferenced vertices"); + if (b) { + ReportWarning("There are unreferenced vertices"); + } // texture channel 2 may not be set if channel 1 is zero ... { @@ -513,13 +513,11 @@ void ValidateDSProcess::Validate( const aiMesh* pMesh) } // ------------------------------------------------------------------------------------------------ -void ValidateDSProcess::Validate( const aiMesh* pMesh, - const aiBone* pBone,float* afSum) -{ +void ValidateDSProcess::Validate( const aiMesh* pMesh, const aiBone* pBone,float* afSum) { this->Validate(&pBone->mName); if (!pBone->mNumWeights) { - ReportError("aiBone::mNumWeights is zero"); + //ReportError("aiBone::mNumWeights is zero"); } // check whether all vertices affected by this bone are valid @@ -557,10 +555,9 @@ void ValidateDSProcess::Validate( const aiAnimation* pAnimation) Validate(pAnimation, pAnimation->mChannels[i]); } } - else ReportError("aiAnimation::mNumChannels is 0. At least one node animation channel must be there."); - - // Animation duration is allowed to be zero in cases where the anim contains only a single key frame. - // if (!pAnimation->mDuration)this->ReportError("aiAnimation::mDuration is zero"); + else { + ReportError("aiAnimation::mNumChannels is 0. At least one node animation channel must be there."); + } } // ------------------------------------------------------------------------------------------------ @@ -577,15 +574,16 @@ void ValidateDSProcess::SearchForInvalidTextures(const aiMaterial* pMaterial, int iNumIndices = 0; int iIndex = -1; - for (unsigned int i = 0; i < pMaterial->mNumProperties;++i) - { - aiMaterialProperty* prop = pMaterial->mProperties[i]; - if (!::strcmp(prop->mKey.data,"$tex.file") && prop->mSemantic == type) { + for (unsigned int i = 0; i < pMaterial->mNumProperties;++i) { + aiMaterialProperty* prop = pMaterial->mProperties[ i ]; + ai_assert(nullptr != prop); + if ( !::strcmp(prop->mKey.data,"$tex.file") && prop->mSemantic == static_cast(type)) { iIndex = std::max(iIndex, (int) prop->mIndex); ++iNumIndices; - if (aiPTI_String != prop->mType) - ReportError("Material property %s is expected to be a string",prop->mKey.data); + if (aiPTI_String != prop->mType) { + ReportError("Material property %s is expected to be a string", prop->mKey.data); + } } } if (iIndex +1 != iNumIndices) { @@ -740,8 +738,9 @@ void ValidateDSProcess::Validate( const aiMaterial* pMaterial) "AI_MATKEY_SHININESS_STRENGTH key is 0.0"); } break; - default: ; - }; + default: + break; + } } if (AI_SUCCESS == aiGetMaterialFloat( pMaterial,AI_MATKEY_OPACITY,&fTemp) && (!fTemp || fTemp > 1.01)) { @@ -773,8 +772,10 @@ void ValidateDSProcess::Validate( const aiTexture* pTexture) } if (pTexture->mHeight) { - if (!pTexture->mWidth)ReportError("aiTexture::mWidth is zero " - "(aiTexture::mHeight is %i, uncompressed texture)",pTexture->mHeight); + if (!pTexture->mWidth){ + ReportError("aiTexture::mWidth is zero (aiTexture::mHeight is %i, uncompressed texture)", + pTexture->mHeight); + } } else { @@ -805,15 +806,15 @@ void ValidateDSProcess::Validate( const aiAnimation* pAnimation, { Validate(&pNodeAnim->mNodeName); - if (!pNodeAnim->mNumPositionKeys && !pNodeAnim->mScalingKeys && !pNodeAnim->mNumRotationKeys) + if (!pNodeAnim->mNumPositionKeys && !pNodeAnim->mScalingKeys && !pNodeAnim->mNumRotationKeys) { ReportError("Empty node animation channel"); - + } // otherwise check whether one of the keys exceeds the total duration of the animation if (pNodeAnim->mNumPositionKeys) { if (!pNodeAnim->mPositionKeys) { - this->ReportError("aiNodeAnim::mPositionKeys is NULL (aiNodeAnim::mNumPositionKeys is %i)", + ReportError("aiNodeAnim::mPositionKeys is NULL (aiNodeAnim::mNumPositionKeys is %i)", pNodeAnim->mNumPositionKeys); } double dLast = -10e10; @@ -844,7 +845,7 @@ void ValidateDSProcess::Validate( const aiAnimation* pAnimation, { if (!pNodeAnim->mRotationKeys) { - this->ReportError("aiNodeAnim::mRotationKeys is NULL (aiNodeAnim::mNumRotationKeys is %i)", + ReportError("aiNodeAnim::mRotationKeys is NULL (aiNodeAnim::mNumRotationKeys is %i)", pNodeAnim->mNumRotationKeys); } double dLast = -10e10; @@ -905,19 +906,23 @@ void ValidateDSProcess::Validate( const aiAnimation* pAnimation, // ------------------------------------------------------------------------------------------------ void ValidateDSProcess::Validate( const aiNode* pNode) { - if (!pNode)ReportError("A node of the scenegraph is NULL"); - if (pNode != mScene->mRootNode && !pNode->mParent) - this->ReportError("A node has no valid parent (aiNode::mParent is NULL)"); - + if (!pNode) { + ReportError("A node of the scenegraph is NULL"); + } + // Validate node name string first so that it's safe to use in below expressions this->Validate(&pNode->mName); + const char* nodeName = (&pNode->mName)->C_Str(); + if (pNode != mScene->mRootNode && !pNode->mParent){ + ReportError("Non-root node %s lacks a valid parent (aiNode::mParent is NULL) ", nodeName); + } // validate all meshes if (pNode->mNumMeshes) { if (!pNode->mMeshes) { - ReportError("aiNode::mMeshes is NULL (aiNode::mNumMeshes is %i)", - pNode->mNumMeshes); + ReportError("aiNode::mMeshes is NULL for node %s (aiNode::mNumMeshes is %i)", + nodeName, pNode->mNumMeshes); } std::vector abHadMesh; abHadMesh.resize(mScene->mNumMeshes,false); @@ -925,13 +930,13 @@ void ValidateDSProcess::Validate( const aiNode* pNode) { if (pNode->mMeshes[i] >= mScene->mNumMeshes) { - ReportError("aiNode::mMeshes[%i] is out of range (maximum is %i)", - pNode->mMeshes[i],mScene->mNumMeshes-1); + ReportError("aiNode::mMeshes[%i] is out of range for node %s (maximum is %i)", + pNode->mMeshes[i], nodeName, mScene->mNumMeshes-1); } if (abHadMesh[pNode->mMeshes[i]]) { - ReportError("aiNode::mMeshes[%i] is already referenced by this node (value: %i)", - i,pNode->mMeshes[i]); + ReportError("aiNode::mMeshes[%i] is already referenced by this node %s (value: %i)", + i, nodeName, pNode->mMeshes[i]); } abHadMesh[pNode->mMeshes[i]] = true; } @@ -939,8 +944,8 @@ void ValidateDSProcess::Validate( const aiNode* pNode) if (pNode->mNumChildren) { if (!pNode->mChildren) { - ReportError("aiNode::mChildren is NULL (aiNode::mNumChildren is %i)", - pNode->mNumChildren); + ReportError("aiNode::mChildren is NULL for node %s (aiNode::mNumChildren is %i)", + nodeName, pNode->mNumChildren); } for (unsigned int i = 0; i < pNode->mNumChildren;++i) { Validate(pNode->mChildren[i]); @@ -953,7 +958,7 @@ void ValidateDSProcess::Validate( const aiString* pString) { if (pString->length > MAXLEN) { - this->ReportError("aiString::length is too large (%i, maximum is %lu)", + ReportError("aiString::length is too large (%lu, maximum is %lu)", pString->length,MAXLEN); } const char* sz = pString->data; @@ -961,12 +966,14 @@ void ValidateDSProcess::Validate( const aiString* pString) { if ('\0' == *sz) { - if (pString->length != (unsigned int)(sz-pString->data)) + if (pString->length != (unsigned int)(sz-pString->data)) { ReportError("aiString::data is invalid: the terminal zero is at a wrong offset"); + } break; } - else if (sz >= &pString->data[MAXLEN]) + else if (sz >= &pString->data[MAXLEN]) { ReportError("aiString::data is invalid. There is no terminal character"); + } ++sz; } } diff --git a/code/glTF2Asset.h b/code/glTF2Asset.h index 0015197c2..2937c0ce9 100644 --- a/code/glTF2Asset.h +++ b/code/glTF2Asset.h @@ -223,7 +223,8 @@ namespace glTF2 ComponentType_FLOAT = 5126 }; - inline unsigned int ComponentTypeSize(ComponentType t) + inline + unsigned int ComponentTypeSize(ComponentType t) { switch (t) { case ComponentType_SHORT: @@ -250,7 +251,7 @@ namespace glTF2 }; //! Values for the Sampler::magFilter field - enum class SamplerMagFilter: unsigned int + enum class SamplerMagFilter : unsigned int { UNSET = 0, SamplerMagFilter_Nearest = 9728, @@ -258,7 +259,7 @@ namespace glTF2 }; //! Values for the Sampler::minFilter field - enum class SamplerMinFilter: unsigned int + enum class SamplerMinFilter : unsigned int { UNSET = 0, SamplerMinFilter_Nearest = 9728, diff --git a/code/glTF2Asset.inl b/code/glTF2Asset.inl old mode 100755 new mode 100644 diff --git a/code/glTF2Importer.cpp b/code/glTF2Importer.cpp old mode 100755 new mode 100644 index 4228db23f..bb0f7ad8d --- a/code/glTF2Importer.cpp +++ b/code/glTF2Importer.cpp @@ -789,13 +789,16 @@ static void BuildVertexWeightMapping(Mesh::Primitive& primitive, std::vector& meshOffsets, glTF2::Ref& ptr) { Node& node = *ptr; - std::string nameOrId = node.name.empty() ? node.id : node.name; - - aiNode* ainode = new aiNode(nameOrId); + aiNode* ainode = new aiNode(GetNodeName(node)); if (!node.children.empty()) { ainode->mNumChildren = unsigned(node.children.size()); @@ -921,7 +924,7 @@ struct AnimationSamplers { aiNodeAnim* CreateNodeAnim(glTF2::Asset& r, Node& node, AnimationSamplers& samplers) { aiNodeAnim* anim = new aiNodeAnim(); - anim->mNodeName = node.name; + anim->mNodeName = GetNodeName(node); static const float kMillisecondsFromSeconds = 1000.f; diff --git a/code/glTFAssetWriter.inl b/code/glTFAssetWriter.inl index 259ad03f6..20afb24e7 100644 --- a/code/glTFAssetWriter.inl +++ b/code/glTFAssetWriter.inl @@ -294,17 +294,17 @@ namespace glTF { // filling object "compressedData" json_comp_data.SetObject(); json_comp_data.AddMember("buffer", ptr_ext_comp->Buffer, w.mAl); - json_comp_data.AddMember("byteOffset", ptr_ext_comp->Offset, w.mAl); + json_comp_data.AddMember("byteOffset", static_cast(ptr_ext_comp->Offset), w.mAl); json_comp_data.AddMember("componentType", 5121, w.mAl); json_comp_data.AddMember("type", "SCALAR", w.mAl); - json_comp_data.AddMember("count", ptr_ext_comp->Count, w.mAl); + json_comp_data.AddMember("count", static_cast(ptr_ext_comp->Count), w.mAl); if(ptr_ext_comp->Binary) json_comp_data.AddMember("mode", "binary", w.mAl); else json_comp_data.AddMember("mode", "ascii", w.mAl); - json_comp_data.AddMember("indicesCount", ptr_ext_comp->IndicesCount, w.mAl); - json_comp_data.AddMember("verticesCount", ptr_ext_comp->VerticesCount, w.mAl); + json_comp_data.AddMember("indicesCount", static_cast(ptr_ext_comp->IndicesCount), w.mAl); + json_comp_data.AddMember("verticesCount", static_cast(ptr_ext_comp->VerticesCount), w.mAl); // filling object "Open3DGC-compression" Value json_o3dgc; diff --git a/code/glTFExporter.cpp b/code/glTFExporter.cpp index 9daf202cb..6bf7415d3 100644 --- a/code/glTFExporter.cpp +++ b/code/glTFExporter.cpp @@ -245,7 +245,7 @@ inline Ref ExportData(Asset& a, std::string& meshName, Ref& bu namespace { void GetMatScalar(const aiMaterial* mat, float& val, const char* propName, int type, int idx) { - if (mat->Get(propName, type, idx, val) == AI_SUCCESS) {} + ai_assert(mat->Get(propName, type, idx, val) == AI_SUCCESS); } } diff --git a/code/glTFImporter.cpp b/code/glTFImporter.cpp old mode 100755 new mode 100644 diff --git a/code/makefile.mingw b/code/makefile.mingw deleted file mode 100644 index 711d57f57..000000000 --- a/code/makefile.mingw +++ /dev/null @@ -1,105 +0,0 @@ -### USE OF THIS MAKEFILE IS NOT RECOMMENDED. -### It is no longer maintained. Use CMAKE instead. - -# --------------------------------------------------------------------------- -# Makefile for Open Asset Import Library (MinGW32-make) -# aramis_acg@users.sourceforge.net -# - just a quick'n'dirty one, could be buggy ... -# -# Usage: mingw32-make -f makefile.mingw - -# TARGETS: -# all Build a shared so from the whole library -# clean Cleanup object files, prepare for rebuild -# static Build a static library (*.a) - -# MACROS: (make clean before you change one) -# NOBOOST=1 Build against boost workaround -# SINGLETHREADED=1 Build single-threaded library -# DEBUG=1 Build debug build of library -# -# --------------------------------------------------------------------------- - -# C++ object files -OBJECTS := $(patsubst %.cpp,%.o, $(wildcard *.cpp)) -OBJECTS += $(patsubst %.cpp,%.o, $(wildcard extra/*.cpp)) -OBJECTS += $(patsubst %.cpp,%.o, $(wildcard ./../contrib/irrXML/*.cpp)) - -# C object files -OBJECTSC := $(patsubst %.c,%.oc, $(wildcard ./../contrib/zlib/*.c)) -OBJECTSC += $(patsubst %.c,%.oc, $(wildcard ./../contrib/ConvertUTF/*.c)) -OBJECTSC += $(patsubst %.c,%.oc, $(wildcard ./../contrib/unzip/*.c)) - -# Include flags for gcc -INCLUDEFLAGS = - -# Preprocessor defines for gcc -DEFINEFLAGS = - -# Suffix for the output binary, represents build type -NAMESUFFIX = - -# Output path for binaries -BINPATH = ../bin/mingw/ - -# GCC compiler flags -CPPFLAGS=-Wall - -# Setup environment for noboost build -ifeq ($(NOBOOST),1) - SINGLETHREADED = 1 - INCLUDEFLAGS += -I./BoostWorkaround/ - DEFINEFLAGS += -DASSIMP_BUILD_BOOST_WORKAROUND -# NAMESUFFIX += -noboost -else - # adjust this manually if your boost is stored elsewhere - INCLUDEFLAGS += -I"C:/Program Files/boost/boost_1_38" - #INCLUDEFLAGS += -I"$(BOOST_DIR)" - -endif - -# Setup environment for st build -ifeq ($(SINGLETHREADED),1) - DEFINEFLAGS += -DASSIMP_BUILD_SINGLETHREADED -# NAMESUFFIX += -st -endif - -# Setup environment for debug build -ifeq ($(DEBUG),1) - DEFINEFLAGS += -D_DEBUG -DDEBUG - CPPFLAGS += -g -# NAMESUFFIX += -debug -else - CPPFLAGS += -O2 -s - DEFINEFLAGS += -DNDEBUG -D_NDEBUG -endif - -# Output name of shared library -SHARED_TARGET = $(BINPATH)/libassimp$(NAMESUFFIX).so - -# Output name of static library -STATIC = $(BINPATH)/libassimp$(NAMESUFFIX).a - -# target: all -# usage : build a shared library (*.so) -all: $(SHARED_TARGET) - -$(SHARED_TARGET): $(OBJECTS) $(OBJECTSC) - gcc -o $@ $(OBJECTS) $(OBJECTSC) -shared -lstdc++ -%.o:%.cpp - $(CXX) -c $(CPPFLAGS) $? -o $@ $(INCLUDEFLAGS) $(DEFINEFLAGS) -%.oc:%.c - $(CXX) -x c -c -ansi $(CPPFLAGS) $? -o $@ - -# target: clean -# usage : cleanup all object files, prepare for a rebuild -.PHONY: clean -clean: - -del *.o .\..\contrib\irrXML\*.o .\..\contrib\zlib\*.oc .\..\contrib\unzip\*.oc .\..\contrib\ConvertUTF\*.oc - -# target: static -# usage : build a static library (*.a) -static: $(STATIC) -$(STATIC): $(OBJECTS) $(OBJECTSC) - ar rcs $@ $(OBJECTS) $(OBJECTSC) - diff --git a/code/res/assimp.rc b/code/res/assimp.rc index 0fe98c05d..14ffdf4f5 100644 --- a/code/res/assimp.rc +++ b/code/res/assimp.rc @@ -1,7 +1,7 @@ // Microsoft Visual C++ generated resource script. // #include "resource.h" -#include "..\..\revision.h" +#include "revision.h" #define APSTUDIO_READONLY_SYMBOLS ///////////////////////////////////////////////////////////////////////////// @@ -31,8 +31,8 @@ LANGUAGE LANG_GERMAN, SUBLANG_GERMAN // VS_VERSION_INFO VERSIONINFO - FILEVERSION 1,1,SVNRevision, 0 - PRODUCTVERSION 1,1,SVNRevision,0 + FILEVERSION VER_FILEVERSION + PRODUCTVERSION VER_FILEVERSION FILEFLAGSMASK 0x17L #ifdef _DEBUG FILEFLAGS 0x1L @@ -50,12 +50,12 @@ BEGIN VALUE "Comments", "Licensed under a 3-clause BSD license" VALUE "CompanyName", "assimp team" VALUE "FileDescription", "Open Asset Import Library" - VALUE "FileVersion", 1,1,SVNRevision,0 + VALUE "FileVersion", VER_FILEVERSION VALUE "InternalName", "assimp " VALUE "LegalCopyright", "Copyright (C) 2006-2010" VALUE "OriginalFilename", "assimpNN.dll" VALUE "ProductName", "Open Asset Import Library" - VALUE "ProductVersion", 1,1,SVNRevision,0 + VALUE "ProductVersion", VER_FILEVERSION_STR ,0 END END diff --git a/code/res/resource.h b/code/res/resource.h index 37d39284f..c28c05073 100644 --- a/code/res/resource.h +++ b/code/res/resource.h @@ -2,8 +2,8 @@ // Microsoft Visual C++ generated include file. // Used by assimp.rc -// Nächste Standardwerte für neue Objekte -// +// Next standard values for new objects +// #ifdef APSTUDIO_INVOKED #ifndef APSTUDIO_READONLY_SYMBOLS #define _APS_NEXT_RESOURCE_VALUE 101 diff --git a/contrib/irrXML/CMakeLists.txt b/contrib/irrXML/CMakeLists.txt index 48941970a..7f58af3d8 100644 --- a/contrib/irrXML/CMakeLists.txt +++ b/contrib/irrXML/CMakeLists.txt @@ -13,10 +13,17 @@ if ( MSVC ) ADD_DEFINITIONS( -D_CRT_SECURE_NO_WARNINGS ) endif ( MSVC ) -add_library(IrrXML STATIC ${IrrXML_SRCS}) +IF(CMAKE_SYSTEM_NAME MATCHES "(Darwin|FreeBSD)") + add_library(IrrXML ${IrrXML_SRCS}) +ELSE() + add_library(IrrXML STATIC ${IrrXML_SRCS}) +ENDIF() set(IRRXML_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}" CACHE INTERNAL "IrrXML_Include" ) set(IRRXML_LIBRARY "IrrXML" CACHE INTERNAL "IrrXML" ) install(TARGETS IrrXML + LIBRARY DESTINATION ${ASSIMP_LIB_INSTALL_DIR} ARCHIVE DESTINATION ${ASSIMP_LIB_INSTALL_DIR} + RUNTIME DESTINATION ${ASSIMP_BIN_INSTALL_DIR} + FRAMEWORK DESTINATION ${ASSIMP_LIB_INSTALL_DIR} COMPONENT ${LIBASSIMP_COMPONENT}) diff --git a/contrib/irrXML/CXMLReaderImpl.h b/contrib/irrXML/CXMLReaderImpl.h index 7d33b9404..6f3bec5fa 100644 --- a/contrib/irrXML/CXMLReaderImpl.h +++ b/contrib/irrXML/CXMLReaderImpl.h @@ -10,8 +10,11 @@ #include "irrArray.h" #include +#include +#include +#include +//using namespace Assimp; -using namespace Assimp; #ifdef _DEBUG #define IRR_DEBUGPRINT(x) printf((x)); @@ -162,7 +165,8 @@ public: return 0; core::stringc c = attr->Value.c_str(); - return fast_atof(c.c_str()); + return static_cast(atof(c.c_str())); + //return fast_atof(c.c_str()); } @@ -174,7 +178,8 @@ public: return 0; core::stringc c = attrvalue; - return fast_atof(c.c_str()); + return static_cast(atof(c.c_str())); + //return fast_atof(c.c_str()); } @@ -428,7 +433,7 @@ private: ++P; // remove trailing whitespace, if any - while( isspace( P[-1])) + while( std::isspace( P[-1])) --P; NodeName = core::string(pBeginClose, (int)(P - pBeginClose)); diff --git a/contrib/irrXML/irrString.h b/contrib/irrXML/irrString.h index af21b8e51..ff0097b71 100644 --- a/contrib/irrXML/irrString.h +++ b/contrib/irrXML/irrString.h @@ -19,7 +19,7 @@ so you can assign unicode to string and ascii to string Note that the conversation between both is not done using an encoding. Known bugs: -Special characters like 'Ä', 'Ü' and 'Ö' are ignored in the +Special characters like 'Ä', 'Ãœ' and 'Ö' are ignored in the methods make_upper, make_lower and equals_ignore_case. */ template diff --git a/contrib/irrXML/irrXML.cpp b/contrib/irrXML/irrXML.cpp index 532eed544..5a4b04507 100644 --- a/contrib/irrXML/irrXML.cpp +++ b/contrib/irrXML/irrXML.cpp @@ -9,7 +9,7 @@ #include "irrXML.h" #include "irrString.h" #include "irrArray.h" -#include +//#include #include "CXMLReaderImpl.h" namespace irr @@ -18,7 +18,7 @@ namespace io { //! Implementation of the file read callback for ordinary files -class CFileReadCallBack : public IFileReadCallBack +class IRRXML_API CFileReadCallBack : public IFileReadCallBack { public: diff --git a/contrib/irrXML/irrXML.h b/contrib/irrXML/irrXML.h index b51ddeb54..d596ec062 100644 --- a/contrib/irrXML/irrXML.h +++ b/contrib/irrXML/irrXML.h @@ -7,6 +7,12 @@ #include +#ifdef _WIN32 +# define IRRXML_API __declspec(dllexport) +#else +# define IRRXML_API __attribute__ ((visibility("default"))) +#endif // _WIN32 + /** \mainpage irrXML 1.2 API documentation
@@ -409,7 +415,7 @@ namespace io \return Returns a pointer to the created xml parser. This pointer should be deleted using 'delete' after no longer needed. Returns 0 if an error occured and the file could not be opened. */ - IrrXMLReader* createIrrXMLReader(const char* filename); + IRRXML_API IrrXMLReader* createIrrXMLReader(const char* filename); //! Creates an instance of an UFT-8 or ASCII character xml parser. /** This means that all character data will be returned in 8 bit ASCII or UTF-8. The file to read can @@ -421,7 +427,7 @@ namespace io \return Returns a pointer to the created xml parser. This pointer should be deleted using 'delete' after no longer needed. Returns 0 if an error occured and the file could not be opened. */ - IrrXMLReader* createIrrXMLReader(FILE* file); + IRRXML_API IrrXMLReader* createIrrXMLReader(FILE* file); //! Creates an instance of an UFT-8 or ASCII character xml parser. /** This means that all character data will be returned in 8 bit ASCII or UTF-8. The file to read can @@ -434,7 +440,7 @@ namespace io \return Returns a pointer to the created xml parser. This pointer should be deleted using 'delete' after no longer needed. Returns 0 if an error occured and the file could not be opened. */ - IrrXMLReader* createIrrXMLReader(IFileReadCallBack* callback); + IRRXML_API IrrXMLReader* createIrrXMLReader(IFileReadCallBack* callback); //! Creates an instance of an UFT-16 xml parser. /** This means that @@ -446,7 +452,7 @@ namespace io \return Returns a pointer to the created xml parser. This pointer should be deleted using 'delete' after no longer needed. Returns 0 if an error occured and the file could not be opened. */ - IrrXMLReaderUTF16* createIrrXMLReaderUTF16(const char* filename); + IRRXML_API IrrXMLReaderUTF16* createIrrXMLReaderUTF16(const char* filename); //! Creates an instance of an UFT-16 xml parser. /** This means that all character data will be returned in UTF-16. The file to read can @@ -458,7 +464,7 @@ namespace io \return Returns a pointer to the created xml parser. This pointer should be deleted using 'delete' after no longer needed. Returns 0 if an error occured and the file could not be opened. */ - IrrXMLReaderUTF16* createIrrXMLReaderUTF16(FILE* file); + IRRXML_API IrrXMLReaderUTF16* createIrrXMLReaderUTF16(FILE* file); //! Creates an instance of an UFT-16 xml parser. /** This means that all character data will be returned in UTF-16. The file to read can @@ -471,7 +477,7 @@ namespace io \return Returns a pointer to the created xml parser. This pointer should be deleted using 'delete' after no longer needed. Returns 0 if an error occured and the file could not be opened. */ - IrrXMLReaderUTF16* createIrrXMLReaderUTF16(IFileReadCallBack* callback); + IRRXML_API IrrXMLReaderUTF16* createIrrXMLReaderUTF16(IFileReadCallBack* callback); //! Creates an instance of an UFT-32 xml parser. @@ -483,7 +489,7 @@ namespace io \return Returns a pointer to the created xml parser. This pointer should be deleted using 'delete' after no longer needed. Returns 0 if an error occured and the file could not be opened. */ - IrrXMLReaderUTF32* createIrrXMLReaderUTF32(const char* filename); + IRRXML_API IrrXMLReaderUTF32* createIrrXMLReaderUTF32(const char* filename); //! Creates an instance of an UFT-32 xml parser. /** This means that all character data will be returned in UTF-32. The file to read can @@ -495,7 +501,7 @@ namespace io \return Returns a pointer to the created xml parser. This pointer should be deleted using 'delete' after no longer needed. Returns 0 if an error occured and the file could not be opened. */ - IrrXMLReaderUTF32* createIrrXMLReaderUTF32(FILE* file); + IRRXML_API IrrXMLReaderUTF32* createIrrXMLReaderUTF32(FILE* file); //! Creates an instance of an UFT-32 xml parser. /** This means that @@ -509,7 +515,7 @@ namespace io \return Returns a pointer to the created xml parser. This pointer should be deleted using 'delete' after no longer needed. Returns 0 if an error occured and the file could not be opened. */ - IrrXMLReaderUTF32* createIrrXMLReaderUTF32(IFileReadCallBack* callback); + IRRXML_API IrrXMLReaderUTF32* createIrrXMLReaderUTF32(IFileReadCallBack* callback); /*! \file irrxml.h diff --git a/contrib/poly2tri/AUTHORS b/contrib/poly2tri/AUTHORS index 1736f14bb..aa390660d 100644 --- a/contrib/poly2tri/AUTHORS +++ b/contrib/poly2tri/AUTHORS @@ -1,7 +1,7 @@ Primary Contributors: Mason Green (C++, Python) - Thomas Åhlén (Java) + Thomas Ã…hlén (Java) Other Contributors: diff --git a/contrib/zip/.gitignore b/contrib/zip/.gitignore index 4f9972c56..a7904a1ef 100644 --- a/contrib/zip/.gitignore +++ b/contrib/zip/.gitignore @@ -36,3 +36,21 @@ # Temporary *.swp .DS_Store + +# CMake +CMakeScripts +*.cmake + +# Xcode +*.build +*.xcodeproj +zip.sln +zip.vcxproj.filters +zip.vcxproj +ALL_BUILD.vcxproj.filters +ALL_BUILD.vcxproj +CMakeFiles/ +zip.dir/ +test/test.exe.vcxproj.filters +test/test.exe.vcxproj +test/test.exe.dir/ diff --git a/contrib/zip/.travis.sh b/contrib/zip/.travis.sh new file mode 100755 index 000000000..22974b1ff --- /dev/null +++ b/contrib/zip/.travis.sh @@ -0,0 +1,18 @@ +#!/bin/bash +# +# Build script for travis-ci.org builds. +# +if [ $ANALYZE = "true" ] && [ "$CC" = "clang" ]; then + # scan-build -h + scan-build cmake -G "Unix Makefiles" + scan-build -enable-checker security.FloatLoopCounter \ + -enable-checker security.insecureAPI.UncheckedReturn \ + --status-bugs -v \ + make -j 8 \ + make -j 8 test +else + cmake -DCMAKE_BUILD_TYPE=Debug -DSANITIZE_ADDRESS=On -DCMAKE_INSTALL_PREFIX=_install + make -j 8 + make install + ASAN_OPTIONS=detect_leaks=0 LSAN_OPTIONS=verbosity=1:log_threads=1 ctest -V +fi \ No newline at end of file diff --git a/contrib/zip/.travis.yml b/contrib/zip/.travis.yml index d8ccb728d..86bac1cca 100644 --- a/contrib/zip/.travis.yml +++ b/contrib/zip/.travis.yml @@ -1,10 +1,22 @@ language: c +addons: + apt: + packages: &1 + - lcov # Compiler selection compiler: - clang - gcc +env: + - ANALYZE=false + - ANALYZE=true # Build steps script: - - mkdir build - - cd build - - cmake -DCMAKE_BUILD_TYPE=Debug .. && make && make test + - ./.travis.sh +after_success: + # Creating report + - cmake -DENABLE_COVERAGE=ON + - make + - make test + # Uploading report to CodeCov + - bash <(curl -s https://codecov.io/bash) \ No newline at end of file diff --git a/contrib/zip/CMakeLists.txt b/contrib/zip/CMakeLists.txt index 450ef3a98..b46dbb1db 100644 --- a/contrib/zip/CMakeLists.txt +++ b/contrib/zip/CMakeLists.txt @@ -1,18 +1,47 @@ cmake_minimum_required(VERSION 2.8) project(zip) +enable_language(C) +set(CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake" ${CMAKE_MODULE_PATH}) if (MSVC) - # Use secure functions by defaualt and suppress warnings about "deprecated" functions - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /D _CRT_SECURE_CPP_OVERLOAD_STANDARD_NAMES=1") - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /D _CRT_SECURE_CPP_OVERLOAD_STANDARD_NAMES_COUNT=1") - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /D _CRT_NONSTDC_NO_WARNINGS=1 /D _CRT_SECURE_NO_WARNINGS=1") + # Use secure functions by defaualt and suppress warnings about "deprecated" functions + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /D _CRT_SECURE_CPP_OVERLOAD_STANDARD_NAMES=1") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /D _CRT_SECURE_CPP_OVERLOAD_STANDARD_NAMES_COUNT=1") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /D _CRT_NONSTDC_NO_WARNINGS=1 /D _CRT_SECURE_NO_WARNINGS=1") +elseif ("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" OR + "${CMAKE_C_COMPILER_ID}" STREQUAL "Clang" OR + "${CMAKE_C_COMPILER_ID}" STREQUAL "AppleClang") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -std=c99 -Wall -Wextra -Werror -pedantic") endif (MSVC) # zip set(SRC src/miniz.h src/zip.h src/zip.c) -add_library(${CMAKE_PROJECT_NAME} ${SRC}) +add_library(${PROJECT_NAME} ${SRC}) +target_include_directories(${PROJECT_NAME} INTERFACE src) # test -enable_testing() -add_subdirectory(test) +if (NOT CMAKE_DISABLE_TESTING) + enable_testing() + add_subdirectory(test) + find_package(Sanitizers) + add_sanitizers(${PROJECT_NAME} test.exe) + add_sanitizers(${PROJECT_NAME} test_miniz.exe) +endif() +install(TARGETS ${PROJECT_NAME} + RUNTIME DESTINATION bin + ARCHIVE DESTINATION lib + LIBRARY DESTINATION lib + COMPONENT library) +install(FILES ${PROJECT_SOURCE_DIR}/src/zip.h DESTINATION include) + +# uninstall target (https://gitlab.kitware.com/cmake/community/wikis/FAQ#can-i-do-make-uninstall-with-cmake) +if(NOT TARGET uninstall) + configure_file( + "${CMAKE_CURRENT_SOURCE_DIR}/cmake/cmake_uninstall.cmake.in" + "${CMAKE_CURRENT_BINARY_DIR}/cmake/cmake_uninstall.cmake" + IMMEDIATE @ONLY) + + add_custom_target(uninstall + COMMAND ${CMAKE_COMMAND} -P ${CMAKE_CURRENT_BINARY_DIR}/cmake/cmake_uninstall.cmake) +endif() diff --git a/contrib/zip/README.md b/contrib/zip/README.md index 24de5e61a..d5fb8cd20 100644 --- a/contrib/zip/README.md +++ b/contrib/zip/README.md @@ -1,12 +1,11 @@ ### A portable (OSX/Linux/Windows), simple zip library written in C This is done by hacking awesome [miniz](https://code.google.com/p/miniz) library and layering functions on top of the miniz v1.15 API. -[![Windows][win-badge]][win-link] [![OS X][osx-linux-badge]][osx-linux-link] +[![Windows](https://ci.appveyor.com/api/projects/status/bph8dr3jacgmjv32/branch/master?svg=true&label=windows)](https://ci.appveyor.com/project/kuba--/zip) +[![Linux](https://travis-ci.org/kuba--/zip.svg?branch=master&label=linux%2fosx)](https://travis-ci.org/kuba--/zip) +[![Version](https://badge.fury.io/gh/kuba--%2Fzip.svg)](https://github.com/kuba--/zip/releases) +[![Codecov](https://codecov.io/gh/kuba--/zip/branch/master/graph/badge.svg)](https://codecov.io/gh/kuba--/zip) -[win-badge]: https://img.shields.io/appveyor/ci/kuba--/zip/master.svg?label=windows "AppVeyor build status" -[win-link]: https://ci.appveyor.com/project/kuba--/zip "AppVeyor build status" -[osx-linux-badge]: https://img.shields.io/travis/kuba--/zip/master.svg?label=linux/osx "Travis CI build status" -[osx-linux-link]: https://travis-ci.org/kuba--/zip "Travis CI build status" # The Idea @@ -23,117 +22,288 @@ It was the reason, why I decided to write zip module on top of the miniz. It req * Create a new zip archive with default compression level. ```c - struct zip_t *zip = zip_open("foo.zip", ZIP_DEFAULT_COMPRESSION_LEVEL, 'w'); +struct zip_t *zip = zip_open("foo.zip", ZIP_DEFAULT_COMPRESSION_LEVEL, 'w'); +{ + zip_entry_open(zip, "foo-1.txt"); { - zip_entry_open(zip, "foo-1.txt"); - { - char *buf = "Some data here..."; - zip_entry_write(zip, buf, strlen(buf)); - } - zip_entry_close(zip); - - zip_entry_open(zip, "foo-2.txt"); - { - // merge 3 files into one entry and compress them on-the-fly. - zip_entry_fwrite(zip, "foo-2.1.txt"); - zip_entry_fwrite(zip, "foo-2.2.txt"); - zip_entry_fwrite(zip, "foo-2.3.txt"); - } - zip_entry_close(zip); + const char *buf = "Some data here...\0"; + zip_entry_write(zip, buf, strlen(buf)); } - zip_close(zip); + zip_entry_close(zip); + + zip_entry_open(zip, "foo-2.txt"); + { + // merge 3 files into one entry and compress them on-the-fly. + zip_entry_fwrite(zip, "foo-2.1.txt"); + zip_entry_fwrite(zip, "foo-2.2.txt"); + zip_entry_fwrite(zip, "foo-2.3.txt"); + } + zip_entry_close(zip); +} +zip_close(zip); ``` * Append to the existing zip archive. ```c - struct zip_t *zip = zip_open("foo.zip", ZIP_DEFAULT_COMPRESSION_LEVEL, 'a'); +struct zip_t *zip = zip_open("foo.zip", ZIP_DEFAULT_COMPRESSION_LEVEL, 'a'); +{ + zip_entry_open(zip, "foo-3.txt"); { - zip_entry_open(zip, "foo-3.txt"); - { - char *buf = "Append some data here..."; - zip_entry_write(zip, buf, strlen(buf)); - } - zip_entry_close(zip); + const char *buf = "Append some data here...\0"; + zip_entry_write(zip, buf, strlen(buf)); } - zip_close(zip); + zip_entry_close(zip); +} +zip_close(zip); ``` * Extract a zip archive into a folder. ```c - int on_extract_entry(const char *filename, void *arg) { - static int i = 0; - int n = *(int *)arg; - printf("Extracted: %s (%d of %d)\n", filename, ++i, n); +int on_extract_entry(const char *filename, void *arg) { + static int i = 0; + int n = *(int *)arg; + printf("Extracted: %s (%d of %d)\n", filename, ++i, n); - return 0; - } + return 0; +} - int arg = 2; - zip_extract("foo.zip", "/tmp", on_extract_entry, &arg); +int arg = 2; +zip_extract("foo.zip", "/tmp", on_extract_entry, &arg); ``` -* Extract a zip entry into memory. +* Extract a zip entry into memory. ```c - void *buf = NULL; - size_t bufsize; +void *buf = NULL; +size_t bufsize; - struct zip_t *zip = zip_open("foo.zip", 0, 'r'); +struct zip_t *zip = zip_open("foo.zip", 0, 'r'); +{ + zip_entry_open(zip, "foo-1.txt"); { - zip_entry_open(zip, "foo-1.txt"); - { - zip_entry_read(zip, &buf, &bufsize); - } - zip_entry_close(zip); + zip_entry_read(zip, &buf, &bufsize); } - zip_close(zip); + zip_entry_close(zip); +} +zip_close(zip); - free(buf); +free(buf); ``` -* Extract a zip entry into memory using callback. +* Extract a zip entry into memory (no internal allocation). ```c - struct buffer_t { - char *data; - size_t size; - }; +unsigned char *buf; +size_t bufsize; - static size_t on_extract(void *arg, unsigned long long offset, const void *data, size_t size) { - struct buffer_t *buf = (struct buffer_t *)arg; - buf->data = realloc(buf->data, buf->size + size + 1); - assert(NULL != buf->data); - - memcpy(&(buf->data[buf->size]), data, size); - buf->size += size; - buf->data[buf->size] = 0; - - return size; - } - - struct buffer_t buf = {0}; - struct zip_t *zip = zip_open("foo.zip", 0, 'r'); +struct zip_t *zip = zip_open("foo.zip", 0, 'r'); +{ + zip_entry_open(zip, "foo-1.txt"); { - zip_entry_open(zip, "foo-1.txt"); - { - zip_entry_extract(zip, on_extract, &buf); - } - zip_entry_close(zip); - } - zip_close(zip); + bufsize = zip_entry_size(zip); + buf = calloc(sizeof(unsigned char), bufsize); - free(buf.data); + zip_entry_noallocread(zip, (void *)buf, bufsize); + } + zip_entry_close(zip); +} +zip_close(zip); + +free(buf); ``` - -* Extract a zip entry into a file. +* Extract a zip entry into memory using callback. ```c - struct zip_t *zip = zip_open("foo.zip", 0, 'r'); +struct buffer_t { + char *data; + size_t size; +}; + +static size_t on_extract(void *arg, unsigned long long offset, const void *data, size_t size) { + struct buffer_t *buf = (struct buffer_t *)arg; + buf->data = realloc(buf->data, buf->size + size + 1); + assert(NULL != buf->data); + + memcpy(&(buf->data[buf->size]), data, size); + buf->size += size; + buf->data[buf->size] = 0; + + return size; +} + +struct buffer_t buf = {0}; +struct zip_t *zip = zip_open("foo.zip", 0, 'r'); +{ + zip_entry_open(zip, "foo-1.txt"); { - zip_entry_open(zip, "foo-2.txt"); - { - zip_entry_fread(zip, "foo-2.txt"); - } - zip_entry_close(zip); + zip_entry_extract(zip, on_extract, &buf); } - zip_close(zip); + zip_entry_close(zip); +} +zip_close(zip); + +free(buf.data); +``` + + +* Extract a zip entry into a file. +```c +struct zip_t *zip = zip_open("foo.zip", 0, 'r'); +{ + zip_entry_open(zip, "foo-2.txt"); + { + zip_entry_fread(zip, "foo-2.txt"); + } + zip_entry_close(zip); +} +zip_close(zip); +``` + +* List of all zip entries +```c +struct zip_t *zip = zip_open("foo.zip", 0, 'r'); +int i, n = zip_total_entries(zip); +for (i = 0; i < n; ++i) { + zip_entry_openbyindex(zip, i); + { + const char *name = zip_entry_name(zip); + int isdir = zip_entry_isdir(zip); + unsigned long long size = zip_entry_size(zip); + unsigned int crc32 = zip_entry_crc32(zip); + } + zip_entry_close(zip); +} +zip_close(zip); +``` + +## Bindings +Compile zip library as a dynamic library. +```shell +$ mkdir build +$ cd build +$ cmake -DBUILD_SHARED_LIBS=true .. +$ make +``` + +### Go (cgo) +```go +package main + +/* +#cgo CFLAGS: -I../src +#cgo LDFLAGS: -L. -lzip +#include +*/ +import "C" +import "unsafe" + +func main() { + path := C.CString("/tmp/go.zip") + zip := C.zip_open(path, 6, 'w') + + entryname := C.CString("test") + C.zip_entry_open(zip, entryname) + + content := "test content" + buf := unsafe.Pointer(C.CString(content)) + bufsize := C.size_t(len(content)) + C.zip_entry_write(zip, buf, bufsize) + + C.zip_entry_close(zip) + + C.zip_close(zip) +} +``` + +### Ruby (ffi) +Install _ffi_ gem. +```shell +$ gem install ffi +``` + +Bind in your module. +```ruby +require 'ffi' + +module Zip + extend FFI::Library + ffi_lib "./libzip.#{::FFI::Platform::LIBSUFFIX}" + + attach_function :zip_open, [:string, :int, :char], :pointer + attach_function :zip_close, [:pointer], :void + + attach_function :zip_entry_open, [:pointer, :string], :int + attach_function :zip_entry_close, [:pointer], :void + attach_function :zip_entry_write, [:pointer, :string, :int], :int +end + +ptr = Zip.zip_open("/tmp/ruby.zip", 6, "w".bytes()[0]) + +status = Zip.zip_entry_open(ptr, "test") + +content = "test content" +status = Zip.zip_entry_write(ptr, content, content.size()) + +Zip.zip_entry_close(ptr) +Zip.zip_close(ptr) +``` + +### Python (cffi) +Install _cffi_ package +```shell +$ pip install cffi +``` + +Bind in your package. +```python +import ctypes.util +from cffi import FFI + +ffi = FFI() +ffi.cdef(""" + struct zip_t *zip_open(const char *zipname, int level, char mode); + void zip_close(struct zip_t *zip); + + int zip_entry_open(struct zip_t *zip, const char *entryname); + int zip_entry_close(struct zip_t *zip); + int zip_entry_write(struct zip_t *zip, const void *buf, size_t bufsize); +""") + +Zip = ffi.dlopen(ctypes.util.find_library("zip")) + +ptr = Zip.zip_open("/tmp/python.zip", 6, 'w') + +status = Zip.zip_entry_open(ptr, "test") + +content = "test content" +status = Zip.zip_entry_write(ptr, content, len(content)) + +Zip.zip_entry_close(ptr) +Zip.zip_close(ptr) +``` + +### Ring +The language comes with RingZip based on this library +```ring +load "ziplib.ring" + +new Zip { + setFileName("myfile.zip") + open("w") + newEntry() { + open("test.c") + writefile("test.c") + close() + } + close() +} +``` + +# Contribution Rules/Coding Standards +No need to throw away your coding style, just do your best to follow default clang-format style. +Apply `clang-format` to the source files before commit: +```sh +for file in $(git ls-files | \grep -E '\.(c|h)$' | \grep -v -- '#') +do + clang-format -i $file +done ``` diff --git a/contrib/zip/appveyor.yml b/contrib/zip/appveyor.yml index 297cad8b0..0be6373ca 100644 --- a/contrib/zip/appveyor.yml +++ b/contrib/zip/appveyor.yml @@ -1,4 +1,4 @@ -version: 1.0.{build} +version: zip-0.1.9.{build} build_script: - cmd: >- cd c:\projects\zip diff --git a/contrib/zip/cmake/asan-wrapper b/contrib/zip/cmake/asan-wrapper new file mode 100755 index 000000000..5d5410337 --- /dev/null +++ b/contrib/zip/cmake/asan-wrapper @@ -0,0 +1,55 @@ +#!/bin/sh + +# The MIT License (MIT) +# +# Copyright (c) +# 2013 Matthew Arsenault +# 2015-2016 RWTH Aachen University, Federal Republic of Germany +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# This script is a wrapper for AddressSanitizer. In some special cases you need +# to preload AddressSanitizer to avoid error messages - e.g. if you're +# preloading another library to your application. At the moment this script will +# only do something, if we're running on a Linux platform. OSX might not be +# affected. + + +# Exit immediately, if platform is not Linux. +if [ "$(uname)" != "Linux" ] +then + exec $@ +fi + + +# Get the used libasan of the application ($1). If a libasan was found, it will +# be prepended to LD_PRELOAD. +libasan=$(ldd $1 | grep libasan | sed "s/^[[:space:]]//" | cut -d' ' -f1) +if [ -n "$libasan" ] +then + if [ -n "$LD_PRELOAD" ] + then + export LD_PRELOAD="$libasan:$LD_PRELOAD" + else + export LD_PRELOAD="$libasan" + fi +fi + +# Execute the application. +exec $@ diff --git a/contrib/zip/cmake/cmake_uninstall.cmake.in b/contrib/zip/cmake/cmake_uninstall.cmake.in new file mode 100644 index 000000000..86ea34d8f --- /dev/null +++ b/contrib/zip/cmake/cmake_uninstall.cmake.in @@ -0,0 +1,23 @@ +# copied from https://gitlab.kitware.com/cmake/community/wikis/FAQ#can-i-do-make-uninstall-with-cmake +if(NOT EXISTS "@CMAKE_BINARY_DIR@/install_manifest.txt") + message(FATAL_ERROR "Cannot find install manifest: @CMAKE_BINARY_DIR@/install_manifest.txt") +endif(NOT EXISTS "@CMAKE_BINARY_DIR@/install_manifest.txt") + +file(READ "@CMAKE_BINARY_DIR@/install_manifest.txt" files) +string(REGEX REPLACE "\n" ";" files "${files}") +foreach(file ${files}) + message(STATUS "Uninstalling $ENV{DESTDIR}${file}") + if(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}") + exec_program( + "@CMAKE_COMMAND@" ARGS "-E remove \"$ENV{DESTDIR}${file}\"" + OUTPUT_VARIABLE rm_out + RETURN_VALUE rm_retval + ) + if(NOT "${rm_retval}" STREQUAL 0) + message(FATAL_ERROR "Problem when removing $ENV{DESTDIR}${file}") + endif(NOT "${rm_retval}" STREQUAL 0) + else(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}") + message(STATUS "File $ENV{DESTDIR}${file} does not exist.") + endif(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}") +endforeach(file) + diff --git a/contrib/zip/src/miniz.h b/contrib/zip/src/miniz.h index 935f7de2e..2c27a94d8 100644 --- a/contrib/zip/src/miniz.h +++ b/contrib/zip/src/miniz.h @@ -1,160 +1,221 @@ /* - miniz.c v1.15 - public domain deflate/inflate, zlib-subset, ZIP reading/writing/appending, PNG writing - See "unlicense" statement at the end of this file. - Rich Geldreich , last updated Oct. 13, 2013 - Implements RFC 1950: http://www.ietf.org/rfc/rfc1950.txt and RFC 1951: http://www.ietf.org/rfc/rfc1951.txt + miniz.c v1.15 - public domain deflate/inflate, zlib-subset, ZIP + reading/writing/appending, PNG writing See "unlicense" statement at the end + of this file. Rich Geldreich , last updated Oct. 13, + 2013 Implements RFC 1950: http://www.ietf.org/rfc/rfc1950.txt and RFC 1951: + http://www.ietf.org/rfc/rfc1951.txt - Most API's defined in miniz.c are optional. For example, to disable the archive related functions just define - MINIZ_NO_ARCHIVE_APIS, or to get rid of all stdio usage define MINIZ_NO_STDIO (see the list below for more macros). + Most API's defined in miniz.c are optional. For example, to disable the + archive related functions just define MINIZ_NO_ARCHIVE_APIS, or to get rid of + all stdio usage define MINIZ_NO_STDIO (see the list below for more macros). * Change History - 10/13/13 v1.15 r4 - Interim bugfix release while I work on the next major release with Zip64 support (almost there!): - - Critical fix for the MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY bug (thanks kahmyong.moon@hp.com) which could cause locate files to not find files. This bug - would only have occured in earlier versions if you explicitly used this flag, OR if you used mz_zip_extract_archive_file_to_heap() or mz_zip_add_mem_to_archive_file_in_place() - (which used this flag). If you can't switch to v1.15 but want to fix this bug, just remove the uses of this flag from both helper funcs (and of course don't use the flag). - - Bugfix in mz_zip_reader_extract_to_mem_no_alloc() from kymoon when pUser_read_buf is not NULL and compressed size is > uncompressed size - - Fixing mz_zip_reader_extract_*() funcs so they don't try to extract compressed data from directory entries, to account for weird zipfiles which contain zero-size compressed data on dir entries. - Hopefully this fix won't cause any issues on weird zip archives, because it assumes the low 16-bits of zip external attributes are DOS attributes (which I believe they always are in practice). - - Fixing mz_zip_reader_is_file_a_directory() so it doesn't check the internal attributes, just the filename and external attributes + 10/13/13 v1.15 r4 - Interim bugfix release while I work on the next major + release with Zip64 support (almost there!): + - Critical fix for the MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY bug + (thanks kahmyong.moon@hp.com) which could cause locate files to not find + files. This bug would only have occured in earlier versions if you explicitly + used this flag, OR if you used mz_zip_extract_archive_file_to_heap() or + mz_zip_add_mem_to_archive_file_in_place() (which used this flag). If you + can't switch to v1.15 but want to fix this bug, just remove the uses of this + flag from both helper funcs (and of course don't use the flag). + - Bugfix in mz_zip_reader_extract_to_mem_no_alloc() from kymoon when + pUser_read_buf is not NULL and compressed size is > uncompressed size + - Fixing mz_zip_reader_extract_*() funcs so they don't try to extract + compressed data from directory entries, to account for weird zipfiles which + contain zero-size compressed data on dir entries. Hopefully this fix won't + cause any issues on weird zip archives, because it assumes the low 16-bits of + zip external attributes are DOS attributes (which I believe they always are + in practice). + - Fixing mz_zip_reader_is_file_a_directory() so it doesn't check the + internal attributes, just the filename and external attributes - mz_zip_reader_init_file() - missing MZ_FCLOSE() call if the seek failed - - Added cmake support for Linux builds which builds all the examples, tested with clang v3.3 and gcc v4.6. + - Added cmake support for Linux builds which builds all the examples, + tested with clang v3.3 and gcc v4.6. - Clang fix for tdefl_write_image_to_png_file_in_memory() from toffaletti - Merged MZ_FORCEINLINE fix from hdeanclark - Fix include before config #ifdef, thanks emil.brink - - Added tdefl_write_image_to_png_file_in_memory_ex(): supports Y flipping (super useful for OpenGL apps), and explicit control over the compression level (so you can - set it to 1 for real-time compression). + - Added tdefl_write_image_to_png_file_in_memory_ex(): supports Y flipping + (super useful for OpenGL apps), and explicit control over the compression + level (so you can set it to 1 for real-time compression). - Merged in some compiler fixes from paulharris's github repro. - - Retested this build under Windows (VS 2010, including static analysis), tcc 0.9.26, gcc v4.6 and clang v3.3. - - Added example6.c, which dumps an image of the mandelbrot set to a PNG file. - - Modified example2 to help test the MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY flag more. - - In r3: Bugfix to mz_zip_writer_add_file() found during merge: Fix possible src file fclose() leak if alignment bytes+local header file write faiiled -  - In r4: Minor bugfix to mz_zip_writer_add_from_zip_reader(): Was pushing the wrong central dir header offset, appears harmless in this release, but it became a problem in the zip64 branch - 5/20/12 v1.14 - MinGW32/64 GCC 4.6.1 compiler fixes: added MZ_FORCEINLINE, #include (thanks fermtect). - 5/19/12 v1.13 - From jason@cornsyrup.org and kelwert@mtu.edu - Fix mz_crc32() so it doesn't compute the wrong CRC-32's when mz_ulong is 64-bit. - - Temporarily/locally slammed in "typedef unsigned long mz_ulong" and re-ran a randomized regression test on ~500k files. + - Retested this build under Windows (VS 2010, including static analysis), + tcc 0.9.26, gcc v4.6 and clang v3.3. + - Added example6.c, which dumps an image of the mandelbrot set to a PNG + file. + - Modified example2 to help test the + MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY flag more. + - In r3: Bugfix to mz_zip_writer_add_file() found during merge: Fix + possible src file fclose() leak if alignment bytes+local header file write + faiiled + - In r4: Minor bugfix to mz_zip_writer_add_from_zip_reader(): Was pushing the + wrong central dir header offset, appears harmless in this release, but it + became a problem in the zip64 branch 5/20/12 v1.14 - MinGW32/64 GCC 4.6.1 + compiler fixes: added MZ_FORCEINLINE, #include (thanks fermtect). + 5/19/12 v1.13 - From jason@cornsyrup.org and kelwert@mtu.edu - Fix + mz_crc32() so it doesn't compute the wrong CRC-32's when mz_ulong is 64-bit. + - Temporarily/locally slammed in "typedef unsigned long mz_ulong" and + re-ran a randomized regression test on ~500k files. - Eliminated a bunch of warnings when compiling with GCC 32-bit/64. - - Ran all examples, miniz.c, and tinfl.c through MSVC 2008's /analyze (static analysis) option and fixed all warnings (except for the silly - "Use of the comma-operator in a tested expression.." analysis warning, which I purposely use to work around a MSVC compiler warning). - - Created 32-bit and 64-bit Codeblocks projects/workspace. Built and tested Linux executables. The codeblocks workspace is compatible with Linux+Win32/x64. - - Added miniz_tester solution/project, which is a useful little app derived from LZHAM's tester app that I use as part of the regression test. - - Ran miniz.c and tinfl.c through another series of regression testing on ~500,000 files and archives. - - Modified example5.c so it purposely disables a bunch of high-level functionality (MINIZ_NO_STDIO, etc.). (Thanks to corysama for the MINIZ_NO_STDIO bug report.) - - Fix ftell() usage in examples so they exit with an error on files which are too large (a limitation of the examples, not miniz itself). - 4/12/12 v1.12 - More comments, added low-level example5.c, fixed a couple minor level_and_flags issues in the archive API's. - level_and_flags can now be set to MZ_DEFAULT_COMPRESSION. Thanks to Bruce Dawson for the feedback/bug report. - 5/28/11 v1.11 - Added statement from unlicense.org - 5/27/11 v1.10 - Substantial compressor optimizations: - - Level 1 is now ~4x faster than before. The L1 compressor's throughput now varies between 70-110MB/sec. on a - - Core i7 (actual throughput varies depending on the type of data, and x64 vs. x86). - - Improved baseline L2-L9 compression perf. Also, greatly improved compression perf. issues on some file types. - - Refactored the compression code for better readability and maintainability. - - Added level 10 compression level (L10 has slightly better ratio than level 9, but could have a potentially large - drop in throughput on some files). - 5/15/11 v1.09 - Initial stable release. + - Ran all examples, miniz.c, and tinfl.c through MSVC 2008's /analyze + (static analysis) option and fixed all warnings (except for the silly "Use of + the comma-operator in a tested expression.." analysis warning, which I + purposely use to work around a MSVC compiler warning). + - Created 32-bit and 64-bit Codeblocks projects/workspace. Built and + tested Linux executables. The codeblocks workspace is compatible with + Linux+Win32/x64. + - Added miniz_tester solution/project, which is a useful little app + derived from LZHAM's tester app that I use as part of the regression test. + - Ran miniz.c and tinfl.c through another series of regression testing on + ~500,000 files and archives. + - Modified example5.c so it purposely disables a bunch of high-level + functionality (MINIZ_NO_STDIO, etc.). (Thanks to corysama for the + MINIZ_NO_STDIO bug report.) + - Fix ftell() usage in examples so they exit with an error on files which + are too large (a limitation of the examples, not miniz itself). 4/12/12 v1.12 + - More comments, added low-level example5.c, fixed a couple minor + level_and_flags issues in the archive API's. level_and_flags can now be set + to MZ_DEFAULT_COMPRESSION. Thanks to Bruce Dawson + for the feedback/bug report. 5/28/11 v1.11 - Added statement from + unlicense.org 5/27/11 v1.10 - Substantial compressor optimizations: + - Level 1 is now ~4x faster than before. The L1 compressor's throughput + now varies between 70-110MB/sec. on a + - Core i7 (actual throughput varies depending on the type of data, and x64 + vs. x86). + - Improved baseline L2-L9 compression perf. Also, greatly improved + compression perf. issues on some file types. + - Refactored the compression code for better readability and + maintainability. + - Added level 10 compression level (L10 has slightly better ratio than + level 9, but could have a potentially large drop in throughput on some + files). 5/15/11 v1.09 - Initial stable release. * Low-level Deflate/Inflate implementation notes: - Compression: Use the "tdefl" API's. The compressor supports raw, static, and dynamic blocks, lazy or - greedy parsing, match length filtering, RLE-only, and Huffman-only streams. It performs and compresses - approximately as well as zlib. + Compression: Use the "tdefl" API's. The compressor supports raw, static, + and dynamic blocks, lazy or greedy parsing, match length filtering, RLE-only, + and Huffman-only streams. It performs and compresses approximately as well as + zlib. - Decompression: Use the "tinfl" API's. The entire decompressor is implemented as a single function - coroutine: see tinfl_decompress(). It supports decompression into a 32KB (or larger power of 2) wrapping buffer, or into a memory - block large enough to hold the entire file. + Decompression: Use the "tinfl" API's. The entire decompressor is + implemented as a single function coroutine: see tinfl_decompress(). It + supports decompression into a 32KB (or larger power of 2) wrapping buffer, or + into a memory block large enough to hold the entire file. - The low-level tdefl/tinfl API's do not make any use of dynamic memory allocation. + The low-level tdefl/tinfl API's do not make any use of dynamic memory + allocation. * zlib-style API notes: - miniz.c implements a fairly large subset of zlib. There's enough functionality present for it to be a drop-in - zlib replacement in many apps: + miniz.c implements a fairly large subset of zlib. There's enough + functionality present for it to be a drop-in zlib replacement in many apps: The z_stream struct, optional memory allocation callbacks deflateInit/deflateInit2/deflate/deflateReset/deflateEnd/deflateBound inflateInit/inflateInit2/inflate/inflateEnd compress, compress2, compressBound, uncompress - CRC-32, Adler-32 - Using modern, minimal code size, CPU cache friendly routines. - Supports raw deflate streams or standard zlib streams with adler-32 checking. + CRC-32, Adler-32 - Using modern, minimal code size, CPU cache friendly + routines. Supports raw deflate streams or standard zlib streams with adler-32 + checking. Limitations: - The callback API's are not implemented yet. No support for gzip headers or zlib static dictionaries. - I've tried to closely emulate zlib's various flavors of stream flushing and return status codes, but - there are no guarantees that miniz.c pulls this off perfectly. + The callback API's are not implemented yet. No support for gzip headers or + zlib static dictionaries. I've tried to closely emulate zlib's various + flavors of stream flushing and return status codes, but there are no + guarantees that miniz.c pulls this off perfectly. - * PNG writing: See the tdefl_write_image_to_png_file_in_memory() function, originally written by - Alex Evans. Supports 1-4 bytes/pixel images. + * PNG writing: See the tdefl_write_image_to_png_file_in_memory() function, + originally written by Alex Evans. Supports 1-4 bytes/pixel images. * ZIP archive API notes: - The ZIP archive API's where designed with simplicity and efficiency in mind, with just enough abstraction to - get the job done with minimal fuss. There are simple API's to retrieve file information, read files from - existing archives, create new archives, append new files to existing archives, or clone archive data from - one archive to another. It supports archives located in memory or the heap, on disk (using stdio.h), - or you can specify custom file read/write callbacks. + The ZIP archive API's where designed with simplicity and efficiency in + mind, with just enough abstraction to get the job done with minimal fuss. + There are simple API's to retrieve file information, read files from existing + archives, create new archives, append new files to existing archives, or + clone archive data from one archive to another. It supports archives located + in memory or the heap, on disk (using stdio.h), or you can specify custom + file read/write callbacks. - - Archive reading: Just call this function to read a single file from a disk archive: + - Archive reading: Just call this function to read a single file from a + disk archive: - void *mz_zip_extract_archive_file_to_heap(const char *pZip_filename, const char *pArchive_name, - size_t *pSize, mz_uint zip_flags); + void *mz_zip_extract_archive_file_to_heap(const char *pZip_filename, const + char *pArchive_name, size_t *pSize, mz_uint zip_flags); - For more complex cases, use the "mz_zip_reader" functions. Upon opening an archive, the entire central - directory is located and read as-is into memory, and subsequent file access only occurs when reading individual files. + For more complex cases, use the "mz_zip_reader" functions. Upon opening an + archive, the entire central directory is located and read as-is into memory, + and subsequent file access only occurs when reading individual files. - - Archives file scanning: The simple way is to use this function to scan a loaded archive for a specific file: + - Archives file scanning: The simple way is to use this function to scan a + loaded archive for a specific file: - int mz_zip_reader_locate_file(mz_zip_archive *pZip, const char *pName, const char *pComment, mz_uint flags); + int mz_zip_reader_locate_file(mz_zip_archive *pZip, const char *pName, + const char *pComment, mz_uint flags); - The locate operation can optionally check file comments too, which (as one example) can be used to identify - multiple versions of the same file in an archive. This function uses a simple linear search through the central + The locate operation can optionally check file comments too, which (as one + example) can be used to identify multiple versions of the same file in an + archive. This function uses a simple linear search through the central directory, so it's not very fast. - Alternately, you can iterate through all the files in an archive (using mz_zip_reader_get_num_files()) and - retrieve detailed info on each file by calling mz_zip_reader_file_stat(). + Alternately, you can iterate through all the files in an archive (using + mz_zip_reader_get_num_files()) and retrieve detailed info on each file by + calling mz_zip_reader_file_stat(). - - Archive creation: Use the "mz_zip_writer" functions. The ZIP writer immediately writes compressed file data - to disk and builds an exact image of the central directory in memory. The central directory image is written - all at once at the end of the archive file when the archive is finalized. + - Archive creation: Use the "mz_zip_writer" functions. The ZIP writer + immediately writes compressed file data to disk and builds an exact image of + the central directory in memory. The central directory image is written all + at once at the end of the archive file when the archive is finalized. - The archive writer can optionally align each file's local header and file data to any power of 2 alignment, - which can be useful when the archive will be read from optical media. Also, the writer supports placing - arbitrary data blobs at the very beginning of ZIP archives. Archives written using either feature are still - readable by any ZIP tool. + The archive writer can optionally align each file's local header and file + data to any power of 2 alignment, which can be useful when the archive will + be read from optical media. Also, the writer supports placing arbitrary data + blobs at the very beginning of ZIP archives. Archives written using either + feature are still readable by any ZIP tool. - - Archive appending: The simple way to add a single file to an archive is to call this function: + - Archive appending: The simple way to add a single file to an archive is + to call this function: - mz_bool mz_zip_add_mem_to_archive_file_in_place(const char *pZip_filename, const char *pArchive_name, - const void *pBuf, size_t buf_size, const void *pComment, mz_uint16 comment_size, mz_uint level_and_flags); + mz_bool mz_zip_add_mem_to_archive_file_in_place(const char *pZip_filename, + const char *pArchive_name, const void *pBuf, size_t buf_size, const void + *pComment, mz_uint16 comment_size, mz_uint level_and_flags); - The archive will be created if it doesn't already exist, otherwise it'll be appended to. - Note the appending is done in-place and is not an atomic operation, so if something goes wrong - during the operation it's possible the archive could be left without a central directory (although the local - file headers and file data will be fine, so the archive will be recoverable). + The archive will be created if it doesn't already exist, otherwise it'll be + appended to. Note the appending is done in-place and is not an atomic + operation, so if something goes wrong during the operation it's possible the + archive could be left without a central directory (although the local file + headers and file data will be fine, so the archive will be recoverable). For more complex archive modification scenarios: - 1. The safest way is to use a mz_zip_reader to read the existing archive, cloning only those bits you want to - preserve into a new archive using using the mz_zip_writer_add_from_zip_reader() function (which compiles the - compressed file data as-is). When you're done, delete the old archive and rename the newly written archive, and - you're done. This is safe but requires a bunch of temporary disk space or heap memory. + 1. The safest way is to use a mz_zip_reader to read the existing archive, + cloning only those bits you want to preserve into a new archive using using + the mz_zip_writer_add_from_zip_reader() function (which compiles the + compressed file data as-is). When you're done, delete the old archive and + rename the newly written archive, and you're done. This is safe but requires + a bunch of temporary disk space or heap memory. - 2. Or, you can convert an mz_zip_reader in-place to an mz_zip_writer using mz_zip_writer_init_from_reader(), - append new files as needed, then finalize the archive which will write an updated central directory to the - original archive. (This is basically what mz_zip_add_mem_to_archive_file_in_place() does.) There's a - possibility that the archive's central directory could be lost with this method if anything goes wrong, though. + 2. Or, you can convert an mz_zip_reader in-place to an mz_zip_writer using + mz_zip_writer_init_from_reader(), append new files as needed, then finalize + the archive which will write an updated central directory to the original + archive. (This is basically what mz_zip_add_mem_to_archive_file_in_place() + does.) There's a possibility that the archive's central directory could be + lost with this method if anything goes wrong, though. - ZIP archive support limitations: - No zip64 or spanning support. Extraction functions can only handle unencrypted, stored or deflated files. - Requires streams capable of seeking. + No zip64 or spanning support. Extraction functions can only handle + unencrypted, stored or deflated files. Requires streams capable of seeking. - * This is a header file library, like stb_image.c. To get only a header file, either cut and paste the - below header, or create miniz.h, #define MINIZ_HEADER_FILE_ONLY, and then include miniz.c from it. + * This is a header file library, like stb_image.c. To get only a header file, + either cut and paste the below header, or create miniz.h, #define + MINIZ_HEADER_FILE_ONLY, and then include miniz.c from it. - * Important: For best perf. be sure to customize the below macros for your target platform: - #define MINIZ_USE_UNALIGNED_LOADS_AND_STORES 1 - #define MINIZ_LITTLE_ENDIAN 1 - #define MINIZ_HAS_64BIT_REGISTERS 1 + * Important: For best perf. be sure to customize the below macros for your + target platform: #define MINIZ_USE_UNALIGNED_LOADS_AND_STORES 1 #define + MINIZ_LITTLE_ENDIAN 1 #define MINIZ_HAS_64BIT_REGISTERS 1 - * On platforms using glibc, Be sure to "#define _LARGEFILE64_SOURCE 1" before including miniz.c to ensure miniz - uses the 64-bit variants: fopen64(), stat64(), etc. Otherwise you won't be able to process large files - (i.e. 32-bit stat() fails for me on files > 0x7FFFFFFF bytes). + * On platforms using glibc, Be sure to "#define _LARGEFILE64_SOURCE 1" before + including miniz.c to ensure miniz uses the 64-bit variants: fopen64(), + stat64(), etc. Otherwise you won't be able to process large files (i.e. + 32-bit stat() fails for me on files > 0x7FFFFFFF bytes). */ #ifndef MINIZ_HEADER_INCLUDED @@ -163,60 +224,80 @@ #include // Defines to completely disable specific portions of miniz.c: -// If all macros here are defined the only functionality remaining will be CRC-32, adler-32, tinfl, and tdefl. +// If all macros here are defined the only functionality remaining will be +// CRC-32, adler-32, tinfl, and tdefl. -// Define MINIZ_NO_STDIO to disable all usage and any functions which rely on stdio for file I/O. +// Define MINIZ_NO_STDIO to disable all usage and any functions which rely on +// stdio for file I/O. //#define MINIZ_NO_STDIO -// If MINIZ_NO_TIME is specified then the ZIP archive functions will not be able to get the current time, or -// get/set file times, and the C run-time funcs that get/set times won't be called. -// The current downside is the times written to your archives will be from 1979. +// If MINIZ_NO_TIME is specified then the ZIP archive functions will not be able +// to get the current time, or get/set file times, and the C run-time funcs that +// get/set times won't be called. The current downside is the times written to +// your archives will be from 1979. //#define MINIZ_NO_TIME // Define MINIZ_NO_ARCHIVE_APIS to disable all ZIP archive API's. //#define MINIZ_NO_ARCHIVE_APIS -// Define MINIZ_NO_ARCHIVE_APIS to disable all writing related ZIP archive API's. +// Define MINIZ_NO_ARCHIVE_APIS to disable all writing related ZIP archive +// API's. //#define MINIZ_NO_ARCHIVE_WRITING_APIS -// Define MINIZ_NO_ZLIB_APIS to remove all ZLIB-style compression/decompression API's. +// Define MINIZ_NO_ZLIB_APIS to remove all ZLIB-style compression/decompression +// API's. //#define MINIZ_NO_ZLIB_APIS -// Define MINIZ_NO_ZLIB_COMPATIBLE_NAME to disable zlib names, to prevent conflicts against stock zlib. +// Define MINIZ_NO_ZLIB_COMPATIBLE_NAME to disable zlib names, to prevent +// conflicts against stock zlib. //#define MINIZ_NO_ZLIB_COMPATIBLE_NAMES // Define MINIZ_NO_MALLOC to disable all calls to malloc, free, and realloc. -// Note if MINIZ_NO_MALLOC is defined then the user must always provide custom user alloc/free/realloc -// callbacks to the zlib and archive API's, and a few stand-alone helper API's which don't provide custom user -// functions (such as tdefl_compress_mem_to_heap() and tinfl_decompress_mem_to_heap()) won't work. +// Note if MINIZ_NO_MALLOC is defined then the user must always provide custom +// user alloc/free/realloc callbacks to the zlib and archive API's, and a few +// stand-alone helper API's which don't provide custom user functions (such as +// tdefl_compress_mem_to_heap() and tinfl_decompress_mem_to_heap()) won't work. //#define MINIZ_NO_MALLOC #if defined(__TINYC__) && (defined(__linux) || defined(__linux__)) - // TODO: Work around "error: include file 'sys\utime.h' when compiling with tcc on Linux - #define MINIZ_NO_TIME +// TODO: Work around "error: include file 'sys\utime.h' when compiling with tcc +// on Linux +#define MINIZ_NO_TIME #endif #if !defined(MINIZ_NO_TIME) && !defined(MINIZ_NO_ARCHIVE_APIS) - #include +#include #endif -#if defined(_M_IX86) || defined(_M_X64) || defined(__i386__) || defined(__i386) || defined(__i486__) || defined(__i486) || defined(i386) || defined(__ia64__) || defined(__x86_64__) +#if defined(_M_IX86) || defined(_M_X64) || defined(__i386__) || \ + defined(__i386) || defined(__i486__) || defined(__i486) || \ + defined(i386) || defined(__ia64__) || defined(__x86_64__) // MINIZ_X86_OR_X64_CPU is only used to help set the below macros. #define MINIZ_X86_OR_X64_CPU 1 #endif -#if (__BYTE_ORDER__==__ORDER_LITTLE_ENDIAN__) || MINIZ_X86_OR_X64_CPU +#if (__BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__) || MINIZ_X86_OR_X64_CPU // Set MINIZ_LITTLE_ENDIAN to 1 if the processor is little endian. #define MINIZ_LITTLE_ENDIAN 1 #endif +/* Set MINIZ_USE_UNALIGNED_LOADS_AND_STORES only if not set */ +#if !defined(MINIZ_USE_UNALIGNED_LOADS_AND_STORES) #if MINIZ_X86_OR_X64_CPU -// Set MINIZ_USE_UNALIGNED_LOADS_AND_STORES to 1 on CPU's that permit efficient integer loads and stores from unaligned addresses. +/* Set MINIZ_USE_UNALIGNED_LOADS_AND_STORES to 1 on CPU's that permit efficient integer loads and stores from unaligned addresses. */ +#define MINIZ_USE_UNALIGNED_LOADS_AND_STORES 1 +#define MINIZ_UNALIGNED_USE_MEMCPY +#else #define MINIZ_USE_UNALIGNED_LOADS_AND_STORES 0 #endif +#endif -#if defined(_M_X64) || defined(_WIN64) || defined(__MINGW64__) || defined(_LP64) || defined(__LP64__) || defined(__ia64__) || defined(__x86_64__) -// Set MINIZ_HAS_64BIT_REGISTERS to 1 if operations on 64-bit integers are reasonably fast (and don't involve compiler generated calls to helper functions). +#if defined(_M_X64) || defined(_WIN64) || defined(__MINGW64__) || \ + defined(_LP64) || defined(__LP64__) || defined(__ia64__) || \ + defined(__x86_64__) +// Set MINIZ_HAS_64BIT_REGISTERS to 1 if operations on 64-bit integers are +// reasonably fast (and don't involve compiler generated calls to helper +// functions). #define MINIZ_HAS_64BIT_REGISTERS 1 #endif @@ -225,6 +306,18 @@ #define fseeko64 fseeko #define fopen64 fopen #define freopen64 freopen + +// Darwin OSX +#define MZ_PLATFORM 19 +#endif + +#ifndef MZ_PLATFORM +#if defined(_WIN64) || defined(_WIN32) || defined(__WIN32__) +#define MZ_PLATFORM 0 +#else +// UNIX +#define MZ_PLATFORM 3 +#endif #endif #ifdef __cplusplus @@ -233,22 +326,33 @@ extern "C" { // ------------------- zlib-style API Definitions. -// For more compatibility with zlib, miniz.c uses unsigned long for some parameters/struct members. Beware: mz_ulong can be either 32 or 64-bits! +// For more compatibility with zlib, miniz.c uses unsigned long for some +// parameters/struct members. Beware: mz_ulong can be either 32 or 64-bits! typedef unsigned long mz_ulong; -// mz_free() internally uses the MZ_FREE() macro (which by default calls free() unless you've modified the MZ_MALLOC macro) to release a block allocated from the heap. +// mz_free() internally uses the MZ_FREE() macro (which by default calls free() +// unless you've modified the MZ_MALLOC macro) to release a block allocated from +// the heap. void mz_free(void *p); #define MZ_ADLER32_INIT (1) -// mz_adler32() returns the initial adler-32 value to use when called with ptr==NULL. +// mz_adler32() returns the initial adler-32 value to use when called with +// ptr==NULL. mz_ulong mz_adler32(mz_ulong adler, const unsigned char *ptr, size_t buf_len); #define MZ_CRC32_INIT (0) -// mz_crc32() returns the initial CRC-32 value to use when called with ptr==NULL. +// mz_crc32() returns the initial CRC-32 value to use when called with +// ptr==NULL. mz_ulong mz_crc32(mz_ulong crc, const unsigned char *ptr, size_t buf_len); // Compression strategies. -enum { MZ_DEFAULT_STRATEGY = 0, MZ_FILTERED = 1, MZ_HUFFMAN_ONLY = 2, MZ_RLE = 3, MZ_FIXED = 4 }; +enum { + MZ_DEFAULT_STRATEGY = 0, + MZ_FILTERED = 1, + MZ_HUFFMAN_ONLY = 2, + MZ_RLE = 3, + MZ_FIXED = 4 +}; // Method #define MZ_DEFLATED 8 @@ -256,26 +360,56 @@ enum { MZ_DEFAULT_STRATEGY = 0, MZ_FILTERED = 1, MZ_HUFFMAN_ONLY = 2, MZ_RLE = 3 #ifndef MINIZ_NO_ZLIB_APIS // Heap allocation callbacks. -// Note that mz_alloc_func parameter types purpsosely differ from zlib's: items/size is size_t, not unsigned long. +// Note that mz_alloc_func parameter types purpsosely differ from zlib's: +// items/size is size_t, not unsigned long. typedef void *(*mz_alloc_func)(void *opaque, size_t items, size_t size); typedef void (*mz_free_func)(void *opaque, void *address); -typedef void *(*mz_realloc_func)(void *opaque, void *address, size_t items, size_t size); +typedef void *(*mz_realloc_func)(void *opaque, void *address, size_t items, + size_t size); -#define MZ_VERSION "9.1.15" -#define MZ_VERNUM 0x91F0 -#define MZ_VER_MAJOR 9 -#define MZ_VER_MINOR 1 -#define MZ_VER_REVISION 15 -#define MZ_VER_SUBREVISION 0 +#define MZ_VERSION "9.1.15" +#define MZ_VERNUM 0x91F0 +#define MZ_VER_MAJOR 9 +#define MZ_VER_MINOR 1 +#define MZ_VER_REVISION 15 +#define MZ_VER_SUBREVISION 0 -// Flush values. For typical usage you only need MZ_NO_FLUSH and MZ_FINISH. The other values are for advanced use (refer to the zlib docs). -enum { MZ_NO_FLUSH = 0, MZ_PARTIAL_FLUSH = 1, MZ_SYNC_FLUSH = 2, MZ_FULL_FLUSH = 3, MZ_FINISH = 4, MZ_BLOCK = 5 }; +// Flush values. For typical usage you only need MZ_NO_FLUSH and MZ_FINISH. The +// other values are for advanced use (refer to the zlib docs). +enum { + MZ_NO_FLUSH = 0, + MZ_PARTIAL_FLUSH = 1, + MZ_SYNC_FLUSH = 2, + MZ_FULL_FLUSH = 3, + MZ_FINISH = 4, + MZ_BLOCK = 5 +}; // Return status codes. MZ_PARAM_ERROR is non-standard. -enum { MZ_OK = 0, MZ_STREAM_END = 1, MZ_NEED_DICT = 2, MZ_ERRNO = -1, MZ_STREAM_ERROR = -2, MZ_DATA_ERROR = -3, MZ_MEM_ERROR = -4, MZ_BUF_ERROR = -5, MZ_VERSION_ERROR = -6, MZ_PARAM_ERROR = -10000 }; +enum { + MZ_OK = 0, + MZ_STREAM_END = 1, + MZ_NEED_DICT = 2, + MZ_ERRNO = -1, + MZ_STREAM_ERROR = -2, + MZ_DATA_ERROR = -3, + MZ_MEM_ERROR = -4, + MZ_BUF_ERROR = -5, + MZ_VERSION_ERROR = -6, + MZ_PARAM_ERROR = -10000 +}; -// Compression levels: 0-9 are the standard zlib-style levels, 10 is best possible compression (not zlib compatible, and may be very slow), MZ_DEFAULT_COMPRESSION=MZ_DEFAULT_LEVEL. -enum { MZ_NO_COMPRESSION = 0, MZ_BEST_SPEED = 1, MZ_BEST_COMPRESSION = 9, MZ_UBER_COMPRESSION = 10, MZ_DEFAULT_LEVEL = 6, MZ_DEFAULT_COMPRESSION = -1 }; +// Compression levels: 0-9 are the standard zlib-style levels, 10 is best +// possible compression (not zlib compatible, and may be very slow), +// MZ_DEFAULT_COMPRESSION=MZ_DEFAULT_LEVEL. +enum { + MZ_NO_COMPRESSION = 0, + MZ_BEST_SPEED = 1, + MZ_BEST_COMPRESSION = 9, + MZ_UBER_COMPRESSION = 10, + MZ_DEFAULT_LEVEL = 6, + MZ_DEFAULT_COMPRESSION = -1 +}; // Window bits #define MZ_DEFAULT_WINDOW_BITS 15 @@ -283,26 +417,26 @@ enum { MZ_NO_COMPRESSION = 0, MZ_BEST_SPEED = 1, MZ_BEST_COMPRESSION = 9, MZ_UBE struct mz_internal_state; // Compression/decompression stream struct. -typedef struct mz_stream_s -{ - const unsigned char *next_in; // pointer to next byte to read - unsigned int avail_in; // number of bytes available at next_in - mz_ulong total_in; // total number of bytes consumed so far +typedef struct mz_stream_s { + const unsigned char *next_in; // pointer to next byte to read + unsigned int avail_in; // number of bytes available at next_in + mz_ulong total_in; // total number of bytes consumed so far - unsigned char *next_out; // pointer to next byte to write - unsigned int avail_out; // number of bytes that can be written to next_out - mz_ulong total_out; // total number of bytes produced so far + unsigned char *next_out; // pointer to next byte to write + unsigned int avail_out; // number of bytes that can be written to next_out + mz_ulong total_out; // total number of bytes produced so far - char *msg; // error msg (unused) - struct mz_internal_state *state; // internal state, allocated by zalloc/zfree + char *msg; // error msg (unused) + struct mz_internal_state *state; // internal state, allocated by zalloc/zfree - mz_alloc_func zalloc; // optional heap allocation function (defaults to malloc) - mz_free_func zfree; // optional heap free function (defaults to free) - void *opaque; // heap alloc function user pointer + mz_alloc_func + zalloc; // optional heap allocation function (defaults to malloc) + mz_free_func zfree; // optional heap free function (defaults to free) + void *opaque; // heap alloc function user pointer - int data_type; // data_type (unused) - mz_ulong adler; // adler32 of the source or uncompressed data - mz_ulong reserved; // not used + int data_type; // data_type (unused) + mz_ulong adler; // adler32 of the source or uncompressed data + mz_ulong reserved; // not used } mz_stream; typedef mz_stream *mz_streamp; @@ -314,8 +448,10 @@ const char *mz_version(void); // Parameters: // pStream must point to an initialized mz_stream struct. // level must be between [MZ_NO_COMPRESSION, MZ_BEST_COMPRESSION]. -// level 1 enables a specially optimized compression function that's been optimized purely for performance, not ratio. -// (This special func. is currently only enabled when MINIZ_USE_UNALIGNED_LOADS_AND_STORES and MINIZ_LITTLE_ENDIAN are defined.) +// level 1 enables a specially optimized compression function that's been +// optimized purely for performance, not ratio. (This special func. is +// currently only enabled when MINIZ_USE_UNALIGNED_LOADS_AND_STORES and +// MINIZ_LITTLE_ENDIAN are defined.) // Return values: // MZ_OK on success. // MZ_STREAM_ERROR if the stream is bogus. @@ -326,23 +462,32 @@ int mz_deflateInit(mz_streamp pStream, int level); // mz_deflateInit2() is like mz_deflate(), except with more control: // Additional parameters: // method must be MZ_DEFLATED -// window_bits must be MZ_DEFAULT_WINDOW_BITS (to wrap the deflate stream with zlib header/adler-32 footer) or -MZ_DEFAULT_WINDOW_BITS (raw deflate/no header or footer) -// mem_level must be between [1, 9] (it's checked but ignored by miniz.c) -int mz_deflateInit2(mz_streamp pStream, int level, int method, int window_bits, int mem_level, int strategy); +// window_bits must be MZ_DEFAULT_WINDOW_BITS (to wrap the deflate stream with +// zlib header/adler-32 footer) or -MZ_DEFAULT_WINDOW_BITS (raw deflate/no +// header or footer) mem_level must be between [1, 9] (it's checked but +// ignored by miniz.c) +int mz_deflateInit2(mz_streamp pStream, int level, int method, int window_bits, + int mem_level, int strategy); -// Quickly resets a compressor without having to reallocate anything. Same as calling mz_deflateEnd() followed by mz_deflateInit()/mz_deflateInit2(). +// Quickly resets a compressor without having to reallocate anything. Same as +// calling mz_deflateEnd() followed by mz_deflateInit()/mz_deflateInit2(). int mz_deflateReset(mz_streamp pStream); -// mz_deflate() compresses the input to output, consuming as much of the input and producing as much output as possible. -// Parameters: -// pStream is the stream to read from and write to. You must initialize/update the next_in, avail_in, next_out, and avail_out members. -// flush may be MZ_NO_FLUSH, MZ_PARTIAL_FLUSH/MZ_SYNC_FLUSH, MZ_FULL_FLUSH, or MZ_FINISH. +// mz_deflate() compresses the input to output, consuming as much of the input +// and producing as much output as possible. Parameters: +// pStream is the stream to read from and write to. You must initialize/update +// the next_in, avail_in, next_out, and avail_out members. flush may be +// MZ_NO_FLUSH, MZ_PARTIAL_FLUSH/MZ_SYNC_FLUSH, MZ_FULL_FLUSH, or MZ_FINISH. // Return values: -// MZ_OK on success (when flushing, or if more input is needed but not available, and/or there's more output to be written but the output buffer is full). -// MZ_STREAM_END if all input has been consumed and all output bytes have been written. Don't call mz_deflate() on the stream anymore. +// MZ_OK on success (when flushing, or if more input is needed but not +// available, and/or there's more output to be written but the output buffer +// is full). MZ_STREAM_END if all input has been consumed and all output bytes +// have been written. Don't call mz_deflate() on the stream anymore. // MZ_STREAM_ERROR if the stream is bogus. // MZ_PARAM_ERROR if one of the parameters is invalid. -// MZ_BUF_ERROR if no forward progress is possible because the input and/or output buffers are empty. (Fill up the input buffer or free up some output space and try again.) +// MZ_BUF_ERROR if no forward progress is possible because the input and/or +// output buffers are empty. (Fill up the input buffer or free up some output +// space and try again.) int mz_deflate(mz_streamp pStream, int flush); // mz_deflateEnd() deinitializes a compressor: @@ -351,123 +496,145 @@ int mz_deflate(mz_streamp pStream, int flush); // MZ_STREAM_ERROR if the stream is bogus. int mz_deflateEnd(mz_streamp pStream); -// mz_deflateBound() returns a (very) conservative upper bound on the amount of data that could be generated by deflate(), assuming flush is set to only MZ_NO_FLUSH or MZ_FINISH. +// mz_deflateBound() returns a (very) conservative upper bound on the amount of +// data that could be generated by deflate(), assuming flush is set to only +// MZ_NO_FLUSH or MZ_FINISH. mz_ulong mz_deflateBound(mz_streamp pStream, mz_ulong source_len); // Single-call compression functions mz_compress() and mz_compress2(): -// Returns MZ_OK on success, or one of the error codes from mz_deflate() on failure. -int mz_compress(unsigned char *pDest, mz_ulong *pDest_len, const unsigned char *pSource, mz_ulong source_len); -int mz_compress2(unsigned char *pDest, mz_ulong *pDest_len, const unsigned char *pSource, mz_ulong source_len, int level); +// Returns MZ_OK on success, or one of the error codes from mz_deflate() on +// failure. +int mz_compress(unsigned char *pDest, mz_ulong *pDest_len, + const unsigned char *pSource, mz_ulong source_len); +int mz_compress2(unsigned char *pDest, mz_ulong *pDest_len, + const unsigned char *pSource, mz_ulong source_len, int level); -// mz_compressBound() returns a (very) conservative upper bound on the amount of data that could be generated by calling mz_compress(). +// mz_compressBound() returns a (very) conservative upper bound on the amount of +// data that could be generated by calling mz_compress(). mz_ulong mz_compressBound(mz_ulong source_len); // Initializes a decompressor. int mz_inflateInit(mz_streamp pStream); -// mz_inflateInit2() is like mz_inflateInit() with an additional option that controls the window size and whether or not the stream has been wrapped with a zlib header/footer: -// window_bits must be MZ_DEFAULT_WINDOW_BITS (to parse zlib header/footer) or -MZ_DEFAULT_WINDOW_BITS (raw deflate). +// mz_inflateInit2() is like mz_inflateInit() with an additional option that +// controls the window size and whether or not the stream has been wrapped with +// a zlib header/footer: window_bits must be MZ_DEFAULT_WINDOW_BITS (to parse +// zlib header/footer) or -MZ_DEFAULT_WINDOW_BITS (raw deflate). int mz_inflateInit2(mz_streamp pStream, int window_bits); -// Decompresses the input stream to the output, consuming only as much of the input as needed, and writing as much to the output as possible. -// Parameters: -// pStream is the stream to read from and write to. You must initialize/update the next_in, avail_in, next_out, and avail_out members. -// flush may be MZ_NO_FLUSH, MZ_SYNC_FLUSH, or MZ_FINISH. -// On the first call, if flush is MZ_FINISH it's assumed the input and output buffers are both sized large enough to decompress the entire stream in a single call (this is slightly faster). -// MZ_FINISH implies that there are no more source bytes available beside what's already in the input buffer, and that the output buffer is large enough to hold the rest of the decompressed data. +// Decompresses the input stream to the output, consuming only as much of the +// input as needed, and writing as much to the output as possible. Parameters: +// pStream is the stream to read from and write to. You must initialize/update +// the next_in, avail_in, next_out, and avail_out members. flush may be +// MZ_NO_FLUSH, MZ_SYNC_FLUSH, or MZ_FINISH. On the first call, if flush is +// MZ_FINISH it's assumed the input and output buffers are both sized large +// enough to decompress the entire stream in a single call (this is slightly +// faster). MZ_FINISH implies that there are no more source bytes available +// beside what's already in the input buffer, and that the output buffer is +// large enough to hold the rest of the decompressed data. // Return values: -// MZ_OK on success. Either more input is needed but not available, and/or there's more output to be written but the output buffer is full. -// MZ_STREAM_END if all needed input has been consumed and all output bytes have been written. For zlib streams, the adler-32 of the decompressed data has also been verified. -// MZ_STREAM_ERROR if the stream is bogus. +// MZ_OK on success. Either more input is needed but not available, and/or +// there's more output to be written but the output buffer is full. +// MZ_STREAM_END if all needed input has been consumed and all output bytes +// have been written. For zlib streams, the adler-32 of the decompressed data +// has also been verified. MZ_STREAM_ERROR if the stream is bogus. // MZ_DATA_ERROR if the deflate stream is invalid. // MZ_PARAM_ERROR if one of the parameters is invalid. -// MZ_BUF_ERROR if no forward progress is possible because the input buffer is empty but the inflater needs more input to continue, or if the output buffer is not large enough. Call mz_inflate() again -// with more input data, or with more room in the output buffer (except when using single call decompression, described above). +// MZ_BUF_ERROR if no forward progress is possible because the input buffer is +// empty but the inflater needs more input to continue, or if the output +// buffer is not large enough. Call mz_inflate() again with more input data, +// or with more room in the output buffer (except when using single call +// decompression, described above). int mz_inflate(mz_streamp pStream, int flush); // Deinitializes a decompressor. int mz_inflateEnd(mz_streamp pStream); // Single-call decompression. -// Returns MZ_OK on success, or one of the error codes from mz_inflate() on failure. -int mz_uncompress(unsigned char *pDest, mz_ulong *pDest_len, const unsigned char *pSource, mz_ulong source_len); +// Returns MZ_OK on success, or one of the error codes from mz_inflate() on +// failure. +int mz_uncompress(unsigned char *pDest, mz_ulong *pDest_len, + const unsigned char *pSource, mz_ulong source_len); -// Returns a string description of the specified error code, or NULL if the error code is invalid. +// Returns a string description of the specified error code, or NULL if the +// error code is invalid. const char *mz_error(int err); -// Redefine zlib-compatible names to miniz equivalents, so miniz.c can be used as a drop-in replacement for the subset of zlib that miniz.c supports. -// Define MINIZ_NO_ZLIB_COMPATIBLE_NAMES to disable zlib-compatibility if you use zlib in the same project. +// Redefine zlib-compatible names to miniz equivalents, so miniz.c can be used +// as a drop-in replacement for the subset of zlib that miniz.c supports. Define +// MINIZ_NO_ZLIB_COMPATIBLE_NAMES to disable zlib-compatibility if you use zlib +// in the same project. #ifndef MINIZ_NO_ZLIB_COMPATIBLE_NAMES - typedef unsigned char Byte; - typedef unsigned int uInt; - typedef mz_ulong uLong; - typedef Byte Bytef; - typedef uInt uIntf; - typedef char charf; - typedef int intf; - typedef void *voidpf; - typedef uLong uLongf; - typedef void *voidp; - typedef void *const voidpc; - #define Z_NULL 0 - #define Z_NO_FLUSH MZ_NO_FLUSH - #define Z_PARTIAL_FLUSH MZ_PARTIAL_FLUSH - #define Z_SYNC_FLUSH MZ_SYNC_FLUSH - #define Z_FULL_FLUSH MZ_FULL_FLUSH - #define Z_FINISH MZ_FINISH - #define Z_BLOCK MZ_BLOCK - #define Z_OK MZ_OK - #define Z_STREAM_END MZ_STREAM_END - #define Z_NEED_DICT MZ_NEED_DICT - #define Z_ERRNO MZ_ERRNO - #define Z_STREAM_ERROR MZ_STREAM_ERROR - #define Z_DATA_ERROR MZ_DATA_ERROR - #define Z_MEM_ERROR MZ_MEM_ERROR - #define Z_BUF_ERROR MZ_BUF_ERROR - #define Z_VERSION_ERROR MZ_VERSION_ERROR - #define Z_PARAM_ERROR MZ_PARAM_ERROR - #define Z_NO_COMPRESSION MZ_NO_COMPRESSION - #define Z_BEST_SPEED MZ_BEST_SPEED - #define Z_BEST_COMPRESSION MZ_BEST_COMPRESSION - #define Z_DEFAULT_COMPRESSION MZ_DEFAULT_COMPRESSION - #define Z_DEFAULT_STRATEGY MZ_DEFAULT_STRATEGY - #define Z_FILTERED MZ_FILTERED - #define Z_HUFFMAN_ONLY MZ_HUFFMAN_ONLY - #define Z_RLE MZ_RLE - #define Z_FIXED MZ_FIXED - #define Z_DEFLATED MZ_DEFLATED - #define Z_DEFAULT_WINDOW_BITS MZ_DEFAULT_WINDOW_BITS - #define alloc_func mz_alloc_func - #define free_func mz_free_func - #define internal_state mz_internal_state - #define z_stream mz_stream - #define deflateInit mz_deflateInit - #define deflateInit2 mz_deflateInit2 - #define deflateReset mz_deflateReset - #define deflate mz_deflate - #define deflateEnd mz_deflateEnd - #define deflateBound mz_deflateBound - #define compress mz_compress - #define compress2 mz_compress2 - #define compressBound mz_compressBound - #define inflateInit mz_inflateInit - #define inflateInit2 mz_inflateInit2 - #define inflate mz_inflate - #define inflateEnd mz_inflateEnd - #define uncompress mz_uncompress - #define crc32 mz_crc32 - #define adler32 mz_adler32 - #define MAX_WBITS 15 - #define MAX_MEM_LEVEL 9 - #define zError mz_error - #define ZLIB_VERSION MZ_VERSION - #define ZLIB_VERNUM MZ_VERNUM - #define ZLIB_VER_MAJOR MZ_VER_MAJOR - #define ZLIB_VER_MINOR MZ_VER_MINOR - #define ZLIB_VER_REVISION MZ_VER_REVISION - #define ZLIB_VER_SUBREVISION MZ_VER_SUBREVISION - #define zlibVersion mz_version - #define zlib_version mz_version() +typedef unsigned char Byte; +typedef unsigned int uInt; +typedef mz_ulong uLong; +typedef Byte Bytef; +typedef uInt uIntf; +typedef char charf; +typedef int intf; +typedef void *voidpf; +typedef uLong uLongf; +typedef void *voidp; +typedef void *const voidpc; +#define Z_NULL 0 +#define Z_NO_FLUSH MZ_NO_FLUSH +#define Z_PARTIAL_FLUSH MZ_PARTIAL_FLUSH +#define Z_SYNC_FLUSH MZ_SYNC_FLUSH +#define Z_FULL_FLUSH MZ_FULL_FLUSH +#define Z_FINISH MZ_FINISH +#define Z_BLOCK MZ_BLOCK +#define Z_OK MZ_OK +#define Z_STREAM_END MZ_STREAM_END +#define Z_NEED_DICT MZ_NEED_DICT +#define Z_ERRNO MZ_ERRNO +#define Z_STREAM_ERROR MZ_STREAM_ERROR +#define Z_DATA_ERROR MZ_DATA_ERROR +#define Z_MEM_ERROR MZ_MEM_ERROR +#define Z_BUF_ERROR MZ_BUF_ERROR +#define Z_VERSION_ERROR MZ_VERSION_ERROR +#define Z_PARAM_ERROR MZ_PARAM_ERROR +#define Z_NO_COMPRESSION MZ_NO_COMPRESSION +#define Z_BEST_SPEED MZ_BEST_SPEED +#define Z_BEST_COMPRESSION MZ_BEST_COMPRESSION +#define Z_DEFAULT_COMPRESSION MZ_DEFAULT_COMPRESSION +#define Z_DEFAULT_STRATEGY MZ_DEFAULT_STRATEGY +#define Z_FILTERED MZ_FILTERED +#define Z_HUFFMAN_ONLY MZ_HUFFMAN_ONLY +#define Z_RLE MZ_RLE +#define Z_FIXED MZ_FIXED +#define Z_DEFLATED MZ_DEFLATED +#define Z_DEFAULT_WINDOW_BITS MZ_DEFAULT_WINDOW_BITS +#define alloc_func mz_alloc_func +#define free_func mz_free_func +#define internal_state mz_internal_state +#define z_stream mz_stream +#define deflateInit mz_deflateInit +#define deflateInit2 mz_deflateInit2 +#define deflateReset mz_deflateReset +#define deflate mz_deflate +#define deflateEnd mz_deflateEnd +#define deflateBound mz_deflateBound +#define compress mz_compress +#define compress2 mz_compress2 +#define compressBound mz_compressBound +#define inflateInit mz_inflateInit +#define inflateInit2 mz_inflateInit2 +#define inflate mz_inflate +#define inflateEnd mz_inflateEnd +#define uncompress mz_uncompress +#define crc32 mz_crc32 +#define adler32 mz_adler32 +#define MAX_WBITS 15 +#define MAX_MEM_LEVEL 9 +#define zError mz_error +#define ZLIB_VERSION MZ_VERSION +#define ZLIB_VERNUM MZ_VERNUM +#define ZLIB_VER_MAJOR MZ_VER_MAJOR +#define ZLIB_VER_MINOR MZ_VER_MINOR +#define ZLIB_VER_REVISION MZ_VER_REVISION +#define ZLIB_VER_SUBREVISION MZ_VER_SUBREVISION +#define zlibVersion mz_version +#define zlib_version mz_version() #endif // #ifndef MINIZ_NO_ZLIB_COMPATIBLE_NAMES #endif // MINIZ_NO_ZLIB_APIS @@ -486,26 +653,25 @@ typedef int mz_bool; #define MZ_FALSE (0) #define MZ_TRUE (1) -// An attempt to work around MSVC's spammy "warning C4127: conditional expression is constant" message. +// An attempt to work around MSVC's spammy "warning C4127: conditional +// expression is constant" message. #ifdef _MSC_VER - #define MZ_MACRO_END while (0, 0) +#define MZ_MACRO_END while (0, 0) #else - #define MZ_MACRO_END while (0) +#define MZ_MACRO_END while (0) #endif // ------------------- ZIP archive reading/writing #ifndef MINIZ_NO_ARCHIVE_APIS -enum -{ - MZ_ZIP_MAX_IO_BUF_SIZE = 64*1024, +enum { + MZ_ZIP_MAX_IO_BUF_SIZE = 64 * 1024, MZ_ZIP_MAX_ARCHIVE_FILENAME_SIZE = 260, MZ_ZIP_MAX_ARCHIVE_FILE_COMMENT_SIZE = 256 }; -typedef struct -{ +typedef struct { mz_uint32 m_file_index; mz_uint32 m_central_dir_ofs; mz_uint16 m_version_made_by; @@ -526,22 +692,22 @@ typedef struct char m_comment[MZ_ZIP_MAX_ARCHIVE_FILE_COMMENT_SIZE]; } mz_zip_archive_file_stat; -typedef size_t (*mz_file_read_func)(void *pOpaque, mz_uint64 file_ofs, void *pBuf, size_t n); -typedef size_t (*mz_file_write_func)(void *pOpaque, mz_uint64 file_ofs, const void *pBuf, size_t n); +typedef size_t (*mz_file_read_func)(void *pOpaque, mz_uint64 file_ofs, + void *pBuf, size_t n); +typedef size_t (*mz_file_write_func)(void *pOpaque, mz_uint64 file_ofs, + const void *pBuf, size_t n); struct mz_zip_internal_state_tag; typedef struct mz_zip_internal_state_tag mz_zip_internal_state; -typedef enum -{ +typedef enum { MZ_ZIP_MODE_INVALID = 0, MZ_ZIP_MODE_READING = 1, MZ_ZIP_MODE_WRITING = 2, MZ_ZIP_MODE_WRITING_HAS_BEEN_FINALIZED = 3 } mz_zip_mode; -typedef struct mz_zip_archive_tag -{ +typedef struct mz_zip_archive_tag { mz_uint64 m_archive_size; mz_uint64 m_central_directory_file_ofs; mz_uint m_total_files; @@ -562,11 +728,10 @@ typedef struct mz_zip_archive_tag } mz_zip_archive; -typedef enum -{ - MZ_ZIP_FLAG_CASE_SENSITIVE = 0x0100, - MZ_ZIP_FLAG_IGNORE_PATH = 0x0200, - MZ_ZIP_FLAG_COMPRESSED_DATA = 0x0400, +typedef enum { + MZ_ZIP_FLAG_CASE_SENSITIVE = 0x0100, + MZ_ZIP_FLAG_IGNORE_PATH = 0x0200, + MZ_ZIP_FLAG_COMPRESSED_DATA = 0x0400, MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY = 0x0800 } mz_zip_flags; @@ -574,56 +739,91 @@ typedef enum // Inits a ZIP archive reader. // These functions read and validate the archive's central directory. -mz_bool mz_zip_reader_init(mz_zip_archive *pZip, mz_uint64 size, mz_uint32 flags); -mz_bool mz_zip_reader_init_mem(mz_zip_archive *pZip, const void *pMem, size_t size, mz_uint32 flags); +mz_bool mz_zip_reader_init(mz_zip_archive *pZip, mz_uint64 size, + mz_uint32 flags); +mz_bool mz_zip_reader_init_mem(mz_zip_archive *pZip, const void *pMem, + size_t size, mz_uint32 flags); #ifndef MINIZ_NO_STDIO -mz_bool mz_zip_reader_init_file(mz_zip_archive *pZip, const char *pFilename, mz_uint32 flags); +mz_bool mz_zip_reader_init_file(mz_zip_archive *pZip, const char *pFilename, + mz_uint32 flags); #endif // Returns the total number of files in the archive. mz_uint mz_zip_reader_get_num_files(mz_zip_archive *pZip); // Returns detailed information about an archive file entry. -mz_bool mz_zip_reader_file_stat(mz_zip_archive *pZip, mz_uint file_index, mz_zip_archive_file_stat *pStat); +mz_bool mz_zip_reader_file_stat(mz_zip_archive *pZip, mz_uint file_index, + mz_zip_archive_file_stat *pStat); // Determines if an archive file entry is a directory entry. -mz_bool mz_zip_reader_is_file_a_directory(mz_zip_archive *pZip, mz_uint file_index); -mz_bool mz_zip_reader_is_file_encrypted(mz_zip_archive *pZip, mz_uint file_index); +mz_bool mz_zip_reader_is_file_a_directory(mz_zip_archive *pZip, + mz_uint file_index); +mz_bool mz_zip_reader_is_file_encrypted(mz_zip_archive *pZip, + mz_uint file_index); // Retrieves the filename of an archive file entry. -// Returns the number of bytes written to pFilename, or if filename_buf_size is 0 this function returns the number of bytes needed to fully store the filename. -mz_uint mz_zip_reader_get_filename(mz_zip_archive *pZip, mz_uint file_index, char *pFilename, mz_uint filename_buf_size); +// Returns the number of bytes written to pFilename, or if filename_buf_size is +// 0 this function returns the number of bytes needed to fully store the +// filename. +mz_uint mz_zip_reader_get_filename(mz_zip_archive *pZip, mz_uint file_index, + char *pFilename, mz_uint filename_buf_size); // Attempts to locates a file in the archive's central directory. // Valid flags: MZ_ZIP_FLAG_CASE_SENSITIVE, MZ_ZIP_FLAG_IGNORE_PATH // Returns -1 if the file cannot be found. -int mz_zip_reader_locate_file(mz_zip_archive *pZip, const char *pName, const char *pComment, mz_uint flags); +int mz_zip_reader_locate_file(mz_zip_archive *pZip, const char *pName, + const char *pComment, mz_uint flags); // Extracts a archive file to a memory buffer using no memory allocation. -mz_bool mz_zip_reader_extract_to_mem_no_alloc(mz_zip_archive *pZip, mz_uint file_index, void *pBuf, size_t buf_size, mz_uint flags, void *pUser_read_buf, size_t user_read_buf_size); -mz_bool mz_zip_reader_extract_file_to_mem_no_alloc(mz_zip_archive *pZip, const char *pFilename, void *pBuf, size_t buf_size, mz_uint flags, void *pUser_read_buf, size_t user_read_buf_size); +mz_bool mz_zip_reader_extract_to_mem_no_alloc(mz_zip_archive *pZip, + mz_uint file_index, void *pBuf, + size_t buf_size, mz_uint flags, + void *pUser_read_buf, + size_t user_read_buf_size); +mz_bool mz_zip_reader_extract_file_to_mem_no_alloc( + mz_zip_archive *pZip, const char *pFilename, void *pBuf, size_t buf_size, + mz_uint flags, void *pUser_read_buf, size_t user_read_buf_size); // Extracts a archive file to a memory buffer. -mz_bool mz_zip_reader_extract_to_mem(mz_zip_archive *pZip, mz_uint file_index, void *pBuf, size_t buf_size, mz_uint flags); -mz_bool mz_zip_reader_extract_file_to_mem(mz_zip_archive *pZip, const char *pFilename, void *pBuf, size_t buf_size, mz_uint flags); +mz_bool mz_zip_reader_extract_to_mem(mz_zip_archive *pZip, mz_uint file_index, + void *pBuf, size_t buf_size, + mz_uint flags); +mz_bool mz_zip_reader_extract_file_to_mem(mz_zip_archive *pZip, + const char *pFilename, void *pBuf, + size_t buf_size, mz_uint flags); // Extracts a archive file to a dynamically allocated heap buffer. -void *mz_zip_reader_extract_to_heap(mz_zip_archive *pZip, mz_uint file_index, size_t *pSize, mz_uint flags); -void *mz_zip_reader_extract_file_to_heap(mz_zip_archive *pZip, const char *pFilename, size_t *pSize, mz_uint flags); +void *mz_zip_reader_extract_to_heap(mz_zip_archive *pZip, mz_uint file_index, + size_t *pSize, mz_uint flags); +void *mz_zip_reader_extract_file_to_heap(mz_zip_archive *pZip, + const char *pFilename, size_t *pSize, + mz_uint flags); // Extracts a archive file using a callback function to output the file's data. -mz_bool mz_zip_reader_extract_to_callback(mz_zip_archive *pZip, mz_uint file_index, mz_file_write_func pCallback, void *pOpaque, mz_uint flags); -mz_bool mz_zip_reader_extract_file_to_callback(mz_zip_archive *pZip, const char *pFilename, mz_file_write_func pCallback, void *pOpaque, mz_uint flags); +mz_bool mz_zip_reader_extract_to_callback(mz_zip_archive *pZip, + mz_uint file_index, + mz_file_write_func pCallback, + void *pOpaque, mz_uint flags); +mz_bool mz_zip_reader_extract_file_to_callback(mz_zip_archive *pZip, + const char *pFilename, + mz_file_write_func pCallback, + void *pOpaque, mz_uint flags); #ifndef MINIZ_NO_STDIO -// Extracts a archive file to a disk file and sets its last accessed and modified times. -// This function only extracts files, not archive directory records. -mz_bool mz_zip_reader_extract_to_file(mz_zip_archive *pZip, mz_uint file_index, const char *pDst_filename, mz_uint flags); -mz_bool mz_zip_reader_extract_file_to_file(mz_zip_archive *pZip, const char *pArchive_filename, const char *pDst_filename, mz_uint flags); +// Extracts a archive file to a disk file and sets its last accessed and +// modified times. This function only extracts files, not archive directory +// records. +mz_bool mz_zip_reader_extract_to_file(mz_zip_archive *pZip, mz_uint file_index, + const char *pDst_filename, mz_uint flags); +mz_bool mz_zip_reader_extract_file_to_file(mz_zip_archive *pZip, + const char *pArchive_filename, + const char *pDst_filename, + mz_uint flags); #endif -// Ends archive reading, freeing all allocations, and closing the input archive file if mz_zip_reader_init_file() was used. +// Ends archive reading, freeing all allocations, and closing the input archive +// file if mz_zip_reader_init_file() was used. mz_bool mz_zip_reader_end(mz_zip_archive *pZip); // ZIP archive writing @@ -632,55 +832,93 @@ mz_bool mz_zip_reader_end(mz_zip_archive *pZip); // Inits a ZIP archive writer. mz_bool mz_zip_writer_init(mz_zip_archive *pZip, mz_uint64 existing_size); -mz_bool mz_zip_writer_init_heap(mz_zip_archive *pZip, size_t size_to_reserve_at_beginning, size_t initial_allocation_size); +mz_bool mz_zip_writer_init_heap(mz_zip_archive *pZip, + size_t size_to_reserve_at_beginning, + size_t initial_allocation_size); #ifndef MINIZ_NO_STDIO -mz_bool mz_zip_writer_init_file(mz_zip_archive *pZip, const char *pFilename, mz_uint64 size_to_reserve_at_beginning); +mz_bool mz_zip_writer_init_file(mz_zip_archive *pZip, const char *pFilename, + mz_uint64 size_to_reserve_at_beginning); #endif -// Converts a ZIP archive reader object into a writer object, to allow efficient in-place file appends to occur on an existing archive. -// For archives opened using mz_zip_reader_init_file, pFilename must be the archive's filename so it can be reopened for writing. If the file can't be reopened, mz_zip_reader_end() will be called. -// For archives opened using mz_zip_reader_init_mem, the memory block must be growable using the realloc callback (which defaults to realloc unless you've overridden it). -// Finally, for archives opened using mz_zip_reader_init, the mz_zip_archive's user provided m_pWrite function cannot be NULL. -// Note: In-place archive modification is not recommended unless you know what you're doing, because if execution stops or something goes wrong before -// the archive is finalized the file's central directory will be hosed. -mz_bool mz_zip_writer_init_from_reader(mz_zip_archive *pZip, const char *pFilename); +// Converts a ZIP archive reader object into a writer object, to allow efficient +// in-place file appends to occur on an existing archive. For archives opened +// using mz_zip_reader_init_file, pFilename must be the archive's filename so it +// can be reopened for writing. If the file can't be reopened, +// mz_zip_reader_end() will be called. For archives opened using +// mz_zip_reader_init_mem, the memory block must be growable using the realloc +// callback (which defaults to realloc unless you've overridden it). Finally, +// for archives opened using mz_zip_reader_init, the mz_zip_archive's user +// provided m_pWrite function cannot be NULL. Note: In-place archive +// modification is not recommended unless you know what you're doing, because if +// execution stops or something goes wrong before the archive is finalized the +// file's central directory will be hosed. +mz_bool mz_zip_writer_init_from_reader(mz_zip_archive *pZip, + const char *pFilename); -// Adds the contents of a memory buffer to an archive. These functions record the current local time into the archive. -// To add a directory entry, call this method with an archive name ending in a forwardslash with empty buffer. -// level_and_flags - compression level (0-10, see MZ_BEST_SPEED, MZ_BEST_COMPRESSION, etc.) logically OR'd with zero or more mz_zip_flags, or just set to MZ_DEFAULT_COMPRESSION. -mz_bool mz_zip_writer_add_mem(mz_zip_archive *pZip, const char *pArchive_name, const void *pBuf, size_t buf_size, mz_uint level_and_flags); -mz_bool mz_zip_writer_add_mem_ex(mz_zip_archive *pZip, const char *pArchive_name, const void *pBuf, size_t buf_size, const void *pComment, mz_uint16 comment_size, mz_uint level_and_flags, mz_uint64 uncomp_size, mz_uint32 uncomp_crc32); +// Adds the contents of a memory buffer to an archive. These functions record +// the current local time into the archive. To add a directory entry, call this +// method with an archive name ending in a forwardslash with empty buffer. +// level_and_flags - compression level (0-10, see MZ_BEST_SPEED, +// MZ_BEST_COMPRESSION, etc.) logically OR'd with zero or more mz_zip_flags, or +// just set to MZ_DEFAULT_COMPRESSION. +mz_bool mz_zip_writer_add_mem(mz_zip_archive *pZip, const char *pArchive_name, + const void *pBuf, size_t buf_size, + mz_uint level_and_flags); +mz_bool mz_zip_writer_add_mem_ex(mz_zip_archive *pZip, + const char *pArchive_name, const void *pBuf, + size_t buf_size, const void *pComment, + mz_uint16 comment_size, + mz_uint level_and_flags, mz_uint64 uncomp_size, + mz_uint32 uncomp_crc32); #ifndef MINIZ_NO_STDIO -// Adds the contents of a disk file to an archive. This function also records the disk file's modified time into the archive. -// level_and_flags - compression level (0-10, see MZ_BEST_SPEED, MZ_BEST_COMPRESSION, etc.) logically OR'd with zero or more mz_zip_flags, or just set to MZ_DEFAULT_COMPRESSION. -mz_bool mz_zip_writer_add_file(mz_zip_archive *pZip, const char *pArchive_name, const char *pSrc_filename, const void *pComment, mz_uint16 comment_size, mz_uint level_and_flags); +// Adds the contents of a disk file to an archive. This function also records +// the disk file's modified time into the archive. level_and_flags - compression +// level (0-10, see MZ_BEST_SPEED, MZ_BEST_COMPRESSION, etc.) logically OR'd +// with zero or more mz_zip_flags, or just set to MZ_DEFAULT_COMPRESSION. +mz_bool mz_zip_writer_add_file(mz_zip_archive *pZip, const char *pArchive_name, + const char *pSrc_filename, const void *pComment, + mz_uint16 comment_size, mz_uint level_and_flags, + mz_uint32 ext_attributes); #endif // Adds a file to an archive by fully cloning the data from another archive. -// This function fully clones the source file's compressed data (no recompression), along with its full filename, extra data, and comment fields. -mz_bool mz_zip_writer_add_from_zip_reader(mz_zip_archive *pZip, mz_zip_archive *pSource_zip, mz_uint file_index); +// This function fully clones the source file's compressed data (no +// recompression), along with its full filename, extra data, and comment fields. +mz_bool mz_zip_writer_add_from_zip_reader(mz_zip_archive *pZip, + mz_zip_archive *pSource_zip, + mz_uint file_index); -// Finalizes the archive by writing the central directory records followed by the end of central directory record. -// After an archive is finalized, the only valid call on the mz_zip_archive struct is mz_zip_writer_end(). -// An archive must be manually finalized by calling this function for it to be valid. +// Finalizes the archive by writing the central directory records followed by +// the end of central directory record. After an archive is finalized, the only +// valid call on the mz_zip_archive struct is mz_zip_writer_end(). An archive +// must be manually finalized by calling this function for it to be valid. mz_bool mz_zip_writer_finalize_archive(mz_zip_archive *pZip); -mz_bool mz_zip_writer_finalize_heap_archive(mz_zip_archive *pZip, void **pBuf, size_t *pSize); +mz_bool mz_zip_writer_finalize_heap_archive(mz_zip_archive *pZip, void **pBuf, + size_t *pSize); -// Ends archive writing, freeing all allocations, and closing the output file if mz_zip_writer_init_file() was used. -// Note for the archive to be valid, it must have been finalized before ending. +// Ends archive writing, freeing all allocations, and closing the output file if +// mz_zip_writer_init_file() was used. Note for the archive to be valid, it must +// have been finalized before ending. mz_bool mz_zip_writer_end(mz_zip_archive *pZip); // Misc. high-level helper functions: -// mz_zip_add_mem_to_archive_file_in_place() efficiently (but not atomically) appends a memory blob to a ZIP archive. -// level_and_flags - compression level (0-10, see MZ_BEST_SPEED, MZ_BEST_COMPRESSION, etc.) logically OR'd with zero or more mz_zip_flags, or just set to MZ_DEFAULT_COMPRESSION. -mz_bool mz_zip_add_mem_to_archive_file_in_place(const char *pZip_filename, const char *pArchive_name, const void *pBuf, size_t buf_size, const void *pComment, mz_uint16 comment_size, mz_uint level_and_flags); +// mz_zip_add_mem_to_archive_file_in_place() efficiently (but not atomically) +// appends a memory blob to a ZIP archive. level_and_flags - compression level +// (0-10, see MZ_BEST_SPEED, MZ_BEST_COMPRESSION, etc.) logically OR'd with zero +// or more mz_zip_flags, or just set to MZ_DEFAULT_COMPRESSION. +mz_bool mz_zip_add_mem_to_archive_file_in_place( + const char *pZip_filename, const char *pArchive_name, const void *pBuf, + size_t buf_size, const void *pComment, mz_uint16 comment_size, + mz_uint level_and_flags); // Reads a single file from an archive into a heap block. // Returns NULL on failure. -void *mz_zip_extract_archive_file_to_heap(const char *pZip_filename, const char *pArchive_name, size_t *pSize, mz_uint zip_flags); +void *mz_zip_extract_archive_file_to_heap(const char *pZip_filename, + const char *pArchive_name, + size_t *pSize, mz_uint zip_flags); #endif // #ifndef MINIZ_NO_ARCHIVE_WRITING_APIS @@ -689,12 +927,17 @@ void *mz_zip_extract_archive_file_to_heap(const char *pZip_filename, const char // ------------------- Low-level Decompression API Definitions // Decompression flags used by tinfl_decompress(). -// TINFL_FLAG_PARSE_ZLIB_HEADER: If set, the input has a valid zlib header and ends with an adler32 checksum (it's a valid zlib stream). Otherwise, the input is a raw deflate stream. -// TINFL_FLAG_HAS_MORE_INPUT: If set, there are more input bytes available beyond the end of the supplied input buffer. If clear, the input buffer contains all remaining input. -// TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF: If set, the output buffer is large enough to hold the entire decompressed stream. If clear, the output buffer is at least the size of the dictionary (typically 32KB). -// TINFL_FLAG_COMPUTE_ADLER32: Force adler-32 checksum computation of the decompressed bytes. -enum -{ +// TINFL_FLAG_PARSE_ZLIB_HEADER: If set, the input has a valid zlib header and +// ends with an adler32 checksum (it's a valid zlib stream). Otherwise, the +// input is a raw deflate stream. TINFL_FLAG_HAS_MORE_INPUT: If set, there are +// more input bytes available beyond the end of the supplied input buffer. If +// clear, the input buffer contains all remaining input. +// TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF: If set, the output buffer is large +// enough to hold the entire decompressed stream. If clear, the output buffer is +// at least the size of the dictionary (typically 32KB). +// TINFL_FLAG_COMPUTE_ADLER32: Force adler-32 checksum computation of the +// decompressed bytes. +enum { TINFL_FLAG_PARSE_ZLIB_HEADER = 1, TINFL_FLAG_HAS_MORE_INPUT = 2, TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF = 4, @@ -702,33 +945,42 @@ enum }; // High level decompression functions: -// tinfl_decompress_mem_to_heap() decompresses a block in memory to a heap block allocated via malloc(). -// On entry: -// pSrc_buf, src_buf_len: Pointer and size of the Deflate or zlib source data to decompress. +// tinfl_decompress_mem_to_heap() decompresses a block in memory to a heap block +// allocated via malloc(). On entry: +// pSrc_buf, src_buf_len: Pointer and size of the Deflate or zlib source data +// to decompress. // On return: // Function returns a pointer to the decompressed data, or NULL on failure. -// *pOut_len will be set to the decompressed data's size, which could be larger than src_buf_len on uncompressible data. -// The caller must call mz_free() on the returned block when it's no longer needed. -void *tinfl_decompress_mem_to_heap(const void *pSrc_buf, size_t src_buf_len, size_t *pOut_len, int flags); +// *pOut_len will be set to the decompressed data's size, which could be larger +// than src_buf_len on uncompressible data. The caller must call mz_free() on +// the returned block when it's no longer needed. +void *tinfl_decompress_mem_to_heap(const void *pSrc_buf, size_t src_buf_len, + size_t *pOut_len, int flags); -// tinfl_decompress_mem_to_mem() decompresses a block in memory to another block in memory. -// Returns TINFL_DECOMPRESS_MEM_TO_MEM_FAILED on failure, or the number of bytes written on success. +// tinfl_decompress_mem_to_mem() decompresses a block in memory to another block +// in memory. Returns TINFL_DECOMPRESS_MEM_TO_MEM_FAILED on failure, or the +// number of bytes written on success. #define TINFL_DECOMPRESS_MEM_TO_MEM_FAILED ((size_t)(-1)) -size_t tinfl_decompress_mem_to_mem(void *pOut_buf, size_t out_buf_len, const void *pSrc_buf, size_t src_buf_len, int flags); +size_t tinfl_decompress_mem_to_mem(void *pOut_buf, size_t out_buf_len, + const void *pSrc_buf, size_t src_buf_len, + int flags); -// tinfl_decompress_mem_to_callback() decompresses a block in memory to an internal 32KB buffer, and a user provided callback function will be called to flush the buffer. -// Returns 1 on success or 0 on failure. -typedef int (*tinfl_put_buf_func_ptr)(const void* pBuf, int len, void *pUser); -int tinfl_decompress_mem_to_callback(const void *pIn_buf, size_t *pIn_buf_size, tinfl_put_buf_func_ptr pPut_buf_func, void *pPut_buf_user, int flags); +// tinfl_decompress_mem_to_callback() decompresses a block in memory to an +// internal 32KB buffer, and a user provided callback function will be called to +// flush the buffer. Returns 1 on success or 0 on failure. +typedef int (*tinfl_put_buf_func_ptr)(const void *pBuf, int len, void *pUser); +int tinfl_decompress_mem_to_callback(const void *pIn_buf, size_t *pIn_buf_size, + tinfl_put_buf_func_ptr pPut_buf_func, + void *pPut_buf_user, int flags); -struct tinfl_decompressor_tag; typedef struct tinfl_decompressor_tag tinfl_decompressor; +struct tinfl_decompressor_tag; +typedef struct tinfl_decompressor_tag tinfl_decompressor; // Max size of LZ dictionary. #define TINFL_LZ_DICT_SIZE 32768 // Return status. -typedef enum -{ +typedef enum { TINFL_STATUS_BAD_PARAM = -3, TINFL_STATUS_ADLER32_MISMATCH = -2, TINFL_STATUS_FAILED = -1, @@ -738,126 +990,196 @@ typedef enum } tinfl_status; // Initializes the decompressor to its initial state. -#define tinfl_init(r) do { (r)->m_state = 0; } MZ_MACRO_END +#define tinfl_init(r) \ + do { \ + (r)->m_state = 0; \ + } \ + MZ_MACRO_END #define tinfl_get_adler32(r) (r)->m_check_adler32 -// Main low-level decompressor coroutine function. This is the only function actually needed for decompression. All the other functions are just high-level helpers for improved usability. -// This is a universal API, i.e. it can be used as a building block to build any desired higher level decompression API. In the limit case, it can be called once per every byte input or output. -tinfl_status tinfl_decompress(tinfl_decompressor *r, const mz_uint8 *pIn_buf_next, size_t *pIn_buf_size, mz_uint8 *pOut_buf_start, mz_uint8 *pOut_buf_next, size_t *pOut_buf_size, const mz_uint32 decomp_flags); +// Main low-level decompressor coroutine function. This is the only function +// actually needed for decompression. All the other functions are just +// high-level helpers for improved usability. This is a universal API, i.e. it +// can be used as a building block to build any desired higher level +// decompression API. In the limit case, it can be called once per every byte +// input or output. +tinfl_status tinfl_decompress(tinfl_decompressor *r, + const mz_uint8 *pIn_buf_next, + size_t *pIn_buf_size, mz_uint8 *pOut_buf_start, + mz_uint8 *pOut_buf_next, size_t *pOut_buf_size, + const mz_uint32 decomp_flags); // Internal/private bits follow. -enum -{ - TINFL_MAX_HUFF_TABLES = 3, TINFL_MAX_HUFF_SYMBOLS_0 = 288, TINFL_MAX_HUFF_SYMBOLS_1 = 32, TINFL_MAX_HUFF_SYMBOLS_2 = 19, - TINFL_FAST_LOOKUP_BITS = 10, TINFL_FAST_LOOKUP_SIZE = 1 << TINFL_FAST_LOOKUP_BITS +enum { + TINFL_MAX_HUFF_TABLES = 3, + TINFL_MAX_HUFF_SYMBOLS_0 = 288, + TINFL_MAX_HUFF_SYMBOLS_1 = 32, + TINFL_MAX_HUFF_SYMBOLS_2 = 19, + TINFL_FAST_LOOKUP_BITS = 10, + TINFL_FAST_LOOKUP_SIZE = 1 << TINFL_FAST_LOOKUP_BITS }; -typedef struct -{ +typedef struct { mz_uint8 m_code_size[TINFL_MAX_HUFF_SYMBOLS_0]; - mz_int16 m_look_up[TINFL_FAST_LOOKUP_SIZE], m_tree[TINFL_MAX_HUFF_SYMBOLS_0 * 2]; + mz_int16 m_look_up[TINFL_FAST_LOOKUP_SIZE], + m_tree[TINFL_MAX_HUFF_SYMBOLS_0 * 2]; } tinfl_huff_table; #if MINIZ_HAS_64BIT_REGISTERS - #define TINFL_USE_64BIT_BITBUF 1 +#define TINFL_USE_64BIT_BITBUF 1 #endif #if TINFL_USE_64BIT_BITBUF - typedef mz_uint64 tinfl_bit_buf_t; - #define TINFL_BITBUF_SIZE (64) +typedef mz_uint64 tinfl_bit_buf_t; +#define TINFL_BITBUF_SIZE (64) #else - typedef mz_uint32 tinfl_bit_buf_t; - #define TINFL_BITBUF_SIZE (32) +typedef mz_uint32 tinfl_bit_buf_t; +#define TINFL_BITBUF_SIZE (32) #endif -struct tinfl_decompressor_tag -{ - mz_uint32 m_state, m_num_bits, m_zhdr0, m_zhdr1, m_z_adler32, m_final, m_type, m_check_adler32, m_dist, m_counter, m_num_extra, m_table_sizes[TINFL_MAX_HUFF_TABLES]; +struct tinfl_decompressor_tag { + mz_uint32 m_state, m_num_bits, m_zhdr0, m_zhdr1, m_z_adler32, m_final, m_type, + m_check_adler32, m_dist, m_counter, m_num_extra, + m_table_sizes[TINFL_MAX_HUFF_TABLES]; tinfl_bit_buf_t m_bit_buf; size_t m_dist_from_out_buf_start; tinfl_huff_table m_tables[TINFL_MAX_HUFF_TABLES]; - mz_uint8 m_raw_header[4], m_len_codes[TINFL_MAX_HUFF_SYMBOLS_0 + TINFL_MAX_HUFF_SYMBOLS_1 + 137]; + mz_uint8 m_raw_header[4], + m_len_codes[TINFL_MAX_HUFF_SYMBOLS_0 + TINFL_MAX_HUFF_SYMBOLS_1 + 137]; }; // ------------------- Low-level Compression API Definitions -// Set TDEFL_LESS_MEMORY to 1 to use less memory (compression will be slightly slower, and raw/dynamic blocks will be output more frequently). +// Set TDEFL_LESS_MEMORY to 1 to use less memory (compression will be slightly +// slower, and raw/dynamic blocks will be output more frequently). #define TDEFL_LESS_MEMORY 0 -// tdefl_init() compression flags logically OR'd together (low 12 bits contain the max. number of probes per dictionary search): -// TDEFL_DEFAULT_MAX_PROBES: The compressor defaults to 128 dictionary probes per dictionary search. 0=Huffman only, 1=Huffman+LZ (fastest/crap compression), 4095=Huffman+LZ (slowest/best compression). -enum -{ - TDEFL_HUFFMAN_ONLY = 0, TDEFL_DEFAULT_MAX_PROBES = 128, TDEFL_MAX_PROBES_MASK = 0xFFF +// tdefl_init() compression flags logically OR'd together (low 12 bits contain +// the max. number of probes per dictionary search): TDEFL_DEFAULT_MAX_PROBES: +// The compressor defaults to 128 dictionary probes per dictionary search. +// 0=Huffman only, 1=Huffman+LZ (fastest/crap compression), 4095=Huffman+LZ +// (slowest/best compression). +enum { + TDEFL_HUFFMAN_ONLY = 0, + TDEFL_DEFAULT_MAX_PROBES = 128, + TDEFL_MAX_PROBES_MASK = 0xFFF }; -// TDEFL_WRITE_ZLIB_HEADER: If set, the compressor outputs a zlib header before the deflate data, and the Adler-32 of the source data at the end. Otherwise, you'll get raw deflate data. -// TDEFL_COMPUTE_ADLER32: Always compute the adler-32 of the input data (even when not writing zlib headers). -// TDEFL_GREEDY_PARSING_FLAG: Set to use faster greedy parsing, instead of more efficient lazy parsing. -// TDEFL_NONDETERMINISTIC_PARSING_FLAG: Enable to decrease the compressor's initialization time to the minimum, but the output may vary from run to run given the same input (depending on the contents of memory). -// TDEFL_RLE_MATCHES: Only look for RLE matches (matches with a distance of 1) -// TDEFL_FILTER_MATCHES: Discards matches <= 5 chars if enabled. +// TDEFL_WRITE_ZLIB_HEADER: If set, the compressor outputs a zlib header before +// the deflate data, and the Adler-32 of the source data at the end. Otherwise, +// you'll get raw deflate data. TDEFL_COMPUTE_ADLER32: Always compute the +// adler-32 of the input data (even when not writing zlib headers). +// TDEFL_GREEDY_PARSING_FLAG: Set to use faster greedy parsing, instead of more +// efficient lazy parsing. TDEFL_NONDETERMINISTIC_PARSING_FLAG: Enable to +// decrease the compressor's initialization time to the minimum, but the output +// may vary from run to run given the same input (depending on the contents of +// memory). TDEFL_RLE_MATCHES: Only look for RLE matches (matches with a +// distance of 1) TDEFL_FILTER_MATCHES: Discards matches <= 5 chars if enabled. // TDEFL_FORCE_ALL_STATIC_BLOCKS: Disable usage of optimized Huffman tables. // TDEFL_FORCE_ALL_RAW_BLOCKS: Only use raw (uncompressed) deflate blocks. -// The low 12 bits are reserved to control the max # of hash probes per dictionary lookup (see TDEFL_MAX_PROBES_MASK). -enum -{ - TDEFL_WRITE_ZLIB_HEADER = 0x01000, - TDEFL_COMPUTE_ADLER32 = 0x02000, - TDEFL_GREEDY_PARSING_FLAG = 0x04000, +// The low 12 bits are reserved to control the max # of hash probes per +// dictionary lookup (see TDEFL_MAX_PROBES_MASK). +enum { + TDEFL_WRITE_ZLIB_HEADER = 0x01000, + TDEFL_COMPUTE_ADLER32 = 0x02000, + TDEFL_GREEDY_PARSING_FLAG = 0x04000, TDEFL_NONDETERMINISTIC_PARSING_FLAG = 0x08000, - TDEFL_RLE_MATCHES = 0x10000, - TDEFL_FILTER_MATCHES = 0x20000, - TDEFL_FORCE_ALL_STATIC_BLOCKS = 0x40000, - TDEFL_FORCE_ALL_RAW_BLOCKS = 0x80000 + TDEFL_RLE_MATCHES = 0x10000, + TDEFL_FILTER_MATCHES = 0x20000, + TDEFL_FORCE_ALL_STATIC_BLOCKS = 0x40000, + TDEFL_FORCE_ALL_RAW_BLOCKS = 0x80000 }; // High level compression functions: -// tdefl_compress_mem_to_heap() compresses a block in memory to a heap block allocated via malloc(). -// On entry: +// tdefl_compress_mem_to_heap() compresses a block in memory to a heap block +// allocated via malloc(). On entry: // pSrc_buf, src_buf_len: Pointer and size of source block to compress. -// flags: The max match finder probes (default is 128) logically OR'd against the above flags. Higher probes are slower but improve compression. +// flags: The max match finder probes (default is 128) logically OR'd against +// the above flags. Higher probes are slower but improve compression. // On return: // Function returns a pointer to the compressed data, or NULL on failure. -// *pOut_len will be set to the compressed data's size, which could be larger than src_buf_len on uncompressible data. -// The caller must free() the returned block when it's no longer needed. -void *tdefl_compress_mem_to_heap(const void *pSrc_buf, size_t src_buf_len, size_t *pOut_len, int flags); +// *pOut_len will be set to the compressed data's size, which could be larger +// than src_buf_len on uncompressible data. The caller must free() the returned +// block when it's no longer needed. +void *tdefl_compress_mem_to_heap(const void *pSrc_buf, size_t src_buf_len, + size_t *pOut_len, int flags); -// tdefl_compress_mem_to_mem() compresses a block in memory to another block in memory. -// Returns 0 on failure. -size_t tdefl_compress_mem_to_mem(void *pOut_buf, size_t out_buf_len, const void *pSrc_buf, size_t src_buf_len, int flags); +// tdefl_compress_mem_to_mem() compresses a block in memory to another block in +// memory. Returns 0 on failure. +size_t tdefl_compress_mem_to_mem(void *pOut_buf, size_t out_buf_len, + const void *pSrc_buf, size_t src_buf_len, + int flags); // Compresses an image to a compressed PNG file in memory. // On entry: -// pImage, w, h, and num_chans describe the image to compress. num_chans may be 1, 2, 3, or 4. -// The image pitch in bytes per scanline will be w*num_chans. The leftmost pixel on the top scanline is stored first in memory. -// level may range from [0,10], use MZ_NO_COMPRESSION, MZ_BEST_SPEED, MZ_BEST_COMPRESSION, etc. or a decent default is MZ_DEFAULT_LEVEL -// If flip is true, the image will be flipped on the Y axis (useful for OpenGL apps). +// pImage, w, h, and num_chans describe the image to compress. num_chans may be +// 1, 2, 3, or 4. The image pitch in bytes per scanline will be w*num_chans. +// The leftmost pixel on the top scanline is stored first in memory. level may +// range from [0,10], use MZ_NO_COMPRESSION, MZ_BEST_SPEED, +// MZ_BEST_COMPRESSION, etc. or a decent default is MZ_DEFAULT_LEVEL If flip is +// true, the image will be flipped on the Y axis (useful for OpenGL apps). // On return: // Function returns a pointer to the compressed data, or NULL on failure. // *pLen_out will be set to the size of the PNG image file. -// The caller must mz_free() the returned heap block (which will typically be larger than *pLen_out) when it's no longer needed. -void *tdefl_write_image_to_png_file_in_memory_ex(const void *pImage, int w, int h, int num_chans, size_t *pLen_out, mz_uint level, mz_bool flip); -void *tdefl_write_image_to_png_file_in_memory(const void *pImage, int w, int h, int num_chans, size_t *pLen_out); +// The caller must mz_free() the returned heap block (which will typically be +// larger than *pLen_out) when it's no longer needed. +void *tdefl_write_image_to_png_file_in_memory_ex(const void *pImage, int w, + int h, int num_chans, + size_t *pLen_out, + mz_uint level, mz_bool flip); +void *tdefl_write_image_to_png_file_in_memory(const void *pImage, int w, int h, + int num_chans, size_t *pLen_out); -// Output stream interface. The compressor uses this interface to write compressed data. It'll typically be called TDEFL_OUT_BUF_SIZE at a time. -typedef mz_bool (*tdefl_put_buf_func_ptr)(const void* pBuf, int len, void *pUser); +// Output stream interface. The compressor uses this interface to write +// compressed data. It'll typically be called TDEFL_OUT_BUF_SIZE at a time. +typedef mz_bool (*tdefl_put_buf_func_ptr)(const void *pBuf, int len, + void *pUser); -// tdefl_compress_mem_to_output() compresses a block to an output stream. The above helpers use this function internally. -mz_bool tdefl_compress_mem_to_output(const void *pBuf, size_t buf_len, tdefl_put_buf_func_ptr pPut_buf_func, void *pPut_buf_user, int flags); +// tdefl_compress_mem_to_output() compresses a block to an output stream. The +// above helpers use this function internally. +mz_bool tdefl_compress_mem_to_output(const void *pBuf, size_t buf_len, + tdefl_put_buf_func_ptr pPut_buf_func, + void *pPut_buf_user, int flags); -enum { TDEFL_MAX_HUFF_TABLES = 3, TDEFL_MAX_HUFF_SYMBOLS_0 = 288, TDEFL_MAX_HUFF_SYMBOLS_1 = 32, TDEFL_MAX_HUFF_SYMBOLS_2 = 19, TDEFL_LZ_DICT_SIZE = 32768, TDEFL_LZ_DICT_SIZE_MASK = TDEFL_LZ_DICT_SIZE - 1, TDEFL_MIN_MATCH_LEN = 3, TDEFL_MAX_MATCH_LEN = 258 }; +enum { + TDEFL_MAX_HUFF_TABLES = 3, + TDEFL_MAX_HUFF_SYMBOLS_0 = 288, + TDEFL_MAX_HUFF_SYMBOLS_1 = 32, + TDEFL_MAX_HUFF_SYMBOLS_2 = 19, + TDEFL_LZ_DICT_SIZE = 32768, + TDEFL_LZ_DICT_SIZE_MASK = TDEFL_LZ_DICT_SIZE - 1, + TDEFL_MIN_MATCH_LEN = 3, + TDEFL_MAX_MATCH_LEN = 258 +}; -// TDEFL_OUT_BUF_SIZE MUST be large enough to hold a single entire compressed output block (using static/fixed Huffman codes). +// TDEFL_OUT_BUF_SIZE MUST be large enough to hold a single entire compressed +// output block (using static/fixed Huffman codes). #if TDEFL_LESS_MEMORY -enum { TDEFL_LZ_CODE_BUF_SIZE = 24 * 1024, TDEFL_OUT_BUF_SIZE = (TDEFL_LZ_CODE_BUF_SIZE * 13 ) / 10, TDEFL_MAX_HUFF_SYMBOLS = 288, TDEFL_LZ_HASH_BITS = 12, TDEFL_LEVEL1_HASH_SIZE_MASK = 4095, TDEFL_LZ_HASH_SHIFT = (TDEFL_LZ_HASH_BITS + 2) / 3, TDEFL_LZ_HASH_SIZE = 1 << TDEFL_LZ_HASH_BITS }; +enum { + TDEFL_LZ_CODE_BUF_SIZE = 24 * 1024, + TDEFL_OUT_BUF_SIZE = (TDEFL_LZ_CODE_BUF_SIZE * 13) / 10, + TDEFL_MAX_HUFF_SYMBOLS = 288, + TDEFL_LZ_HASH_BITS = 12, + TDEFL_LEVEL1_HASH_SIZE_MASK = 4095, + TDEFL_LZ_HASH_SHIFT = (TDEFL_LZ_HASH_BITS + 2) / 3, + TDEFL_LZ_HASH_SIZE = 1 << TDEFL_LZ_HASH_BITS +}; #else -enum { TDEFL_LZ_CODE_BUF_SIZE = 64 * 1024, TDEFL_OUT_BUF_SIZE = (TDEFL_LZ_CODE_BUF_SIZE * 13 ) / 10, TDEFL_MAX_HUFF_SYMBOLS = 288, TDEFL_LZ_HASH_BITS = 15, TDEFL_LEVEL1_HASH_SIZE_MASK = 4095, TDEFL_LZ_HASH_SHIFT = (TDEFL_LZ_HASH_BITS + 2) / 3, TDEFL_LZ_HASH_SIZE = 1 << TDEFL_LZ_HASH_BITS }; +enum { + TDEFL_LZ_CODE_BUF_SIZE = 64 * 1024, + TDEFL_OUT_BUF_SIZE = (TDEFL_LZ_CODE_BUF_SIZE * 13) / 10, + TDEFL_MAX_HUFF_SYMBOLS = 288, + TDEFL_LZ_HASH_BITS = 15, + TDEFL_LEVEL1_HASH_SIZE_MASK = 4095, + TDEFL_LZ_HASH_SHIFT = (TDEFL_LZ_HASH_BITS + 2) / 3, + TDEFL_LZ_HASH_SIZE = 1 << TDEFL_LZ_HASH_BITS +}; #endif -// The low-level tdefl functions below may be used directly if the above helper functions aren't flexible enough. The low-level functions don't make any heap allocations, unlike the above helper functions. -typedef enum -{ +// The low-level tdefl functions below may be used directly if the above helper +// functions aren't flexible enough. The low-level functions don't make any heap +// allocations, unlike the above helper functions. +typedef enum { TDEFL_STATUS_BAD_PARAM = -2, TDEFL_STATUS_PUT_BUF_FAILED = -1, TDEFL_STATUS_OKAY = 0, @@ -865,8 +1187,7 @@ typedef enum } tdefl_status; // Must map to MZ_NO_FLUSH, MZ_SYNC_FLUSH, etc. enums -typedef enum -{ +typedef enum { TDEFL_NO_FLUSH = 0, TDEFL_SYNC_FLUSH = 2, TDEFL_FULL_FLUSH = 3, @@ -874,16 +1195,18 @@ typedef enum } tdefl_flush; // tdefl's compression state structure. -typedef struct -{ +typedef struct { tdefl_put_buf_func_ptr m_pPut_buf_func; void *m_pPut_buf_user; mz_uint m_flags, m_max_probes[2]; int m_greedy_parsing; mz_uint m_adler32, m_lookahead_pos, m_lookahead_size, m_dict_size; mz_uint8 *m_pLZ_code_buf, *m_pLZ_flags, *m_pOutput_buf, *m_pOutput_buf_end; - mz_uint m_num_flags_left, m_total_lz_bytes, m_lz_code_buf_dict_pos, m_bits_in, m_bit_buffer; - mz_uint m_saved_match_dist, m_saved_match_len, m_saved_lit, m_output_flush_ofs, m_output_flush_remaining, m_finished, m_block_index, m_wants_to_finish; + mz_uint m_num_flags_left, m_total_lz_bytes, m_lz_code_buf_dict_pos, m_bits_in, + m_bit_buffer; + mz_uint m_saved_match_dist, m_saved_match_len, m_saved_lit, + m_output_flush_ofs, m_output_flush_remaining, m_finished, m_block_index, + m_wants_to_finish; tdefl_status m_prev_return_status; const void *m_pIn_buf; void *m_pOut_buf; @@ -902,29 +1225,42 @@ typedef struct } tdefl_compressor; // Initializes the compressor. -// There is no corresponding deinit() function because the tdefl API's do not dynamically allocate memory. -// pBut_buf_func: If NULL, output data will be supplied to the specified callback. In this case, the user should call the tdefl_compress_buffer() API for compression. -// If pBut_buf_func is NULL the user should always call the tdefl_compress() API. -// flags: See the above enums (TDEFL_HUFFMAN_ONLY, TDEFL_WRITE_ZLIB_HEADER, etc.) -tdefl_status tdefl_init(tdefl_compressor *d, tdefl_put_buf_func_ptr pPut_buf_func, void *pPut_buf_user, int flags); +// There is no corresponding deinit() function because the tdefl API's do not +// dynamically allocate memory. pBut_buf_func: If NULL, output data will be +// supplied to the specified callback. In this case, the user should call the +// tdefl_compress_buffer() API for compression. If pBut_buf_func is NULL the +// user should always call the tdefl_compress() API. flags: See the above enums +// (TDEFL_HUFFMAN_ONLY, TDEFL_WRITE_ZLIB_HEADER, etc.) +tdefl_status tdefl_init(tdefl_compressor *d, + tdefl_put_buf_func_ptr pPut_buf_func, + void *pPut_buf_user, int flags); -// Compresses a block of data, consuming as much of the specified input buffer as possible, and writing as much compressed data to the specified output buffer as possible. -tdefl_status tdefl_compress(tdefl_compressor *d, const void *pIn_buf, size_t *pIn_buf_size, void *pOut_buf, size_t *pOut_buf_size, tdefl_flush flush); +// Compresses a block of data, consuming as much of the specified input buffer +// as possible, and writing as much compressed data to the specified output +// buffer as possible. +tdefl_status tdefl_compress(tdefl_compressor *d, const void *pIn_buf, + size_t *pIn_buf_size, void *pOut_buf, + size_t *pOut_buf_size, tdefl_flush flush); -// tdefl_compress_buffer() is only usable when the tdefl_init() is called with a non-NULL tdefl_put_buf_func_ptr. -// tdefl_compress_buffer() always consumes the entire input buffer. -tdefl_status tdefl_compress_buffer(tdefl_compressor *d, const void *pIn_buf, size_t in_buf_size, tdefl_flush flush); +// tdefl_compress_buffer() is only usable when the tdefl_init() is called with a +// non-NULL tdefl_put_buf_func_ptr. tdefl_compress_buffer() always consumes the +// entire input buffer. +tdefl_status tdefl_compress_buffer(tdefl_compressor *d, const void *pIn_buf, + size_t in_buf_size, tdefl_flush flush); tdefl_status tdefl_get_prev_return_status(tdefl_compressor *d); mz_uint32 tdefl_get_adler32(tdefl_compressor *d); -// Can't use tdefl_create_comp_flags_from_zip_params if MINIZ_NO_ZLIB_APIS isn't defined, because it uses some of its macros. +// Can't use tdefl_create_comp_flags_from_zip_params if MINIZ_NO_ZLIB_APIS isn't +// defined, because it uses some of its macros. #ifndef MINIZ_NO_ZLIB_APIS // Create tdefl_compress() flags given zlib-style compression parameters. -// level may range from [0,10] (where 10 is absolute max compression, but may be much slower on some files) -// window_bits may be -15 (raw deflate) or 15 (zlib) -// strategy may be either MZ_DEFAULT_STRATEGY, MZ_FILTERED, MZ_HUFFMAN_ONLY, MZ_RLE, or MZ_FIXED -mz_uint tdefl_create_comp_flags_from_zip_params(int level, int window_bits, int strategy); +// level may range from [0,10] (where 10 is absolute max compression, but may be +// much slower on some files) window_bits may be -15 (raw deflate) or 15 (zlib) +// strategy may be either MZ_DEFAULT_STRATEGY, MZ_FILTERED, MZ_HUFFMAN_ONLY, +// MZ_RLE, or MZ_FIXED +mz_uint tdefl_create_comp_flags_from_zip_params(int level, int window_bits, + int strategy); #endif // #ifndef MINIZ_NO_ZLIB_APIS #ifdef __cplusplus @@ -933,109 +1269,145 @@ mz_uint tdefl_create_comp_flags_from_zip_params(int level, int window_bits, int #endif // MINIZ_HEADER_INCLUDED -// ------------------- End of Header: Implementation follows. (If you only want the header, define MINIZ_HEADER_FILE_ONLY.) +// ------------------- End of Header: Implementation follows. (If you only want +// the header, define MINIZ_HEADER_FILE_ONLY.) #ifndef MINIZ_HEADER_FILE_ONLY -typedef unsigned char mz_validate_uint16[sizeof(mz_uint16)==2 ? 1 : -1]; -typedef unsigned char mz_validate_uint32[sizeof(mz_uint32)==4 ? 1 : -1]; -typedef unsigned char mz_validate_uint64[sizeof(mz_uint64)==8 ? 1 : -1]; +typedef unsigned char mz_validate_uint16[sizeof(mz_uint16) == 2 ? 1 : -1]; +typedef unsigned char mz_validate_uint32[sizeof(mz_uint32) == 4 ? 1 : -1]; +typedef unsigned char mz_validate_uint64[sizeof(mz_uint64) == 8 ? 1 : -1]; -#include #include +#include #define MZ_ASSERT(x) assert(x) #ifdef MINIZ_NO_MALLOC - #define MZ_MALLOC(x) NULL - #define MZ_FREE(x) (void)x, ((void)0) - #define MZ_REALLOC(p, x) NULL +#define MZ_MALLOC(x) NULL +#define MZ_FREE(x) (void)x, ((void)0) +#define MZ_REALLOC(p, x) NULL #else - #define MZ_MALLOC(x) malloc(x) - #define MZ_FREE(x) free(x) - #define MZ_REALLOC(p, x) realloc(p, x) +#define MZ_MALLOC(x) malloc(x) +#define MZ_FREE(x) free(x) +#define MZ_REALLOC(p, x) realloc(p, x) #endif -#define MZ_MAX(a,b) (((a)>(b))?(a):(b)) -#define MZ_MIN(a,b) (((a)<(b))?(a):(b)) +#define MZ_MAX(a, b) (((a) > (b)) ? (a) : (b)) +#define MZ_MIN(a, b) (((a) < (b)) ? (a) : (b)) #define MZ_CLEAR_OBJ(obj) memset(&(obj), 0, sizeof(obj)) #if MINIZ_USE_UNALIGNED_LOADS_AND_STORES && MINIZ_LITTLE_ENDIAN - #define MZ_READ_LE16(p) *((const mz_uint16 *)(p)) - #define MZ_READ_LE32(p) *((const mz_uint32 *)(p)) +#define MZ_READ_LE16(p) *((const mz_uint16 *)(p)) +#define MZ_READ_LE32(p) *((const mz_uint32 *)(p)) #else - #define MZ_READ_LE16(p) ((mz_uint32)(((const mz_uint8 *)(p))[0]) | ((mz_uint32)(((const mz_uint8 *)(p))[1]) << 8U)) - #define MZ_READ_LE32(p) ((mz_uint32)(((const mz_uint8 *)(p))[0]) | ((mz_uint32)(((const mz_uint8 *)(p))[1]) << 8U) | ((mz_uint32)(((const mz_uint8 *)(p))[2]) << 16U) | ((mz_uint32)(((const mz_uint8 *)(p))[3]) << 24U)) +#define MZ_READ_LE16(p) \ + ((mz_uint32)(((const mz_uint8 *)(p))[0]) | \ + ((mz_uint32)(((const mz_uint8 *)(p))[1]) << 8U)) +#define MZ_READ_LE32(p) \ + ((mz_uint32)(((const mz_uint8 *)(p))[0]) | \ + ((mz_uint32)(((const mz_uint8 *)(p))[1]) << 8U) | \ + ((mz_uint32)(((const mz_uint8 *)(p))[2]) << 16U) | \ + ((mz_uint32)(((const mz_uint8 *)(p))[3]) << 24U)) #endif #ifdef _MSC_VER - #define MZ_FORCEINLINE __forceinline +#define MZ_FORCEINLINE __forceinline #elif defined(__GNUC__) - #define MZ_FORCEINLINE inline __attribute__((__always_inline__)) +#define MZ_FORCEINLINE inline __attribute__((__always_inline__)) #else - #define MZ_FORCEINLINE inline +#define MZ_FORCEINLINE inline #endif #ifdef __cplusplus - extern "C" { +extern "C" { #endif // ------------------- zlib-style API's -mz_ulong mz_adler32(mz_ulong adler, const unsigned char *ptr, size_t buf_len) -{ - mz_uint32 i, s1 = (mz_uint32)(adler & 0xffff), s2 = (mz_uint32)(adler >> 16); size_t block_len = buf_len % 5552; - if (!ptr) return MZ_ADLER32_INIT; +mz_ulong mz_adler32(mz_ulong adler, const unsigned char *ptr, size_t buf_len) { + mz_uint32 i, s1 = (mz_uint32)(adler & 0xffff), s2 = (mz_uint32)(adler >> 16); + size_t block_len = buf_len % 5552; + if (!ptr) + return MZ_ADLER32_INIT; while (buf_len) { for (i = 0; i + 7 < block_len; i += 8, ptr += 8) { - s1 += ptr[0], s2 += s1; s1 += ptr[1], s2 += s1; s1 += ptr[2], s2 += s1; s1 += ptr[3], s2 += s1; - s1 += ptr[4], s2 += s1; s1 += ptr[5], s2 += s1; s1 += ptr[6], s2 += s1; s1 += ptr[7], s2 += s1; + s1 += ptr[0], s2 += s1; + s1 += ptr[1], s2 += s1; + s1 += ptr[2], s2 += s1; + s1 += ptr[3], s2 += s1; + s1 += ptr[4], s2 += s1; + s1 += ptr[5], s2 += s1; + s1 += ptr[6], s2 += s1; + s1 += ptr[7], s2 += s1; } - for ( ; i < block_len; ++i) s1 += *ptr++, s2 += s1; - s1 %= 65521U, s2 %= 65521U; buf_len -= block_len; block_len = 5552; + for (; i < block_len; ++i) + s1 += *ptr++, s2 += s1; + s1 %= 65521U, s2 %= 65521U; + buf_len -= block_len; + block_len = 5552; } return (s2 << 16) + s1; } -// Karl Malbrain's compact CRC-32. See "A compact CCITT crc16 and crc32 C implementation that balances processor cache usage against speed": http://www.geocities.com/malbrain/ -mz_ulong mz_crc32(mz_ulong crc, const mz_uint8 *ptr, size_t buf_len) -{ - static const mz_uint32 s_crc32[16] = { 0, 0x1db71064, 0x3b6e20c8, 0x26d930ac, 0x76dc4190, 0x6b6b51f4, 0x4db26158, 0x5005713c, - 0xedb88320, 0xf00f9344, 0xd6d6a3e8, 0xcb61b38c, 0x9b64c2b0, 0x86d3d2d4, 0xa00ae278, 0xbdbdf21c }; +// Karl Malbrain's compact CRC-32. See "A compact CCITT crc16 and crc32 C +// implementation that balances processor cache usage against speed": +// http://www.geocities.com/malbrain/ +mz_ulong mz_crc32(mz_ulong crc, const mz_uint8 *ptr, size_t buf_len) { + static const mz_uint32 s_crc32[16] = { + 0, 0x1db71064, 0x3b6e20c8, 0x26d930ac, 0x76dc4190, 0x6b6b51f4, + 0x4db26158, 0x5005713c, 0xedb88320, 0xf00f9344, 0xd6d6a3e8, 0xcb61b38c, + 0x9b64c2b0, 0x86d3d2d4, 0xa00ae278, 0xbdbdf21c}; mz_uint32 crcu32 = (mz_uint32)crc; - if (!ptr) return MZ_CRC32_INIT; - crcu32 = ~crcu32; while (buf_len--) { mz_uint8 b = *ptr++; crcu32 = (crcu32 >> 4) ^ s_crc32[(crcu32 & 0xF) ^ (b & 0xF)]; crcu32 = (crcu32 >> 4) ^ s_crc32[(crcu32 & 0xF) ^ (b >> 4)]; } + if (!ptr) + return MZ_CRC32_INIT; + crcu32 = ~crcu32; + while (buf_len--) { + mz_uint8 b = *ptr++; + crcu32 = (crcu32 >> 4) ^ s_crc32[(crcu32 & 0xF) ^ (b & 0xF)]; + crcu32 = (crcu32 >> 4) ^ s_crc32[(crcu32 & 0xF) ^ (b >> 4)]; + } return ~crcu32; } -void mz_free(void *p) -{ - MZ_FREE(p); -} +void mz_free(void *p) { MZ_FREE(p); } #ifndef MINIZ_NO_ZLIB_APIS -static void *def_alloc_func(void *opaque, size_t items, size_t size) { (void)opaque, (void)items, (void)size; return MZ_MALLOC(items * size); } -static void def_free_func(void *opaque, void *address) { (void)opaque, (void)address; MZ_FREE(address); } -static void *def_realloc_func(void *opaque, void *address, size_t items, size_t size) { (void)opaque, (void)address, (void)items, (void)size; return MZ_REALLOC(address, items * size); } - -const char *mz_version(void) -{ - return MZ_VERSION; +static void *def_alloc_func(void *opaque, size_t items, size_t size) { + (void)opaque, (void)items, (void)size; + return MZ_MALLOC(items * size); +} +static void def_free_func(void *opaque, void *address) { + (void)opaque, (void)address; + MZ_FREE(address); +} +static void *def_realloc_func(void *opaque, void *address, size_t items, + size_t size) { + (void)opaque, (void)address, (void)items, (void)size; + return MZ_REALLOC(address, items * size); } -int mz_deflateInit(mz_streamp pStream, int level) -{ - return mz_deflateInit2(pStream, level, MZ_DEFLATED, MZ_DEFAULT_WINDOW_BITS, 9, MZ_DEFAULT_STRATEGY); +const char *mz_version(void) { return MZ_VERSION; } + +int mz_deflateInit(mz_streamp pStream, int level) { + return mz_deflateInit2(pStream, level, MZ_DEFLATED, MZ_DEFAULT_WINDOW_BITS, 9, + MZ_DEFAULT_STRATEGY); } -int mz_deflateInit2(mz_streamp pStream, int level, int method, int window_bits, int mem_level, int strategy) -{ +int mz_deflateInit2(mz_streamp pStream, int level, int method, int window_bits, + int mem_level, int strategy) { tdefl_compressor *pComp; - mz_uint comp_flags = TDEFL_COMPUTE_ADLER32 | tdefl_create_comp_flags_from_zip_params(level, window_bits, strategy); + mz_uint comp_flags = + TDEFL_COMPUTE_ADLER32 | + tdefl_create_comp_flags_from_zip_params(level, window_bits, strategy); - if (!pStream) return MZ_STREAM_ERROR; - if ((method != MZ_DEFLATED) || ((mem_level < 1) || (mem_level > 9)) || ((window_bits != MZ_DEFAULT_WINDOW_BITS) && (-window_bits != MZ_DEFAULT_WINDOW_BITS))) return MZ_PARAM_ERROR; + if (!pStream) + return MZ_STREAM_ERROR; + if ((method != MZ_DEFLATED) || ((mem_level < 1) || (mem_level > 9)) || + ((window_bits != MZ_DEFAULT_WINDOW_BITS) && + (-window_bits != MZ_DEFAULT_WINDOW_BITS))) + return MZ_PARAM_ERROR; pStream->data_type = 0; pStream->adler = MZ_ADLER32_INIT; @@ -1043,17 +1415,19 @@ int mz_deflateInit2(mz_streamp pStream, int level, int method, int window_bits, pStream->reserved = 0; pStream->total_in = 0; pStream->total_out = 0; - if (!pStream->zalloc) pStream->zalloc = def_alloc_func; - if (!pStream->zfree) pStream->zfree = def_free_func; + if (!pStream->zalloc) + pStream->zalloc = def_alloc_func; + if (!pStream->zfree) + pStream->zfree = def_free_func; - pComp = (tdefl_compressor *)pStream->zalloc(pStream->opaque, 1, sizeof(tdefl_compressor)); + pComp = (tdefl_compressor *)pStream->zalloc(pStream->opaque, 1, + sizeof(tdefl_compressor)); if (!pComp) return MZ_MEM_ERROR; pStream->state = (struct mz_internal_state *)pComp; - if (tdefl_init(pComp, NULL, NULL, comp_flags) != TDEFL_STATUS_OKAY) - { + if (tdefl_init(pComp, NULL, NULL, comp_flags) != TDEFL_STATUS_OKAY) { mz_deflateEnd(pStream); return MZ_PARAM_ERROR; } @@ -1061,56 +1435,64 @@ int mz_deflateInit2(mz_streamp pStream, int level, int method, int window_bits, return MZ_OK; } -int mz_deflateReset(mz_streamp pStream) -{ - if ((!pStream) || (!pStream->state) || (!pStream->zalloc) || (!pStream->zfree)) return MZ_STREAM_ERROR; +int mz_deflateReset(mz_streamp pStream) { + if ((!pStream) || (!pStream->state) || (!pStream->zalloc) || + (!pStream->zfree)) + return MZ_STREAM_ERROR; pStream->total_in = pStream->total_out = 0; - tdefl_init((tdefl_compressor*)pStream->state, NULL, NULL, ((tdefl_compressor*)pStream->state)->m_flags); + tdefl_init((tdefl_compressor *)pStream->state, NULL, NULL, + ((tdefl_compressor *)pStream->state)->m_flags); return MZ_OK; } -int mz_deflate(mz_streamp pStream, int flush) -{ +int mz_deflate(mz_streamp pStream, int flush) { size_t in_bytes, out_bytes; mz_ulong orig_total_in, orig_total_out; int mz_status = MZ_OK; - if ((!pStream) || (!pStream->state) || (flush < 0) || (flush > MZ_FINISH) || (!pStream->next_out)) return MZ_STREAM_ERROR; - if (!pStream->avail_out) return MZ_BUF_ERROR; + if ((!pStream) || (!pStream->state) || (flush < 0) || (flush > MZ_FINISH) || + (!pStream->next_out)) + return MZ_STREAM_ERROR; + if (!pStream->avail_out) + return MZ_BUF_ERROR; - if (flush == MZ_PARTIAL_FLUSH) flush = MZ_SYNC_FLUSH; + if (flush == MZ_PARTIAL_FLUSH) + flush = MZ_SYNC_FLUSH; - if (((tdefl_compressor*)pStream->state)->m_prev_return_status == TDEFL_STATUS_DONE) + if (((tdefl_compressor *)pStream->state)->m_prev_return_status == + TDEFL_STATUS_DONE) return (flush == MZ_FINISH) ? MZ_STREAM_END : MZ_BUF_ERROR; - orig_total_in = pStream->total_in; orig_total_out = pStream->total_out; - for ( ; ; ) - { + orig_total_in = pStream->total_in; + orig_total_out = pStream->total_out; + for (;;) { tdefl_status defl_status; - in_bytes = pStream->avail_in; out_bytes = pStream->avail_out; + in_bytes = pStream->avail_in; + out_bytes = pStream->avail_out; - defl_status = tdefl_compress((tdefl_compressor*)pStream->state, pStream->next_in, &in_bytes, pStream->next_out, &out_bytes, (tdefl_flush)flush); - pStream->next_in += (mz_uint)in_bytes; pStream->avail_in -= (mz_uint)in_bytes; - pStream->total_in += (mz_uint)in_bytes; pStream->adler = tdefl_get_adler32((tdefl_compressor*)pStream->state); + defl_status = tdefl_compress((tdefl_compressor *)pStream->state, + pStream->next_in, &in_bytes, pStream->next_out, + &out_bytes, (tdefl_flush)flush); + pStream->next_in += (mz_uint)in_bytes; + pStream->avail_in -= (mz_uint)in_bytes; + pStream->total_in += (mz_uint)in_bytes; + pStream->adler = tdefl_get_adler32((tdefl_compressor *)pStream->state); - pStream->next_out += (mz_uint)out_bytes; pStream->avail_out -= (mz_uint)out_bytes; + pStream->next_out += (mz_uint)out_bytes; + pStream->avail_out -= (mz_uint)out_bytes; pStream->total_out += (mz_uint)out_bytes; - if (defl_status < 0) - { + if (defl_status < 0) { mz_status = MZ_STREAM_ERROR; break; - } - else if (defl_status == TDEFL_STATUS_DONE) - { + } else if (defl_status == TDEFL_STATUS_DONE) { mz_status = MZ_STREAM_END; break; - } - else if (!pStream->avail_out) + } else if (!pStream->avail_out) break; - else if ((!pStream->avail_in) && (flush != MZ_FINISH)) - { - if ((flush) || (pStream->total_in != orig_total_in) || (pStream->total_out != orig_total_out)) + else if ((!pStream->avail_in) && (flush != MZ_FINISH)) { + if ((flush) || (pStream->total_in != orig_total_in) || + (pStream->total_out != orig_total_out)) break; return MZ_BUF_ERROR; // Can't make forward progress without some input. } @@ -1118,32 +1500,33 @@ int mz_deflate(mz_streamp pStream, int flush) return mz_status; } -int mz_deflateEnd(mz_streamp pStream) -{ - if (!pStream) return MZ_STREAM_ERROR; - if (pStream->state) - { +int mz_deflateEnd(mz_streamp pStream) { + if (!pStream) + return MZ_STREAM_ERROR; + if (pStream->state) { pStream->zfree(pStream->opaque, pStream->state); pStream->state = NULL; } return MZ_OK; } -mz_ulong mz_deflateBound(mz_streamp pStream, mz_ulong source_len) -{ +mz_ulong mz_deflateBound(mz_streamp pStream, mz_ulong source_len) { (void)pStream; - // This is really over conservative. (And lame, but it's actually pretty tricky to compute a true upper bound given the way tdefl's blocking works.) - return MZ_MAX(128 + (source_len * 110) / 100, 128 + source_len + ((source_len / (31 * 1024)) + 1) * 5); + // This is really over conservative. (And lame, but it's actually pretty + // tricky to compute a true upper bound given the way tdefl's blocking works.) + return MZ_MAX(128 + (source_len * 110) / 100, + 128 + source_len + ((source_len / (31 * 1024)) + 1) * 5); } -int mz_compress2(unsigned char *pDest, mz_ulong *pDest_len, const unsigned char *pSource, mz_ulong source_len, int level) -{ +int mz_compress2(unsigned char *pDest, mz_ulong *pDest_len, + const unsigned char *pSource, mz_ulong source_len, int level) { int status; mz_stream stream; memset(&stream, 0, sizeof(stream)); // In case mz_ulong is 64-bits (argh I hate longs). - if ((source_len | *pDest_len) > 0xFFFFFFFFU) return MZ_PARAM_ERROR; + if ((source_len | *pDest_len) > 0xFFFFFFFFU) + return MZ_PARAM_ERROR; stream.next_in = pSource; stream.avail_in = (mz_uint32)source_len; @@ -1151,11 +1534,11 @@ int mz_compress2(unsigned char *pDest, mz_ulong *pDest_len, const unsigned char stream.avail_out = (mz_uint32)*pDest_len; status = mz_deflateInit(&stream, level); - if (status != MZ_OK) return status; + if (status != MZ_OK) + return status; status = mz_deflate(&stream, MZ_FINISH); - if (status != MZ_STREAM_END) - { + if (status != MZ_STREAM_END) { mz_deflateEnd(&stream); return (status == MZ_OK) ? MZ_BUF_ERROR : status; } @@ -1164,29 +1547,31 @@ int mz_compress2(unsigned char *pDest, mz_ulong *pDest_len, const unsigned char return mz_deflateEnd(&stream); } -int mz_compress(unsigned char *pDest, mz_ulong *pDest_len, const unsigned char *pSource, mz_ulong source_len) -{ - return mz_compress2(pDest, pDest_len, pSource, source_len, MZ_DEFAULT_COMPRESSION); +int mz_compress(unsigned char *pDest, mz_ulong *pDest_len, + const unsigned char *pSource, mz_ulong source_len) { + return mz_compress2(pDest, pDest_len, pSource, source_len, + MZ_DEFAULT_COMPRESSION); } -mz_ulong mz_compressBound(mz_ulong source_len) -{ +mz_ulong mz_compressBound(mz_ulong source_len) { return mz_deflateBound(NULL, source_len); } -typedef struct -{ +typedef struct { tinfl_decompressor m_decomp; - mz_uint m_dict_ofs, m_dict_avail, m_first_call, m_has_flushed; int m_window_bits; + mz_uint m_dict_ofs, m_dict_avail, m_first_call, m_has_flushed; + int m_window_bits; mz_uint8 m_dict[TINFL_LZ_DICT_SIZE]; tinfl_status m_last_status; } inflate_state; -int mz_inflateInit2(mz_streamp pStream, int window_bits) -{ +int mz_inflateInit2(mz_streamp pStream, int window_bits) { inflate_state *pDecomp; - if (!pStream) return MZ_STREAM_ERROR; - if ((window_bits != MZ_DEFAULT_WINDOW_BITS) && (-window_bits != MZ_DEFAULT_WINDOW_BITS)) return MZ_PARAM_ERROR; + if (!pStream) + return MZ_STREAM_ERROR; + if ((window_bits != MZ_DEFAULT_WINDOW_BITS) && + (-window_bits != MZ_DEFAULT_WINDOW_BITS)) + return MZ_PARAM_ERROR; pStream->data_type = 0; pStream->adler = 0; @@ -1194,11 +1579,15 @@ int mz_inflateInit2(mz_streamp pStream, int window_bits) pStream->total_in = 0; pStream->total_out = 0; pStream->reserved = 0; - if (!pStream->zalloc) pStream->zalloc = def_alloc_func; - if (!pStream->zfree) pStream->zfree = def_free_func; + if (!pStream->zalloc) + pStream->zalloc = def_alloc_func; + if (!pStream->zfree) + pStream->zfree = def_free_func; - pDecomp = (inflate_state*)pStream->zalloc(pStream->opaque, 1, sizeof(inflate_state)); - if (!pDecomp) return MZ_MEM_ERROR; + pDecomp = (inflate_state *)pStream->zalloc(pStream->opaque, 1, + sizeof(inflate_state)); + if (!pDecomp) + return MZ_MEM_ERROR; pStream->state = (struct mz_internal_state *)pDecomp; @@ -1213,122 +1602,152 @@ int mz_inflateInit2(mz_streamp pStream, int window_bits) return MZ_OK; } -int mz_inflateInit(mz_streamp pStream) -{ - return mz_inflateInit2(pStream, MZ_DEFAULT_WINDOW_BITS); +int mz_inflateInit(mz_streamp pStream) { + return mz_inflateInit2(pStream, MZ_DEFAULT_WINDOW_BITS); } -int mz_inflate(mz_streamp pStream, int flush) -{ - inflate_state* pState; +int mz_inflate(mz_streamp pStream, int flush) { + inflate_state *pState; mz_uint n, first_call, decomp_flags = TINFL_FLAG_COMPUTE_ADLER32; size_t in_bytes, out_bytes, orig_avail_in; tinfl_status status; - if ((!pStream) || (!pStream->state)) return MZ_STREAM_ERROR; - if (flush == MZ_PARTIAL_FLUSH) flush = MZ_SYNC_FLUSH; - if ((flush) && (flush != MZ_SYNC_FLUSH) && (flush != MZ_FINISH)) return MZ_STREAM_ERROR; + if ((!pStream) || (!pStream->state)) + return MZ_STREAM_ERROR; + if (flush == MZ_PARTIAL_FLUSH) + flush = MZ_SYNC_FLUSH; + if ((flush) && (flush != MZ_SYNC_FLUSH) && (flush != MZ_FINISH)) + return MZ_STREAM_ERROR; - pState = (inflate_state*)pStream->state; - if (pState->m_window_bits > 0) decomp_flags |= TINFL_FLAG_PARSE_ZLIB_HEADER; + pState = (inflate_state *)pStream->state; + if (pState->m_window_bits > 0) + decomp_flags |= TINFL_FLAG_PARSE_ZLIB_HEADER; orig_avail_in = pStream->avail_in; - first_call = pState->m_first_call; pState->m_first_call = 0; - if (pState->m_last_status < 0) return MZ_DATA_ERROR; + first_call = pState->m_first_call; + pState->m_first_call = 0; + if (pState->m_last_status < 0) + return MZ_DATA_ERROR; - if (pState->m_has_flushed && (flush != MZ_FINISH)) return MZ_STREAM_ERROR; + if (pState->m_has_flushed && (flush != MZ_FINISH)) + return MZ_STREAM_ERROR; pState->m_has_flushed |= (flush == MZ_FINISH); - if ((flush == MZ_FINISH) && (first_call)) - { - // MZ_FINISH on the first call implies that the input and output buffers are large enough to hold the entire compressed/decompressed file. + if ((flush == MZ_FINISH) && (first_call)) { + // MZ_FINISH on the first call implies that the input and output buffers are + // large enough to hold the entire compressed/decompressed file. decomp_flags |= TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF; - in_bytes = pStream->avail_in; out_bytes = pStream->avail_out; - status = tinfl_decompress(&pState->m_decomp, pStream->next_in, &in_bytes, pStream->next_out, pStream->next_out, &out_bytes, decomp_flags); + in_bytes = pStream->avail_in; + out_bytes = pStream->avail_out; + status = tinfl_decompress(&pState->m_decomp, pStream->next_in, &in_bytes, + pStream->next_out, pStream->next_out, &out_bytes, + decomp_flags); pState->m_last_status = status; - pStream->next_in += (mz_uint)in_bytes; pStream->avail_in -= (mz_uint)in_bytes; pStream->total_in += (mz_uint)in_bytes; + pStream->next_in += (mz_uint)in_bytes; + pStream->avail_in -= (mz_uint)in_bytes; + pStream->total_in += (mz_uint)in_bytes; pStream->adler = tinfl_get_adler32(&pState->m_decomp); - pStream->next_out += (mz_uint)out_bytes; pStream->avail_out -= (mz_uint)out_bytes; pStream->total_out += (mz_uint)out_bytes; + pStream->next_out += (mz_uint)out_bytes; + pStream->avail_out -= (mz_uint)out_bytes; + pStream->total_out += (mz_uint)out_bytes; if (status < 0) return MZ_DATA_ERROR; - else if (status != TINFL_STATUS_DONE) - { + else if (status != TINFL_STATUS_DONE) { pState->m_last_status = TINFL_STATUS_FAILED; return MZ_BUF_ERROR; } return MZ_STREAM_END; } // flush != MZ_FINISH then we must assume there's more input. - if (flush != MZ_FINISH) decomp_flags |= TINFL_FLAG_HAS_MORE_INPUT; + if (flush != MZ_FINISH) + decomp_flags |= TINFL_FLAG_HAS_MORE_INPUT; - if (pState->m_dict_avail) - { + if (pState->m_dict_avail) { n = MZ_MIN(pState->m_dict_avail, pStream->avail_out); memcpy(pStream->next_out, pState->m_dict + pState->m_dict_ofs, n); - pStream->next_out += n; pStream->avail_out -= n; pStream->total_out += n; - pState->m_dict_avail -= n; pState->m_dict_ofs = (pState->m_dict_ofs + n) & (TINFL_LZ_DICT_SIZE - 1); - return ((pState->m_last_status == TINFL_STATUS_DONE) && (!pState->m_dict_avail)) ? MZ_STREAM_END : MZ_OK; + pStream->next_out += n; + pStream->avail_out -= n; + pStream->total_out += n; + pState->m_dict_avail -= n; + pState->m_dict_ofs = (pState->m_dict_ofs + n) & (TINFL_LZ_DICT_SIZE - 1); + return ((pState->m_last_status == TINFL_STATUS_DONE) && + (!pState->m_dict_avail)) + ? MZ_STREAM_END + : MZ_OK; } - for ( ; ; ) - { + for (;;) { in_bytes = pStream->avail_in; out_bytes = TINFL_LZ_DICT_SIZE - pState->m_dict_ofs; - status = tinfl_decompress(&pState->m_decomp, pStream->next_in, &in_bytes, pState->m_dict, pState->m_dict + pState->m_dict_ofs, &out_bytes, decomp_flags); + status = tinfl_decompress( + &pState->m_decomp, pStream->next_in, &in_bytes, pState->m_dict, + pState->m_dict + pState->m_dict_ofs, &out_bytes, decomp_flags); pState->m_last_status = status; - pStream->next_in += (mz_uint)in_bytes; pStream->avail_in -= (mz_uint)in_bytes; - pStream->total_in += (mz_uint)in_bytes; pStream->adler = tinfl_get_adler32(&pState->m_decomp); + pStream->next_in += (mz_uint)in_bytes; + pStream->avail_in -= (mz_uint)in_bytes; + pStream->total_in += (mz_uint)in_bytes; + pStream->adler = tinfl_get_adler32(&pState->m_decomp); pState->m_dict_avail = (mz_uint)out_bytes; n = MZ_MIN(pState->m_dict_avail, pStream->avail_out); memcpy(pStream->next_out, pState->m_dict + pState->m_dict_ofs, n); - pStream->next_out += n; pStream->avail_out -= n; pStream->total_out += n; - pState->m_dict_avail -= n; pState->m_dict_ofs = (pState->m_dict_ofs + n) & (TINFL_LZ_DICT_SIZE - 1); + pStream->next_out += n; + pStream->avail_out -= n; + pStream->total_out += n; + pState->m_dict_avail -= n; + pState->m_dict_ofs = (pState->m_dict_ofs + n) & (TINFL_LZ_DICT_SIZE - 1); if (status < 0) - return MZ_DATA_ERROR; // Stream is corrupted (there could be some uncompressed data left in the output dictionary - oh well). + return MZ_DATA_ERROR; // Stream is corrupted (there could be some + // uncompressed data left in the output dictionary - + // oh well). else if ((status == TINFL_STATUS_NEEDS_MORE_INPUT) && (!orig_avail_in)) - return MZ_BUF_ERROR; // Signal caller that we can't make forward progress without supplying more input or by setting flush to MZ_FINISH. - else if (flush == MZ_FINISH) - { - // The output buffer MUST be large to hold the remaining uncompressed data when flush==MZ_FINISH. - if (status == TINFL_STATUS_DONE) - return pState->m_dict_avail ? MZ_BUF_ERROR : MZ_STREAM_END; - // status here must be TINFL_STATUS_HAS_MORE_OUTPUT, which means there's at least 1 more byte on the way. If there's no more room left in the output buffer then something is wrong. - else if (!pStream->avail_out) - return MZ_BUF_ERROR; - } - else if ((status == TINFL_STATUS_DONE) || (!pStream->avail_in) || (!pStream->avail_out) || (pState->m_dict_avail)) + return MZ_BUF_ERROR; // Signal caller that we can't make forward progress + // without supplying more input or by setting flush + // to MZ_FINISH. + else if (flush == MZ_FINISH) { + // The output buffer MUST be large to hold the remaining uncompressed data + // when flush==MZ_FINISH. + if (status == TINFL_STATUS_DONE) + return pState->m_dict_avail ? MZ_BUF_ERROR : MZ_STREAM_END; + // status here must be TINFL_STATUS_HAS_MORE_OUTPUT, which means there's + // at least 1 more byte on the way. If there's no more room left in the + // output buffer then something is wrong. + else if (!pStream->avail_out) + return MZ_BUF_ERROR; + } else if ((status == TINFL_STATUS_DONE) || (!pStream->avail_in) || + (!pStream->avail_out) || (pState->m_dict_avail)) break; } - return ((status == TINFL_STATUS_DONE) && (!pState->m_dict_avail)) ? MZ_STREAM_END : MZ_OK; + return ((status == TINFL_STATUS_DONE) && (!pState->m_dict_avail)) + ? MZ_STREAM_END + : MZ_OK; } -int mz_inflateEnd(mz_streamp pStream) -{ +int mz_inflateEnd(mz_streamp pStream) { if (!pStream) return MZ_STREAM_ERROR; - if (pStream->state) - { + if (pStream->state) { pStream->zfree(pStream->opaque, pStream->state); pStream->state = NULL; } return MZ_OK; } -int mz_uncompress(unsigned char *pDest, mz_ulong *pDest_len, const unsigned char *pSource, mz_ulong source_len) -{ +int mz_uncompress(unsigned char *pDest, mz_ulong *pDest_len, + const unsigned char *pSource, mz_ulong source_len) { mz_stream stream; int status; memset(&stream, 0, sizeof(stream)); // In case mz_ulong is 64-bits (argh I hate longs). - if ((source_len | *pDest_len) > 0xFFFFFFFFU) return MZ_PARAM_ERROR; + if ((source_len | *pDest_len) > 0xFFFFFFFFU) + return MZ_PARAM_ERROR; stream.next_in = pSource; stream.avail_in = (mz_uint32)source_len; @@ -1340,268 +1759,482 @@ int mz_uncompress(unsigned char *pDest, mz_ulong *pDest_len, const unsigned char return status; status = mz_inflate(&stream, MZ_FINISH); - if (status != MZ_STREAM_END) - { + if (status != MZ_STREAM_END) { mz_inflateEnd(&stream); - return ((status == MZ_BUF_ERROR) && (!stream.avail_in)) ? MZ_DATA_ERROR : status; + return ((status == MZ_BUF_ERROR) && (!stream.avail_in)) ? MZ_DATA_ERROR + : status; } *pDest_len = stream.total_out; return mz_inflateEnd(&stream); } -const char *mz_error(int err) -{ - static struct { int m_err; const char *m_pDesc; } s_error_descs[] = - { - { MZ_OK, "" }, { MZ_STREAM_END, "stream end" }, { MZ_NEED_DICT, "need dictionary" }, { MZ_ERRNO, "file error" }, { MZ_STREAM_ERROR, "stream error" }, - { MZ_DATA_ERROR, "data error" }, { MZ_MEM_ERROR, "out of memory" }, { MZ_BUF_ERROR, "buf error" }, { MZ_VERSION_ERROR, "version error" }, { MZ_PARAM_ERROR, "parameter error" } - }; - mz_uint i; for (i = 0; i < sizeof(s_error_descs) / sizeof(s_error_descs[0]); ++i) if (s_error_descs[i].m_err == err) return s_error_descs[i].m_pDesc; +const char *mz_error(int err) { + static struct { + int m_err; + const char *m_pDesc; + } s_error_descs[] = {{MZ_OK, ""}, + {MZ_STREAM_END, "stream end"}, + {MZ_NEED_DICT, "need dictionary"}, + {MZ_ERRNO, "file error"}, + {MZ_STREAM_ERROR, "stream error"}, + {MZ_DATA_ERROR, "data error"}, + {MZ_MEM_ERROR, "out of memory"}, + {MZ_BUF_ERROR, "buf error"}, + {MZ_VERSION_ERROR, "version error"}, + {MZ_PARAM_ERROR, "parameter error"}}; + mz_uint i; + for (i = 0; i < sizeof(s_error_descs) / sizeof(s_error_descs[0]); ++i) + if (s_error_descs[i].m_err == err) + return s_error_descs[i].m_pDesc; return NULL; } -#endif //MINIZ_NO_ZLIB_APIS +#endif // MINIZ_NO_ZLIB_APIS -// ------------------- Low-level Decompression (completely independent from all compression API's) +// ------------------- Low-level Decompression (completely independent from all +// compression API's) #define TINFL_MEMCPY(d, s, l) memcpy(d, s, l) #define TINFL_MEMSET(p, c, l) memset(p, c, l) -#define TINFL_CR_BEGIN switch(r->m_state) { case 0: -#define TINFL_CR_RETURN(state_index, result) do { status = result; r->m_state = state_index; goto common_exit; case state_index:; } MZ_MACRO_END -#define TINFL_CR_RETURN_FOREVER(state_index, result) do { for ( ; ; ) { TINFL_CR_RETURN(state_index, result); } } MZ_MACRO_END +#define TINFL_CR_BEGIN \ + switch (r->m_state) { \ + case 0: +#define TINFL_CR_RETURN(state_index, result) \ + do { \ + status = result; \ + r->m_state = state_index; \ + goto common_exit; \ + case state_index:; \ + } \ + MZ_MACRO_END +#define TINFL_CR_RETURN_FOREVER(state_index, result) \ + do { \ + for (;;) { \ + TINFL_CR_RETURN(state_index, result); \ + } \ + } \ + MZ_MACRO_END #define TINFL_CR_FINISH } -// TODO: If the caller has indicated that there's no more input, and we attempt to read beyond the input buf, then something is wrong with the input because the inflator never -// reads ahead more than it needs to. Currently TINFL_GET_BYTE() pads the end of the stream with 0's in this scenario. -#define TINFL_GET_BYTE(state_index, c) do { \ - if (pIn_buf_cur >= pIn_buf_end) { \ - for ( ; ; ) { \ - if (decomp_flags & TINFL_FLAG_HAS_MORE_INPUT) { \ - TINFL_CR_RETURN(state_index, TINFL_STATUS_NEEDS_MORE_INPUT); \ - if (pIn_buf_cur < pIn_buf_end) { \ - c = *pIn_buf_cur++; \ - break; \ - } \ - } else { \ - c = 0; \ - break; \ - } \ - } \ - } else c = *pIn_buf_cur++; } MZ_MACRO_END +// TODO: If the caller has indicated that there's no more input, and we attempt +// to read beyond the input buf, then something is wrong with the input because +// the inflator never reads ahead more than it needs to. Currently +// TINFL_GET_BYTE() pads the end of the stream with 0's in this scenario. +#define TINFL_GET_BYTE(state_index, c) \ + do { \ + if (pIn_buf_cur >= pIn_buf_end) { \ + for (;;) { \ + if (decomp_flags & TINFL_FLAG_HAS_MORE_INPUT) { \ + TINFL_CR_RETURN(state_index, TINFL_STATUS_NEEDS_MORE_INPUT); \ + if (pIn_buf_cur < pIn_buf_end) { \ + c = *pIn_buf_cur++; \ + break; \ + } \ + } else { \ + c = 0; \ + break; \ + } \ + } \ + } else \ + c = *pIn_buf_cur++; \ + } \ + MZ_MACRO_END -#define TINFL_NEED_BITS(state_index, n) do { mz_uint c; TINFL_GET_BYTE(state_index, c); bit_buf |= (((tinfl_bit_buf_t)c) << num_bits); num_bits += 8; } while (num_bits < (mz_uint)(n)) -#define TINFL_SKIP_BITS(state_index, n) do { if (num_bits < (mz_uint)(n)) { TINFL_NEED_BITS(state_index, n); } bit_buf >>= (n); num_bits -= (n); } MZ_MACRO_END -#define TINFL_GET_BITS(state_index, b, n) do { if (num_bits < (mz_uint)(n)) { TINFL_NEED_BITS(state_index, n); } b = bit_buf & ((1 << (n)) - 1); bit_buf >>= (n); num_bits -= (n); } MZ_MACRO_END +#define TINFL_NEED_BITS(state_index, n) \ + do { \ + mz_uint c; \ + TINFL_GET_BYTE(state_index, c); \ + bit_buf |= (((tinfl_bit_buf_t)c) << num_bits); \ + num_bits += 8; \ + } while (num_bits < (mz_uint)(n)) +#define TINFL_SKIP_BITS(state_index, n) \ + do { \ + if (num_bits < (mz_uint)(n)) { \ + TINFL_NEED_BITS(state_index, n); \ + } \ + bit_buf >>= (n); \ + num_bits -= (n); \ + } \ + MZ_MACRO_END +#define TINFL_GET_BITS(state_index, b, n) \ + do { \ + if (num_bits < (mz_uint)(n)) { \ + TINFL_NEED_BITS(state_index, n); \ + } \ + b = bit_buf & ((1 << (n)) - 1); \ + bit_buf >>= (n); \ + num_bits -= (n); \ + } \ + MZ_MACRO_END -// TINFL_HUFF_BITBUF_FILL() is only used rarely, when the number of bytes remaining in the input buffer falls below 2. -// It reads just enough bytes from the input stream that are needed to decode the next Huffman code (and absolutely no more). It works by trying to fully decode a -// Huffman code by using whatever bits are currently present in the bit buffer. If this fails, it reads another byte, and tries again until it succeeds or until the -// bit buffer contains >=15 bits (deflate's max. Huffman code size). -#define TINFL_HUFF_BITBUF_FILL(state_index, pHuff) \ - do { \ - temp = (pHuff)->m_look_up[bit_buf & (TINFL_FAST_LOOKUP_SIZE - 1)]; \ - if (temp >= 0) { \ - code_len = temp >> 9; \ - if ((code_len) && (num_bits >= code_len)) \ - break; \ - } else if (num_bits > TINFL_FAST_LOOKUP_BITS) { \ - code_len = TINFL_FAST_LOOKUP_BITS; \ - do { \ - temp = (pHuff)->m_tree[~temp + ((bit_buf >> code_len++) & 1)]; \ - } while ((temp < 0) && (num_bits >= (code_len + 1))); if (temp >= 0) break; \ - } TINFL_GET_BYTE(state_index, c); bit_buf |= (((tinfl_bit_buf_t)c) << num_bits); num_bits += 8; \ +// TINFL_HUFF_BITBUF_FILL() is only used rarely, when the number of bytes +// remaining in the input buffer falls below 2. It reads just enough bytes from +// the input stream that are needed to decode the next Huffman code (and +// absolutely no more). It works by trying to fully decode a Huffman code by +// using whatever bits are currently present in the bit buffer. If this fails, +// it reads another byte, and tries again until it succeeds or until the bit +// buffer contains >=15 bits (deflate's max. Huffman code size). +#define TINFL_HUFF_BITBUF_FILL(state_index, pHuff) \ + do { \ + temp = (pHuff)->m_look_up[bit_buf & (TINFL_FAST_LOOKUP_SIZE - 1)]; \ + if (temp >= 0) { \ + code_len = temp >> 9; \ + if ((code_len) && (num_bits >= code_len)) \ + break; \ + } else if (num_bits > TINFL_FAST_LOOKUP_BITS) { \ + code_len = TINFL_FAST_LOOKUP_BITS; \ + do { \ + temp = (pHuff)->m_tree[~temp + ((bit_buf >> code_len++) & 1)]; \ + } while ((temp < 0) && (num_bits >= (code_len + 1))); \ + if (temp >= 0) \ + break; \ + } \ + TINFL_GET_BYTE(state_index, c); \ + bit_buf |= (((tinfl_bit_buf_t)c) << num_bits); \ + num_bits += 8; \ } while (num_bits < 15); -// TINFL_HUFF_DECODE() decodes the next Huffman coded symbol. It's more complex than you would initially expect because the zlib API expects the decompressor to never read -// beyond the final byte of the deflate stream. (In other words, when this macro wants to read another byte from the input, it REALLY needs another byte in order to fully -// decode the next Huffman code.) Handling this properly is particularly important on raw deflate (non-zlib) streams, which aren't followed by a byte aligned adler-32. -// The slow path is only executed at the very end of the input buffer. -#define TINFL_HUFF_DECODE(state_index, sym, pHuff) do { \ - int temp; mz_uint code_len, c; \ - if (num_bits < 15) { \ - if ((pIn_buf_end - pIn_buf_cur) < 2) { \ - TINFL_HUFF_BITBUF_FILL(state_index, pHuff); \ - } else { \ - bit_buf |= (((tinfl_bit_buf_t)pIn_buf_cur[0]) << num_bits) | (((tinfl_bit_buf_t)pIn_buf_cur[1]) << (num_bits + 8)); pIn_buf_cur += 2; num_bits += 16; \ - } \ - } \ - if ((temp = (pHuff)->m_look_up[bit_buf & (TINFL_FAST_LOOKUP_SIZE - 1)]) >= 0) \ - code_len = temp >> 9, temp &= 511; \ - else { \ - code_len = TINFL_FAST_LOOKUP_BITS; do { temp = (pHuff)->m_tree[~temp + ((bit_buf >> code_len++) & 1)]; } while (temp < 0); \ - } sym = temp; bit_buf >>= code_len; num_bits -= code_len; } MZ_MACRO_END +// TINFL_HUFF_DECODE() decodes the next Huffman coded symbol. It's more complex +// than you would initially expect because the zlib API expects the decompressor +// to never read beyond the final byte of the deflate stream. (In other words, +// when this macro wants to read another byte from the input, it REALLY needs +// another byte in order to fully decode the next Huffman code.) Handling this +// properly is particularly important on raw deflate (non-zlib) streams, which +// aren't followed by a byte aligned adler-32. The slow path is only executed at +// the very end of the input buffer. +#define TINFL_HUFF_DECODE(state_index, sym, pHuff) \ + do { \ + int temp; \ + mz_uint code_len, c; \ + if (num_bits < 15) { \ + if ((pIn_buf_end - pIn_buf_cur) < 2) { \ + TINFL_HUFF_BITBUF_FILL(state_index, pHuff); \ + } else { \ + bit_buf |= (((tinfl_bit_buf_t)pIn_buf_cur[0]) << num_bits) | \ + (((tinfl_bit_buf_t)pIn_buf_cur[1]) << (num_bits + 8)); \ + pIn_buf_cur += 2; \ + num_bits += 16; \ + } \ + } \ + if ((temp = (pHuff)->m_look_up[bit_buf & (TINFL_FAST_LOOKUP_SIZE - 1)]) >= \ + 0) \ + code_len = temp >> 9, temp &= 511; \ + else { \ + code_len = TINFL_FAST_LOOKUP_BITS; \ + do { \ + temp = (pHuff)->m_tree[~temp + ((bit_buf >> code_len++) & 1)]; \ + } while (temp < 0); \ + } \ + sym = temp; \ + bit_buf >>= code_len; \ + num_bits -= code_len; \ + } \ + MZ_MACRO_END -tinfl_status tinfl_decompress(tinfl_decompressor *r, const mz_uint8 *pIn_buf_next, size_t *pIn_buf_size, mz_uint8 *pOut_buf_start, mz_uint8 *pOut_buf_next, size_t *pOut_buf_size, const mz_uint32 decomp_flags) -{ - static const int s_length_base[31] = { 3,4,5,6,7,8,9,10,11,13, 15,17,19,23,27,31,35,43,51,59, 67,83,99,115,131,163,195,227,258,0,0 }; - static const int s_length_extra[31]= { 0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0 }; - static const int s_dist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, 257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0}; - static const int s_dist_extra[32] = { 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13}; - static const mz_uint8 s_length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; - static const int s_min_table_sizes[3] = { 257, 1, 4 }; +tinfl_status tinfl_decompress(tinfl_decompressor *r, + const mz_uint8 *pIn_buf_next, + size_t *pIn_buf_size, mz_uint8 *pOut_buf_start, + mz_uint8 *pOut_buf_next, size_t *pOut_buf_size, + const mz_uint32 decomp_flags) { + static const int s_length_base[31] = { + 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 17, 19, 23, 27, 31, + 35, 43, 51, 59, 67, 83, 99, 115, 131, 163, 195, 227, 258, 0, 0}; + static const int s_length_extra[31] = {0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, + 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, + 4, 4, 5, 5, 5, 5, 0, 0, 0}; + static const int s_dist_base[32] = { + 1, 2, 3, 4, 5, 7, 9, 13, 17, 25, 33, + 49, 65, 97, 129, 193, 257, 385, 513, 769, 1025, 1537, + 2049, 3073, 4097, 6145, 8193, 12289, 16385, 24577, 0, 0}; + static const int s_dist_extra[32] = {0, 0, 0, 0, 1, 1, 2, 2, 3, 3, + 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, + 9, 9, 10, 10, 11, 11, 12, 12, 13, 13}; + static const mz_uint8 s_length_dezigzag[19] = { + 16, 17, 18, 0, 8, 7, 9, 6, 10, 5, 11, 4, 12, 3, 13, 2, 14, 1, 15}; + static const int s_min_table_sizes[3] = {257, 1, 4}; - tinfl_status status = TINFL_STATUS_FAILED; mz_uint32 num_bits, dist, counter, num_extra; tinfl_bit_buf_t bit_buf; - const mz_uint8 *pIn_buf_cur = pIn_buf_next, *const pIn_buf_end = pIn_buf_next + *pIn_buf_size; - mz_uint8 *pOut_buf_cur = pOut_buf_next, *const pOut_buf_end = pOut_buf_next + *pOut_buf_size; - size_t out_buf_size_mask = (decomp_flags & TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF) ? (size_t)-1 : ((pOut_buf_next - pOut_buf_start) + *pOut_buf_size) - 1, dist_from_out_buf_start; + tinfl_status status = TINFL_STATUS_FAILED; + mz_uint32 num_bits, dist, counter, num_extra; + tinfl_bit_buf_t bit_buf; + const mz_uint8 *pIn_buf_cur = pIn_buf_next, *const pIn_buf_end = + pIn_buf_next + *pIn_buf_size; + mz_uint8 *pOut_buf_cur = pOut_buf_next, *const pOut_buf_end = + pOut_buf_next + *pOut_buf_size; + size_t out_buf_size_mask = + (decomp_flags & TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF) + ? (size_t)-1 + : ((pOut_buf_next - pOut_buf_start) + *pOut_buf_size) - 1, + dist_from_out_buf_start; - // Ensure the output buffer's size is a power of 2, unless the output buffer is large enough to hold the entire output file (in which case it doesn't matter). - if (((out_buf_size_mask + 1) & out_buf_size_mask) || (pOut_buf_next < pOut_buf_start)) { *pIn_buf_size = *pOut_buf_size = 0; return TINFL_STATUS_BAD_PARAM; } - - num_bits = r->m_num_bits; bit_buf = r->m_bit_buf; dist = r->m_dist; counter = r->m_counter; num_extra = r->m_num_extra; dist_from_out_buf_start = r->m_dist_from_out_buf_start; - TINFL_CR_BEGIN - - bit_buf = num_bits = dist = counter = num_extra = r->m_zhdr0 = r->m_zhdr1 = 0; r->m_z_adler32 = r->m_check_adler32 = 1; - if (decomp_flags & TINFL_FLAG_PARSE_ZLIB_HEADER) - { - TINFL_GET_BYTE(1, r->m_zhdr0); TINFL_GET_BYTE(2, r->m_zhdr1); - counter = (((r->m_zhdr0 * 256 + r->m_zhdr1) % 31 != 0) || (r->m_zhdr1 & 32) || ((r->m_zhdr0 & 15) != 8)); - if (!(decomp_flags & TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF)) counter |= (((1U << (8U + (r->m_zhdr0 >> 4))) > 32768U) || ((out_buf_size_mask + 1) < (size_t)(1U << (8U + (r->m_zhdr0 >> 4))))); - if (counter) { TINFL_CR_RETURN_FOREVER(36, TINFL_STATUS_FAILED); } + // Ensure the output buffer's size is a power of 2, unless the output buffer + // is large enough to hold the entire output file (in which case it doesn't + // matter). + if (((out_buf_size_mask + 1) & out_buf_size_mask) || + (pOut_buf_next < pOut_buf_start)) { + *pIn_buf_size = *pOut_buf_size = 0; + return TINFL_STATUS_BAD_PARAM; } - do - { - TINFL_GET_BITS(3, r->m_final, 3); r->m_type = r->m_final >> 1; - if (r->m_type == 0) - { + num_bits = r->m_num_bits; + bit_buf = r->m_bit_buf; + dist = r->m_dist; + counter = r->m_counter; + num_extra = r->m_num_extra; + dist_from_out_buf_start = r->m_dist_from_out_buf_start; + TINFL_CR_BEGIN + + bit_buf = num_bits = dist = counter = num_extra = r->m_zhdr0 = r->m_zhdr1 = 0; + r->m_z_adler32 = r->m_check_adler32 = 1; + if (decomp_flags & TINFL_FLAG_PARSE_ZLIB_HEADER) { + TINFL_GET_BYTE(1, r->m_zhdr0); + TINFL_GET_BYTE(2, r->m_zhdr1); + counter = (((r->m_zhdr0 * 256 + r->m_zhdr1) % 31 != 0) || + (r->m_zhdr1 & 32) || ((r->m_zhdr0 & 15) != 8)); + if (!(decomp_flags & TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF)) + counter |= (((1U << (8U + (r->m_zhdr0 >> 4))) > 32768U) || + ((out_buf_size_mask + 1) < + (size_t)(1U << (8U + (r->m_zhdr0 >> 4))))); + if (counter) { + TINFL_CR_RETURN_FOREVER(36, TINFL_STATUS_FAILED); + } + } + + do { + TINFL_GET_BITS(3, r->m_final, 3); + r->m_type = r->m_final >> 1; + if (r->m_type == 0) { TINFL_SKIP_BITS(5, num_bits & 7); - for (counter = 0; counter < 4; ++counter) { if (num_bits) TINFL_GET_BITS(6, r->m_raw_header[counter], 8); else TINFL_GET_BYTE(7, r->m_raw_header[counter]); } - if ((counter = (r->m_raw_header[0] | (r->m_raw_header[1] << 8))) != (mz_uint)(0xFFFF ^ (r->m_raw_header[2] | (r->m_raw_header[3] << 8)))) { TINFL_CR_RETURN_FOREVER(39, TINFL_STATUS_FAILED); } - while ((counter) && (num_bits)) - { + for (counter = 0; counter < 4; ++counter) { + if (num_bits) + TINFL_GET_BITS(6, r->m_raw_header[counter], 8); + else + TINFL_GET_BYTE(7, r->m_raw_header[counter]); + } + if ((counter = (r->m_raw_header[0] | (r->m_raw_header[1] << 8))) != + (mz_uint)(0xFFFF ^ + (r->m_raw_header[2] | (r->m_raw_header[3] << 8)))) { + TINFL_CR_RETURN_FOREVER(39, TINFL_STATUS_FAILED); + } + while ((counter) && (num_bits)) { TINFL_GET_BITS(51, dist, 8); - while (pOut_buf_cur >= pOut_buf_end) { TINFL_CR_RETURN(52, TINFL_STATUS_HAS_MORE_OUTPUT); } + while (pOut_buf_cur >= pOut_buf_end) { + TINFL_CR_RETURN(52, TINFL_STATUS_HAS_MORE_OUTPUT); + } *pOut_buf_cur++ = (mz_uint8)dist; counter--; } - while (counter) - { - size_t n; while (pOut_buf_cur >= pOut_buf_end) { TINFL_CR_RETURN(9, TINFL_STATUS_HAS_MORE_OUTPUT); } - while (pIn_buf_cur >= pIn_buf_end) - { - if (decomp_flags & TINFL_FLAG_HAS_MORE_INPUT) - { + while (counter) { + size_t n; + while (pOut_buf_cur >= pOut_buf_end) { + TINFL_CR_RETURN(9, TINFL_STATUS_HAS_MORE_OUTPUT); + } + while (pIn_buf_cur >= pIn_buf_end) { + if (decomp_flags & TINFL_FLAG_HAS_MORE_INPUT) { TINFL_CR_RETURN(38, TINFL_STATUS_NEEDS_MORE_INPUT); - } - else - { + } else { TINFL_CR_RETURN_FOREVER(40, TINFL_STATUS_FAILED); } } - n = MZ_MIN(MZ_MIN((size_t)(pOut_buf_end - pOut_buf_cur), (size_t)(pIn_buf_end - pIn_buf_cur)), counter); - TINFL_MEMCPY(pOut_buf_cur, pIn_buf_cur, n); pIn_buf_cur += n; pOut_buf_cur += n; counter -= (mz_uint)n; + n = MZ_MIN(MZ_MIN((size_t)(pOut_buf_end - pOut_buf_cur), + (size_t)(pIn_buf_end - pIn_buf_cur)), + counter); + TINFL_MEMCPY(pOut_buf_cur, pIn_buf_cur, n); + pIn_buf_cur += n; + pOut_buf_cur += n; + counter -= (mz_uint)n; } - } - else if (r->m_type == 3) - { + } else if (r->m_type == 3) { TINFL_CR_RETURN_FOREVER(10, TINFL_STATUS_FAILED); - } - else - { - if (r->m_type == 1) - { - mz_uint8 *p = r->m_tables[0].m_code_size; mz_uint i; - r->m_table_sizes[0] = 288; r->m_table_sizes[1] = 32; TINFL_MEMSET(r->m_tables[1].m_code_size, 5, 32); - for ( i = 0; i <= 143; ++i) *p++ = 8; for ( ; i <= 255; ++i) *p++ = 9; for ( ; i <= 279; ++i) *p++ = 7; for ( ; i <= 287; ++i) *p++ = 8; - } - else - { - for (counter = 0; counter < 3; counter++) { TINFL_GET_BITS(11, r->m_table_sizes[counter], "\05\05\04"[counter]); r->m_table_sizes[counter] += s_min_table_sizes[counter]; } - MZ_CLEAR_OBJ(r->m_tables[2].m_code_size); for (counter = 0; counter < r->m_table_sizes[2]; counter++) { mz_uint s; TINFL_GET_BITS(14, s, 3); r->m_tables[2].m_code_size[s_length_dezigzag[counter]] = (mz_uint8)s; } + } else { + if (r->m_type == 1) { + mz_uint8 *p = r->m_tables[0].m_code_size; + mz_uint i; + r->m_table_sizes[0] = 288; + r->m_table_sizes[1] = 32; + TINFL_MEMSET(r->m_tables[1].m_code_size, 5, 32); + for (i = 0; i <= 143; ++i) + *p++ = 8; + for (; i <= 255; ++i) + *p++ = 9; + for (; i <= 279; ++i) + *p++ = 7; + for (; i <= 287; ++i) + *p++ = 8; + } else { + for (counter = 0; counter < 3; counter++) { + TINFL_GET_BITS(11, r->m_table_sizes[counter], "\05\05\04"[counter]); + r->m_table_sizes[counter] += s_min_table_sizes[counter]; + } + MZ_CLEAR_OBJ(r->m_tables[2].m_code_size); + for (counter = 0; counter < r->m_table_sizes[2]; counter++) { + mz_uint s; + TINFL_GET_BITS(14, s, 3); + r->m_tables[2].m_code_size[s_length_dezigzag[counter]] = (mz_uint8)s; + } r->m_table_sizes[2] = 19; } - for ( ; (int)r->m_type >= 0; r->m_type--) - { - int tree_next, tree_cur; tinfl_huff_table *pTable; - mz_uint i, j, used_syms, total, sym_index, next_code[17], total_syms[16]; pTable = &r->m_tables[r->m_type]; MZ_CLEAR_OBJ(total_syms); MZ_CLEAR_OBJ(pTable->m_look_up); MZ_CLEAR_OBJ(pTable->m_tree); - for (i = 0; i < r->m_table_sizes[r->m_type]; ++i) total_syms[pTable->m_code_size[i]]++; - used_syms = 0, total = 0; next_code[0] = next_code[1] = 0; - for (i = 1; i <= 15; ++i) { used_syms += total_syms[i]; next_code[i + 1] = (total = ((total + total_syms[i]) << 1)); } - if ((65536 != total) && (used_syms > 1)) - { + for (; (int)r->m_type >= 0; r->m_type--) { + int tree_next, tree_cur; + tinfl_huff_table *pTable; + mz_uint i, j, used_syms, total, sym_index, next_code[17], + total_syms[16]; + pTable = &r->m_tables[r->m_type]; + MZ_CLEAR_OBJ(total_syms); + MZ_CLEAR_OBJ(pTable->m_look_up); + MZ_CLEAR_OBJ(pTable->m_tree); + for (i = 0; i < r->m_table_sizes[r->m_type]; ++i) + total_syms[pTable->m_code_size[i]]++; + used_syms = 0, total = 0; + next_code[0] = next_code[1] = 0; + for (i = 1; i <= 15; ++i) { + used_syms += total_syms[i]; + next_code[i + 1] = (total = ((total + total_syms[i]) << 1)); + } + if ((65536 != total) && (used_syms > 1)) { TINFL_CR_RETURN_FOREVER(35, TINFL_STATUS_FAILED); } - for (tree_next = -1, sym_index = 0; sym_index < r->m_table_sizes[r->m_type]; ++sym_index) - { - mz_uint rev_code = 0, l, cur_code, code_size = pTable->m_code_size[sym_index]; if (!code_size) continue; - cur_code = next_code[code_size]++; for (l = code_size; l > 0; l--, cur_code >>= 1) rev_code = (rev_code << 1) | (cur_code & 1); - if (code_size <= TINFL_FAST_LOOKUP_BITS) { mz_int16 k = (mz_int16)((code_size << 9) | sym_index); while (rev_code < TINFL_FAST_LOOKUP_SIZE) { pTable->m_look_up[rev_code] = k; rev_code += (1 << code_size); } continue; } - if (0 == (tree_cur = pTable->m_look_up[rev_code & (TINFL_FAST_LOOKUP_SIZE - 1)])) { pTable->m_look_up[rev_code & (TINFL_FAST_LOOKUP_SIZE - 1)] = (mz_int16)tree_next; tree_cur = tree_next; tree_next -= 2; } - rev_code >>= (TINFL_FAST_LOOKUP_BITS - 1); - for (j = code_size; j > (TINFL_FAST_LOOKUP_BITS + 1); j--) - { - tree_cur -= ((rev_code >>= 1) & 1); - if (!pTable->m_tree[-tree_cur - 1]) { pTable->m_tree[-tree_cur - 1] = (mz_int16)tree_next; tree_cur = tree_next; tree_next -= 2; } else tree_cur = pTable->m_tree[-tree_cur - 1]; + for (tree_next = -1, sym_index = 0; + sym_index < r->m_table_sizes[r->m_type]; ++sym_index) { + mz_uint rev_code = 0, l, cur_code, + code_size = pTable->m_code_size[sym_index]; + if (!code_size) + continue; + cur_code = next_code[code_size]++; + for (l = code_size; l > 0; l--, cur_code >>= 1) + rev_code = (rev_code << 1) | (cur_code & 1); + if (code_size <= TINFL_FAST_LOOKUP_BITS) { + mz_int16 k = (mz_int16)((code_size << 9) | sym_index); + while (rev_code < TINFL_FAST_LOOKUP_SIZE) { + pTable->m_look_up[rev_code] = k; + rev_code += (1 << code_size); + } + continue; } - tree_cur -= ((rev_code >>= 1) & 1); pTable->m_tree[-tree_cur - 1] = (mz_int16)sym_index; + if (0 == + (tree_cur = pTable->m_look_up[rev_code & + (TINFL_FAST_LOOKUP_SIZE - 1)])) { + pTable->m_look_up[rev_code & (TINFL_FAST_LOOKUP_SIZE - 1)] = + (mz_int16)tree_next; + tree_cur = tree_next; + tree_next -= 2; + } + rev_code >>= (TINFL_FAST_LOOKUP_BITS - 1); + for (j = code_size; j > (TINFL_FAST_LOOKUP_BITS + 1); j--) { + tree_cur -= ((rev_code >>= 1) & 1); + if (!pTable->m_tree[-tree_cur - 1]) { + pTable->m_tree[-tree_cur - 1] = (mz_int16)tree_next; + tree_cur = tree_next; + tree_next -= 2; + } else + tree_cur = pTable->m_tree[-tree_cur - 1]; + } + tree_cur -= ((rev_code >>= 1) & 1); + pTable->m_tree[-tree_cur - 1] = (mz_int16)sym_index; } - if (r->m_type == 2) - { - for (counter = 0; counter < (r->m_table_sizes[0] + r->m_table_sizes[1]); ) - { - mz_uint s; TINFL_HUFF_DECODE(16, dist, &r->m_tables[2]); if (dist < 16) { r->m_len_codes[counter++] = (mz_uint8)dist; continue; } - if ((dist == 16) && (!counter)) - { + if (r->m_type == 2) { + for (counter = 0; + counter < (r->m_table_sizes[0] + r->m_table_sizes[1]);) { + mz_uint s; + TINFL_HUFF_DECODE(16, dist, &r->m_tables[2]); + if (dist < 16) { + r->m_len_codes[counter++] = (mz_uint8)dist; + continue; + } + if ((dist == 16) && (!counter)) { TINFL_CR_RETURN_FOREVER(17, TINFL_STATUS_FAILED); } - num_extra = "\02\03\07"[dist - 16]; TINFL_GET_BITS(18, s, num_extra); s += "\03\03\013"[dist - 16]; - TINFL_MEMSET(r->m_len_codes + counter, (dist == 16) ? r->m_len_codes[counter - 1] : 0, s); counter += s; + num_extra = "\02\03\07"[dist - 16]; + TINFL_GET_BITS(18, s, num_extra); + s += "\03\03\013"[dist - 16]; + TINFL_MEMSET(r->m_len_codes + counter, + (dist == 16) ? r->m_len_codes[counter - 1] : 0, s); + counter += s; } - if ((r->m_table_sizes[0] + r->m_table_sizes[1]) != counter) - { + if ((r->m_table_sizes[0] + r->m_table_sizes[1]) != counter) { TINFL_CR_RETURN_FOREVER(21, TINFL_STATUS_FAILED); } - TINFL_MEMCPY(r->m_tables[0].m_code_size, r->m_len_codes, r->m_table_sizes[0]); TINFL_MEMCPY(r->m_tables[1].m_code_size, r->m_len_codes + r->m_table_sizes[0], r->m_table_sizes[1]); + TINFL_MEMCPY(r->m_tables[0].m_code_size, r->m_len_codes, + r->m_table_sizes[0]); + TINFL_MEMCPY(r->m_tables[1].m_code_size, + r->m_len_codes + r->m_table_sizes[0], + r->m_table_sizes[1]); } } - for ( ; ; ) - { + for (;;) { mz_uint8 *pSrc; - for ( ; ; ) - { - if (((pIn_buf_end - pIn_buf_cur) < 4) || ((pOut_buf_end - pOut_buf_cur) < 2)) - { + for (;;) { + if (((pIn_buf_end - pIn_buf_cur) < 4) || + ((pOut_buf_end - pOut_buf_cur) < 2)) { TINFL_HUFF_DECODE(23, counter, &r->m_tables[0]); if (counter >= 256) break; - while (pOut_buf_cur >= pOut_buf_end) { TINFL_CR_RETURN(24, TINFL_STATUS_HAS_MORE_OUTPUT); } - *pOut_buf_cur++ = (mz_uint8)counter; - } - else - { - int sym2; mz_uint code_len; -#if TINFL_USE_64BIT_BITBUF - if (num_bits < 30) { bit_buf |= (((tinfl_bit_buf_t)MZ_READ_LE32(pIn_buf_cur)) << num_bits); pIn_buf_cur += 4; num_bits += 32; } -#else - if (num_bits < 15) { bit_buf |= (((tinfl_bit_buf_t)MZ_READ_LE16(pIn_buf_cur)) << num_bits); pIn_buf_cur += 2; num_bits += 16; } -#endif - if ((sym2 = r->m_tables[0].m_look_up[bit_buf & (TINFL_FAST_LOOKUP_SIZE - 1)]) >= 0) - code_len = sym2 >> 9; - else - { - code_len = TINFL_FAST_LOOKUP_BITS; do { sym2 = r->m_tables[0].m_tree[~sym2 + ((bit_buf >> code_len++) & 1)]; } while (sym2 < 0); + while (pOut_buf_cur >= pOut_buf_end) { + TINFL_CR_RETURN(24, TINFL_STATUS_HAS_MORE_OUTPUT); } - counter = sym2; bit_buf >>= code_len; num_bits -= code_len; + *pOut_buf_cur++ = (mz_uint8)counter; + } else { + int sym2; + mz_uint code_len; +#if TINFL_USE_64BIT_BITBUF + if (num_bits < 30) { + bit_buf |= + (((tinfl_bit_buf_t)MZ_READ_LE32(pIn_buf_cur)) << num_bits); + pIn_buf_cur += 4; + num_bits += 32; + } +#else + if (num_bits < 15) { + bit_buf |= + (((tinfl_bit_buf_t)MZ_READ_LE16(pIn_buf_cur)) << num_bits); + pIn_buf_cur += 2; + num_bits += 16; + } +#endif + if ((sym2 = + r->m_tables[0] + .m_look_up[bit_buf & (TINFL_FAST_LOOKUP_SIZE - 1)]) >= + 0) + code_len = sym2 >> 9; + else { + code_len = TINFL_FAST_LOOKUP_BITS; + do { + sym2 = r->m_tables[0] + .m_tree[~sym2 + ((bit_buf >> code_len++) & 1)]; + } while (sym2 < 0); + } + counter = sym2; + bit_buf >>= code_len; + num_bits -= code_len; if (counter & 256) break; #if !TINFL_USE_64BIT_BITBUF - if (num_bits < 15) { bit_buf |= (((tinfl_bit_buf_t)MZ_READ_LE16(pIn_buf_cur)) << num_bits); pIn_buf_cur += 2; num_bits += 16; } -#endif - if ((sym2 = r->m_tables[0].m_look_up[bit_buf & (TINFL_FAST_LOOKUP_SIZE - 1)]) >= 0) - code_len = sym2 >> 9; - else - { - code_len = TINFL_FAST_LOOKUP_BITS; do { sym2 = r->m_tables[0].m_tree[~sym2 + ((bit_buf >> code_len++) & 1)]; } while (sym2 < 0); + if (num_bits < 15) { + bit_buf |= + (((tinfl_bit_buf_t)MZ_READ_LE16(pIn_buf_cur)) << num_bits); + pIn_buf_cur += 2; + num_bits += 16; } - bit_buf >>= code_len; num_bits -= code_len; +#endif + if ((sym2 = + r->m_tables[0] + .m_look_up[bit_buf & (TINFL_FAST_LOOKUP_SIZE - 1)]) >= + 0) + code_len = sym2 >> 9; + else { + code_len = TINFL_FAST_LOOKUP_BITS; + do { + sym2 = r->m_tables[0] + .m_tree[~sym2 + ((bit_buf >> code_len++) & 1)]; + } while (sym2 < 0); + } + bit_buf >>= code_len; + num_bits -= code_len; pOut_buf_cur[0] = (mz_uint8)counter; - if (sym2 & 256) - { + if (sym2 & 256) { pOut_buf_cur++; counter = sym2; break; @@ -1610,46 +2243,56 @@ tinfl_status tinfl_decompress(tinfl_decompressor *r, const mz_uint8 *pIn_buf_nex pOut_buf_cur += 2; } } - if ((counter &= 511) == 256) break; + if ((counter &= 511) == 256) + break; - num_extra = s_length_extra[counter - 257]; counter = s_length_base[counter - 257]; - if (num_extra) { mz_uint extra_bits; TINFL_GET_BITS(25, extra_bits, num_extra); counter += extra_bits; } + num_extra = s_length_extra[counter - 257]; + counter = s_length_base[counter - 257]; + if (num_extra) { + mz_uint extra_bits; + TINFL_GET_BITS(25, extra_bits, num_extra); + counter += extra_bits; + } TINFL_HUFF_DECODE(26, dist, &r->m_tables[1]); - num_extra = s_dist_extra[dist]; dist = s_dist_base[dist]; - if (num_extra) { mz_uint extra_bits; TINFL_GET_BITS(27, extra_bits, num_extra); dist += extra_bits; } + num_extra = s_dist_extra[dist]; + dist = s_dist_base[dist]; + if (num_extra) { + mz_uint extra_bits; + TINFL_GET_BITS(27, extra_bits, num_extra); + dist += extra_bits; + } dist_from_out_buf_start = pOut_buf_cur - pOut_buf_start; - if ((dist > dist_from_out_buf_start) && (decomp_flags & TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF)) - { + if ((dist > dist_from_out_buf_start) && + (decomp_flags & TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF)) { TINFL_CR_RETURN_FOREVER(37, TINFL_STATUS_FAILED); } - pSrc = pOut_buf_start + ((dist_from_out_buf_start - dist) & out_buf_size_mask); + pSrc = pOut_buf_start + + ((dist_from_out_buf_start - dist) & out_buf_size_mask); - if ((MZ_MAX(pOut_buf_cur, pSrc) + counter) > pOut_buf_end) - { - while (counter--) - { - while (pOut_buf_cur >= pOut_buf_end) { TINFL_CR_RETURN(53, TINFL_STATUS_HAS_MORE_OUTPUT); } - *pOut_buf_cur++ = pOut_buf_start[(dist_from_out_buf_start++ - dist) & out_buf_size_mask]; + if ((MZ_MAX(pOut_buf_cur, pSrc) + counter) > pOut_buf_end) { + while (counter--) { + while (pOut_buf_cur >= pOut_buf_end) { + TINFL_CR_RETURN(53, TINFL_STATUS_HAS_MORE_OUTPUT); + } + *pOut_buf_cur++ = + pOut_buf_start[(dist_from_out_buf_start++ - dist) & + out_buf_size_mask]; } continue; } #if MINIZ_USE_UNALIGNED_LOADS_AND_STORES - else if ((counter >= 9) && (counter <= dist)) - { + else if ((counter >= 9) && (counter <= dist)) { const mz_uint8 *pSrc_end = pSrc + (counter & ~7); - do - { + do { ((mz_uint32 *)pOut_buf_cur)[0] = ((const mz_uint32 *)pSrc)[0]; ((mz_uint32 *)pOut_buf_cur)[1] = ((const mz_uint32 *)pSrc)[1]; pOut_buf_cur += 8; } while ((pSrc += 8) < pSrc_end); - if ((counter &= 7) < 3) - { - if (counter) - { + if ((counter &= 7) < 3) { + if (counter) { pOut_buf_cur[0] = pSrc[0]; if (counter > 1) pOut_buf_cur[1] = pSrc[1]; @@ -1659,15 +2302,14 @@ tinfl_status tinfl_decompress(tinfl_decompressor *r, const mz_uint8 *pIn_buf_nex } } #endif - do - { + do { pOut_buf_cur[0] = pSrc[0]; pOut_buf_cur[1] = pSrc[1]; pOut_buf_cur[2] = pSrc[2]; - pOut_buf_cur += 3; pSrc += 3; + pOut_buf_cur += 3; + pSrc += 3; } while ((int)(counter -= 3) > 2); - if ((int)counter > 0) - { + if ((int)counter > 0) { pOut_buf_cur[0] = pSrc[0]; if ((int)counter > 1) pOut_buf_cur[1] = pSrc[1]; @@ -1676,89 +2318,142 @@ tinfl_status tinfl_decompress(tinfl_decompressor *r, const mz_uint8 *pIn_buf_nex } } } while (!(r->m_final & 1)); - if (decomp_flags & TINFL_FLAG_PARSE_ZLIB_HEADER) - { - TINFL_SKIP_BITS(32, num_bits & 7); for (counter = 0; counter < 4; ++counter) { mz_uint s; if (num_bits) TINFL_GET_BITS(41, s, 8); else TINFL_GET_BYTE(42, s); r->m_z_adler32 = (r->m_z_adler32 << 8) | s; } + if (decomp_flags & TINFL_FLAG_PARSE_ZLIB_HEADER) { + TINFL_SKIP_BITS(32, num_bits & 7); + for (counter = 0; counter < 4; ++counter) { + mz_uint s; + if (num_bits) + TINFL_GET_BITS(41, s, 8); + else + TINFL_GET_BYTE(42, s); + r->m_z_adler32 = (r->m_z_adler32 << 8) | s; + } } TINFL_CR_RETURN_FOREVER(34, TINFL_STATUS_DONE); TINFL_CR_FINISH common_exit: - r->m_num_bits = num_bits; r->m_bit_buf = bit_buf; r->m_dist = dist; r->m_counter = counter; r->m_num_extra = num_extra; r->m_dist_from_out_buf_start = dist_from_out_buf_start; - *pIn_buf_size = pIn_buf_cur - pIn_buf_next; *pOut_buf_size = pOut_buf_cur - pOut_buf_next; - if ((decomp_flags & (TINFL_FLAG_PARSE_ZLIB_HEADER | TINFL_FLAG_COMPUTE_ADLER32)) && (status >= 0)) - { - const mz_uint8 *ptr = pOut_buf_next; size_t buf_len = *pOut_buf_size; - mz_uint32 i, s1 = r->m_check_adler32 & 0xffff, s2 = r->m_check_adler32 >> 16; size_t block_len = buf_len % 5552; - while (buf_len) - { - for (i = 0; i + 7 < block_len; i += 8, ptr += 8) - { - s1 += ptr[0], s2 += s1; s1 += ptr[1], s2 += s1; s1 += ptr[2], s2 += s1; s1 += ptr[3], s2 += s1; - s1 += ptr[4], s2 += s1; s1 += ptr[5], s2 += s1; s1 += ptr[6], s2 += s1; s1 += ptr[7], s2 += s1; + r->m_num_bits = num_bits; + r->m_bit_buf = bit_buf; + r->m_dist = dist; + r->m_counter = counter; + r->m_num_extra = num_extra; + r->m_dist_from_out_buf_start = dist_from_out_buf_start; + *pIn_buf_size = pIn_buf_cur - pIn_buf_next; + *pOut_buf_size = pOut_buf_cur - pOut_buf_next; + if ((decomp_flags & + (TINFL_FLAG_PARSE_ZLIB_HEADER | TINFL_FLAG_COMPUTE_ADLER32)) && + (status >= 0)) { + const mz_uint8 *ptr = pOut_buf_next; + size_t buf_len = *pOut_buf_size; + mz_uint32 i, s1 = r->m_check_adler32 & 0xffff, + s2 = r->m_check_adler32 >> 16; + size_t block_len = buf_len % 5552; + while (buf_len) { + for (i = 0; i + 7 < block_len; i += 8, ptr += 8) { + s1 += ptr[0], s2 += s1; + s1 += ptr[1], s2 += s1; + s1 += ptr[2], s2 += s1; + s1 += ptr[3], s2 += s1; + s1 += ptr[4], s2 += s1; + s1 += ptr[5], s2 += s1; + s1 += ptr[6], s2 += s1; + s1 += ptr[7], s2 += s1; } - for ( ; i < block_len; ++i) s1 += *ptr++, s2 += s1; - s1 %= 65521U, s2 %= 65521U; buf_len -= block_len; block_len = 5552; + for (; i < block_len; ++i) + s1 += *ptr++, s2 += s1; + s1 %= 65521U, s2 %= 65521U; + buf_len -= block_len; + block_len = 5552; } - r->m_check_adler32 = (s2 << 16) + s1; if ((status == TINFL_STATUS_DONE) && (decomp_flags & TINFL_FLAG_PARSE_ZLIB_HEADER) && (r->m_check_adler32 != r->m_z_adler32)) status = TINFL_STATUS_ADLER32_MISMATCH; + r->m_check_adler32 = (s2 << 16) + s1; + if ((status == TINFL_STATUS_DONE) && + (decomp_flags & TINFL_FLAG_PARSE_ZLIB_HEADER) && + (r->m_check_adler32 != r->m_z_adler32)) + status = TINFL_STATUS_ADLER32_MISMATCH; } return status; } // Higher level helper functions. -void *tinfl_decompress_mem_to_heap(const void *pSrc_buf, size_t src_buf_len, size_t *pOut_len, int flags) -{ - tinfl_decompressor decomp; void *pBuf = NULL, *pNew_buf; size_t src_buf_ofs = 0, out_buf_capacity = 0; +void *tinfl_decompress_mem_to_heap(const void *pSrc_buf, size_t src_buf_len, + size_t *pOut_len, int flags) { + tinfl_decompressor decomp; + void *pBuf = NULL, *pNew_buf; + size_t src_buf_ofs = 0, out_buf_capacity = 0; *pOut_len = 0; tinfl_init(&decomp); - for ( ; ; ) - { - size_t src_buf_size = src_buf_len - src_buf_ofs, dst_buf_size = out_buf_capacity - *pOut_len, new_out_buf_capacity; - tinfl_status status = tinfl_decompress(&decomp, (const mz_uint8*)pSrc_buf + src_buf_ofs, &src_buf_size, (mz_uint8*)pBuf, pBuf ? (mz_uint8*)pBuf + *pOut_len : NULL, &dst_buf_size, - (flags & ~TINFL_FLAG_HAS_MORE_INPUT) | TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF); - if ((status < 0) || (status == TINFL_STATUS_NEEDS_MORE_INPUT)) - { - MZ_FREE(pBuf); *pOut_len = 0; return NULL; + for (;;) { + size_t src_buf_size = src_buf_len - src_buf_ofs, + dst_buf_size = out_buf_capacity - *pOut_len, new_out_buf_capacity; + tinfl_status status = tinfl_decompress( + &decomp, (const mz_uint8 *)pSrc_buf + src_buf_ofs, &src_buf_size, + (mz_uint8 *)pBuf, pBuf ? (mz_uint8 *)pBuf + *pOut_len : NULL, + &dst_buf_size, + (flags & ~TINFL_FLAG_HAS_MORE_INPUT) | + TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF); + if ((status < 0) || (status == TINFL_STATUS_NEEDS_MORE_INPUT)) { + MZ_FREE(pBuf); + *pOut_len = 0; + return NULL; } src_buf_ofs += src_buf_size; *pOut_len += dst_buf_size; - if (status == TINFL_STATUS_DONE) break; - new_out_buf_capacity = out_buf_capacity * 2; if (new_out_buf_capacity < 128) new_out_buf_capacity = 128; + if (status == TINFL_STATUS_DONE) + break; + new_out_buf_capacity = out_buf_capacity * 2; + if (new_out_buf_capacity < 128) + new_out_buf_capacity = 128; pNew_buf = MZ_REALLOC(pBuf, new_out_buf_capacity); - if (!pNew_buf) - { - MZ_FREE(pBuf); *pOut_len = 0; return NULL; + if (!pNew_buf) { + MZ_FREE(pBuf); + *pOut_len = 0; + return NULL; } - pBuf = pNew_buf; out_buf_capacity = new_out_buf_capacity; + pBuf = pNew_buf; + out_buf_capacity = new_out_buf_capacity; } return pBuf; } -size_t tinfl_decompress_mem_to_mem(void *pOut_buf, size_t out_buf_len, const void *pSrc_buf, size_t src_buf_len, int flags) -{ - tinfl_decompressor decomp; tinfl_status status; tinfl_init(&decomp); - status = tinfl_decompress(&decomp, (const mz_uint8*)pSrc_buf, &src_buf_len, (mz_uint8*)pOut_buf, (mz_uint8*)pOut_buf, &out_buf_len, (flags & ~TINFL_FLAG_HAS_MORE_INPUT) | TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF); - return (status != TINFL_STATUS_DONE) ? TINFL_DECOMPRESS_MEM_TO_MEM_FAILED : out_buf_len; +size_t tinfl_decompress_mem_to_mem(void *pOut_buf, size_t out_buf_len, + const void *pSrc_buf, size_t src_buf_len, + int flags) { + tinfl_decompressor decomp; + tinfl_status status; + tinfl_init(&decomp); + status = + tinfl_decompress(&decomp, (const mz_uint8 *)pSrc_buf, &src_buf_len, + (mz_uint8 *)pOut_buf, (mz_uint8 *)pOut_buf, &out_buf_len, + (flags & ~TINFL_FLAG_HAS_MORE_INPUT) | + TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF); + return (status != TINFL_STATUS_DONE) ? TINFL_DECOMPRESS_MEM_TO_MEM_FAILED + : out_buf_len; } -int tinfl_decompress_mem_to_callback(const void *pIn_buf, size_t *pIn_buf_size, tinfl_put_buf_func_ptr pPut_buf_func, void *pPut_buf_user, int flags) -{ +int tinfl_decompress_mem_to_callback(const void *pIn_buf, size_t *pIn_buf_size, + tinfl_put_buf_func_ptr pPut_buf_func, + void *pPut_buf_user, int flags) { int result = 0; tinfl_decompressor decomp; - mz_uint8 *pDict = (mz_uint8*)MZ_MALLOC(TINFL_LZ_DICT_SIZE); size_t in_buf_ofs = 0, dict_ofs = 0; + mz_uint8 *pDict = (mz_uint8 *)MZ_MALLOC(TINFL_LZ_DICT_SIZE); + size_t in_buf_ofs = 0, dict_ofs = 0; if (!pDict) return TINFL_STATUS_FAILED; tinfl_init(&decomp); - for ( ; ; ) - { - size_t in_buf_size = *pIn_buf_size - in_buf_ofs, dst_buf_size = TINFL_LZ_DICT_SIZE - dict_ofs; - tinfl_status status = tinfl_decompress(&decomp, (const mz_uint8*)pIn_buf + in_buf_ofs, &in_buf_size, pDict, pDict + dict_ofs, &dst_buf_size, - (flags & ~(TINFL_FLAG_HAS_MORE_INPUT | TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF))); + for (;;) { + size_t in_buf_size = *pIn_buf_size - in_buf_ofs, + dst_buf_size = TINFL_LZ_DICT_SIZE - dict_ofs; + tinfl_status status = + tinfl_decompress(&decomp, (const mz_uint8 *)pIn_buf + in_buf_ofs, + &in_buf_size, pDict, pDict + dict_ofs, &dst_buf_size, + (flags & ~(TINFL_FLAG_HAS_MORE_INPUT | + TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF))); in_buf_ofs += in_buf_size; - if ((dst_buf_size) && (!(*pPut_buf_func)(pDict + dict_ofs, (int)dst_buf_size, pPut_buf_user))) + if ((dst_buf_size) && + (!(*pPut_buf_func)(pDict + dict_ofs, (int)dst_buf_size, pPut_buf_user))) break; - if (status != TINFL_STATUS_HAS_MORE_OUTPUT) - { + if (status != TINFL_STATUS_HAS_MORE_OUTPUT) { result = (status == TINFL_STATUS_DONE); break; } @@ -1769,220 +2464,390 @@ int tinfl_decompress_mem_to_callback(const void *pIn_buf, size_t *pIn_buf_size, return result; } -// ------------------- Low-level Compression (independent from all decompression API's) +// ------------------- Low-level Compression (independent from all decompression +// API's) // Purposely making these tables static for faster init and thread safety. static const mz_uint16 s_tdefl_len_sym[256] = { - 257,258,259,260,261,262,263,264,265,265,266,266,267,267,268,268,269,269,269,269,270,270,270,270,271,271,271,271,272,272,272,272, - 273,273,273,273,273,273,273,273,274,274,274,274,274,274,274,274,275,275,275,275,275,275,275,275,276,276,276,276,276,276,276,276, - 277,277,277,277,277,277,277,277,277,277,277,277,277,277,277,277,278,278,278,278,278,278,278,278,278,278,278,278,278,278,278,278, - 279,279,279,279,279,279,279,279,279,279,279,279,279,279,279,279,280,280,280,280,280,280,280,280,280,280,280,280,280,280,280,280, - 281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281,281, - 282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282,282, - 283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283,283, - 284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,284,285 }; + 257, 258, 259, 260, 261, 262, 263, 264, 265, 265, 266, 266, 267, 267, 268, + 268, 269, 269, 269, 269, 270, 270, 270, 270, 271, 271, 271, 271, 272, 272, + 272, 272, 273, 273, 273, 273, 273, 273, 273, 273, 274, 274, 274, 274, 274, + 274, 274, 274, 275, 275, 275, 275, 275, 275, 275, 275, 276, 276, 276, 276, + 276, 276, 276, 276, 277, 277, 277, 277, 277, 277, 277, 277, 277, 277, 277, + 277, 277, 277, 277, 277, 278, 278, 278, 278, 278, 278, 278, 278, 278, 278, + 278, 278, 278, 278, 278, 278, 279, 279, 279, 279, 279, 279, 279, 279, 279, + 279, 279, 279, 279, 279, 279, 279, 280, 280, 280, 280, 280, 280, 280, 280, + 280, 280, 280, 280, 280, 280, 280, 280, 281, 281, 281, 281, 281, 281, 281, + 281, 281, 281, 281, 281, 281, 281, 281, 281, 281, 281, 281, 281, 281, 281, + 281, 281, 281, 281, 281, 281, 281, 281, 281, 281, 282, 282, 282, 282, 282, + 282, 282, 282, 282, 282, 282, 282, 282, 282, 282, 282, 282, 282, 282, 282, + 282, 282, 282, 282, 282, 282, 282, 282, 282, 282, 282, 282, 283, 283, 283, + 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, + 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, 284, + 284, 284, 284, 284, 284, 284, 284, 284, 284, 284, 284, 284, 284, 284, 284, + 284, 284, 284, 284, 284, 284, 284, 284, 284, 284, 284, 284, 284, 284, 284, + 285}; static const mz_uint8 s_tdefl_len_extra[256] = { - 0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3, - 4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4, - 5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5, - 5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,0 }; + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, + 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, + 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, + 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, + 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, + 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, + 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, + 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0}; static const mz_uint8 s_tdefl_small_dist_sym[512] = { - 0,1,2,3,4,4,5,5,6,6,6,6,7,7,7,7,8,8,8,8,8,8,8,8,9,9,9,9,9,9,9,9,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,11,11,11,11,11,11, - 11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,13, - 13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,14,14,14,14,14,14,14,14,14,14,14,14, - 14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14, - 14,14,14,14,14,14,14,14,14,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15, - 15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,16,16,16,16,16,16,16,16,16,16,16,16,16, - 16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16, - 16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16, - 16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,17,17,17,17,17,17,17,17,17,17,17,17,17,17, - 17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17, - 17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17, - 17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17 }; + 0, 1, 2, 3, 4, 4, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, + 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, + 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, + 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, + 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, + 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, + 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, + 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, + 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, + 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, + 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, + 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, + 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, + 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, + 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, + 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, + 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, + 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, + 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, + 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, + 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, + 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, + 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, + 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, + 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, + 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17}; static const mz_uint8 s_tdefl_small_dist_extra[512] = { - 0,0,0,0,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5, - 5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6, - 6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6, - 6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, - 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, - 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, - 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, - 7,7,7,7,7,7,7,7 }; + 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, + 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, + 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, + 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, + 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, + 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, + 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, + 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, + 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, + 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, + 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, + 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, + 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, + 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, + 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, + 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, + 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, + 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, + 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, + 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7}; static const mz_uint8 s_tdefl_large_dist_sym[128] = { - 0,0,18,19,20,20,21,21,22,22,22,22,23,23,23,23,24,24,24,24,24,24,24,24,25,25,25,25,25,25,25,25,26,26,26,26,26,26,26,26,26,26,26,26, - 26,26,26,26,27,27,27,27,27,27,27,27,27,27,27,27,27,27,27,27,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28, - 28,28,28,28,28,28,28,28,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29 }; + 0, 0, 18, 19, 20, 20, 21, 21, 22, 22, 22, 22, 23, 23, 23, 23, 24, 24, 24, + 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, + 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, + 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, + 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, + 28, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, + 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29}; static const mz_uint8 s_tdefl_large_dist_extra[128] = { - 0,0,8,8,9,9,9,9,10,10,10,10,10,10,10,10,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12, - 12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13, - 13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13 }; + 0, 0, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, + 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, + 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, + 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, + 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, + 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, + 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13}; -// Radix sorts tdefl_sym_freq[] array by 16-bit key m_key. Returns ptr to sorted values. -typedef struct { mz_uint16 m_key, m_sym_index; } tdefl_sym_freq; -static tdefl_sym_freq* tdefl_radix_sort_syms(mz_uint num_syms, tdefl_sym_freq* pSyms0, tdefl_sym_freq* pSyms1) -{ - mz_uint32 total_passes = 2, pass_shift, pass, i, hist[256 * 2]; tdefl_sym_freq* pCur_syms = pSyms0, *pNew_syms = pSyms1; MZ_CLEAR_OBJ(hist); - for (i = 0; i < num_syms; i++) { mz_uint freq = pSyms0[i].m_key; hist[freq & 0xFF]++; hist[256 + ((freq >> 8) & 0xFF)]++; } - while ((total_passes > 1) && (num_syms == hist[(total_passes - 1) * 256])) total_passes--; - for (pass_shift = 0, pass = 0; pass < total_passes; pass++, pass_shift += 8) - { - const mz_uint32* pHist = &hist[pass << 8]; +// Radix sorts tdefl_sym_freq[] array by 16-bit key m_key. Returns ptr to sorted +// values. +typedef struct { + mz_uint16 m_key, m_sym_index; +} tdefl_sym_freq; +static tdefl_sym_freq *tdefl_radix_sort_syms(mz_uint num_syms, + tdefl_sym_freq *pSyms0, + tdefl_sym_freq *pSyms1) { + mz_uint32 total_passes = 2, pass_shift, pass, i, hist[256 * 2]; + tdefl_sym_freq *pCur_syms = pSyms0, *pNew_syms = pSyms1; + MZ_CLEAR_OBJ(hist); + for (i = 0; i < num_syms; i++) { + mz_uint freq = pSyms0[i].m_key; + hist[freq & 0xFF]++; + hist[256 + ((freq >> 8) & 0xFF)]++; + } + while ((total_passes > 1) && (num_syms == hist[(total_passes - 1) * 256])) + total_passes--; + for (pass_shift = 0, pass = 0; pass < total_passes; pass++, pass_shift += 8) { + const mz_uint32 *pHist = &hist[pass << 8]; mz_uint offsets[256], cur_ofs = 0; - for (i = 0; i < 256; i++) { offsets[i] = cur_ofs; cur_ofs += pHist[i]; } - for (i = 0; i < num_syms; i++) pNew_syms[offsets[(pCur_syms[i].m_key >> pass_shift) & 0xFF]++] = pCur_syms[i]; - { tdefl_sym_freq* t = pCur_syms; pCur_syms = pNew_syms; pNew_syms = t; } + for (i = 0; i < 256; i++) { + offsets[i] = cur_ofs; + cur_ofs += pHist[i]; + } + for (i = 0; i < num_syms; i++) + pNew_syms[offsets[(pCur_syms[i].m_key >> pass_shift) & 0xFF]++] = + pCur_syms[i]; + { + tdefl_sym_freq *t = pCur_syms; + pCur_syms = pNew_syms; + pNew_syms = t; + } } return pCur_syms; } -// tdefl_calculate_minimum_redundancy() originally written by: Alistair Moffat, alistair@cs.mu.oz.au, Jyrki Katajainen, jyrki@diku.dk, November 1996. -static void tdefl_calculate_minimum_redundancy(tdefl_sym_freq *A, int n) -{ +// tdefl_calculate_minimum_redundancy() originally written by: Alistair Moffat, +// alistair@cs.mu.oz.au, Jyrki Katajainen, jyrki@diku.dk, November 1996. +static void tdefl_calculate_minimum_redundancy(tdefl_sym_freq *A, int n) { int root, leaf, next, avbl, used, dpth; - if (n==0) return; else if (n==1) { A[0].m_key = 1; return; } - A[0].m_key += A[1].m_key; root = 0; leaf = 2; - for (next=1; next < n-1; next++) - { - if (leaf>=n || A[root].m_key=n || (root=0; next--) A[next].m_key = A[A[next].m_key].m_key+1; - avbl = 1; used = dpth = 0; root = n-2; next = n-1; - while (avbl>0) - { - while (root>=0 && (int)A[root].m_key==dpth) { used++; root--; } - while (avbl>used) { A[next--].m_key = (mz_uint16)(dpth); avbl--; } - avbl = 2*used; dpth++; used = 0; + A[0].m_key += A[1].m_key; + root = 0; + leaf = 2; + for (next = 1; next < n - 1; next++) { + if (leaf >= n || A[root].m_key < A[leaf].m_key) { + A[next].m_key = A[root].m_key; + A[root++].m_key = (mz_uint16)next; + } else + A[next].m_key = A[leaf++].m_key; + if (leaf >= n || (root < next && A[root].m_key < A[leaf].m_key)) { + A[next].m_key = (mz_uint16)(A[next].m_key + A[root].m_key); + A[root++].m_key = (mz_uint16)next; + } else + A[next].m_key = (mz_uint16)(A[next].m_key + A[leaf++].m_key); + } + A[n - 2].m_key = 0; + for (next = n - 3; next >= 0; next--) + A[next].m_key = A[A[next].m_key].m_key + 1; + avbl = 1; + used = dpth = 0; + root = n - 2; + next = n - 1; + while (avbl > 0) { + while (root >= 0 && (int)A[root].m_key == dpth) { + used++; + root--; + } + while (avbl > used) { + A[next--].m_key = (mz_uint16)(dpth); + avbl--; + } + avbl = 2 * used; + dpth++; + used = 0; } } // Limits canonical Huffman code table's max code size. enum { TDEFL_MAX_SUPPORTED_HUFF_CODESIZE = 32 }; -static void tdefl_huffman_enforce_max_code_size(int *pNum_codes, int code_list_len, int max_code_size) -{ - int i; mz_uint32 total = 0; if (code_list_len <= 1) return; - for (i = max_code_size + 1; i <= TDEFL_MAX_SUPPORTED_HUFF_CODESIZE; i++) pNum_codes[max_code_size] += pNum_codes[i]; - for (i = max_code_size; i > 0; i--) total += (((mz_uint32)pNum_codes[i]) << (max_code_size - i)); - while (total != (1UL << max_code_size)) - { +static void tdefl_huffman_enforce_max_code_size(int *pNum_codes, + int code_list_len, + int max_code_size) { + int i; + mz_uint32 total = 0; + if (code_list_len <= 1) + return; + for (i = max_code_size + 1; i <= TDEFL_MAX_SUPPORTED_HUFF_CODESIZE; i++) + pNum_codes[max_code_size] += pNum_codes[i]; + for (i = max_code_size; i > 0; i--) + total += (((mz_uint32)pNum_codes[i]) << (max_code_size - i)); + while (total != (1UL << max_code_size)) { pNum_codes[max_code_size]--; - for (i = max_code_size - 1; i > 0; i--) if (pNum_codes[i]) { pNum_codes[i]--; pNum_codes[i + 1] += 2; break; } + for (i = max_code_size - 1; i > 0; i--) + if (pNum_codes[i]) { + pNum_codes[i]--; + pNum_codes[i + 1] += 2; + break; + } total--; } } -static void tdefl_optimize_huffman_table(tdefl_compressor *d, int table_num, int table_len, int code_size_limit, int static_table) -{ - int i, j, l, num_codes[1 + TDEFL_MAX_SUPPORTED_HUFF_CODESIZE]; mz_uint next_code[TDEFL_MAX_SUPPORTED_HUFF_CODESIZE + 1]; MZ_CLEAR_OBJ(num_codes); - if (static_table) - { - for (i = 0; i < table_len; i++) num_codes[d->m_huff_code_sizes[table_num][i]]++; - } - else - { - tdefl_sym_freq syms0[TDEFL_MAX_HUFF_SYMBOLS], syms1[TDEFL_MAX_HUFF_SYMBOLS], *pSyms; +static void tdefl_optimize_huffman_table(tdefl_compressor *d, int table_num, + int table_len, int code_size_limit, + int static_table) { + int i, j, l, num_codes[1 + TDEFL_MAX_SUPPORTED_HUFF_CODESIZE]; + mz_uint next_code[TDEFL_MAX_SUPPORTED_HUFF_CODESIZE + 1]; + MZ_CLEAR_OBJ(num_codes); + if (static_table) { + for (i = 0; i < table_len; i++) + num_codes[d->m_huff_code_sizes[table_num][i]]++; + } else { + tdefl_sym_freq syms0[TDEFL_MAX_HUFF_SYMBOLS], syms1[TDEFL_MAX_HUFF_SYMBOLS], + *pSyms; int num_used_syms = 0; const mz_uint16 *pSym_count = &d->m_huff_count[table_num][0]; - for (i = 0; i < table_len; i++) if (pSym_count[i]) { syms0[num_used_syms].m_key = (mz_uint16)pSym_count[i]; syms0[num_used_syms++].m_sym_index = (mz_uint16)i; } + for (i = 0; i < table_len; i++) + if (pSym_count[i]) { + syms0[num_used_syms].m_key = (mz_uint16)pSym_count[i]; + syms0[num_used_syms++].m_sym_index = (mz_uint16)i; + } - pSyms = tdefl_radix_sort_syms(num_used_syms, syms0, syms1); tdefl_calculate_minimum_redundancy(pSyms, num_used_syms); + pSyms = tdefl_radix_sort_syms(num_used_syms, syms0, syms1); + tdefl_calculate_minimum_redundancy(pSyms, num_used_syms); - for (i = 0; i < num_used_syms; i++) num_codes[pSyms[i].m_key]++; + for (i = 0; i < num_used_syms; i++) + num_codes[pSyms[i].m_key]++; - tdefl_huffman_enforce_max_code_size(num_codes, num_used_syms, code_size_limit); + tdefl_huffman_enforce_max_code_size(num_codes, num_used_syms, + code_size_limit); - MZ_CLEAR_OBJ(d->m_huff_code_sizes[table_num]); MZ_CLEAR_OBJ(d->m_huff_codes[table_num]); + MZ_CLEAR_OBJ(d->m_huff_code_sizes[table_num]); + MZ_CLEAR_OBJ(d->m_huff_codes[table_num]); for (i = 1, j = num_used_syms; i <= code_size_limit; i++) - for (l = num_codes[i]; l > 0; l--) d->m_huff_code_sizes[table_num][pSyms[--j].m_sym_index] = (mz_uint8)(i); + for (l = num_codes[i]; l > 0; l--) + d->m_huff_code_sizes[table_num][pSyms[--j].m_sym_index] = (mz_uint8)(i); } - next_code[1] = 0; for (j = 0, i = 2; i <= code_size_limit; i++) next_code[i] = j = ((j + num_codes[i - 1]) << 1); + next_code[1] = 0; + for (j = 0, i = 2; i <= code_size_limit; i++) + next_code[i] = j = ((j + num_codes[i - 1]) << 1); - for (i = 0; i < table_len; i++) - { - mz_uint rev_code = 0, code, code_size; if ((code_size = d->m_huff_code_sizes[table_num][i]) == 0) continue; - code = next_code[code_size]++; for (l = code_size; l > 0; l--, code >>= 1) rev_code = (rev_code << 1) | (code & 1); + for (i = 0; i < table_len; i++) { + mz_uint rev_code = 0, code, code_size; + if ((code_size = d->m_huff_code_sizes[table_num][i]) == 0) + continue; + code = next_code[code_size]++; + for (l = code_size; l > 0; l--, code >>= 1) + rev_code = (rev_code << 1) | (code & 1); d->m_huff_codes[table_num][i] = (mz_uint16)rev_code; } } -#define TDEFL_PUT_BITS(b, l) do { \ - mz_uint bits = b; mz_uint len = l; MZ_ASSERT(bits <= ((1U << len) - 1U)); \ - d->m_bit_buffer |= (bits << d->m_bits_in); d->m_bits_in += len; \ - while (d->m_bits_in >= 8) { \ - if (d->m_pOutput_buf < d->m_pOutput_buf_end) \ - *d->m_pOutput_buf++ = (mz_uint8)(d->m_bit_buffer); \ - d->m_bit_buffer >>= 8; \ - d->m_bits_in -= 8; \ - } \ -} MZ_MACRO_END +#define TDEFL_PUT_BITS(b, l) \ + do { \ + mz_uint bits = b; \ + mz_uint len = l; \ + MZ_ASSERT(bits <= ((1U << len) - 1U)); \ + d->m_bit_buffer |= (bits << d->m_bits_in); \ + d->m_bits_in += len; \ + while (d->m_bits_in >= 8) { \ + if (d->m_pOutput_buf < d->m_pOutput_buf_end) \ + *d->m_pOutput_buf++ = (mz_uint8)(d->m_bit_buffer); \ + d->m_bit_buffer >>= 8; \ + d->m_bits_in -= 8; \ + } \ + } \ + MZ_MACRO_END -#define TDEFL_RLE_PREV_CODE_SIZE() { if (rle_repeat_count) { \ - if (rle_repeat_count < 3) { \ - d->m_huff_count[2][prev_code_size] = (mz_uint16)(d->m_huff_count[2][prev_code_size] + rle_repeat_count); \ - while (rle_repeat_count--) packed_code_sizes[num_packed_code_sizes++] = prev_code_size; \ - } else { \ - d->m_huff_count[2][16] = (mz_uint16)(d->m_huff_count[2][16] + 1); packed_code_sizes[num_packed_code_sizes++] = 16; packed_code_sizes[num_packed_code_sizes++] = (mz_uint8)(rle_repeat_count - 3); \ -} rle_repeat_count = 0; } } +#define TDEFL_RLE_PREV_CODE_SIZE() \ + { \ + if (rle_repeat_count) { \ + if (rle_repeat_count < 3) { \ + d->m_huff_count[2][prev_code_size] = (mz_uint16)( \ + d->m_huff_count[2][prev_code_size] + rle_repeat_count); \ + while (rle_repeat_count--) \ + packed_code_sizes[num_packed_code_sizes++] = prev_code_size; \ + } else { \ + d->m_huff_count[2][16] = (mz_uint16)(d->m_huff_count[2][16] + 1); \ + packed_code_sizes[num_packed_code_sizes++] = 16; \ + packed_code_sizes[num_packed_code_sizes++] = \ + (mz_uint8)(rle_repeat_count - 3); \ + } \ + rle_repeat_count = 0; \ + } \ + } -#define TDEFL_RLE_ZERO_CODE_SIZE() { if (rle_z_count) { \ - if (rle_z_count < 3) { \ - d->m_huff_count[2][0] = (mz_uint16)(d->m_huff_count[2][0] + rle_z_count); while (rle_z_count--) packed_code_sizes[num_packed_code_sizes++] = 0; \ - } else if (rle_z_count <= 10) { \ - d->m_huff_count[2][17] = (mz_uint16)(d->m_huff_count[2][17] + 1); packed_code_sizes[num_packed_code_sizes++] = 17; packed_code_sizes[num_packed_code_sizes++] = (mz_uint8)(rle_z_count - 3); \ - } else { \ - d->m_huff_count[2][18] = (mz_uint16)(d->m_huff_count[2][18] + 1); packed_code_sizes[num_packed_code_sizes++] = 18; packed_code_sizes[num_packed_code_sizes++] = (mz_uint8)(rle_z_count - 11); \ -} rle_z_count = 0; } } +#define TDEFL_RLE_ZERO_CODE_SIZE() \ + { \ + if (rle_z_count) { \ + if (rle_z_count < 3) { \ + d->m_huff_count[2][0] = \ + (mz_uint16)(d->m_huff_count[2][0] + rle_z_count); \ + while (rle_z_count--) \ + packed_code_sizes[num_packed_code_sizes++] = 0; \ + } else if (rle_z_count <= 10) { \ + d->m_huff_count[2][17] = (mz_uint16)(d->m_huff_count[2][17] + 1); \ + packed_code_sizes[num_packed_code_sizes++] = 17; \ + packed_code_sizes[num_packed_code_sizes++] = \ + (mz_uint8)(rle_z_count - 3); \ + } else { \ + d->m_huff_count[2][18] = (mz_uint16)(d->m_huff_count[2][18] + 1); \ + packed_code_sizes[num_packed_code_sizes++] = 18; \ + packed_code_sizes[num_packed_code_sizes++] = \ + (mz_uint8)(rle_z_count - 11); \ + } \ + rle_z_count = 0; \ + } \ + } -static mz_uint8 s_tdefl_packed_code_size_syms_swizzle[] = { 16, 17, 18, 0, 8, 7, 9, 6, 10, 5, 11, 4, 12, 3, 13, 2, 14, 1, 15 }; +static mz_uint8 s_tdefl_packed_code_size_syms_swizzle[] = { + 16, 17, 18, 0, 8, 7, 9, 6, 10, 5, 11, 4, 12, 3, 13, 2, 14, 1, 15}; -static void tdefl_start_dynamic_block(tdefl_compressor *d) -{ - int num_lit_codes, num_dist_codes, num_bit_lengths; mz_uint i, total_code_sizes_to_pack, num_packed_code_sizes, rle_z_count, rle_repeat_count, packed_code_sizes_index; - mz_uint8 code_sizes_to_pack[TDEFL_MAX_HUFF_SYMBOLS_0 + TDEFL_MAX_HUFF_SYMBOLS_1], packed_code_sizes[TDEFL_MAX_HUFF_SYMBOLS_0 + TDEFL_MAX_HUFF_SYMBOLS_1], prev_code_size = 0xFF; +static void tdefl_start_dynamic_block(tdefl_compressor *d) { + int num_lit_codes, num_dist_codes, num_bit_lengths; + mz_uint i, total_code_sizes_to_pack, num_packed_code_sizes, rle_z_count, + rle_repeat_count, packed_code_sizes_index; + mz_uint8 + code_sizes_to_pack[TDEFL_MAX_HUFF_SYMBOLS_0 + TDEFL_MAX_HUFF_SYMBOLS_1], + packed_code_sizes[TDEFL_MAX_HUFF_SYMBOLS_0 + TDEFL_MAX_HUFF_SYMBOLS_1], + prev_code_size = 0xFF; d->m_huff_count[0][256] = 1; tdefl_optimize_huffman_table(d, 0, TDEFL_MAX_HUFF_SYMBOLS_0, 15, MZ_FALSE); tdefl_optimize_huffman_table(d, 1, TDEFL_MAX_HUFF_SYMBOLS_1, 15, MZ_FALSE); - for (num_lit_codes = 286; num_lit_codes > 257; num_lit_codes--) if (d->m_huff_code_sizes[0][num_lit_codes - 1]) break; - for (num_dist_codes = 30; num_dist_codes > 1; num_dist_codes--) if (d->m_huff_code_sizes[1][num_dist_codes - 1]) break; + for (num_lit_codes = 286; num_lit_codes > 257; num_lit_codes--) + if (d->m_huff_code_sizes[0][num_lit_codes - 1]) + break; + for (num_dist_codes = 30; num_dist_codes > 1; num_dist_codes--) + if (d->m_huff_code_sizes[1][num_dist_codes - 1]) + break; - memcpy(code_sizes_to_pack, &d->m_huff_code_sizes[0][0], num_lit_codes); - memcpy(code_sizes_to_pack + num_lit_codes, &d->m_huff_code_sizes[1][0], num_dist_codes); - total_code_sizes_to_pack = num_lit_codes + num_dist_codes; num_packed_code_sizes = 0; rle_z_count = 0; rle_repeat_count = 0; + memcpy(code_sizes_to_pack, &d->m_huff_code_sizes[0][0], + sizeof(mz_uint8) * num_lit_codes); + memcpy(code_sizes_to_pack + num_lit_codes, &d->m_huff_code_sizes[1][0], + sizeof(mz_uint8) * num_dist_codes); + total_code_sizes_to_pack = num_lit_codes + num_dist_codes; + num_packed_code_sizes = 0; + rle_z_count = 0; + rle_repeat_count = 0; - memset(&d->m_huff_count[2][0], 0, sizeof(d->m_huff_count[2][0]) * TDEFL_MAX_HUFF_SYMBOLS_2); - for (i = 0; i < total_code_sizes_to_pack; i++) - { + memset(&d->m_huff_count[2][0], 0, + sizeof(d->m_huff_count[2][0]) * TDEFL_MAX_HUFF_SYMBOLS_2); + for (i = 0; i < total_code_sizes_to_pack; i++) { mz_uint8 code_size = code_sizes_to_pack[i]; - if (!code_size) - { + if (!code_size) { TDEFL_RLE_PREV_CODE_SIZE(); - if (++rle_z_count == 138) { TDEFL_RLE_ZERO_CODE_SIZE(); } - } - else - { - TDEFL_RLE_ZERO_CODE_SIZE(); - if (code_size != prev_code_size) - { - TDEFL_RLE_PREV_CODE_SIZE(); - d->m_huff_count[2][code_size] = (mz_uint16)(d->m_huff_count[2][code_size] + 1); packed_code_sizes[num_packed_code_sizes++] = code_size; + if (++rle_z_count == 138) { + TDEFL_RLE_ZERO_CODE_SIZE(); } - else if (++rle_repeat_count == 6) - { + } else { + TDEFL_RLE_ZERO_CODE_SIZE(); + if (code_size != prev_code_size) { + TDEFL_RLE_PREV_CODE_SIZE(); + d->m_huff_count[2][code_size] = + (mz_uint16)(d->m_huff_count[2][code_size] + 1); + packed_code_sizes[num_packed_code_sizes++] = code_size; + } else if (++rle_repeat_count == 6) { TDEFL_RLE_PREV_CODE_SIZE(); } } prev_code_size = code_size; } - if (rle_repeat_count) { TDEFL_RLE_PREV_CODE_SIZE(); } else { TDEFL_RLE_ZERO_CODE_SIZE(); } + if (rle_repeat_count) { + TDEFL_RLE_PREV_CODE_SIZE(); + } else { + TDEFL_RLE_ZERO_CODE_SIZE(); + } tdefl_optimize_huffman_table(d, 2, TDEFL_MAX_HUFF_SYMBOLS_2, 7, MZ_FALSE); @@ -1991,27 +2856,39 @@ static void tdefl_start_dynamic_block(tdefl_compressor *d) TDEFL_PUT_BITS(num_lit_codes - 257, 5); TDEFL_PUT_BITS(num_dist_codes - 1, 5); - for (num_bit_lengths = 18; num_bit_lengths >= 0; num_bit_lengths--) if (d->m_huff_code_sizes[2][s_tdefl_packed_code_size_syms_swizzle[num_bit_lengths]]) break; - num_bit_lengths = MZ_MAX(4, (num_bit_lengths + 1)); TDEFL_PUT_BITS(num_bit_lengths - 4, 4); - for (i = 0; (int)i < num_bit_lengths; i++) TDEFL_PUT_BITS(d->m_huff_code_sizes[2][s_tdefl_packed_code_size_syms_swizzle[i]], 3); + for (num_bit_lengths = 18; num_bit_lengths >= 0; num_bit_lengths--) + if (d->m_huff_code_sizes + [2][s_tdefl_packed_code_size_syms_swizzle[num_bit_lengths]]) + break; + num_bit_lengths = MZ_MAX(4, (num_bit_lengths + 1)); + TDEFL_PUT_BITS(num_bit_lengths - 4, 4); + for (i = 0; (int)i < num_bit_lengths; i++) + TDEFL_PUT_BITS( + d->m_huff_code_sizes[2][s_tdefl_packed_code_size_syms_swizzle[i]], 3); - for (packed_code_sizes_index = 0; packed_code_sizes_index < num_packed_code_sizes; ) - { - mz_uint code = packed_code_sizes[packed_code_sizes_index++]; MZ_ASSERT(code < TDEFL_MAX_HUFF_SYMBOLS_2); + for (packed_code_sizes_index = 0; + packed_code_sizes_index < num_packed_code_sizes;) { + mz_uint code = packed_code_sizes[packed_code_sizes_index++]; + MZ_ASSERT(code < TDEFL_MAX_HUFF_SYMBOLS_2); TDEFL_PUT_BITS(d->m_huff_codes[2][code], d->m_huff_code_sizes[2][code]); - if (code >= 16) TDEFL_PUT_BITS(packed_code_sizes[packed_code_sizes_index++], "\02\03\07"[code - 16]); + if (code >= 16) + TDEFL_PUT_BITS(packed_code_sizes[packed_code_sizes_index++], + "\02\03\07"[code - 16]); } } -static void tdefl_start_static_block(tdefl_compressor *d) -{ +static void tdefl_start_static_block(tdefl_compressor *d) { mz_uint i; mz_uint8 *p = &d->m_huff_code_sizes[0][0]; - for (i = 0; i <= 143; ++i) *p++ = 8; - for ( ; i <= 255; ++i) *p++ = 9; - for ( ; i <= 279; ++i) *p++ = 7; - for ( ; i <= 287; ++i) *p++ = 8; + for (i = 0; i <= 143; ++i) + *p++ = 8; + for (; i <= 255; ++i) + *p++ = 9; + for (; i <= 279; ++i) + *p++ = 7; + for (; i <= 287; ++i) + *p++ = 8; memset(d->m_huff_code_sizes[1], 5, 32); @@ -2021,11 +2898,13 @@ static void tdefl_start_static_block(tdefl_compressor *d) TDEFL_PUT_BITS(1, 2); } -static const mz_uint mz_bitmasks[17] = { 0x0000, 0x0001, 0x0003, 0x0007, 0x000F, 0x001F, 0x003F, 0x007F, 0x00FF, 0x01FF, 0x03FF, 0x07FF, 0x0FFF, 0x1FFF, 0x3FFF, 0x7FFF, 0xFFFF }; +static const mz_uint mz_bitmasks[17] = { + 0x0000, 0x0001, 0x0003, 0x0007, 0x000F, 0x001F, 0x003F, 0x007F, 0x00FF, + 0x01FF, 0x03FF, 0x07FF, 0x0FFF, 0x1FFF, 0x3FFF, 0x7FFF, 0xFFFF}; -#if MINIZ_USE_UNALIGNED_LOADS_AND_STORES && MINIZ_LITTLE_ENDIAN && MINIZ_HAS_64BIT_REGISTERS -static mz_bool tdefl_compress_lz_codes(tdefl_compressor *d) -{ +#if MINIZ_USE_UNALIGNED_LOADS_AND_STORES && MINIZ_LITTLE_ENDIAN && \ + MINIZ_HAS_64BIT_REGISTERS +static mz_bool tdefl_compress_lz_codes(tdefl_compressor *d) { mz_uint flags; mz_uint8 *pLZ_codes; mz_uint8 *pOutput_buf = d->m_pOutput_buf; @@ -2033,22 +2912,29 @@ static mz_bool tdefl_compress_lz_codes(tdefl_compressor *d) mz_uint64 bit_buffer = d->m_bit_buffer; mz_uint bits_in = d->m_bits_in; -#define TDEFL_PUT_BITS_FAST(b, l) { bit_buffer |= (((mz_uint64)(b)) << bits_in); bits_in += (l); } +#define TDEFL_PUT_BITS_FAST(b, l) \ + { \ + bit_buffer |= (((mz_uint64)(b)) << bits_in); \ + bits_in += (l); \ + } flags = 1; - for (pLZ_codes = d->m_lz_code_buf; pLZ_codes < pLZ_code_buf_end; flags >>= 1) - { + for (pLZ_codes = d->m_lz_code_buf; pLZ_codes < pLZ_code_buf_end; + flags >>= 1) { if (flags == 1) flags = *pLZ_codes++ | 0x100; - if (flags & 1) - { + if (flags & 1) { mz_uint s0, s1, n0, n1, sym, num_extra_bits; - mz_uint match_len = pLZ_codes[0], match_dist = *(const mz_uint16 *)(pLZ_codes + 1); pLZ_codes += 3; + mz_uint match_len = pLZ_codes[0], + match_dist = *(const mz_uint16 *)(pLZ_codes + 1); + pLZ_codes += 3; MZ_ASSERT(d->m_huff_code_sizes[0][s_tdefl_len_sym[match_len]]); - TDEFL_PUT_BITS_FAST(d->m_huff_codes[0][s_tdefl_len_sym[match_len]], d->m_huff_code_sizes[0][s_tdefl_len_sym[match_len]]); - TDEFL_PUT_BITS_FAST(match_len & mz_bitmasks[s_tdefl_len_extra[match_len]], s_tdefl_len_extra[match_len]); + TDEFL_PUT_BITS_FAST(d->m_huff_codes[0][s_tdefl_len_sym[match_len]], + d->m_huff_code_sizes[0][s_tdefl_len_sym[match_len]]); + TDEFL_PUT_BITS_FAST(match_len & mz_bitmasks[s_tdefl_len_extra[match_len]], + s_tdefl_len_extra[match_len]); // This sequence coaxes MSVC into using cmov's vs. jmp's. s0 = s_tdefl_small_dist_sym[match_dist & 511]; @@ -2059,28 +2945,29 @@ static mz_bool tdefl_compress_lz_codes(tdefl_compressor *d) num_extra_bits = (match_dist < 512) ? n0 : n1; MZ_ASSERT(d->m_huff_code_sizes[1][sym]); - TDEFL_PUT_BITS_FAST(d->m_huff_codes[1][sym], d->m_huff_code_sizes[1][sym]); - TDEFL_PUT_BITS_FAST(match_dist & mz_bitmasks[num_extra_bits], num_extra_bits); - } - else - { + TDEFL_PUT_BITS_FAST(d->m_huff_codes[1][sym], + d->m_huff_code_sizes[1][sym]); + TDEFL_PUT_BITS_FAST(match_dist & mz_bitmasks[num_extra_bits], + num_extra_bits); + } else { mz_uint lit = *pLZ_codes++; MZ_ASSERT(d->m_huff_code_sizes[0][lit]); - TDEFL_PUT_BITS_FAST(d->m_huff_codes[0][lit], d->m_huff_code_sizes[0][lit]); + TDEFL_PUT_BITS_FAST(d->m_huff_codes[0][lit], + d->m_huff_code_sizes[0][lit]); - if (((flags & 2) == 0) && (pLZ_codes < pLZ_code_buf_end)) - { + if (((flags & 2) == 0) && (pLZ_codes < pLZ_code_buf_end)) { flags >>= 1; lit = *pLZ_codes++; MZ_ASSERT(d->m_huff_code_sizes[0][lit]); - TDEFL_PUT_BITS_FAST(d->m_huff_codes[0][lit], d->m_huff_code_sizes[0][lit]); + TDEFL_PUT_BITS_FAST(d->m_huff_codes[0][lit], + d->m_huff_code_sizes[0][lit]); - if (((flags & 2) == 0) && (pLZ_codes < pLZ_code_buf_end)) - { + if (((flags & 2) == 0) && (pLZ_codes < pLZ_code_buf_end)) { flags >>= 1; lit = *pLZ_codes++; MZ_ASSERT(d->m_huff_code_sizes[0][lit]); - TDEFL_PUT_BITS_FAST(d->m_huff_codes[0][lit], d->m_huff_code_sizes[0][lit]); + TDEFL_PUT_BITS_FAST(d->m_huff_codes[0][lit], + d->m_huff_code_sizes[0][lit]); } } } @@ -2088,7 +2975,7 @@ static mz_bool tdefl_compress_lz_codes(tdefl_compressor *d) if (pOutput_buf >= d->m_pOutput_buf_end) return MZ_FALSE; - *(mz_uint64*)pOutput_buf = bit_buffer; + *(mz_uint64 *)pOutput_buf = bit_buffer; pOutput_buf += (bits_in >> 3); bit_buffer >>= (bits_in & ~7); bits_in &= 7; @@ -2100,8 +2987,7 @@ static mz_bool tdefl_compress_lz_codes(tdefl_compressor *d) d->m_bits_in = 0; d->m_bit_buffer = 0; - while (bits_in) - { + while (bits_in) { mz_uint32 n = MZ_MIN(bits_in, 16); TDEFL_PUT_BITS((mz_uint)bit_buffer & mz_bitmasks[n], n); bit_buffer >>= n; @@ -2113,39 +2999,37 @@ static mz_bool tdefl_compress_lz_codes(tdefl_compressor *d) return (d->m_pOutput_buf < d->m_pOutput_buf_end); } #else -static mz_bool tdefl_compress_lz_codes(tdefl_compressor *d) -{ +static mz_bool tdefl_compress_lz_codes(tdefl_compressor *d) { mz_uint flags; mz_uint8 *pLZ_codes; flags = 1; - for (pLZ_codes = d->m_lz_code_buf; pLZ_codes < d->m_pLZ_code_buf; flags >>= 1) - { + for (pLZ_codes = d->m_lz_code_buf; pLZ_codes < d->m_pLZ_code_buf; + flags >>= 1) { if (flags == 1) flags = *pLZ_codes++ | 0x100; - if (flags & 1) - { + if (flags & 1) { mz_uint sym, num_extra_bits; - mz_uint match_len = pLZ_codes[0], match_dist = (pLZ_codes[1] | (pLZ_codes[2] << 8)); pLZ_codes += 3; + mz_uint match_len = pLZ_codes[0], + match_dist = (pLZ_codes[1] | (pLZ_codes[2] << 8)); + pLZ_codes += 3; MZ_ASSERT(d->m_huff_code_sizes[0][s_tdefl_len_sym[match_len]]); - TDEFL_PUT_BITS(d->m_huff_codes[0][s_tdefl_len_sym[match_len]], d->m_huff_code_sizes[0][s_tdefl_len_sym[match_len]]); - TDEFL_PUT_BITS(match_len & mz_bitmasks[s_tdefl_len_extra[match_len]], s_tdefl_len_extra[match_len]); + TDEFL_PUT_BITS(d->m_huff_codes[0][s_tdefl_len_sym[match_len]], + d->m_huff_code_sizes[0][s_tdefl_len_sym[match_len]]); + TDEFL_PUT_BITS(match_len & mz_bitmasks[s_tdefl_len_extra[match_len]], + s_tdefl_len_extra[match_len]); - if (match_dist < 512) - { - sym = s_tdefl_small_dist_sym[match_dist]; num_extra_bits = s_tdefl_small_dist_extra[match_dist]; + if (match_dist < 512) { + sym = s_tdefl_small_dist_sym[match_dist]; + num_extra_bits = s_tdefl_small_dist_extra[match_dist]; + } else { + sym = s_tdefl_large_dist_sym[match_dist >> 8]; + num_extra_bits = s_tdefl_large_dist_extra[match_dist >> 8]; } - else - { - sym = s_tdefl_large_dist_sym[match_dist >> 8]; num_extra_bits = s_tdefl_large_dist_extra[match_dist >> 8]; - } - MZ_ASSERT(d->m_huff_code_sizes[1][sym]); TDEFL_PUT_BITS(d->m_huff_codes[1][sym], d->m_huff_code_sizes[1][sym]); TDEFL_PUT_BITS(match_dist & mz_bitmasks[num_extra_bits], num_extra_bits); - } - else - { + } else { mz_uint lit = *pLZ_codes++; MZ_ASSERT(d->m_huff_code_sizes[0][lit]); TDEFL_PUT_BITS(d->m_huff_codes[0][lit], d->m_huff_code_sizes[0][lit]); @@ -2156,10 +3040,10 @@ static mz_bool tdefl_compress_lz_codes(tdefl_compressor *d) return (d->m_pOutput_buf < d->m_pOutput_buf_end); } -#endif // MINIZ_USE_UNALIGNED_LOADS_AND_STORES && MINIZ_LITTLE_ENDIAN && MINIZ_HAS_64BIT_REGISTERS +#endif // MINIZ_USE_UNALIGNED_LOADS_AND_STORES && MINIZ_LITTLE_ENDIAN && + // MINIZ_HAS_64BIT_REGISTERS -static mz_bool tdefl_compress_block(tdefl_compressor *d, mz_bool static_block) -{ +static mz_bool tdefl_compress_block(tdefl_compressor *d, mz_bool static_block) { if (static_block) tdefl_start_static_block(d); else @@ -2167,13 +3051,18 @@ static mz_bool tdefl_compress_block(tdefl_compressor *d, mz_bool static_block) return tdefl_compress_lz_codes(d); } -static int tdefl_flush_block(tdefl_compressor *d, int flush) -{ +static int tdefl_flush_block(tdefl_compressor *d, int flush) { mz_uint saved_bit_buf, saved_bits_in; mz_uint8 *pSaved_output_buf; mz_bool comp_block_succeeded = MZ_FALSE; - int n, use_raw_block = ((d->m_flags & TDEFL_FORCE_ALL_RAW_BLOCKS) != 0) && (d->m_lookahead_pos - d->m_lz_code_buf_dict_pos) <= d->m_dict_size; - mz_uint8 *pOutput_buf_start = ((d->m_pPut_buf_func == NULL) && ((*d->m_pOut_buf_size - d->m_out_buf_ofs) >= TDEFL_OUT_BUF_SIZE)) ? ((mz_uint8 *)d->m_pOut_buf + d->m_out_buf_ofs) : d->m_output_buf; + int n, use_raw_block = + ((d->m_flags & TDEFL_FORCE_ALL_RAW_BLOCKS) != 0) && + (d->m_lookahead_pos - d->m_lz_code_buf_dict_pos) <= d->m_dict_size; + mz_uint8 *pOutput_buf_start = + ((d->m_pPut_buf_func == NULL) && + ((*d->m_pOut_buf_size - d->m_out_buf_ofs) >= TDEFL_OUT_BUF_SIZE)) + ? ((mz_uint8 *)d->m_pOut_buf + d->m_out_buf_ofs) + : d->m_output_buf; d->m_pOutput_buf = pOutput_buf_start; d->m_pOutput_buf_end = d->m_pOutput_buf + TDEFL_OUT_BUF_SIZE - 16; @@ -2185,82 +3074,106 @@ static int tdefl_flush_block(tdefl_compressor *d, int flush) *d->m_pLZ_flags = (mz_uint8)(*d->m_pLZ_flags >> d->m_num_flags_left); d->m_pLZ_code_buf -= (d->m_num_flags_left == 8); - if ((d->m_flags & TDEFL_WRITE_ZLIB_HEADER) && (!d->m_block_index)) - { - TDEFL_PUT_BITS(0x78, 8); TDEFL_PUT_BITS(0x01, 8); + if ((d->m_flags & TDEFL_WRITE_ZLIB_HEADER) && (!d->m_block_index)) { + TDEFL_PUT_BITS(0x78, 8); + TDEFL_PUT_BITS(0x01, 8); } TDEFL_PUT_BITS(flush == TDEFL_FINISH, 1); - pSaved_output_buf = d->m_pOutput_buf; saved_bit_buf = d->m_bit_buffer; saved_bits_in = d->m_bits_in; + pSaved_output_buf = d->m_pOutput_buf; + saved_bit_buf = d->m_bit_buffer; + saved_bits_in = d->m_bits_in; if (!use_raw_block) - comp_block_succeeded = tdefl_compress_block(d, (d->m_flags & TDEFL_FORCE_ALL_STATIC_BLOCKS) || (d->m_total_lz_bytes < 48)); + comp_block_succeeded = + tdefl_compress_block(d, (d->m_flags & TDEFL_FORCE_ALL_STATIC_BLOCKS) || + (d->m_total_lz_bytes < 48)); - // If the block gets expanded, forget the current contents of the output buffer and send a raw block instead. - if ( ((use_raw_block) || ((d->m_total_lz_bytes) && ((d->m_pOutput_buf - pSaved_output_buf + 1U) >= d->m_total_lz_bytes))) && - ((d->m_lookahead_pos - d->m_lz_code_buf_dict_pos) <= d->m_dict_size) ) - { - mz_uint i; d->m_pOutput_buf = pSaved_output_buf; d->m_bit_buffer = saved_bit_buf, d->m_bits_in = saved_bits_in; + // If the block gets expanded, forget the current contents of the output + // buffer and send a raw block instead. + if (((use_raw_block) || + ((d->m_total_lz_bytes) && ((d->m_pOutput_buf - pSaved_output_buf + 1U) >= + d->m_total_lz_bytes))) && + ((d->m_lookahead_pos - d->m_lz_code_buf_dict_pos) <= d->m_dict_size)) { + mz_uint i; + d->m_pOutput_buf = pSaved_output_buf; + d->m_bit_buffer = saved_bit_buf, d->m_bits_in = saved_bits_in; TDEFL_PUT_BITS(0, 2); - if (d->m_bits_in) { TDEFL_PUT_BITS(0, 8 - d->m_bits_in); } - for (i = 2; i; --i, d->m_total_lz_bytes ^= 0xFFFF) - { + if (d->m_bits_in) { + TDEFL_PUT_BITS(0, 8 - d->m_bits_in); + } + for (i = 2; i; --i, d->m_total_lz_bytes ^= 0xFFFF) { TDEFL_PUT_BITS(d->m_total_lz_bytes & 0xFFFF, 16); } - for (i = 0; i < d->m_total_lz_bytes; ++i) - { - TDEFL_PUT_BITS(d->m_dict[(d->m_lz_code_buf_dict_pos + i) & TDEFL_LZ_DICT_SIZE_MASK], 8); + for (i = 0; i < d->m_total_lz_bytes; ++i) { + TDEFL_PUT_BITS( + d->m_dict[(d->m_lz_code_buf_dict_pos + i) & TDEFL_LZ_DICT_SIZE_MASK], + 8); } } - // Check for the extremely unlikely (if not impossible) case of the compressed block not fitting into the output buffer when using dynamic codes. - else if (!comp_block_succeeded) - { - d->m_pOutput_buf = pSaved_output_buf; d->m_bit_buffer = saved_bit_buf, d->m_bits_in = saved_bits_in; + // Check for the extremely unlikely (if not impossible) case of the compressed + // block not fitting into the output buffer when using dynamic codes. + else if (!comp_block_succeeded) { + d->m_pOutput_buf = pSaved_output_buf; + d->m_bit_buffer = saved_bit_buf, d->m_bits_in = saved_bits_in; tdefl_compress_block(d, MZ_TRUE); } - if (flush) - { - if (flush == TDEFL_FINISH) - { - if (d->m_bits_in) { TDEFL_PUT_BITS(0, 8 - d->m_bits_in); } - if (d->m_flags & TDEFL_WRITE_ZLIB_HEADER) { mz_uint i, a = d->m_adler32; for (i = 0; i < 4; i++) { TDEFL_PUT_BITS((a >> 24) & 0xFF, 8); a <<= 8; } } - } - else - { - mz_uint i, z = 0; TDEFL_PUT_BITS(0, 3); if (d->m_bits_in) { TDEFL_PUT_BITS(0, 8 - d->m_bits_in); } for (i = 2; i; --i, z ^= 0xFFFF) { TDEFL_PUT_BITS(z & 0xFFFF, 16); } + if (flush) { + if (flush == TDEFL_FINISH) { + if (d->m_bits_in) { + TDEFL_PUT_BITS(0, 8 - d->m_bits_in); + } + if (d->m_flags & TDEFL_WRITE_ZLIB_HEADER) { + mz_uint i, a = d->m_adler32; + for (i = 0; i < 4; i++) { + TDEFL_PUT_BITS((a >> 24) & 0xFF, 8); + a <<= 8; + } + } + } else { + mz_uint i, z = 0; + TDEFL_PUT_BITS(0, 3); + if (d->m_bits_in) { + TDEFL_PUT_BITS(0, 8 - d->m_bits_in); + } + for (i = 2; i; --i, z ^= 0xFFFF) { + TDEFL_PUT_BITS(z & 0xFFFF, 16); + } } } MZ_ASSERT(d->m_pOutput_buf < d->m_pOutput_buf_end); - memset(&d->m_huff_count[0][0], 0, sizeof(d->m_huff_count[0][0]) * TDEFL_MAX_HUFF_SYMBOLS_0); - memset(&d->m_huff_count[1][0], 0, sizeof(d->m_huff_count[1][0]) * TDEFL_MAX_HUFF_SYMBOLS_1); + memset(&d->m_huff_count[0][0], 0, + sizeof(d->m_huff_count[0][0]) * TDEFL_MAX_HUFF_SYMBOLS_0); + memset(&d->m_huff_count[1][0], 0, + sizeof(d->m_huff_count[1][0]) * TDEFL_MAX_HUFF_SYMBOLS_1); - d->m_pLZ_code_buf = d->m_lz_code_buf + 1; d->m_pLZ_flags = d->m_lz_code_buf; d->m_num_flags_left = 8; d->m_lz_code_buf_dict_pos += d->m_total_lz_bytes; d->m_total_lz_bytes = 0; d->m_block_index++; + d->m_pLZ_code_buf = d->m_lz_code_buf + 1; + d->m_pLZ_flags = d->m_lz_code_buf; + d->m_num_flags_left = 8; + d->m_lz_code_buf_dict_pos += d->m_total_lz_bytes; + d->m_total_lz_bytes = 0; + d->m_block_index++; - if ((n = (int)(d->m_pOutput_buf - pOutput_buf_start)) != 0) - { - if (d->m_pPut_buf_func) - { + if ((n = (int)(d->m_pOutput_buf - pOutput_buf_start)) != 0) { + if (d->m_pPut_buf_func) { *d->m_pIn_buf_size = d->m_pSrc - (const mz_uint8 *)d->m_pIn_buf; if (!(*d->m_pPut_buf_func)(d->m_output_buf, n, d->m_pPut_buf_user)) return (d->m_prev_return_status = TDEFL_STATUS_PUT_BUF_FAILED); - } - else if (pOutput_buf_start == d->m_output_buf) - { - int bytes_to_copy = (int)MZ_MIN((size_t)n, (size_t)(*d->m_pOut_buf_size - d->m_out_buf_ofs)); - memcpy((mz_uint8 *)d->m_pOut_buf + d->m_out_buf_ofs, d->m_output_buf, bytes_to_copy); + } else if (pOutput_buf_start == d->m_output_buf) { + int bytes_to_copy = (int)MZ_MIN( + (size_t)n, (size_t)(*d->m_pOut_buf_size - d->m_out_buf_ofs)); + memcpy((mz_uint8 *)d->m_pOut_buf + d->m_out_buf_ofs, d->m_output_buf, + bytes_to_copy); d->m_out_buf_ofs += bytes_to_copy; - if ((n -= bytes_to_copy) != 0) - { + if ((n -= bytes_to_copy) != 0) { d->m_output_flush_ofs = bytes_to_copy; d->m_output_flush_remaining = n; } - } - else - { + } else { d->m_out_buf_ofs += n; } } @@ -2269,133 +3182,192 @@ static int tdefl_flush_block(tdefl_compressor *d, int flush) } #if MINIZ_USE_UNALIGNED_LOADS_AND_STORES -#define TDEFL_READ_UNALIGNED_WORD(p) *(const mz_uint16*)(p) -static MZ_FORCEINLINE void tdefl_find_match(tdefl_compressor *d, mz_uint lookahead_pos, mz_uint max_dist, mz_uint max_match_len, mz_uint *pMatch_dist, mz_uint *pMatch_len) -{ - mz_uint dist, pos = lookahead_pos & TDEFL_LZ_DICT_SIZE_MASK, match_len = *pMatch_len, probe_pos = pos, next_probe_pos, probe_len; +#define TDEFL_READ_UNALIGNED_WORD(p) ((p)[0] | (p)[1] << 8) +static MZ_FORCEINLINE void +tdefl_find_match(tdefl_compressor *d, mz_uint lookahead_pos, mz_uint max_dist, + mz_uint max_match_len, mz_uint *pMatch_dist, + mz_uint *pMatch_len) { + mz_uint dist, pos = lookahead_pos & TDEFL_LZ_DICT_SIZE_MASK, + match_len = *pMatch_len, probe_pos = pos, next_probe_pos, + probe_len; mz_uint num_probes_left = d->m_max_probes[match_len >= 32]; - const mz_uint16 *s = (const mz_uint16*)(d->m_dict + pos), *p, *q; - mz_uint16 c01 = TDEFL_READ_UNALIGNED_WORD(&d->m_dict[pos + match_len - 1]), s01 = TDEFL_READ_UNALIGNED_WORD(s); - MZ_ASSERT(max_match_len <= TDEFL_MAX_MATCH_LEN); if (max_match_len <= match_len) return; - for ( ; ; ) - { - for ( ; ; ) - { - if (--num_probes_left == 0) return; - #define TDEFL_PROBE \ - next_probe_pos = d->m_next[probe_pos]; \ - if ((!next_probe_pos) || ((dist = (mz_uint16)(lookahead_pos - next_probe_pos)) > max_dist)) return; \ - probe_pos = next_probe_pos & TDEFL_LZ_DICT_SIZE_MASK; \ - if (TDEFL_READ_UNALIGNED_WORD(&d->m_dict[probe_pos + match_len - 1]) == c01) break; - TDEFL_PROBE; TDEFL_PROBE; TDEFL_PROBE; + const mz_uint16 *s = (const mz_uint16 *)(d->m_dict + pos), *p, *q; + mz_uint16 c01 = TDEFL_READ_UNALIGNED_WORD(&d->m_dict[pos + match_len - 1]), + s01 = *s; + MZ_ASSERT(max_match_len <= TDEFL_MAX_MATCH_LEN); + if (max_match_len <= match_len) + return; + for (;;) { + for (;;) { + if (--num_probes_left == 0) + return; +#define TDEFL_PROBE \ + next_probe_pos = d->m_next[probe_pos]; \ + if ((!next_probe_pos) || \ + ((dist = (mz_uint16)(lookahead_pos - next_probe_pos)) > max_dist)) \ + return; \ + probe_pos = next_probe_pos & TDEFL_LZ_DICT_SIZE_MASK; \ + if (TDEFL_READ_UNALIGNED_WORD(&d->m_dict[probe_pos + match_len - 1]) == c01) \ + break; + TDEFL_PROBE; + TDEFL_PROBE; + TDEFL_PROBE; } - if (!dist) break; q = (const mz_uint16*)(d->m_dict + probe_pos); if (TDEFL_READ_UNALIGNED_WORD(q) != s01) continue; p = s; probe_len = 32; - do { } while ( (TDEFL_READ_UNALIGNED_WORD(++p) == TDEFL_READ_UNALIGNED_WORD(++q)) && (TDEFL_READ_UNALIGNED_WORD(++p) == TDEFL_READ_UNALIGNED_WORD(++q)) && - (TDEFL_READ_UNALIGNED_WORD(++p) == TDEFL_READ_UNALIGNED_WORD(++q)) && (TDEFL_READ_UNALIGNED_WORD(++p) == TDEFL_READ_UNALIGNED_WORD(++q)) && (--probe_len > 0) ); - if (!probe_len) - { - *pMatch_dist = dist; *pMatch_len = MZ_MIN(max_match_len, TDEFL_MAX_MATCH_LEN); break; - } - else if ((probe_len = ((mz_uint)(p - s) * 2) + (mz_uint)(*(const mz_uint8*)p == *(const mz_uint8*)q)) > match_len) - { - *pMatch_dist = dist; if ((*pMatch_len = match_len = MZ_MIN(max_match_len, probe_len)) == max_match_len) break; + if (!dist) + break; + q = (const mz_uint16 *)(d->m_dict + probe_pos); + if (*q != s01) + continue; + p = s; + probe_len = 32; + do { + } while ((*(++p) == *(++q)) && (*(++p) == *(++q)) && (*(++p) == *(++q)) && + (*(++p) == *(++q)) && (--probe_len > 0)); + if (!probe_len) { + *pMatch_dist = dist; + *pMatch_len = MZ_MIN(max_match_len, TDEFL_MAX_MATCH_LEN); + break; + } else if ((probe_len = ((mz_uint)(p - s) * 2) + + (mz_uint)(*(const mz_uint8 *)p == + *(const mz_uint8 *)q)) > match_len) { + *pMatch_dist = dist; + if ((*pMatch_len = match_len = MZ_MIN(max_match_len, probe_len)) == + max_match_len) + break; c01 = TDEFL_READ_UNALIGNED_WORD(&d->m_dict[pos + match_len - 1]); } } } #else -static MZ_FORCEINLINE void tdefl_find_match(tdefl_compressor *d, mz_uint lookahead_pos, mz_uint max_dist, mz_uint max_match_len, mz_uint *pMatch_dist, mz_uint *pMatch_len) -{ - mz_uint dist, pos = lookahead_pos & TDEFL_LZ_DICT_SIZE_MASK, match_len = *pMatch_len, probe_pos = pos, next_probe_pos, probe_len; +static MZ_FORCEINLINE void +tdefl_find_match(tdefl_compressor *d, mz_uint lookahead_pos, mz_uint max_dist, + mz_uint max_match_len, mz_uint *pMatch_dist, + mz_uint *pMatch_len) { + mz_uint dist, pos = lookahead_pos & TDEFL_LZ_DICT_SIZE_MASK, + match_len = *pMatch_len, probe_pos = pos, next_probe_pos, + probe_len; mz_uint num_probes_left = d->m_max_probes[match_len >= 32]; const mz_uint8 *s = d->m_dict + pos, *p, *q; mz_uint8 c0 = d->m_dict[pos + match_len], c1 = d->m_dict[pos + match_len - 1]; - MZ_ASSERT(max_match_len <= TDEFL_MAX_MATCH_LEN); if (max_match_len <= match_len) return; - for ( ; ; ) - { - for ( ; ; ) - { - if (--num_probes_left == 0) return; - #define TDEFL_PROBE \ - next_probe_pos = d->m_next[probe_pos]; \ - if ((!next_probe_pos) || ((dist = (mz_uint16)(lookahead_pos - next_probe_pos)) > max_dist)) return; \ - probe_pos = next_probe_pos & TDEFL_LZ_DICT_SIZE_MASK; \ - if ((d->m_dict[probe_pos + match_len] == c0) && (d->m_dict[probe_pos + match_len - 1] == c1)) break; - TDEFL_PROBE; TDEFL_PROBE; TDEFL_PROBE; + MZ_ASSERT(max_match_len <= TDEFL_MAX_MATCH_LEN); + if (max_match_len <= match_len) + return; + for (;;) { + for (;;) { + if (--num_probes_left == 0) + return; +#define TDEFL_PROBE \ + next_probe_pos = d->m_next[probe_pos]; \ + if ((!next_probe_pos) || \ + ((dist = (mz_uint16)(lookahead_pos - next_probe_pos)) > max_dist)) \ + return; \ + probe_pos = next_probe_pos & TDEFL_LZ_DICT_SIZE_MASK; \ + if ((d->m_dict[probe_pos + match_len] == c0) && \ + (d->m_dict[probe_pos + match_len - 1] == c1)) \ + break; + TDEFL_PROBE; + TDEFL_PROBE; + TDEFL_PROBE; } - if (!dist) break; p = s; q = d->m_dict + probe_pos; for (probe_len = 0; probe_len < max_match_len; probe_len++) if (*p++ != *q++) break; - if (probe_len > match_len) - { - *pMatch_dist = dist; if ((*pMatch_len = match_len = probe_len) == max_match_len) return; - c0 = d->m_dict[pos + match_len]; c1 = d->m_dict[pos + match_len - 1]; + if (!dist) + break; + p = s; + q = d->m_dict + probe_pos; + for (probe_len = 0; probe_len < max_match_len; probe_len++) + if (*p++ != *q++) + break; + if (probe_len > match_len) { + *pMatch_dist = dist; + if ((*pMatch_len = match_len = probe_len) == max_match_len) + return; + c0 = d->m_dict[pos + match_len]; + c1 = d->m_dict[pos + match_len - 1]; } } } #endif // #if MINIZ_USE_UNALIGNED_LOADS_AND_STORES #if MINIZ_USE_UNALIGNED_LOADS_AND_STORES && MINIZ_LITTLE_ENDIAN -static mz_bool tdefl_compress_fast(tdefl_compressor *d) -{ - // Faster, minimally featured LZRW1-style match+parse loop with better register utilization. Intended for applications where raw throughput is valued more highly than ratio. - mz_uint lookahead_pos = d->m_lookahead_pos, lookahead_size = d->m_lookahead_size, dict_size = d->m_dict_size, total_lz_bytes = d->m_total_lz_bytes, num_flags_left = d->m_num_flags_left; +static mz_bool tdefl_compress_fast(tdefl_compressor *d) { + // Faster, minimally featured LZRW1-style match+parse loop with better + // register utilization. Intended for applications where raw throughput is + // valued more highly than ratio. + mz_uint lookahead_pos = d->m_lookahead_pos, + lookahead_size = d->m_lookahead_size, dict_size = d->m_dict_size, + total_lz_bytes = d->m_total_lz_bytes, + num_flags_left = d->m_num_flags_left; mz_uint8 *pLZ_code_buf = d->m_pLZ_code_buf, *pLZ_flags = d->m_pLZ_flags; mz_uint cur_pos = lookahead_pos & TDEFL_LZ_DICT_SIZE_MASK; - while ((d->m_src_buf_left) || ((d->m_flush) && (lookahead_size))) - { + while ((d->m_src_buf_left) || ((d->m_flush) && (lookahead_size))) { const mz_uint TDEFL_COMP_FAST_LOOKAHEAD_SIZE = 4096; - mz_uint dst_pos = (lookahead_pos + lookahead_size) & TDEFL_LZ_DICT_SIZE_MASK; - mz_uint num_bytes_to_process = (mz_uint)MZ_MIN(d->m_src_buf_left, TDEFL_COMP_FAST_LOOKAHEAD_SIZE - lookahead_size); + mz_uint dst_pos = + (lookahead_pos + lookahead_size) & TDEFL_LZ_DICT_SIZE_MASK; + mz_uint num_bytes_to_process = (mz_uint)MZ_MIN( + d->m_src_buf_left, TDEFL_COMP_FAST_LOOKAHEAD_SIZE - lookahead_size); d->m_src_buf_left -= num_bytes_to_process; lookahead_size += num_bytes_to_process; - while (num_bytes_to_process) - { + while (num_bytes_to_process) { mz_uint32 n = MZ_MIN(TDEFL_LZ_DICT_SIZE - dst_pos, num_bytes_to_process); memcpy(d->m_dict + dst_pos, d->m_pSrc, n); if (dst_pos < (TDEFL_MAX_MATCH_LEN - 1)) - memcpy(d->m_dict + TDEFL_LZ_DICT_SIZE + dst_pos, d->m_pSrc, MZ_MIN(n, (TDEFL_MAX_MATCH_LEN - 1) - dst_pos)); + memcpy(d->m_dict + TDEFL_LZ_DICT_SIZE + dst_pos, d->m_pSrc, + MZ_MIN(n, (TDEFL_MAX_MATCH_LEN - 1) - dst_pos)); d->m_pSrc += n; dst_pos = (dst_pos + n) & TDEFL_LZ_DICT_SIZE_MASK; num_bytes_to_process -= n; } dict_size = MZ_MIN(TDEFL_LZ_DICT_SIZE - lookahead_size, dict_size); - if ((!d->m_flush) && (lookahead_size < TDEFL_COMP_FAST_LOOKAHEAD_SIZE)) break; + if ((!d->m_flush) && (lookahead_size < TDEFL_COMP_FAST_LOOKAHEAD_SIZE)) + break; - while (lookahead_size >= 4) - { + while (lookahead_size >= 4) { mz_uint cur_match_dist, cur_match_len = 1; mz_uint8 *pCur_dict = d->m_dict + cur_pos; mz_uint first_trigram = (*(const mz_uint32 *)pCur_dict) & 0xFFFFFF; - mz_uint hash = (first_trigram ^ (first_trigram >> (24 - (TDEFL_LZ_HASH_BITS - 8)))) & TDEFL_LEVEL1_HASH_SIZE_MASK; + mz_uint hash = + (first_trigram ^ (first_trigram >> (24 - (TDEFL_LZ_HASH_BITS - 8)))) & + TDEFL_LEVEL1_HASH_SIZE_MASK; mz_uint probe_pos = d->m_hash[hash]; d->m_hash[hash] = (mz_uint16)lookahead_pos; - if (((cur_match_dist = (mz_uint16)(lookahead_pos - probe_pos)) <= dict_size) && ((*(const mz_uint32 *)(d->m_dict + (probe_pos &= TDEFL_LZ_DICT_SIZE_MASK)) & 0xFFFFFF) == first_trigram)) - { + if (((cur_match_dist = (mz_uint16)(lookahead_pos - probe_pos)) <= + dict_size) && + ((mz_uint32)( + *(d->m_dict + (probe_pos & TDEFL_LZ_DICT_SIZE_MASK)) | + (*(d->m_dict + ((probe_pos & TDEFL_LZ_DICT_SIZE_MASK) + 1)) + << 8) | + (*(d->m_dict + ((probe_pos & TDEFL_LZ_DICT_SIZE_MASK) + 2)) + << 16)) == first_trigram)) { const mz_uint16 *p = (const mz_uint16 *)pCur_dict; - const mz_uint16 *q = (const mz_uint16 *)(d->m_dict + probe_pos); + const mz_uint16 *q = + (const mz_uint16 *)(d->m_dict + + (probe_pos & TDEFL_LZ_DICT_SIZE_MASK)); mz_uint32 probe_len = 32; - do { } while ( (TDEFL_READ_UNALIGNED_WORD(++p) == TDEFL_READ_UNALIGNED_WORD(++q)) && (TDEFL_READ_UNALIGNED_WORD(++p) == TDEFL_READ_UNALIGNED_WORD(++q)) && - (TDEFL_READ_UNALIGNED_WORD(++p) == TDEFL_READ_UNALIGNED_WORD(++q)) && (TDEFL_READ_UNALIGNED_WORD(++p) == TDEFL_READ_UNALIGNED_WORD(++q)) && (--probe_len > 0) ); - cur_match_len = ((mz_uint)(p - (const mz_uint16 *)pCur_dict) * 2) + (mz_uint)(*(const mz_uint8 *)p == *(const mz_uint8 *)q); + do { + } while ((*(++p) == *(++q)) && (*(++p) == *(++q)) && + (*(++p) == *(++q)) && (*(++p) == *(++q)) && (--probe_len > 0)); + cur_match_len = ((mz_uint)(p - (const mz_uint16 *)pCur_dict) * 2) + + (mz_uint)(*(const mz_uint8 *)p == *(const mz_uint8 *)q); if (!probe_len) cur_match_len = cur_match_dist ? TDEFL_MAX_MATCH_LEN : 0; - if ((cur_match_len < TDEFL_MIN_MATCH_LEN) || ((cur_match_len == TDEFL_MIN_MATCH_LEN) && (cur_match_dist >= 8U*1024U))) - { + if ((cur_match_len < TDEFL_MIN_MATCH_LEN) || + ((cur_match_len == TDEFL_MIN_MATCH_LEN) && + (cur_match_dist >= 8U * 1024U))) { cur_match_len = 1; *pLZ_code_buf++ = (mz_uint8)first_trigram; *pLZ_flags = (mz_uint8)(*pLZ_flags >> 1); d->m_huff_count[0][(mz_uint8)first_trigram]++; - } - else - { + } else { mz_uint32 s0, s1; cur_match_len = MZ_MIN(cur_match_len, lookahead_size); - MZ_ASSERT((cur_match_len >= TDEFL_MIN_MATCH_LEN) && (cur_match_dist >= 1) && (cur_match_dist <= TDEFL_LZ_DICT_SIZE)); + MZ_ASSERT((cur_match_len >= TDEFL_MIN_MATCH_LEN) && + (cur_match_dist >= 1) && + (cur_match_dist <= TDEFL_LZ_DICT_SIZE)); cur_match_dist--; @@ -2408,17 +3380,19 @@ static mz_bool tdefl_compress_fast(tdefl_compressor *d) s1 = s_tdefl_large_dist_sym[cur_match_dist >> 8]; d->m_huff_count[1][(cur_match_dist < 512) ? s0 : s1]++; - d->m_huff_count[0][s_tdefl_len_sym[cur_match_len - TDEFL_MIN_MATCH_LEN]]++; + d->m_huff_count[0][s_tdefl_len_sym[cur_match_len - + TDEFL_MIN_MATCH_LEN]]++; } - } - else - { + } else { *pLZ_code_buf++ = (mz_uint8)first_trigram; *pLZ_flags = (mz_uint8)(*pLZ_flags >> 1); d->m_huff_count[0][(mz_uint8)first_trigram]++; } - if (--num_flags_left == 0) { num_flags_left = 8; pLZ_flags = pLZ_code_buf++; } + if (--num_flags_left == 0) { + num_flags_left = 8; + pLZ_flags = pLZ_code_buf++; + } total_lz_bytes += cur_match_len; lookahead_pos += cur_match_len; @@ -2427,25 +3401,34 @@ static mz_bool tdefl_compress_fast(tdefl_compressor *d) MZ_ASSERT(lookahead_size >= cur_match_len); lookahead_size -= cur_match_len; - if (pLZ_code_buf > &d->m_lz_code_buf[TDEFL_LZ_CODE_BUF_SIZE - 8]) - { + if (pLZ_code_buf > &d->m_lz_code_buf[TDEFL_LZ_CODE_BUF_SIZE - 8]) { int n; - d->m_lookahead_pos = lookahead_pos; d->m_lookahead_size = lookahead_size; d->m_dict_size = dict_size; - d->m_total_lz_bytes = total_lz_bytes; d->m_pLZ_code_buf = pLZ_code_buf; d->m_pLZ_flags = pLZ_flags; d->m_num_flags_left = num_flags_left; + d->m_lookahead_pos = lookahead_pos; + d->m_lookahead_size = lookahead_size; + d->m_dict_size = dict_size; + d->m_total_lz_bytes = total_lz_bytes; + d->m_pLZ_code_buf = pLZ_code_buf; + d->m_pLZ_flags = pLZ_flags; + d->m_num_flags_left = num_flags_left; if ((n = tdefl_flush_block(d, 0)) != 0) return (n < 0) ? MZ_FALSE : MZ_TRUE; - total_lz_bytes = d->m_total_lz_bytes; pLZ_code_buf = d->m_pLZ_code_buf; pLZ_flags = d->m_pLZ_flags; num_flags_left = d->m_num_flags_left; + total_lz_bytes = d->m_total_lz_bytes; + pLZ_code_buf = d->m_pLZ_code_buf; + pLZ_flags = d->m_pLZ_flags; + num_flags_left = d->m_num_flags_left; } } - while (lookahead_size) - { + while (lookahead_size) { mz_uint8 lit = d->m_dict[cur_pos]; total_lz_bytes++; *pLZ_code_buf++ = lit; *pLZ_flags = (mz_uint8)(*pLZ_flags >> 1); - if (--num_flags_left == 0) { num_flags_left = 8; pLZ_flags = pLZ_code_buf++; } + if (--num_flags_left == 0) { + num_flags_left = 8; + pLZ_flags = pLZ_code_buf++; + } d->m_huff_count[0][lit]++; @@ -2454,37 +3437,54 @@ static mz_bool tdefl_compress_fast(tdefl_compressor *d) cur_pos = (cur_pos + 1) & TDEFL_LZ_DICT_SIZE_MASK; lookahead_size--; - if (pLZ_code_buf > &d->m_lz_code_buf[TDEFL_LZ_CODE_BUF_SIZE - 8]) - { + if (pLZ_code_buf > &d->m_lz_code_buf[TDEFL_LZ_CODE_BUF_SIZE - 8]) { int n; - d->m_lookahead_pos = lookahead_pos; d->m_lookahead_size = lookahead_size; d->m_dict_size = dict_size; - d->m_total_lz_bytes = total_lz_bytes; d->m_pLZ_code_buf = pLZ_code_buf; d->m_pLZ_flags = pLZ_flags; d->m_num_flags_left = num_flags_left; + d->m_lookahead_pos = lookahead_pos; + d->m_lookahead_size = lookahead_size; + d->m_dict_size = dict_size; + d->m_total_lz_bytes = total_lz_bytes; + d->m_pLZ_code_buf = pLZ_code_buf; + d->m_pLZ_flags = pLZ_flags; + d->m_num_flags_left = num_flags_left; if ((n = tdefl_flush_block(d, 0)) != 0) return (n < 0) ? MZ_FALSE : MZ_TRUE; - total_lz_bytes = d->m_total_lz_bytes; pLZ_code_buf = d->m_pLZ_code_buf; pLZ_flags = d->m_pLZ_flags; num_flags_left = d->m_num_flags_left; + total_lz_bytes = d->m_total_lz_bytes; + pLZ_code_buf = d->m_pLZ_code_buf; + pLZ_flags = d->m_pLZ_flags; + num_flags_left = d->m_num_flags_left; } } } - d->m_lookahead_pos = lookahead_pos; d->m_lookahead_size = lookahead_size; d->m_dict_size = dict_size; - d->m_total_lz_bytes = total_lz_bytes; d->m_pLZ_code_buf = pLZ_code_buf; d->m_pLZ_flags = pLZ_flags; d->m_num_flags_left = num_flags_left; + d->m_lookahead_pos = lookahead_pos; + d->m_lookahead_size = lookahead_size; + d->m_dict_size = dict_size; + d->m_total_lz_bytes = total_lz_bytes; + d->m_pLZ_code_buf = pLZ_code_buf; + d->m_pLZ_flags = pLZ_flags; + d->m_num_flags_left = num_flags_left; return MZ_TRUE; } #endif // MINIZ_USE_UNALIGNED_LOADS_AND_STORES && MINIZ_LITTLE_ENDIAN -static MZ_FORCEINLINE void tdefl_record_literal(tdefl_compressor *d, mz_uint8 lit) -{ +static MZ_FORCEINLINE void tdefl_record_literal(tdefl_compressor *d, + mz_uint8 lit) { d->m_total_lz_bytes++; *d->m_pLZ_code_buf++ = lit; - *d->m_pLZ_flags = (mz_uint8)(*d->m_pLZ_flags >> 1); if (--d->m_num_flags_left == 0) { d->m_num_flags_left = 8; d->m_pLZ_flags = d->m_pLZ_code_buf++; } + *d->m_pLZ_flags = (mz_uint8)(*d->m_pLZ_flags >> 1); + if (--d->m_num_flags_left == 0) { + d->m_num_flags_left = 8; + d->m_pLZ_flags = d->m_pLZ_code_buf++; + } d->m_huff_count[0][lit]++; } -static MZ_FORCEINLINE void tdefl_record_match(tdefl_compressor *d, mz_uint match_len, mz_uint match_dist) -{ +static MZ_FORCEINLINE void +tdefl_record_match(tdefl_compressor *d, mz_uint match_len, mz_uint match_dist) { mz_uint32 s0, s1; - MZ_ASSERT((match_len >= TDEFL_MIN_MATCH_LEN) && (match_dist >= 1) && (match_dist <= TDEFL_LZ_DICT_SIZE)); + MZ_ASSERT((match_len >= TDEFL_MIN_MATCH_LEN) && (match_dist >= 1) && + (match_dist <= TDEFL_LZ_DICT_SIZE)); d->m_total_lz_bytes += match_len; @@ -2492,145 +3492,176 @@ static MZ_FORCEINLINE void tdefl_record_match(tdefl_compressor *d, mz_uint match match_dist -= 1; d->m_pLZ_code_buf[1] = (mz_uint8)(match_dist & 0xFF); - d->m_pLZ_code_buf[2] = (mz_uint8)(match_dist >> 8); d->m_pLZ_code_buf += 3; + d->m_pLZ_code_buf[2] = (mz_uint8)(match_dist >> 8); + d->m_pLZ_code_buf += 3; - *d->m_pLZ_flags = (mz_uint8)((*d->m_pLZ_flags >> 1) | 0x80); if (--d->m_num_flags_left == 0) { d->m_num_flags_left = 8; d->m_pLZ_flags = d->m_pLZ_code_buf++; } + *d->m_pLZ_flags = (mz_uint8)((*d->m_pLZ_flags >> 1) | 0x80); + if (--d->m_num_flags_left == 0) { + d->m_num_flags_left = 8; + d->m_pLZ_flags = d->m_pLZ_code_buf++; + } - s0 = s_tdefl_small_dist_sym[match_dist & 511]; s1 = s_tdefl_large_dist_sym[(match_dist >> 8) & 127]; + s0 = s_tdefl_small_dist_sym[match_dist & 511]; + s1 = s_tdefl_large_dist_sym[(match_dist >> 8) & 127]; d->m_huff_count[1][(match_dist < 512) ? s0 : s1]++; - if (match_len >= TDEFL_MIN_MATCH_LEN) d->m_huff_count[0][s_tdefl_len_sym[match_len - TDEFL_MIN_MATCH_LEN]]++; + if (match_len >= TDEFL_MIN_MATCH_LEN) + d->m_huff_count[0][s_tdefl_len_sym[match_len - TDEFL_MIN_MATCH_LEN]]++; } -static mz_bool tdefl_compress_normal(tdefl_compressor *d) -{ - const mz_uint8 *pSrc = d->m_pSrc; size_t src_buf_left = d->m_src_buf_left; +static mz_bool tdefl_compress_normal(tdefl_compressor *d) { + const mz_uint8 *pSrc = d->m_pSrc; + size_t src_buf_left = d->m_src_buf_left; tdefl_flush flush = d->m_flush; - while ((src_buf_left) || ((flush) && (d->m_lookahead_size))) - { + while ((src_buf_left) || ((flush) && (d->m_lookahead_size))) { mz_uint len_to_move, cur_match_dist, cur_match_len, cur_pos; - // Update dictionary and hash chains. Keeps the lookahead size equal to TDEFL_MAX_MATCH_LEN. - if ((d->m_lookahead_size + d->m_dict_size) >= (TDEFL_MIN_MATCH_LEN - 1)) - { - mz_uint dst_pos = (d->m_lookahead_pos + d->m_lookahead_size) & TDEFL_LZ_DICT_SIZE_MASK, ins_pos = d->m_lookahead_pos + d->m_lookahead_size - 2; - mz_uint hash = (d->m_dict[ins_pos & TDEFL_LZ_DICT_SIZE_MASK] << TDEFL_LZ_HASH_SHIFT) ^ d->m_dict[(ins_pos + 1) & TDEFL_LZ_DICT_SIZE_MASK]; - mz_uint num_bytes_to_process = (mz_uint)MZ_MIN(src_buf_left, TDEFL_MAX_MATCH_LEN - d->m_lookahead_size); + // Update dictionary and hash chains. Keeps the lookahead size equal to + // TDEFL_MAX_MATCH_LEN. + if ((d->m_lookahead_size + d->m_dict_size) >= (TDEFL_MIN_MATCH_LEN - 1)) { + mz_uint dst_pos = (d->m_lookahead_pos + d->m_lookahead_size) & + TDEFL_LZ_DICT_SIZE_MASK, + ins_pos = d->m_lookahead_pos + d->m_lookahead_size - 2; + mz_uint hash = (d->m_dict[ins_pos & TDEFL_LZ_DICT_SIZE_MASK] + << TDEFL_LZ_HASH_SHIFT) ^ + d->m_dict[(ins_pos + 1) & TDEFL_LZ_DICT_SIZE_MASK]; + mz_uint num_bytes_to_process = (mz_uint)MZ_MIN( + src_buf_left, TDEFL_MAX_MATCH_LEN - d->m_lookahead_size); const mz_uint8 *pSrc_end = pSrc + num_bytes_to_process; src_buf_left -= num_bytes_to_process; d->m_lookahead_size += num_bytes_to_process; - while (pSrc != pSrc_end) - { - mz_uint8 c = *pSrc++; d->m_dict[dst_pos] = c; if (dst_pos < (TDEFL_MAX_MATCH_LEN - 1)) d->m_dict[TDEFL_LZ_DICT_SIZE + dst_pos] = c; - hash = ((hash << TDEFL_LZ_HASH_SHIFT) ^ c) & (TDEFL_LZ_HASH_SIZE - 1); - d->m_next[ins_pos & TDEFL_LZ_DICT_SIZE_MASK] = d->m_hash[hash]; d->m_hash[hash] = (mz_uint16)(ins_pos); - dst_pos = (dst_pos + 1) & TDEFL_LZ_DICT_SIZE_MASK; ins_pos++; - } - } - else - { - while ((src_buf_left) && (d->m_lookahead_size < TDEFL_MAX_MATCH_LEN)) - { + while (pSrc != pSrc_end) { mz_uint8 c = *pSrc++; - mz_uint dst_pos = (d->m_lookahead_pos + d->m_lookahead_size) & TDEFL_LZ_DICT_SIZE_MASK; + d->m_dict[dst_pos] = c; + if (dst_pos < (TDEFL_MAX_MATCH_LEN - 1)) + d->m_dict[TDEFL_LZ_DICT_SIZE + dst_pos] = c; + hash = ((hash << TDEFL_LZ_HASH_SHIFT) ^ c) & (TDEFL_LZ_HASH_SIZE - 1); + d->m_next[ins_pos & TDEFL_LZ_DICT_SIZE_MASK] = d->m_hash[hash]; + d->m_hash[hash] = (mz_uint16)(ins_pos); + dst_pos = (dst_pos + 1) & TDEFL_LZ_DICT_SIZE_MASK; + ins_pos++; + } + } else { + while ((src_buf_left) && (d->m_lookahead_size < TDEFL_MAX_MATCH_LEN)) { + mz_uint8 c = *pSrc++; + mz_uint dst_pos = (d->m_lookahead_pos + d->m_lookahead_size) & + TDEFL_LZ_DICT_SIZE_MASK; src_buf_left--; d->m_dict[dst_pos] = c; if (dst_pos < (TDEFL_MAX_MATCH_LEN - 1)) d->m_dict[TDEFL_LZ_DICT_SIZE + dst_pos] = c; - if ((++d->m_lookahead_size + d->m_dict_size) >= TDEFL_MIN_MATCH_LEN) - { + if ((++d->m_lookahead_size + d->m_dict_size) >= TDEFL_MIN_MATCH_LEN) { mz_uint ins_pos = d->m_lookahead_pos + (d->m_lookahead_size - 1) - 2; - mz_uint hash = ((d->m_dict[ins_pos & TDEFL_LZ_DICT_SIZE_MASK] << (TDEFL_LZ_HASH_SHIFT * 2)) ^ (d->m_dict[(ins_pos + 1) & TDEFL_LZ_DICT_SIZE_MASK] << TDEFL_LZ_HASH_SHIFT) ^ c) & (TDEFL_LZ_HASH_SIZE - 1); - d->m_next[ins_pos & TDEFL_LZ_DICT_SIZE_MASK] = d->m_hash[hash]; d->m_hash[hash] = (mz_uint16)(ins_pos); + mz_uint hash = ((d->m_dict[ins_pos & TDEFL_LZ_DICT_SIZE_MASK] + << (TDEFL_LZ_HASH_SHIFT * 2)) ^ + (d->m_dict[(ins_pos + 1) & TDEFL_LZ_DICT_SIZE_MASK] + << TDEFL_LZ_HASH_SHIFT) ^ + c) & + (TDEFL_LZ_HASH_SIZE - 1); + d->m_next[ins_pos & TDEFL_LZ_DICT_SIZE_MASK] = d->m_hash[hash]; + d->m_hash[hash] = (mz_uint16)(ins_pos); } } } - d->m_dict_size = MZ_MIN(TDEFL_LZ_DICT_SIZE - d->m_lookahead_size, d->m_dict_size); + d->m_dict_size = + MZ_MIN(TDEFL_LZ_DICT_SIZE - d->m_lookahead_size, d->m_dict_size); if ((!flush) && (d->m_lookahead_size < TDEFL_MAX_MATCH_LEN)) break; // Simple lazy/greedy parsing state machine. - len_to_move = 1; cur_match_dist = 0; cur_match_len = d->m_saved_match_len ? d->m_saved_match_len : (TDEFL_MIN_MATCH_LEN - 1); cur_pos = d->m_lookahead_pos & TDEFL_LZ_DICT_SIZE_MASK; - if (d->m_flags & (TDEFL_RLE_MATCHES | TDEFL_FORCE_ALL_RAW_BLOCKS)) - { - if ((d->m_dict_size) && (!(d->m_flags & TDEFL_FORCE_ALL_RAW_BLOCKS))) - { + len_to_move = 1; + cur_match_dist = 0; + cur_match_len = + d->m_saved_match_len ? d->m_saved_match_len : (TDEFL_MIN_MATCH_LEN - 1); + cur_pos = d->m_lookahead_pos & TDEFL_LZ_DICT_SIZE_MASK; + if (d->m_flags & (TDEFL_RLE_MATCHES | TDEFL_FORCE_ALL_RAW_BLOCKS)) { + if ((d->m_dict_size) && (!(d->m_flags & TDEFL_FORCE_ALL_RAW_BLOCKS))) { mz_uint8 c = d->m_dict[(cur_pos - 1) & TDEFL_LZ_DICT_SIZE_MASK]; - cur_match_len = 0; while (cur_match_len < d->m_lookahead_size) { if (d->m_dict[cur_pos + cur_match_len] != c) break; cur_match_len++; } - if (cur_match_len < TDEFL_MIN_MATCH_LEN) cur_match_len = 0; else cur_match_dist = 1; + cur_match_len = 0; + while (cur_match_len < d->m_lookahead_size) { + if (d->m_dict[cur_pos + cur_match_len] != c) + break; + cur_match_len++; + } + if (cur_match_len < TDEFL_MIN_MATCH_LEN) + cur_match_len = 0; + else + cur_match_dist = 1; } + } else { + tdefl_find_match(d, d->m_lookahead_pos, d->m_dict_size, + d->m_lookahead_size, &cur_match_dist, &cur_match_len); } - else - { - tdefl_find_match(d, d->m_lookahead_pos, d->m_dict_size, d->m_lookahead_size, &cur_match_dist, &cur_match_len); - } - if (((cur_match_len == TDEFL_MIN_MATCH_LEN) && (cur_match_dist >= 8U*1024U)) || (cur_pos == cur_match_dist) || ((d->m_flags & TDEFL_FILTER_MATCHES) && (cur_match_len <= 5))) - { + if (((cur_match_len == TDEFL_MIN_MATCH_LEN) && + (cur_match_dist >= 8U * 1024U)) || + (cur_pos == cur_match_dist) || + ((d->m_flags & TDEFL_FILTER_MATCHES) && (cur_match_len <= 5))) { cur_match_dist = cur_match_len = 0; } - if (d->m_saved_match_len) - { - if (cur_match_len > d->m_saved_match_len) - { + if (d->m_saved_match_len) { + if (cur_match_len > d->m_saved_match_len) { tdefl_record_literal(d, (mz_uint8)d->m_saved_lit); - if (cur_match_len >= 128) - { + if (cur_match_len >= 128) { tdefl_record_match(d, cur_match_len, cur_match_dist); - d->m_saved_match_len = 0; len_to_move = cur_match_len; + d->m_saved_match_len = 0; + len_to_move = cur_match_len; + } else { + d->m_saved_lit = d->m_dict[cur_pos]; + d->m_saved_match_dist = cur_match_dist; + d->m_saved_match_len = cur_match_len; } - else - { - d->m_saved_lit = d->m_dict[cur_pos]; d->m_saved_match_dist = cur_match_dist; d->m_saved_match_len = cur_match_len; - } - } - else - { + } else { tdefl_record_match(d, d->m_saved_match_len, d->m_saved_match_dist); - len_to_move = d->m_saved_match_len - 1; d->m_saved_match_len = 0; + len_to_move = d->m_saved_match_len - 1; + d->m_saved_match_len = 0; } - } - else if (!cur_match_dist) - tdefl_record_literal(d, d->m_dict[MZ_MIN(cur_pos, sizeof(d->m_dict) - 1)]); - else if ((d->m_greedy_parsing) || (d->m_flags & TDEFL_RLE_MATCHES) || (cur_match_len >= 128)) - { + } else if (!cur_match_dist) + tdefl_record_literal(d, + d->m_dict[MZ_MIN(cur_pos, sizeof(d->m_dict) - 1)]); + else if ((d->m_greedy_parsing) || (d->m_flags & TDEFL_RLE_MATCHES) || + (cur_match_len >= 128)) { tdefl_record_match(d, cur_match_len, cur_match_dist); len_to_move = cur_match_len; - } - else - { - d->m_saved_lit = d->m_dict[MZ_MIN(cur_pos, sizeof(d->m_dict) - 1)]; d->m_saved_match_dist = cur_match_dist; d->m_saved_match_len = cur_match_len; + } else { + d->m_saved_lit = d->m_dict[MZ_MIN(cur_pos, sizeof(d->m_dict) - 1)]; + d->m_saved_match_dist = cur_match_dist; + d->m_saved_match_len = cur_match_len; } // Move the lookahead forward by len_to_move bytes. d->m_lookahead_pos += len_to_move; MZ_ASSERT(d->m_lookahead_size >= len_to_move); d->m_lookahead_size -= len_to_move; d->m_dict_size = MZ_MIN(d->m_dict_size + len_to_move, TDEFL_LZ_DICT_SIZE); - // Check if it's time to flush the current LZ codes to the internal output buffer. - if ( (d->m_pLZ_code_buf > &d->m_lz_code_buf[TDEFL_LZ_CODE_BUF_SIZE - 8]) || - ( (d->m_total_lz_bytes > 31*1024) && (((((mz_uint)(d->m_pLZ_code_buf - d->m_lz_code_buf) * 115) >> 7) >= d->m_total_lz_bytes) || (d->m_flags & TDEFL_FORCE_ALL_RAW_BLOCKS))) ) - { + // Check if it's time to flush the current LZ codes to the internal output + // buffer. + if ((d->m_pLZ_code_buf > &d->m_lz_code_buf[TDEFL_LZ_CODE_BUF_SIZE - 8]) || + ((d->m_total_lz_bytes > 31 * 1024) && + (((((mz_uint)(d->m_pLZ_code_buf - d->m_lz_code_buf) * 115) >> 7) >= + d->m_total_lz_bytes) || + (d->m_flags & TDEFL_FORCE_ALL_RAW_BLOCKS)))) { int n; - d->m_pSrc = pSrc; d->m_src_buf_left = src_buf_left; + d->m_pSrc = pSrc; + d->m_src_buf_left = src_buf_left; if ((n = tdefl_flush_block(d, 0)) != 0) return (n < 0) ? MZ_FALSE : MZ_TRUE; } } - d->m_pSrc = pSrc; d->m_src_buf_left = src_buf_left; + d->m_pSrc = pSrc; + d->m_src_buf_left = src_buf_left; return MZ_TRUE; } -static tdefl_status tdefl_flush_output_buffer(tdefl_compressor *d) -{ - if (d->m_pIn_buf_size) - { +static tdefl_status tdefl_flush_output_buffer(tdefl_compressor *d) { + if (d->m_pIn_buf_size) { *d->m_pIn_buf_size = d->m_pSrc - (const mz_uint8 *)d->m_pIn_buf; } - if (d->m_pOut_buf_size) - { - size_t n = MZ_MIN(*d->m_pOut_buf_size - d->m_out_buf_ofs, d->m_output_flush_remaining); - memcpy((mz_uint8 *)d->m_pOut_buf + d->m_out_buf_ofs, d->m_output_buf + d->m_output_flush_ofs, n); + if (d->m_pOut_buf_size) { + size_t n = MZ_MIN(*d->m_pOut_buf_size - d->m_out_buf_ofs, + d->m_output_flush_remaining); + memcpy((mz_uint8 *)d->m_pOut_buf + d->m_out_buf_ofs, + d->m_output_buf + d->m_output_flush_ofs, n); d->m_output_flush_ofs += (mz_uint)n; d->m_output_flush_remaining -= (mz_uint)n; d->m_out_buf_ofs += n; @@ -2638,29 +3669,40 @@ static tdefl_status tdefl_flush_output_buffer(tdefl_compressor *d) *d->m_pOut_buf_size = d->m_out_buf_ofs; } - return (d->m_finished && !d->m_output_flush_remaining) ? TDEFL_STATUS_DONE : TDEFL_STATUS_OKAY; + return (d->m_finished && !d->m_output_flush_remaining) ? TDEFL_STATUS_DONE + : TDEFL_STATUS_OKAY; } -tdefl_status tdefl_compress(tdefl_compressor *d, const void *pIn_buf, size_t *pIn_buf_size, void *pOut_buf, size_t *pOut_buf_size, tdefl_flush flush) -{ - if (!d) - { - if (pIn_buf_size) *pIn_buf_size = 0; - if (pOut_buf_size) *pOut_buf_size = 0; +tdefl_status tdefl_compress(tdefl_compressor *d, const void *pIn_buf, + size_t *pIn_buf_size, void *pOut_buf, + size_t *pOut_buf_size, tdefl_flush flush) { + if (!d) { + if (pIn_buf_size) + *pIn_buf_size = 0; + if (pOut_buf_size) + *pOut_buf_size = 0; return TDEFL_STATUS_BAD_PARAM; } - d->m_pIn_buf = pIn_buf; d->m_pIn_buf_size = pIn_buf_size; - d->m_pOut_buf = pOut_buf; d->m_pOut_buf_size = pOut_buf_size; - d->m_pSrc = (const mz_uint8 *)(pIn_buf); d->m_src_buf_left = pIn_buf_size ? *pIn_buf_size : 0; + d->m_pIn_buf = pIn_buf; + d->m_pIn_buf_size = pIn_buf_size; + d->m_pOut_buf = pOut_buf; + d->m_pOut_buf_size = pOut_buf_size; + d->m_pSrc = (const mz_uint8 *)(pIn_buf); + d->m_src_buf_left = pIn_buf_size ? *pIn_buf_size : 0; d->m_out_buf_ofs = 0; d->m_flush = flush; - if ( ((d->m_pPut_buf_func != NULL) == ((pOut_buf != NULL) || (pOut_buf_size != NULL))) || (d->m_prev_return_status != TDEFL_STATUS_OKAY) || - (d->m_wants_to_finish && (flush != TDEFL_FINISH)) || (pIn_buf_size && *pIn_buf_size && !pIn_buf) || (pOut_buf_size && *pOut_buf_size && !pOut_buf) ) - { - if (pIn_buf_size) *pIn_buf_size = 0; - if (pOut_buf_size) *pOut_buf_size = 0; + if (((d->m_pPut_buf_func != NULL) == + ((pOut_buf != NULL) || (pOut_buf_size != NULL))) || + (d->m_prev_return_status != TDEFL_STATUS_OKAY) || + (d->m_wants_to_finish && (flush != TDEFL_FINISH)) || + (pIn_buf_size && *pIn_buf_size && !pIn_buf) || + (pOut_buf_size && *pOut_buf_size && !pOut_buf)) { + if (pIn_buf_size) + *pIn_buf_size = 0; + if (pOut_buf_size) + *pOut_buf_size = 0; return (d->m_prev_return_status = TDEFL_STATUS_BAD_PARAM); } d->m_wants_to_finish |= (flush == TDEFL_FINISH); @@ -2671,179 +3713,329 @@ tdefl_status tdefl_compress(tdefl_compressor *d, const void *pIn_buf, size_t *pI #if MINIZ_USE_UNALIGNED_LOADS_AND_STORES && MINIZ_LITTLE_ENDIAN if (((d->m_flags & TDEFL_MAX_PROBES_MASK) == 1) && ((d->m_flags & TDEFL_GREEDY_PARSING_FLAG) != 0) && - ((d->m_flags & (TDEFL_FILTER_MATCHES | TDEFL_FORCE_ALL_RAW_BLOCKS | TDEFL_RLE_MATCHES)) == 0)) - { + ((d->m_flags & (TDEFL_FILTER_MATCHES | TDEFL_FORCE_ALL_RAW_BLOCKS | + TDEFL_RLE_MATCHES)) == 0)) { if (!tdefl_compress_fast(d)) return d->m_prev_return_status; - } - else + } else #endif // #if MINIZ_USE_UNALIGNED_LOADS_AND_STORES && MINIZ_LITTLE_ENDIAN { if (!tdefl_compress_normal(d)) return d->m_prev_return_status; } - if ((d->m_flags & (TDEFL_WRITE_ZLIB_HEADER | TDEFL_COMPUTE_ADLER32)) && (pIn_buf)) - d->m_adler32 = (mz_uint32)mz_adler32(d->m_adler32, (const mz_uint8 *)pIn_buf, d->m_pSrc - (const mz_uint8 *)pIn_buf); + if ((d->m_flags & (TDEFL_WRITE_ZLIB_HEADER | TDEFL_COMPUTE_ADLER32)) && + (pIn_buf)) + d->m_adler32 = + (mz_uint32)mz_adler32(d->m_adler32, (const mz_uint8 *)pIn_buf, + d->m_pSrc - (const mz_uint8 *)pIn_buf); - if ((flush) && (!d->m_lookahead_size) && (!d->m_src_buf_left) && (!d->m_output_flush_remaining)) - { + if ((flush) && (!d->m_lookahead_size) && (!d->m_src_buf_left) && + (!d->m_output_flush_remaining)) { if (tdefl_flush_block(d, flush) < 0) return d->m_prev_return_status; d->m_finished = (flush == TDEFL_FINISH); - if (flush == TDEFL_FULL_FLUSH) { MZ_CLEAR_OBJ(d->m_hash); MZ_CLEAR_OBJ(d->m_next); d->m_dict_size = 0; } + if (flush == TDEFL_FULL_FLUSH) { + MZ_CLEAR_OBJ(d->m_hash); + MZ_CLEAR_OBJ(d->m_next); + d->m_dict_size = 0; + } } return (d->m_prev_return_status = tdefl_flush_output_buffer(d)); } -tdefl_status tdefl_compress_buffer(tdefl_compressor *d, const void *pIn_buf, size_t in_buf_size, tdefl_flush flush) -{ - MZ_ASSERT(d->m_pPut_buf_func); return tdefl_compress(d, pIn_buf, &in_buf_size, NULL, NULL, flush); +tdefl_status tdefl_compress_buffer(tdefl_compressor *d, const void *pIn_buf, + size_t in_buf_size, tdefl_flush flush) { + MZ_ASSERT(d->m_pPut_buf_func); + return tdefl_compress(d, pIn_buf, &in_buf_size, NULL, NULL, flush); } -tdefl_status tdefl_init(tdefl_compressor *d, tdefl_put_buf_func_ptr pPut_buf_func, void *pPut_buf_user, int flags) -{ - d->m_pPut_buf_func = pPut_buf_func; d->m_pPut_buf_user = pPut_buf_user; - d->m_flags = (mz_uint)(flags); d->m_max_probes[0] = 1 + ((flags & 0xFFF) + 2) / 3; d->m_greedy_parsing = (flags & TDEFL_GREEDY_PARSING_FLAG) != 0; +tdefl_status tdefl_init(tdefl_compressor *d, + tdefl_put_buf_func_ptr pPut_buf_func, + void *pPut_buf_user, int flags) { + d->m_pPut_buf_func = pPut_buf_func; + d->m_pPut_buf_user = pPut_buf_user; + d->m_flags = (mz_uint)(flags); + d->m_max_probes[0] = 1 + ((flags & 0xFFF) + 2) / 3; + d->m_greedy_parsing = (flags & TDEFL_GREEDY_PARSING_FLAG) != 0; d->m_max_probes[1] = 1 + (((flags & 0xFFF) >> 2) + 2) / 3; - if (!(flags & TDEFL_NONDETERMINISTIC_PARSING_FLAG)) MZ_CLEAR_OBJ(d->m_hash); - d->m_lookahead_pos = d->m_lookahead_size = d->m_dict_size = d->m_total_lz_bytes = d->m_lz_code_buf_dict_pos = d->m_bits_in = 0; - d->m_output_flush_ofs = d->m_output_flush_remaining = d->m_finished = d->m_block_index = d->m_bit_buffer = d->m_wants_to_finish = 0; - d->m_pLZ_code_buf = d->m_lz_code_buf + 1; d->m_pLZ_flags = d->m_lz_code_buf; d->m_num_flags_left = 8; - d->m_pOutput_buf = d->m_output_buf; d->m_pOutput_buf_end = d->m_output_buf; d->m_prev_return_status = TDEFL_STATUS_OKAY; - d->m_saved_match_dist = d->m_saved_match_len = d->m_saved_lit = 0; d->m_adler32 = 1; - d->m_pIn_buf = NULL; d->m_pOut_buf = NULL; - d->m_pIn_buf_size = NULL; d->m_pOut_buf_size = NULL; - d->m_flush = TDEFL_NO_FLUSH; d->m_pSrc = NULL; d->m_src_buf_left = 0; d->m_out_buf_ofs = 0; - memset(&d->m_huff_count[0][0], 0, sizeof(d->m_huff_count[0][0]) * TDEFL_MAX_HUFF_SYMBOLS_0); - memset(&d->m_huff_count[1][0], 0, sizeof(d->m_huff_count[1][0]) * TDEFL_MAX_HUFF_SYMBOLS_1); + if (!(flags & TDEFL_NONDETERMINISTIC_PARSING_FLAG)) + MZ_CLEAR_OBJ(d->m_hash); + d->m_lookahead_pos = d->m_lookahead_size = d->m_dict_size = + d->m_total_lz_bytes = d->m_lz_code_buf_dict_pos = d->m_bits_in = 0; + d->m_output_flush_ofs = d->m_output_flush_remaining = d->m_finished = + d->m_block_index = d->m_bit_buffer = d->m_wants_to_finish = 0; + d->m_pLZ_code_buf = d->m_lz_code_buf + 1; + d->m_pLZ_flags = d->m_lz_code_buf; + d->m_num_flags_left = 8; + d->m_pOutput_buf = d->m_output_buf; + d->m_pOutput_buf_end = d->m_output_buf; + d->m_prev_return_status = TDEFL_STATUS_OKAY; + d->m_saved_match_dist = d->m_saved_match_len = d->m_saved_lit = 0; + d->m_adler32 = 1; + d->m_pIn_buf = NULL; + d->m_pOut_buf = NULL; + d->m_pIn_buf_size = NULL; + d->m_pOut_buf_size = NULL; + d->m_flush = TDEFL_NO_FLUSH; + d->m_pSrc = NULL; + d->m_src_buf_left = 0; + d->m_out_buf_ofs = 0; + memset(&d->m_huff_count[0][0], 0, + sizeof(d->m_huff_count[0][0]) * TDEFL_MAX_HUFF_SYMBOLS_0); + memset(&d->m_huff_count[1][0], 0, + sizeof(d->m_huff_count[1][0]) * TDEFL_MAX_HUFF_SYMBOLS_1); return TDEFL_STATUS_OKAY; } -tdefl_status tdefl_get_prev_return_status(tdefl_compressor *d) -{ +tdefl_status tdefl_get_prev_return_status(tdefl_compressor *d) { return d->m_prev_return_status; } -mz_uint32 tdefl_get_adler32(tdefl_compressor *d) -{ - return d->m_adler32; +mz_uint32 tdefl_get_adler32(tdefl_compressor *d) { return d->m_adler32; } + +mz_bool tdefl_compress_mem_to_output(const void *pBuf, size_t buf_len, + tdefl_put_buf_func_ptr pPut_buf_func, + void *pPut_buf_user, int flags) { + tdefl_compressor *pComp; + mz_bool succeeded; + if (((buf_len) && (!pBuf)) || (!pPut_buf_func)) + return MZ_FALSE; + pComp = (tdefl_compressor *)MZ_MALLOC(sizeof(tdefl_compressor)); + if (!pComp) + return MZ_FALSE; + succeeded = (tdefl_init(pComp, pPut_buf_func, pPut_buf_user, flags) == + TDEFL_STATUS_OKAY); + succeeded = + succeeded && (tdefl_compress_buffer(pComp, pBuf, buf_len, TDEFL_FINISH) == + TDEFL_STATUS_DONE); + MZ_FREE(pComp); + return succeeded; } -mz_bool tdefl_compress_mem_to_output(const void *pBuf, size_t buf_len, tdefl_put_buf_func_ptr pPut_buf_func, void *pPut_buf_user, int flags) -{ - tdefl_compressor *pComp; mz_bool succeeded; if (((buf_len) && (!pBuf)) || (!pPut_buf_func)) return MZ_FALSE; - pComp = (tdefl_compressor*)MZ_MALLOC(sizeof(tdefl_compressor)); if (!pComp) return MZ_FALSE; - succeeded = (tdefl_init(pComp, pPut_buf_func, pPut_buf_user, flags) == TDEFL_STATUS_OKAY); - succeeded = succeeded && (tdefl_compress_buffer(pComp, pBuf, buf_len, TDEFL_FINISH) == TDEFL_STATUS_DONE); - MZ_FREE(pComp); return succeeded; -} - -typedef struct -{ +typedef struct { size_t m_size, m_capacity; mz_uint8 *m_pBuf; mz_bool m_expandable; } tdefl_output_buffer; -static mz_bool tdefl_output_buffer_putter(const void *pBuf, int len, void *pUser) -{ +static mz_bool tdefl_output_buffer_putter(const void *pBuf, int len, + void *pUser) { tdefl_output_buffer *p = (tdefl_output_buffer *)pUser; size_t new_size = p->m_size + len; - if (new_size > p->m_capacity) - { - size_t new_capacity = p->m_capacity; mz_uint8 *pNew_buf; if (!p->m_expandable) return MZ_FALSE; - do { new_capacity = MZ_MAX(128U, new_capacity << 1U); } while (new_size > new_capacity); - pNew_buf = (mz_uint8*)MZ_REALLOC(p->m_pBuf, new_capacity); if (!pNew_buf) return MZ_FALSE; - p->m_pBuf = pNew_buf; p->m_capacity = new_capacity; + if (new_size > p->m_capacity) { + size_t new_capacity = p->m_capacity; + mz_uint8 *pNew_buf; + if (!p->m_expandable) + return MZ_FALSE; + do { + new_capacity = MZ_MAX(128U, new_capacity << 1U); + } while (new_size > new_capacity); + pNew_buf = (mz_uint8 *)MZ_REALLOC(p->m_pBuf, new_capacity); + if (!pNew_buf) + return MZ_FALSE; + p->m_pBuf = pNew_buf; + p->m_capacity = new_capacity; } - memcpy((mz_uint8*)p->m_pBuf + p->m_size, pBuf, len); p->m_size = new_size; + memcpy((mz_uint8 *)p->m_pBuf + p->m_size, pBuf, len); + p->m_size = new_size; return MZ_TRUE; } -void *tdefl_compress_mem_to_heap(const void *pSrc_buf, size_t src_buf_len, size_t *pOut_len, int flags) -{ - tdefl_output_buffer out_buf; MZ_CLEAR_OBJ(out_buf); - if (!pOut_len) return MZ_FALSE; else *pOut_len = 0; +void *tdefl_compress_mem_to_heap(const void *pSrc_buf, size_t src_buf_len, + size_t *pOut_len, int flags) { + tdefl_output_buffer out_buf; + MZ_CLEAR_OBJ(out_buf); + if (!pOut_len) + return MZ_FALSE; + else + *pOut_len = 0; out_buf.m_expandable = MZ_TRUE; - if (!tdefl_compress_mem_to_output(pSrc_buf, src_buf_len, tdefl_output_buffer_putter, &out_buf, flags)) return NULL; - *pOut_len = out_buf.m_size; return out_buf.m_pBuf; + if (!tdefl_compress_mem_to_output( + pSrc_buf, src_buf_len, tdefl_output_buffer_putter, &out_buf, flags)) + return NULL; + *pOut_len = out_buf.m_size; + return out_buf.m_pBuf; } -size_t tdefl_compress_mem_to_mem(void *pOut_buf, size_t out_buf_len, const void *pSrc_buf, size_t src_buf_len, int flags) -{ - tdefl_output_buffer out_buf; MZ_CLEAR_OBJ(out_buf); - if (!pOut_buf) return 0; - out_buf.m_pBuf = (mz_uint8*)pOut_buf; out_buf.m_capacity = out_buf_len; - if (!tdefl_compress_mem_to_output(pSrc_buf, src_buf_len, tdefl_output_buffer_putter, &out_buf, flags)) return 0; +size_t tdefl_compress_mem_to_mem(void *pOut_buf, size_t out_buf_len, + const void *pSrc_buf, size_t src_buf_len, + int flags) { + tdefl_output_buffer out_buf; + MZ_CLEAR_OBJ(out_buf); + if (!pOut_buf) + return 0; + out_buf.m_pBuf = (mz_uint8 *)pOut_buf; + out_buf.m_capacity = out_buf_len; + if (!tdefl_compress_mem_to_output( + pSrc_buf, src_buf_len, tdefl_output_buffer_putter, &out_buf, flags)) + return 0; return out_buf.m_size; } #ifndef MINIZ_NO_ZLIB_APIS -static const mz_uint s_tdefl_num_probes[11] = { 0, 1, 6, 32, 16, 32, 128, 256, 512, 768, 1500 }; +static const mz_uint s_tdefl_num_probes[11] = {0, 1, 6, 32, 16, 32, + 128, 256, 512, 768, 1500}; -// level may actually range from [0,10] (10 is a "hidden" max level, where we want a bit more compression and it's fine if throughput to fall off a cliff on some files). -mz_uint tdefl_create_comp_flags_from_zip_params(int level, int window_bits, int strategy) -{ - mz_uint comp_flags = s_tdefl_num_probes[(level >= 0) ? MZ_MIN(10, level) : MZ_DEFAULT_LEVEL] | ((level <= 3) ? TDEFL_GREEDY_PARSING_FLAG : 0); - if (window_bits > 0) comp_flags |= TDEFL_WRITE_ZLIB_HEADER; +// level may actually range from [0,10] (10 is a "hidden" max level, where we +// want a bit more compression and it's fine if throughput to fall off a cliff +// on some files). +mz_uint tdefl_create_comp_flags_from_zip_params(int level, int window_bits, + int strategy) { + mz_uint comp_flags = + s_tdefl_num_probes[(level >= 0) ? MZ_MIN(10, level) : MZ_DEFAULT_LEVEL] | + ((level <= 3) ? TDEFL_GREEDY_PARSING_FLAG : 0); + if (window_bits > 0) + comp_flags |= TDEFL_WRITE_ZLIB_HEADER; - if (!level) comp_flags |= TDEFL_FORCE_ALL_RAW_BLOCKS; - else if (strategy == MZ_FILTERED) comp_flags |= TDEFL_FILTER_MATCHES; - else if (strategy == MZ_HUFFMAN_ONLY) comp_flags &= ~TDEFL_MAX_PROBES_MASK; - else if (strategy == MZ_FIXED) comp_flags |= TDEFL_FORCE_ALL_STATIC_BLOCKS; - else if (strategy == MZ_RLE) comp_flags |= TDEFL_RLE_MATCHES; + if (!level) + comp_flags |= TDEFL_FORCE_ALL_RAW_BLOCKS; + else if (strategy == MZ_FILTERED) + comp_flags |= TDEFL_FILTER_MATCHES; + else if (strategy == MZ_HUFFMAN_ONLY) + comp_flags &= ~TDEFL_MAX_PROBES_MASK; + else if (strategy == MZ_FIXED) + comp_flags |= TDEFL_FORCE_ALL_STATIC_BLOCKS; + else if (strategy == MZ_RLE) + comp_flags |= TDEFL_RLE_MATCHES; return comp_flags; } -#endif //MINIZ_NO_ZLIB_APIS +#endif // MINIZ_NO_ZLIB_APIS #ifdef _MSC_VER -#pragma warning (push) -#pragma warning (disable:4204) // nonstandard extension used : non-constant aggregate initializer (also supported by GNU C and C99, so no big deal) +#pragma warning(push) +#pragma warning(disable : 4204) // nonstandard extension used : non-constant + // aggregate initializer (also supported by GNU + // C and C99, so no big deal) #endif -// Simple PNG writer function by Alex Evans, 2011. Released into the public domain: https://gist.github.com/908299, more context at +// Simple PNG writer function by Alex Evans, 2011. Released into the public +// domain: https://gist.github.com/908299, more context at // http://altdevblogaday.org/2011/04/06/a-smaller-jpg-encoder/. -// This is actually a modification of Alex's original code so PNG files generated by this function pass pngcheck. -void *tdefl_write_image_to_png_file_in_memory_ex(const void *pImage, int w, int h, int num_chans, size_t *pLen_out, mz_uint level, mz_bool flip) -{ - // Using a local copy of this array here in case MINIZ_NO_ZLIB_APIS was defined. - static const mz_uint s_tdefl_png_num_probes[11] = { 0, 1, 6, 32, 16, 32, 128, 256, 512, 768, 1500 }; - tdefl_compressor *pComp = (tdefl_compressor *)MZ_MALLOC(sizeof(tdefl_compressor)); tdefl_output_buffer out_buf; int i, bpl = w * num_chans, y, z; mz_uint32 c; *pLen_out = 0; - if (!pComp) return NULL; - MZ_CLEAR_OBJ(out_buf); out_buf.m_expandable = MZ_TRUE; out_buf.m_capacity = 57+MZ_MAX(64, (1+bpl)*h); if (NULL == (out_buf.m_pBuf = (mz_uint8*)MZ_MALLOC(out_buf.m_capacity))) { MZ_FREE(pComp); return NULL; } +// This is actually a modification of Alex's original code so PNG files +// generated by this function pass pngcheck. +void *tdefl_write_image_to_png_file_in_memory_ex(const void *pImage, int w, + int h, int num_chans, + size_t *pLen_out, + mz_uint level, mz_bool flip) { + // Using a local copy of this array here in case MINIZ_NO_ZLIB_APIS was + // defined. + static const mz_uint s_tdefl_png_num_probes[11] = { + 0, 1, 6, 32, 16, 32, 128, 256, 512, 768, 1500}; + tdefl_compressor *pComp = + (tdefl_compressor *)MZ_MALLOC(sizeof(tdefl_compressor)); + tdefl_output_buffer out_buf; + int i, bpl = w * num_chans, y, z; + mz_uint32 c; + *pLen_out = 0; + if (!pComp) + return NULL; + MZ_CLEAR_OBJ(out_buf); + out_buf.m_expandable = MZ_TRUE; + out_buf.m_capacity = 57 + MZ_MAX(64, (1 + bpl) * h); + if (NULL == (out_buf.m_pBuf = (mz_uint8 *)MZ_MALLOC(out_buf.m_capacity))) { + MZ_FREE(pComp); + return NULL; + } // write dummy header - for (z = 41; z; --z) tdefl_output_buffer_putter(&z, 1, &out_buf); + for (z = 41; z; --z) + tdefl_output_buffer_putter(&z, 1, &out_buf); // compress image data - tdefl_init(pComp, tdefl_output_buffer_putter, &out_buf, s_tdefl_png_num_probes[MZ_MIN(10, level)] | TDEFL_WRITE_ZLIB_HEADER); - for (y = 0; y < h; ++y) { tdefl_compress_buffer(pComp, &z, 1, TDEFL_NO_FLUSH); tdefl_compress_buffer(pComp, (mz_uint8*)pImage + (flip ? (h - 1 - y) : y) * bpl, bpl, TDEFL_NO_FLUSH); } - if (tdefl_compress_buffer(pComp, NULL, 0, TDEFL_FINISH) != TDEFL_STATUS_DONE) { MZ_FREE(pComp); MZ_FREE(out_buf.m_pBuf); return NULL; } + tdefl_init(pComp, tdefl_output_buffer_putter, &out_buf, + s_tdefl_png_num_probes[MZ_MIN(10, level)] | + TDEFL_WRITE_ZLIB_HEADER); + for (y = 0; y < h; ++y) { + tdefl_compress_buffer(pComp, &z, 1, TDEFL_NO_FLUSH); + tdefl_compress_buffer(pComp, + (mz_uint8 *)pImage + (flip ? (h - 1 - y) : y) * bpl, + bpl, TDEFL_NO_FLUSH); + } + if (tdefl_compress_buffer(pComp, NULL, 0, TDEFL_FINISH) != + TDEFL_STATUS_DONE) { + MZ_FREE(pComp); + MZ_FREE(out_buf.m_pBuf); + return NULL; + } // write real header - *pLen_out = out_buf.m_size-41; + *pLen_out = out_buf.m_size - 41; { static const mz_uint8 chans[] = {0x00, 0x00, 0x04, 0x02, 0x06}; - mz_uint8 pnghdr[41]={0x89,0x50,0x4e,0x47,0x0d,0x0a,0x1a,0x0a,0x00,0x00,0x00,0x0d,0x49,0x48,0x44,0x52, - 0,0,(mz_uint8)(w>>8),(mz_uint8)w,0,0,(mz_uint8)(h>>8),(mz_uint8)h,8,chans[num_chans],0,0,0,0,0,0,0, - (mz_uint8)(*pLen_out>>24),(mz_uint8)(*pLen_out>>16),(mz_uint8)(*pLen_out>>8),(mz_uint8)*pLen_out,0x49,0x44,0x41,0x54}; - c=(mz_uint32)mz_crc32(MZ_CRC32_INIT,pnghdr+12,17); for (i=0; i<4; ++i, c<<=8) ((mz_uint8*)(pnghdr+29))[i]=(mz_uint8)(c>>24); + mz_uint8 pnghdr[41] = {0x89, + 0x50, + 0x4e, + 0x47, + 0x0d, + 0x0a, + 0x1a, + 0x0a, + 0x00, + 0x00, + 0x00, + 0x0d, + 0x49, + 0x48, + 0x44, + 0x52, + 0, + 0, + (mz_uint8)(w >> 8), + (mz_uint8)w, + 0, + 0, + (mz_uint8)(h >> 8), + (mz_uint8)h, + 8, + chans[num_chans], + 0, + 0, + 0, + 0, + 0, + 0, + 0, + (mz_uint8)(*pLen_out >> 24), + (mz_uint8)(*pLen_out >> 16), + (mz_uint8)(*pLen_out >> 8), + (mz_uint8)*pLen_out, + 0x49, + 0x44, + 0x41, + 0x54}; + c = (mz_uint32)mz_crc32(MZ_CRC32_INIT, pnghdr + 12, 17); + for (i = 0; i < 4; ++i, c <<= 8) + ((mz_uint8 *)(pnghdr + 29))[i] = (mz_uint8)(c >> 24); memcpy(out_buf.m_pBuf, pnghdr, 41); } // write footer (IDAT CRC-32, followed by IEND chunk) - if (!tdefl_output_buffer_putter("\0\0\0\0\0\0\0\0\x49\x45\x4e\x44\xae\x42\x60\x82", 16, &out_buf)) { *pLen_out = 0; MZ_FREE(pComp); MZ_FREE(out_buf.m_pBuf); return NULL; } - c = (mz_uint32)mz_crc32(MZ_CRC32_INIT,out_buf.m_pBuf+41-4, *pLen_out+4); for (i=0; i<4; ++i, c<<=8) (out_buf.m_pBuf+out_buf.m_size-16)[i] = (mz_uint8)(c >> 24); + if (!tdefl_output_buffer_putter( + "\0\0\0\0\0\0\0\0\x49\x45\x4e\x44\xae\x42\x60\x82", 16, &out_buf)) { + *pLen_out = 0; + MZ_FREE(pComp); + MZ_FREE(out_buf.m_pBuf); + return NULL; + } + c = (mz_uint32)mz_crc32(MZ_CRC32_INIT, out_buf.m_pBuf + 41 - 4, + *pLen_out + 4); + for (i = 0; i < 4; ++i, c <<= 8) + (out_buf.m_pBuf + out_buf.m_size - 16)[i] = (mz_uint8)(c >> 24); // compute final size of file, grab compressed data buffer and return - *pLen_out += 57; MZ_FREE(pComp); return out_buf.m_pBuf; + *pLen_out += 57; + MZ_FREE(pComp); + return out_buf.m_pBuf; } -void *tdefl_write_image_to_png_file_in_memory(const void *pImage, int w, int h, int num_chans, size_t *pLen_out) -{ - // Level 6 corresponds to TDEFL_DEFAULT_MAX_PROBES or MZ_DEFAULT_LEVEL (but we can't depend on MZ_DEFAULT_LEVEL being available in case the zlib API's where #defined out) - return tdefl_write_image_to_png_file_in_memory_ex(pImage, w, h, num_chans, pLen_out, 6, MZ_FALSE); +void *tdefl_write_image_to_png_file_in_memory(const void *pImage, int w, int h, + int num_chans, size_t *pLen_out) { + // Level 6 corresponds to TDEFL_DEFAULT_MAX_PROBES or MZ_DEFAULT_LEVEL (but we + // can't depend on MZ_DEFAULT_LEVEL being available in case the zlib API's + // where #defined out) + return tdefl_write_image_to_png_file_in_memory_ex(pImage, w, h, num_chans, + pLen_out, 6, MZ_FALSE); } #ifdef _MSC_VER -#pragma warning (pop) +#pragma warning(pop) #endif // ------------------- .ZIP archive reading @@ -2851,138 +4043,171 @@ void *tdefl_write_image_to_png_file_in_memory(const void *pImage, int w, int h, #ifndef MINIZ_NO_ARCHIVE_APIS #ifdef MINIZ_NO_STDIO - #define MZ_FILE void * +#define MZ_FILE void * #else - #include - #include +#include +#include - #if defined(_MSC_VER) || defined(__MINGW64__) - static FILE *mz_fopen(const char *pFilename, const char *pMode) - { - FILE* pFile = NULL; - fopen_s(&pFile, pFilename, pMode); - return pFile; - } - static FILE *mz_freopen(const char *pPath, const char *pMode, FILE *pStream) - { - FILE* pFile = NULL; - if (freopen_s(&pFile, pPath, pMode, pStream)) - return NULL; - return pFile; - } - #ifndef MINIZ_NO_TIME - #include - #endif - #define MZ_FILE FILE - #define MZ_FOPEN mz_fopen - #define MZ_FCLOSE fclose - #define MZ_FREAD fread - #define MZ_FWRITE fwrite - #define MZ_FTELL64 _ftelli64 - #define MZ_FSEEK64 _fseeki64 - #define MZ_FILE_STAT_STRUCT _stat - #define MZ_FILE_STAT _stat - #define MZ_FFLUSH fflush - #define MZ_FREOPEN mz_freopen - #define MZ_DELETE_FILE remove - #elif defined(__MINGW32__) - #ifndef MINIZ_NO_TIME - #include - #endif - #define MZ_FILE FILE - #define MZ_FOPEN(f, m) fopen(f, m) - #define MZ_FCLOSE fclose - #define MZ_FREAD fread - #define MZ_FWRITE fwrite - #define MZ_FTELL64 ftello64 - #define MZ_FSEEK64 fseeko64 - #define MZ_FILE_STAT_STRUCT _stat - #define MZ_FILE_STAT _stat - #define MZ_FFLUSH fflush - #define MZ_FREOPEN(f, m, s) freopen(f, m, s) - #define MZ_DELETE_FILE remove - #elif defined(__TINYC__) - #ifndef MINIZ_NO_TIME - #include - #endif - #define MZ_FILE FILE - #define MZ_FOPEN(f, m) fopen(f, m) - #define MZ_FCLOSE fclose - #define MZ_FREAD fread - #define MZ_FWRITE fwrite - #define MZ_FTELL64 ftell - #define MZ_FSEEK64 fseek - #define MZ_FILE_STAT_STRUCT stat - #define MZ_FILE_STAT stat - #define MZ_FFLUSH fflush - #define MZ_FREOPEN(f, m, s) freopen(f, m, s) - #define MZ_DELETE_FILE remove - #elif defined(__GNUC__) && _LARGEFILE64_SOURCE - #ifndef MINIZ_NO_TIME - #include - #endif - #define MZ_FILE FILE - #define MZ_FOPEN(f, m) fopen64(f, m) - #define MZ_FCLOSE fclose - #define MZ_FREAD fread - #define MZ_FWRITE fwrite - #define MZ_FTELL64 ftello64 - #define MZ_FSEEK64 fseeko64 - #define MZ_FILE_STAT_STRUCT stat64 - #define MZ_FILE_STAT stat64 - #define MZ_FFLUSH fflush - #define MZ_FREOPEN(p, m, s) freopen64(p, m, s) - #define MZ_DELETE_FILE remove - #else - #ifndef MINIZ_NO_TIME - #include - #endif - #define MZ_FILE FILE - #define MZ_FOPEN(f, m) fopen(f, m) - #define MZ_FCLOSE fclose - #define MZ_FREAD fread - #define MZ_FWRITE fwrite - #define MZ_FTELL64 ftello - #define MZ_FSEEK64 fseeko - #define MZ_FILE_STAT_STRUCT stat - #define MZ_FILE_STAT stat - #define MZ_FFLUSH fflush - #define MZ_FREOPEN(f, m, s) freopen(f, m, s) - #define MZ_DELETE_FILE remove - #endif // #ifdef _MSC_VER +#if defined(_MSC_VER) +static FILE *mz_fopen(const char *pFilename, const char *pMode) { + FILE *pFile = NULL; + fopen_s(&pFile, pFilename, pMode); + return pFile; +} +static FILE *mz_freopen(const char *pPath, const char *pMode, FILE *pStream) { + FILE *pFile = NULL; + if (freopen_s(&pFile, pPath, pMode, pStream)) + return NULL; + return pFile; +} +#ifndef MINIZ_NO_TIME +#include +#endif +#define MZ_FILE FILE +#define MZ_FOPEN mz_fopen +#define MZ_FCLOSE fclose +#define MZ_FREAD fread +#define MZ_FWRITE fwrite +#define MZ_FTELL64 _ftelli64 +#define MZ_FSEEK64 _fseeki64 +#define MZ_FILE_STAT_STRUCT _stat +#define MZ_FILE_STAT _stat +#define MZ_FFLUSH fflush +#define MZ_FREOPEN mz_freopen +#define MZ_DELETE_FILE remove +#elif defined(__MINGW32__) +#ifndef MINIZ_NO_TIME +#include +#endif +#define MZ_FILE FILE +#define MZ_FOPEN(f, m) fopen(f, m) +#define MZ_FCLOSE fclose +#define MZ_FREAD fread +#define MZ_FWRITE fwrite +#define MZ_FTELL64 ftell +#define MZ_FSEEK64 fseek +#define MZ_FILE_STAT_STRUCT _stat +#define MZ_FILE_STAT _stat +#define MZ_FFLUSH fflush +#define MZ_FREOPEN(f, m, s) freopen(f, m, s) +#define MZ_DELETE_FILE remove +#elif defined(__TINYC__) +#ifndef MINIZ_NO_TIME +#include +#endif +#define MZ_FILE FILE +#define MZ_FOPEN(f, m) fopen(f, m) +#define MZ_FCLOSE fclose +#define MZ_FREAD fread +#define MZ_FWRITE fwrite +#define MZ_FTELL64 ftell +#define MZ_FSEEK64 fseek +#define MZ_FILE_STAT_STRUCT stat +#define MZ_FILE_STAT stat +#define MZ_FFLUSH fflush +#define MZ_FREOPEN(f, m, s) freopen(f, m, s) +#define MZ_DELETE_FILE remove +#elif defined(__GNUC__) && _LARGEFILE64_SOURCE +#ifndef MINIZ_NO_TIME +#include +#endif +#define MZ_FILE FILE +#define MZ_FOPEN(f, m) fopen64(f, m) +#define MZ_FCLOSE fclose +#define MZ_FREAD fread +#define MZ_FWRITE fwrite +#define MZ_FTELL64 ftello64 +#define MZ_FSEEK64 fseeko64 +#define MZ_FILE_STAT_STRUCT stat64 +#define MZ_FILE_STAT stat64 +#define MZ_FFLUSH fflush +#define MZ_FREOPEN(p, m, s) freopen64(p, m, s) +#define MZ_DELETE_FILE remove +#else +#ifndef MINIZ_NO_TIME +#include +#endif +#define MZ_FILE FILE +#define MZ_FOPEN(f, m) fopen(f, m) +#define MZ_FCLOSE fclose +#define MZ_FREAD fread +#define MZ_FWRITE fwrite +#if _FILE_OFFSET_BITS == 64 || _POSIX_C_SOURCE >= 200112L +#define MZ_FTELL64 ftello +#define MZ_FSEEK64 fseeko +#else +#define MZ_FTELL64 ftell +#define MZ_FSEEK64 fseek +#endif +#define MZ_FILE_STAT_STRUCT stat +#define MZ_FILE_STAT stat +#define MZ_FFLUSH fflush +#define MZ_FREOPEN(f, m, s) freopen(f, m, s) +#define MZ_DELETE_FILE remove +#endif // #ifdef _MSC_VER #endif // #ifdef MINIZ_NO_STDIO #define MZ_TOLOWER(c) ((((c) >= 'A') && ((c) <= 'Z')) ? ((c) - 'A' + 'a') : (c)) -// Various ZIP archive enums. To completely avoid cross platform compiler alignment and platform endian issues, miniz.c doesn't use structs for any of this stuff. -enum -{ +// Various ZIP archive enums. To completely avoid cross platform compiler +// alignment and platform endian issues, miniz.c doesn't use structs for any of +// this stuff. +enum { // ZIP archive identifiers and record sizes - MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIG = 0x06054b50, MZ_ZIP_CENTRAL_DIR_HEADER_SIG = 0x02014b50, MZ_ZIP_LOCAL_DIR_HEADER_SIG = 0x04034b50, - MZ_ZIP_LOCAL_DIR_HEADER_SIZE = 30, MZ_ZIP_CENTRAL_DIR_HEADER_SIZE = 46, MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIZE = 22, + MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIG = 0x06054b50, + MZ_ZIP_CENTRAL_DIR_HEADER_SIG = 0x02014b50, + MZ_ZIP_LOCAL_DIR_HEADER_SIG = 0x04034b50, + MZ_ZIP_LOCAL_DIR_HEADER_SIZE = 30, + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE = 46, + MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIZE = 22, // Central directory header record offsets - MZ_ZIP_CDH_SIG_OFS = 0, MZ_ZIP_CDH_VERSION_MADE_BY_OFS = 4, MZ_ZIP_CDH_VERSION_NEEDED_OFS = 6, MZ_ZIP_CDH_BIT_FLAG_OFS = 8, - MZ_ZIP_CDH_METHOD_OFS = 10, MZ_ZIP_CDH_FILE_TIME_OFS = 12, MZ_ZIP_CDH_FILE_DATE_OFS = 14, MZ_ZIP_CDH_CRC32_OFS = 16, - MZ_ZIP_CDH_COMPRESSED_SIZE_OFS = 20, MZ_ZIP_CDH_DECOMPRESSED_SIZE_OFS = 24, MZ_ZIP_CDH_FILENAME_LEN_OFS = 28, MZ_ZIP_CDH_EXTRA_LEN_OFS = 30, - MZ_ZIP_CDH_COMMENT_LEN_OFS = 32, MZ_ZIP_CDH_DISK_START_OFS = 34, MZ_ZIP_CDH_INTERNAL_ATTR_OFS = 36, MZ_ZIP_CDH_EXTERNAL_ATTR_OFS = 38, MZ_ZIP_CDH_LOCAL_HEADER_OFS = 42, + MZ_ZIP_CDH_SIG_OFS = 0, + MZ_ZIP_CDH_VERSION_MADE_BY_OFS = 4, + MZ_ZIP_CDH_VERSION_NEEDED_OFS = 6, + MZ_ZIP_CDH_BIT_FLAG_OFS = 8, + MZ_ZIP_CDH_METHOD_OFS = 10, + MZ_ZIP_CDH_FILE_TIME_OFS = 12, + MZ_ZIP_CDH_FILE_DATE_OFS = 14, + MZ_ZIP_CDH_CRC32_OFS = 16, + MZ_ZIP_CDH_COMPRESSED_SIZE_OFS = 20, + MZ_ZIP_CDH_DECOMPRESSED_SIZE_OFS = 24, + MZ_ZIP_CDH_FILENAME_LEN_OFS = 28, + MZ_ZIP_CDH_EXTRA_LEN_OFS = 30, + MZ_ZIP_CDH_COMMENT_LEN_OFS = 32, + MZ_ZIP_CDH_DISK_START_OFS = 34, + MZ_ZIP_CDH_INTERNAL_ATTR_OFS = 36, + MZ_ZIP_CDH_EXTERNAL_ATTR_OFS = 38, + MZ_ZIP_CDH_LOCAL_HEADER_OFS = 42, // Local directory header offsets - MZ_ZIP_LDH_SIG_OFS = 0, MZ_ZIP_LDH_VERSION_NEEDED_OFS = 4, MZ_ZIP_LDH_BIT_FLAG_OFS = 6, MZ_ZIP_LDH_METHOD_OFS = 8, MZ_ZIP_LDH_FILE_TIME_OFS = 10, - MZ_ZIP_LDH_FILE_DATE_OFS = 12, MZ_ZIP_LDH_CRC32_OFS = 14, MZ_ZIP_LDH_COMPRESSED_SIZE_OFS = 18, MZ_ZIP_LDH_DECOMPRESSED_SIZE_OFS = 22, - MZ_ZIP_LDH_FILENAME_LEN_OFS = 26, MZ_ZIP_LDH_EXTRA_LEN_OFS = 28, + MZ_ZIP_LDH_SIG_OFS = 0, + MZ_ZIP_LDH_VERSION_NEEDED_OFS = 4, + MZ_ZIP_LDH_BIT_FLAG_OFS = 6, + MZ_ZIP_LDH_METHOD_OFS = 8, + MZ_ZIP_LDH_FILE_TIME_OFS = 10, + MZ_ZIP_LDH_FILE_DATE_OFS = 12, + MZ_ZIP_LDH_CRC32_OFS = 14, + MZ_ZIP_LDH_COMPRESSED_SIZE_OFS = 18, + MZ_ZIP_LDH_DECOMPRESSED_SIZE_OFS = 22, + MZ_ZIP_LDH_FILENAME_LEN_OFS = 26, + MZ_ZIP_LDH_EXTRA_LEN_OFS = 28, // End of central directory offsets - MZ_ZIP_ECDH_SIG_OFS = 0, MZ_ZIP_ECDH_NUM_THIS_DISK_OFS = 4, MZ_ZIP_ECDH_NUM_DISK_CDIR_OFS = 6, MZ_ZIP_ECDH_CDIR_NUM_ENTRIES_ON_DISK_OFS = 8, - MZ_ZIP_ECDH_CDIR_TOTAL_ENTRIES_OFS = 10, MZ_ZIP_ECDH_CDIR_SIZE_OFS = 12, MZ_ZIP_ECDH_CDIR_OFS_OFS = 16, MZ_ZIP_ECDH_COMMENT_SIZE_OFS = 20, + MZ_ZIP_ECDH_SIG_OFS = 0, + MZ_ZIP_ECDH_NUM_THIS_DISK_OFS = 4, + MZ_ZIP_ECDH_NUM_DISK_CDIR_OFS = 6, + MZ_ZIP_ECDH_CDIR_NUM_ENTRIES_ON_DISK_OFS = 8, + MZ_ZIP_ECDH_CDIR_TOTAL_ENTRIES_OFS = 10, + MZ_ZIP_ECDH_CDIR_SIZE_OFS = 12, + MZ_ZIP_ECDH_CDIR_OFS_OFS = 16, + MZ_ZIP_ECDH_COMMENT_SIZE_OFS = 20, }; -typedef struct -{ +typedef struct { void *m_p; size_t m_size, m_capacity; mz_uint m_element_size; } mz_zip_array; -struct mz_zip_internal_state_tag -{ +struct mz_zip_internal_state_tag { mz_zip_array m_central_dir; mz_zip_array m_central_dir_offsets; mz_zip_array m_sorted_central_dir_offsets; @@ -2992,232 +4217,324 @@ struct mz_zip_internal_state_tag size_t m_mem_capacity; }; -#define MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(array_ptr, element_size) (array_ptr)->m_element_size = element_size -#define MZ_ZIP_ARRAY_ELEMENT(array_ptr, element_type, index) ((element_type *)((array_ptr)->m_p))[index] +#define MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(array_ptr, element_size) \ + (array_ptr)->m_element_size = element_size +#define MZ_ZIP_ARRAY_ELEMENT(array_ptr, element_type, index) \ + ((element_type *)((array_ptr)->m_p))[index] -static MZ_FORCEINLINE void mz_zip_array_clear(mz_zip_archive *pZip, mz_zip_array *pArray) -{ +static MZ_FORCEINLINE void mz_zip_array_clear(mz_zip_archive *pZip, + mz_zip_array *pArray) { pZip->m_pFree(pZip->m_pAlloc_opaque, pArray->m_p); memset(pArray, 0, sizeof(mz_zip_array)); } -static mz_bool mz_zip_array_ensure_capacity(mz_zip_archive *pZip, mz_zip_array *pArray, size_t min_new_capacity, mz_uint growing) -{ - void *pNew_p; size_t new_capacity = min_new_capacity; MZ_ASSERT(pArray->m_element_size); if (pArray->m_capacity >= min_new_capacity) return MZ_TRUE; - if (growing) { new_capacity = MZ_MAX(1, pArray->m_capacity); while (new_capacity < min_new_capacity) new_capacity *= 2; } - if (NULL == (pNew_p = pZip->m_pRealloc(pZip->m_pAlloc_opaque, pArray->m_p, pArray->m_element_size, new_capacity))) return MZ_FALSE; - pArray->m_p = pNew_p; pArray->m_capacity = new_capacity; +static mz_bool mz_zip_array_ensure_capacity(mz_zip_archive *pZip, + mz_zip_array *pArray, + size_t min_new_capacity, + mz_uint growing) { + void *pNew_p; + size_t new_capacity = min_new_capacity; + MZ_ASSERT(pArray->m_element_size); + if (pArray->m_capacity >= min_new_capacity) + return MZ_TRUE; + if (growing) { + new_capacity = MZ_MAX(1, pArray->m_capacity); + while (new_capacity < min_new_capacity) + new_capacity *= 2; + } + if (NULL == (pNew_p = pZip->m_pRealloc(pZip->m_pAlloc_opaque, pArray->m_p, + pArray->m_element_size, new_capacity))) + return MZ_FALSE; + pArray->m_p = pNew_p; + pArray->m_capacity = new_capacity; return MZ_TRUE; } -static MZ_FORCEINLINE mz_bool mz_zip_array_reserve(mz_zip_archive *pZip, mz_zip_array *pArray, size_t new_capacity, mz_uint growing) -{ - if (new_capacity > pArray->m_capacity) { if (!mz_zip_array_ensure_capacity(pZip, pArray, new_capacity, growing)) return MZ_FALSE; } +static MZ_FORCEINLINE mz_bool mz_zip_array_reserve(mz_zip_archive *pZip, + mz_zip_array *pArray, + size_t new_capacity, + mz_uint growing) { + if (new_capacity > pArray->m_capacity) { + if (!mz_zip_array_ensure_capacity(pZip, pArray, new_capacity, growing)) + return MZ_FALSE; + } return MZ_TRUE; } -static MZ_FORCEINLINE mz_bool mz_zip_array_resize(mz_zip_archive *pZip, mz_zip_array *pArray, size_t new_size, mz_uint growing) -{ - if (new_size > pArray->m_capacity) { if (!mz_zip_array_ensure_capacity(pZip, pArray, new_size, growing)) return MZ_FALSE; } +static MZ_FORCEINLINE mz_bool mz_zip_array_resize(mz_zip_archive *pZip, + mz_zip_array *pArray, + size_t new_size, + mz_uint growing) { + if (new_size > pArray->m_capacity) { + if (!mz_zip_array_ensure_capacity(pZip, pArray, new_size, growing)) + return MZ_FALSE; + } pArray->m_size = new_size; return MZ_TRUE; } -static MZ_FORCEINLINE mz_bool mz_zip_array_ensure_room(mz_zip_archive *pZip, mz_zip_array *pArray, size_t n) -{ +static MZ_FORCEINLINE mz_bool mz_zip_array_ensure_room(mz_zip_archive *pZip, + mz_zip_array *pArray, + size_t n) { return mz_zip_array_reserve(pZip, pArray, pArray->m_size + n, MZ_TRUE); } -static MZ_FORCEINLINE mz_bool mz_zip_array_push_back(mz_zip_archive *pZip, mz_zip_array *pArray, const void *pElements, size_t n) -{ - if (n==0) return MZ_TRUE; - assert(NULL!=pElements); - size_t orig_size = pArray->m_size; if (!mz_zip_array_resize(pZip, pArray, orig_size + n, MZ_TRUE)) return MZ_FALSE; - memcpy((mz_uint8*)pArray->m_p + orig_size * pArray->m_element_size, pElements, n * pArray->m_element_size); +static MZ_FORCEINLINE mz_bool mz_zip_array_push_back(mz_zip_archive *pZip, + mz_zip_array *pArray, + const void *pElements, + size_t n) { + if (0 == n) + return MZ_TRUE; + if (!pElements) + return MZ_FALSE; + + size_t orig_size = pArray->m_size; + if (!mz_zip_array_resize(pZip, pArray, orig_size + n, MZ_TRUE)) + return MZ_FALSE; + memcpy((mz_uint8 *)pArray->m_p + orig_size * pArray->m_element_size, + pElements, n * pArray->m_element_size); return MZ_TRUE; } #ifndef MINIZ_NO_TIME -static time_t mz_zip_dos_to_time_t(int dos_time, int dos_date) -{ +static time_t mz_zip_dos_to_time_t(int dos_time, int dos_date) { struct tm tm; - memset(&tm, 0, sizeof(tm)); tm.tm_isdst = -1; - tm.tm_year = ((dos_date >> 9) & 127) + 1980 - 1900; tm.tm_mon = ((dos_date >> 5) & 15) - 1; tm.tm_mday = dos_date & 31; - tm.tm_hour = (dos_time >> 11) & 31; tm.tm_min = (dos_time >> 5) & 63; tm.tm_sec = (dos_time << 1) & 62; + memset(&tm, 0, sizeof(tm)); + tm.tm_isdst = -1; + tm.tm_year = ((dos_date >> 9) & 127) + 1980 - 1900; + tm.tm_mon = ((dos_date >> 5) & 15) - 1; + tm.tm_mday = dos_date & 31; + tm.tm_hour = (dos_time >> 11) & 31; + tm.tm_min = (dos_time >> 5) & 63; + tm.tm_sec = (dos_time << 1) & 62; return mktime(&tm); } -static void mz_zip_time_to_dos_time(time_t time, mz_uint16 *pDOS_time, mz_uint16 *pDOS_date) -{ +#ifndef MINIZ_NO_ARCHIVE_WRITING_APIS +static void mz_zip_time_t_to_dos_time(time_t time, mz_uint16 *pDOS_time, + mz_uint16 *pDOS_date) { #ifdef _MSC_VER struct tm tm_struct; struct tm *tm = &tm_struct; errno_t err = localtime_s(tm, &time); - if (err) - { - *pDOS_date = 0; *pDOS_time = 0; + if (err) { + *pDOS_date = 0; + *pDOS_time = 0; return; } #else struct tm *tm = localtime(&time); -#endif - *pDOS_time = (mz_uint16)(((tm->tm_hour) << 11) + ((tm->tm_min) << 5) + ((tm->tm_sec) >> 1)); - *pDOS_date = (mz_uint16)(((tm->tm_year + 1900 - 1980) << 9) + ((tm->tm_mon + 1) << 5) + tm->tm_mday); +#endif /* #ifdef _MSC_VER */ + + *pDOS_time = (mz_uint16)(((tm->tm_hour) << 11) + ((tm->tm_min) << 5) + + ((tm->tm_sec) >> 1)); + *pDOS_date = (mz_uint16)(((tm->tm_year + 1900 - 1980) << 9) + + ((tm->tm_mon + 1) << 5) + tm->tm_mday); } -#endif +#endif /* MINIZ_NO_ARCHIVE_WRITING_APIS */ #ifndef MINIZ_NO_STDIO -static mz_bool mz_zip_get_file_modified_time(const char *pFilename, mz_uint16 *pDOS_time, mz_uint16 *pDOS_date) -{ -#ifdef MINIZ_NO_TIME - (void)pFilename; *pDOS_date = *pDOS_time = 0; -#else +#ifndef MINIZ_NO_ARCHIVE_WRITING_APIS +static mz_bool mz_zip_get_file_modified_time(const char *pFilename, + time_t *pTime) { struct MZ_FILE_STAT_STRUCT file_stat; - // On Linux with x86 glibc, this call will fail on large files (>= 0x80000000 bytes) unless you compiled with _LARGEFILE64_SOURCE. Argh. + + /* On Linux with x86 glibc, this call will fail on large files (I think >= + * 0x80000000 bytes) unless you compiled with _LARGEFILE64_SOURCE. Argh. */ if (MZ_FILE_STAT(pFilename, &file_stat) != 0) return MZ_FALSE; - mz_zip_time_to_dos_time(file_stat.st_mtime, pDOS_time, pDOS_date); -#endif // #ifdef MINIZ_NO_TIME + + *pTime = file_stat.st_mtime; + return MZ_TRUE; } +#endif /* #ifndef MINIZ_NO_ARCHIVE_WRITING_APIS*/ + +static mz_bool mz_zip_set_file_times(const char *pFilename, time_t access_time, + time_t modified_time) { + struct utimbuf t; + + memset(&t, 0, sizeof(t)); + t.actime = access_time; + t.modtime = modified_time; -#ifndef MINIZ_NO_TIME -static mz_bool mz_zip_set_file_times(const char *pFilename, time_t access_time, time_t modified_time) -{ - struct utimbuf t; t.actime = access_time; t.modtime = modified_time; return !utime(pFilename, &t); } -#endif // #ifndef MINIZ_NO_TIME -#endif // #ifndef MINIZ_NO_STDIO +#endif /* #ifndef MINIZ_NO_STDIO */ +#endif /* #ifndef MINIZ_NO_TIME */ -static mz_bool mz_zip_reader_init_internal(mz_zip_archive *pZip, mz_uint32 flags) -{ +static mz_bool mz_zip_reader_init_internal(mz_zip_archive *pZip, + mz_uint32 flags) { (void)flags; if ((!pZip) || (pZip->m_pState) || (pZip->m_zip_mode != MZ_ZIP_MODE_INVALID)) return MZ_FALSE; - if (!pZip->m_pAlloc) pZip->m_pAlloc = def_alloc_func; - if (!pZip->m_pFree) pZip->m_pFree = def_free_func; - if (!pZip->m_pRealloc) pZip->m_pRealloc = def_realloc_func; + if (!pZip->m_pAlloc) + pZip->m_pAlloc = def_alloc_func; + if (!pZip->m_pFree) + pZip->m_pFree = def_free_func; + if (!pZip->m_pRealloc) + pZip->m_pRealloc = def_realloc_func; pZip->m_zip_mode = MZ_ZIP_MODE_READING; pZip->m_archive_size = 0; pZip->m_central_directory_file_ofs = 0; pZip->m_total_files = 0; - if (NULL == (pZip->m_pState = (mz_zip_internal_state *)pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, sizeof(mz_zip_internal_state)))) + if (NULL == (pZip->m_pState = (mz_zip_internal_state *)pZip->m_pAlloc( + pZip->m_pAlloc_opaque, 1, sizeof(mz_zip_internal_state)))) return MZ_FALSE; memset(pZip->m_pState, 0, sizeof(mz_zip_internal_state)); - MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(&pZip->m_pState->m_central_dir, sizeof(mz_uint8)); - MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(&pZip->m_pState->m_central_dir_offsets, sizeof(mz_uint32)); - MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(&pZip->m_pState->m_sorted_central_dir_offsets, sizeof(mz_uint32)); + MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(&pZip->m_pState->m_central_dir, + sizeof(mz_uint8)); + MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(&pZip->m_pState->m_central_dir_offsets, + sizeof(mz_uint32)); + MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(&pZip->m_pState->m_sorted_central_dir_offsets, + sizeof(mz_uint32)); return MZ_TRUE; } -static MZ_FORCEINLINE mz_bool mz_zip_reader_filename_less(const mz_zip_array *pCentral_dir_array, const mz_zip_array *pCentral_dir_offsets, mz_uint l_index, mz_uint r_index) -{ - const mz_uint8 *pL = &MZ_ZIP_ARRAY_ELEMENT(pCentral_dir_array, mz_uint8, MZ_ZIP_ARRAY_ELEMENT(pCentral_dir_offsets, mz_uint32, l_index)), *pE; - const mz_uint8 *pR = &MZ_ZIP_ARRAY_ELEMENT(pCentral_dir_array, mz_uint8, MZ_ZIP_ARRAY_ELEMENT(pCentral_dir_offsets, mz_uint32, r_index)); - mz_uint l_len = MZ_READ_LE16(pL + MZ_ZIP_CDH_FILENAME_LEN_OFS), r_len = MZ_READ_LE16(pR + MZ_ZIP_CDH_FILENAME_LEN_OFS); +static MZ_FORCEINLINE mz_bool +mz_zip_reader_filename_less(const mz_zip_array *pCentral_dir_array, + const mz_zip_array *pCentral_dir_offsets, + mz_uint l_index, mz_uint r_index) { + const mz_uint8 *pL = &MZ_ZIP_ARRAY_ELEMENT( + pCentral_dir_array, mz_uint8, + MZ_ZIP_ARRAY_ELEMENT(pCentral_dir_offsets, mz_uint32, + l_index)), + *pE; + const mz_uint8 *pR = &MZ_ZIP_ARRAY_ELEMENT( + pCentral_dir_array, mz_uint8, + MZ_ZIP_ARRAY_ELEMENT(pCentral_dir_offsets, mz_uint32, r_index)); + mz_uint l_len = MZ_READ_LE16(pL + MZ_ZIP_CDH_FILENAME_LEN_OFS), + r_len = MZ_READ_LE16(pR + MZ_ZIP_CDH_FILENAME_LEN_OFS); mz_uint8 l = 0, r = 0; - pL += MZ_ZIP_CENTRAL_DIR_HEADER_SIZE; pR += MZ_ZIP_CENTRAL_DIR_HEADER_SIZE; + pL += MZ_ZIP_CENTRAL_DIR_HEADER_SIZE; + pR += MZ_ZIP_CENTRAL_DIR_HEADER_SIZE; pE = pL + MZ_MIN(l_len, r_len); - while (pL < pE) - { + while (pL < pE) { if ((l = MZ_TOLOWER(*pL)) != (r = MZ_TOLOWER(*pR))) break; - pL++; pR++; + pL++; + pR++; } return (pL == pE) ? (l_len < r_len) : (l < r); } -#define MZ_SWAP_UINT32(a, b) do { mz_uint32 t = a; a = b; b = t; } MZ_MACRO_END +#define MZ_SWAP_UINT32(a, b) \ + do { \ + mz_uint32 t = a; \ + a = b; \ + b = t; \ + } \ + MZ_MACRO_END -// Heap sort of lowercased filenames, used to help accelerate plain central directory searches by mz_zip_reader_locate_file(). (Could also use qsort(), but it could allocate memory.) -static void mz_zip_reader_sort_central_dir_offsets_by_filename(mz_zip_archive *pZip) -{ +// Heap sort of lowercased filenames, used to help accelerate plain central +// directory searches by mz_zip_reader_locate_file(). (Could also use qsort(), +// but it could allocate memory.) +static void +mz_zip_reader_sort_central_dir_offsets_by_filename(mz_zip_archive *pZip) { mz_zip_internal_state *pState = pZip->m_pState; const mz_zip_array *pCentral_dir_offsets = &pState->m_central_dir_offsets; const mz_zip_array *pCentral_dir = &pState->m_central_dir; - mz_uint32 *pIndices = &MZ_ZIP_ARRAY_ELEMENT(&pState->m_sorted_central_dir_offsets, mz_uint32, 0); + mz_uint32 *pIndices = &MZ_ZIP_ARRAY_ELEMENT( + &pState->m_sorted_central_dir_offsets, mz_uint32, 0); const int size = pZip->m_total_files; int start = (size - 2) >> 1, end; - while (start >= 0) - { + while (start >= 0) { int child, root = start; - for ( ; ; ) - { + for (;;) { if ((child = (root << 1) + 1) >= size) break; - child += (((child + 1) < size) && (mz_zip_reader_filename_less(pCentral_dir, pCentral_dir_offsets, pIndices[child], pIndices[child + 1]))); - if (!mz_zip_reader_filename_less(pCentral_dir, pCentral_dir_offsets, pIndices[root], pIndices[child])) + child += + (((child + 1) < size) && + (mz_zip_reader_filename_less(pCentral_dir, pCentral_dir_offsets, + pIndices[child], pIndices[child + 1]))); + if (!mz_zip_reader_filename_less(pCentral_dir, pCentral_dir_offsets, + pIndices[root], pIndices[child])) break; - MZ_SWAP_UINT32(pIndices[root], pIndices[child]); root = child; + MZ_SWAP_UINT32(pIndices[root], pIndices[child]); + root = child; } start--; } end = size - 1; - while (end > 0) - { + while (end > 0) { int child, root = 0; MZ_SWAP_UINT32(pIndices[end], pIndices[0]); - for ( ; ; ) - { + for (;;) { if ((child = (root << 1) + 1) >= end) break; - child += (((child + 1) < end) && mz_zip_reader_filename_less(pCentral_dir, pCentral_dir_offsets, pIndices[child], pIndices[child + 1])); - if (!mz_zip_reader_filename_less(pCentral_dir, pCentral_dir_offsets, pIndices[root], pIndices[child])) + child += + (((child + 1) < end) && + mz_zip_reader_filename_less(pCentral_dir, pCentral_dir_offsets, + pIndices[child], pIndices[child + 1])); + if (!mz_zip_reader_filename_less(pCentral_dir, pCentral_dir_offsets, + pIndices[root], pIndices[child])) break; - MZ_SWAP_UINT32(pIndices[root], pIndices[child]); root = child; + MZ_SWAP_UINT32(pIndices[root], pIndices[child]); + root = child; } end--; } } -static mz_bool mz_zip_reader_read_central_dir(mz_zip_archive *pZip, mz_uint32 flags) -{ +static mz_bool mz_zip_reader_read_central_dir(mz_zip_archive *pZip, + mz_uint32 flags) { mz_uint cdir_size, num_this_disk, cdir_disk_index; mz_uint64 cdir_ofs; mz_int64 cur_file_ofs; const mz_uint8 *p; - mz_uint32 buf_u32[4096 / sizeof(mz_uint32)]; mz_uint8 *pBuf = (mz_uint8 *)buf_u32; - mz_bool sort_central_dir = ((flags & MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY) == 0); - // Basic sanity checks - reject files which are too small, and check the first 4 bytes of the file to make sure a local header is there. + mz_uint32 buf_u32[4096 / sizeof(mz_uint32)]; + mz_uint8 *pBuf = (mz_uint8 *)buf_u32; + mz_bool sort_central_dir = + ((flags & MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY) == 0); + // Basic sanity checks - reject files which are too small, and check the first + // 4 bytes of the file to make sure a local header is there. if (pZip->m_archive_size < MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIZE) return MZ_FALSE; - // Find the end of central directory record by scanning the file from the end towards the beginning. - cur_file_ofs = MZ_MAX((mz_int64)pZip->m_archive_size - (mz_int64)sizeof(buf_u32), 0); - for ( ; ; ) - { - int i, n = (int)MZ_MIN(sizeof(buf_u32), pZip->m_archive_size - cur_file_ofs); + // Find the end of central directory record by scanning the file from the end + // towards the beginning. + cur_file_ofs = + MZ_MAX((mz_int64)pZip->m_archive_size - (mz_int64)sizeof(buf_u32), 0); + for (;;) { + int i, + n = (int)MZ_MIN(sizeof(buf_u32), pZip->m_archive_size - cur_file_ofs); if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pBuf, n) != (mz_uint)n) return MZ_FALSE; for (i = n - 4; i >= 0; --i) if (MZ_READ_LE32(pBuf + i) == MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIG) break; - if (i >= 0) - { + if (i >= 0) { cur_file_ofs += i; break; } - if ((!cur_file_ofs) || ((pZip->m_archive_size - cur_file_ofs) >= (0xFFFF + MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIZE))) + if ((!cur_file_ofs) || ((pZip->m_archive_size - cur_file_ofs) >= + (0xFFFF + MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIZE))) return MZ_FALSE; cur_file_ofs = MZ_MAX(cur_file_ofs - (sizeof(buf_u32) - 3), 0); } // Read and verify the end of central directory record. - if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pBuf, MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIZE) != MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIZE) + if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pBuf, + MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIZE) != + MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIZE) return MZ_FALSE; - if ((MZ_READ_LE32(pBuf + MZ_ZIP_ECDH_SIG_OFS) != MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIG) || - ((pZip->m_total_files = MZ_READ_LE16(pBuf + MZ_ZIP_ECDH_CDIR_TOTAL_ENTRIES_OFS)) != MZ_READ_LE16(pBuf + MZ_ZIP_ECDH_CDIR_NUM_ENTRIES_ON_DISK_OFS))) + if ((MZ_READ_LE32(pBuf + MZ_ZIP_ECDH_SIG_OFS) != + MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIG) || + ((pZip->m_total_files = + MZ_READ_LE16(pBuf + MZ_ZIP_ECDH_CDIR_TOTAL_ENTRIES_OFS)) != + MZ_READ_LE16(pBuf + MZ_ZIP_ECDH_CDIR_NUM_ENTRIES_ON_DISK_OFS))) return MZ_FALSE; num_this_disk = MZ_READ_LE16(pBuf + MZ_ZIP_ECDH_NUM_THIS_DISK_OFS); cdir_disk_index = MZ_READ_LE16(pBuf + MZ_ZIP_ECDH_NUM_DISK_CDIR_OFS); - if (((num_this_disk | cdir_disk_index) != 0) && ((num_this_disk != 1) || (cdir_disk_index != 1))) + if (((num_this_disk | cdir_disk_index) != 0) && + ((num_this_disk != 1) || (cdir_disk_index != 1))) return MZ_FALSE; - if ((cdir_size = MZ_READ_LE32(pBuf + MZ_ZIP_ECDH_CDIR_SIZE_OFS)) < pZip->m_total_files * MZ_ZIP_CENTRAL_DIR_HEADER_SIZE) + if ((cdir_size = MZ_READ_LE32(pBuf + MZ_ZIP_ECDH_CDIR_SIZE_OFS)) < + pZip->m_total_files * MZ_ZIP_CENTRAL_DIR_HEADER_SIZE) return MZ_FALSE; cdir_ofs = MZ_READ_LE32(pBuf + MZ_ZIP_ECDH_CDIR_OFS_OFS); @@ -3226,46 +4543,66 @@ static mz_bool mz_zip_reader_read_central_dir(mz_zip_archive *pZip, mz_uint32 fl pZip->m_central_directory_file_ofs = cdir_ofs; - if (pZip->m_total_files) - { - mz_uint i, n; + if (pZip->m_total_files) { + mz_uint i, n; - // Read the entire central directory into a heap block, and allocate another heap block to hold the unsorted central dir file record offsets, and another to hold the sorted indices. - if ((!mz_zip_array_resize(pZip, &pZip->m_pState->m_central_dir, cdir_size, MZ_FALSE)) || - (!mz_zip_array_resize(pZip, &pZip->m_pState->m_central_dir_offsets, pZip->m_total_files, MZ_FALSE))) + // Read the entire central directory into a heap block, and allocate another + // heap block to hold the unsorted central dir file record offsets, and + // another to hold the sorted indices. + if ((!mz_zip_array_resize(pZip, &pZip->m_pState->m_central_dir, cdir_size, + MZ_FALSE)) || + (!mz_zip_array_resize(pZip, &pZip->m_pState->m_central_dir_offsets, + pZip->m_total_files, MZ_FALSE))) return MZ_FALSE; - if (sort_central_dir) - { - if (!mz_zip_array_resize(pZip, &pZip->m_pState->m_sorted_central_dir_offsets, pZip->m_total_files, MZ_FALSE)) + if (sort_central_dir) { + if (!mz_zip_array_resize(pZip, + &pZip->m_pState->m_sorted_central_dir_offsets, + pZip->m_total_files, MZ_FALSE)) return MZ_FALSE; } - if (pZip->m_pRead(pZip->m_pIO_opaque, cdir_ofs, pZip->m_pState->m_central_dir.m_p, cdir_size) != cdir_size) + if (pZip->m_pRead(pZip->m_pIO_opaque, cdir_ofs, + pZip->m_pState->m_central_dir.m_p, + cdir_size) != cdir_size) return MZ_FALSE; - // Now create an index into the central directory file records, do some basic sanity checking on each record, and check for zip64 entries (which are not yet supported). + // Now create an index into the central directory file records, do some + // basic sanity checking on each record, and check for zip64 entries (which + // are not yet supported). p = (const mz_uint8 *)pZip->m_pState->m_central_dir.m_p; - for (n = cdir_size, i = 0; i < pZip->m_total_files; ++i) - { + for (n = cdir_size, i = 0; i < pZip->m_total_files; ++i) { mz_uint total_header_size, comp_size, decomp_size, disk_index; - if ((n < MZ_ZIP_CENTRAL_DIR_HEADER_SIZE) || (MZ_READ_LE32(p) != MZ_ZIP_CENTRAL_DIR_HEADER_SIG)) + if ((n < MZ_ZIP_CENTRAL_DIR_HEADER_SIZE) || + (MZ_READ_LE32(p) != MZ_ZIP_CENTRAL_DIR_HEADER_SIG)) return MZ_FALSE; - MZ_ZIP_ARRAY_ELEMENT(&pZip->m_pState->m_central_dir_offsets, mz_uint32, i) = (mz_uint32)(p - (const mz_uint8 *)pZip->m_pState->m_central_dir.m_p); + MZ_ZIP_ARRAY_ELEMENT(&pZip->m_pState->m_central_dir_offsets, mz_uint32, + i) = + (mz_uint32)(p - (const mz_uint8 *)pZip->m_pState->m_central_dir.m_p); if (sort_central_dir) - MZ_ZIP_ARRAY_ELEMENT(&pZip->m_pState->m_sorted_central_dir_offsets, mz_uint32, i) = i; + MZ_ZIP_ARRAY_ELEMENT(&pZip->m_pState->m_sorted_central_dir_offsets, + mz_uint32, i) = i; comp_size = MZ_READ_LE32(p + MZ_ZIP_CDH_COMPRESSED_SIZE_OFS); decomp_size = MZ_READ_LE32(p + MZ_ZIP_CDH_DECOMPRESSED_SIZE_OFS); - if (((!MZ_READ_LE32(p + MZ_ZIP_CDH_METHOD_OFS)) && (decomp_size != comp_size)) || (decomp_size && !comp_size) || (decomp_size == 0xFFFFFFFF) || (comp_size == 0xFFFFFFFF)) + if (((!MZ_READ_LE32(p + MZ_ZIP_CDH_METHOD_OFS)) && + (decomp_size != comp_size)) || + (decomp_size && !comp_size) || (decomp_size == 0xFFFFFFFF) || + (comp_size == 0xFFFFFFFF)) return MZ_FALSE; disk_index = MZ_READ_LE16(p + MZ_ZIP_CDH_DISK_START_OFS); if ((disk_index != num_this_disk) && (disk_index != 1)) return MZ_FALSE; - if (((mz_uint64)MZ_READ_LE32(p + MZ_ZIP_CDH_LOCAL_HEADER_OFS) + MZ_ZIP_LOCAL_DIR_HEADER_SIZE + comp_size) > pZip->m_archive_size) + if (((mz_uint64)MZ_READ_LE32(p + MZ_ZIP_CDH_LOCAL_HEADER_OFS) + + MZ_ZIP_LOCAL_DIR_HEADER_SIZE + comp_size) > pZip->m_archive_size) return MZ_FALSE; - if ((total_header_size = MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + MZ_READ_LE16(p + MZ_ZIP_CDH_FILENAME_LEN_OFS) + MZ_READ_LE16(p + MZ_ZIP_CDH_EXTRA_LEN_OFS) + MZ_READ_LE16(p + MZ_ZIP_CDH_COMMENT_LEN_OFS)) > n) + if ((total_header_size = MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + + MZ_READ_LE16(p + MZ_ZIP_CDH_FILENAME_LEN_OFS) + + MZ_READ_LE16(p + MZ_ZIP_CDH_EXTRA_LEN_OFS) + + MZ_READ_LE16(p + MZ_ZIP_CDH_COMMENT_LEN_OFS)) > + n) return MZ_FALSE; - n -= total_header_size; p += total_header_size; + n -= total_header_size; + p += total_header_size; } } @@ -3275,31 +4612,32 @@ static mz_bool mz_zip_reader_read_central_dir(mz_zip_archive *pZip, mz_uint32 fl return MZ_TRUE; } -mz_bool mz_zip_reader_init(mz_zip_archive *pZip, mz_uint64 size, mz_uint32 flags) -{ +mz_bool mz_zip_reader_init(mz_zip_archive *pZip, mz_uint64 size, + mz_uint32 flags) { if ((!pZip) || (!pZip->m_pRead)) return MZ_FALSE; if (!mz_zip_reader_init_internal(pZip, flags)) return MZ_FALSE; pZip->m_archive_size = size; - if (!mz_zip_reader_read_central_dir(pZip, flags)) - { + if (!mz_zip_reader_read_central_dir(pZip, flags)) { mz_zip_reader_end(pZip); return MZ_FALSE; } return MZ_TRUE; } -static size_t mz_zip_mem_read_func(void *pOpaque, mz_uint64 file_ofs, void *pBuf, size_t n) -{ +static size_t mz_zip_mem_read_func(void *pOpaque, mz_uint64 file_ofs, + void *pBuf, size_t n) { mz_zip_archive *pZip = (mz_zip_archive *)pOpaque; - size_t s = (file_ofs >= pZip->m_archive_size) ? 0 : (size_t)MZ_MIN(pZip->m_archive_size - file_ofs, n); + size_t s = (file_ofs >= pZip->m_archive_size) + ? 0 + : (size_t)MZ_MIN(pZip->m_archive_size - file_ofs, n); memcpy(pBuf, (const mz_uint8 *)pZip->m_pState->m_pMem + file_ofs, s); return s; } -mz_bool mz_zip_reader_init_mem(mz_zip_archive *pZip, const void *pMem, size_t size, mz_uint32 flags) -{ +mz_bool mz_zip_reader_init_mem(mz_zip_archive *pZip, const void *pMem, + size_t size, mz_uint32 flags) { if (!mz_zip_reader_init_internal(pZip, flags)) return MZ_FALSE; pZip->m_archive_size = size; @@ -3311,8 +4649,7 @@ mz_bool mz_zip_reader_init_mem(mz_zip_archive *pZip, const void *pMem, size_t si pZip->m_pState->m_pMem = (void *)pMem; #endif pZip->m_pState->m_mem_size = size; - if (!mz_zip_reader_read_central_dir(pZip, flags)) - { + if (!mz_zip_reader_read_central_dir(pZip, flags)) { mz_zip_reader_end(pZip); return MZ_FALSE; } @@ -3320,29 +4657,29 @@ mz_bool mz_zip_reader_init_mem(mz_zip_archive *pZip, const void *pMem, size_t si } #ifndef MINIZ_NO_STDIO -static size_t mz_zip_file_read_func(void *pOpaque, mz_uint64 file_ofs, void *pBuf, size_t n) -{ +static size_t mz_zip_file_read_func(void *pOpaque, mz_uint64 file_ofs, + void *pBuf, size_t n) { mz_zip_archive *pZip = (mz_zip_archive *)pOpaque; mz_int64 cur_ofs = MZ_FTELL64(pZip->m_pState->m_pFile); - if (((mz_int64)file_ofs < 0) || (((cur_ofs != (mz_int64)file_ofs)) && (MZ_FSEEK64(pZip->m_pState->m_pFile, (mz_int64)file_ofs, SEEK_SET)))) + if (((mz_int64)file_ofs < 0) || + (((cur_ofs != (mz_int64)file_ofs)) && + (MZ_FSEEK64(pZip->m_pState->m_pFile, (mz_int64)file_ofs, SEEK_SET)))) return 0; return MZ_FREAD(pBuf, 1, n, pZip->m_pState->m_pFile); } -mz_bool mz_zip_reader_init_file(mz_zip_archive *pZip, const char *pFilename, mz_uint32 flags) -{ +mz_bool mz_zip_reader_init_file(mz_zip_archive *pZip, const char *pFilename, + mz_uint32 flags) { mz_uint64 file_size; MZ_FILE *pFile = MZ_FOPEN(pFilename, "rb"); if (!pFile) return MZ_FALSE; - if (MZ_FSEEK64(pFile, 0, SEEK_END)) - { + if (MZ_FSEEK64(pFile, 0, SEEK_END)) { MZ_FCLOSE(pFile); return MZ_FALSE; } file_size = MZ_FTELL64(pFile); - if (!mz_zip_reader_init_internal(pZip, flags)) - { + if (!mz_zip_reader_init_internal(pZip, flags)) { MZ_FCLOSE(pFile); return MZ_FALSE; } @@ -3350,8 +4687,7 @@ mz_bool mz_zip_reader_init_file(mz_zip_archive *pZip, const char *pFilename, mz_ pZip->m_pIO_opaque = pZip; pZip->m_pState->m_pFile = pFile; pZip->m_archive_size = file_size; - if (!mz_zip_reader_read_central_dir(pZip, flags)) - { + if (!mz_zip_reader_read_central_dir(pZip, flags)) { mz_zip_reader_end(pZip); return MZ_FALSE; } @@ -3359,20 +4695,23 @@ mz_bool mz_zip_reader_init_file(mz_zip_archive *pZip, const char *pFilename, mz_ } #endif // #ifndef MINIZ_NO_STDIO -mz_uint mz_zip_reader_get_num_files(mz_zip_archive *pZip) -{ +mz_uint mz_zip_reader_get_num_files(mz_zip_archive *pZip) { return pZip ? pZip->m_total_files : 0; } -static MZ_FORCEINLINE const mz_uint8 *mz_zip_reader_get_cdh(mz_zip_archive *pZip, mz_uint file_index) -{ - if ((!pZip) || (!pZip->m_pState) || (file_index >= pZip->m_total_files) || (pZip->m_zip_mode != MZ_ZIP_MODE_READING)) +static MZ_FORCEINLINE const mz_uint8 * +mz_zip_reader_get_cdh(mz_zip_archive *pZip, mz_uint file_index) { + if ((!pZip) || (!pZip->m_pState) || (file_index >= pZip->m_total_files) || + (pZip->m_zip_mode != MZ_ZIP_MODE_READING)) return NULL; - return &MZ_ZIP_ARRAY_ELEMENT(&pZip->m_pState->m_central_dir, mz_uint8, MZ_ZIP_ARRAY_ELEMENT(&pZip->m_pState->m_central_dir_offsets, mz_uint32, file_index)); + return &MZ_ZIP_ARRAY_ELEMENT( + &pZip->m_pState->m_central_dir, mz_uint8, + MZ_ZIP_ARRAY_ELEMENT(&pZip->m_pState->m_central_dir_offsets, mz_uint32, + file_index)); } -mz_bool mz_zip_reader_is_file_encrypted(mz_zip_archive *pZip, mz_uint file_index) -{ +mz_bool mz_zip_reader_is_file_encrypted(mz_zip_archive *pZip, + mz_uint file_index) { mz_uint m_bit_flag; const mz_uint8 *p = mz_zip_reader_get_cdh(pZip, file_index); if (!p) @@ -3381,8 +4720,8 @@ mz_bool mz_zip_reader_is_file_encrypted(mz_zip_archive *pZip, mz_uint file_index return (m_bit_flag & 1); } -mz_bool mz_zip_reader_is_file_a_directory(mz_zip_archive *pZip, mz_uint file_index) -{ +mz_bool mz_zip_reader_is_file_a_directory(mz_zip_archive *pZip, + mz_uint file_index) { mz_uint filename_len, external_attr; const mz_uint8 *p = mz_zip_reader_get_cdh(pZip, file_index); if (!p) @@ -3390,14 +4729,15 @@ mz_bool mz_zip_reader_is_file_a_directory(mz_zip_archive *pZip, mz_uint file_ind // First see if the filename ends with a '/' character. filename_len = MZ_READ_LE16(p + MZ_ZIP_CDH_FILENAME_LEN_OFS); - if (filename_len) - { + if (filename_len) { if (*(p + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + filename_len - 1) == '/') return MZ_TRUE; } - // Bugfix: This code was also checking if the internal attribute was non-zero, which wasn't correct. - // Most/all zip writers (hopefully) set DOS file/directory attributes in the low 16-bits, so check for the DOS directory flag and ignore the source OS ID in the created by field. + // Bugfix: This code was also checking if the internal attribute was non-zero, + // which wasn't correct. Most/all zip writers (hopefully) set DOS + // file/directory attributes in the low 16-bits, so check for the DOS + // directory flag and ignore the source OS ID in the created by field. // FIXME: Remove this check? Is it necessary - we already check the filename. external_attr = MZ_READ_LE32(p + MZ_ZIP_CDH_EXTERNAL_ATTR_OFS); if ((external_attr & 0x10) != 0) @@ -3406,8 +4746,8 @@ mz_bool mz_zip_reader_is_file_a_directory(mz_zip_archive *pZip, mz_uint file_ind return MZ_FALSE; } -mz_bool mz_zip_reader_file_stat(mz_zip_archive *pZip, mz_uint file_index, mz_zip_archive_file_stat *pStat) -{ +mz_bool mz_zip_reader_file_stat(mz_zip_archive *pZip, mz_uint file_index, + mz_zip_archive_file_stat *pStat) { mz_uint n; const mz_uint8 *p = mz_zip_reader_get_cdh(pZip, file_index); if ((!p) || (!pStat)) @@ -3415,13 +4755,16 @@ mz_bool mz_zip_reader_file_stat(mz_zip_archive *pZip, mz_uint file_index, mz_zip // Unpack the central directory record. pStat->m_file_index = file_index; - pStat->m_central_dir_ofs = MZ_ZIP_ARRAY_ELEMENT(&pZip->m_pState->m_central_dir_offsets, mz_uint32, file_index); + pStat->m_central_dir_ofs = MZ_ZIP_ARRAY_ELEMENT( + &pZip->m_pState->m_central_dir_offsets, mz_uint32, file_index); pStat->m_version_made_by = MZ_READ_LE16(p + MZ_ZIP_CDH_VERSION_MADE_BY_OFS); pStat->m_version_needed = MZ_READ_LE16(p + MZ_ZIP_CDH_VERSION_NEEDED_OFS); pStat->m_bit_flag = MZ_READ_LE16(p + MZ_ZIP_CDH_BIT_FLAG_OFS); pStat->m_method = MZ_READ_LE16(p + MZ_ZIP_CDH_METHOD_OFS); #ifndef MINIZ_NO_TIME - pStat->m_time = mz_zip_dos_to_time_t(MZ_READ_LE16(p + MZ_ZIP_CDH_FILE_TIME_OFS), MZ_READ_LE16(p + MZ_ZIP_CDH_FILE_DATE_OFS)); + pStat->m_time = + mz_zip_dos_to_time_t(MZ_READ_LE16(p + MZ_ZIP_CDH_FILE_TIME_OFS), + MZ_READ_LE16(p + MZ_ZIP_CDH_FILE_DATE_OFS)); #endif pStat->m_crc32 = MZ_READ_LE32(p + MZ_ZIP_CDH_CRC32_OFS); pStat->m_comp_size = MZ_READ_LE32(p + MZ_ZIP_CDH_COMPRESSED_SIZE_OFS); @@ -3431,24 +4774,35 @@ mz_bool mz_zip_reader_file_stat(mz_zip_archive *pZip, mz_uint file_index, mz_zip pStat->m_local_header_ofs = MZ_READ_LE32(p + MZ_ZIP_CDH_LOCAL_HEADER_OFS); // Copy as much of the filename and comment as possible. - n = MZ_READ_LE16(p + MZ_ZIP_CDH_FILENAME_LEN_OFS); n = MZ_MIN(n, MZ_ZIP_MAX_ARCHIVE_FILENAME_SIZE - 1); - memcpy(pStat->m_filename, p + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE, n); pStat->m_filename[n] = '\0'; + n = MZ_READ_LE16(p + MZ_ZIP_CDH_FILENAME_LEN_OFS); + n = MZ_MIN(n, MZ_ZIP_MAX_ARCHIVE_FILENAME_SIZE - 1); + memcpy(pStat->m_filename, p + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE, n); + pStat->m_filename[n] = '\0'; - n = MZ_READ_LE16(p + MZ_ZIP_CDH_COMMENT_LEN_OFS); n = MZ_MIN(n, MZ_ZIP_MAX_ARCHIVE_FILE_COMMENT_SIZE - 1); + n = MZ_READ_LE16(p + MZ_ZIP_CDH_COMMENT_LEN_OFS); + n = MZ_MIN(n, MZ_ZIP_MAX_ARCHIVE_FILE_COMMENT_SIZE - 1); pStat->m_comment_size = n; - memcpy(pStat->m_comment, p + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + MZ_READ_LE16(p + MZ_ZIP_CDH_FILENAME_LEN_OFS) + MZ_READ_LE16(p + MZ_ZIP_CDH_EXTRA_LEN_OFS), n); pStat->m_comment[n] = '\0'; + memcpy(pStat->m_comment, + p + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + + MZ_READ_LE16(p + MZ_ZIP_CDH_FILENAME_LEN_OFS) + + MZ_READ_LE16(p + MZ_ZIP_CDH_EXTRA_LEN_OFS), + n); + pStat->m_comment[n] = '\0'; return MZ_TRUE; } -mz_uint mz_zip_reader_get_filename(mz_zip_archive *pZip, mz_uint file_index, char *pFilename, mz_uint filename_buf_size) -{ +mz_uint mz_zip_reader_get_filename(mz_zip_archive *pZip, mz_uint file_index, + char *pFilename, mz_uint filename_buf_size) { mz_uint n; const mz_uint8 *p = mz_zip_reader_get_cdh(pZip, file_index); - if (!p) { if (filename_buf_size) pFilename[0] = '\0'; return 0; } + if (!p) { + if (filename_buf_size) + pFilename[0] = '\0'; + return 0; + } n = MZ_READ_LE16(p + MZ_ZIP_CDH_FILENAME_LEN_OFS); - if (filename_buf_size) - { + if (filename_buf_size) { n = MZ_MIN(n, filename_buf_size - 1); memcpy(pFilename, p + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE, n); pFilename[n] = '\0'; @@ -3456,8 +4810,10 @@ mz_uint mz_zip_reader_get_filename(mz_zip_archive *pZip, mz_uint file_index, cha return n + 1; } -static MZ_FORCEINLINE mz_bool mz_zip_reader_string_equal(const char *pA, const char *pB, mz_uint len, mz_uint flags) -{ +static MZ_FORCEINLINE mz_bool mz_zip_reader_string_equal(const char *pA, + const char *pB, + mz_uint len, + mz_uint flags) { mz_uint i; if (flags & MZ_ZIP_FLAG_CASE_SENSITIVE) return 0 == memcmp(pA, pB, len); @@ -3467,34 +4823,43 @@ static MZ_FORCEINLINE mz_bool mz_zip_reader_string_equal(const char *pA, const c return MZ_TRUE; } -static MZ_FORCEINLINE int mz_zip_reader_filename_compare(const mz_zip_array *pCentral_dir_array, const mz_zip_array *pCentral_dir_offsets, mz_uint l_index, const char *pR, mz_uint r_len) -{ - const mz_uint8 *pL = &MZ_ZIP_ARRAY_ELEMENT(pCentral_dir_array, mz_uint8, MZ_ZIP_ARRAY_ELEMENT(pCentral_dir_offsets, mz_uint32, l_index)), *pE; +static MZ_FORCEINLINE int +mz_zip_reader_filename_compare(const mz_zip_array *pCentral_dir_array, + const mz_zip_array *pCentral_dir_offsets, + mz_uint l_index, const char *pR, mz_uint r_len) { + const mz_uint8 *pL = &MZ_ZIP_ARRAY_ELEMENT( + pCentral_dir_array, mz_uint8, + MZ_ZIP_ARRAY_ELEMENT(pCentral_dir_offsets, mz_uint32, + l_index)), + *pE; mz_uint l_len = MZ_READ_LE16(pL + MZ_ZIP_CDH_FILENAME_LEN_OFS); mz_uint8 l = 0, r = 0; pL += MZ_ZIP_CENTRAL_DIR_HEADER_SIZE; pE = pL + MZ_MIN(l_len, r_len); - while (pL < pE) - { + while (pL < pE) { if ((l = MZ_TOLOWER(*pL)) != (r = MZ_TOLOWER(*pR))) break; - pL++; pR++; + pL++; + pR++; } return (pL == pE) ? (int)(l_len - r_len) : (l - r); } -static int mz_zip_reader_locate_file_binary_search(mz_zip_archive *pZip, const char *pFilename) -{ +static int mz_zip_reader_locate_file_binary_search(mz_zip_archive *pZip, + const char *pFilename) { mz_zip_internal_state *pState = pZip->m_pState; const mz_zip_array *pCentral_dir_offsets = &pState->m_central_dir_offsets; const mz_zip_array *pCentral_dir = &pState->m_central_dir; - mz_uint32 *pIndices = &MZ_ZIP_ARRAY_ELEMENT(&pState->m_sorted_central_dir_offsets, mz_uint32, 0); + mz_uint32 *pIndices = &MZ_ZIP_ARRAY_ELEMENT( + &pState->m_sorted_central_dir_offsets, mz_uint32, 0); const int size = pZip->m_total_files; const mz_uint filename_len = (mz_uint)strlen(pFilename); int l = 0, h = size - 1; - while (l <= h) - { - int m = (l + h) >> 1, file_index = pIndices[m], comp = mz_zip_reader_filename_compare(pCentral_dir, pCentral_dir_offsets, file_index, pFilename, filename_len); + while (l <= h) { + int m = (l + h) >> 1, file_index = pIndices[m], + comp = + mz_zip_reader_filename_compare(pCentral_dir, pCentral_dir_offsets, + file_index, pFilename, filename_len); if (!comp) return file_index; else if (comp < 0) @@ -3505,53 +4870,74 @@ static int mz_zip_reader_locate_file_binary_search(mz_zip_archive *pZip, const c return -1; } -int mz_zip_reader_locate_file(mz_zip_archive *pZip, const char *pName, const char *pComment, mz_uint flags) -{ - mz_uint file_index; size_t name_len, comment_len; - if ((!pZip) || (!pZip->m_pState) || (!pName) || (pZip->m_zip_mode != MZ_ZIP_MODE_READING)) +int mz_zip_reader_locate_file(mz_zip_archive *pZip, const char *pName, + const char *pComment, mz_uint flags) { + mz_uint file_index; + size_t name_len, comment_len; + if ((!pZip) || (!pZip->m_pState) || (!pName) || + (pZip->m_zip_mode != MZ_ZIP_MODE_READING)) return -1; - if (((flags & (MZ_ZIP_FLAG_IGNORE_PATH | MZ_ZIP_FLAG_CASE_SENSITIVE)) == 0) && (!pComment) && (pZip->m_pState->m_sorted_central_dir_offsets.m_size)) + if (((flags & (MZ_ZIP_FLAG_IGNORE_PATH | MZ_ZIP_FLAG_CASE_SENSITIVE)) == 0) && + (!pComment) && (pZip->m_pState->m_sorted_central_dir_offsets.m_size)) return mz_zip_reader_locate_file_binary_search(pZip, pName); - name_len = strlen(pName); if (name_len > 0xFFFF) return -1; - comment_len = pComment ? strlen(pComment) : 0; if (comment_len > 0xFFFF) return -1; - for (file_index = 0; file_index < pZip->m_total_files; file_index++) - { - const mz_uint8 *pHeader = &MZ_ZIP_ARRAY_ELEMENT(&pZip->m_pState->m_central_dir, mz_uint8, MZ_ZIP_ARRAY_ELEMENT(&pZip->m_pState->m_central_dir_offsets, mz_uint32, file_index)); + name_len = strlen(pName); + if (name_len > 0xFFFF) + return -1; + comment_len = pComment ? strlen(pComment) : 0; + if (comment_len > 0xFFFF) + return -1; + for (file_index = 0; file_index < pZip->m_total_files; file_index++) { + const mz_uint8 *pHeader = &MZ_ZIP_ARRAY_ELEMENT( + &pZip->m_pState->m_central_dir, mz_uint8, + MZ_ZIP_ARRAY_ELEMENT(&pZip->m_pState->m_central_dir_offsets, mz_uint32, + file_index)); mz_uint filename_len = MZ_READ_LE16(pHeader + MZ_ZIP_CDH_FILENAME_LEN_OFS); - const char *pFilename = (const char *)pHeader + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE; + const char *pFilename = + (const char *)pHeader + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE; if (filename_len < name_len) continue; - if (comment_len) - { - mz_uint file_extra_len = MZ_READ_LE16(pHeader + MZ_ZIP_CDH_EXTRA_LEN_OFS), file_comment_len = MZ_READ_LE16(pHeader + MZ_ZIP_CDH_COMMENT_LEN_OFS); + if (comment_len) { + mz_uint file_extra_len = MZ_READ_LE16(pHeader + MZ_ZIP_CDH_EXTRA_LEN_OFS), + file_comment_len = + MZ_READ_LE16(pHeader + MZ_ZIP_CDH_COMMENT_LEN_OFS); const char *pFile_comment = pFilename + filename_len + file_extra_len; - if ((file_comment_len != comment_len) || (!mz_zip_reader_string_equal(pComment, pFile_comment, file_comment_len, flags))) + if ((file_comment_len != comment_len) || + (!mz_zip_reader_string_equal(pComment, pFile_comment, + file_comment_len, flags))) continue; } - if ((flags & MZ_ZIP_FLAG_IGNORE_PATH) && (filename_len)) - { + if ((flags & MZ_ZIP_FLAG_IGNORE_PATH) && (filename_len)) { int ofs = filename_len - 1; - do - { - if ((pFilename[ofs] == '/') || (pFilename[ofs] == '\\') || (pFilename[ofs] == ':')) + do { + if ((pFilename[ofs] == '/') || (pFilename[ofs] == '\\') || + (pFilename[ofs] == ':')) break; } while (--ofs >= 0); ofs++; - pFilename += ofs; filename_len -= ofs; + pFilename += ofs; + filename_len -= ofs; } - if ((filename_len == name_len) && (mz_zip_reader_string_equal(pName, pFilename, filename_len, flags))) + if ((filename_len == name_len) && + (mz_zip_reader_string_equal(pName, pFilename, filename_len, flags))) return file_index; } return -1; } -mz_bool mz_zip_reader_extract_to_mem_no_alloc(mz_zip_archive *pZip, mz_uint file_index, void *pBuf, size_t buf_size, mz_uint flags, void *pUser_read_buf, size_t user_read_buf_size) -{ +mz_bool mz_zip_reader_extract_to_mem_no_alloc(mz_zip_archive *pZip, + mz_uint file_index, void *pBuf, + size_t buf_size, mz_uint flags, + void *pUser_read_buf, + size_t user_read_buf_size) { int status = TINFL_STATUS_DONE; - mz_uint64 needed_size, cur_file_ofs, comp_remaining, out_buf_ofs = 0, read_buf_size, read_buf_ofs = 0, read_buf_avail; + mz_uint64 needed_size, cur_file_ofs, comp_remaining, + out_buf_ofs = 0, read_buf_size, read_buf_ofs = 0, read_buf_avail; mz_zip_archive_file_stat file_stat; void *pRead_buf; - mz_uint32 local_header_u32[(MZ_ZIP_LOCAL_DIR_HEADER_SIZE + sizeof(mz_uint32) - 1) / sizeof(mz_uint32)]; mz_uint8 *pLocal_header = (mz_uint8 *)local_header_u32; + mz_uint32 + local_header_u32[(MZ_ZIP_LOCAL_DIR_HEADER_SIZE + sizeof(mz_uint32) - 1) / + sizeof(mz_uint32)]; + mz_uint8 *pLocal_header = (mz_uint8 *)local_header_u32; tinfl_decompressor inflator; if ((buf_size) && (!pBuf)) @@ -3560,12 +4946,15 @@ mz_bool mz_zip_reader_extract_to_mem_no_alloc(mz_zip_archive *pZip, mz_uint file if (!mz_zip_reader_file_stat(pZip, file_index, &file_stat)) return MZ_FALSE; - // Empty file, or a directory (but not always a directory - I've seen odd zips with directories that have compressed data which inflates to 0 bytes) + // Empty file, or a directory (but not always a directory - I've seen odd zips + // with directories that have compressed data which inflates to 0 bytes) if (!file_stat.m_comp_size) return MZ_TRUE; - // Entry is a subdirectory (I've seen old zips with dir entries which have compressed deflate data which inflates to 0 bytes, but these entries claim to uncompress to 512 bytes in the headers). - // I'm torn how to handle this case - should it fail instead? + // Entry is a subdirectory (I've seen old zips with dir entries which have + // compressed deflate data which inflates to 0 bytes, but these entries claim + // to uncompress to 512 bytes in the headers). I'm torn how to handle this + // case - should it fail instead? if (mz_zip_reader_is_file_a_directory(pZip, file_index)) return MZ_TRUE; @@ -3574,45 +4963,51 @@ mz_bool mz_zip_reader_extract_to_mem_no_alloc(mz_zip_archive *pZip, mz_uint file return MZ_FALSE; // This function only supports stored and deflate. - if ((!(flags & MZ_ZIP_FLAG_COMPRESSED_DATA)) && (file_stat.m_method != 0) && (file_stat.m_method != MZ_DEFLATED)) + if ((!(flags & MZ_ZIP_FLAG_COMPRESSED_DATA)) && (file_stat.m_method != 0) && + (file_stat.m_method != MZ_DEFLATED)) return MZ_FALSE; // Ensure supplied output buffer is large enough. - needed_size = (flags & MZ_ZIP_FLAG_COMPRESSED_DATA) ? file_stat.m_comp_size : file_stat.m_uncomp_size; + needed_size = (flags & MZ_ZIP_FLAG_COMPRESSED_DATA) ? file_stat.m_comp_size + : file_stat.m_uncomp_size; if (buf_size < needed_size) return MZ_FALSE; // Read and parse the local directory entry. cur_file_ofs = file_stat.m_local_header_ofs; - if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pLocal_header, MZ_ZIP_LOCAL_DIR_HEADER_SIZE) != MZ_ZIP_LOCAL_DIR_HEADER_SIZE) + if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pLocal_header, + MZ_ZIP_LOCAL_DIR_HEADER_SIZE) != + MZ_ZIP_LOCAL_DIR_HEADER_SIZE) return MZ_FALSE; if (MZ_READ_LE32(pLocal_header) != MZ_ZIP_LOCAL_DIR_HEADER_SIG) return MZ_FALSE; - cur_file_ofs += MZ_ZIP_LOCAL_DIR_HEADER_SIZE + MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_FILENAME_LEN_OFS) + MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_EXTRA_LEN_OFS); + cur_file_ofs += MZ_ZIP_LOCAL_DIR_HEADER_SIZE + + MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_FILENAME_LEN_OFS) + + MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_EXTRA_LEN_OFS); if ((cur_file_ofs + file_stat.m_comp_size) > pZip->m_archive_size) return MZ_FALSE; - if ((flags & MZ_ZIP_FLAG_COMPRESSED_DATA) || (!file_stat.m_method)) - { + if ((flags & MZ_ZIP_FLAG_COMPRESSED_DATA) || (!file_stat.m_method)) { // The file is stored or the caller has requested the compressed data. - if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pBuf, (size_t)needed_size) != needed_size) + if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pBuf, + (size_t)needed_size) != needed_size) return MZ_FALSE; - return ((flags & MZ_ZIP_FLAG_COMPRESSED_DATA) != 0) || (mz_crc32(MZ_CRC32_INIT, (const mz_uint8 *)pBuf, (size_t)file_stat.m_uncomp_size) == file_stat.m_crc32); + return ((flags & MZ_ZIP_FLAG_COMPRESSED_DATA) != 0) || + (mz_crc32(MZ_CRC32_INIT, (const mz_uint8 *)pBuf, + (size_t)file_stat.m_uncomp_size) == file_stat.m_crc32); } - // Decompress the file either directly from memory or from a file input buffer. + // Decompress the file either directly from memory or from a file input + // buffer. tinfl_init(&inflator); - if (pZip->m_pState->m_pMem) - { + if (pZip->m_pState->m_pMem) { // Read directly from the archive in memory. pRead_buf = (mz_uint8 *)pZip->m_pState->m_pMem + cur_file_ofs; read_buf_size = read_buf_avail = file_stat.m_comp_size; comp_remaining = 0; - } - else if (pUser_read_buf) - { + } else if (pUser_read_buf) { // Use a user provided read buffer. if (!user_read_buf_size) return MZ_FALSE; @@ -3620,31 +5015,30 @@ mz_bool mz_zip_reader_extract_to_mem_no_alloc(mz_zip_archive *pZip, mz_uint file read_buf_size = user_read_buf_size; read_buf_avail = 0; comp_remaining = file_stat.m_comp_size; - } - else - { + } else { // Temporarily allocate a read buffer. read_buf_size = MZ_MIN(file_stat.m_comp_size, MZ_ZIP_MAX_IO_BUF_SIZE); #ifdef _MSC_VER - if (((0, sizeof(size_t) == sizeof(mz_uint32))) && (read_buf_size > 0x7FFFFFFF)) + if (((0, sizeof(size_t) == sizeof(mz_uint32))) && + (read_buf_size > 0x7FFFFFFF)) #else if (((sizeof(size_t) == sizeof(mz_uint32))) && (read_buf_size > 0x7FFFFFFF)) #endif return MZ_FALSE; - if (NULL == (pRead_buf = pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, (size_t)read_buf_size))) + if (NULL == (pRead_buf = pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, + (size_t)read_buf_size))) return MZ_FALSE; read_buf_avail = 0; comp_remaining = file_stat.m_comp_size; } - do - { - size_t in_buf_size, out_buf_size = (size_t)(file_stat.m_uncomp_size - out_buf_ofs); - if ((!read_buf_avail) && (!pZip->m_pState->m_pMem)) - { + do { + size_t in_buf_size, + out_buf_size = (size_t)(file_stat.m_uncomp_size - out_buf_ofs); + if ((!read_buf_avail) && (!pZip->m_pState->m_pMem)) { read_buf_avail = MZ_MIN(read_buf_size, comp_remaining); - if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pRead_buf, (size_t)read_buf_avail) != read_buf_avail) - { + if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pRead_buf, + (size_t)read_buf_avail) != read_buf_avail) { status = TINFL_STATUS_FAILED; break; } @@ -3653,16 +5047,21 @@ mz_bool mz_zip_reader_extract_to_mem_no_alloc(mz_zip_archive *pZip, mz_uint file read_buf_ofs = 0; } in_buf_size = (size_t)read_buf_avail; - status = tinfl_decompress(&inflator, (mz_uint8 *)pRead_buf + read_buf_ofs, &in_buf_size, (mz_uint8 *)pBuf, (mz_uint8 *)pBuf + out_buf_ofs, &out_buf_size, TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF | (comp_remaining ? TINFL_FLAG_HAS_MORE_INPUT : 0)); + status = tinfl_decompress( + &inflator, (mz_uint8 *)pRead_buf + read_buf_ofs, &in_buf_size, + (mz_uint8 *)pBuf, (mz_uint8 *)pBuf + out_buf_ofs, &out_buf_size, + TINFL_FLAG_USING_NON_WRAPPING_OUTPUT_BUF | + (comp_remaining ? TINFL_FLAG_HAS_MORE_INPUT : 0)); read_buf_avail -= in_buf_size; read_buf_ofs += in_buf_size; out_buf_ofs += out_buf_size; } while (status == TINFL_STATUS_NEEDS_MORE_INPUT); - if (status == TINFL_STATUS_DONE) - { + if (status == TINFL_STATUS_DONE) { // Make sure the entire file was decompressed, and check its CRC. - if ((out_buf_ofs != file_stat.m_uncomp_size) || (mz_crc32(MZ_CRC32_INIT, (const mz_uint8 *)pBuf, (size_t)file_stat.m_uncomp_size) != file_stat.m_crc32)) + if ((out_buf_ofs != file_stat.m_uncomp_size) || + (mz_crc32(MZ_CRC32_INIT, (const mz_uint8 *)pBuf, + (size_t)file_stat.m_uncomp_size) != file_stat.m_crc32)) status = TINFL_STATUS_FAILED; } @@ -3672,26 +5071,33 @@ mz_bool mz_zip_reader_extract_to_mem_no_alloc(mz_zip_archive *pZip, mz_uint file return status == TINFL_STATUS_DONE; } -mz_bool mz_zip_reader_extract_file_to_mem_no_alloc(mz_zip_archive *pZip, const char *pFilename, void *pBuf, size_t buf_size, mz_uint flags, void *pUser_read_buf, size_t user_read_buf_size) -{ +mz_bool mz_zip_reader_extract_file_to_mem_no_alloc( + mz_zip_archive *pZip, const char *pFilename, void *pBuf, size_t buf_size, + mz_uint flags, void *pUser_read_buf, size_t user_read_buf_size) { int file_index = mz_zip_reader_locate_file(pZip, pFilename, NULL, flags); if (file_index < 0) return MZ_FALSE; - return mz_zip_reader_extract_to_mem_no_alloc(pZip, file_index, pBuf, buf_size, flags, pUser_read_buf, user_read_buf_size); + return mz_zip_reader_extract_to_mem_no_alloc(pZip, file_index, pBuf, buf_size, + flags, pUser_read_buf, + user_read_buf_size); } -mz_bool mz_zip_reader_extract_to_mem(mz_zip_archive *pZip, mz_uint file_index, void *pBuf, size_t buf_size, mz_uint flags) -{ - return mz_zip_reader_extract_to_mem_no_alloc(pZip, file_index, pBuf, buf_size, flags, NULL, 0); +mz_bool mz_zip_reader_extract_to_mem(mz_zip_archive *pZip, mz_uint file_index, + void *pBuf, size_t buf_size, + mz_uint flags) { + return mz_zip_reader_extract_to_mem_no_alloc(pZip, file_index, pBuf, buf_size, + flags, NULL, 0); } -mz_bool mz_zip_reader_extract_file_to_mem(mz_zip_archive *pZip, const char *pFilename, void *pBuf, size_t buf_size, mz_uint flags) -{ - return mz_zip_reader_extract_file_to_mem_no_alloc(pZip, pFilename, pBuf, buf_size, flags, NULL, 0); +mz_bool mz_zip_reader_extract_file_to_mem(mz_zip_archive *pZip, + const char *pFilename, void *pBuf, + size_t buf_size, mz_uint flags) { + return mz_zip_reader_extract_file_to_mem_no_alloc(pZip, pFilename, pBuf, + buf_size, flags, NULL, 0); } -void *mz_zip_reader_extract_to_heap(mz_zip_archive *pZip, mz_uint file_index, size_t *pSize, mz_uint flags) -{ +void *mz_zip_reader_extract_to_heap(mz_zip_archive *pZip, mz_uint file_index, + size_t *pSize, mz_uint flags) { mz_uint64 comp_size, uncomp_size, alloc_size; const mz_uint8 *p = mz_zip_reader_get_cdh(pZip, file_index); void *pBuf; @@ -3711,47 +5117,61 @@ void *mz_zip_reader_extract_to_heap(mz_zip_archive *pZip, mz_uint file_index, si if (((sizeof(size_t) == sizeof(mz_uint32))) && (alloc_size > 0x7FFFFFFF)) #endif return NULL; - if (NULL == (pBuf = pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, (size_t)alloc_size))) + if (NULL == + (pBuf = pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, (size_t)alloc_size))) return NULL; - if (!mz_zip_reader_extract_to_mem(pZip, file_index, pBuf, (size_t)alloc_size, flags)) - { + if (!mz_zip_reader_extract_to_mem(pZip, file_index, pBuf, (size_t)alloc_size, + flags)) { pZip->m_pFree(pZip->m_pAlloc_opaque, pBuf); return NULL; } - if (pSize) *pSize = (size_t)alloc_size; + if (pSize) + *pSize = (size_t)alloc_size; return pBuf; } -void *mz_zip_reader_extract_file_to_heap(mz_zip_archive *pZip, const char *pFilename, size_t *pSize, mz_uint flags) -{ +void *mz_zip_reader_extract_file_to_heap(mz_zip_archive *pZip, + const char *pFilename, size_t *pSize, + mz_uint flags) { int file_index = mz_zip_reader_locate_file(pZip, pFilename, NULL, flags); - if (file_index < 0) - { - if (pSize) *pSize = 0; + if (file_index < 0) { + if (pSize) + *pSize = 0; return MZ_FALSE; } return mz_zip_reader_extract_to_heap(pZip, file_index, pSize, flags); } -mz_bool mz_zip_reader_extract_to_callback(mz_zip_archive *pZip, mz_uint file_index, mz_file_write_func pCallback, void *pOpaque, mz_uint flags) -{ - int status = TINFL_STATUS_DONE; mz_uint file_crc32 = MZ_CRC32_INIT; - mz_uint64 read_buf_size, read_buf_ofs = 0, read_buf_avail, comp_remaining, out_buf_ofs = 0, cur_file_ofs; +mz_bool mz_zip_reader_extract_to_callback(mz_zip_archive *pZip, + mz_uint file_index, + mz_file_write_func pCallback, + void *pOpaque, mz_uint flags) { + int status = TINFL_STATUS_DONE; + mz_uint file_crc32 = MZ_CRC32_INIT; + mz_uint64 read_buf_size, read_buf_ofs = 0, read_buf_avail, comp_remaining, + out_buf_ofs = 0, cur_file_ofs; mz_zip_archive_file_stat file_stat; - void *pRead_buf = NULL; void *pWrite_buf = NULL; - mz_uint32 local_header_u32[(MZ_ZIP_LOCAL_DIR_HEADER_SIZE + sizeof(mz_uint32) - 1) / sizeof(mz_uint32)]; mz_uint8 *pLocal_header = (mz_uint8 *)local_header_u32; + void *pRead_buf = NULL; + void *pWrite_buf = NULL; + mz_uint32 + local_header_u32[(MZ_ZIP_LOCAL_DIR_HEADER_SIZE + sizeof(mz_uint32) - 1) / + sizeof(mz_uint32)]; + mz_uint8 *pLocal_header = (mz_uint8 *)local_header_u32; if (!mz_zip_reader_file_stat(pZip, file_index, &file_stat)) return MZ_FALSE; - // Empty file, or a directory (but not always a directory - I've seen odd zips with directories that have compressed data which inflates to 0 bytes) + // Empty file, or a directory (but not always a directory - I've seen odd zips + // with directories that have compressed data which inflates to 0 bytes) if (!file_stat.m_comp_size) return MZ_TRUE; - // Entry is a subdirectory (I've seen old zips with dir entries which have compressed deflate data which inflates to 0 bytes, but these entries claim to uncompress to 512 bytes in the headers). - // I'm torn how to handle this case - should it fail instead? + // Entry is a subdirectory (I've seen old zips with dir entries which have + // compressed deflate data which inflates to 0 bytes, but these entries claim + // to uncompress to 512 bytes in the headers). I'm torn how to handle this + // case - should it fail instead? if (mz_zip_reader_is_file_a_directory(pZip, file_index)) return MZ_TRUE; @@ -3760,69 +5180,76 @@ mz_bool mz_zip_reader_extract_to_callback(mz_zip_archive *pZip, mz_uint file_ind return MZ_FALSE; // This function only supports stored and deflate. - if ((!(flags & MZ_ZIP_FLAG_COMPRESSED_DATA)) && (file_stat.m_method != 0) && (file_stat.m_method != MZ_DEFLATED)) + if ((!(flags & MZ_ZIP_FLAG_COMPRESSED_DATA)) && (file_stat.m_method != 0) && + (file_stat.m_method != MZ_DEFLATED)) return MZ_FALSE; // Read and parse the local directory entry. cur_file_ofs = file_stat.m_local_header_ofs; - if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pLocal_header, MZ_ZIP_LOCAL_DIR_HEADER_SIZE) != MZ_ZIP_LOCAL_DIR_HEADER_SIZE) + if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pLocal_header, + MZ_ZIP_LOCAL_DIR_HEADER_SIZE) != + MZ_ZIP_LOCAL_DIR_HEADER_SIZE) return MZ_FALSE; if (MZ_READ_LE32(pLocal_header) != MZ_ZIP_LOCAL_DIR_HEADER_SIG) return MZ_FALSE; - cur_file_ofs += MZ_ZIP_LOCAL_DIR_HEADER_SIZE + MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_FILENAME_LEN_OFS) + MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_EXTRA_LEN_OFS); + cur_file_ofs += MZ_ZIP_LOCAL_DIR_HEADER_SIZE + + MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_FILENAME_LEN_OFS) + + MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_EXTRA_LEN_OFS); if ((cur_file_ofs + file_stat.m_comp_size) > pZip->m_archive_size) return MZ_FALSE; - // Decompress the file either directly from memory or from a file input buffer. - if (pZip->m_pState->m_pMem) - { + // Decompress the file either directly from memory or from a file input + // buffer. + if (pZip->m_pState->m_pMem) { pRead_buf = (mz_uint8 *)pZip->m_pState->m_pMem + cur_file_ofs; read_buf_size = read_buf_avail = file_stat.m_comp_size; comp_remaining = 0; - } - else - { + } else { read_buf_size = MZ_MIN(file_stat.m_comp_size, MZ_ZIP_MAX_IO_BUF_SIZE); - if (NULL == (pRead_buf = pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, (size_t)read_buf_size))) + if (NULL == (pRead_buf = pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, + (size_t)read_buf_size))) return MZ_FALSE; read_buf_avail = 0; comp_remaining = file_stat.m_comp_size; } - if ((flags & MZ_ZIP_FLAG_COMPRESSED_DATA) || (!file_stat.m_method)) - { + if ((flags & MZ_ZIP_FLAG_COMPRESSED_DATA) || (!file_stat.m_method)) { // The file is stored or the caller has requested the compressed data. - if (pZip->m_pState->m_pMem) - { + if (pZip->m_pState->m_pMem) { #ifdef _MSC_VER - if (((0, sizeof(size_t) == sizeof(mz_uint32))) && (file_stat.m_comp_size > 0xFFFFFFFF)) + if (((0, sizeof(size_t) == sizeof(mz_uint32))) && + (file_stat.m_comp_size > 0xFFFFFFFF)) #else - if (((sizeof(size_t) == sizeof(mz_uint32))) && (file_stat.m_comp_size > 0xFFFFFFFF)) + if (((sizeof(size_t) == sizeof(mz_uint32))) && + (file_stat.m_comp_size > 0xFFFFFFFF)) #endif return MZ_FALSE; - if (pCallback(pOpaque, out_buf_ofs, pRead_buf, (size_t)file_stat.m_comp_size) != file_stat.m_comp_size) + if (pCallback(pOpaque, out_buf_ofs, pRead_buf, + (size_t)file_stat.m_comp_size) != file_stat.m_comp_size) status = TINFL_STATUS_FAILED; else if (!(flags & MZ_ZIP_FLAG_COMPRESSED_DATA)) - file_crc32 = (mz_uint32)mz_crc32(file_crc32, (const mz_uint8 *)pRead_buf, (size_t)file_stat.m_comp_size); + file_crc32 = + (mz_uint32)mz_crc32(file_crc32, (const mz_uint8 *)pRead_buf, + (size_t)file_stat.m_comp_size); + // cur_file_ofs += file_stat.m_comp_size; out_buf_ofs += file_stat.m_comp_size; - } - else - { - while (comp_remaining) - { + // comp_remaining = 0; + } else { + while (comp_remaining) { read_buf_avail = MZ_MIN(read_buf_size, comp_remaining); - if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pRead_buf, (size_t)read_buf_avail) != read_buf_avail) - { + if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pRead_buf, + (size_t)read_buf_avail) != read_buf_avail) { status = TINFL_STATUS_FAILED; break; } if (!(flags & MZ_ZIP_FLAG_COMPRESSED_DATA)) - file_crc32 = (mz_uint32)mz_crc32(file_crc32, (const mz_uint8 *)pRead_buf, (size_t)read_buf_avail); + file_crc32 = (mz_uint32)mz_crc32( + file_crc32, (const mz_uint8 *)pRead_buf, (size_t)read_buf_avail); - if (pCallback(pOpaque, out_buf_ofs, pRead_buf, (size_t)read_buf_avail) != read_buf_avail) - { + if (pCallback(pOpaque, out_buf_ofs, pRead_buf, + (size_t)read_buf_avail) != read_buf_avail) { status = TINFL_STATUS_FAILED; break; } @@ -3831,25 +5258,24 @@ mz_bool mz_zip_reader_extract_to_callback(mz_zip_archive *pZip, mz_uint file_ind comp_remaining -= read_buf_avail; } } - } - else - { + } else { tinfl_decompressor inflator; tinfl_init(&inflator); - if (NULL == (pWrite_buf = pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, TINFL_LZ_DICT_SIZE))) + if (NULL == (pWrite_buf = pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, + TINFL_LZ_DICT_SIZE))) status = TINFL_STATUS_FAILED; - else - { - do - { - mz_uint8 *pWrite_buf_cur = (mz_uint8 *)pWrite_buf + (out_buf_ofs & (TINFL_LZ_DICT_SIZE - 1)); - size_t in_buf_size, out_buf_size = TINFL_LZ_DICT_SIZE - (out_buf_ofs & (TINFL_LZ_DICT_SIZE - 1)); - if ((!read_buf_avail) && (!pZip->m_pState->m_pMem)) - { + else { + do { + mz_uint8 *pWrite_buf_cur = + (mz_uint8 *)pWrite_buf + (out_buf_ofs & (TINFL_LZ_DICT_SIZE - 1)); + size_t in_buf_size, + out_buf_size = + TINFL_LZ_DICT_SIZE - (out_buf_ofs & (TINFL_LZ_DICT_SIZE - 1)); + if ((!read_buf_avail) && (!pZip->m_pState->m_pMem)) { read_buf_avail = MZ_MIN(read_buf_size, comp_remaining); - if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pRead_buf, (size_t)read_buf_avail) != read_buf_avail) - { + if (pZip->m_pRead(pZip->m_pIO_opaque, cur_file_ofs, pRead_buf, + (size_t)read_buf_avail) != read_buf_avail) { status = TINFL_STATUS_FAILED; break; } @@ -3859,32 +5285,36 @@ mz_bool mz_zip_reader_extract_to_callback(mz_zip_archive *pZip, mz_uint file_ind } in_buf_size = (size_t)read_buf_avail; - status = tinfl_decompress(&inflator, (const mz_uint8 *)pRead_buf + read_buf_ofs, &in_buf_size, (mz_uint8 *)pWrite_buf, pWrite_buf_cur, &out_buf_size, comp_remaining ? TINFL_FLAG_HAS_MORE_INPUT : 0); + status = tinfl_decompress( + &inflator, (const mz_uint8 *)pRead_buf + read_buf_ofs, &in_buf_size, + (mz_uint8 *)pWrite_buf, pWrite_buf_cur, &out_buf_size, + comp_remaining ? TINFL_FLAG_HAS_MORE_INPUT : 0); read_buf_avail -= in_buf_size; read_buf_ofs += in_buf_size; - if (out_buf_size) - { - if (pCallback(pOpaque, out_buf_ofs, pWrite_buf_cur, out_buf_size) != out_buf_size) - { + if (out_buf_size) { + if (pCallback(pOpaque, out_buf_ofs, pWrite_buf_cur, out_buf_size) != + out_buf_size) { status = TINFL_STATUS_FAILED; break; } - file_crc32 = (mz_uint32)mz_crc32(file_crc32, pWrite_buf_cur, out_buf_size); - if ((out_buf_ofs += out_buf_size) > file_stat.m_uncomp_size) - { + file_crc32 = + (mz_uint32)mz_crc32(file_crc32, pWrite_buf_cur, out_buf_size); + if ((out_buf_ofs += out_buf_size) > file_stat.m_uncomp_size) { status = TINFL_STATUS_FAILED; break; } } - } while ((status == TINFL_STATUS_NEEDS_MORE_INPUT) || (status == TINFL_STATUS_HAS_MORE_OUTPUT)); + } while ((status == TINFL_STATUS_NEEDS_MORE_INPUT) || + (status == TINFL_STATUS_HAS_MORE_OUTPUT)); } } - if ((status == TINFL_STATUS_DONE) && (!(flags & MZ_ZIP_FLAG_COMPRESSED_DATA))) - { + if ((status == TINFL_STATUS_DONE) && + (!(flags & MZ_ZIP_FLAG_COMPRESSED_DATA))) { // Make sure the entire file was decompressed, and check its CRC. - if ((out_buf_ofs != file_stat.m_uncomp_size) || (file_crc32 != file_stat.m_crc32)) + if ((out_buf_ofs != file_stat.m_uncomp_size) || + (file_crc32 != file_stat.m_crc32)) status = TINFL_STATUS_FAILED; } @@ -3896,72 +5326,82 @@ mz_bool mz_zip_reader_extract_to_callback(mz_zip_archive *pZip, mz_uint file_ind return status == TINFL_STATUS_DONE; } -mz_bool mz_zip_reader_extract_file_to_callback(mz_zip_archive *pZip, const char *pFilename, mz_file_write_func pCallback, void *pOpaque, mz_uint flags) -{ +mz_bool mz_zip_reader_extract_file_to_callback(mz_zip_archive *pZip, + const char *pFilename, + mz_file_write_func pCallback, + void *pOpaque, mz_uint flags) { int file_index = mz_zip_reader_locate_file(pZip, pFilename, NULL, flags); if (file_index < 0) return MZ_FALSE; - return mz_zip_reader_extract_to_callback(pZip, file_index, pCallback, pOpaque, flags); + return mz_zip_reader_extract_to_callback(pZip, file_index, pCallback, pOpaque, + flags); } #ifndef MINIZ_NO_STDIO -static size_t mz_zip_file_write_callback(void *pOpaque, mz_uint64 ofs, const void *pBuf, size_t n) -{ - (void)ofs; return MZ_FWRITE(pBuf, 1, n, (MZ_FILE*)pOpaque); +static size_t mz_zip_file_write_callback(void *pOpaque, mz_uint64 ofs, + const void *pBuf, size_t n) { + (void)ofs; + return MZ_FWRITE(pBuf, 1, n, (MZ_FILE *)pOpaque); } -mz_bool mz_zip_reader_extract_to_file(mz_zip_archive *pZip, mz_uint file_index, const char *pDst_filename, mz_uint flags) -{ +mz_bool mz_zip_reader_extract_to_file(mz_zip_archive *pZip, mz_uint file_index, + const char *pDst_filename, + mz_uint flags) { mz_bool status; mz_zip_archive_file_stat file_stat; MZ_FILE *pFile; if (!mz_zip_reader_file_stat(pZip, file_index, &file_stat)) return MZ_FALSE; + pFile = MZ_FOPEN(pDst_filename, "wb"); if (!pFile) return MZ_FALSE; - status = mz_zip_reader_extract_to_callback(pZip, file_index, mz_zip_file_write_callback, pFile, flags); + status = mz_zip_reader_extract_to_callback( + pZip, file_index, mz_zip_file_write_callback, pFile, flags); if (MZ_FCLOSE(pFile) == EOF) return MZ_FALSE; #ifndef MINIZ_NO_TIME - if (status) + if (status) { mz_zip_set_file_times(pDst_filename, file_stat.m_time, file_stat.m_time); + } #endif + return status; } #endif // #ifndef MINIZ_NO_STDIO -mz_bool mz_zip_reader_end(mz_zip_archive *pZip) -{ - if ((!pZip) || (!pZip->m_pState) || (!pZip->m_pAlloc) || (!pZip->m_pFree) || (pZip->m_zip_mode != MZ_ZIP_MODE_READING)) +mz_bool mz_zip_reader_end(mz_zip_archive *pZip) { + if ((!pZip) || (!pZip->m_pState) || (!pZip->m_pAlloc) || (!pZip->m_pFree) || + (pZip->m_zip_mode != MZ_ZIP_MODE_READING)) return MZ_FALSE; - if (pZip->m_pState) - { - mz_zip_internal_state *pState = pZip->m_pState; pZip->m_pState = NULL; - mz_zip_array_clear(pZip, &pState->m_central_dir); - mz_zip_array_clear(pZip, &pState->m_central_dir_offsets); - mz_zip_array_clear(pZip, &pState->m_sorted_central_dir_offsets); + mz_zip_internal_state *pState = pZip->m_pState; + pZip->m_pState = NULL; + mz_zip_array_clear(pZip, &pState->m_central_dir); + mz_zip_array_clear(pZip, &pState->m_central_dir_offsets); + mz_zip_array_clear(pZip, &pState->m_sorted_central_dir_offsets); #ifndef MINIZ_NO_STDIO - if (pState->m_pFile) - { - MZ_FCLOSE(pState->m_pFile); - pState->m_pFile = NULL; - } + if (pState->m_pFile) { + MZ_FCLOSE(pState->m_pFile); + pState->m_pFile = NULL; + } #endif // #ifndef MINIZ_NO_STDIO - pZip->m_pFree(pZip->m_pAlloc_opaque, pState); - } + pZip->m_pFree(pZip->m_pAlloc_opaque, pState); + pZip->m_zip_mode = MZ_ZIP_MODE_INVALID; return MZ_TRUE; } #ifndef MINIZ_NO_STDIO -mz_bool mz_zip_reader_extract_file_to_file(mz_zip_archive *pZip, const char *pArchive_filename, const char *pDst_filename, mz_uint flags) -{ - int file_index = mz_zip_reader_locate_file(pZip, pArchive_filename, NULL, flags); +mz_bool mz_zip_reader_extract_file_to_file(mz_zip_archive *pZip, + const char *pArchive_filename, + const char *pDst_filename, + mz_uint flags) { + int file_index = + mz_zip_reader_locate_file(pZip, pArchive_filename, NULL, flags); if (file_index < 0) return MZ_FALSE; return mz_zip_reader_extract_to_file(pZip, file_index, pDst_filename, flags); @@ -3972,75 +5412,92 @@ mz_bool mz_zip_reader_extract_file_to_file(mz_zip_archive *pZip, const char *pAr #ifndef MINIZ_NO_ARCHIVE_WRITING_APIS -static void mz_write_le16(mz_uint8 *p, mz_uint16 v) { p[0] = (mz_uint8)v; p[1] = (mz_uint8)(v >> 8); } -static void mz_write_le32(mz_uint8 *p, mz_uint32 v) { p[0] = (mz_uint8)v; p[1] = (mz_uint8)(v >> 8); p[2] = (mz_uint8)(v >> 16); p[3] = (mz_uint8)(v >> 24); } +static void mz_write_le16(mz_uint8 *p, mz_uint16 v) { + p[0] = (mz_uint8)v; + p[1] = (mz_uint8)(v >> 8); +} +static void mz_write_le32(mz_uint8 *p, mz_uint32 v) { + p[0] = (mz_uint8)v; + p[1] = (mz_uint8)(v >> 8); + p[2] = (mz_uint8)(v >> 16); + p[3] = (mz_uint8)(v >> 24); +} #define MZ_WRITE_LE16(p, v) mz_write_le16((mz_uint8 *)(p), (mz_uint16)(v)) #define MZ_WRITE_LE32(p, v) mz_write_le32((mz_uint8 *)(p), (mz_uint32)(v)) -mz_bool mz_zip_writer_init(mz_zip_archive *pZip, mz_uint64 existing_size) -{ - if ((!pZip) || (pZip->m_pState) || (!pZip->m_pWrite) || (pZip->m_zip_mode != MZ_ZIP_MODE_INVALID)) +mz_bool mz_zip_writer_init(mz_zip_archive *pZip, mz_uint64 existing_size) { + if ((!pZip) || (pZip->m_pState) || (!pZip->m_pWrite) || + (pZip->m_zip_mode != MZ_ZIP_MODE_INVALID)) return MZ_FALSE; - if (pZip->m_file_offset_alignment) - { + if (pZip->m_file_offset_alignment) { // Ensure user specified file offset alignment is a power of 2. if (pZip->m_file_offset_alignment & (pZip->m_file_offset_alignment - 1)) return MZ_FALSE; } - if (!pZip->m_pAlloc) pZip->m_pAlloc = def_alloc_func; - if (!pZip->m_pFree) pZip->m_pFree = def_free_func; - if (!pZip->m_pRealloc) pZip->m_pRealloc = def_realloc_func; + if (!pZip->m_pAlloc) + pZip->m_pAlloc = def_alloc_func; + if (!pZip->m_pFree) + pZip->m_pFree = def_free_func; + if (!pZip->m_pRealloc) + pZip->m_pRealloc = def_realloc_func; pZip->m_zip_mode = MZ_ZIP_MODE_WRITING; pZip->m_archive_size = existing_size; pZip->m_central_directory_file_ofs = 0; pZip->m_total_files = 0; - if (NULL == (pZip->m_pState = (mz_zip_internal_state *)pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, sizeof(mz_zip_internal_state)))) + if (NULL == (pZip->m_pState = (mz_zip_internal_state *)pZip->m_pAlloc( + pZip->m_pAlloc_opaque, 1, sizeof(mz_zip_internal_state)))) return MZ_FALSE; memset(pZip->m_pState, 0, sizeof(mz_zip_internal_state)); - MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(&pZip->m_pState->m_central_dir, sizeof(mz_uint8)); - MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(&pZip->m_pState->m_central_dir_offsets, sizeof(mz_uint32)); - MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(&pZip->m_pState->m_sorted_central_dir_offsets, sizeof(mz_uint32)); + MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(&pZip->m_pState->m_central_dir, + sizeof(mz_uint8)); + MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(&pZip->m_pState->m_central_dir_offsets, + sizeof(mz_uint32)); + MZ_ZIP_ARRAY_SET_ELEMENT_SIZE(&pZip->m_pState->m_sorted_central_dir_offsets, + sizeof(mz_uint32)); return MZ_TRUE; } -static size_t mz_zip_heap_write_func(void *pOpaque, mz_uint64 file_ofs, const void *pBuf, size_t n) -{ +static size_t mz_zip_heap_write_func(void *pOpaque, mz_uint64 file_ofs, + const void *pBuf, size_t n) { mz_zip_archive *pZip = (mz_zip_archive *)pOpaque; mz_zip_internal_state *pState = pZip->m_pState; mz_uint64 new_size = MZ_MAX(file_ofs + n, pState->m_mem_size); -#ifdef _MSC_VER - if ((!n) || ((0, sizeof(size_t) == sizeof(mz_uint32)) && (new_size > 0x7FFFFFFF))) -#else - if ((!n) || ((sizeof(size_t) == sizeof(mz_uint32)) && (new_size > 0x7FFFFFFF))) -#endif + + if ((!n) || + ((sizeof(size_t) == sizeof(mz_uint32)) && (new_size > 0x7FFFFFFF))) return 0; - if (new_size > pState->m_mem_capacity) - { + + if (new_size > pState->m_mem_capacity) { void *pNew_block; - size_t new_capacity = MZ_MAX(64, pState->m_mem_capacity); while (new_capacity < new_size) new_capacity *= 2; - if (NULL == (pNew_block = pZip->m_pRealloc(pZip->m_pAlloc_opaque, pState->m_pMem, 1, new_capacity))) + size_t new_capacity = MZ_MAX(64, pState->m_mem_capacity); + while (new_capacity < new_size) + new_capacity *= 2; + if (NULL == (pNew_block = pZip->m_pRealloc( + pZip->m_pAlloc_opaque, pState->m_pMem, 1, new_capacity))) return 0; - pState->m_pMem = pNew_block; pState->m_mem_capacity = new_capacity; + pState->m_pMem = pNew_block; + pState->m_mem_capacity = new_capacity; } memcpy((mz_uint8 *)pState->m_pMem + file_ofs, pBuf, n); pState->m_mem_size = (size_t)new_size; return n; } -mz_bool mz_zip_writer_init_heap(mz_zip_archive *pZip, size_t size_to_reserve_at_beginning, size_t initial_allocation_size) -{ +mz_bool mz_zip_writer_init_heap(mz_zip_archive *pZip, + size_t size_to_reserve_at_beginning, + size_t initial_allocation_size) { pZip->m_pWrite = mz_zip_heap_write_func; pZip->m_pIO_opaque = pZip; if (!mz_zip_writer_init(pZip, size_to_reserve_at_beginning)) return MZ_FALSE; - if (0 != (initial_allocation_size = MZ_MAX(initial_allocation_size, size_to_reserve_at_beginning))) - { - if (NULL == (pZip->m_pState->m_pMem = pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, initial_allocation_size))) - { + if (0 != (initial_allocation_size = MZ_MAX(initial_allocation_size, + size_to_reserve_at_beginning))) { + if (NULL == (pZip->m_pState->m_pMem = pZip->m_pAlloc( + pZip->m_pAlloc_opaque, 1, initial_allocation_size))) { mz_zip_writer_end(pZip); return MZ_FALSE; } @@ -4050,61 +5507,65 @@ mz_bool mz_zip_writer_init_heap(mz_zip_archive *pZip, size_t size_to_reserve_at_ } #ifndef MINIZ_NO_STDIO -static size_t mz_zip_file_write_func(void *pOpaque, mz_uint64 file_ofs, const void *pBuf, size_t n) -{ +static size_t mz_zip_file_write_func(void *pOpaque, mz_uint64 file_ofs, + const void *pBuf, size_t n) { mz_zip_archive *pZip = (mz_zip_archive *)pOpaque; mz_int64 cur_ofs = MZ_FTELL64(pZip->m_pState->m_pFile); - if (((mz_int64)file_ofs < 0) || (((cur_ofs != (mz_int64)file_ofs)) && (MZ_FSEEK64(pZip->m_pState->m_pFile, (mz_int64)file_ofs, SEEK_SET)))) + if (((mz_int64)file_ofs < 0) || + (((cur_ofs != (mz_int64)file_ofs)) && + (MZ_FSEEK64(pZip->m_pState->m_pFile, (mz_int64)file_ofs, SEEK_SET)))) return 0; return MZ_FWRITE(pBuf, 1, n, pZip->m_pState->m_pFile); } -mz_bool mz_zip_writer_init_file(mz_zip_archive *pZip, const char *pFilename, mz_uint64 size_to_reserve_at_beginning) -{ +mz_bool mz_zip_writer_init_file(mz_zip_archive *pZip, const char *pFilename, + mz_uint64 size_to_reserve_at_beginning) { MZ_FILE *pFile; pZip->m_pWrite = mz_zip_file_write_func; pZip->m_pIO_opaque = pZip; if (!mz_zip_writer_init(pZip, size_to_reserve_at_beginning)) return MZ_FALSE; - if (NULL == (pFile = MZ_FOPEN(pFilename, "wb"))) - { + if (NULL == (pFile = MZ_FOPEN(pFilename, "wb"))) { mz_zip_writer_end(pZip); return MZ_FALSE; } pZip->m_pState->m_pFile = pFile; - if (size_to_reserve_at_beginning) - { - mz_uint64 cur_ofs = 0; char buf[4096]; MZ_CLEAR_OBJ(buf); - do - { + if (size_to_reserve_at_beginning) { + mz_uint64 cur_ofs = 0; + char buf[4096]; + MZ_CLEAR_OBJ(buf); + do { size_t n = (size_t)MZ_MIN(sizeof(buf), size_to_reserve_at_beginning); - if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_ofs, buf, n) != n) - { + if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_ofs, buf, n) != n) { mz_zip_writer_end(pZip); return MZ_FALSE; } - cur_ofs += n; size_to_reserve_at_beginning -= n; + cur_ofs += n; + size_to_reserve_at_beginning -= n; } while (size_to_reserve_at_beginning); } return MZ_TRUE; } #endif // #ifndef MINIZ_NO_STDIO -mz_bool mz_zip_writer_init_from_reader(mz_zip_archive *pZip, const char *pFilename) -{ +mz_bool mz_zip_writer_init_from_reader(mz_zip_archive *pZip, + const char *pFilename) { mz_zip_internal_state *pState; if ((!pZip) || (!pZip->m_pState) || (pZip->m_zip_mode != MZ_ZIP_MODE_READING)) return MZ_FALSE; - // No sense in trying to write to an archive that's already at the support max size - if ((pZip->m_total_files == 0xFFFF) || ((pZip->m_archive_size + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + MZ_ZIP_LOCAL_DIR_HEADER_SIZE) > 0xFFFFFFFF)) + // No sense in trying to write to an archive that's already at the support max + // size + if ((pZip->m_total_files == 0xFFFF) || + ((pZip->m_archive_size + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + + MZ_ZIP_LOCAL_DIR_HEADER_SIZE) > 0xFFFFFFFF)) return MZ_FALSE; pState = pZip->m_pState; - if (pState->m_pFile) - { + if (pState->m_pFile) { #ifdef MINIZ_NO_STDIO - pFilename; return MZ_FALSE; + pFilename; + return MZ_FALSE; #else // Archive is being read from stdio - try to reopen as writable. if (pZip->m_pIO_opaque != pZip) @@ -4112,27 +5573,29 @@ mz_bool mz_zip_writer_init_from_reader(mz_zip_archive *pZip, const char *pFilena if (!pFilename) return MZ_FALSE; pZip->m_pWrite = mz_zip_file_write_func; - if (NULL == (pState->m_pFile = MZ_FREOPEN(pFilename, "r+b", pState->m_pFile))) - { - // The mz_zip_archive is now in a bogus state because pState->m_pFile is NULL, so just close it. + if (NULL == + (pState->m_pFile = MZ_FREOPEN(pFilename, "r+b", pState->m_pFile))) { + // The mz_zip_archive is now in a bogus state because pState->m_pFile is + // NULL, so just close it. mz_zip_reader_end(pZip); return MZ_FALSE; } #endif // #ifdef MINIZ_NO_STDIO - } - else if (pState->m_pMem) - { - // Archive lives in a memory block. Assume it's from the heap that we can resize using the realloc callback. + } else if (pState->m_pMem) { + // Archive lives in a memory block. Assume it's from the heap that we can + // resize using the realloc callback. if (pZip->m_pIO_opaque != pZip) return MZ_FALSE; pState->m_mem_capacity = pState->m_mem_size; pZip->m_pWrite = mz_zip_heap_write_func; } - // Archive is being read via a user provided read function - make sure the user has specified a write function too. + // Archive is being read via a user provided read function - make sure the + // user has specified a write function too. else if (!pZip->m_pWrite) return MZ_FALSE; - // Start writing new files at the archive's current central directory location. + // Start writing new files at the archive's current central directory + // location. pZip->m_archive_size = pZip->m_central_directory_file_ofs; pZip->m_zip_mode = MZ_ZIP_MODE_WRITING; pZip->m_central_directory_file_ofs = 0; @@ -4140,30 +5603,36 @@ mz_bool mz_zip_writer_init_from_reader(mz_zip_archive *pZip, const char *pFilena return MZ_TRUE; } -mz_bool mz_zip_writer_add_mem(mz_zip_archive *pZip, const char *pArchive_name, const void *pBuf, size_t buf_size, mz_uint level_and_flags) -{ - return mz_zip_writer_add_mem_ex(pZip, pArchive_name, pBuf, buf_size, NULL, 0, level_and_flags, 0, 0); +mz_bool mz_zip_writer_add_mem(mz_zip_archive *pZip, const char *pArchive_name, + const void *pBuf, size_t buf_size, + mz_uint level_and_flags) { + return mz_zip_writer_add_mem_ex(pZip, pArchive_name, pBuf, buf_size, NULL, 0, + level_and_flags, 0, 0); } -typedef struct -{ +typedef struct { mz_zip_archive *m_pZip; mz_uint64 m_cur_archive_file_ofs; mz_uint64 m_comp_size; } mz_zip_writer_add_state; -static mz_bool mz_zip_writer_add_put_buf_callback(const void* pBuf, int len, void *pUser) -{ +static mz_bool mz_zip_writer_add_put_buf_callback(const void *pBuf, int len, + void *pUser) { mz_zip_writer_add_state *pState = (mz_zip_writer_add_state *)pUser; - if ((int)pState->m_pZip->m_pWrite(pState->m_pZip->m_pIO_opaque, pState->m_cur_archive_file_ofs, pBuf, len) != len) + if ((int)pState->m_pZip->m_pWrite(pState->m_pZip->m_pIO_opaque, + pState->m_cur_archive_file_ofs, pBuf, + len) != len) return MZ_FALSE; pState->m_cur_archive_file_ofs += len; pState->m_comp_size += len; return MZ_TRUE; } -static mz_bool mz_zip_writer_create_local_dir_header(mz_zip_archive *pZip, mz_uint8 *pDst, mz_uint16 filename_size, mz_uint16 extra_size, mz_uint64 uncomp_size, mz_uint64 comp_size, mz_uint32 uncomp_crc32, mz_uint16 method, mz_uint16 bit_flags, mz_uint16 dos_time, mz_uint16 dos_date) -{ +static mz_bool mz_zip_writer_create_local_dir_header( + mz_zip_archive *pZip, mz_uint8 *pDst, mz_uint16 filename_size, + mz_uint16 extra_size, mz_uint64 uncomp_size, mz_uint64 comp_size, + mz_uint32 uncomp_crc32, mz_uint16 method, mz_uint16 bit_flags, + mz_uint16 dos_time, mz_uint16 dos_date) { (void)pZip; memset(pDst, 0, MZ_ZIP_LOCAL_DIR_HEADER_SIZE); MZ_WRITE_LE32(pDst + MZ_ZIP_LDH_SIG_OFS, MZ_ZIP_LOCAL_DIR_HEADER_SIG); @@ -4180,11 +5649,19 @@ static mz_bool mz_zip_writer_create_local_dir_header(mz_zip_archive *pZip, mz_ui return MZ_TRUE; } -static mz_bool mz_zip_writer_create_central_dir_header(mz_zip_archive *pZip, mz_uint8 *pDst, mz_uint16 filename_size, mz_uint16 extra_size, mz_uint16 comment_size, mz_uint64 uncomp_size, mz_uint64 comp_size, mz_uint32 uncomp_crc32, mz_uint16 method, mz_uint16 bit_flags, mz_uint16 dos_time, mz_uint16 dos_date, mz_uint64 local_header_ofs, mz_uint32 ext_attributes) -{ +static mz_bool mz_zip_writer_create_central_dir_header( + mz_zip_archive *pZip, mz_uint8 *pDst, mz_uint16 filename_size, + mz_uint16 extra_size, mz_uint16 comment_size, mz_uint64 uncomp_size, + mz_uint64 comp_size, mz_uint32 uncomp_crc32, mz_uint16 method, + mz_uint16 bit_flags, mz_uint16 dos_time, mz_uint16 dos_date, + mz_uint64 local_header_ofs, mz_uint32 ext_attributes) { (void)pZip; + mz_uint16 version_made_by = 10 * MZ_VER_MAJOR + MZ_VER_MINOR; + version_made_by |= (MZ_PLATFORM << 8); + memset(pDst, 0, MZ_ZIP_CENTRAL_DIR_HEADER_SIZE); MZ_WRITE_LE32(pDst + MZ_ZIP_CDH_SIG_OFS, MZ_ZIP_CENTRAL_DIR_HEADER_SIG); + MZ_WRITE_LE16(pDst + MZ_ZIP_CDH_VERSION_MADE_BY_OFS, version_made_by); MZ_WRITE_LE16(pDst + MZ_ZIP_CDH_VERSION_NEEDED_OFS, method ? 20 : 0); MZ_WRITE_LE16(pDst + MZ_ZIP_CDH_BIT_FLAG_OFS, bit_flags); MZ_WRITE_LE16(pDst + MZ_ZIP_CDH_METHOD_OFS, method); @@ -4201,41 +5678,57 @@ static mz_bool mz_zip_writer_create_central_dir_header(mz_zip_archive *pZip, mz_ return MZ_TRUE; } -static mz_bool mz_zip_writer_add_to_central_dir(mz_zip_archive *pZip, const char *pFilename, mz_uint16 filename_size, const void *pExtra, mz_uint16 extra_size, const void *pComment, mz_uint16 comment_size, mz_uint64 uncomp_size, mz_uint64 comp_size, mz_uint32 uncomp_crc32, mz_uint16 method, mz_uint16 bit_flags, mz_uint16 dos_time, mz_uint16 dos_date, mz_uint64 local_header_ofs, mz_uint32 ext_attributes) -{ +static mz_bool mz_zip_writer_add_to_central_dir( + mz_zip_archive *pZip, const char *pFilename, mz_uint16 filename_size, + const void *pExtra, mz_uint16 extra_size, const void *pComment, + mz_uint16 comment_size, mz_uint64 uncomp_size, mz_uint64 comp_size, + mz_uint32 uncomp_crc32, mz_uint16 method, mz_uint16 bit_flags, + mz_uint16 dos_time, mz_uint16 dos_date, mz_uint64 local_header_ofs, + mz_uint32 ext_attributes) { mz_zip_internal_state *pState = pZip->m_pState; mz_uint32 central_dir_ofs = (mz_uint32)pState->m_central_dir.m_size; size_t orig_central_dir_size = pState->m_central_dir.m_size; mz_uint8 central_dir_header[MZ_ZIP_CENTRAL_DIR_HEADER_SIZE]; // No zip64 support yet - if ((local_header_ofs > 0xFFFFFFFF) || (((mz_uint64)pState->m_central_dir.m_size + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + filename_size + extra_size + comment_size) > 0xFFFFFFFF)) + if ((local_header_ofs > 0xFFFFFFFF) || + (((mz_uint64)pState->m_central_dir.m_size + + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + filename_size + extra_size + + comment_size) > 0xFFFFFFFF)) return MZ_FALSE; - if (!mz_zip_writer_create_central_dir_header(pZip, central_dir_header, filename_size, extra_size, comment_size, uncomp_size, comp_size, uncomp_crc32, method, bit_flags, dos_time, dos_date, local_header_ofs, ext_attributes)) + if (!mz_zip_writer_create_central_dir_header( + pZip, central_dir_header, filename_size, extra_size, comment_size, + uncomp_size, comp_size, uncomp_crc32, method, bit_flags, dos_time, + dos_date, local_header_ofs, ext_attributes)) return MZ_FALSE; - if ((!mz_zip_array_push_back(pZip, &pState->m_central_dir, central_dir_header, MZ_ZIP_CENTRAL_DIR_HEADER_SIZE)) || - (!mz_zip_array_push_back(pZip, &pState->m_central_dir, pFilename, filename_size)) || - (!mz_zip_array_push_back(pZip, &pState->m_central_dir, pExtra, extra_size)) || - (!mz_zip_array_push_back(pZip, &pState->m_central_dir, pComment, comment_size)) || - (!mz_zip_array_push_back(pZip, &pState->m_central_dir_offsets, ¢ral_dir_ofs, 1))) - { + if ((!mz_zip_array_push_back(pZip, &pState->m_central_dir, central_dir_header, + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE)) || + (!mz_zip_array_push_back(pZip, &pState->m_central_dir, pFilename, + filename_size)) || + (!mz_zip_array_push_back(pZip, &pState->m_central_dir, pExtra, + extra_size)) || + (!mz_zip_array_push_back(pZip, &pState->m_central_dir, pComment, + comment_size)) || + (!mz_zip_array_push_back(pZip, &pState->m_central_dir_offsets, + ¢ral_dir_ofs, 1))) { // Try to push the central directory array back into its original state. - mz_zip_array_resize(pZip, &pState->m_central_dir, orig_central_dir_size, MZ_FALSE); + mz_zip_array_resize(pZip, &pState->m_central_dir, orig_central_dir_size, + MZ_FALSE); return MZ_FALSE; } return MZ_TRUE; } -static mz_bool mz_zip_writer_validate_archive_name(const char *pArchive_name) -{ - // Basic ZIP archive filename validity checks: Valid filenames cannot start with a forward slash, cannot contain a drive letter, and cannot use DOS-style backward slashes. +static mz_bool mz_zip_writer_validate_archive_name(const char *pArchive_name) { + // Basic ZIP archive filename validity checks: Valid filenames cannot start + // with a forward slash, cannot contain a drive letter, and cannot use + // DOS-style backward slashes. if (*pArchive_name == '/') return MZ_FALSE; - while (*pArchive_name) - { + while (*pArchive_name) { if ((*pArchive_name == '\\') || (*pArchive_name == ':')) return MZ_FALSE; pArchive_name++; @@ -4243,33 +5736,39 @@ static mz_bool mz_zip_writer_validate_archive_name(const char *pArchive_name) return MZ_TRUE; } -static mz_uint mz_zip_writer_compute_padding_needed_for_file_alignment(mz_zip_archive *pZip) -{ +static mz_uint +mz_zip_writer_compute_padding_needed_for_file_alignment(mz_zip_archive *pZip) { mz_uint32 n; if (!pZip->m_file_offset_alignment) return 0; n = (mz_uint32)(pZip->m_archive_size & (pZip->m_file_offset_alignment - 1)); - return (pZip->m_file_offset_alignment - n) & (pZip->m_file_offset_alignment - 1); + return (pZip->m_file_offset_alignment - n) & + (pZip->m_file_offset_alignment - 1); } -static mz_bool mz_zip_writer_write_zeros(mz_zip_archive *pZip, mz_uint64 cur_file_ofs, mz_uint32 n) -{ +static mz_bool mz_zip_writer_write_zeros(mz_zip_archive *pZip, + mz_uint64 cur_file_ofs, mz_uint32 n) { char buf[4096]; memset(buf, 0, MZ_MIN(sizeof(buf), n)); - while (n) - { + while (n) { mz_uint32 s = MZ_MIN(sizeof(buf), n); if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_file_ofs, buf, s) != s) return MZ_FALSE; - cur_file_ofs += s; n -= s; + cur_file_ofs += s; + n -= s; } return MZ_TRUE; } -mz_bool mz_zip_writer_add_mem_ex(mz_zip_archive *pZip, const char *pArchive_name, const void *pBuf, size_t buf_size, const void *pComment, mz_uint16 comment_size, mz_uint level_and_flags, mz_uint64 uncomp_size, mz_uint32 uncomp_crc32) -{ +mz_bool mz_zip_writer_add_mem_ex(mz_zip_archive *pZip, + const char *pArchive_name, const void *pBuf, + size_t buf_size, const void *pComment, + mz_uint16 comment_size, + mz_uint level_and_flags, mz_uint64 uncomp_size, + mz_uint32 uncomp_crc32) { + mz_uint32 ext_attributes = 0; mz_uint16 method = 0, dos_time = 0, dos_date = 0; - mz_uint level, ext_attributes = 0, num_alignment_padding_bytes; + mz_uint level, num_alignment_padding_bytes; mz_uint64 local_dir_header_ofs, cur_archive_file_ofs, comp_size = 0; size_t archive_name_size; mz_uint8 local_dir_header[MZ_ZIP_LOCAL_DIR_HEADER_SIZE]; @@ -4280,9 +5779,13 @@ mz_bool mz_zip_writer_add_mem_ex(mz_zip_archive *pZip, const char *pArchive_name if ((int)level_and_flags < 0) level_and_flags = MZ_DEFAULT_LEVEL; level = level_and_flags & 0xF; - store_data_uncompressed = ((!level) || (level_and_flags & MZ_ZIP_FLAG_COMPRESSED_DATA)); + store_data_uncompressed = + ((!level) || (level_and_flags & MZ_ZIP_FLAG_COMPRESSED_DATA)); - if ((!pZip) || (!pZip->m_pState) || (pZip->m_zip_mode != MZ_ZIP_MODE_WRITING) || ((buf_size) && (!pBuf)) || (!pArchive_name) || ((comment_size) && (!pComment)) || (pZip->m_total_files == 0xFFFF) || (level > MZ_UBER_COMPRESSION)) + if ((!pZip) || (!pZip->m_pState) || + (pZip->m_zip_mode != MZ_ZIP_MODE_WRITING) || ((buf_size) && (!pBuf)) || + (!pArchive_name) || ((comment_size) && (!pComment)) || + (pZip->m_total_files == 0xFFFF) || (level > MZ_UBER_COMPRESSION)) return MZ_FALSE; local_dir_header_ofs = cur_archive_file_ofs = pZip->m_archive_size; @@ -4298,8 +5801,9 @@ mz_bool mz_zip_writer_add_mem_ex(mz_zip_archive *pZip, const char *pArchive_name #ifndef MINIZ_NO_TIME { - time_t cur_time; time(&cur_time); - mz_zip_time_to_dos_time(cur_time, &dos_time, &dos_date); + time_t cur_time; + time(&cur_time); + mz_zip_time_t_to_dos_time(cur_time, &dos_time, &dos_date); } #endif // #ifndef MINIZ_NO_TIME @@ -4307,14 +5811,17 @@ mz_bool mz_zip_writer_add_mem_ex(mz_zip_archive *pZip, const char *pArchive_name if (archive_name_size > 0xFFFF) return MZ_FALSE; - num_alignment_padding_bytes = mz_zip_writer_compute_padding_needed_for_file_alignment(pZip); + num_alignment_padding_bytes = + mz_zip_writer_compute_padding_needed_for_file_alignment(pZip); // no zip64 support yet - if ((pZip->m_total_files == 0xFFFF) || ((pZip->m_archive_size + num_alignment_padding_bytes + MZ_ZIP_LOCAL_DIR_HEADER_SIZE + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + comment_size + archive_name_size) > 0xFFFFFFFF)) + if ((pZip->m_total_files == 0xFFFF) || + ((pZip->m_archive_size + num_alignment_padding_bytes + + MZ_ZIP_LOCAL_DIR_HEADER_SIZE + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + + comment_size + archive_name_size) > 0xFFFFFFFF)) return MZ_FALSE; - if ((archive_name_size) && (pArchive_name[archive_name_size - 1] == '/')) - { + if ((archive_name_size) && (pArchive_name[archive_name_size - 1] == '/')) { // Set DOS Subdirectory attribute bit. ext_attributes |= 0x10; // Subdirectories cannot contain data. @@ -4322,48 +5829,56 @@ mz_bool mz_zip_writer_add_mem_ex(mz_zip_archive *pZip, const char *pArchive_name return MZ_FALSE; } - // Try to do any allocations before writing to the archive, so if an allocation fails the file remains unmodified. (A good idea if we're doing an in-place modification.) - if ((!mz_zip_array_ensure_room(pZip, &pState->m_central_dir, MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + archive_name_size + comment_size)) || (!mz_zip_array_ensure_room(pZip, &pState->m_central_dir_offsets, 1))) + // Try to do any allocations before writing to the archive, so if an + // allocation fails the file remains unmodified. (A good idea if we're doing + // an in-place modification.) + if ((!mz_zip_array_ensure_room(pZip, &pState->m_central_dir, + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + + archive_name_size + comment_size)) || + (!mz_zip_array_ensure_room(pZip, &pState->m_central_dir_offsets, 1))) return MZ_FALSE; - if ((!store_data_uncompressed) && (buf_size)) - { - if (NULL == (pComp = (tdefl_compressor *)pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, sizeof(tdefl_compressor)))) + if ((!store_data_uncompressed) && (buf_size)) { + if (NULL == (pComp = (tdefl_compressor *)pZip->m_pAlloc( + pZip->m_pAlloc_opaque, 1, sizeof(tdefl_compressor)))) return MZ_FALSE; } - if (!mz_zip_writer_write_zeros(pZip, cur_archive_file_ofs, num_alignment_padding_bytes + sizeof(local_dir_header))) - { + if (!mz_zip_writer_write_zeros(pZip, cur_archive_file_ofs, + num_alignment_padding_bytes + + sizeof(local_dir_header))) { pZip->m_pFree(pZip->m_pAlloc_opaque, pComp); return MZ_FALSE; } local_dir_header_ofs += num_alignment_padding_bytes; - if (pZip->m_file_offset_alignment) { MZ_ASSERT((local_dir_header_ofs & (pZip->m_file_offset_alignment - 1)) == 0); } - cur_archive_file_ofs += num_alignment_padding_bytes + sizeof(local_dir_header); + if (pZip->m_file_offset_alignment) { + MZ_ASSERT((local_dir_header_ofs & (pZip->m_file_offset_alignment - 1)) == + 0); + } + cur_archive_file_ofs += + num_alignment_padding_bytes + sizeof(local_dir_header); MZ_CLEAR_OBJ(local_dir_header); - if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_archive_file_ofs, pArchive_name, archive_name_size) != archive_name_size) - { + if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_archive_file_ofs, pArchive_name, + archive_name_size) != archive_name_size) { pZip->m_pFree(pZip->m_pAlloc_opaque, pComp); return MZ_FALSE; } cur_archive_file_ofs += archive_name_size; - if (!(level_and_flags & MZ_ZIP_FLAG_COMPRESSED_DATA)) - { - uncomp_crc32 = (mz_uint32)mz_crc32(MZ_CRC32_INIT, (const mz_uint8*)pBuf, buf_size); + if (!(level_and_flags & MZ_ZIP_FLAG_COMPRESSED_DATA)) { + uncomp_crc32 = + (mz_uint32)mz_crc32(MZ_CRC32_INIT, (const mz_uint8 *)pBuf, buf_size); uncomp_size = buf_size; - if (uncomp_size <= 3) - { + if (uncomp_size <= 3) { level = 0; store_data_uncompressed = MZ_TRUE; } } - if (store_data_uncompressed) - { - if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_archive_file_ofs, pBuf, buf_size) != buf_size) - { + if (store_data_uncompressed) { + if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_archive_file_ofs, pBuf, + buf_size) != buf_size) { pZip->m_pFree(pZip->m_pAlloc_opaque, pComp); return MZ_FALSE; } @@ -4373,18 +5888,19 @@ mz_bool mz_zip_writer_add_mem_ex(mz_zip_archive *pZip, const char *pArchive_name if (level_and_flags & MZ_ZIP_FLAG_COMPRESSED_DATA) method = MZ_DEFLATED; - } - else if (buf_size) - { + } else if (buf_size) { mz_zip_writer_add_state state; state.m_pZip = pZip; state.m_cur_archive_file_ofs = cur_archive_file_ofs; state.m_comp_size = 0; - if ((tdefl_init(pComp, mz_zip_writer_add_put_buf_callback, &state, tdefl_create_comp_flags_from_zip_params(level, -15, MZ_DEFAULT_STRATEGY)) != TDEFL_STATUS_OKAY) || - (tdefl_compress_buffer(pComp, pBuf, buf_size, TDEFL_FINISH) != TDEFL_STATUS_DONE)) - { + if ((tdefl_init(pComp, mz_zip_writer_add_put_buf_callback, &state, + tdefl_create_comp_flags_from_zip_params( + level, -15, MZ_DEFAULT_STRATEGY)) != + TDEFL_STATUS_OKAY) || + (tdefl_compress_buffer(pComp, pBuf, buf_size, TDEFL_FINISH) != + TDEFL_STATUS_DONE)) { pZip->m_pFree(pZip->m_pAlloc_opaque, pComp); return MZ_FALSE; } @@ -4402,13 +5918,19 @@ mz_bool mz_zip_writer_add_mem_ex(mz_zip_archive *pZip, const char *pArchive_name if ((comp_size > 0xFFFFFFFF) || (cur_archive_file_ofs > 0xFFFFFFFF)) return MZ_FALSE; - if (!mz_zip_writer_create_local_dir_header(pZip, local_dir_header, (mz_uint16)archive_name_size, 0, uncomp_size, comp_size, uncomp_crc32, method, 0, dos_time, dos_date)) + if (!mz_zip_writer_create_local_dir_header( + pZip, local_dir_header, (mz_uint16)archive_name_size, 0, uncomp_size, + comp_size, uncomp_crc32, method, 0, dos_time, dos_date)) return MZ_FALSE; - if (pZip->m_pWrite(pZip->m_pIO_opaque, local_dir_header_ofs, local_dir_header, sizeof(local_dir_header)) != sizeof(local_dir_header)) + if (pZip->m_pWrite(pZip->m_pIO_opaque, local_dir_header_ofs, local_dir_header, + sizeof(local_dir_header)) != sizeof(local_dir_header)) return MZ_FALSE; - if (!mz_zip_writer_add_to_central_dir(pZip, pArchive_name, (mz_uint16)archive_name_size, NULL, 0, pComment, comment_size, uncomp_size, comp_size, uncomp_crc32, method, 0, dos_time, dos_date, local_dir_header_ofs, ext_attributes)) + if (!mz_zip_writer_add_to_central_dir( + pZip, pArchive_name, (mz_uint16)archive_name_size, NULL, 0, pComment, + comment_size, uncomp_size, comp_size, uncomp_crc32, method, 0, + dos_time, dos_date, local_dir_header_ofs, ext_attributes)) return MZ_FALSE; pZip->m_total_files++; @@ -4418,24 +5940,30 @@ mz_bool mz_zip_writer_add_mem_ex(mz_zip_archive *pZip, const char *pArchive_name } #ifndef MINIZ_NO_STDIO -mz_bool mz_zip_writer_add_file(mz_zip_archive *pZip, const char *pArchive_name, const char *pSrc_filename, const void *pComment, mz_uint16 comment_size, mz_uint level_and_flags) -{ +mz_bool mz_zip_writer_add_file(mz_zip_archive *pZip, const char *pArchive_name, + const char *pSrc_filename, const void *pComment, + mz_uint16 comment_size, mz_uint level_and_flags, + mz_uint32 ext_attributes) { mz_uint uncomp_crc32 = MZ_CRC32_INIT, level, num_alignment_padding_bytes; - mz_uint16 method = 0, dos_time = 0, dos_date = 0, ext_attributes = 0; - mz_uint64 local_dir_header_ofs, cur_archive_file_ofs, uncomp_size = 0, comp_size = 0; + mz_uint16 method = 0, dos_time = 0, dos_date = 0; + time_t file_modified_time; + mz_uint64 local_dir_header_ofs, cur_archive_file_ofs, uncomp_size = 0, + comp_size = 0; size_t archive_name_size; mz_uint8 local_dir_header[MZ_ZIP_LOCAL_DIR_HEADER_SIZE]; MZ_FILE *pSrc_file = NULL; - if ((!pZip) || (!pZip->m_pState) || (pZip->m_zip_mode != MZ_ZIP_MODE_WRITING) || (!pArchive_name) || ((comment_size) && (!pComment)) || ( level_and_flags > MZ_UBER_COMPRESSION)) - return MZ_FALSE; - - local_dir_header_ofs = cur_archive_file_ofs = pZip->m_archive_size; - if ((int)level_and_flags < 0) level_and_flags = MZ_DEFAULT_LEVEL; level = level_and_flags & 0xF; + if ((!pZip) || (!pZip->m_pState) || + (pZip->m_zip_mode != MZ_ZIP_MODE_WRITING) || (!pArchive_name) || + ((comment_size) && (!pComment)) || (level > MZ_UBER_COMPRESSION)) + return MZ_FALSE; + + local_dir_header_ofs = cur_archive_file_ofs = pZip->m_archive_size; + if (level_and_flags & MZ_ZIP_FLAG_COMPRESSED_DATA) return MZ_FALSE; if (!mz_zip_writer_validate_archive_name(pArchive_name)) @@ -4445,14 +5973,20 @@ mz_bool mz_zip_writer_add_file(mz_zip_archive *pZip, const char *pArchive_name, if (archive_name_size > 0xFFFF) return MZ_FALSE; - num_alignment_padding_bytes = mz_zip_writer_compute_padding_needed_for_file_alignment(pZip); + num_alignment_padding_bytes = + mz_zip_writer_compute_padding_needed_for_file_alignment(pZip); // no zip64 support yet - if ((pZip->m_total_files == 0xFFFF) || ((pZip->m_archive_size + num_alignment_padding_bytes + MZ_ZIP_LOCAL_DIR_HEADER_SIZE + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + comment_size + archive_name_size) > 0xFFFFFFFF)) + if ((pZip->m_total_files == 0xFFFF) || + ((pZip->m_archive_size + num_alignment_padding_bytes + + MZ_ZIP_LOCAL_DIR_HEADER_SIZE + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + + comment_size + archive_name_size) > 0xFFFFFFFF)) return MZ_FALSE; - if (!mz_zip_get_file_modified_time(pSrc_filename, &dos_time, &dos_date)) + memset(&file_modified_time, 0, sizeof(file_modified_time)); + if (!mz_zip_get_file_modified_time(pSrc_filename, &file_modified_time)) return MZ_FALSE; + mz_zip_time_t_to_dos_time(file_modified_time, &dos_time, &dos_date); pSrc_file = MZ_FOPEN(pSrc_filename, "rb"); if (!pSrc_file) @@ -4461,8 +5995,7 @@ mz_bool mz_zip_writer_add_file(mz_zip_archive *pZip, const char *pArchive_name, uncomp_size = MZ_FTELL64(pSrc_file); MZ_FSEEK64(pSrc_file, 0, SEEK_SET); - if (uncomp_size > 0xFFFFFFFF) - { + if (uncomp_size > 0xFFFFFFFF) { // No zip64 support yet MZ_FCLOSE(pSrc_file); return MZ_FALSE; @@ -4470,57 +6003,59 @@ mz_bool mz_zip_writer_add_file(mz_zip_archive *pZip, const char *pArchive_name, if (uncomp_size <= 3) level = 0; - if (!mz_zip_writer_write_zeros(pZip, cur_archive_file_ofs, num_alignment_padding_bytes + sizeof(local_dir_header))) - { + if (!mz_zip_writer_write_zeros(pZip, cur_archive_file_ofs, + num_alignment_padding_bytes + + sizeof(local_dir_header))) { MZ_FCLOSE(pSrc_file); return MZ_FALSE; } local_dir_header_ofs += num_alignment_padding_bytes; - if (pZip->m_file_offset_alignment) { MZ_ASSERT((local_dir_header_ofs & (pZip->m_file_offset_alignment - 1)) == 0); } - cur_archive_file_ofs += num_alignment_padding_bytes + sizeof(local_dir_header); + if (pZip->m_file_offset_alignment) { + MZ_ASSERT((local_dir_header_ofs & (pZip->m_file_offset_alignment - 1)) == + 0); + } + cur_archive_file_ofs += + num_alignment_padding_bytes + sizeof(local_dir_header); MZ_CLEAR_OBJ(local_dir_header); - if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_archive_file_ofs, pArchive_name, archive_name_size) != archive_name_size) - { + if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_archive_file_ofs, pArchive_name, + archive_name_size) != archive_name_size) { MZ_FCLOSE(pSrc_file); return MZ_FALSE; } cur_archive_file_ofs += archive_name_size; - if (uncomp_size) - { + if (uncomp_size) { mz_uint64 uncomp_remaining = uncomp_size; - void *pRead_buf = pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, MZ_ZIP_MAX_IO_BUF_SIZE); - if (!pRead_buf) - { + void *pRead_buf = + pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, MZ_ZIP_MAX_IO_BUF_SIZE); + if (!pRead_buf) { MZ_FCLOSE(pSrc_file); return MZ_FALSE; } - if (!level) - { - while (uncomp_remaining) - { + if (!level) { + while (uncomp_remaining) { mz_uint n = (mz_uint)MZ_MIN(MZ_ZIP_MAX_IO_BUF_SIZE, uncomp_remaining); - if ((MZ_FREAD(pRead_buf, 1, n, pSrc_file) != n) || (pZip->m_pWrite(pZip->m_pIO_opaque, cur_archive_file_ofs, pRead_buf, n) != n)) - { + if ((MZ_FREAD(pRead_buf, 1, n, pSrc_file) != n) || + (pZip->m_pWrite(pZip->m_pIO_opaque, cur_archive_file_ofs, pRead_buf, + n) != n)) { pZip->m_pFree(pZip->m_pAlloc_opaque, pRead_buf); MZ_FCLOSE(pSrc_file); return MZ_FALSE; } - uncomp_crc32 = (mz_uint32)mz_crc32(uncomp_crc32, (const mz_uint8 *)pRead_buf, n); + uncomp_crc32 = + (mz_uint32)mz_crc32(uncomp_crc32, (const mz_uint8 *)pRead_buf, n); uncomp_remaining -= n; cur_archive_file_ofs += n; } comp_size = uncomp_size; - } - else - { + } else { mz_bool result = MZ_FALSE; mz_zip_writer_add_state state; - tdefl_compressor *pComp = (tdefl_compressor *)pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, sizeof(tdefl_compressor)); - if (!pComp) - { + tdefl_compressor *pComp = (tdefl_compressor *)pZip->m_pAlloc( + pZip->m_pAlloc_opaque, 1, sizeof(tdefl_compressor)); + if (!pComp) { pZip->m_pFree(pZip->m_pAlloc_opaque, pRead_buf); MZ_FCLOSE(pSrc_file); return MZ_FALSE; @@ -4530,39 +6065,41 @@ mz_bool mz_zip_writer_add_file(mz_zip_archive *pZip, const char *pArchive_name, state.m_cur_archive_file_ofs = cur_archive_file_ofs; state.m_comp_size = 0; - if (tdefl_init(pComp, mz_zip_writer_add_put_buf_callback, &state, tdefl_create_comp_flags_from_zip_params(level, -15, MZ_DEFAULT_STRATEGY)) != TDEFL_STATUS_OKAY) - { + if (tdefl_init(pComp, mz_zip_writer_add_put_buf_callback, &state, + tdefl_create_comp_flags_from_zip_params( + level, -15, MZ_DEFAULT_STRATEGY)) != + TDEFL_STATUS_OKAY) { pZip->m_pFree(pZip->m_pAlloc_opaque, pComp); pZip->m_pFree(pZip->m_pAlloc_opaque, pRead_buf); MZ_FCLOSE(pSrc_file); return MZ_FALSE; } - for ( ; ; ) - { - size_t in_buf_size = (mz_uint32)MZ_MIN(uncomp_remaining, MZ_ZIP_MAX_IO_BUF_SIZE); + for (;;) { + size_t in_buf_size = + (mz_uint32)MZ_MIN(uncomp_remaining, MZ_ZIP_MAX_IO_BUF_SIZE); tdefl_status status; if (MZ_FREAD(pRead_buf, 1, in_buf_size, pSrc_file) != in_buf_size) break; - uncomp_crc32 = (mz_uint32)mz_crc32(uncomp_crc32, (const mz_uint8 *)pRead_buf, in_buf_size); + uncomp_crc32 = (mz_uint32)mz_crc32( + uncomp_crc32, (const mz_uint8 *)pRead_buf, in_buf_size); uncomp_remaining -= in_buf_size; - status = tdefl_compress_buffer(pComp, pRead_buf, in_buf_size, uncomp_remaining ? TDEFL_NO_FLUSH : TDEFL_FINISH); - if (status == TDEFL_STATUS_DONE) - { + status = tdefl_compress_buffer(pComp, pRead_buf, in_buf_size, + uncomp_remaining ? TDEFL_NO_FLUSH + : TDEFL_FINISH); + if (status == TDEFL_STATUS_DONE) { result = MZ_TRUE; break; - } - else if (status != TDEFL_STATUS_OKAY) + } else if (status != TDEFL_STATUS_OKAY) break; } pZip->m_pFree(pZip->m_pAlloc_opaque, pComp); - if (!result) - { + if (!result) { pZip->m_pFree(pZip->m_pAlloc_opaque, pRead_buf); MZ_FCLOSE(pSrc_file); return MZ_FALSE; @@ -4577,19 +6114,26 @@ mz_bool mz_zip_writer_add_file(mz_zip_archive *pZip, const char *pArchive_name, pZip->m_pFree(pZip->m_pAlloc_opaque, pRead_buf); } - MZ_FCLOSE(pSrc_file); pSrc_file = NULL; + MZ_FCLOSE(pSrc_file); + pSrc_file = NULL; // no zip64 support yet if ((comp_size > 0xFFFFFFFF) || (cur_archive_file_ofs > 0xFFFFFFFF)) return MZ_FALSE; - if (!mz_zip_writer_create_local_dir_header(pZip, local_dir_header, (mz_uint16)archive_name_size, 0, uncomp_size, comp_size, uncomp_crc32, method, 0, dos_time, dos_date)) + if (!mz_zip_writer_create_local_dir_header( + pZip, local_dir_header, (mz_uint16)archive_name_size, 0, uncomp_size, + comp_size, uncomp_crc32, method, 0, dos_time, dos_date)) return MZ_FALSE; - if (pZip->m_pWrite(pZip->m_pIO_opaque, local_dir_header_ofs, local_dir_header, sizeof(local_dir_header)) != sizeof(local_dir_header)) + if (pZip->m_pWrite(pZip->m_pIO_opaque, local_dir_header_ofs, local_dir_header, + sizeof(local_dir_header)) != sizeof(local_dir_header)) return MZ_FALSE; - if (!mz_zip_writer_add_to_central_dir(pZip, pArchive_name, (mz_uint16)archive_name_size, NULL, 0, pComment, comment_size, uncomp_size, comp_size, uncomp_crc32, method, 0, dos_time, dos_date, local_dir_header_ofs, ext_attributes)) + if (!mz_zip_writer_add_to_central_dir( + pZip, pArchive_name, (mz_uint16)archive_name_size, NULL, 0, pComment, + comment_size, uncomp_size, comp_size, uncomp_crc32, method, 0, + dos_time, dos_date, local_dir_header_ofs, ext_attributes)) return MZ_FALSE; pZip->m_total_files++; @@ -4599,66 +6143,89 @@ mz_bool mz_zip_writer_add_file(mz_zip_archive *pZip, const char *pArchive_name, } #endif // #ifndef MINIZ_NO_STDIO -mz_bool mz_zip_writer_add_from_zip_reader(mz_zip_archive *pZip, mz_zip_archive *pSource_zip, mz_uint file_index) -{ +mz_bool mz_zip_writer_add_from_zip_reader(mz_zip_archive *pZip, + mz_zip_archive *pSource_zip, + mz_uint file_index) { mz_uint n, bit_flags, num_alignment_padding_bytes; mz_uint64 comp_bytes_remaining, local_dir_header_ofs; mz_uint64 cur_src_file_ofs, cur_dst_file_ofs; - mz_uint32 local_header_u32[(MZ_ZIP_LOCAL_DIR_HEADER_SIZE + sizeof(mz_uint32) - 1) / sizeof(mz_uint32)]; mz_uint8 *pLocal_header = (mz_uint8 *)local_header_u32; + mz_uint32 + local_header_u32[(MZ_ZIP_LOCAL_DIR_HEADER_SIZE + sizeof(mz_uint32) - 1) / + sizeof(mz_uint32)]; + mz_uint8 *pLocal_header = (mz_uint8 *)local_header_u32; mz_uint8 central_header[MZ_ZIP_CENTRAL_DIR_HEADER_SIZE]; size_t orig_central_dir_size; mz_zip_internal_state *pState; - void *pBuf; const mz_uint8 *pSrc_central_header; + void *pBuf; + const mz_uint8 *pSrc_central_header; if ((!pZip) || (!pZip->m_pState) || (pZip->m_zip_mode != MZ_ZIP_MODE_WRITING)) return MZ_FALSE; - if (NULL == (pSrc_central_header = mz_zip_reader_get_cdh(pSource_zip, file_index))) + if (NULL == + (pSrc_central_header = mz_zip_reader_get_cdh(pSource_zip, file_index))) return MZ_FALSE; pState = pZip->m_pState; - num_alignment_padding_bytes = mz_zip_writer_compute_padding_needed_for_file_alignment(pZip); + num_alignment_padding_bytes = + mz_zip_writer_compute_padding_needed_for_file_alignment(pZip); // no zip64 support yet - if ((pZip->m_total_files == 0xFFFF) || ((pZip->m_archive_size + num_alignment_padding_bytes + MZ_ZIP_LOCAL_DIR_HEADER_SIZE + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE) > 0xFFFFFFFF)) + if ((pZip->m_total_files == 0xFFFF) || + ((pZip->m_archive_size + num_alignment_padding_bytes + + MZ_ZIP_LOCAL_DIR_HEADER_SIZE + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE) > + 0xFFFFFFFF)) return MZ_FALSE; - cur_src_file_ofs = MZ_READ_LE32(pSrc_central_header + MZ_ZIP_CDH_LOCAL_HEADER_OFS); + cur_src_file_ofs = + MZ_READ_LE32(pSrc_central_header + MZ_ZIP_CDH_LOCAL_HEADER_OFS); cur_dst_file_ofs = pZip->m_archive_size; - if (pSource_zip->m_pRead(pSource_zip->m_pIO_opaque, cur_src_file_ofs, pLocal_header, MZ_ZIP_LOCAL_DIR_HEADER_SIZE) != MZ_ZIP_LOCAL_DIR_HEADER_SIZE) + if (pSource_zip->m_pRead(pSource_zip->m_pIO_opaque, cur_src_file_ofs, + pLocal_header, MZ_ZIP_LOCAL_DIR_HEADER_SIZE) != + MZ_ZIP_LOCAL_DIR_HEADER_SIZE) return MZ_FALSE; if (MZ_READ_LE32(pLocal_header) != MZ_ZIP_LOCAL_DIR_HEADER_SIG) return MZ_FALSE; cur_src_file_ofs += MZ_ZIP_LOCAL_DIR_HEADER_SIZE; - if (!mz_zip_writer_write_zeros(pZip, cur_dst_file_ofs, num_alignment_padding_bytes)) + if (!mz_zip_writer_write_zeros(pZip, cur_dst_file_ofs, + num_alignment_padding_bytes)) return MZ_FALSE; cur_dst_file_ofs += num_alignment_padding_bytes; local_dir_header_ofs = cur_dst_file_ofs; - if (pZip->m_file_offset_alignment) { MZ_ASSERT((local_dir_header_ofs & (pZip->m_file_offset_alignment - 1)) == 0); } + if (pZip->m_file_offset_alignment) { + MZ_ASSERT((local_dir_header_ofs & (pZip->m_file_offset_alignment - 1)) == + 0); + } - if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_dst_file_ofs, pLocal_header, MZ_ZIP_LOCAL_DIR_HEADER_SIZE) != MZ_ZIP_LOCAL_DIR_HEADER_SIZE) + if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_dst_file_ofs, pLocal_header, + MZ_ZIP_LOCAL_DIR_HEADER_SIZE) != + MZ_ZIP_LOCAL_DIR_HEADER_SIZE) return MZ_FALSE; cur_dst_file_ofs += MZ_ZIP_LOCAL_DIR_HEADER_SIZE; - n = MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_FILENAME_LEN_OFS) + MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_EXTRA_LEN_OFS); - comp_bytes_remaining = n + MZ_READ_LE32(pSrc_central_header + MZ_ZIP_CDH_COMPRESSED_SIZE_OFS); + n = MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_FILENAME_LEN_OFS) + + MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_EXTRA_LEN_OFS); + comp_bytes_remaining = + n + MZ_READ_LE32(pSrc_central_header + MZ_ZIP_CDH_COMPRESSED_SIZE_OFS); - if (NULL == (pBuf = pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, (size_t)MZ_MAX(sizeof(mz_uint32) * 4, MZ_MIN(MZ_ZIP_MAX_IO_BUF_SIZE, comp_bytes_remaining))))) + if (NULL == + (pBuf = pZip->m_pAlloc(pZip->m_pAlloc_opaque, 1, + (size_t)MZ_MAX(sizeof(mz_uint32) * 4, + MZ_MIN(MZ_ZIP_MAX_IO_BUF_SIZE, + comp_bytes_remaining))))) return MZ_FALSE; - while (comp_bytes_remaining) - { + while (comp_bytes_remaining) { n = (mz_uint)MZ_MIN(MZ_ZIP_MAX_IO_BUF_SIZE, comp_bytes_remaining); - if (pSource_zip->m_pRead(pSource_zip->m_pIO_opaque, cur_src_file_ofs, pBuf, n) != n) - { + if (pSource_zip->m_pRead(pSource_zip->m_pIO_opaque, cur_src_file_ofs, pBuf, + n) != n) { pZip->m_pFree(pZip->m_pAlloc_opaque, pBuf); return MZ_FALSE; } cur_src_file_ofs += n; - if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_dst_file_ofs, pBuf, n) != n) - { + if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_dst_file_ofs, pBuf, n) != n) { pZip->m_pFree(pZip->m_pAlloc_opaque, pBuf); return MZ_FALSE; } @@ -4668,22 +6235,21 @@ mz_bool mz_zip_writer_add_from_zip_reader(mz_zip_archive *pZip, mz_zip_archive * } bit_flags = MZ_READ_LE16(pLocal_header + MZ_ZIP_LDH_BIT_FLAG_OFS); - if (bit_flags & 8) - { + if (bit_flags & 8) { // Copy data descriptor - if (pSource_zip->m_pRead(pSource_zip->m_pIO_opaque, cur_src_file_ofs, pBuf, sizeof(mz_uint32) * 4) != sizeof(mz_uint32) * 4) - { + if (pSource_zip->m_pRead(pSource_zip->m_pIO_opaque, cur_src_file_ofs, pBuf, + sizeof(mz_uint32) * 4) != sizeof(mz_uint32) * 4) { pZip->m_pFree(pZip->m_pAlloc_opaque, pBuf); return MZ_FALSE; } n = sizeof(mz_uint32) * ((MZ_READ_LE32(pBuf) == 0x08074b50) ? 4 : 3); - if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_dst_file_ofs, pBuf, n) != n) - { + if (pZip->m_pWrite(pZip->m_pIO_opaque, cur_dst_file_ofs, pBuf, n) != n) { pZip->m_pFree(pZip->m_pAlloc_opaque, pBuf); return MZ_FALSE; } + // cur_src_file_ofs += n; cur_dst_file_ofs += n; } pZip->m_pFree(pZip->m_pAlloc_opaque, pBuf); @@ -4695,23 +6261,29 @@ mz_bool mz_zip_writer_add_from_zip_reader(mz_zip_archive *pZip, mz_zip_archive * orig_central_dir_size = pState->m_central_dir.m_size; memcpy(central_header, pSrc_central_header, MZ_ZIP_CENTRAL_DIR_HEADER_SIZE); - MZ_WRITE_LE32(central_header + MZ_ZIP_CDH_LOCAL_HEADER_OFS, local_dir_header_ofs); - if (!mz_zip_array_push_back(pZip, &pState->m_central_dir, central_header, MZ_ZIP_CENTRAL_DIR_HEADER_SIZE)) + MZ_WRITE_LE32(central_header + MZ_ZIP_CDH_LOCAL_HEADER_OFS, + local_dir_header_ofs); + if (!mz_zip_array_push_back(pZip, &pState->m_central_dir, central_header, + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE)) return MZ_FALSE; - n = MZ_READ_LE16(pSrc_central_header + MZ_ZIP_CDH_FILENAME_LEN_OFS) + MZ_READ_LE16(pSrc_central_header + MZ_ZIP_CDH_EXTRA_LEN_OFS) + MZ_READ_LE16(pSrc_central_header + MZ_ZIP_CDH_COMMENT_LEN_OFS); - if (!mz_zip_array_push_back(pZip, &pState->m_central_dir, pSrc_central_header + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE, n)) - { - mz_zip_array_resize(pZip, &pState->m_central_dir, orig_central_dir_size, MZ_FALSE); + n = MZ_READ_LE16(pSrc_central_header + MZ_ZIP_CDH_FILENAME_LEN_OFS) + + MZ_READ_LE16(pSrc_central_header + MZ_ZIP_CDH_EXTRA_LEN_OFS) + + MZ_READ_LE16(pSrc_central_header + MZ_ZIP_CDH_COMMENT_LEN_OFS); + if (!mz_zip_array_push_back( + pZip, &pState->m_central_dir, + pSrc_central_header + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE, n)) { + mz_zip_array_resize(pZip, &pState->m_central_dir, orig_central_dir_size, + MZ_FALSE); return MZ_FALSE; } if (pState->m_central_dir.m_size > 0xFFFFFFFF) return MZ_FALSE; n = (mz_uint32)orig_central_dir_size; - if (!mz_zip_array_push_back(pZip, &pState->m_central_dir_offsets, &n, 1)) - { - mz_zip_array_resize(pZip, &pState->m_central_dir, orig_central_dir_size, MZ_FALSE); + if (!mz_zip_array_push_back(pZip, &pState->m_central_dir_offsets, &n, 1)) { + mz_zip_array_resize(pZip, &pState->m_central_dir, orig_central_dir_size, + MZ_FALSE); return MZ_FALSE; } @@ -4721,8 +6293,7 @@ mz_bool mz_zip_writer_add_from_zip_reader(mz_zip_archive *pZip, mz_zip_archive * return MZ_TRUE; } -mz_bool mz_zip_writer_finalize_archive(mz_zip_archive *pZip) -{ +mz_bool mz_zip_writer_finalize_archive(mz_zip_archive *pZip) { mz_zip_internal_state *pState; mz_uint64 central_dir_ofs, central_dir_size; mz_uint8 hdr[MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIZE]; @@ -4733,31 +6304,37 @@ mz_bool mz_zip_writer_finalize_archive(mz_zip_archive *pZip) pState = pZip->m_pState; // no zip64 support yet - if ((pZip->m_total_files > 0xFFFF) || ((pZip->m_archive_size + pState->m_central_dir.m_size + MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIZE) > 0xFFFFFFFF)) + if ((pZip->m_total_files > 0xFFFF) || + ((pZip->m_archive_size + pState->m_central_dir.m_size + + MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIZE) > 0xFFFFFFFF)) return MZ_FALSE; central_dir_ofs = 0; central_dir_size = 0; - if (pZip->m_total_files) - { + if (pZip->m_total_files) { // Write central directory central_dir_ofs = pZip->m_archive_size; central_dir_size = pState->m_central_dir.m_size; pZip->m_central_directory_file_ofs = central_dir_ofs; - if (pZip->m_pWrite(pZip->m_pIO_opaque, central_dir_ofs, pState->m_central_dir.m_p, (size_t)central_dir_size) != central_dir_size) + if (pZip->m_pWrite(pZip->m_pIO_opaque, central_dir_ofs, + pState->m_central_dir.m_p, + (size_t)central_dir_size) != central_dir_size) return MZ_FALSE; pZip->m_archive_size += central_dir_size; } // Write end of central directory record MZ_CLEAR_OBJ(hdr); - MZ_WRITE_LE32(hdr + MZ_ZIP_ECDH_SIG_OFS, MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIG); - MZ_WRITE_LE16(hdr + MZ_ZIP_ECDH_CDIR_NUM_ENTRIES_ON_DISK_OFS, pZip->m_total_files); + MZ_WRITE_LE32(hdr + MZ_ZIP_ECDH_SIG_OFS, + MZ_ZIP_END_OF_CENTRAL_DIR_HEADER_SIG); + MZ_WRITE_LE16(hdr + MZ_ZIP_ECDH_CDIR_NUM_ENTRIES_ON_DISK_OFS, + pZip->m_total_files); MZ_WRITE_LE16(hdr + MZ_ZIP_ECDH_CDIR_TOTAL_ENTRIES_OFS, pZip->m_total_files); MZ_WRITE_LE32(hdr + MZ_ZIP_ECDH_CDIR_SIZE_OFS, central_dir_size); MZ_WRITE_LE32(hdr + MZ_ZIP_ECDH_CDIR_OFS_OFS, central_dir_ofs); - if (pZip->m_pWrite(pZip->m_pIO_opaque, pZip->m_archive_size, hdr, sizeof(hdr)) != sizeof(hdr)) + if (pZip->m_pWrite(pZip->m_pIO_opaque, pZip->m_archive_size, hdr, + sizeof(hdr)) != sizeof(hdr)) return MZ_FALSE; #ifndef MINIZ_NO_STDIO if ((pState->m_pFile) && (MZ_FFLUSH(pState->m_pFile) == EOF)) @@ -4770,8 +6347,8 @@ mz_bool mz_zip_writer_finalize_archive(mz_zip_archive *pZip) return MZ_TRUE; } -mz_bool mz_zip_writer_finalize_heap_archive(mz_zip_archive *pZip, void **pBuf, size_t *pSize) -{ +mz_bool mz_zip_writer_finalize_heap_archive(mz_zip_archive *pZip, void **pBuf, + size_t *pSize) { if ((!pZip) || (!pZip->m_pState) || (!pBuf) || (!pSize)) return MZ_FALSE; if (pZip->m_pWrite != mz_zip_heap_write_func) @@ -4786,11 +6363,12 @@ mz_bool mz_zip_writer_finalize_heap_archive(mz_zip_archive *pZip, void **pBuf, s return MZ_TRUE; } -mz_bool mz_zip_writer_end(mz_zip_archive *pZip) -{ +mz_bool mz_zip_writer_end(mz_zip_archive *pZip) { mz_zip_internal_state *pState; mz_bool status = MZ_TRUE; - if ((!pZip) || (!pZip->m_pState) || (!pZip->m_pAlloc) || (!pZip->m_pFree) || ((pZip->m_zip_mode != MZ_ZIP_MODE_WRITING) && (pZip->m_zip_mode != MZ_ZIP_MODE_WRITING_HAS_BEEN_FINALIZED))) + if ((!pZip) || (!pZip->m_pState) || (!pZip->m_pAlloc) || (!pZip->m_pFree) || + ((pZip->m_zip_mode != MZ_ZIP_MODE_WRITING) && + (pZip->m_zip_mode != MZ_ZIP_MODE_WRITING_HAS_BEEN_FINALIZED))) return MZ_FALSE; pState = pZip->m_pState; @@ -4800,15 +6378,13 @@ mz_bool mz_zip_writer_end(mz_zip_archive *pZip) mz_zip_array_clear(pZip, &pState->m_sorted_central_dir_offsets); #ifndef MINIZ_NO_STDIO - if (pState->m_pFile) - { + if (pState->m_pFile) { MZ_FCLOSE(pState->m_pFile); pState->m_pFile = NULL; } #endif // #ifndef MINIZ_NO_STDIO - if ((pZip->m_pWrite == mz_zip_heap_write_func) && (pState->m_pMem)) - { + if ((pZip->m_pWrite == mz_zip_heap_write_func) && (pState->m_pMem)) { pZip->m_pFree(pZip->m_pAlloc_opaque, pState->m_pMem); pState->m_pMem = NULL; } @@ -4819,44 +6395,48 @@ mz_bool mz_zip_writer_end(mz_zip_archive *pZip) } #ifndef MINIZ_NO_STDIO -mz_bool mz_zip_add_mem_to_archive_file_in_place(const char *pZip_filename, const char *pArchive_name, const void *pBuf, size_t buf_size, const void *pComment, mz_uint16 comment_size, mz_uint level_and_flags) -{ +mz_bool mz_zip_add_mem_to_archive_file_in_place( + const char *pZip_filename, const char *pArchive_name, const void *pBuf, + size_t buf_size, const void *pComment, mz_uint16 comment_size, + mz_uint level_and_flags) { mz_bool status, created_new_archive = MZ_FALSE; mz_zip_archive zip_archive; struct MZ_FILE_STAT_STRUCT file_stat; MZ_CLEAR_OBJ(zip_archive); if ((int)level_and_flags < 0) - level_and_flags = MZ_DEFAULT_LEVEL; - if ((!pZip_filename) || (!pArchive_name) || ((buf_size) && (!pBuf)) || ((comment_size) && (!pComment)) || ((level_and_flags & 0xF) > MZ_UBER_COMPRESSION)) + level_and_flags = MZ_DEFAULT_LEVEL; + if ((!pZip_filename) || (!pArchive_name) || ((buf_size) && (!pBuf)) || + ((comment_size) && (!pComment)) || + ((level_and_flags & 0xF) > MZ_UBER_COMPRESSION)) return MZ_FALSE; if (!mz_zip_writer_validate_archive_name(pArchive_name)) return MZ_FALSE; - if (MZ_FILE_STAT(pZip_filename, &file_stat) != 0) - { + if (MZ_FILE_STAT(pZip_filename, &file_stat) != 0) { // Create a new archive. if (!mz_zip_writer_init_file(&zip_archive, pZip_filename, 0)) return MZ_FALSE; created_new_archive = MZ_TRUE; - } - else - { + } else { // Append to an existing archive. - if (!mz_zip_reader_init_file(&zip_archive, pZip_filename, level_and_flags | MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY)) + if (!mz_zip_reader_init_file(&zip_archive, pZip_filename, + level_and_flags | + MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY)) return MZ_FALSE; - if (!mz_zip_writer_init_from_reader(&zip_archive, pZip_filename)) - { + if (!mz_zip_writer_init_from_reader(&zip_archive, pZip_filename)) { mz_zip_reader_end(&zip_archive); return MZ_FALSE; } } - status = mz_zip_writer_add_mem_ex(&zip_archive, pArchive_name, pBuf, buf_size, pComment, comment_size, level_and_flags, 0, 0); - // Always finalize, even if adding failed for some reason, so we have a valid central directory. (This may not always succeed, but we can try.) + status = + mz_zip_writer_add_mem_ex(&zip_archive, pArchive_name, pBuf, buf_size, + pComment, comment_size, level_and_flags, 0, 0); + // Always finalize, even if adding failed for some reason, so we have a valid + // central directory. (This may not always succeed, but we can try.) if (!mz_zip_writer_finalize_archive(&zip_archive)) status = MZ_FALSE; if (!mz_zip_writer_end(&zip_archive)) status = MZ_FALSE; - if ((!status) && (created_new_archive)) - { + if ((!status) && (created_new_archive)) { // It's a new archive and something went wrong, so just delete it. int ignoredStatus = MZ_DELETE_FILE(pZip_filename); (void)ignoredStatus; @@ -4864,8 +6444,9 @@ mz_bool mz_zip_add_mem_to_archive_file_in_place(const char *pZip_filename, const return status; } -void *mz_zip_extract_archive_file_to_heap(const char *pZip_filename, const char *pArchive_name, size_t *pSize, mz_uint flags) -{ +void *mz_zip_extract_archive_file_to_heap(const char *pZip_filename, + const char *pArchive_name, + size_t *pSize, mz_uint flags) { int file_index; mz_zip_archive zip_archive; void *p = NULL; @@ -4877,10 +6458,13 @@ void *mz_zip_extract_archive_file_to_heap(const char *pZip_filename, const char return NULL; MZ_CLEAR_OBJ(zip_archive); - if (!mz_zip_reader_init_file(&zip_archive, pZip_filename, flags | MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY)) + if (!mz_zip_reader_init_file(&zip_archive, pZip_filename, + flags | + MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY)) return NULL; - if ((file_index = mz_zip_reader_locate_file(&zip_archive, pArchive_name, NULL, flags)) >= 0) + if ((file_index = mz_zip_reader_locate_file(&zip_archive, pArchive_name, NULL, + flags)) >= 0) p = mz_zip_reader_extract_to_heap(&zip_archive, file_index, pSize, flags); mz_zip_reader_end(&zip_archive); diff --git a/contrib/zip/src/zip.c b/contrib/zip/src/zip.c index 80573096b..ff3a8fe1e 100644 --- a/contrib/zip/src/zip.c +++ b/contrib/zip/src/zip.c @@ -7,29 +7,40 @@ * ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR * OTHER DEALINGS IN THE SOFTWARE. */ - -#include "zip.h" -#include "miniz.h" +#define __STDC_WANT_LIB_EXT1__ 1 #include #include #include -#if defined _WIN32 || defined __WIN32__ -/* Win32, DOS */ +#if defined(_WIN32) || defined(__WIN32__) || defined(_MSC_VER) || \ + defined(__MINGW32__) +/* Win32, DOS, MSVC, MSVS */ #include #define MKDIR(DIRNAME) _mkdir(DIRNAME) #define STRCLONE(STR) ((STR) ? _strdup(STR) : NULL) #define HAS_DEVICE(P) \ - ((((P)[0] >= 'A' && (P)[0] <= 'Z') || ((P)[0] >= 'a' && (P)[0] <= 'z')) && \ - (P)[1] == ':') + ((((P)[0] >= 'A' && (P)[0] <= 'Z') || ((P)[0] >= 'a' && (P)[0] <= 'z')) && \ + (P)[1] == ':') #define FILESYSTEM_PREFIX_LEN(P) (HAS_DEVICE(P) ? 2 : 0) #define ISSLASH(C) ((C) == '/' || (C) == '\\') #else + +#include // needed for symlink() on BSD +int symlink(const char *target, const char *linkpath); // needed on Linux + #define MKDIR(DIRNAME) mkdir(DIRNAME, 0755) #define STRCLONE(STR) ((STR) ? strdup(STR) : NULL) + +#endif + +#include "miniz.h" +#include "zip.h" + +#ifndef HAS_DEVICE +#define HAS_DEVICE(P) 0 #endif #ifndef FILESYSTEM_PREFIX_LEN @@ -40,601 +51,876 @@ #define ISSLASH(C) ((C) == '/') #endif -#define CLEANUP(ptr) \ - do { \ - if (ptr) { \ - free((void *)ptr); \ - ptr = NULL; \ - } \ - } while (0) +#define CLEANUP(ptr) \ + do { \ + if (ptr) { \ + free((void *)ptr); \ + ptr = NULL; \ + } \ + } while (0) -static char *basename(const char *name) { - char const *p; - char const *base = name += FILESYSTEM_PREFIX_LEN(name); - int all_slashes = 1; +static const char *base_name(const char *name) { + char const *p; + char const *base = name += FILESYSTEM_PREFIX_LEN(name); + int all_slashes = 1; - for (p = name; *p; p++) { - if (ISSLASH(*p)) - base = p + 1; - else - all_slashes = 0; - } + for (p = name; *p; p++) { + if (ISSLASH(*p)) + base = p + 1; + else + all_slashes = 0; + } - /* If NAME is all slashes, arrange to return `/'. */ - if (*base == '\0' && ISSLASH(*name) && all_slashes) --base; + /* If NAME is all slashes, arrange to return `/'. */ + if (*base == '\0' && ISSLASH(*name) && all_slashes) + --base; - return (char *)base; + return base; } static int mkpath(const char *path) { - char const *p; - char npath[MAX_PATH + 1] = {0}; - int len = 0; + char const *p; + char npath[MAX_PATH + 1]; + int len = 0; + int has_device = HAS_DEVICE(path); - for (p = path; *p && len < MAX_PATH; p++) { - if (ISSLASH(*p) && len > 0) { - if (MKDIR(npath) == -1) - if (errno != EEXIST) return -1; - } - npath[len++] = *p; + memset(npath, 0, MAX_PATH + 1); + +#ifdef _WIN32 + // only on windows fix the path + npath[0] = path[0]; + npath[1] = path[1]; + len = 2; +#endif // _WIN32 + + for (p = path + len; *p && len < MAX_PATH; p++) { + if (ISSLASH(*p) && ((!has_device && len > 0) || (has_device && len > 2))) { + if (MKDIR(npath) == -1) + if (errno != EEXIST) + return -1; } + npath[len++] = *p; + } - return 0; + return 0; } -static char *strrpl(const char *str, char oldchar, char newchar) { - char *rpl = (char *)malloc(sizeof(char) * (1 + strlen(str))); - char *begin = rpl; - char c; - while((c = *str++)) { - if (c == oldchar) { - c = newchar; - } - *rpl++ = c; - } - *rpl = '\0'; +static char *strrpl(const char *str, size_t n, char oldchar, char newchar) { + char c; + size_t i; + char *rpl = (char *)calloc((1 + n), sizeof(char)); + char *begin = rpl; + if (!rpl) { + return NULL; + } - return begin; + for (i = 0; (i < n) && (c = *str++); ++i) { + if (c == oldchar) { + c = newchar; + } + *rpl++ = c; + } + + return begin; } struct zip_entry_t { - int index; - const char *name; - mz_uint64 uncomp_size; - mz_uint64 comp_size; - mz_uint32 uncomp_crc32; - mz_uint64 offset; - mz_uint8 header[MZ_ZIP_LOCAL_DIR_HEADER_SIZE]; - mz_uint64 header_offset; - mz_uint16 method; - mz_zip_writer_add_state state; - tdefl_compressor comp; + int index; + char *name; + mz_uint64 uncomp_size; + mz_uint64 comp_size; + mz_uint32 uncomp_crc32; + mz_uint64 offset; + mz_uint8 header[MZ_ZIP_LOCAL_DIR_HEADER_SIZE]; + mz_uint64 header_offset; + mz_uint16 method; + mz_zip_writer_add_state state; + tdefl_compressor comp; + mz_uint32 external_attr; + time_t m_time; }; struct zip_t { - mz_zip_archive archive; - mz_uint level; - struct zip_entry_t entry; - char mode; + mz_zip_archive archive; + mz_uint level; + struct zip_entry_t entry; }; struct zip_t *zip_open(const char *zipname, int level, char mode) { - struct zip_t *zip = NULL; + struct zip_t *zip = NULL; - if (!zipname || strlen(zipname) < 1) { - // zip_t archive name is empty or NULL - goto cleanup; + if (!zipname || strlen(zipname) < 1) { + // zip_t archive name is empty or NULL + goto cleanup; + } + + if (level < 0) + level = MZ_DEFAULT_LEVEL; + if ((level & 0xF) > MZ_UBER_COMPRESSION) { + // Wrong compression level + goto cleanup; + } + + zip = (struct zip_t *)calloc((size_t)1, sizeof(struct zip_t)); + if (!zip) + goto cleanup; + + zip->level = (mz_uint)level; + switch (mode) { + case 'w': + // Create a new archive. + if (!mz_zip_writer_init_file(&(zip->archive), zipname, 0)) { + // Cannot initialize zip_archive writer + goto cleanup; } + break; - if (level < 0) level = MZ_DEFAULT_LEVEL; - if ((level & 0xF) > MZ_UBER_COMPRESSION) { - // Wrong compression level - goto cleanup; + case 'r': + case 'a': + if (!mz_zip_reader_init_file( + &(zip->archive), zipname, + zip->level | MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY)) { + // An archive file does not exist or cannot initialize + // zip_archive reader + goto cleanup; } - - zip = (struct zip_t *)calloc((size_t)1, sizeof(struct zip_t)); - if (!zip) goto cleanup; - - zip->level = level; - zip->mode = mode; - switch (mode) { - case 'w': - // Create a new archive. - if (!mz_zip_writer_init_file(&(zip->archive), zipname, 0)) { - // Cannot initialize zip_archive writer - goto cleanup; - } - break; - - case 'r': - case 'a': - if (!mz_zip_reader_init_file( - &(zip->archive), zipname, - level | MZ_ZIP_FLAG_DO_NOT_SORT_CENTRAL_DIRECTORY)) { - // An archive file does not exist or cannot initialize - // zip_archive reader - goto cleanup; - } - - if (mode == 'a' && - !mz_zip_writer_init_from_reader(&(zip->archive), zipname)) { - mz_zip_reader_end(&(zip->archive)); - goto cleanup; - } - - break; - - default: - goto cleanup; + if (mode == 'a' && + !mz_zip_writer_init_from_reader(&(zip->archive), zipname)) { + mz_zip_reader_end(&(zip->archive)); + goto cleanup; } + break; - return zip; + default: + goto cleanup; + } + + return zip; cleanup: - CLEANUP(zip); - return NULL; + CLEANUP(zip); + return NULL; } void zip_close(struct zip_t *zip) { - if (zip) { - // Always finalize, even if adding failed for some reason, so we have a - // valid central directory. - mz_zip_writer_finalize_archive(&(zip->archive)); + if (zip) { + // Always finalize, even if adding failed for some reason, so we have a + // valid central directory. + mz_zip_writer_finalize_archive(&(zip->archive)); - mz_zip_writer_end(&(zip->archive)); - mz_zip_reader_end(&(zip->archive)); + mz_zip_writer_end(&(zip->archive)); + mz_zip_reader_end(&(zip->archive)); - CLEANUP(zip); - } + CLEANUP(zip); + } } int zip_entry_open(struct zip_t *zip, const char *entryname) { - char *locname = NULL; - size_t entrylen = 0; - mz_zip_archive *pzip = NULL; - mz_uint num_alignment_padding_bytes, level; + size_t entrylen = 0; + mz_zip_archive *pzip = NULL; + mz_uint num_alignment_padding_bytes, level; + mz_zip_archive_file_stat stats; - if (!zip || !entryname) { - return -1; + if (!zip || !entryname) { + return -1; + } + + entrylen = strlen(entryname); + if (entrylen < 1) { + return -1; + } + + /* + .ZIP File Format Specification Version: 6.3.3 + + 4.4.17.1 The name of the file, with optional relative path. + The path stored MUST not contain a drive or + device letter, or a leading slash. All slashes + MUST be forward slashes '/' as opposed to + backwards slashes '\' for compatibility with Amiga + and UNIX file systems etc. If input came from standard + input, there is no file name field. + */ + zip->entry.name = strrpl(entryname, entrylen, '\\', '/'); + if (!zip->entry.name) { + // Cannot parse zip entry name + return -1; + } + + pzip = &(zip->archive); + if (pzip->m_zip_mode == MZ_ZIP_MODE_READING) { + zip->entry.index = + mz_zip_reader_locate_file(pzip, zip->entry.name, NULL, 0); + if (zip->entry.index < 0) { + goto cleanup; } - entrylen = strlen(entryname); - if (entrylen < 1) { - return -1; + if (!mz_zip_reader_file_stat(pzip, (mz_uint)zip->entry.index, &stats)) { + goto cleanup; } - pzip = &(zip->archive); - /* - .ZIP File Format Specification Version: 6.3.3 - - 4.4.17.1 The name of the file, with optional relative path. - The path stored MUST not contain a drive or - device letter, or a leading slash. All slashes - MUST be forward slashes '/' as opposed to - backwards slashes '\' for compatibility with Amiga - and UNIX file systems etc. If input came from standard - input, there is no file name field. - */ - locname = strrpl(entryname, '\\', '/'); - - if (zip->mode == 'r') { - zip->entry.index = mz_zip_reader_locate_file(pzip, locname, NULL, 0); - CLEANUP(locname); - return (zip->entry.index < 0) ? -1 : 0; - } - - zip->entry.index = zip->archive.m_total_files; - zip->entry.name = locname; - if (!zip->entry.name) { - // Cannot parse zip entry name - return -1; - } - - zip->entry.comp_size = 0; - zip->entry.uncomp_size = 0; - zip->entry.uncomp_crc32 = MZ_CRC32_INIT; - zip->entry.offset = zip->archive.m_archive_size; - zip->entry.header_offset = zip->archive.m_archive_size; - memset(zip->entry.header, 0, - MZ_ZIP_LOCAL_DIR_HEADER_SIZE * sizeof(mz_uint8)); - zip->entry.method = 0; - - num_alignment_padding_bytes = - mz_zip_writer_compute_padding_needed_for_file_alignment(pzip); - - if (!pzip->m_pState || (pzip->m_zip_mode != MZ_ZIP_MODE_WRITING)) { - // Wrong zip mode - return -1; - } - if (zip->level & MZ_ZIP_FLAG_COMPRESSED_DATA) { - // Wrong zip compression level - return -1; - } - // no zip64 support yet - if ((pzip->m_total_files == 0xFFFF) || - ((pzip->m_archive_size + num_alignment_padding_bytes + - MZ_ZIP_LOCAL_DIR_HEADER_SIZE + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + - entrylen) > 0xFFFFFFFF)) { - // No zip64 support yet - return -1; - } - if (!mz_zip_writer_write_zeros( - pzip, zip->entry.offset, - num_alignment_padding_bytes + sizeof(zip->entry.header))) { - // Cannot memset zip entry header - return -1; - } - - zip->entry.header_offset += num_alignment_padding_bytes; - if (pzip->m_file_offset_alignment) { - MZ_ASSERT((zip->entry.header_offset & - (pzip->m_file_offset_alignment - 1)) == 0); - } - zip->entry.offset += - num_alignment_padding_bytes + sizeof(zip->entry.header); - - if (pzip->m_pWrite(pzip->m_pIO_opaque, zip->entry.offset, zip->entry.name, - entrylen) != entrylen) { - // Cannot write data to zip entry - return -1; - } - - zip->entry.offset += entrylen; - level = zip->level & 0xF; - if (level) { - zip->entry.state.m_pZip = pzip; - zip->entry.state.m_cur_archive_file_ofs = zip->entry.offset; - zip->entry.state.m_comp_size = 0; - - if (tdefl_init(&(zip->entry.comp), mz_zip_writer_add_put_buf_callback, - &(zip->entry.state), - tdefl_create_comp_flags_from_zip_params( - level, -15, MZ_DEFAULT_STRATEGY)) != - TDEFL_STATUS_OKAY) { - // Cannot initialize the zip compressor - return -1; - } - } + zip->entry.comp_size = stats.m_comp_size; + zip->entry.uncomp_size = stats.m_uncomp_size; + zip->entry.uncomp_crc32 = stats.m_crc32; + zip->entry.offset = stats.m_central_dir_ofs; + zip->entry.header_offset = stats.m_local_header_ofs; + zip->entry.method = stats.m_method; + zip->entry.external_attr = stats.m_external_attr; + zip->entry.m_time = stats.m_time; return 0; + } + + zip->entry.index = (int)zip->archive.m_total_files; + zip->entry.comp_size = 0; + zip->entry.uncomp_size = 0; + zip->entry.uncomp_crc32 = MZ_CRC32_INIT; + zip->entry.offset = zip->archive.m_archive_size; + zip->entry.header_offset = zip->archive.m_archive_size; + memset(zip->entry.header, 0, MZ_ZIP_LOCAL_DIR_HEADER_SIZE * sizeof(mz_uint8)); + zip->entry.method = 0; + zip->entry.external_attr = 0; + + num_alignment_padding_bytes = + mz_zip_writer_compute_padding_needed_for_file_alignment(pzip); + + if (!pzip->m_pState || (pzip->m_zip_mode != MZ_ZIP_MODE_WRITING)) { + // Wrong zip mode + goto cleanup; + } + if (zip->level & MZ_ZIP_FLAG_COMPRESSED_DATA) { + // Wrong zip compression level + goto cleanup; + } + // no zip64 support yet + if ((pzip->m_total_files == 0xFFFF) || + ((pzip->m_archive_size + num_alignment_padding_bytes + + MZ_ZIP_LOCAL_DIR_HEADER_SIZE + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE + + entrylen) > 0xFFFFFFFF)) { + // No zip64 support yet + goto cleanup; + } + if (!mz_zip_writer_write_zeros(pzip, zip->entry.offset, + num_alignment_padding_bytes + + sizeof(zip->entry.header))) { + // Cannot memset zip entry header + goto cleanup; + } + + zip->entry.header_offset += num_alignment_padding_bytes; + if (pzip->m_file_offset_alignment) { + MZ_ASSERT( + (zip->entry.header_offset & (pzip->m_file_offset_alignment - 1)) == 0); + } + zip->entry.offset += num_alignment_padding_bytes + sizeof(zip->entry.header); + + if (pzip->m_pWrite(pzip->m_pIO_opaque, zip->entry.offset, zip->entry.name, + entrylen) != entrylen) { + // Cannot write data to zip entry + goto cleanup; + } + + zip->entry.offset += entrylen; + level = zip->level & 0xF; + if (level) { + zip->entry.state.m_pZip = pzip; + zip->entry.state.m_cur_archive_file_ofs = zip->entry.offset; + zip->entry.state.m_comp_size = 0; + + if (tdefl_init(&(zip->entry.comp), mz_zip_writer_add_put_buf_callback, + &(zip->entry.state), + (int)tdefl_create_comp_flags_from_zip_params( + (int)level, -15, MZ_DEFAULT_STRATEGY)) != + TDEFL_STATUS_OKAY) { + // Cannot initialize the zip compressor + goto cleanup; + } + } + + zip->entry.m_time = time(NULL); + + return 0; + +cleanup: + CLEANUP(zip->entry.name); + return -1; +} + +int zip_entry_openbyindex(struct zip_t *zip, int index) { + mz_zip_archive *pZip = NULL; + mz_zip_archive_file_stat stats; + mz_uint namelen; + const mz_uint8 *pHeader; + const char *pFilename; + + if (!zip) { + // zip_t handler is not initialized + return -1; + } + + pZip = &(zip->archive); + if (pZip->m_zip_mode != MZ_ZIP_MODE_READING) { + // open by index requires readonly mode + return -1; + } + + if (index < 0 || (mz_uint)index >= pZip->m_total_files) { + // index out of range + return -1; + } + + if (!(pHeader = &MZ_ZIP_ARRAY_ELEMENT( + &pZip->m_pState->m_central_dir, mz_uint8, + MZ_ZIP_ARRAY_ELEMENT(&pZip->m_pState->m_central_dir_offsets, + mz_uint32, index)))) { + // cannot find header in central directory + return -1; + } + + namelen = MZ_READ_LE16(pHeader + MZ_ZIP_CDH_FILENAME_LEN_OFS); + pFilename = (const char *)pHeader + MZ_ZIP_CENTRAL_DIR_HEADER_SIZE; + + /* + .ZIP File Format Specification Version: 6.3.3 + + 4.4.17.1 The name of the file, with optional relative path. + The path stored MUST not contain a drive or + device letter, or a leading slash. All slashes + MUST be forward slashes '/' as opposed to + backwards slashes '\' for compatibility with Amiga + and UNIX file systems etc. If input came from standard + input, there is no file name field. + */ + zip->entry.name = strrpl(pFilename, namelen, '\\', '/'); + if (!zip->entry.name) { + // local entry name is NULL + return -1; + } + + if (!mz_zip_reader_file_stat(pZip, (mz_uint)index, &stats)) { + return -1; + } + + zip->entry.index = index; + zip->entry.comp_size = stats.m_comp_size; + zip->entry.uncomp_size = stats.m_uncomp_size; + zip->entry.uncomp_crc32 = stats.m_crc32; + zip->entry.offset = stats.m_central_dir_ofs; + zip->entry.header_offset = stats.m_local_header_ofs; + zip->entry.method = stats.m_method; + zip->entry.external_attr = stats.m_external_attr; + zip->entry.m_time = stats.m_time; + + return 0; } int zip_entry_close(struct zip_t *zip) { - mz_zip_archive *pzip = NULL; - mz_uint level; - tdefl_status done; - mz_uint16 entrylen; - time_t t; - struct tm *tm; - mz_uint16 dos_time, dos_date; - int status = -1; + mz_zip_archive *pzip = NULL; + mz_uint level; + tdefl_status done; + mz_uint16 entrylen; + mz_uint16 dos_time, dos_date; + int status = -1; - if (!zip) { - // zip_t handler is not initialized - return -1; - } + if (!zip) { + // zip_t handler is not initialized + goto cleanup; + } - if (zip->mode == 'r') { - return 0; - } - - pzip = &(zip->archive); - level = zip->level & 0xF; - if (level) { - done = tdefl_compress_buffer(&(zip->entry.comp), "", 0, TDEFL_FINISH); - if (done != TDEFL_STATUS_DONE && done != TDEFL_STATUS_OKAY) { - // Cannot flush compressed buffer - goto cleanup; - } - zip->entry.comp_size = zip->entry.state.m_comp_size; - zip->entry.offset = zip->entry.state.m_cur_archive_file_ofs; - zip->entry.method = MZ_DEFLATED; - } - - entrylen = (mz_uint16)strlen(zip->entry.name); - t = time(NULL); - tm = localtime(&t); - dos_time = (mz_uint16)(((tm->tm_hour) << 11) + ((tm->tm_min) << 5) + - ((tm->tm_sec) >> 1)); - dos_date = (mz_uint16)(((tm->tm_year + 1900 - 1980) << 9) + - ((tm->tm_mon + 1) << 5) + tm->tm_mday); - - // no zip64 support yet - if ((zip->entry.comp_size > 0xFFFFFFFF) || - (zip->entry.offset > 0xFFFFFFFF)) { - // No zip64 support, yet - goto cleanup; - } - - if (!mz_zip_writer_create_local_dir_header( - pzip, zip->entry.header, entrylen, 0, zip->entry.uncomp_size, - zip->entry.comp_size, zip->entry.uncomp_crc32, zip->entry.method, 0, - dos_time, dos_date)) { - // Cannot create zip entry header - goto cleanup; - } - - if (pzip->m_pWrite(pzip->m_pIO_opaque, zip->entry.header_offset, - zip->entry.header, sizeof(zip->entry.header)) != - sizeof(zip->entry.header)) { - // Cannot write zip entry header - goto cleanup; - } - - if (!mz_zip_writer_add_to_central_dir( - pzip, zip->entry.name, entrylen, NULL, 0, "", 0, - zip->entry.uncomp_size, zip->entry.comp_size, - zip->entry.uncomp_crc32, zip->entry.method, 0, dos_time, dos_date, - zip->entry.header_offset, 0)) { - // Cannot write to zip central dir - goto cleanup; - } - - pzip->m_total_files++; - pzip->m_archive_size = zip->entry.offset; + pzip = &(zip->archive); + if (pzip->m_zip_mode == MZ_ZIP_MODE_READING) { status = 0; + goto cleanup; + } + + level = zip->level & 0xF; + if (level) { + done = tdefl_compress_buffer(&(zip->entry.comp), "", 0, TDEFL_FINISH); + if (done != TDEFL_STATUS_DONE && done != TDEFL_STATUS_OKAY) { + // Cannot flush compressed buffer + goto cleanup; + } + zip->entry.comp_size = zip->entry.state.m_comp_size; + zip->entry.offset = zip->entry.state.m_cur_archive_file_ofs; + zip->entry.method = MZ_DEFLATED; + } + + entrylen = (mz_uint16)strlen(zip->entry.name); + // no zip64 support yet + if ((zip->entry.comp_size > 0xFFFFFFFF) || (zip->entry.offset > 0xFFFFFFFF)) { + // No zip64 support, yet + goto cleanup; + } + + mz_zip_time_t_to_dos_time(zip->entry.m_time, &dos_time, &dos_date); + if (!mz_zip_writer_create_local_dir_header( + pzip, zip->entry.header, entrylen, 0, zip->entry.uncomp_size, + zip->entry.comp_size, zip->entry.uncomp_crc32, zip->entry.method, 0, + dos_time, dos_date)) { + // Cannot create zip entry header + goto cleanup; + } + + if (pzip->m_pWrite(pzip->m_pIO_opaque, zip->entry.header_offset, + zip->entry.header, + sizeof(zip->entry.header)) != sizeof(zip->entry.header)) { + // Cannot write zip entry header + goto cleanup; + } + + if (!mz_zip_writer_add_to_central_dir( + pzip, zip->entry.name, entrylen, NULL, 0, "", 0, + zip->entry.uncomp_size, zip->entry.comp_size, zip->entry.uncomp_crc32, + zip->entry.method, 0, dos_time, dos_date, zip->entry.header_offset, + zip->entry.external_attr)) { + // Cannot write to zip central dir + goto cleanup; + } + + pzip->m_total_files++; + pzip->m_archive_size = zip->entry.offset; + status = 0; cleanup: + if (zip) { + zip->entry.m_time = 0; CLEANUP(zip->entry.name); - return status; + } + return status; +} + +const char *zip_entry_name(struct zip_t *zip) { + if (!zip) { + // zip_t handler is not initialized + return NULL; + } + + return zip->entry.name; +} + +int zip_entry_index(struct zip_t *zip) { + if (!zip) { + // zip_t handler is not initialized + return -1; + } + + return zip->entry.index; +} + +int zip_entry_isdir(struct zip_t *zip) { + if (!zip) { + // zip_t handler is not initialized + return -1; + } + + if (zip->entry.index < 0) { + // zip entry is not opened + return -1; + } + + return (int)mz_zip_reader_is_file_a_directory(&zip->archive, + (mz_uint)zip->entry.index); +} + +unsigned long long zip_entry_size(struct zip_t *zip) { + return zip ? zip->entry.uncomp_size : 0; +} + +unsigned int zip_entry_crc32(struct zip_t *zip) { + return zip ? zip->entry.uncomp_crc32 : 0; } int zip_entry_write(struct zip_t *zip, const void *buf, size_t bufsize) { - mz_uint level; - mz_zip_archive *pzip = NULL; - tdefl_status status; + mz_uint level; + mz_zip_archive *pzip = NULL; + tdefl_status status; - if (!zip) { - // zip_t handler is not initialized + if (!zip) { + // zip_t handler is not initialized + return -1; + } + + pzip = &(zip->archive); + if (buf && bufsize > 0) { + zip->entry.uncomp_size += bufsize; + zip->entry.uncomp_crc32 = (mz_uint32)mz_crc32( + zip->entry.uncomp_crc32, (const mz_uint8 *)buf, bufsize); + + level = zip->level & 0xF; + if (!level) { + if ((pzip->m_pWrite(pzip->m_pIO_opaque, zip->entry.offset, buf, + bufsize) != bufsize)) { + // Cannot write buffer return -1; + } + zip->entry.offset += bufsize; + zip->entry.comp_size += bufsize; + } else { + status = tdefl_compress_buffer(&(zip->entry.comp), buf, bufsize, + TDEFL_NO_FLUSH); + if (status != TDEFL_STATUS_DONE && status != TDEFL_STATUS_OKAY) { + // Cannot compress buffer + return -1; + } } + } - pzip = &(zip->archive); - if (buf && bufsize > 0) { - zip->entry.uncomp_size += bufsize; - zip->entry.uncomp_crc32 = (mz_uint32)mz_crc32( - zip->entry.uncomp_crc32, (const mz_uint8 *)buf, bufsize); - - level = zip->level & 0xF; - if (!level) { - if ((pzip->m_pWrite(pzip->m_pIO_opaque, zip->entry.offset, buf, - bufsize) != bufsize)) { - // Cannot write buffer - return -1; - } - zip->entry.offset += bufsize; - zip->entry.comp_size += bufsize; - } else { - status = tdefl_compress_buffer(&(zip->entry.comp), buf, bufsize, - TDEFL_NO_FLUSH); - if (status != TDEFL_STATUS_DONE && status != TDEFL_STATUS_OKAY) { - // Cannot compress buffer - return -1; - } - } - } - - return 0; + return 0; } int zip_entry_fwrite(struct zip_t *zip, const char *filename) { - int status = 0; - size_t n = 0; - FILE *stream = NULL; - mz_uint8 buf[MZ_ZIP_MAX_IO_BUF_SIZE] = {0}; + int status = 0; + size_t n = 0; + FILE *stream = NULL; + mz_uint8 buf[MZ_ZIP_MAX_IO_BUF_SIZE]; + struct MZ_FILE_STAT_STRUCT file_stat; - if (!zip) { - // zip_t handler is not initialized - return -1; + if (!zip) { + // zip_t handler is not initialized + return -1; + } + + memset(buf, 0, MZ_ZIP_MAX_IO_BUF_SIZE); + memset((void *)&file_stat, 0, sizeof(struct MZ_FILE_STAT_STRUCT)); + if (MZ_FILE_STAT(filename, &file_stat) != 0) { + // problem getting information - check errno + return -1; + } + + if ((file_stat.st_mode & 0200) == 0) { + // MS-DOS read-only attribute + zip->entry.external_attr |= 0x01; + } + zip->entry.external_attr |= (mz_uint32)((file_stat.st_mode & 0xFFFF) << 16); + zip->entry.m_time = file_stat.st_mtime; + +#if defined(_MSC_VER) + if (fopen_s(&stream, filename, "rb")) +#else + if (!(stream = fopen(filename, "rb"))) +#endif + { + // Cannot open filename + return -1; + } + + while ((n = fread(buf, sizeof(mz_uint8), MZ_ZIP_MAX_IO_BUF_SIZE, stream)) > + 0) { + if (zip_entry_write(zip, buf, n) < 0) { + status = -1; + break; } + } + fclose(stream); - stream = fopen(filename, "rb"); - if (!stream) { - // Cannot open filename - return -1; - } - - while ((n = fread(buf, sizeof(mz_uint8), MZ_ZIP_MAX_IO_BUF_SIZE, stream)) > - 0) { - if (zip_entry_write(zip, buf, n) < 0) { - status = -1; - break; - } - } - fclose(stream); - - return status; + return status; } -int zip_entry_read(struct zip_t *zip, void **buf, size_t *bufsize) { - mz_zip_archive *pzip = NULL; - mz_uint idx; +ssize_t zip_entry_read(struct zip_t *zip, void **buf, size_t *bufsize) { + mz_zip_archive *pzip = NULL; + mz_uint idx; + size_t size = 0; - if (!zip) { - // zip_t handler is not initialized - return -1; - } + if (!zip) { + // zip_t handler is not initialized + return -1; + } - if (zip->mode != 'r' || zip->entry.index < 0) { - // the entry is not found or we do not have read access - return -1; - } + pzip = &(zip->archive); + if (pzip->m_zip_mode != MZ_ZIP_MODE_READING || zip->entry.index < 0) { + // the entry is not found or we do not have read access + return -1; + } - pzip = &(zip->archive); - idx = (mz_uint)zip->entry.index; - if (mz_zip_reader_is_file_a_directory(pzip, idx)) { - // the entry is a directory - return -1; - } + idx = (mz_uint)zip->entry.index; + if (mz_zip_reader_is_file_a_directory(pzip, idx)) { + // the entry is a directory + return -1; + } - *buf = mz_zip_reader_extract_to_heap(pzip, idx, bufsize, 0); - return (*buf) ? 0 : -1; + *buf = mz_zip_reader_extract_to_heap(pzip, idx, &size, 0); + if (*buf && bufsize) { + *bufsize = size; + } + return size; +} + +ssize_t zip_entry_noallocread(struct zip_t *zip, void *buf, size_t bufsize) { + mz_zip_archive *pzip = NULL; + + if (!zip) { + // zip_t handler is not initialized + return -1; + } + + pzip = &(zip->archive); + if (pzip->m_zip_mode != MZ_ZIP_MODE_READING || zip->entry.index < 0) { + // the entry is not found or we do not have read access + return -1; + } + + if (!mz_zip_reader_extract_to_mem_no_alloc(pzip, (mz_uint)zip->entry.index, + buf, bufsize, 0, NULL, 0)) { + return -1; + } + + return (ssize_t)zip->entry.uncomp_size; } int zip_entry_fread(struct zip_t *zip, const char *filename) { - mz_zip_archive *pzip = NULL; - mz_uint idx; + mz_zip_archive *pzip = NULL; + mz_uint idx; +#if defined(_MSC_VER) +#else + mz_uint32 xattr = 0; +#endif + mz_zip_archive_file_stat info; - if (!zip) { - // zip_t handler is not initialized - return -1; + if (!zip) { + // zip_t handler is not initialized + return -1; + } + + memset((void *)&info, 0, sizeof(mz_zip_archive_file_stat)); + pzip = &(zip->archive); + if (pzip->m_zip_mode != MZ_ZIP_MODE_READING || zip->entry.index < 0) { + // the entry is not found or we do not have read access + return -1; + } + + idx = (mz_uint)zip->entry.index; + if (mz_zip_reader_is_file_a_directory(pzip, idx)) { + // the entry is a directory + return -1; + } + + if (!mz_zip_reader_extract_to_file(pzip, idx, filename, 0)) { + return -1; + } + +#if defined(_MSC_VER) +#else + if (!mz_zip_reader_file_stat(pzip, idx, &info)) { + // Cannot get information about zip archive; + return -1; + } + + xattr = (info.m_external_attr >> 16) & 0xFFFF; + if (xattr > 0) { + if (chmod(filename, (mode_t)xattr) < 0) { + return -1; } + } +#endif - if (zip->mode != 'r' || zip->entry.index < 0) { - // the entry is not found or we do not have read access - return -1; - } - - pzip = &(zip->archive); - idx = (mz_uint)zip->entry.index; - if (mz_zip_reader_is_file_a_directory(pzip, idx)) { - // the entry is a directory - return -1; - } - - return (mz_zip_reader_extract_to_file(pzip, idx, filename, 0)) ? 0 : -1; + return 0; } int zip_entry_extract(struct zip_t *zip, size_t (*on_extract)(void *arg, unsigned long long offset, const void *buf, size_t bufsize), void *arg) { - mz_zip_archive *pzip = NULL; - mz_uint idx; + mz_zip_archive *pzip = NULL; + mz_uint idx; - if (!zip) { - // zip_t handler is not initialized - return -1; - } + if (!zip) { + // zip_t handler is not initialized + return -1; + } - if (zip->mode != 'r' || zip->entry.index < 0) { - // the entry is not found or we do not have read access - return -1; - } + pzip = &(zip->archive); + if (pzip->m_zip_mode != MZ_ZIP_MODE_READING || zip->entry.index < 0) { + // the entry is not found or we do not have read access + return -1; + } - pzip = &(zip->archive); - idx = (mz_uint)zip->entry.index; + idx = (mz_uint)zip->entry.index; + return (mz_zip_reader_extract_to_callback(pzip, idx, on_extract, arg, 0)) + ? 0 + : -1; +} - return (mz_zip_reader_extract_to_callback(pzip, idx, on_extract, arg, 0)) - ? 0 - : -1; +int zip_total_entries(struct zip_t *zip) { + if (!zip) { + // zip_t handler is not initialized + return -1; + } + + return (int)zip->archive.m_total_files; } int zip_create(const char *zipname, const char *filenames[], size_t len) { - int status = 0; - size_t i; - mz_zip_archive zip_archive; + int status = 0; + size_t i; + mz_zip_archive zip_archive; + struct MZ_FILE_STAT_STRUCT file_stat; + mz_uint32 ext_attributes = 0; - if (!zipname || strlen(zipname) < 1) { - // zip_t archive name is empty or NULL - return -1; + if (!zipname || strlen(zipname) < 1) { + // zip_t archive name is empty or NULL + return -1; + } + + // Create a new archive. + if (!memset(&(zip_archive), 0, sizeof(zip_archive))) { + // Cannot memset zip archive + return -1; + } + + if (!mz_zip_writer_init_file(&zip_archive, zipname, 0)) { + // Cannot initialize zip_archive writer + return -1; + } + + memset((void *)&file_stat, 0, sizeof(struct MZ_FILE_STAT_STRUCT)); + + for (i = 0; i < len; ++i) { + const char *name = filenames[i]; + if (!name) { + status = -1; + break; } - // Create a new archive. - if (!memset(&(zip_archive), 0, sizeof(zip_archive))) { - // Cannot memset zip archive - return -1; + if (MZ_FILE_STAT(name, &file_stat) != 0) { + // problem getting information - check errno + return -1; } - if (!mz_zip_writer_init_file(&zip_archive, zipname, 0)) { - // Cannot initialize zip_archive writer - return -1; + if ((file_stat.st_mode & 0200) == 0) { + // MS-DOS read-only attribute + ext_attributes |= 0x01; } + ext_attributes |= (mz_uint32)((file_stat.st_mode & 0xFFFF) << 16); - for (i = 0; i < len; ++i) { - const char *name = filenames[i]; - if (!name) { - status = -1; - break; - } - - if (!mz_zip_writer_add_file(&zip_archive, basename(name), name, "", 0, - ZIP_DEFAULT_COMPRESSION_LEVEL)) { - // Cannot add file to zip_archive - status = -1; - break; - } + if (!mz_zip_writer_add_file(&zip_archive, base_name(name), name, "", 0, + ZIP_DEFAULT_COMPRESSION_LEVEL, + ext_attributes)) { + // Cannot add file to zip_archive + status = -1; + break; } + } - mz_zip_writer_finalize_archive(&zip_archive); - mz_zip_writer_end(&zip_archive); - return status; + mz_zip_writer_finalize_archive(&zip_archive); + mz_zip_writer_end(&zip_archive); + return status; } int zip_extract(const char *zipname, const char *dir, int (*on_extract)(const char *filename, void *arg), void *arg) { - int status = -1; - mz_uint i, n; - char path[MAX_PATH + 1] = {0}; - mz_zip_archive zip_archive; - mz_zip_archive_file_stat info; - size_t dirlen = 0; + int status = -1; + mz_uint i, n; + char path[MAX_PATH + 1]; + char symlink_to[MAX_PATH + 1]; + mz_zip_archive zip_archive; + mz_zip_archive_file_stat info; + size_t dirlen = 0; + mz_uint32 xattr = 0; - if (!memset(&(zip_archive), 0, sizeof(zip_archive))) { - // Cannot memset zip archive - return -1; - } + memset(path, 0, sizeof(path)); + memset(symlink_to, 0, sizeof(symlink_to)); + if (!memset(&(zip_archive), 0, sizeof(zip_archive))) { + // Cannot memset zip archive + return -1; + } - if (!zipname || !dir) { - // Cannot parse zip archive name - return -1; - } + if (!zipname || !dir) { + // Cannot parse zip archive name + return -1; + } - dirlen = strlen(dir); - if (dirlen + 1 > MAX_PATH) { - return -1; - } + dirlen = strlen(dir); + if (dirlen + 1 > MAX_PATH) { + return -1; + } - // Now try to open the archive. - if (!mz_zip_reader_init_file(&zip_archive, zipname, 0)) { - // Cannot initialize zip_archive reader - return -1; - } + // Now try to open the archive. + if (!mz_zip_reader_init_file(&zip_archive, zipname, 0)) { + // Cannot initialize zip_archive reader + return -1; + } - strcpy(path, dir); - if (!ISSLASH(path[dirlen - 1])) { -#if defined _WIN32 || defined __WIN32__ - path[dirlen] = '\\'; + memset((void *)&info, 0, sizeof(mz_zip_archive_file_stat)); + +#if defined(_MSC_VER) + strcpy_s(path, MAX_PATH, dir); #else - path[dirlen] = '/'; + strcpy(path, dir); #endif - ++dirlen; + + if (!ISSLASH(path[dirlen - 1])) { +#if defined(_WIN32) || defined(__WIN32__) + path[dirlen] = '\\'; +#else + path[dirlen] = '/'; +#endif + ++dirlen; + } + + // Get and print information about each file in the archive. + n = mz_zip_reader_get_num_files(&zip_archive); + for (i = 0; i < n; ++i) { + if (!mz_zip_reader_file_stat(&zip_archive, i, &info)) { + // Cannot get information about zip archive; + goto out; + } +#if defined(_MSC_VER) + strncpy_s(&path[dirlen], MAX_PATH - dirlen, info.m_filename, + MAX_PATH - dirlen); +#else + strncpy(&path[dirlen], info.m_filename, MAX_PATH - dirlen); +#endif + if (mkpath(path) < 0) { + // Cannot make a path + goto out; } - // Get and print information about each file in the archive. - n = mz_zip_reader_get_num_files(&zip_archive); - for (i = 0; i < n; ++i) { - if (!mz_zip_reader_file_stat(&zip_archive, i, &info)) { - // Cannot get information about zip archive; - goto out; - } - strncpy(&path[dirlen], info.m_filename, MAX_PATH - dirlen); - if (mkpath(path) < 0) { - // Cannot make a path - goto out; + if ((((info.m_version_made_by >> 8) == 3) || ((info.m_version_made_by >> 8) == 19)) // if zip is produced on Unix or macOS (3 and 19 from section 4.4.2.2 of zip standard) + && info.m_external_attr & (0x20 << 24)) { // and has sym link attribute (0x80 is file, 0x40 is directory) +#if defined(_WIN32) || defined(__WIN32__) || defined(_MSC_VER) || \ + defined(__MINGW32__) +#else + if (info.m_uncomp_size > MAX_PATH || !mz_zip_reader_extract_to_mem_no_alloc(&zip_archive, i, symlink_to, MAX_PATH, 0, NULL, 0)) { + goto out; + } + symlink_to[info.m_uncomp_size] = '\0'; + if (symlink(symlink_to, path) != 0) { + goto out; + } +#endif + } else { + if (!mz_zip_reader_is_file_a_directory(&zip_archive, i)) { + if (!mz_zip_reader_extract_to_file(&zip_archive, i, path, 0)) { + // Cannot extract zip archive to file + goto out; } + } - if (!mz_zip_reader_is_file_a_directory(&zip_archive, i)) { - if (!mz_zip_reader_extract_to_file(&zip_archive, i, path, 0)) { - // Cannot extract zip archive to file - goto out; - } - } - - if (on_extract) { - if (on_extract(path, arg) < 0) { - goto out; - } +#if defined(_MSC_VER) +#else + xattr = (info.m_external_attr >> 16) & 0xFFFF; + if (xattr > 0) { + if (chmod(path, (mode_t)xattr) < 0) { + goto out; } + } +#endif } - status = 0; + + if (on_extract) { + if (on_extract(path, arg) < 0) { + goto out; + } + } + } + status = 0; out: - // Close the archive, freeing any resources it was using - if (!mz_zip_reader_end(&zip_archive)) { - // Cannot end zip reader - status = -1; - } + // Close the archive, freeing any resources it was using + if (!mz_zip_reader_end(&zip_archive)) { + // Cannot end zip reader + status = -1; + } - return status; + return status; } diff --git a/contrib/zip/src/zip.h b/contrib/zip/src/zip.h index 1611b5417..5f39df50a 100644 --- a/contrib/zip/src/zip.h +++ b/contrib/zip/src/zip.h @@ -13,11 +13,24 @@ #define ZIP_H #include +#include #ifdef __cplusplus extern "C" { #endif +#if !defined(_SSIZE_T_DEFINED) && !defined(_SSIZE_T_DEFINED_) && \ + !defined(_SSIZE_T) && !defined(_SSIZE_T_) && !defined(__ssize_t_defined) +#define _SSIZE_T +// 64-bit Windows is the only mainstream platform +// where sizeof(long) != sizeof(void*) +#ifdef _WIN64 +typedef long long ssize_t; /* byte count or error */ +#else +typedef long ssize_t; /* byte count or error */ +#endif +#endif + #ifndef MAX_PATH #define MAX_PATH 32767 /* # chars in a path name including NULL */ #endif @@ -47,7 +60,7 @@ struct zip_t; extern struct zip_t *zip_open(const char *zipname, int level, char mode); /* - Closes zip archive, releases resources - always finalize. + Closes the zip archive, releases resources - always finalize. Args: zip: zip archive handler. @@ -55,7 +68,10 @@ extern struct zip_t *zip_open(const char *zipname, int level, char mode); extern void zip_close(struct zip_t *zip); /* - Opens a new entry for writing in a zip archive. + Opens an entry by name in the zip archive. + For zip archive opened in 'w' or 'a' mode the function will append + a new entry. In readonly mode the function tries to locate the entry + in global dictionary. Args: zip: zip archive handler. @@ -66,6 +82,19 @@ extern void zip_close(struct zip_t *zip); */ extern int zip_entry_open(struct zip_t *zip, const char *entryname); +/* + Opens a new entry by index in the zip archive. + This function is only valid if zip archive was opened in 'r' (readonly) mode. + + Args: + zip: zip archive handler. + index: index in local dictionary. + + Returns: + The return code - 0 on success, negative number (< 0) on error. +*/ +extern int zip_entry_openbyindex(struct zip_t *zip, int index); + /* Closes a zip entry, flushes buffer and releases resources. @@ -77,6 +106,67 @@ extern int zip_entry_open(struct zip_t *zip, const char *entryname); */ extern int zip_entry_close(struct zip_t *zip); +/* + Returns a local name of the current zip entry. + The main difference between user's entry name and local entry name + is optional relative path. + Following .ZIP File Format Specification - the path stored MUST not contain + a drive or device letter, or a leading slash. + All slashes MUST be forward slashes '/' as opposed to backwards slashes '\' + for compatibility with Amiga and UNIX file systems etc. + + Args: + zip: zip archive handler. + + Returns: + The pointer to the current zip entry name, or NULL on error. +*/ +extern const char *zip_entry_name(struct zip_t *zip); + +/* + Returns an index of the current zip entry. + + Args: + zip: zip archive handler. + + Returns: + The index on success, negative number (< 0) on error. +*/ +extern int zip_entry_index(struct zip_t *zip); + +/* + Determines if the current zip entry is a directory entry. + + Args: + zip: zip archive handler. + + Returns: + The return code - 1 (true), 0 (false), negative number (< 0) on error. +*/ +extern int zip_entry_isdir(struct zip_t *zip); + +/* + Returns an uncompressed size of the current zip entry. + + Args: + zip: zip archive handler. + + Returns: + The uncompressed size in bytes. +*/ +extern unsigned long long zip_entry_size(struct zip_t *zip); + +/* + Returns CRC-32 checksum of the current zip entry. + + Args: + zip: zip archive handler. + + Returns: + The CRC-32 checksum. +*/ +extern unsigned int zip_entry_crc32(struct zip_t *zip); + /* Compresses an input buffer for the current zip entry. @@ -116,9 +206,31 @@ extern int zip_entry_fwrite(struct zip_t *zip, const char *filename); - for large entries, please take a look at zip_entry_extract function. Returns: - The return code - 0 on success, negative number (< 0) on error. + The return code - the number of bytes actually read on success. + Otherwise a -1 on error. */ -extern int zip_entry_read(struct zip_t *zip, void **buf, size_t *bufsize); +extern ssize_t zip_entry_read(struct zip_t *zip, void **buf, size_t *bufsize); + +/* + Extracts the current zip entry into a memory buffer using no memory + allocation. + + Args: + zip: zip archive handler. + buf: preallocated output buffer. + bufsize: output buffer size (in bytes). + + Note: + - ensure supplied output buffer is large enough. + - zip_entry_size function (returns uncompressed size for the current entry) + can be handy to estimate how big buffer is needed. + - for large entries, please take a look at zip_entry_extract function. + + Returns: + The return code - the number of bytes actually read on success. + Otherwise a -1 on error (e.g. bufsize is not large enough). +*/ +extern ssize_t zip_entry_noallocread(struct zip_t *zip, void *buf, size_t bufsize); /* Extracts the current zip entry into output file. @@ -133,9 +245,9 @@ extern int zip_entry_read(struct zip_t *zip, void **buf, size_t *bufsize); extern int zip_entry_fread(struct zip_t *zip, const char *filename); /* - Extract the current zip entry using a callback function (on_extract). + Extracts the current zip entry using a callback function (on_extract). - Args: + Args: zip: zip archive handler. on_extract: callback function. arg: opaque pointer (optional argument, @@ -144,12 +256,23 @@ extern int zip_entry_fread(struct zip_t *zip, const char *filename); Returns: The return code - 0 on success, negative number (< 0) on error. */ -extern int zip_entry_extract(struct zip_t *zip, - size_t (*on_extract)(void *arg, - unsigned long long offset, - const void *data, - size_t size), - void *arg); +extern int +zip_entry_extract(struct zip_t *zip, + size_t (*on_extract)(void *arg, unsigned long long offset, + const void *data, size_t size), + void *arg); + +/* + Returns the number of all entries (files and directories) in the zip archive. + + Args: + zip: zip archive handler. + + Returns: + The return code - the number of entries on success, + negative number (< 0) on error. +*/ +extern int zip_total_entries(struct zip_t *zip); /* Creates a new archive and puts files into a single zip archive. diff --git a/contrib/zip/test/CMakeLists.txt b/contrib/zip/test/CMakeLists.txt index 7734dcbe7..9b2a8db10 100644 --- a/contrib/zip/test/CMakeLists.txt +++ b/contrib/zip/test/CMakeLists.txt @@ -1,7 +1,19 @@ cmake_minimum_required(VERSION 2.8) +if ("${CMAKE_C_COMPILER_ID}" STREQUAL "GNU" OR "${CMAKE_C_COMPILER_ID}" STREQUAL "Clang" OR "${CMAKE_C_COMPILER_ID}" STREQUAL "AppleClang") + if(ENABLE_COVERAGE) + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -g ") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -O0") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fprofile-arcs") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -ftest-coverage") + set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} --coverage") + endif() +endif () + # test include_directories(../src) add_executable(test.exe test.c ../src/zip.c) +add_executable(test_miniz.exe test_miniz.c) add_test(NAME test COMMAND test.exe) +add_test(NAME test_miniz COMMAND test_miniz.exe) diff --git a/contrib/zip/test/test.c b/contrib/zip/test/test.c index 0b9c9f7b6..454430533 100644 --- a/contrib/zip/test/test.c +++ b/contrib/zip/test/test.c @@ -4,102 +4,456 @@ #include #include #include +#include -#define ZIPNAME "test.zip" +#if defined(_MSC_VER) || defined(__MINGW64__) || defined(__MINGW32__) +#define MZ_FILE_STAT_STRUCT _stat +#define MZ_FILE_STAT _stat +#else +#define MZ_FILE_STAT_STRUCT stat +#define MZ_FILE_STAT stat +#endif + +#define ZIPNAME "test.zip\0" #define TESTDATA1 "Some test data 1...\0" +#define CRC32DATA1 2220805626 #define TESTDATA2 "Some test data 2...\0" +#define CRC32DATA2 2532008468 + +#define RFILE "4.txt\0" +#define RMODE 0100444 + +#define WFILE "6.txt\0" +#define WMODE 0100666 + +#define XFILE "7.txt\0" +#define XMODE 0100777 + +#define UNUSED(x) (void)x + +static int total_entries = 0; static void test_write(void) { - struct zip_t *zip = zip_open(ZIPNAME, ZIP_DEFAULT_COMPRESSION_LEVEL, 'w'); - assert(zip != NULL); + struct zip_t *zip = zip_open(ZIPNAME, ZIP_DEFAULT_COMPRESSION_LEVEL, 'w'); + assert(zip != NULL); - assert(0 == zip_entry_open(zip, "test/test-1.txt")); - assert(0 == zip_entry_write(zip, TESTDATA1, strlen(TESTDATA1))); - assert(0 == zip_entry_close(zip)); + assert(0 == zip_entry_open(zip, "test/test-1.txt")); + assert(0 == zip_entry_write(zip, TESTDATA1, strlen(TESTDATA1))); + assert(0 == strcmp(zip_entry_name(zip), "test/test-1.txt")); + assert(total_entries == zip_entry_index(zip)); + assert(strlen(TESTDATA1) == zip_entry_size(zip)); + assert(CRC32DATA1 == zip_entry_crc32(zip)); + ++total_entries; + assert(0 == zip_entry_close(zip)); - zip_close(zip); + zip_close(zip); } static void test_append(void) { - struct zip_t *zip = zip_open(ZIPNAME, ZIP_DEFAULT_COMPRESSION_LEVEL, 'a'); - assert(zip != NULL); + struct zip_t *zip = zip_open(ZIPNAME, ZIP_DEFAULT_COMPRESSION_LEVEL, 'a'); + assert(zip != NULL); - assert(0 == zip_entry_open(zip, "test\\test-2.txt")); - assert(0 == zip_entry_write(zip, TESTDATA2, strlen(TESTDATA2))); - assert(0 == zip_entry_close(zip)); + assert(0 == zip_entry_open(zip, "test\\test-2.txt")); + assert(0 == strcmp(zip_entry_name(zip), "test/test-2.txt")); + assert(total_entries == zip_entry_index(zip)); + assert(0 == zip_entry_write(zip, TESTDATA2, strlen(TESTDATA2))); + assert(strlen(TESTDATA2) == zip_entry_size(zip)); + assert(CRC32DATA2 == zip_entry_crc32(zip)); - zip_close(zip); + ++total_entries; + assert(0 == zip_entry_close(zip)); + + assert(0 == zip_entry_open(zip, "test\\empty/")); + assert(0 == strcmp(zip_entry_name(zip), "test/empty/")); + assert(0 == zip_entry_size(zip)); + assert(0 == zip_entry_crc32(zip)); + + assert(total_entries == zip_entry_index(zip)); + ++total_entries; + assert(0 == zip_entry_close(zip)); + + assert(0 == zip_entry_open(zip, "empty/")); + assert(0 == strcmp(zip_entry_name(zip), "empty/")); + assert(0 == zip_entry_size(zip)); + assert(0 == zip_entry_crc32(zip)); + + assert(total_entries == zip_entry_index(zip)); + ++total_entries; + assert(0 == zip_entry_close(zip)); + + zip_close(zip); } static void test_read(void) { - char *buf = NULL; - size_t bufsize; - struct zip_t *zip = zip_open(ZIPNAME, 0, 'r'); - assert(zip != NULL); + char *buf = NULL; + ssize_t bufsize; + size_t buftmp; + struct zip_t *zip = zip_open(ZIPNAME, 0, 'r'); + assert(zip != NULL); - assert(0 == zip_entry_open(zip, "test\\test-1.txt")); - assert(0 == zip_entry_read(zip, (void **)&buf, &bufsize)); - assert(bufsize == strlen(TESTDATA1)); - assert(0 == strncmp(buf, TESTDATA1, bufsize)); - assert(0 == zip_entry_close(zip)); - free(buf); - buf = NULL; - bufsize = 0; + assert(0 == zip_entry_open(zip, "test\\test-1.txt")); + assert(strlen(TESTDATA1) == zip_entry_size(zip)); + assert(CRC32DATA1 == zip_entry_crc32(zip)); - assert(0 == zip_entry_open(zip, "test/test-2.txt")); - assert(0 == zip_entry_read(zip, (void **)&buf, &bufsize)); - assert(bufsize == strlen(TESTDATA2)); - assert(0 == strncmp(buf, TESTDATA2, bufsize)); - assert(0 == zip_entry_close(zip)); - free(buf); - buf = NULL; - bufsize = 0; + bufsize = zip_entry_read(zip, (void **)&buf, &buftmp); + assert(bufsize == strlen(TESTDATA1)); + assert((size_t)bufsize == buftmp); + assert(0 == strncmp(buf, TESTDATA1, bufsize)); + assert(0 == zip_entry_close(zip)); + free(buf); + buf = NULL; + + assert(0 == zip_entry_open(zip, "test/test-2.txt")); + assert(strlen(TESTDATA2) == zip_entry_size(zip)); + assert(CRC32DATA2 == zip_entry_crc32(zip)); - zip_close(zip); + bufsize = zip_entry_read(zip, (void **)&buf, NULL); + assert((size_t)bufsize == strlen(TESTDATA2)); + assert(0 == strncmp(buf, TESTDATA2, (size_t)bufsize)); + assert(0 == zip_entry_close(zip)); + free(buf); + buf = NULL; + bufsize = 0; + + assert(0 == zip_entry_open(zip, "test\\empty/")); + assert(0 == strcmp(zip_entry_name(zip), "test/empty/")); + assert(0 == zip_entry_size(zip)); + assert(0 == zip_entry_crc32(zip)); + assert(0 == zip_entry_close(zip)); + + buftmp = strlen(TESTDATA2); + buf = calloc(buftmp, sizeof(char)); + assert(0 == zip_entry_open(zip, "test/test-2.txt")); + + bufsize = zip_entry_noallocread(zip, (void *)buf, buftmp); + assert(buftmp == (size_t)bufsize); + assert(0 == strncmp(buf, TESTDATA2, buftmp)); + assert(0 == zip_entry_close(zip)); + free(buf); + buf = NULL; + + buftmp = strlen(TESTDATA1); + buf = calloc(buftmp, sizeof(char)); + assert(0 == zip_entry_open(zip, "test/test-1.txt")); + + bufsize = zip_entry_noallocread(zip, (void *)buf, buftmp); + assert(buftmp == (size_t)bufsize); + assert(0 == strncmp(buf, TESTDATA1, buftmp)); + assert(0 == zip_entry_close(zip)); + free(buf); + buf = NULL; + bufsize = 0; + + zip_close(zip); } struct buffer_t { - char *data; - size_t size; + char *data; + size_t size; }; static size_t on_extract(void *arg, unsigned long long offset, const void *data, size_t size) { - struct buffer_t *buf = (struct buffer_t *)arg; - buf->data = realloc(buf->data, buf->size + size + 1); - assert(NULL != buf->data); + UNUSED(offset); - memcpy(&(buf->data[buf->size]), data, size); - buf->size += size; - buf->data[buf->size] = 0; + struct buffer_t *buf = (struct buffer_t *)arg; + buf->data = realloc(buf->data, buf->size + size + 1); + assert(NULL != buf->data); - return size; + memcpy(&(buf->data[buf->size]), data, size); + buf->size += size; + buf->data[buf->size] = 0; + + return size; } static void test_extract(void) { - struct buffer_t buf = {0}; + struct buffer_t buf; - struct zip_t *zip = zip_open(ZIPNAME, 0, 'r'); - assert(zip != NULL); + struct zip_t *zip = zip_open(ZIPNAME, 0, 'r'); + assert(zip != NULL); + memset((void *)&buf, 0, sizeof(struct buffer_t)); - assert(0 == zip_entry_open(zip, "test/test-1.txt")); - assert(0 == zip_entry_extract(zip, on_extract, &buf)); + assert(0 == zip_entry_open(zip, "test/test-1.txt")); + assert(0 == zip_entry_extract(zip, on_extract, &buf)); - assert(buf.size == strlen(TESTDATA1)); - assert(0 == strncmp(buf.data, TESTDATA1, buf.size)); + assert(buf.size == strlen(TESTDATA1)); + assert(0 == strncmp(buf.data, TESTDATA1, buf.size)); + assert(0 == zip_entry_close(zip)); + free(buf.data); + buf.data = NULL; + buf.size = 0; + + zip_close(zip); +} + +static void test_total_entries(void) { + struct zip_t *zip = zip_open(ZIPNAME, 0, 'r'); + assert(zip != NULL); + + int n = zip_total_entries(zip); + zip_close(zip); + + assert(n == total_entries); +} + +static void test_entry_name(void) { + struct zip_t *zip = zip_open(ZIPNAME, 0, 'r'); + assert(zip != NULL); + + assert(zip_entry_name(zip) == NULL); + + assert(0 == zip_entry_open(zip, "test\\test-1.txt")); + assert(NULL != zip_entry_name(zip)); + assert(0 == strcmp(zip_entry_name(zip), "test/test-1.txt")); + assert(strlen(TESTDATA1) == zip_entry_size(zip)); + assert(CRC32DATA1 == zip_entry_crc32(zip)); + assert(0 == zip_entry_index(zip)); + + assert(0 == zip_entry_close(zip)); + + assert(0 == zip_entry_open(zip, "test/test-2.txt")); + assert(NULL != zip_entry_name(zip)); + assert(0 == strcmp(zip_entry_name(zip), "test/test-2.txt")); + assert(strlen(TESTDATA2) == zip_entry_size(zip)); + assert(CRC32DATA2 == zip_entry_crc32(zip)); + assert(1 == zip_entry_index(zip)); + + assert(0 == zip_entry_close(zip)); + + zip_close(zip); +} + +static void test_entry_index(void) { + struct zip_t *zip = zip_open(ZIPNAME, 0, 'r'); + assert(zip != NULL); + + assert(0 == zip_entry_open(zip, "test\\test-1.txt")); + assert(0 == zip_entry_index(zip)); + assert(0 == strcmp(zip_entry_name(zip), "test/test-1.txt")); + assert(strlen(TESTDATA1) == zip_entry_size(zip)); + assert(CRC32DATA1 == zip_entry_crc32(zip)); + assert(0 == zip_entry_close(zip)); + + assert(0 == zip_entry_open(zip, "test/test-2.txt")); + assert(1 == zip_entry_index(zip)); + assert(0 == strcmp(zip_entry_name(zip), "test/test-2.txt")); + assert(strlen(TESTDATA2) == zip_entry_size(zip)); + assert(CRC32DATA2 == zip_entry_crc32(zip)); + assert(0 == zip_entry_close(zip)); + + zip_close(zip); +} + +static void test_entry_openbyindex(void) { + struct zip_t *zip = zip_open(ZIPNAME, 0, 'r'); + assert(zip != NULL); + + assert(0 == zip_entry_openbyindex(zip, 1)); + assert(1 == zip_entry_index(zip)); + assert(strlen(TESTDATA2) == zip_entry_size(zip)); + assert(CRC32DATA2 == zip_entry_crc32(zip)); + assert(0 == strcmp(zip_entry_name(zip), "test/test-2.txt")); + assert(0 == zip_entry_close(zip)); + + assert(0 == zip_entry_openbyindex(zip, 0)); + assert(0 == zip_entry_index(zip)); + assert(strlen(TESTDATA1) == zip_entry_size(zip)); + assert(CRC32DATA1 == zip_entry_crc32(zip)); + assert(0 == strcmp(zip_entry_name(zip), "test/test-1.txt")); + assert(0 == zip_entry_close(zip)); + + zip_close(zip); +} + +static void test_list_entries(void) { + struct zip_t *zip = zip_open(ZIPNAME, 0, 'r'); + assert(zip != NULL); + + int i = 0, n = zip_total_entries(zip); + for (; i < n; ++i) { + assert(0 == zip_entry_openbyindex(zip, i)); + fprintf(stdout, "[%d]: %s", i, zip_entry_name(zip)); + if (zip_entry_isdir(zip)) { + fprintf(stdout, " (DIR)"); + } + fprintf(stdout, "\n"); assert(0 == zip_entry_close(zip)); - free(buf.data); - buf.data = NULL; - buf.size = 0; + } - zip_close(zip); + zip_close(zip); +} + +static void test_fwrite(void) { + const char *filename = WFILE; + FILE *stream = NULL; + struct zip_t *zip = NULL; +#if defined(_MSC_VER) + if (0 != fopen_s(&stream, filename, "w+")) +#else + if (!(stream = fopen(filename, "w+"))) +#endif + { + // Cannot open filename + fprintf(stdout, "Cannot open filename\n"); + assert(0 == -1); + } + fwrite(TESTDATA1, sizeof(char), strlen(TESTDATA1), stream); + assert(0 == fclose(stream)); + + zip = zip_open(ZIPNAME, 9, 'w'); + assert(zip != NULL); + assert(0 == zip_entry_open(zip, WFILE)); + assert(0 == zip_entry_fwrite(zip, WFILE)); + assert(0 == zip_entry_close(zip)); + + zip_close(zip); + remove(WFILE); + remove(ZIPNAME); +} + +static void test_exe_permissions(void) { +#if defined(_WIN32) || defined(__WIN32__) +#else + struct MZ_FILE_STAT_STRUCT file_stats; + const char *filenames[] = {XFILE}; + FILE *f = fopen(XFILE, "w"); + fclose(f); + chmod(XFILE, XMODE); + + remove(ZIPNAME); + + assert(0 == zip_create(ZIPNAME, filenames, 1)); + + remove(XFILE); + + assert(0 == zip_extract(ZIPNAME, ".", NULL, NULL)); + + assert(0 == MZ_FILE_STAT(XFILE, &file_stats)); + assert(XMODE == file_stats.st_mode); + + remove(XFILE); + remove(ZIPNAME); +#endif +} + +static void test_read_permissions(void) { +#if defined(_MSC_VER) +#else + + struct MZ_FILE_STAT_STRUCT file_stats; + const char *filenames[] = {RFILE}; + FILE *f = fopen(RFILE, "w"); + fclose(f); + chmod(RFILE, RMODE); + + remove(ZIPNAME); + + assert(0 == zip_create(ZIPNAME, filenames, 1)); + + // chmod from 444 to 666 to be able delete the file on windows + chmod(RFILE, WMODE); + remove(RFILE); + + assert(0 == zip_extract(ZIPNAME, ".", NULL, NULL)); + + assert(0 == MZ_FILE_STAT(RFILE, &file_stats)); + assert(RMODE == file_stats.st_mode); + + chmod(RFILE, WMODE); + remove(RFILE); + remove(ZIPNAME); +#endif +} + +static void test_write_permissions(void) { +#if defined(_MSC_VER) +#else + + struct MZ_FILE_STAT_STRUCT file_stats; + const char *filenames[] = {WFILE}; + FILE *f = fopen(WFILE, "w"); + fclose(f); + chmod(WFILE, WMODE); + + remove(ZIPNAME); + + assert(0 == zip_create(ZIPNAME, filenames, 1)); + + remove(WFILE); + + assert(0 == zip_extract(ZIPNAME, ".", NULL, NULL)); + + assert(0 == MZ_FILE_STAT(WFILE, &file_stats)); + assert(WMODE == file_stats.st_mode); + + remove(WFILE); + remove(ZIPNAME); +#endif +} + +static void test_mtime(void) { + struct MZ_FILE_STAT_STRUCT file_stat1, file_stat2; + + const char *filename = WFILE; + FILE *stream = NULL; + struct zip_t *zip = NULL; +#if defined(_MSC_VER) + if (0 != fopen_s(&stream, filename, "w+")) +#else + if (!(stream = fopen(filename, "w+"))) +#endif + { + // Cannot open filename + fprintf(stdout, "Cannot open filename\n"); + assert(0 == -1); + } + fwrite(TESTDATA1, sizeof(char), strlen(TESTDATA1), stream); + assert(0 == fclose(stream)); + + memset(&file_stat1, 0, sizeof(file_stat1)); + memset(&file_stat2, 0, sizeof(file_stat2)); + zip = zip_open(ZIPNAME, ZIP_DEFAULT_COMPRESSION_LEVEL, 'w'); + assert(zip != NULL); + assert(0 == zip_entry_open(zip, filename)); + assert(0 == zip_entry_fwrite(zip, filename)); + assert(0 == zip_entry_close(zip)); + zip_close(zip); + + assert(0 == MZ_FILE_STAT(filename, &file_stat1)); + + remove(filename); + assert(0 == zip_extract(ZIPNAME, ".", NULL, NULL)); + assert(0 == MZ_FILE_STAT(filename, &file_stat2)); + fprintf(stdout, "file_stat1.st_mtime: %lu\n", file_stat1.st_mtime); + fprintf(stdout, "file_stat2.st_mtime: %lu\n", file_stat2.st_mtime); + assert(labs(file_stat1.st_mtime - file_stat2.st_mtime) <= 1); + + remove(filename); + remove(ZIPNAME); } int main(int argc, char *argv[]) { - test_write(); - test_append(); - test_read(); - test_extract(); + UNUSED(argc); + UNUSED(argv); - return remove(ZIPNAME); + remove(ZIPNAME); + + test_write(); + test_append(); + test_read(); + test_extract(); + test_total_entries(); + test_entry_name(); + test_entry_index(); + test_entry_openbyindex(); + test_list_entries(); + test_fwrite(); + test_read_permissions(); + test_write_permissions(); + test_exe_permissions(); + test_mtime(); + + remove(ZIPNAME); + return 0; } diff --git a/contrib/zip/test/test_miniz.c b/contrib/zip/test/test_miniz.c new file mode 100644 index 000000000..ebc0564dc --- /dev/null +++ b/contrib/zip/test/test_miniz.c @@ -0,0 +1,104 @@ +// Demonstrates miniz.c's compress() and uncompress() functions +// (same as zlib's). Public domain, May 15 2011, Rich Geldreich, +// richgel99@gmail.com. See "unlicense" statement at the end of tinfl.c. + +#include +#include + +typedef unsigned char uint8; +typedef unsigned short uint16; +typedef unsigned int uint; + +// The string to compress. +static const char *s_pStr = + "Good morning Dr. Chandra. This is Hal. I am ready for my first lesson." + "Good morning Dr. Chandra. This is Hal. I am ready for my first lesson." + "Good morning Dr. Chandra. This is Hal. I am ready for my first lesson." + "Good morning Dr. Chandra. This is Hal. I am ready for my first lesson." + "Good morning Dr. Chandra. This is Hal. I am ready for my first lesson." + "Good morning Dr. Chandra. This is Hal. I am ready for my first lesson." + "Good morning Dr. Chandra. This is Hal. I am ready for my first lesson."; + +int main(int argc, char *argv[]) { + uint step = 0; + int cmp_status; + uLong src_len = (uLong)strlen(s_pStr); + uLong cmp_len = compressBound(src_len); + uLong uncomp_len = src_len; + uint8 *pCmp, *pUncomp; + uint total_succeeded = 0; + (void)argc, (void)argv; + + printf("miniz.c version: %s\n", MZ_VERSION); + + do { + // Allocate buffers to hold compressed and uncompressed data. + pCmp = (mz_uint8 *)malloc((size_t)cmp_len); + pUncomp = (mz_uint8 *)malloc((size_t)src_len); + if ((!pCmp) || (!pUncomp)) { + printf("Out of memory!\n"); + return EXIT_FAILURE; + } + + // Compress the string. + cmp_status = + compress(pCmp, &cmp_len, (const unsigned char *)s_pStr, src_len); + if (cmp_status != Z_OK) { + printf("compress() failed!\n"); + free(pCmp); + free(pUncomp); + return EXIT_FAILURE; + } + + printf("Compressed from %u to %u bytes\n", (mz_uint32)src_len, + (mz_uint32)cmp_len); + + if (step) { + // Purposely corrupt the compressed data if fuzzy testing (this is a + // very crude fuzzy test). + uint n = 1 + (rand() % 3); + while (n--) { + uint i = rand() % cmp_len; + pCmp[i] ^= (rand() & 0xFF); + } + } + + // Decompress. + cmp_status = uncompress(pUncomp, &uncomp_len, pCmp, cmp_len); + total_succeeded += (cmp_status == Z_OK); + + if (step) { + printf("Simple fuzzy test: step %u total_succeeded: %u\n", step, + total_succeeded); + } else { + if (cmp_status != Z_OK) { + printf("uncompress failed!\n"); + free(pCmp); + free(pUncomp); + return EXIT_FAILURE; + } + + printf("Decompressed from %u to %u bytes\n", (mz_uint32)cmp_len, + (mz_uint32)uncomp_len); + + // Ensure uncompress() returned the expected data. + if ((uncomp_len != src_len) || + (memcmp(pUncomp, s_pStr, (size_t)src_len))) { + printf("Decompression failed!\n"); + free(pCmp); + free(pUncomp); + return EXIT_FAILURE; + } + } + + free(pCmp); + free(pUncomp); + + step++; + + // Keep on fuzzy testing if there's a non-empty command line. + } while (argc >= 2); + + printf("Success.\n"); + return EXIT_SUCCESS; +} diff --git a/contrib/zlib/CMakeLists.txt b/contrib/zlib/CMakeLists.txt index 5f1368adb..9d1fcc943 100644 --- a/contrib/zlib/CMakeLists.txt +++ b/contrib/zlib/CMakeLists.txt @@ -5,12 +5,17 @@ set(CMAKE_ALLOW_LOOSE_LOOP_CONSTRUCTS ON) # See http://www.cmake.org/cmake/help/v3.0/policy/CMP0048.html cmake_policy(PUSH) if(CMAKE_MAJOR_VERSION GREATER 2) - cmake_policy(SET CMP0048 OLD) + cmake_policy(SET CMP0048 NEW) endif() project(zlib C) +SET (ZLIB_VERSION_MAJOR 1) +SET (ZLIB_VERSION_MINOR 2) +SET (ZLIB_VERSION_PATCH 11) +SET (ZLIB_VERSION ${ZLIB_VERSION_MAJOR}.${ZLIB_VERSION_MINOR}.${ZLIB_VERSION_PATCH}) +SET (ZLIB_SOVERSION 1) +SET (PROJECT_VERSION "${ZLIB_VERSION}") cmake_policy(POP) -set(VERSION "1.2.11.1") option(ASM686 "Enable building i686 assembly implementation") option(AMD64 "Enable building amd64 assembly implementation") diff --git a/contrib/zlib/contrib/dotzlib/DotZLib/ChecksumImpl.cs b/contrib/zlib/contrib/dotzlib/DotZLib/ChecksumImpl.cs index b110dae6a..cd1ef44eb 100644 --- a/contrib/zlib/contrib/dotzlib/DotZLib/ChecksumImpl.cs +++ b/contrib/zlib/contrib/dotzlib/DotZLib/ChecksumImpl.cs @@ -1,5 +1,5 @@ // -// © Copyright Henrik Ravn 2004 +// © Copyright Henrik Ravn 2004 // // Use, modification and distribution are subject to the Boost Software License, Version 1.0. // (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt) @@ -199,4 +199,4 @@ namespace DotZLib } #endregion -} \ No newline at end of file +} diff --git a/contrib/zlib/contrib/dotzlib/DotZLib/CircularBuffer.cs b/contrib/zlib/contrib/dotzlib/DotZLib/CircularBuffer.cs index 9c8d60195..e7a88b9f4 100644 --- a/contrib/zlib/contrib/dotzlib/DotZLib/CircularBuffer.cs +++ b/contrib/zlib/contrib/dotzlib/DotZLib/CircularBuffer.cs @@ -1,5 +1,5 @@ // -// © Copyright Henrik Ravn 2004 +// © Copyright Henrik Ravn 2004 // // Use, modification and distribution are subject to the Boost Software License, Version 1.0. // (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt) diff --git a/contrib/zlib/contrib/dotzlib/DotZLib/CodecBase.cs b/contrib/zlib/contrib/dotzlib/DotZLib/CodecBase.cs index b0eb78a02..6ef6d8fab 100644 --- a/contrib/zlib/contrib/dotzlib/DotZLib/CodecBase.cs +++ b/contrib/zlib/contrib/dotzlib/DotZLib/CodecBase.cs @@ -1,5 +1,5 @@ // -// © Copyright Henrik Ravn 2004 +// © Copyright Henrik Ravn 2004 // // Use, modification and distribution are subject to the Boost Software License, Version 1.0. // (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt) diff --git a/contrib/zlib/contrib/dotzlib/DotZLib/Deflater.cs b/contrib/zlib/contrib/dotzlib/DotZLib/Deflater.cs index 9039f41f6..778a6794d 100644 --- a/contrib/zlib/contrib/dotzlib/DotZLib/Deflater.cs +++ b/contrib/zlib/contrib/dotzlib/DotZLib/Deflater.cs @@ -1,5 +1,5 @@ // -// © Copyright Henrik Ravn 2004 +// © Copyright Henrik Ravn 2004 // // Use, modification and distribution are subject to the Boost Software License, Version 1.0. // (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt) diff --git a/contrib/zlib/contrib/dotzlib/DotZLib/DotZLib.cs b/contrib/zlib/contrib/dotzlib/DotZLib/DotZLib.cs index 90c7c3b38..a48ed4974 100644 --- a/contrib/zlib/contrib/dotzlib/DotZLib/DotZLib.cs +++ b/contrib/zlib/contrib/dotzlib/DotZLib/DotZLib.cs @@ -1,5 +1,5 @@ // -// © Copyright Henrik Ravn 2004 +// © Copyright Henrik Ravn 2004 // // Use, modification and distribution are subject to the Boost Software License, Version 1.0. // (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt) diff --git a/contrib/zlib/contrib/dotzlib/DotZLib/GZipStream.cs b/contrib/zlib/contrib/dotzlib/DotZLib/GZipStream.cs index f0eada1d2..07b2f7a9b 100644 --- a/contrib/zlib/contrib/dotzlib/DotZLib/GZipStream.cs +++ b/contrib/zlib/contrib/dotzlib/DotZLib/GZipStream.cs @@ -1,5 +1,5 @@ // -// © Copyright Henrik Ravn 2004 +// © Copyright Henrik Ravn 2004 // // Use, modification and distribution are subject to the Boost Software License, Version 1.0. // (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt) diff --git a/contrib/zlib/contrib/dotzlib/DotZLib/Inflater.cs b/contrib/zlib/contrib/dotzlib/DotZLib/Inflater.cs index d295f2680..8e900ae20 100644 --- a/contrib/zlib/contrib/dotzlib/DotZLib/Inflater.cs +++ b/contrib/zlib/contrib/dotzlib/DotZLib/Inflater.cs @@ -1,5 +1,5 @@ // -// © Copyright Henrik Ravn 2004 +// © Copyright Henrik Ravn 2004 // // Use, modification and distribution are subject to the Boost Software License, Version 1.0. // (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt) diff --git a/doc/dox.h b/doc/dox.h index d63c8a806..910e77eae 100644 --- a/doc/dox.h +++ b/doc/dox.h @@ -1173,6 +1173,18 @@ float4 PimpMyPixel (float4 prev) @endcode +@section shdacc How to access shader-code from a texture (AI_MATKEY_GLOBAL_SHADERLANG and AI_MATKEY_SHADER_VERTEX, ...) + +You can get assigned shader sources by using the following material keys: + +
  • AI_MATKEY_GLOBAL_SHADERLANG
    • To get the used shader language. +
  • AI_MATKEY_SHADER_VERTEX
    • Assigned vertex shader code stored as a string. +
  • AI_MATKEY_SHADER_FRAGMENT
    • Assigned fragment shader code stored as a string. +
  • AI_MATKEY_SHADER_GEO
    • Assigned geometry shader code stored as a string. +
  • AI_MATKEY_SHADER_TESSELATION
    • Assigned tesselation shader code stored as a string. +
  • AI_MATKEY_SHADER_PRIMITIVE
    • Assigned primitive shader code stored as a string. +
  • AI_MATKEY_SHADER_COMPUTE
    • Assigned compute shader code stored as a string. + */ diff --git a/include/assimp/Exporter.hpp b/include/assimp/Exporter.hpp index bf0096e7e..ea0303e80 100644 --- a/include/assimp/Exporter.hpp +++ b/include/assimp/Exporter.hpp @@ -190,7 +190,7 @@ public: * @note Use aiCopyScene() to get a modifiable copy of a previously * imported scene. */ const aiExportDataBlob* ExportToBlob(const aiScene* pScene, const char* pFormatId, - unsigned int pPreprocessing = 0u, const ExportProperties* = nullptr); + unsigned int pPreprocessing = 0u, const ExportProperties* pProperties = nullptr); const aiExportDataBlob* ExportToBlob( const aiScene* pScene, const std::string& pFormatId, unsigned int pPreprocessing = 0u, const ExportProperties* pProperties = nullptr); diff --git a/include/assimp/ai_assert.h b/include/assimp/ai_assert.h index daae23454..e5de5d3f3 100644 --- a/include/assimp/ai_assert.h +++ b/include/assimp/ai_assert.h @@ -46,9 +46,12 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #ifdef ASSIMP_BUILD_DEBUG # include -# define ai_assert(expression) assert(expression) +# define ai_assert(expression) assert( expression ) +# define ai_assert_entry() assert( false ) #else # define ai_assert(expression) -#endif // +# define ai_assert_entry() +#endif // ASSIMP_BUILD_DEBUG #endif // AI_ASSERT_H_INC + diff --git a/include/assimp/color4.inl b/include/assimp/color4.inl index 3192d55f3..afa53dcb5 100644 --- a/include/assimp/color4.inl +++ b/include/assimp/color4.inl @@ -85,6 +85,8 @@ AI_FORCE_INLINE TReal aiColor4t::operator[](unsigned int i) const { return g; case 2: return b; + case 3: + return a; default: break; } @@ -100,6 +102,8 @@ AI_FORCE_INLINE TReal& aiColor4t::operator[](unsigned int i) { return g; case 2: return b; + case 3: + return a; default: break; } diff --git a/include/assimp/config.h.in b/include/assimp/config.h.in index a37ff0b8c..c42aa63da 100644 --- a/include/assimp/config.h.in +++ b/include/assimp/config.h.in @@ -651,13 +651,28 @@ enum aiComponent // --------------------------------------------------------------------------- /** @brief Set whether the fbx importer will use the legacy embedded texture naming. -* -* The default value is false (0) -* Property type: bool -*/ + * + * The default value is false (0) + * Property type: bool + */ #define AI_CONFIG_IMPORT_FBX_EMBEDDED_TEXTURES_LEGACY_NAMING \ "AI_CONFIG_IMPORT_FBX_EMBEDDED_TEXTURES_LEGACY_NAMING" - + +// --------------------------------------------------------------------------- +/** @brief Set wether the importer shall not remove empty bones. + * + * Empty bone are often used to define connections for other models. + */ +#define AI_CONFIG_IMPORT_REMOVE_EMPTY_BONES \ + "AI_CONFIG_IMPORT_REMOVE_EMPTY_BONES" + + +// --------------------------------------------------------------------------- +/** @brief Set wether the FBX importer shall convert the unit from cm to m. + */ +#define AI_CONFIG_FBX_CONVERT_TO_M \ + "AI_CONFIG_FBX_CONVERT_TO_M" + // --------------------------------------------------------------------------- /** @brief Set the vertex animation keyframe to be imported * diff --git a/include/assimp/defs.h b/include/assimp/defs.h index 4a177e3c3..2631263f5 100644 --- a/include/assimp/defs.h +++ b/include/assimp/defs.h @@ -293,7 +293,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #ifndef _MSC_VER # define AI_NO_EXCEPT noexcept #else -# if (_MSC_VER == 1915 ) +# if (_MSC_VER >= 1915 ) # define AI_NO_EXCEPT noexcept # else # define AI_NO_EXCEPT diff --git a/include/assimp/irrXMLWrapper.h b/include/assimp/irrXMLWrapper.h index ec8ee7c76..290cf67ea 100644 --- a/include/assimp/irrXMLWrapper.h +++ b/include/assimp/irrXMLWrapper.h @@ -91,14 +91,15 @@ public: stream->Read(&data[0],data.size(),1); // Remove null characters from the input sequence otherwise the parsing will utterly fail - unsigned int size = 0; - unsigned int size_max = static_cast(data.size()); - for(unsigned int i = 0; i < size_max; i++) { - if(data[i] != '\0') { - data[size++] = data[i]; - } + // std::find is usually much faster than manually iterating + // It is very unlikely that there will be any null characters + auto null_char_iter = std::find(data.begin(), data.end(), '\0'); + + while (null_char_iter != data.end()) + { + null_char_iter = data.erase(null_char_iter); + null_char_iter = std::find(null_char_iter, data.end(), '\0'); } - data.resize(size); BaseImporter::ConvertToUTF8(data); } diff --git a/include/assimp/material.h b/include/assimp/material.h index 81b5fb05f..4b5a1293d 100644 --- a/include/assimp/material.h +++ b/include/assimp/material.h @@ -198,8 +198,6 @@ enum aiTextureType */ aiTextureType_NONE = 0x0, - - /** The texture is combined with the result of the diffuse * lighting equation. */ @@ -278,7 +276,7 @@ enum aiTextureType * * A texture reference that does not match any of the definitions * above is considered to be 'unknown'. It is still imported, - * but is excluded from any further postprocessing. + * but is excluded from any further post-processing. */ aiTextureType_UNKNOWN = 0xC, @@ -375,7 +373,7 @@ enum aiShadingMode */ enum aiTextureFlags { - /** The texture's color values have to be inverted (componentwise 1-n) + /** The texture's color values have to be inverted (component-wise 1-n) */ aiTextureFlags_Invert = 0x1, @@ -902,6 +900,7 @@ extern "C" { #define AI_MATKEY_ENABLE_WIREFRAME "$mat.wireframe",0,0 #define AI_MATKEY_BLEND_FUNC "$mat.blend",0,0 #define AI_MATKEY_OPACITY "$mat.opacity",0,0 +#define AI_MATKEY_TRANSPARENCYFACTOR "$mat.transparencyfactor",0,0 #define AI_MATKEY_BUMPSCALING "$mat.bumpscaling",0,0 #define AI_MATKEY_SHININESS "$mat.shininess",0,0 #define AI_MATKEY_REFLECTIVITY "$mat.reflectivity",0,0 @@ -914,6 +913,13 @@ extern "C" { #define AI_MATKEY_COLOR_TRANSPARENT "$clr.transparent",0,0 #define AI_MATKEY_COLOR_REFLECTIVE "$clr.reflective",0,0 #define AI_MATKEY_GLOBAL_BACKGROUND_IMAGE "?bg.global",0,0 +#define AI_MATKEY_GLOBAL_SHADERLANG "?sh.lang",0,0 +#define AI_MATKEY_SHADER_VERTEX "?sh.vs",0,0 +#define AI_MATKEY_SHADER_FRAGMENT "?sh.fs",0,0 +#define AI_MATKEY_SHADER_GEO "?sh.gs",0,0 +#define AI_MATKEY_SHADER_TESSELATION "?sh.ts",0,0 +#define AI_MATKEY_SHADER_PRIMITIVE "?sh.ps",0,0 +#define AI_MATKEY_SHADER_COMPUTE "?sh.cs",0,0 // --------------------------------------------------------------------------- // Pure key names for all texture-related properties @@ -1457,8 +1463,6 @@ inline aiReturn aiGetMaterialInteger(const C_STRUCT aiMaterial* pMat, #endif //!__cplusplus - - // --------------------------------------------------------------------------- /** @brief Retrieve a color value from the material property table * diff --git a/include/assimp/mesh.h b/include/assimp/mesh.h index 8d539c59e..36f3ed2af 100644 --- a/include/assimp/mesh.h +++ b/include/assimp/mesh.h @@ -469,10 +469,10 @@ struct aiAnimMesh { // fixme consider moving this to the ctor initializer list as well for( unsigned int a = 0; a < AI_MAX_NUMBER_OF_TEXTURECOORDS; a++){ - mTextureCoords[a] = NULL; + mTextureCoords[a] = nullptr; } for( unsigned int a = 0; a < AI_MAX_NUMBER_OF_COLOR_SETS; a++) { - mColors[a] = NULL; + mColors[a] = nullptr; } } @@ -493,34 +493,34 @@ struct aiAnimMesh /** Check whether the anim mesh overrides the vertex positions * of its host mesh*/ bool HasPositions() const { - return mVertices != NULL; + return mVertices != nullptr; } /** Check whether the anim mesh overrides the vertex normals * of its host mesh*/ bool HasNormals() const { - return mNormals != NULL; + return mNormals != nullptr; } /** Check whether the anim mesh overrides the vertex tangents * and bitangents of its host mesh. As for aiMesh, * tangents and bitangents always go together. */ bool HasTangentsAndBitangents() const { - return mTangents != NULL; + return mTangents != nullptr; } /** Check whether the anim mesh overrides a particular * set of vertex colors on his host mesh. * @param pIndex 0= AI_MAX_NUMBER_OF_COLOR_SETS ? false : mColors[pIndex] != NULL; + return pIndex >= AI_MAX_NUMBER_OF_COLOR_SETS ? false : mColors[pIndex] != nullptr; } /** Check whether the anim mesh overrides a particular * set of texture coordinates on his host mesh. * @param pIndex 0= AI_MAX_NUMBER_OF_TEXTURECOORDS ? false : mTextureCoords[pIndex] != NULL; + return pIndex >= AI_MAX_NUMBER_OF_TEXTURECOORDS ? false : mTextureCoords[pIndex] != nullptr; } #endif diff --git a/include/assimp/scene.h b/include/assimp/scene.h index de0239702..e973f6c5b 100644 --- a/include/assimp/scene.h +++ b/include/assimp/scene.h @@ -389,6 +389,14 @@ struct aiScene //! Returns an embedded texture const aiTexture* GetEmbeddedTexture(const char* filename) const { + // lookup using texture ID (if referenced like: "*1", "*2", etc.) + if ('*' == *filename) { + int index = std::atoi(filename + 1); + if (0 > index || mNumTextures <= static_cast(index)) + return nullptr; + return mTextures[index]; + } + // lookup using filename const char* shortFilename = GetShortFilename(filename); for (unsigned int i = 0; i < mNumTextures; i++) { const char* shortTextureFilename = GetShortFilename(mTextures[i]->mFilename.C_Str()); diff --git a/packaging/windows-innosetup/script.iss b/packaging/windows-innosetup/script.iss deleted file mode 100644 index 695740679..000000000 --- a/packaging/windows-innosetup/script.iss +++ /dev/null @@ -1,103 +0,0 @@ -; Setup script for use with Inno Setup. - -[Setup] -AppName=Open Asset Import Library - SDK -AppVerName=Open Asset Import Library - SDK (v4.1.0) -DefaultDirName={pf}\Assimp -DefaultGroupName=Assimp -UninstallDisplayIcon={app}\bin\x86\assimp.exe -OutputDir=out -AppCopyright=Assimp Development Team -SetupIconFile=..\..\tools\shared\assimp_tools_icon.ico -WizardImageFile=compiler:WizModernImage-IS.BMP -WizardSmallImageFile=compiler:WizModernSmallImage-IS.BMP -LicenseFile=License.rtf -OutputBaseFileName=assimp-sdk-4.1.0-setup -VersionInfoVersion=4.1.0.0 -VersionInfoTextVersion=4.1.0 -VersionInfoCompany=Assimp Development Team -ArchitecturesInstallIn64BitMode=x64 - -[Types] -Name: "full"; Description: "Full installation" -Name: "compact"; Description: "Compact installation, no test models or language bindings" -Name: "custom"; Description: "Custom installation"; Flags: iscustom - -[Components] -Name: "main"; Description: "Main Files (32 and 64 Bit)"; Types: full compact custom; Flags: fixed -Name: "tools"; Description: "Asset Viewer & Command Line Tools (32 and 64 Bit)"; Types: full compact -Name: "help"; Description: "Help Files"; Types: full compact -Name: "samples"; Description: "Samples"; Types: full -Name: "test"; Description: "Test Models (BSD-licensed)"; Types: full -Name: "test_nonbsd"; Description: "Test Models (other (free) licenses)"; Types: full -;Name: "pyassimp"; Description: "Python Bindings"; Types: full -;Name: "dassimp"; Description: "D Bindings"; Types: full -;Name: "assimp_net"; Description: "C#/.NET Bindings"; Types: full - -[Run] -;Filename: "{app}\stub\vc_redist.x86.exe"; Parameters: "/qb"; StatusMsg: "Installing VS2017 redistributable package (32 Bit)"; Check: not IsWin64 -Filename: "{app}\stub\vc_redist.x64.exe"; Parameters: "/qb"; StatusMsg: "Installing VS2017 redistributable package (64 Bit)"; Check: IsWin64 - -[Files] -Source: "readme_installer.txt"; DestDir: "{app}"; Flags: isreadme - -; Installer stub -;Source: "vc_redist.x86.exe"; DestDir: "{app}\stub\"; Check: not IsWin64 -Source: "vc_redist.x64.exe"; DestDir: "{app}\stub\"; Check: IsWin64 - -; Common stuff -Source: "..\..\CREDITS"; DestDir: "{app}" -Source: "..\..\LICENSE"; DestDir: "{app}" -Source: "..\..\README"; DestDir: "{app}" -Source: "WEB"; DestDir: "{app}" - -Source: "..\..\scripts\*"; DestDir: "{app}\scripts"; Flags: recursesubdirs - -; x86 binaries -;Source: "..\..\bin\release\x86\assimp-vc140-mt.dll"; DestDir: "{app}\bin\x86" -;Source: "..\..\bin\release\x86\assimp_viewer.exe"; DestDir: "{app}\bin\x86"; Components: tools -;Source: "C:\Windows\SysWOW64\D3DCompiler_42.dll"; DestDir: "{app}\bin\x86"; Components: tools -;Source: "C:\Windows\SysWOW64\D3DX9_42.dll"; DestDir: "{app}\bin\x86"; Components: tools -;Source: "..\..\bin\release\x86\assimp.exe"; DestDir: "{app}\bin\x86"; Components: tools - -; x64 binaries -Source: "..\..\bin\release\assimp-vc140-mt.dll"; DestDir: "{app}\bin\x64" -Source: "..\..\bin\release\assimp_viewer.exe"; DestDir: "{app}\bin\x64"; Components: tools -Source: "C:\Windows\SysWOW64\D3DCompiler_42.dll"; DestDir: "{app}\bin\x64"; DestName: "D3DCompiler_42.dll"; Components: tools -Source: "C:\Windows\SysWOW64\D3DX9_42.dll"; DestDir: "{app}\bin\x64"; DestName: "D3DX9_42.dll"; Components: tools -Source: "..\..\bin\release\assimp.exe"; DestDir: "{app}\bin\x64"; Components: tools - -; Documentation -;Source: "..\..\doc\AssimpDoc_Html\AssimpDoc.chm"; DestDir: "{app}\doc"; Components: help -;Source: "..\..\doc\AssimpCmdDoc_Html\AssimpCmdDoc.chm"; DestDir: "{app}\doc"; Components: help -;Source: "..\..\doc\datastructure.xml"; DestDir: "{app}\doc"; Components: help - -; Import libraries -;Source: "..\..\lib\release\x86\assimp.lib"; DestDir: "{app}\lib\x86" -Source: "..\..\lib\release\assimp-vc140-mt.lib"; DestDir: "{app}\lib\x64" - -; Samples -Source: "..\..\samples\*"; DestDir: "{app}\samples"; Flags: recursesubdirs; Components: samples - -; Include files -Source: "..\..\include\*"; DestDir: "{app}\include"; Flags: recursesubdirs - -; dAssimp -;Source: "..\..\port\dAssimp\*"; DestDir: "{app}\port\D"; Flags: recursesubdirs; Components: dassimp - -; Assimp.NET -;Source: "..\..\port\Assimp.NET\*"; DestDir: "{app}\port\C#"; Flags: recursesubdirs; Components: assimp_net - -; PyAssimp -;Source: "..\..\port\PyAssimp\*"; DestDir: "{app}\port\Python"; Excludes: "*.pyc,*.dll"; Flags: recursesubdirs; Components: pyassimp - -; Test repository -;Source: "..\..\test\models\*"; DestDir: "{app}\test\models"; Flags: recursesubdirs; Components: test -;Source: "..\..\test\regression\*"; DestDir: "{app}\test\regression"; Flags: recursesubdirs; Components: test -;Source: "..\..\test\models-nonbsd\*"; DestDir: "{app}\test\models-nonbsd"; Flags: recursesubdirs; Components: test_nonbsd - -[Icons] -Name: "{group}\Assimp Manual"; Filename: "{app}\doc\AssimpDoc.chm" ; Components: help -Name: "{group}\Assimp Command Line Manual"; Filename: "{app}\doc\AssimpCmdDoc.chm"; Components: help -Name: "{group}\AssimpView"; Filename: "{app}\bin\x64\assimp_view.exe"; Components: tools; Check: IsWin64 -Name: "{group}\AssimpView"; Filename: "{app}\bin\x86\assimp_view.exe"; Components: tools; Check: not IsWin64 diff --git a/packaging/windows-innosetup/script_x64.iss b/packaging/windows-innosetup/script_x64.iss new file mode 100644 index 000000000..4d1b67cd0 --- /dev/null +++ b/packaging/windows-innosetup/script_x64.iss @@ -0,0 +1,74 @@ +; Setup script for use with Inno Setup. + +[Setup] +AppName=Open Asset Import Library - SDK +AppVerName=Open Asset Import Library - SDK (v5.0.0) +DefaultDirName={pf}\Assimp +DefaultGroupName=Assimp +UninstallDisplayIcon={app}\bin\x64\assimp.exe +OutputDir=out +AppCopyright=Assimp Development Team +SetupIconFile=..\..\tools\shared\assimp_tools_icon.ico +WizardImageFile=compiler:WizModernImage-IS.BMP +WizardSmallImageFile=compiler:WizModernSmallImage-IS.BMP +LicenseFile=License.rtf +OutputBaseFileName=assimp-sdk-5.0.0-setup +VersionInfoVersion=5.0.0.0 +VersionInfoTextVersion=5.0.0 +VersionInfoCompany=Assimp Development Team +ArchitecturesInstallIn64BitMode=x64 + +[Types] +Name: "full"; Description: "Full installation" +Name: "compact"; Description: "Compact installation, no test models or language bindings" +Name: "custom"; Description: "Custom installation"; Flags: iscustom + +[Components] +Name: "main"; Description: "Main Files ( 64 Bit )"; Types: full compact custom; Flags: fixed +Name: "tools"; Description: "Asset Viewer & Command Line Tools (32 and 64 Bit)"; Types: full compact +Name: "help"; Description: "Help Files"; Types: full compact +Name: "samples"; Description: "Samples"; Types: full +Name: "test"; Description: "Test Models (BSD-licensed)"; Types: full +Name: "test_nonbsd"; Description: "Test Models (other (free) licenses)"; Types: full + +[Run] +Filename: "{app}\stub\vc_redist.x64.exe"; Parameters: "/qb /passive /quiet"; StatusMsg: "Installing VS2017 redistributable package (64 Bit)"; Check: IsWin64 + +[Files] +Source: "readme_installer.txt"; DestDir: "{app}"; Flags: isreadme + +; Installer stub +Source: "vc_redist.x64.exe"; DestDir: "{app}\stub\"; Check: IsWin64 + +; Common stuff +Source: "..\..\CREDITS"; DestDir: "{app}" +Source: "..\..\LICENSE"; DestDir: "{app}" +Source: "..\..\README"; DestDir: "{app}" +Source: "WEB"; DestDir: "{app}" + +Source: "..\..\scripts\*"; DestDir: "{app}\scripts"; Flags: recursesubdirs + +; x64 binaries +Source: "..\..\bin\release\assimp-vc141-mt.dll"; DestDir: "{app}\bin\x64" +Source: "..\..\bin\release\assimp_viewer.exe"; DestDir: "{app}\bin\x64"; Components: tools +Source: "C:\Windows\SysWOW64\D3DCompiler_42.dll"; DestDir: "{app}\bin\x64"; DestName: "D3DCompiler_42.dll"; Components: tools +Source: "C:\Windows\SysWOW64\D3DX9_42.dll"; DestDir: "{app}\bin\x64"; DestName: "D3DX9_42.dll"; Components: tools +Source: "..\..\bin\release\assimp.exe"; DestDir: "{app}\bin\x64"; Components: tools + +; Import libraries +Source: "..\..\lib\release\assimp-vc141-mt.lib"; DestDir: "{app}\lib\x64" + +; Samples +Source: "..\..\samples\*"; DestDir: "{app}\samples"; Flags: recursesubdirs; Components: samples + +; Include files +Source: "..\..\include\*"; DestDir: "{app}\include"; Flags: recursesubdirs + +; CMake files +Source: "..\..\cmake-modules\*"; DestDir: "{app}\cmake-modules"; Flags: recursesubdirs + +[Icons] +; Name: "{group}\Assimp Manual"; Filename: "{app}\doc\AssimpDoc.chm" ; Components: help +; Name: "{group}\Assimp Command Line Manual"; Filename: "{app}\doc\AssimpCmdDoc.chm"; Components: help +; Name: "{group}\AssimpView"; Filename: "{app}\bin\x64\assimp_view.exe"; Components: tools; Check: IsWin64 +; Name: "{group}\AssimpView"; Filename: "{app}\bin\x86\assimp_view.exe"; Components: tools; Check: not IsWin64 diff --git a/packaging/windows-innosetup/script_x86.iss b/packaging/windows-innosetup/script_x86.iss new file mode 100644 index 000000000..d22d23b64 --- /dev/null +++ b/packaging/windows-innosetup/script_x86.iss @@ -0,0 +1,75 @@ +; Setup script for use with Inno Setup. + +[Setup] +AppName=Open Asset Import Library - SDK +AppVerName=Open Asset Import Library - SDK (v5.0.0) +DefaultDirName={pf}\Assimp +DefaultGroupName=Assimp +UninstallDisplayIcon={app}\bin\x86\assimp.exe +OutputDir=out +AppCopyright=Assimp Development Team +SetupIconFile=..\..\tools\shared\assimp_tools_icon.ico +WizardImageFile=compiler:WizModernImage-IS.BMP +WizardSmallImageFile=compiler:WizModernSmallImage-IS.BMP +LicenseFile=License.rtf +OutputBaseFileName=assimp-sdk-5.0.0-setup +VersionInfoVersion=4.1.0.0 +VersionInfoTextVersion=4.1.0 +VersionInfoCompany=Assimp Development Team +;ArchitecturesInstallIn64BitMode=x64 + +[Types] +Name: "full"; Description: "Full installation" +Name: "compact"; Description: "Compact installation, no test models or language bindings" +Name: "custom"; Description: "Custom installation"; Flags: iscustom + +[Components] +Name: "main"; Description: "Main Files (32 and 64 Bit)"; Types: full compact custom; Flags: fixed +Name: "tools"; Description: "Asset Viewer & Command Line Tools (32 and 64 Bit)"; Types: full compact +Name: "help"; Description: "Help Files"; Types: full compact +Name: "samples"; Description: "Samples"; Types: full +Name: "test"; Description: "Test Models (BSD-licensed)"; Types: full +Name: "test_nonbsd"; Description: "Test Models (other (free) licenses)"; Types: full + +[Run] +Filename: "{app}\stub\vc_redist.x86.exe"; Parameters: "/qb /passive /quiet"; StatusMsg: "Installing VS2017 redistributable package (32 Bit)"; Check: not IsWin64 + +[Files] +Source: "readme_installer.txt"; DestDir: "{app}"; Flags: isreadme + +; Installer stub +Source: "vc_redist.x86.exe"; DestDir: "{app}\stub\"; Check: not IsWin64 + +; Common stuff +Source: "..\..\CREDITS"; DestDir: "{app}" +Source: "..\..\LICENSE"; DestDir: "{app}" +Source: "..\..\README"; DestDir: "{app}" +Source: "WEB"; DestDir: "{app}" + +Source: "..\..\scripts\*"; DestDir: "{app}\scripts"; Flags: recursesubdirs + +; x86 binaries +Source: "..\..\bin\release\assimp-vc141-mt.dll"; DestDir: "{app}\bin\x86" +Source: "..\..\bin\release\assimp_viewer.exe"; DestDir: "{app}\bin\x86"; Components: tools +Source: "C:\Windows\SysWOW64\D3DCompiler_42.dll"; DestDir: "{app}\bin\x86"; Components: tools +Source: "C:\Windows\SysWOW64\D3DX9_42.dll"; DestDir: "{app}\bin\x86"; Components: tools +Source: "..\..\bin\release\assimp.exe"; DestDir: "{app}\bin\x86"; Components: tools + + +; Import libraries +Source: "..\..\lib\release\assimp-vc141-mt.lib"; DestDir: "{app}\lib\x86" + +; Samples +Source: "..\..\samples\*"; DestDir: "{app}\samples"; Flags: recursesubdirs; Components: samples + +; Include files +Source: "..\..\include\*"; DestDir: "{app}\include"; Flags: recursesubdirs + +; CMake files +Source: "..\..\cmake-modules\*"; DestDir: "{app}\cmake-modules"; Flags: recursesubdirs + +[Icons] +; Name: "{group}\Assimp Manual"; Filename: "{app}\doc\AssimpDoc.chm" ; Components: help +; Name: "{group}\Assimp Command Line Manual"; Filename: "{app}\doc\AssimpCmdDoc.chm"; Components: help +; Name: "{group}\AssimpView"; Filename: "{app}\bin\x64\assimp_view.exe"; Components: tools; Check: IsWin64 +; Name: "{group}\AssimpView"; Filename: "{app}\bin\x86\assimp_view.exe"; Components: tools; Check: not IsWin64 diff --git a/port/AndroidJNI/README.md b/port/AndroidJNI/README.md index 7998fa3ef..0b95efd04 100644 --- a/port/AndroidJNI/README.md +++ b/port/AndroidJNI/README.md @@ -1,6 +1,6 @@ Build Asset Importer Lib for Android ==================================== -This module provides a fascade for the io-stream-access to files behind the android-asset-management within +This module provides a facade for the io-stream-access to files behind the android-asset-management within an Android-native application. - It is built as a static library - It requires Android NDK with android API > 9 support. diff --git a/port/PyAssimp/pyassimp/core.py b/port/PyAssimp/pyassimp/core.py index 25821ff9b..c346e2652 100644 --- a/port/PyAssimp/pyassimp/core.py +++ b/port/PyAssimp/pyassimp/core.py @@ -1,547 +1,546 @@ -""" -PyAssimp - -This is the main-module of PyAssimp. -""" - -import sys -if sys.version_info < (2,6): - raise 'pyassimp: need python 2.6 or newer' - -# xrange was renamed range in Python 3 and the original range from Python 2 was removed. -# To keep compatibility with both Python 2 and 3, xrange is set to range for version 3.0 and up. -if sys.version_info >= (3,0): - xrange = range - -import ctypes -import os - -try: import numpy -except: numpy = None - -import logging -logger = logging.getLogger("pyassimp") -# attach default null handler to logger so it doesn't complain -# even if you don't attach another handler to logger -logger.addHandler(logging.NullHandler()) - -from . import structs -from . import helper -from . import postprocess -from .errors import AssimpError - -class AssimpLib(object): - """ - Assimp-Singleton - """ - load, load_mem, export, export_blob, release, dll = helper.search_library() -_assimp_lib = AssimpLib() - -def make_tuple(ai_obj, type = None): - res = None - - #notes: - # ai_obj._fields_ = [ ("attr", c_type), ... ] - # getattr(ai_obj, e[0]).__class__ == float - - if isinstance(ai_obj, structs.Matrix4x4): - if numpy: - res = numpy.array([getattr(ai_obj, e[0]) for e in ai_obj._fields_]).reshape((4,4)) - #import pdb;pdb.set_trace() - else: - res = [getattr(ai_obj, e[0]) for e in ai_obj._fields_] - res = [res[i:i+4] for i in xrange(0,16,4)] - elif isinstance(ai_obj, structs.Matrix3x3): - if numpy: - res = numpy.array([getattr(ai_obj, e[0]) for e in ai_obj._fields_]).reshape((3,3)) - else: - res = [getattr(ai_obj, e[0]) for e in ai_obj._fields_] - res = [res[i:i+3] for i in xrange(0,9,3)] - else: - if numpy: - res = numpy.array([getattr(ai_obj, e[0]) for e in ai_obj._fields_]) - else: - res = [getattr(ai_obj, e[0]) for e in ai_obj._fields_] - - return res - -# Returns unicode object for Python 2, and str object for Python 3. -def _convert_assimp_string(assimp_string): - try: - return unicode(assimp_string.data, errors='ignore') - except: - return str(assimp_string.data, errors='ignore') - -# It is faster and more correct to have an init function for each assimp class -def _init_face(aiFace): - aiFace.indices = [aiFace.mIndices[i] for i in range(aiFace.mNumIndices)] -assimp_struct_inits = { structs.Face : _init_face } - -def call_init(obj, caller = None): - if helper.hasattr_silent(obj,'contents'): #pointer - _init(obj.contents, obj, caller) - else: - _init(obj,parent=caller) - -def _is_init_type(obj): - if helper.hasattr_silent(obj,'contents'): #pointer - return _is_init_type(obj[0]) - # null-pointer case that arises when we reach a mesh attribute - # like mBitangents which use mNumVertices rather than mNumBitangents - # so it breaks the 'is iterable' check. - # Basically: - # FIXME! - elif not bool(obj): - return False - tname = obj.__class__.__name__ - return not (tname[:2] == 'c_' or tname == 'Structure' \ - or tname == 'POINTER') and not isinstance(obj,int) - -def _init(self, target = None, parent = None): - """ - Custom initialize() for C structs, adds safely accessible member functionality. - - :param target: set the object which receive the added methods. Useful when manipulating - pointers, to skip the intermediate 'contents' deferencing. - """ - if not target: - target = self - - dirself = dir(self) - for m in dirself: - - if m.startswith("_"): - continue - - if m.startswith('mNum'): - if 'm' + m[4:] in dirself: - continue # will be processed later on - else: - name = m[1:].lower() - - obj = getattr(self, m) - setattr(target, name, obj) - continue - - if m == 'mName': - target.name = str(_convert_assimp_string(self.mName)) - target.__class__.__repr__ = lambda x: str(x.__class__) + "(" + getattr(x, 'name','') + ")" - target.__class__.__str__ = lambda x: getattr(x, 'name', '') - continue - - name = m[1:].lower() - - obj = getattr(self, m) - - # Create tuples - if isinstance(obj, structs.assimp_structs_as_tuple): - setattr(target, name, make_tuple(obj)) - logger.debug(str(self) + ": Added array " + str(getattr(target, name)) + " as self." + name.lower()) - continue - - if m.startswith('m'): - - if name == "parent": - setattr(target, name, parent) - logger.debug("Added a parent as self." + name) - continue - - if helper.hasattr_silent(self, 'mNum' + m[1:]): - - length = getattr(self, 'mNum' + m[1:]) - - # -> special case: properties are - # stored as a dict. - if m == 'mProperties': - setattr(target, name, _get_properties(obj, length)) - continue - - - if not length: # empty! - setattr(target, name, []) - logger.debug(str(self) + ": " + name + " is an empty list.") - continue - - - try: - if obj._type_ in structs.assimp_structs_as_tuple: - if numpy: - setattr(target, name, numpy.array([make_tuple(obj[i]) for i in range(length)], dtype=numpy.float32)) - - logger.debug(str(self) + ": Added an array of numpy arrays (type "+ str(type(obj)) + ") as self." + name) - else: - setattr(target, name, [make_tuple(obj[i]) for i in range(length)]) - - logger.debug(str(self) + ": Added a list of lists (type "+ str(type(obj)) + ") as self." + name) - - else: - setattr(target, name, [obj[i] for i in range(length)]) #TODO: maybe not necessary to recreate an array? - - logger.debug(str(self) + ": Added list of " + str(obj) + " " + name + " as self." + name + " (type: " + str(type(obj)) + ")") - - # initialize array elements - try: - init = assimp_struct_inits[type(obj[0])] - except KeyError: - if _is_init_type(obj[0]): - for e in getattr(target, name): - call_init(e, target) - else: - for e in getattr(target, name): - init(e) - - - except IndexError: - logger.error("in " + str(self) +" : mismatch between mNum" + name + " and the actual amount of data in m" + name + ". This may be due to version mismatch between libassimp and pyassimp. Quitting now.") - sys.exit(1) - - except ValueError as e: - - logger.error("In " + str(self) + "->" + name + ": " + str(e) + ". Quitting now.") - if "setting an array element with a sequence" in str(e): - logger.error("Note that pyassimp does not currently " - "support meshes with mixed triangles " - "and quads. Try to load your mesh with" - " a post-processing to triangulate your" - " faces.") - raise e - - - - else: # starts with 'm' but not iterable - setattr(target, name, obj) - logger.debug("Added " + name + " as self." + name + " (type: " + str(type(obj)) + ")") - - if _is_init_type(obj): - call_init(obj, target) - - if isinstance(self, structs.Mesh): - _finalize_mesh(self, target) - - if isinstance(self, structs.Texture): - _finalize_texture(self, target) - - if isinstance(self, structs.Metadata): - _finalize_metadata(self, target) - - - return self - - -def pythonize_assimp(type, obj, scene): - """ This method modify the Assimp data structures - to make them easier to work with in Python. - - Supported operations: - - MESH: replace a list of mesh IDs by reference to these meshes - - ADDTRANSFORMATION: add a reference to an object's transformation taken from their associated node. - - :param type: the type of modification to operate (cf above) - :param obj: the input object to modify - :param scene: a reference to the whole scene - """ - - if type == "MESH": - meshes = [] - for i in obj: - meshes.append(scene.meshes[i]) - return meshes - - if type == "ADDTRANSFORMATION": - def getnode(node, name): - if node.name == name: return node - for child in node.children: - n = getnode(child, name) - if n: return n - - node = getnode(scene.rootnode, obj.name) - if not node: - raise AssimpError("Object " + str(obj) + " has no associated node!") - setattr(obj, "transformation", node.transformation) - -def recur_pythonize(node, scene): - ''' - Recursively call pythonize_assimp on - nodes tree to apply several post-processing to - pythonize the assimp datastructures. - ''' - node.meshes = pythonize_assimp("MESH", node.meshes, scene) - for mesh in node.meshes: - mesh.material = scene.materials[mesh.materialindex] - for cam in scene.cameras: - pythonize_assimp("ADDTRANSFORMATION", cam, scene) - for c in node.children: - recur_pythonize(c, scene) - -def load(filename, - file_type = None, - processing = postprocess.aiProcess_Triangulate): - ''' - Load a model into a scene. On failure throws AssimpError. - - Arguments - --------- - filename: Either a filename or a file object to load model from. - If a file object is passed, file_type MUST be specified - Otherwise Assimp has no idea which importer to use. - This is named 'filename' so as to not break legacy code. - processing: assimp postprocessing parameters. Verbose keywords are imported - from postprocessing, and the parameters can be combined bitwise to - generate the final processing value. Note that the default value will - triangulate quad faces. Example of generating other possible values: - processing = (pyassimp.postprocess.aiProcess_Triangulate | - pyassimp.postprocess.aiProcess_OptimizeMeshes) - file_type: string of file extension, such as 'stl' - - Returns - --------- - Scene object with model data - ''' - - if hasattr(filename, 'read'): - # This is the case where a file object has been passed to load. - # It is calling the following function: - # const aiScene* aiImportFileFromMemory(const char* pBuffer, - # unsigned int pLength, - # unsigned int pFlags, - # const char* pHint) - if file_type == None: - raise AssimpError('File type must be specified when passing file objects!') - data = filename.read() - model = _assimp_lib.load_mem(data, - len(data), - processing, - file_type) - else: - # a filename string has been passed - model = _assimp_lib.load(filename.encode(sys.getfilesystemencoding()), processing) - - if not model: - raise AssimpError('Could not import file!') - scene = _init(model.contents) - recur_pythonize(scene.rootnode, scene) - return scene - -def export(scene, - filename, - file_type = None, - processing = postprocess.aiProcess_Triangulate): - ''' - Export a scene. On failure throws AssimpError. - - Arguments - --------- - scene: scene to export. - filename: Filename that the scene should be exported to. - file_type: string of file exporter to use. For example "collada". - processing: assimp postprocessing parameters. Verbose keywords are imported - from postprocessing, and the parameters can be combined bitwise to - generate the final processing value. Note that the default value will - triangulate quad faces. Example of generating other possible values: - processing = (pyassimp.postprocess.aiProcess_Triangulate | - pyassimp.postprocess.aiProcess_OptimizeMeshes) - - ''' - - from ctypes import pointer - exportStatus = _assimp_lib.export(pointer(scene), file_type.encode("ascii"), filename.encode(sys.getfilesystemencoding()), processing) - - if exportStatus != 0: - raise AssimpError('Could not export scene!') - -def export_blob(scene, - file_type = None, - processing = postprocess.aiProcess_Triangulate): - ''' - Export a scene and return a blob in the correct format. On failure throws AssimpError. - - Arguments - --------- - scene: scene to export. - file_type: string of file exporter to use. For example "collada". - processing: assimp postprocessing parameters. Verbose keywords are imported - from postprocessing, and the parameters can be combined bitwise to - generate the final processing value. Note that the default value will - triangulate quad faces. Example of generating other possible values: - processing = (pyassimp.postprocess.aiProcess_Triangulate | - pyassimp.postprocess.aiProcess_OptimizeMeshes) - Returns - --------- - Pointer to structs.ExportDataBlob - ''' - from ctypes import pointer - exportBlobPtr = _assimp_lib.export_blob(pointer(scene), file_type.encode("ascii"), processing) - - if exportBlobPtr == 0: - raise AssimpError('Could not export scene to blob!') - return exportBlobPtr - -def release(scene): - from ctypes import pointer - _assimp_lib.release(pointer(scene)) - -def _finalize_texture(tex, target): - setattr(target, "achformathint", tex.achFormatHint) - if numpy: - data = numpy.array([make_tuple(getattr(tex, "pcData")[i]) for i in range(tex.mWidth * tex.mHeight)]) - else: - data = [make_tuple(getattr(tex, "pcData")[i]) for i in range(tex.mWidth * tex.mHeight)] - setattr(target, "data", data) - -def _finalize_mesh(mesh, target): - """ Building of meshes is a bit specific. - - We override here the various datasets that can - not be process as regular fields. - - For instance, the length of the normals array is - mNumVertices (no mNumNormals is available) - """ - nb_vertices = getattr(mesh, "mNumVertices") - - def fill(name): - mAttr = getattr(mesh, name) - if numpy: - if mAttr: - data = numpy.array([make_tuple(getattr(mesh, name)[i]) for i in range(nb_vertices)], dtype=numpy.float32) - setattr(target, name[1:].lower(), data) - else: - setattr(target, name[1:].lower(), numpy.array([], dtype="float32")) - else: - if mAttr: - data = [make_tuple(getattr(mesh, name)[i]) for i in range(nb_vertices)] - setattr(target, name[1:].lower(), data) - else: - setattr(target, name[1:].lower(), []) - - def fillarray(name): - mAttr = getattr(mesh, name) - - data = [] - for index, mSubAttr in enumerate(mAttr): - if mSubAttr: - data.append([make_tuple(getattr(mesh, name)[index][i]) for i in range(nb_vertices)]) - - if numpy: - setattr(target, name[1:].lower(), numpy.array(data, dtype=numpy.float32)) - else: - setattr(target, name[1:].lower(), data) - - fill("mNormals") - fill("mTangents") - fill("mBitangents") - - fillarray("mColors") - fillarray("mTextureCoords") - - # prepare faces - if numpy: - faces = numpy.array([f.indices for f in target.faces], dtype=numpy.int32) - else: - faces = [f.indices for f in target.faces] - setattr(target, 'faces', faces) - -def _init_metadata_entry(entry): - from ctypes import POINTER, c_bool, c_int32, c_uint64, c_float, c_double, cast - - entry.type = entry.mType - if entry.type == structs.MetadataEntry.AI_BOOL: - entry.data = cast(entry.mData, POINTER(c_bool)).contents.value - elif entry.type == structs.MetadataEntry.AI_INT32: - entry.data = cast(entry.mData, POINTER(c_int32)).contents.value - elif entry.type == structs.MetadataEntry.AI_UINT64: - entry.data = cast(entry.mData, POINTER(c_uint64)).contents.value - elif entry.type == structs.MetadataEntry.AI_FLOAT: - entry.data = cast(entry.mData, POINTER(c_float)).contents.value - elif entry.type == structs.MetadataEntry.AI_DOUBLE: - entry.data = cast(entry.mData, POINTER(c_double)).contents.value - elif entry.type == structs.MetadataEntry.AI_AISTRING: - assimp_string = cast(entry.mData, POINTER(structs.String)).contents - entry.data = _convert_assimp_string(assimp_string) - elif entry.type == structs.MetadataEntry.AI_AIVECTOR3D: - assimp_vector = cast(entry.mData, POINTER(structs.Vector3D)).contents - entry.data = make_tuple(assimp_vector) - - return entry - -def _finalize_metadata(metadata, target): - """ Building the metadata object is a bit specific. - - Firstly, there are two separate arrays: one with metadata keys and one - with metadata values, and there are no corresponding mNum* attributes, - so the C arrays are not converted to Python arrays using the generic - code in the _init function. - - Secondly, a metadata entry value has to be cast according to declared - metadata entry type. - """ - length = metadata.mNumProperties - setattr(target, 'keys', [str(_convert_assimp_string(metadata.mKeys[i])) for i in range(length)]) - setattr(target, 'values', [_init_metadata_entry(metadata.mValues[i]) for i in range(length)]) - -class PropertyGetter(dict): - def __getitem__(self, key): - semantic = 0 - if isinstance(key, tuple): - key, semantic = key - - return dict.__getitem__(self, (key, semantic)) - - def keys(self): - for k in dict.keys(self): - yield k[0] - - def __iter__(self): - return self.keys() - - def items(self): - for k, v in dict.items(self): - yield k[0], v - - -def _get_properties(properties, length): - """ - Convenience Function to get the material properties as a dict - and values in a python format. - """ - result = {} - #read all properties - for p in [properties[i] for i in range(length)]: - #the name - p = p.contents - key = str(_convert_assimp_string(p.mKey)) - key = (key.split('.')[1], p.mSemantic) - - #the data - from ctypes import POINTER, cast, c_int, c_float, sizeof - if p.mType == 1: - arr = cast(p.mData, POINTER(c_float * int(p.mDataLength/sizeof(c_float)) )).contents - value = [x for x in arr] - elif p.mType == 3: #string can't be an array - value = _convert_assimp_string(cast(p.mData, POINTER(structs.MaterialPropertyString)).contents) - - elif p.mType == 4: - arr = cast(p.mData, POINTER(c_int * int(p.mDataLength/sizeof(c_int)) )).contents - value = [x for x in arr] - else: - value = p.mData[:p.mDataLength] - - if len(value) == 1: - [value] = value - - result[key] = value - - return PropertyGetter(result) - -def decompose_matrix(matrix): - if not isinstance(matrix, structs.Matrix4x4): - raise AssimpError("pyassimp.decompose_matrix failed: Not a Matrix4x4!") - - scaling = structs.Vector3D() - rotation = structs.Quaternion() - position = structs.Vector3D() - - from ctypes import byref, pointer - _assimp_lib.dll.aiDecomposeMatrix(pointer(matrix), byref(scaling), byref(rotation), byref(position)) - return scaling._init(), rotation._init(), position._init() - +""" +PyAssimp + +This is the main-module of PyAssimp. +""" + +import sys +if sys.version_info < (2,6): + raise RuntimeError('pyassimp: need python 2.6 or newer') + +# xrange was renamed range in Python 3 and the original range from Python 2 was removed. +# To keep compatibility with both Python 2 and 3, xrange is set to range for version 3.0 and up. +if sys.version_info >= (3,0): + xrange = range + + +try: import numpy +except ImportError: numpy = None +import logging +import ctypes +logger = logging.getLogger("pyassimp") +# attach default null handler to logger so it doesn't complain +# even if you don't attach another handler to logger +logger.addHandler(logging.NullHandler()) + +from . import structs +from . import helper +from . import postprocess +from .errors import AssimpError + +class AssimpLib(object): + """ + Assimp-Singleton + """ + load, load_mem, export, export_blob, release, dll = helper.search_library() +_assimp_lib = AssimpLib() + +def make_tuple(ai_obj, type = None): + res = None + + #notes: + # ai_obj._fields_ = [ ("attr", c_type), ... ] + # getattr(ai_obj, e[0]).__class__ == float + + if isinstance(ai_obj, structs.Matrix4x4): + if numpy: + res = numpy.array([getattr(ai_obj, e[0]) for e in ai_obj._fields_]).reshape((4,4)) + #import pdb;pdb.set_trace() + else: + res = [getattr(ai_obj, e[0]) for e in ai_obj._fields_] + res = [res[i:i+4] for i in xrange(0,16,4)] + elif isinstance(ai_obj, structs.Matrix3x3): + if numpy: + res = numpy.array([getattr(ai_obj, e[0]) for e in ai_obj._fields_]).reshape((3,3)) + else: + res = [getattr(ai_obj, e[0]) for e in ai_obj._fields_] + res = [res[i:i+3] for i in xrange(0,9,3)] + else: + if numpy: + res = numpy.array([getattr(ai_obj, e[0]) for e in ai_obj._fields_]) + else: + res = [getattr(ai_obj, e[0]) for e in ai_obj._fields_] + + return res + +# Returns unicode object for Python 2, and str object for Python 3. +def _convert_assimp_string(assimp_string): + if sys.version_info >= (3, 0): + return str(assimp_string.data, errors='ignore') + else: + return unicode(assimp_string.data, errors='ignore') + +# It is faster and more correct to have an init function for each assimp class +def _init_face(aiFace): + aiFace.indices = [aiFace.mIndices[i] for i in range(aiFace.mNumIndices)] +assimp_struct_inits = { structs.Face : _init_face } + +def call_init(obj, caller = None): + if helper.hasattr_silent(obj,'contents'): #pointer + _init(obj.contents, obj, caller) + else: + _init(obj,parent=caller) + +def _is_init_type(obj): + + if obj and helper.hasattr_silent(obj,'contents'): #pointer + return _is_init_type(obj[0]) + # null-pointer case that arises when we reach a mesh attribute + # like mBitangents which use mNumVertices rather than mNumBitangents + # so it breaks the 'is iterable' check. + # Basically: + # FIXME! + elif not bool(obj): + return False + tname = obj.__class__.__name__ + return not (tname[:2] == 'c_' or tname == 'Structure' \ + or tname == 'POINTER') and not isinstance(obj, (int, str, bytes)) + +def _init(self, target = None, parent = None): + """ + Custom initialize() for C structs, adds safely accessible member functionality. + + :param target: set the object which receive the added methods. Useful when manipulating + pointers, to skip the intermediate 'contents' deferencing. + """ + if not target: + target = self + + dirself = dir(self) + for m in dirself: + + if m.startswith("_"): + continue + + if m.startswith('mNum'): + if 'm' + m[4:] in dirself: + continue # will be processed later on + else: + name = m[1:].lower() + + obj = getattr(self, m) + setattr(target, name, obj) + continue + + if m == 'mName': + target.name = str(_convert_assimp_string(self.mName)) + target.__class__.__repr__ = lambda x: str(x.__class__) + "(" + getattr(x, 'name','') + ")" + target.__class__.__str__ = lambda x: getattr(x, 'name', '') + continue + + name = m[1:].lower() + + obj = getattr(self, m) + + # Create tuples + if isinstance(obj, structs.assimp_structs_as_tuple): + setattr(target, name, make_tuple(obj)) + logger.debug(str(self) + ": Added array " + str(getattr(target, name)) + " as self." + name.lower()) + continue + + if m.startswith('m'): + + if name == "parent": + setattr(target, name, parent) + logger.debug("Added a parent as self." + name) + continue + + if helper.hasattr_silent(self, 'mNum' + m[1:]): + + length = getattr(self, 'mNum' + m[1:]) + + # -> special case: properties are + # stored as a dict. + if m == 'mProperties': + setattr(target, name, _get_properties(obj, length)) + continue + + + if not length: # empty! + setattr(target, name, []) + logger.debug(str(self) + ": " + name + " is an empty list.") + continue + + + try: + if obj._type_ in structs.assimp_structs_as_tuple: + if numpy: + setattr(target, name, numpy.array([make_tuple(obj[i]) for i in range(length)], dtype=numpy.float32)) + + logger.debug(str(self) + ": Added an array of numpy arrays (type "+ str(type(obj)) + ") as self." + name) + else: + setattr(target, name, [make_tuple(obj[i]) for i in range(length)]) + + logger.debug(str(self) + ": Added a list of lists (type "+ str(type(obj)) + ") as self." + name) + + else: + setattr(target, name, [obj[i] for i in range(length)]) #TODO: maybe not necessary to recreate an array? + + logger.debug(str(self) + ": Added list of " + str(obj) + " " + name + " as self." + name + " (type: " + str(type(obj)) + ")") + + # initialize array elements + try: + init = assimp_struct_inits[type(obj[0])] + except KeyError: + if _is_init_type(obj[0]): + for e in getattr(target, name): + call_init(e, target) + else: + for e in getattr(target, name): + init(e) + + + except IndexError: + logger.error("in " + str(self) +" : mismatch between mNum" + name + " and the actual amount of data in m" + name + ". This may be due to version mismatch between libassimp and pyassimp. Quitting now.") + sys.exit(1) + + except ValueError as e: + + logger.error("In " + str(self) + "->" + name + ": " + str(e) + ". Quitting now.") + if "setting an array element with a sequence" in str(e): + logger.error("Note that pyassimp does not currently " + "support meshes with mixed triangles " + "and quads. Try to load your mesh with" + " a post-processing to triangulate your" + " faces.") + raise e + + + + else: # starts with 'm' but not iterable + setattr(target, name, obj) + logger.debug("Added " + name + " as self." + name + " (type: " + str(type(obj)) + ")") + + if _is_init_type(obj): + call_init(obj, target) + + if isinstance(self, structs.Mesh): + _finalize_mesh(self, target) + + if isinstance(self, structs.Texture): + _finalize_texture(self, target) + + if isinstance(self, structs.Metadata): + _finalize_metadata(self, target) + + + return self + + +def pythonize_assimp(type, obj, scene): + """ This method modify the Assimp data structures + to make them easier to work with in Python. + + Supported operations: + - MESH: replace a list of mesh IDs by reference to these meshes + - ADDTRANSFORMATION: add a reference to an object's transformation taken from their associated node. + + :param type: the type of modification to operate (cf above) + :param obj: the input object to modify + :param scene: a reference to the whole scene + """ + + if type == "MESH": + meshes = [] + for i in obj: + meshes.append(scene.meshes[i]) + return meshes + + if type == "ADDTRANSFORMATION": + def getnode(node, name): + if node.name == name: return node + for child in node.children: + n = getnode(child, name) + if n: return n + + node = getnode(scene.rootnode, obj.name) + if not node: + raise AssimpError("Object " + str(obj) + " has no associated node!") + setattr(obj, "transformation", node.transformation) + +def recur_pythonize(node, scene): + ''' + Recursively call pythonize_assimp on + nodes tree to apply several post-processing to + pythonize the assimp datastructures. + ''' + node.meshes = pythonize_assimp("MESH", node.meshes, scene) + for mesh in node.meshes: + mesh.material = scene.materials[mesh.materialindex] + for cam in scene.cameras: + pythonize_assimp("ADDTRANSFORMATION", cam, scene) + for c in node.children: + recur_pythonize(c, scene) + +def load(filename, + file_type = None, + processing = postprocess.aiProcess_Triangulate): + ''' + Load a model into a scene. On failure throws AssimpError. + + Arguments + --------- + filename: Either a filename or a file object to load model from. + If a file object is passed, file_type MUST be specified + Otherwise Assimp has no idea which importer to use. + This is named 'filename' so as to not break legacy code. + processing: assimp postprocessing parameters. Verbose keywords are imported + from postprocessing, and the parameters can be combined bitwise to + generate the final processing value. Note that the default value will + triangulate quad faces. Example of generating other possible values: + processing = (pyassimp.postprocess.aiProcess_Triangulate | + pyassimp.postprocess.aiProcess_OptimizeMeshes) + file_type: string of file extension, such as 'stl' + + Returns + --------- + Scene object with model data + ''' + + if hasattr(filename, 'read'): + # This is the case where a file object has been passed to load. + # It is calling the following function: + # const aiScene* aiImportFileFromMemory(const char* pBuffer, + # unsigned int pLength, + # unsigned int pFlags, + # const char* pHint) + if file_type is None: + raise AssimpError('File type must be specified when passing file objects!') + data = filename.read() + model = _assimp_lib.load_mem(data, + len(data), + processing, + file_type) + else: + # a filename string has been passed + model = _assimp_lib.load(filename.encode(sys.getfilesystemencoding()), processing) + + if not model: + raise AssimpError('Could not import file!') + scene = _init(model.contents) + recur_pythonize(scene.rootnode, scene) + return scene + +def export(scene, + filename, + file_type = None, + processing = postprocess.aiProcess_Triangulate): + ''' + Export a scene. On failure throws AssimpError. + + Arguments + --------- + scene: scene to export. + filename: Filename that the scene should be exported to. + file_type: string of file exporter to use. For example "collada". + processing: assimp postprocessing parameters. Verbose keywords are imported + from postprocessing, and the parameters can be combined bitwise to + generate the final processing value. Note that the default value will + triangulate quad faces. Example of generating other possible values: + processing = (pyassimp.postprocess.aiProcess_Triangulate | + pyassimp.postprocess.aiProcess_OptimizeMeshes) + + ''' + + exportStatus = _assimp_lib.export(ctypes.pointer(scene), file_type.encode("ascii"), filename.encode(sys.getfilesystemencoding()), processing) + + if exportStatus != 0: + raise AssimpError('Could not export scene!') + +def export_blob(scene, + file_type = None, + processing = postprocess.aiProcess_Triangulate): + ''' + Export a scene and return a blob in the correct format. On failure throws AssimpError. + + Arguments + --------- + scene: scene to export. + file_type: string of file exporter to use. For example "collada". + processing: assimp postprocessing parameters. Verbose keywords are imported + from postprocessing, and the parameters can be combined bitwise to + generate the final processing value. Note that the default value will + triangulate quad faces. Example of generating other possible values: + processing = (pyassimp.postprocess.aiProcess_Triangulate | + pyassimp.postprocess.aiProcess_OptimizeMeshes) + Returns + --------- + Pointer to structs.ExportDataBlob + ''' + exportBlobPtr = _assimp_lib.export_blob(ctypes.pointer(scene), file_type.encode("ascii"), processing) + + if exportBlobPtr == 0: + raise AssimpError('Could not export scene to blob!') + return exportBlobPtr + +def release(scene): + _assimp_lib.release(ctypes.pointer(scene)) + +def _finalize_texture(tex, target): + setattr(target, "achformathint", tex.achFormatHint) + if numpy: + data = numpy.array([make_tuple(getattr(tex, "pcData")[i]) for i in range(tex.mWidth * tex.mHeight)]) + else: + data = [make_tuple(getattr(tex, "pcData")[i]) for i in range(tex.mWidth * tex.mHeight)] + setattr(target, "data", data) + +def _finalize_mesh(mesh, target): + """ Building of meshes is a bit specific. + + We override here the various datasets that can + not be process as regular fields. + + For instance, the length of the normals array is + mNumVertices (no mNumNormals is available) + """ + nb_vertices = getattr(mesh, "mNumVertices") + + def fill(name): + mAttr = getattr(mesh, name) + if numpy: + if mAttr: + data = numpy.array([make_tuple(getattr(mesh, name)[i]) for i in range(nb_vertices)], dtype=numpy.float32) + setattr(target, name[1:].lower(), data) + else: + setattr(target, name[1:].lower(), numpy.array([], dtype="float32")) + else: + if mAttr: + data = [make_tuple(getattr(mesh, name)[i]) for i in range(nb_vertices)] + setattr(target, name[1:].lower(), data) + else: + setattr(target, name[1:].lower(), []) + + def fillarray(name): + mAttr = getattr(mesh, name) + + data = [] + for index, mSubAttr in enumerate(mAttr): + if mSubAttr: + data.append([make_tuple(getattr(mesh, name)[index][i]) for i in range(nb_vertices)]) + + if numpy: + setattr(target, name[1:].lower(), numpy.array(data, dtype=numpy.float32)) + else: + setattr(target, name[1:].lower(), data) + + fill("mNormals") + fill("mTangents") + fill("mBitangents") + + fillarray("mColors") + fillarray("mTextureCoords") + + # prepare faces + if numpy: + faces = numpy.array([f.indices for f in target.faces], dtype=numpy.int32) + else: + faces = [f.indices for f in target.faces] + setattr(target, 'faces', faces) + +def _init_metadata_entry(entry): + entry.type = entry.mType + if entry.type == structs.MetadataEntry.AI_BOOL: + entry.data = ctypes.cast(entry.mData, ctypes.POINTER(ctypes.c_bool)).contents.value + elif entry.type == structs.MetadataEntry.AI_INT32: + entry.data = ctypes.cast(entry.mData, ctypes.POINTER(ctypes.c_int32)).contents.value + elif entry.type == structs.MetadataEntry.AI_UINT64: + entry.data = ctypes.cast(entry.mData, ctypes.POINTER(ctypes.c_uint64)).contents.value + elif entry.type == structs.MetadataEntry.AI_FLOAT: + entry.data = ctypes.cast(entry.mData, ctypes.POINTER(ctypes.c_float)).contents.value + elif entry.type == structs.MetadataEntry.AI_DOUBLE: + entry.data = ctypes.cast(entry.mData, ctypes.POINTER(ctypes.c_double)).contents.value + elif entry.type == structs.MetadataEntry.AI_AISTRING: + assimp_string = ctypes.cast(entry.mData, ctypes.POINTER(structs.String)).contents + entry.data = _convert_assimp_string(assimp_string) + elif entry.type == structs.MetadataEntry.AI_AIVECTOR3D: + assimp_vector = ctypes.cast(entry.mData, ctypes.POINTER(structs.Vector3D)).contents + entry.data = make_tuple(assimp_vector) + + return entry + +def _finalize_metadata(metadata, target): + """ Building the metadata object is a bit specific. + + Firstly, there are two separate arrays: one with metadata keys and one + with metadata values, and there are no corresponding mNum* attributes, + so the C arrays are not converted to Python arrays using the generic + code in the _init function. + + Secondly, a metadata entry value has to be cast according to declared + metadata entry type. + """ + length = metadata.mNumProperties + setattr(target, 'keys', [str(_convert_assimp_string(metadata.mKeys[i])) for i in range(length)]) + setattr(target, 'values', [_init_metadata_entry(metadata.mValues[i]) for i in range(length)]) + +class PropertyGetter(dict): + def __getitem__(self, key): + semantic = 0 + if isinstance(key, tuple): + key, semantic = key + + return dict.__getitem__(self, (key, semantic)) + + def keys(self): + for k in dict.keys(self): + yield k[0] + + def __iter__(self): + return self.keys() + + def items(self): + for k, v in dict.items(self): + yield k[0], v + + +def _get_properties(properties, length): + """ + Convenience Function to get the material properties as a dict + and values in a python format. + """ + result = {} + #read all properties + for p in [properties[i] for i in range(length)]: + #the name + p = p.contents + key = str(_convert_assimp_string(p.mKey)) + key = (key.split('.')[1], p.mSemantic) + + #the data + if p.mType == 1: + arr = ctypes.cast(p.mData, + ctypes.POINTER(ctypes.c_float * int(p.mDataLength/ctypes.sizeof(ctypes.c_float))) + ).contents + value = [x for x in arr] + elif p.mType == 3: #string can't be an array + value = _convert_assimp_string(ctypes.cast(p.mData, ctypes.POINTER(structs.MaterialPropertyString)).contents) + + elif p.mType == 4: + arr = ctypes.cast(p.mData, + ctypes.POINTER(ctypes.c_int * int(p.mDataLength/ctypes.sizeof(ctypes.c_int))) + ).contents + value = [x for x in arr] + else: + value = p.mData[:p.mDataLength] + + if len(value) == 1: + [value] = value + + result[key] = value + + return PropertyGetter(result) + +def decompose_matrix(matrix): + if not isinstance(matrix, structs.Matrix4x4): + raise AssimpError("pyassimp.decompose_matrix failed: Not a Matrix4x4!") + + scaling = structs.Vector3D() + rotation = structs.Quaternion() + position = structs.Vector3D() + + _assimp_lib.dll.aiDecomposeMatrix(ctypes.pointer(matrix), + ctypes.byref(scaling), + ctypes.byref(rotation), + ctypes.byref(position)) + return scaling._init(), rotation._init(), position._init() + diff --git a/port/PyAssimp/pyassimp/formats.py b/port/PyAssimp/pyassimp/formats.py index baba1645c..5d454e5b7 100644 --- a/port/PyAssimp/pyassimp/formats.py +++ b/port/PyAssimp/pyassimp/formats.py @@ -21,7 +21,7 @@ FORMATS = ["CSM", "STL", "IRR", "Q3O", - "Q3D" + "Q3D", "MS3D", "Q3S", "ZGL", diff --git a/port/PyAssimp/pyassimp/helper.py b/port/PyAssimp/pyassimp/helper.py index 4e9f10e94..62384f54f 100644 --- a/port/PyAssimp/pyassimp/helper.py +++ b/port/PyAssimp/pyassimp/helper.py @@ -1,280 +1,279 @@ -#-*- coding: UTF-8 -*- - -""" -Some fancy helper functions. -""" - -import os -import ctypes -from ctypes import POINTER -import operator - -from distutils.sysconfig import get_python_lib -import re -import sys - -try: import numpy -except: numpy = None - -import logging;logger = logging.getLogger("pyassimp") - -from .errors import AssimpError - -additional_dirs, ext_whitelist = [],[] - -# populate search directories and lists of allowed file extensions -# depending on the platform we're running on. -if os.name=='posix': - additional_dirs.append('./') - additional_dirs.append('/usr/lib/') - additional_dirs.append('/usr/lib/x86_64-linux-gnu/') - additional_dirs.append('/usr/local/lib/') - - if 'LD_LIBRARY_PATH' in os.environ: - additional_dirs.extend([item for item in os.environ['LD_LIBRARY_PATH'].split(':') if item]) - - # check if running from anaconda. - if "conda" or "continuum" in sys.version.lower(): - cur_path = get_python_lib() - pattern = re.compile('.*\/lib\/') - conda_lib = pattern.match(cur_path).group() - logger.info("Adding Anaconda lib path:"+ conda_lib) - additional_dirs.append(conda_lib) - - # note - this won't catch libassimp.so.N.n, but - # currently there's always a symlink called - # libassimp.so in /usr/local/lib. - ext_whitelist.append('.so') - # libassimp.dylib in /usr/local/lib - ext_whitelist.append('.dylib') - -elif os.name=='nt': - ext_whitelist.append('.dll') - path_dirs = os.environ['PATH'].split(';') - additional_dirs.extend(path_dirs) - -def vec2tuple(x): - """ Converts a VECTOR3D to a Tuple """ - return (x.x, x.y, x.z) - -def transform(vector3, matrix4x4): - """ Apply a transformation matrix on a 3D vector. - - :param vector3: array with 3 elements - :param matrix4x4: 4x4 matrix - """ - if numpy: - return numpy.dot(matrix4x4, numpy.append(vector3, 1.)) - else: - m0,m1,m2,m3 = matrix4x4; x,y,z = vector3 - return [ - m0[0]*x + m0[1]*y + m0[2]*z + m0[3], - m1[0]*x + m1[1]*y + m1[2]*z + m1[3], - m2[0]*x + m2[1]*y + m2[2]*z + m2[3], - m3[0]*x + m3[1]*y + m3[2]*z + m3[3] - ] - -def _inv(matrix4x4): - m0,m1,m2,m3 = matrix4x4 - - det = m0[3]*m1[2]*m2[1]*m3[0] - m0[2]*m1[3]*m2[1]*m3[0] - \ - m0[3]*m1[1]*m2[2]*m3[0] + m0[1]*m1[3]*m2[2]*m3[0] + \ - m0[2]*m1[1]*m2[3]*m3[0] - m0[1]*m1[2]*m2[3]*m3[0] - \ - m0[3]*m1[2]*m2[0]*m3[1] + m0[2]*m1[3]*m2[0]*m3[1] + \ - m0[3]*m1[0]*m2[2]*m3[1] - m0[0]*m1[3]*m2[2]*m3[1] - \ - m0[2]*m1[0]*m2[3]*m3[1] + m0[0]*m1[2]*m2[3]*m3[1] + \ - m0[3]*m1[1]*m2[0]*m3[2] - m0[1]*m1[3]*m2[0]*m3[2] - \ - m0[3]*m1[0]*m2[1]*m3[2] + m0[0]*m1[3]*m2[1]*m3[2] + \ - m0[1]*m1[0]*m2[3]*m3[2] - m0[0]*m1[1]*m2[3]*m3[2] - \ - m0[2]*m1[1]*m2[0]*m3[3] + m0[1]*m1[2]*m2[0]*m3[3] + \ - m0[2]*m1[0]*m2[1]*m3[3] - m0[0]*m1[2]*m2[1]*m3[3] - \ - m0[1]*m1[0]*m2[2]*m3[3] + m0[0]*m1[1]*m2[2]*m3[3] - - return[[( m1[2]*m2[3]*m3[1] - m1[3]*m2[2]*m3[1] + m1[3]*m2[1]*m3[2] - m1[1]*m2[3]*m3[2] - m1[2]*m2[1]*m3[3] + m1[1]*m2[2]*m3[3]) /det, - ( m0[3]*m2[2]*m3[1] - m0[2]*m2[3]*m3[1] - m0[3]*m2[1]*m3[2] + m0[1]*m2[3]*m3[2] + m0[2]*m2[1]*m3[3] - m0[1]*m2[2]*m3[3]) /det, - ( m0[2]*m1[3]*m3[1] - m0[3]*m1[2]*m3[1] + m0[3]*m1[1]*m3[2] - m0[1]*m1[3]*m3[2] - m0[2]*m1[1]*m3[3] + m0[1]*m1[2]*m3[3]) /det, - ( m0[3]*m1[2]*m2[1] - m0[2]*m1[3]*m2[1] - m0[3]*m1[1]*m2[2] + m0[1]*m1[3]*m2[2] + m0[2]*m1[1]*m2[3] - m0[1]*m1[2]*m2[3]) /det], - [( m1[3]*m2[2]*m3[0] - m1[2]*m2[3]*m3[0] - m1[3]*m2[0]*m3[2] + m1[0]*m2[3]*m3[2] + m1[2]*m2[0]*m3[3] - m1[0]*m2[2]*m3[3]) /det, - ( m0[2]*m2[3]*m3[0] - m0[3]*m2[2]*m3[0] + m0[3]*m2[0]*m3[2] - m0[0]*m2[3]*m3[2] - m0[2]*m2[0]*m3[3] + m0[0]*m2[2]*m3[3]) /det, - ( m0[3]*m1[2]*m3[0] - m0[2]*m1[3]*m3[0] - m0[3]*m1[0]*m3[2] + m0[0]*m1[3]*m3[2] + m0[2]*m1[0]*m3[3] - m0[0]*m1[2]*m3[3]) /det, - ( m0[2]*m1[3]*m2[0] - m0[3]*m1[2]*m2[0] + m0[3]*m1[0]*m2[2] - m0[0]*m1[3]*m2[2] - m0[2]*m1[0]*m2[3] + m0[0]*m1[2]*m2[3]) /det], - [( m1[1]*m2[3]*m3[0] - m1[3]*m2[1]*m3[0] + m1[3]*m2[0]*m3[1] - m1[0]*m2[3]*m3[1] - m1[1]*m2[0]*m3[3] + m1[0]*m2[1]*m3[3]) /det, - ( m0[3]*m2[1]*m3[0] - m0[1]*m2[3]*m3[0] - m0[3]*m2[0]*m3[1] + m0[0]*m2[3]*m3[1] + m0[1]*m2[0]*m3[3] - m0[0]*m2[1]*m3[3]) /det, - ( m0[1]*m1[3]*m3[0] - m0[3]*m1[1]*m3[0] + m0[3]*m1[0]*m3[1] - m0[0]*m1[3]*m3[1] - m0[1]*m1[0]*m3[3] + m0[0]*m1[1]*m3[3]) /det, - ( m0[3]*m1[1]*m2[0] - m0[1]*m1[3]*m2[0] - m0[3]*m1[0]*m2[1] + m0[0]*m1[3]*m2[1] + m0[1]*m1[0]*m2[3] - m0[0]*m1[1]*m2[3]) /det], - [( m1[2]*m2[1]*m3[0] - m1[1]*m2[2]*m3[0] - m1[2]*m2[0]*m3[1] + m1[0]*m2[2]*m3[1] + m1[1]*m2[0]*m3[2] - m1[0]*m2[1]*m3[2]) /det, - ( m0[1]*m2[2]*m3[0] - m0[2]*m2[1]*m3[0] + m0[2]*m2[0]*m3[1] - m0[0]*m2[2]*m3[1] - m0[1]*m2[0]*m3[2] + m0[0]*m2[1]*m3[2]) /det, - ( m0[2]*m1[1]*m3[0] - m0[1]*m1[2]*m3[0] - m0[2]*m1[0]*m3[1] + m0[0]*m1[2]*m3[1] + m0[1]*m1[0]*m3[2] - m0[0]*m1[1]*m3[2]) /det, - ( m0[1]*m1[2]*m2[0] - m0[2]*m1[1]*m2[0] + m0[2]*m1[0]*m2[1] - m0[0]*m1[2]*m2[1] - m0[1]*m1[0]*m2[2] + m0[0]*m1[1]*m2[2]) /det]] - -def get_bounding_box(scene): - bb_min = [1e10, 1e10, 1e10] # x,y,z - bb_max = [-1e10, -1e10, -1e10] # x,y,z - inv = numpy.linalg.inv if numpy else _inv - return get_bounding_box_for_node(scene.rootnode, bb_min, bb_max, inv(scene.rootnode.transformation)) - -def get_bounding_box_for_node(node, bb_min, bb_max, transformation): - - if numpy: - transformation = numpy.dot(transformation, node.transformation) - else: - t0,t1,t2,t3 = transformation - T0,T1,T2,T3 = node.transformation - transformation = [ [ - t0[0]*T0[0] + t0[1]*T1[0] + t0[2]*T2[0] + t0[3]*T3[0], - t0[0]*T0[1] + t0[1]*T1[1] + t0[2]*T2[1] + t0[3]*T3[1], - t0[0]*T0[2] + t0[1]*T1[2] + t0[2]*T2[2] + t0[3]*T3[2], - t0[0]*T0[3] + t0[1]*T1[3] + t0[2]*T2[3] + t0[3]*T3[3] - ],[ - t1[0]*T0[0] + t1[1]*T1[0] + t1[2]*T2[0] + t1[3]*T3[0], - t1[0]*T0[1] + t1[1]*T1[1] + t1[2]*T2[1] + t1[3]*T3[1], - t1[0]*T0[2] + t1[1]*T1[2] + t1[2]*T2[2] + t1[3]*T3[2], - t1[0]*T0[3] + t1[1]*T1[3] + t1[2]*T2[3] + t1[3]*T3[3] - ],[ - t2[0]*T0[0] + t2[1]*T1[0] + t2[2]*T2[0] + t2[3]*T3[0], - t2[0]*T0[1] + t2[1]*T1[1] + t2[2]*T2[1] + t2[3]*T3[1], - t2[0]*T0[2] + t2[1]*T1[2] + t2[2]*T2[2] + t2[3]*T3[2], - t2[0]*T0[3] + t2[1]*T1[3] + t2[2]*T2[3] + t2[3]*T3[3] - ],[ - t3[0]*T0[0] + t3[1]*T1[0] + t3[2]*T2[0] + t3[3]*T3[0], - t3[0]*T0[1] + t3[1]*T1[1] + t3[2]*T2[1] + t3[3]*T3[1], - t3[0]*T0[2] + t3[1]*T1[2] + t3[2]*T2[2] + t3[3]*T3[2], - t3[0]*T0[3] + t3[1]*T1[3] + t3[2]*T2[3] + t3[3]*T3[3] - ] ] - - for mesh in node.meshes: - for v in mesh.vertices: - v = transform(v, transformation) - bb_min[0] = min(bb_min[0], v[0]) - bb_min[1] = min(bb_min[1], v[1]) - bb_min[2] = min(bb_min[2], v[2]) - bb_max[0] = max(bb_max[0], v[0]) - bb_max[1] = max(bb_max[1], v[1]) - bb_max[2] = max(bb_max[2], v[2]) - - - for child in node.children: - bb_min, bb_max = get_bounding_box_for_node(child, bb_min, bb_max, transformation) - - return bb_min, bb_max - -def try_load_functions(library_path, dll): - ''' - Try to bind to aiImportFile and aiReleaseImport - - Arguments - --------- - library_path: path to current lib - dll: ctypes handle to library - - Returns - --------- - If unsuccessful: - None - If successful: - Tuple containing (library_path, - load from filename function, - load from memory function, - export to filename function, - export to blob function, - release function, - ctypes handle to assimp library) - ''' - - try: - load = dll.aiImportFile - release = dll.aiReleaseImport - load_mem = dll.aiImportFileFromMemory - export = dll.aiExportScene - export2blob = dll.aiExportSceneToBlob - except AttributeError: - #OK, this is a library, but it doesn't have the functions we need - return None - - # library found! - from .structs import Scene, ExportDataBlob - load.restype = POINTER(Scene) - load_mem.restype = POINTER(Scene) - export2blob.restype = POINTER(ExportDataBlob) - return (library_path, load, load_mem, export, export2blob, release, dll) - -def search_library(): - ''' - Loads the assimp library. - Throws exception AssimpError if no library_path is found - - Returns: tuple, (load from filename function, - load from memory function, - export to filename function, - export to blob function, - release function, - dll) - ''' - #this path - folder = os.path.dirname(__file__) - - # silence 'DLL not found' message boxes on win - try: - ctypes.windll.kernel32.SetErrorMode(0x8007) - except AttributeError: - pass - - candidates = [] - # test every file - for curfolder in [folder]+additional_dirs: - if os.path.isdir(curfolder): - for filename in os.listdir(curfolder): - # our minimum requirement for candidates is that - # they should contain 'assimp' somewhere in - # their name - if filename.lower().find('assimp')==-1 : - continue - is_out=1 - for et in ext_whitelist: - if et in filename.lower(): - is_out=0 - break - if is_out: - continue - - library_path = os.path.join(curfolder, filename) - logger.debug('Try ' + library_path) - try: - dll = ctypes.cdll.LoadLibrary(library_path) - except Exception as e: - logger.warning(str(e)) - # OK, this except is evil. But different OSs will throw different - # errors. So just ignore any errors. - continue - # see if the functions we need are in the dll - loaded = try_load_functions(library_path, dll) - if loaded: candidates.append(loaded) - - if not candidates: - # no library found - raise AssimpError("assimp library not found") - else: - # get the newest library_path - candidates = map(lambda x: (os.lstat(x[0])[-2], x), candidates) - res = max(candidates, key=operator.itemgetter(0))[1] - logger.debug('Using assimp library located at ' + res[0]) - - # XXX: if there are 1000 dll/so files containing 'assimp' - # in their name, do we have all of them in our address - # space now until gc kicks in? - - # XXX: take version postfix of the .so on linux? - return res[1:] - -def hasattr_silent(object, name): - """ - Calls hasttr() with the given parameters and preserves the legacy (pre-Python 3.2) - functionality of silently catching exceptions. - - Returns the result of hasatter() or False if an exception was raised. - """ - - try: - return hasattr(object, name) - except: - return False +#-*- coding: UTF-8 -*- + +""" +Some fancy helper functions. +""" + +import os +import ctypes +import operator + +from distutils.sysconfig import get_python_lib +import re +import sys + +try: import numpy +except ImportError: numpy = None + +import logging;logger = logging.getLogger("pyassimp") + +from .errors import AssimpError + +additional_dirs, ext_whitelist = [],[] + +# populate search directories and lists of allowed file extensions +# depending on the platform we're running on. +if os.name=='posix': + additional_dirs.append('./') + additional_dirs.append('/usr/lib/') + additional_dirs.append('/usr/lib/x86_64-linux-gnu/') + additional_dirs.append('/usr/local/lib/') + + if 'LD_LIBRARY_PATH' in os.environ: + additional_dirs.extend([item for item in os.environ['LD_LIBRARY_PATH'].split(':') if item]) + + # check if running from anaconda. + if "conda" or "continuum" in sys.version.lower(): + cur_path = get_python_lib() + pattern = re.compile('.*\/lib\/') + conda_lib = pattern.match(cur_path).group() + logger.info("Adding Anaconda lib path:"+ conda_lib) + additional_dirs.append(conda_lib) + + # note - this won't catch libassimp.so.N.n, but + # currently there's always a symlink called + # libassimp.so in /usr/local/lib. + ext_whitelist.append('.so') + # libassimp.dylib in /usr/local/lib + ext_whitelist.append('.dylib') + +elif os.name=='nt': + ext_whitelist.append('.dll') + path_dirs = os.environ['PATH'].split(';') + additional_dirs.extend(path_dirs) + +def vec2tuple(x): + """ Converts a VECTOR3D to a Tuple """ + return (x.x, x.y, x.z) + +def transform(vector3, matrix4x4): + """ Apply a transformation matrix on a 3D vector. + + :param vector3: array with 3 elements + :param matrix4x4: 4x4 matrix + """ + if numpy: + return numpy.dot(matrix4x4, numpy.append(vector3, 1.)) + else: + m0,m1,m2,m3 = matrix4x4; x,y,z = vector3 + return [ + m0[0]*x + m0[1]*y + m0[2]*z + m0[3], + m1[0]*x + m1[1]*y + m1[2]*z + m1[3], + m2[0]*x + m2[1]*y + m2[2]*z + m2[3], + m3[0]*x + m3[1]*y + m3[2]*z + m3[3] + ] + +def _inv(matrix4x4): + m0,m1,m2,m3 = matrix4x4 + + det = m0[3]*m1[2]*m2[1]*m3[0] - m0[2]*m1[3]*m2[1]*m3[0] - \ + m0[3]*m1[1]*m2[2]*m3[0] + m0[1]*m1[3]*m2[2]*m3[0] + \ + m0[2]*m1[1]*m2[3]*m3[0] - m0[1]*m1[2]*m2[3]*m3[0] - \ + m0[3]*m1[2]*m2[0]*m3[1] + m0[2]*m1[3]*m2[0]*m3[1] + \ + m0[3]*m1[0]*m2[2]*m3[1] - m0[0]*m1[3]*m2[2]*m3[1] - \ + m0[2]*m1[0]*m2[3]*m3[1] + m0[0]*m1[2]*m2[3]*m3[1] + \ + m0[3]*m1[1]*m2[0]*m3[2] - m0[1]*m1[3]*m2[0]*m3[2] - \ + m0[3]*m1[0]*m2[1]*m3[2] + m0[0]*m1[3]*m2[1]*m3[2] + \ + m0[1]*m1[0]*m2[3]*m3[2] - m0[0]*m1[1]*m2[3]*m3[2] - \ + m0[2]*m1[1]*m2[0]*m3[3] + m0[1]*m1[2]*m2[0]*m3[3] + \ + m0[2]*m1[0]*m2[1]*m3[3] - m0[0]*m1[2]*m2[1]*m3[3] - \ + m0[1]*m1[0]*m2[2]*m3[3] + m0[0]*m1[1]*m2[2]*m3[3] + + return[[( m1[2]*m2[3]*m3[1] - m1[3]*m2[2]*m3[1] + m1[3]*m2[1]*m3[2] - m1[1]*m2[3]*m3[2] - m1[2]*m2[1]*m3[3] + m1[1]*m2[2]*m3[3]) /det, + ( m0[3]*m2[2]*m3[1] - m0[2]*m2[3]*m3[1] - m0[3]*m2[1]*m3[2] + m0[1]*m2[3]*m3[2] + m0[2]*m2[1]*m3[3] - m0[1]*m2[2]*m3[3]) /det, + ( m0[2]*m1[3]*m3[1] - m0[3]*m1[2]*m3[1] + m0[3]*m1[1]*m3[2] - m0[1]*m1[3]*m3[2] - m0[2]*m1[1]*m3[3] + m0[1]*m1[2]*m3[3]) /det, + ( m0[3]*m1[2]*m2[1] - m0[2]*m1[3]*m2[1] - m0[3]*m1[1]*m2[2] + m0[1]*m1[3]*m2[2] + m0[2]*m1[1]*m2[3] - m0[1]*m1[2]*m2[3]) /det], + [( m1[3]*m2[2]*m3[0] - m1[2]*m2[3]*m3[0] - m1[3]*m2[0]*m3[2] + m1[0]*m2[3]*m3[2] + m1[2]*m2[0]*m3[3] - m1[0]*m2[2]*m3[3]) /det, + ( m0[2]*m2[3]*m3[0] - m0[3]*m2[2]*m3[0] + m0[3]*m2[0]*m3[2] - m0[0]*m2[3]*m3[2] - m0[2]*m2[0]*m3[3] + m0[0]*m2[2]*m3[3]) /det, + ( m0[3]*m1[2]*m3[0] - m0[2]*m1[3]*m3[0] - m0[3]*m1[0]*m3[2] + m0[0]*m1[3]*m3[2] + m0[2]*m1[0]*m3[3] - m0[0]*m1[2]*m3[3]) /det, + ( m0[2]*m1[3]*m2[0] - m0[3]*m1[2]*m2[0] + m0[3]*m1[0]*m2[2] - m0[0]*m1[3]*m2[2] - m0[2]*m1[0]*m2[3] + m0[0]*m1[2]*m2[3]) /det], + [( m1[1]*m2[3]*m3[0] - m1[3]*m2[1]*m3[0] + m1[3]*m2[0]*m3[1] - m1[0]*m2[3]*m3[1] - m1[1]*m2[0]*m3[3] + m1[0]*m2[1]*m3[3]) /det, + ( m0[3]*m2[1]*m3[0] - m0[1]*m2[3]*m3[0] - m0[3]*m2[0]*m3[1] + m0[0]*m2[3]*m3[1] + m0[1]*m2[0]*m3[3] - m0[0]*m2[1]*m3[3]) /det, + ( m0[1]*m1[3]*m3[0] - m0[3]*m1[1]*m3[0] + m0[3]*m1[0]*m3[1] - m0[0]*m1[3]*m3[1] - m0[1]*m1[0]*m3[3] + m0[0]*m1[1]*m3[3]) /det, + ( m0[3]*m1[1]*m2[0] - m0[1]*m1[3]*m2[0] - m0[3]*m1[0]*m2[1] + m0[0]*m1[3]*m2[1] + m0[1]*m1[0]*m2[3] - m0[0]*m1[1]*m2[3]) /det], + [( m1[2]*m2[1]*m3[0] - m1[1]*m2[2]*m3[0] - m1[2]*m2[0]*m3[1] + m1[0]*m2[2]*m3[1] + m1[1]*m2[0]*m3[2] - m1[0]*m2[1]*m3[2]) /det, + ( m0[1]*m2[2]*m3[0] - m0[2]*m2[1]*m3[0] + m0[2]*m2[0]*m3[1] - m0[0]*m2[2]*m3[1] - m0[1]*m2[0]*m3[2] + m0[0]*m2[1]*m3[2]) /det, + ( m0[2]*m1[1]*m3[0] - m0[1]*m1[2]*m3[0] - m0[2]*m1[0]*m3[1] + m0[0]*m1[2]*m3[1] + m0[1]*m1[0]*m3[2] - m0[0]*m1[1]*m3[2]) /det, + ( m0[1]*m1[2]*m2[0] - m0[2]*m1[1]*m2[0] + m0[2]*m1[0]*m2[1] - m0[0]*m1[2]*m2[1] - m0[1]*m1[0]*m2[2] + m0[0]*m1[1]*m2[2]) /det]] + +def get_bounding_box(scene): + bb_min = [1e10, 1e10, 1e10] # x,y,z + bb_max = [-1e10, -1e10, -1e10] # x,y,z + inv = numpy.linalg.inv if numpy else _inv + return get_bounding_box_for_node(scene.rootnode, bb_min, bb_max, inv(scene.rootnode.transformation)) + +def get_bounding_box_for_node(node, bb_min, bb_max, transformation): + + if numpy: + transformation = numpy.dot(transformation, node.transformation) + else: + t0,t1,t2,t3 = transformation + T0,T1,T2,T3 = node.transformation + transformation = [ [ + t0[0]*T0[0] + t0[1]*T1[0] + t0[2]*T2[0] + t0[3]*T3[0], + t0[0]*T0[1] + t0[1]*T1[1] + t0[2]*T2[1] + t0[3]*T3[1], + t0[0]*T0[2] + t0[1]*T1[2] + t0[2]*T2[2] + t0[3]*T3[2], + t0[0]*T0[3] + t0[1]*T1[3] + t0[2]*T2[3] + t0[3]*T3[3] + ],[ + t1[0]*T0[0] + t1[1]*T1[0] + t1[2]*T2[0] + t1[3]*T3[0], + t1[0]*T0[1] + t1[1]*T1[1] + t1[2]*T2[1] + t1[3]*T3[1], + t1[0]*T0[2] + t1[1]*T1[2] + t1[2]*T2[2] + t1[3]*T3[2], + t1[0]*T0[3] + t1[1]*T1[3] + t1[2]*T2[3] + t1[3]*T3[3] + ],[ + t2[0]*T0[0] + t2[1]*T1[0] + t2[2]*T2[0] + t2[3]*T3[0], + t2[0]*T0[1] + t2[1]*T1[1] + t2[2]*T2[1] + t2[3]*T3[1], + t2[0]*T0[2] + t2[1]*T1[2] + t2[2]*T2[2] + t2[3]*T3[2], + t2[0]*T0[3] + t2[1]*T1[3] + t2[2]*T2[3] + t2[3]*T3[3] + ],[ + t3[0]*T0[0] + t3[1]*T1[0] + t3[2]*T2[0] + t3[3]*T3[0], + t3[0]*T0[1] + t3[1]*T1[1] + t3[2]*T2[1] + t3[3]*T3[1], + t3[0]*T0[2] + t3[1]*T1[2] + t3[2]*T2[2] + t3[3]*T3[2], + t3[0]*T0[3] + t3[1]*T1[3] + t3[2]*T2[3] + t3[3]*T3[3] + ] ] + + for mesh in node.meshes: + for v in mesh.vertices: + v = transform(v, transformation) + bb_min[0] = min(bb_min[0], v[0]) + bb_min[1] = min(bb_min[1], v[1]) + bb_min[2] = min(bb_min[2], v[2]) + bb_max[0] = max(bb_max[0], v[0]) + bb_max[1] = max(bb_max[1], v[1]) + bb_max[2] = max(bb_max[2], v[2]) + + + for child in node.children: + bb_min, bb_max = get_bounding_box_for_node(child, bb_min, bb_max, transformation) + + return bb_min, bb_max + +def try_load_functions(library_path, dll): + ''' + Try to bind to aiImportFile and aiReleaseImport + + Arguments + --------- + library_path: path to current lib + dll: ctypes handle to library + + Returns + --------- + If unsuccessful: + None + If successful: + Tuple containing (library_path, + load from filename function, + load from memory function, + export to filename function, + export to blob function, + release function, + ctypes handle to assimp library) + ''' + + try: + load = dll.aiImportFile + release = dll.aiReleaseImport + load_mem = dll.aiImportFileFromMemory + export = dll.aiExportScene + export2blob = dll.aiExportSceneToBlob + except AttributeError: + #OK, this is a library, but it doesn't have the functions we need + return None + + # library found! + from .structs import Scene, ExportDataBlob + load.restype = ctypes.POINTER(Scene) + load_mem.restype = ctypes.POINTER(Scene) + export2blob.restype = ctypes.POINTER(ExportDataBlob) + return (library_path, load, load_mem, export, export2blob, release, dll) + +def search_library(): + ''' + Loads the assimp library. + Throws exception AssimpError if no library_path is found + + Returns: tuple, (load from filename function, + load from memory function, + export to filename function, + export to blob function, + release function, + dll) + ''' + #this path + folder = os.path.dirname(__file__) + + # silence 'DLL not found' message boxes on win + try: + ctypes.windll.kernel32.SetErrorMode(0x8007) + except AttributeError: + pass + + candidates = [] + # test every file + for curfolder in [folder]+additional_dirs: + if os.path.isdir(curfolder): + for filename in os.listdir(curfolder): + # our minimum requirement for candidates is that + # they should contain 'assimp' somewhere in + # their name + if filename.lower().find('assimp')==-1 : + continue + is_out=1 + for et in ext_whitelist: + if et in filename.lower(): + is_out=0 + break + if is_out: + continue + + library_path = os.path.join(curfolder, filename) + logger.debug('Try ' + library_path) + try: + dll = ctypes.cdll.LoadLibrary(library_path) + except Exception as e: + logger.warning(str(e)) + # OK, this except is evil. But different OSs will throw different + # errors. So just ignore any errors. + continue + # see if the functions we need are in the dll + loaded = try_load_functions(library_path, dll) + if loaded: candidates.append(loaded) + + if not candidates: + # no library found + raise AssimpError("assimp library not found") + else: + # get the newest library_path + candidates = map(lambda x: (os.lstat(x[0])[-2], x), candidates) + res = max(candidates, key=operator.itemgetter(0))[1] + logger.debug('Using assimp library located at ' + res[0]) + + # XXX: if there are 1000 dll/so files containing 'assimp' + # in their name, do we have all of them in our address + # space now until gc kicks in? + + # XXX: take version postfix of the .so on linux? + return res[1:] + +def hasattr_silent(object, name): + """ + Calls hasttr() with the given parameters and preserves the legacy (pre-Python 3.2) + functionality of silently catching exceptions. + + Returns the result of hasatter() or False if an exception was raised. + """ + + try: + return hasattr(object, name) + except AttributeError: + return False diff --git a/port/PyAssimp/pyassimp/postprocess.py b/port/PyAssimp/pyassimp/postprocess.py index 932c7c660..0c55d6798 100644 --- a/port/PyAssimp/pyassimp/postprocess.py +++ b/port/PyAssimp/pyassimp/postprocess.py @@ -435,6 +435,7 @@ aiProcess_Debone = 0x4000000 aiProcess_GenEntityMeshes = 0x100000 aiProcess_OptimizeAnimations = 0x200000 aiProcess_FixTexturePaths = 0x200000 +aiProcess_EmbedTextures = 0x10000000, ## @def aiProcess_ConvertToLeftHanded # @brief Shortcut flag for Direct3D-based applications. diff --git a/port/PyAssimp/pyassimp/structs.py b/port/PyAssimp/pyassimp/structs.py index ddfd87f8a..d478b861b 100644 --- a/port/PyAssimp/pyassimp/structs.py +++ b/port/PyAssimp/pyassimp/structs.py @@ -1,6 +1,6 @@ #-*- coding: UTF-8 -*- -from ctypes import POINTER, c_void_p, c_int, c_uint, c_char, c_float, Structure, c_char_p, c_double, c_ubyte, c_size_t, c_uint32 +from ctypes import POINTER, c_void_p, c_uint, c_char, c_float, Structure, c_char_p, c_double, c_ubyte, c_size_t, c_uint32 class Vector2D(Structure): @@ -70,7 +70,7 @@ class String(Structure): See 'types.h' for details. """ - MAXLEN = 1024 + MAXLEN = 1024 _fields_ = [ # Binary length of the string excluding the terminal 0. This is NOT the diff --git a/port/PyAssimp/scripts/fixed_pipeline_3d_viewer.py b/port/PyAssimp/scripts/fixed_pipeline_3d_viewer.py index 7f98b9004..c2f6cebb7 100755 --- a/port/PyAssimp/scripts/fixed_pipeline_3d_viewer.py +++ b/port/PyAssimp/scripts/fixed_pipeline_3d_viewer.py @@ -24,12 +24,13 @@ This sample is based on several sources, including: - ASSIMP's C++ SimpleOpenGL viewer """ -import os, sys +import sys from OpenGL.GLUT import * from OpenGL.GLU import * from OpenGL.GL import * -import logging;logger = logging.getLogger("pyassimp_opengl") +import logging +logger = logging.getLogger("pyassimp_opengl") logging.basicConfig(level=logging.INFO) import math diff --git a/port/PyAssimp/scripts/sample.py b/port/PyAssimp/scripts/sample.py index 9012eb374..3cd4b3ec4 100755 --- a/port/PyAssimp/scripts/sample.py +++ b/port/PyAssimp/scripts/sample.py @@ -5,7 +5,7 @@ This module demonstrates the functionality of PyAssimp. """ -import os, sys +import sys import logging logging.basicConfig(level=logging.INFO) @@ -50,8 +50,8 @@ def main(filename=None): print(" colors:" + str(len(mesh.colors))) tcs = mesh.texturecoords if tcs.any(): - for index, tc in enumerate(tcs): - print(" texture-coords "+ str(index) + ":" + str(len(tcs[index])) + "first3:" + str(tcs[index][:3])) + for tc_index, tc in enumerate(tcs): + print(" texture-coords "+ str(tc_index) + ":" + str(len(tcs[tc_index])) + "first3:" + str(tcs[tc_index][:3])) else: print(" no texture coordinates") diff --git a/revision.h.in b/revision.h.in index 87c41fa89..0ad58feb7 100644 --- a/revision.h.in +++ b/revision.h.in @@ -4,4 +4,15 @@ #define GitVersion 0x@GIT_COMMIT_HASH@ #define GitBranch "@GIT_BRANCH@" +#define VER_MAJOR @ASSIMP_VERSION_MAJOR@ +#define VER_MINOR @ASSIMP_VERSION_MINOR@ +#define VER_PATCH @ASSIMP_VERSION_PATCH@ +#define VER_BUILD @ASSIMP_PACKAGE_VERSION@ + +#define STR_HELP(x) #x +#define STR(x) STR_HELP(x) + +#define VER_FILEVERSION VER_MAJOR,VER_MINOR,VER_PATCH,VER_BUILD +#define VER_FILEVERSION_STR STR(VER_MAJOR) "." STR(VER_MINOR) "." STR(VER_PATCH) "." STR(VER_BUILD) + #endif // ASSIMP_REVISION_H_INC diff --git a/samples/README b/samples/README index 995a84f0c..a9f44cec2 100644 --- a/samples/README +++ b/samples/README @@ -12,22 +12,25 @@ use in various real-world environments. Workspaces to build the samples can be found in the respective directories. The VC workspaces copy the created executables to the ./bin directory. -All GL-based samples depend on GLUT, some on DevIL. For convenience, -these libraries are included in the repository in their respective - Windows/x86 prebuilt versions. To build on linux, install the - required packages using the package manager of your choice. +All GL-based samples depend on FreeGLUT, the image loading will be done +by a header-only library. For convenience, these libraries are included +in the repository in their respective Windows/x86 prebuilt versions. +To build on linux, install freeglut using the package manager of your +choice. For instance on Ubuntu to install freeglut you can use the following +command: -Also note that the VS workspaces link against the DLL version of the -Assimp library, thus you need to build it in the first place ( -assimp-release-dll build configuration). The Assimp DLL needs to be -copied to ./bin as well (the VS workspaces will try to do this -automatically). +> sudo apt install freeglut + +All samples will be placed at + +Win32: //bin + +or Linux : /bin 2. List of samples ------------------ - SimpleOpenGL A very simple and straightforward OpenGL sample. It loads a diff --git a/scripts/BlenderImporter/genblenddna.py b/scripts/BlenderImporter/genblenddna.py index caa58ded7..cca595eca 100644 --- a/scripts/BlenderImporter/genblenddna.py +++ b/scripts/BlenderImporter/genblenddna.py @@ -291,7 +291,9 @@ def main(): #s += "#endif\n" output.write(templt.replace("",s)) - + + # we got here, so no error + return 0 if __name__ == "__main__": sys.exit(main()) diff --git a/scripts/StepImporter/CppGenerator.py b/scripts/StepImporter/CppGenerator.py index 156f19523..b9dba9902 100644 --- a/scripts/StepImporter/CppGenerator.py +++ b/scripts/StepImporter/CppGenerator.py @@ -52,8 +52,8 @@ use_ifc_template = False input_step_template_h = 'StepReaderGen.h.template' input_step_template_cpp = 'StepReaderGen.cpp.template' -input_ifc_template_h = 'IFCReaderGen.h.template' -input_ifc_template_cpp = 'IFCReaderGen.cpp.template' +input_ifc_template_h = 'IFCReaderGen.h.template' +input_ifc_template_cpp = 'IFCReaderGen.cpp.template' cpp_keywords = "class" @@ -87,7 +87,7 @@ template_type = r""" template_stub_decl = '\tDECL_CONV_STUB({type});\n' template_schema = '\t\tSchemaEntry("{normalized_name}",&STEP::ObjectHelper<{type},{argcnt}>::Construct )\n' -template_schema_type = '\t\tSchemaEntry("{normalized_name}",NULL )\n' +template_schema_type = '\t\tSchemaEntry("{normalized_name}",nullptr )\n' template_converter = r""" // ----------------------------------------------------------------------------------------------------------- template <> size_t GenericFill<{type}>(const DB& db, const LIST& params, {type}* in) @@ -99,7 +99,7 @@ template_converter_prologue_a = '\tsize_t base = GenericFill(db,params,static_ca template_converter_prologue_b = '\tsize_t base = 0;\n' template_converter_check_argcnt = '\tif (params.GetSize() < {max_arg}) {{ throw STEP::TypeError("expected {max_arg} arguments to {name}"); }}' template_converter_code_per_field = r""" do {{ // convert the '{fieldname}' argument - boost::shared_ptr arg = params[base++];{handle_unset}{convert} + std::shared_ptr arg = params[base++];{handle_unset}{convert} }} while(0); """ template_allow_optional = r""" @@ -151,11 +151,8 @@ def handle_unset_args(field,entity,schema,argnum): return n+template_allow_optional.format() def get_single_conversion(field,schema,argnum=0,classname='?'): - typen = field.type name = field.name - if field.collection: - typen = 'LIST' - return template_convert_single.format(type=typen,name=name,argnum=argnum,classname=classname,full_type=field.fullspec) + return template_convert_single.format(name=name,argnum=argnum,classname=classname,full_type=field.fullspec) def count_args_up(entity,schema): return len(entity.members) + (count_args_up(schema.entities[entity.parent],schema) if entity.parent else 0) @@ -218,7 +215,7 @@ def get_derived(e,schema): return res def get_hierarchy(e,schema): - return get_derived(e.schema)+[e.name]+get_base_classes(e,schema) + return get_derived(e, schema)+[e.name]+get_base_classes(e,schema) def sort_entity_list(schema): deps = [] @@ -300,5 +297,8 @@ def work(filename): with open(output_file_cpp,'wt') as outp: outp.write(inp.read().replace('{schema-static-table}',schema_table).replace('{converter-impl}',converters)) + # Finished without error, so return 0 + return 0 + if __name__ == "__main__": sys.exit(work(sys.argv[1] if len(sys.argv)>1 else 'schema.exp')) diff --git a/scripts/StepImporter/ExpressReader.py b/scripts/StepImporter/ExpressReader.py index 84aaadbdc..c2a39e70b 100644 --- a/scripts/StepImporter/ExpressReader.py +++ b/scripts/StepImporter/ExpressReader.py @@ -43,7 +43,8 @@ """Parse an EXPRESS file and extract basic information on all entities and data types contained""" -import sys, os, re +import sys +import re from collections import OrderedDict re_match_entity = re.compile(r""" diff --git a/scripts/StepImporter/StepReaderGen.cpp.template b/scripts/StepImporter/StepReaderGen.cpp.template index 91705f7a4..f3240c099 100644 --- a/scripts/StepImporter/StepReaderGen.cpp.template +++ b/scripts/StepImporter/StepReaderGen.cpp.template @@ -2,7 +2,7 @@ Open Asset Import Library (ASSIMP) ---------------------------------------------------------------------- -Copyright (c) 2006-2018, ASSIMP Development Team +Copyright (c) 2006-2019, ASSIMP Development Team All rights reserved. Redistribution and use of this software in source and binary forms, @@ -66,7 +66,7 @@ namespace STEP { // ----------------------------------------------------------------------------------------------------------- template <> size_t GenericFill(const STEP::DB& db, const LIST& params, NotImplemented* in) { - return 0; + return 0u; } diff --git a/scripts/StepImporter/StepReaderGen.h.template b/scripts/StepImporter/StepReaderGen.h.template index 5f9a2fd5d..7d4d77ef4 100644 --- a/scripts/StepImporter/StepReaderGen.h.template +++ b/scripts/StepImporter/StepReaderGen.h.template @@ -2,7 +2,7 @@ Open Asset Import Library (ASSIMP) ---------------------------------------------------------------------- -Copyright (c) 2006-2018, ASSIMP Development Team +Copyright (c) 2006-2019, ASSIMP Development Team All rights reserved. Redistribution and use of this software in source and binary forms, @@ -47,25 +47,23 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. namespace Assimp { namespace StepFile { - using namespace STEP; - using namespace STEP::EXPRESS; - - - struct NotImplemented : public ObjectHelper { - - }; - - // ****************************************************************************** - // StepFile Custom data types - // ****************************************************************************** +using namespace STEP; +using namespace STEP::EXPRESS; + +struct NotImplemented : public ObjectHelper { + +}; + +// ****************************************************************************** +// StepFile Custom data types +// ****************************************************************************** {types} - - // ****************************************************************************** - // StepFile Entities - // ****************************************************************************** +// ****************************************************************************** +// StepFile Entities +// ****************************************************************************** {predefs} {entities} @@ -73,11 +71,12 @@ namespace StepFile { void GetSchema(EXPRESS::ConversionSchema& out); } //! StepFile + namespace STEP { - // ****************************************************************************** - // Converter stubs - // ****************************************************************************** +// ****************************************************************************** +// Converter stubs +// ****************************************************************************** #define DECL_CONV_STUB(type) template <> size_t GenericFill(const STEP::DB& db, const EXPRESS::LIST& params, IFC::type* in) diff --git a/test/models/FBX/boxWithCompressedCTypeArray.FBX b/test/models/FBX/boxWithCompressedCTypeArray.FBX new file mode 100644 index 000000000..2f71bc7ee Binary files /dev/null and b/test/models/FBX/boxWithCompressedCTypeArray.FBX differ diff --git a/test/models/FBX/boxWithUncompressedCTypeArray.FBX b/test/models/FBX/boxWithUncompressedCTypeArray.FBX new file mode 100644 index 000000000..2ec3d77d7 Binary files /dev/null and b/test/models/FBX/boxWithUncompressedCTypeArray.FBX differ diff --git a/test/models/FBX/cubes_nonames.fbx b/test/models/FBX/cubes_nonames.fbx new file mode 100644 index 000000000..810657aff --- /dev/null +++ b/test/models/FBX/cubes_nonames.fbx @@ -0,0 +1,852 @@ +; FBX 7.5.0 project file +; ---------------------------------------------------- + +FBXHeaderExtension: { + FBXHeaderVersion: 1003 + FBXVersion: 7500 + CreationTimeStamp: { + Version: 1000 + Year: 2019 + Month: 1 + Day: 7 + Hour: 16 + Minute: 17 + Second: 31 + Millisecond: 730 + } + Creator: "FBX SDK/FBX Plugins version 2018.1.1" + SceneInfo: "SceneInfo::GlobalInfo", "UserData" { + Type: "UserData" + Version: 100 + MetaData: { + Version: 100 + Title: "" + Subject: "" + Author: "" + Keywords: "" + Revision: "" + Comment: "" + } + Properties70: { + P: "DocumentUrl", "KString", "Url", "", "U:\Some\Absolute\Path\cubes_noname.fbx" + P: "SrcDocumentUrl", "KString", "Url", "", "U:\Some\Absolute\Path\cubes_noname.fbx" + P: "Original", 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", 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" + } + Texture: 1826343863344, "Texture::Map #2", "" { + Type: "TextureVideoClip" + Version: 202 + TextureName: "Texture::Map #2" + Properties70: { + P: "CurrentTextureBlendMode", "enum", "", "",0 + P: "UVSet", "KString", "", "", "UVChannel_1" + P: "UseMaterial", "bool", "", "",1 + } + Media: "Video::Map #2" + FileName: "U:/Some/Absolute/Path/paper.png" + RelativeFilename: "paper.png" + ModelUVTranslation: 0,0 + ModelUVScaling: 1,1 + Texture_Alpha_Source: "None" + Cropping: 0,0,0,0 + } + AnimationStack: 1827077611568, "AnimStack::Take 001", "" { + Properties70: { + P: "LocalStop", "KTime", "Time", "",153953860000 + P: "ReferenceStop", "KTime", "Time", "",153953860000 + } + } + AnimationCurveNode: 1827077610320, "AnimCurveNode::mr displacement use global settings", "" { + Properties70: { + P: "d|mr displacement use global settings", "Bool", "", "A",1 + } + } + AnimationCurveNode: 1827077600128, "AnimCurveNode::mr displacement view dependent", "" { + Properties70: { + P: "d|mr displacement view dependent", "Bool", "", "A",1 + } + } + AnimationCurveNode: 1827077602624, "AnimCurveNode::mr displacement method", "" { + Properties70: { + P: "d|mr displacement method", "Integer", "", "A",6 + } + } + AnimationCurveNode: 1827077604912, "AnimCurveNode::mr displacement smoothing on", "" { + Properties70: { + P: "d|mr displacement smoothing on", "Bool", "", "A",1 + } + } + AnimationCurveNode: 1827077603664, "AnimCurveNode::mr displacement edge length", "" { + Properties70: { + P: "d|mr displacement edge length", "Number", "", "A",2 + } + } + AnimationCurveNode: 1827077606160, "AnimCurveNode::mr displacement max displace", "" { + Properties70: { + P: "d|mr displacement max displace", "Number", "", "A",20 + } + } + AnimationCurveNode: 1827077607824, "AnimCurveNode::mr displacement parametric subdivision level", "" { + Properties70: { + P: "d|mr displacement parametric subdivision level", "Integer", "", "A",5 + } + } + AnimationCurveNode: 1827077611360, "AnimCurveNode::MaxHandle", "" { + Properties70: { + P: "d|MaxHandle", "Integer", "", "A",1 + } + } + AnimationLayer: 1827070138064, "AnimLayer::BaseLayer", "" { + } + CollectionExclusive: 1827093511360, "DisplayLayer::Box", "DisplayLayer" { + Properties70: { + P: "Color", "ColorRGB", "Color", "",0.607999980449677,0,0.157000005245209 + } + } +} + +; Object connections +;------------------------------------------------------------------ + +Connections: { + + ;Model::Box, Model::RootNode + C: "OO",1826985145024,0 + + ;AnimLayer::BaseLayer, AnimStack::Take 001 + C: "OO",1827070138064,1827077611568 + + ;AnimCurveNode::mr displacement use global settings, AnimLayer::BaseLayer + C: "OO",1827077610320,1827070138064 + + ;AnimCurveNode::mr displacement view dependent, AnimLayer::BaseLayer + C: "OO",1827077600128,1827070138064 + + ;AnimCurveNode::mr displacement method, AnimLayer::BaseLayer + C: "OO",1827077602624,1827070138064 + + ;AnimCurveNode::mr displacement smoothing on, AnimLayer::BaseLayer + C: "OO",1827077604912,1827070138064 + + ;AnimCurveNode::mr displacement edge length, AnimLayer::BaseLayer + C: "OO",1827077603664,1827070138064 + + ;AnimCurveNode::mr displacement max displace, AnimLayer::BaseLayer + C: "OO",1827077606160,1827070138064 + + ;AnimCurveNode::mr displacement parametric subdivision level, AnimLayer::BaseLayer + C: "OO",1827077607824,1827070138064 + + ;AnimCurveNode::MaxHandle, AnimLayer::BaseLayer + C: "OO",1827077611360,1827070138064 + + ;Texture::Map #2, Material::Default + C: "OP",1826343863344,1826343864784, "DiffuseColor" + + ;Texture::Map #2, Material::Default + C: "OP",1826343863344,1826343864784, "TransparentColor" + + ;Video::Map #2, Texture::Map #2 + C: "OO",1826343863824,1826343863344 + + ;Geometry::, Model::Box + C: "OO",1827080161440,1826985145024 + + ;Material::Default, Model::Box + C: "OO",1826343864784,1826985145024 + + ;AnimCurveNode::mr displacement use global settings, Model::Box + C: "OP",1827077610320,1826985145024, "mr displacement use global settings" + + ;AnimCurveNode::mr displacement view dependent, Model::Box + C: "OP",1827077600128,1826985145024, "mr displacement view dependent" + + ;AnimCurveNode::mr displacement method, Model::Box + C: "OP",1827077602624,1826985145024, "mr displacement method" + + ;AnimCurveNode::mr displacement smoothing on, Model::Box + C: "OP",1827077604912,1826985145024, "mr displacement smoothing on" + + ;AnimCurveNode::mr displacement edge length, Model::Box + C: "OP",1827077603664,1826985145024, "mr displacement edge length" + + ;AnimCurveNode::mr displacement max displace, Model::Box + C: "OP",1827077606160,1826985145024, "mr displacement max displace" + + ;AnimCurveNode::mr displacement parametric subdivision level, Model::Box + C: "OP",1827077607824,1826985145024, "mr displacement parametric subdivision level" + + ;AnimCurveNode::MaxHandle, Model::Box + C: "OP",1827077611360,1826985145024, "MaxHandle" + + ;Model::Box, DisplayLayer::Box + C: "OO",1826985145024,1827093511360 +} +;Takes section +;---------------------------------------------------- + +Takes: { + Current: "Take 001" + Take: "Take 001" { + FileName: "Take_001.tak" + LocalTime: 0,153953860000 + ReferenceTime: 0,153953860000 + } +} diff --git a/test/models/PLY/cube_test.ply b/test/models/PLY/cube_test.ply new file mode 100644 index 000000000..a86d022a5 --- /dev/null +++ b/test/models/PLY/cube_test.ply @@ -0,0 +1,24 @@ +ply +format ascii 1.0 +comment Created by Open Asset Import Library - http://assimp.sf.net (v4.1.3297435427) +element vertex 8 +property float x +property float y +property float z +element face 6 +property list uchar int vertex_index +end_header +0 0 0 +0 0 1 +0 1 1 +0 1 0 +1 0 0 +1 0 1 +1 1 1 +1 1 0 +4 0 1 2 3 +4 7 6 5 4 +4 0 4 5 1 +4 1 5 6 2 +4 2 6 7 3 +4 3 7 4 0 diff --git a/test/models/PLY/points.ply b/test/models/PLY/points.ply new file mode 100644 index 000000000..91f4bb83b --- /dev/null +++ b/test/models/PLY/points.ply @@ -0,0 +1,17 @@ +ply +format ascii 1.0 +element vertex 4 +property float x +property float y +property float z +property uchar red +property uchar green +property uchar blue +property float nx +property float ny +property float nz +end_header +0.0 0.0 0.0 255 255 255 0.0 1.0 0.0 +0.0 0.0 1.0 255 0 255 0.0 0.0 1.0 +0.0 1.0 0.0 255 255 0 1.0 0.0 0.0 +0.0 1.0 1.0 0 255 255 1.0 1.0 0.0 diff --git a/test/models/glTF2/simple_skin/simple_skin.gltf b/test/models/glTF2/simple_skin/simple_skin.gltf new file mode 100644 index 000000000..e075bb34c --- /dev/null +++ b/test/models/glTF2/simple_skin/simple_skin.gltf @@ -0,0 +1,148 @@ +{ + "scenes" : [ { + "nodes" : [ 0 ] + } ], + + "nodes" : [ { + "skin" : 0, + "mesh" : 0, + "children" : [ 1 ] + }, { + "children" : [ 2 ], + "translation" : [ 0.0, 1.0, 0.0 ] + }, { + "rotation" : [ 0.0, 0.0, 0.0, 1.0 ] + } ], + + "meshes" : [ { + "primitives" : [ { + "attributes" : { + "POSITION" : 1, + "JOINTS_0" : 2, + "WEIGHTS_0" : 3 + }, + "indices" : 0 + } ] + } ], + + "skins" : [ { + "inverseBindMatrices" : 4, + "joints" : [ 1, 2 ] + } ], + + "animations" : [ { + "channels" : [ { + "sampler" : 0, + "target" : { + "node" : 2, + "path" : "rotation" + } + } ], + "samplers" : [ { + "input" : 5, + "interpolation" : "LINEAR", + "output" : 6 + } ] + } ], + + "buffers" : [ { + "uri" : "data:application/gltf-buffer;base64,AAABAAMAAAADAAIAAgADAAUAAgAFAAQABAAFAAcABAAHAAYABgAHAAkABgAJAAgAAAAAAAAAAAAAAAAAAACAPwAAAAAAAAAAAAAAAAAAAD8AAAAAAACAPwAAAD8AAAAAAAAAAAAAgD8AAAAAAACAPwAAgD8AAAAAAAAAAAAAwD8AAAAAAACAPwAAwD8AAAAAAAAAAAAAAEAAAAAAAACAPwAAAEAAAAAA", + "byteLength" : 168 + }, { + "uri" : "data:application/gltf-buffer;base64,AAABAAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAgD8AAAAAAAAAAAAAAAAAAIA/AAAAAAAAAAAAAAAAAABAPwAAgD4AAAAAAAAAAAAAQD8AAIA+AAAAAAAAAAAAAAA/AAAAPwAAAAAAAAAAAAAAPwAAAD8AAAAAAAAAAAAAgD4AAEA/AAAAAAAAAAAAAIA+AABAPwAAAAAAAAAAAAAAAAAAgD8AAAAAAAAAAAAAAAAAAIA/AAAAAAAAAAA=", + "byteLength" : 320 + }, { + "uri" : "data:application/gltf-buffer;base64,AACAPwAAAAAAAAAAAAAAAAAAAAAAAIA/AAAAAAAAAAAAAAAAAAAAAAAAgD8AAAAAAAAAvwAAgL8AAAAAAACAPwAAgD8AAAAAAAAAAAAAAAAAAAAAAACAPwAAAAAAAAAAAAAAAAAAAAAAAIA/AAAAAAAAAL8AAIC/AAAAAAAAgD8=", + "byteLength" : 128 + }, { + "uri" : "data:application/gltf-buffer;base64,AAAAAAAAAD8AAIA/AADAPwAAAEAAACBAAABAQAAAYEAAAIBAAACQQAAAoEAAALBAAAAAAAAAAAAAAAAAAACAPwAAAAAAAAAAkxjEPkSLbD8AAAAAAAAAAPT9ND/0/TQ/AAAAAAAAAAD0/TQ/9P00PwAAAAAAAAAAkxjEPkSLbD8AAAAAAAAAAAAAAAAAAIA/AAAAAAAAAAAAAAAAAACAPwAAAAAAAAAAkxjEvkSLbD8AAAAAAAAAAPT9NL/0/TQ/AAAAAAAAAAD0/TS/9P00PwAAAAAAAAAAkxjEvkSLbD8AAAAAAAAAAAAAAAAAAIA/", + "byteLength" : 240 + } ], + + "bufferViews" : [ { + "buffer" : 0, + "byteOffset" : 0, + "byteLength" : 48, + "target" : 34963 + }, { + "buffer" : 0, + "byteOffset" : 48, + "byteLength" : 120, + "target" : 34962 + }, { + "buffer" : 1, + "byteOffset" : 0, + "byteLength" : 320, + "byteStride" : 16 + }, { + "buffer" : 2, + "byteOffset" : 0, + "byteLength" : 128 + }, { + "buffer" : 3, + "byteOffset" : 0, + "byteLength" : 240 + } ], + + "accessors" : [ { + "bufferView" : 0, + "byteOffset" : 0, + "componentType" : 5123, + "count" : 24, + "type" : "SCALAR", + "max" : [ 9 ], + "min" : [ 0 ] + }, { + "bufferView" : 1, + "byteOffset" : 0, + "componentType" : 5126, + "count" : 10, + "type" : "VEC3", + "max" : [ 1.0, 2.0, 0.0 ], + "min" : [ 0.0, 0.0, 0.0 ] + }, { + "bufferView" : 2, + "byteOffset" : 0, + "componentType" : 5123, + "count" : 10, + "type" : "VEC4", + "max" : [ 0, 1, 0, 0 ], + "min" : [ 0, 1, 0, 0 ] + }, { + "bufferView" : 2, + "byteOffset" : 160, + "componentType" : 5126, + "count" : 10, + "type" : "VEC4", + "max" : [ 1.0, 1.0, 0.0, 0.0 ], + "min" : [ 0.0, 0.0, 0.0, 0.0 ] + }, { + "bufferView" : 3, + "byteOffset" : 0, + "componentType" : 5126, + "count" : 2, + "type" : "MAT4", + "max" : [ 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, -0.5, -1.0, 0.0, 1.0 ], + "min" : [ 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, -0.5, -1.0, 0.0, 1.0 ] + }, { + "bufferView" : 4, + "byteOffset" : 0, + "componentType" : 5126, + "count" : 12, + "type" : "SCALAR", + "max" : [ 5.5 ], + "min" : [ 0.0 ] + }, { + "bufferView" : 4, + "byteOffset" : 48, + "componentType" : 5126, + "count" : 12, + "type" : "VEC4", + "max" : [ 0.0, 0.0, 0.707, 1.0 ], + "min" : [ 0.0, 0.0, -0.707, 0.707 ] + } ], + + "asset" : { + "version" : "2.0" + } +} \ No newline at end of file diff --git a/test/unit/utColladaExportCamera.cpp b/test/unit/utColladaExportCamera.cpp index 3c7412e0b..ff84422e5 100644 --- a/test/unit/utColladaExportCamera.cpp +++ b/test/unit/utColladaExportCamera.cpp @@ -52,18 +52,17 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. class ColladaExportCamera : public ::testing::Test { public: - - virtual void SetUp() - { + void SetUp() override{ ex = new Assimp::Exporter(); im = new Assimp::Importer(); } - virtual void TearDown() - { + void TearDown() override { delete ex; + ex = nullptr; delete im; + im = nullptr; } protected: @@ -71,16 +70,15 @@ protected: Assimp::Importer* im; }; -TEST_F(ColladaExportCamera, testExportCamera) -{ +TEST_F(ColladaExportCamera, testExportCamera) { const char* file = "cameraExp.dae"; const aiScene* pTest = im->ReadFile(ASSIMP_TEST_MODELS_DIR "/Collada/cameras.dae", aiProcess_ValidateDataStructure); - ASSERT_TRUE(pTest!=NULL); + ASSERT_NE( nullptr, pTest ); ASSERT_TRUE(pTest->HasCameras()); - EXPECT_EQ(AI_SUCCESS,ex->Export(pTest,"collada",file)); + EXPECT_EQ( AI_SUCCESS, ex->Export(pTest,"collada",file)); const unsigned int origNumCams( pTest->mNumCameras ); std::unique_ptr origFOV( new float[ origNumCams ] ); std::unique_ptr orifClipPlaneNear( new float[ origNumCams ] ); @@ -89,7 +87,7 @@ TEST_F(ColladaExportCamera, testExportCamera) std::unique_ptr pos( new aiVector3D[ origNumCams ] ); for (size_t i = 0; i < origNumCams; i++) { const aiCamera *orig = pTest->mCameras[ i ]; - ASSERT_TRUE( orig != nullptr ); + ASSERT_NE(nullptr, orig ); origFOV[ i ] = orig->mHorizontalFOV; orifClipPlaneNear[ i ] = orig->mClipPlaneNear; @@ -99,7 +97,7 @@ TEST_F(ColladaExportCamera, testExportCamera) } const aiScene* imported = im->ReadFile(file, aiProcess_ValidateDataStructure); - ASSERT_TRUE(imported!=NULL); + ASSERT_NE(nullptr, imported ); EXPECT_TRUE( imported->HasCameras() ); EXPECT_EQ( origNumCams, imported->mNumCameras ); @@ -119,5 +117,3 @@ TEST_F(ColladaExportCamera, testExportCamera) } #endif // ASSIMP_BUILD_NO_EXPORT - - diff --git a/test/unit/utFBXImporterExporter.cpp b/test/unit/utFBXImporterExporter.cpp index 5aca810a0..77d7e9523 100644 --- a/test/unit/utFBXImporterExporter.cpp +++ b/test/unit/utFBXImporterExporter.cpp @@ -76,6 +76,119 @@ TEST_F( utFBXImporterExporter, importBareBoxWithoutColorsAndTextureCoords ) { EXPECT_EQ(mesh->mNumVertices, 36); } +TEST_F(utFBXImporterExporter, importCubesWithNoNames) { + Assimp::Importer importer; + const aiScene *scene = importer.ReadFile(ASSIMP_TEST_MODELS_DIR "/FBX/cubes_nonames.fbx", aiProcess_ValidateDataStructure); + ASSERT_TRUE(scene); + + ASSERT_TRUE(scene->mRootNode); + const auto root = scene->mRootNode; + ASSERT_STREQ(root->mName.C_Str(), "RootNode"); + ASSERT_TRUE(root->mChildren); + ASSERT_EQ(root->mNumChildren, 2); + + const auto child0 = root->mChildren[0]; + ASSERT_TRUE(child0); + ASSERT_STREQ(child0->mName.C_Str(), "RootNode001"); + ASSERT_TRUE(child0->mChildren); + ASSERT_EQ(child0->mNumChildren, 1); + + const auto child00 = child0->mChildren[0]; + ASSERT_TRUE(child00); + ASSERT_STREQ(child00->mName.C_Str(), "RootNode001001"); + + const auto child1 = root->mChildren[1]; + ASSERT_TRUE(child1); + ASSERT_STREQ(child1->mName.C_Str(), "RootNode002"); + ASSERT_TRUE(child1->mChildren); + ASSERT_EQ(child1->mNumChildren, 1); + + const auto child10 = child1->mChildren[0]; + ASSERT_TRUE(child10); + ASSERT_STREQ(child10->mName.C_Str(), "RootNode002001"); +} + +TEST_F(utFBXImporterExporter, importCubesWithUnicodeDuplicatedNames) { + Assimp::Importer importer; + const aiScene *scene = importer.ReadFile(ASSIMP_TEST_MODELS_DIR "/FBX/cubes_with_names.fbx", aiProcess_ValidateDataStructure); + ASSERT_TRUE(scene); + + ASSERT_TRUE(scene->mRootNode); + const auto root = scene->mRootNode; + ASSERT_STREQ(root->mName.C_Str(), "RootNode"); + ASSERT_TRUE(root->mChildren); + ASSERT_EQ(root->mNumChildren, 2); + + const auto child0 = root->mChildren[0]; + ASSERT_TRUE(child0); + ASSERT_STREQ(child0->mName.C_Str(), "Cube2"); + ASSERT_TRUE(child0->mChildren); + ASSERT_EQ(child0->mNumChildren, 1); + + const auto child00 = child0->mChildren[0]; + ASSERT_TRUE(child00); + ASSERT_STREQ(child00->mName.C_Str(), "\xd0\x9a\xd1\x83\xd0\xb1\x31"); + + const auto child1 = root->mChildren[1]; + ASSERT_TRUE(child1); + ASSERT_STREQ(child1->mName.C_Str(), "Cube3"); + ASSERT_TRUE(child1->mChildren); + ASSERT_EQ(child1->mNumChildren, 1); + + const auto child10 = child1->mChildren[0]; + ASSERT_TRUE(child10); + ASSERT_STREQ(child10->mName.C_Str(), "\xd0\x9a\xd1\x83\xd0\xb1\x31""001"); +} + +TEST_F(utFBXImporterExporter, importCubesComplexTransform) { + Assimp::Importer importer; + const aiScene *scene = importer.ReadFile(ASSIMP_TEST_MODELS_DIR "/FBX/cubes_with_mirroring_and_pivot.fbx", aiProcess_ValidateDataStructure); + ASSERT_TRUE(scene); + + ASSERT_TRUE(scene->mRootNode); + const auto root = scene->mRootNode; + ASSERT_STREQ(root->mName.C_Str(), "RootNode"); + ASSERT_TRUE(root->mChildren); + ASSERT_EQ(root->mNumChildren, 2); + + const auto child0 = root->mChildren[0]; + ASSERT_TRUE(child0); + ASSERT_STREQ(child0->mName.C_Str(), "Cube2"); + ASSERT_TRUE(child0->mChildren); + ASSERT_EQ(child0->mNumChildren, 1); + + const auto child00 = child0->mChildren[0]; + ASSERT_TRUE(child00); + ASSERT_STREQ(child00->mName.C_Str(), "Cube1"); + + const auto child1 = root->mChildren[1]; + ASSERT_TRUE(child1); + ASSERT_STREQ(child1->mName.C_Str(), "Cube3"); + + auto parent = child1; + const size_t chain_length = 8u; + const char* chainStr[chain_length] = { + "Cube1001_$AssimpFbx$_Translation", + "Cube1001_$AssimpFbx$_RotationPivot", + "Cube1001_$AssimpFbx$_RotationPivotInverse", + "Cube1001_$AssimpFbx$_ScalingOffset", + "Cube1001_$AssimpFbx$_ScalingPivot", + "Cube1001_$AssimpFbx$_Scaling", + "Cube1001_$AssimpFbx$_ScalingPivotInverse", + "Cube1001" + }; + for (size_t i = 0; i < chain_length; ++i) + { + ASSERT_TRUE(parent->mChildren); + ASSERT_EQ(parent->mNumChildren, 1); + auto node = parent->mChildren[0]; + ASSERT_TRUE(node); + ASSERT_STREQ(node->mName.C_Str(), chainStr[i]); + parent = node; + } + ASSERT_EQ(0, parent->mNumChildren) << "Leaf node"; +} + TEST_F( utFBXImporterExporter, importPhongMaterial ) { Assimp::Importer importer; const aiScene *scene = importer.ReadFile( ASSIMP_TEST_MODELS_DIR "/FBX/phong_cube.fbx", aiProcess_ValidateDataStructure ); @@ -114,3 +227,42 @@ TEST_F(utFBXImporterExporter, importUnitScaleFactor) { scene->mMetaData->Get("UnitScaleFactor", factor); EXPECT_DOUBLE_EQ(500.0, factor); } + +TEST_F(utFBXImporterExporter, importEmbeddedAsciiTest) { + // see https://github.com/assimp/assimp/issues/1957 + Assimp::Importer importer; + const aiScene *scene = importer.ReadFile(ASSIMP_TEST_MODELS_DIR "/FBX/embedded_ascii/box.FBX", aiProcess_ValidateDataStructure); + EXPECT_NE(nullptr, scene); + + EXPECT_EQ(1, scene->mNumMaterials); + aiMaterial *mat = scene->mMaterials[0]; + ASSERT_NE(nullptr, mat); + + aiString path; + aiTextureMapMode modes[2]; + EXPECT_EQ(aiReturn_SUCCESS, mat->GetTexture(aiTextureType_DIFFUSE, 0, &path, nullptr, nullptr, nullptr, nullptr, modes)); + + ASSERT_EQ(1, scene->mNumTextures); + ASSERT_TRUE(scene->mTextures[0]->pcData); + ASSERT_EQ(439176u, scene->mTextures[0]->mWidth) << "FBX ASCII base64 compression splits data by 512Kb, it should be two parts for this texture"; +} + +TEST_F(utFBXImporterExporter, importEmbeddedFragmentedAsciiTest) { + // see https://github.com/assimp/assimp/issues/1957 + Assimp::Importer importer; + const aiScene *scene = importer.ReadFile(ASSIMP_TEST_MODELS_DIR "/FBX/embedded_ascii/box_embedded_texture_fragmented.fbx", aiProcess_ValidateDataStructure); + EXPECT_NE(nullptr, scene); + + EXPECT_EQ(1, scene->mNumMaterials); + aiMaterial *mat = scene->mMaterials[0]; + ASSERT_NE(nullptr, mat); + + aiString path; + aiTextureMapMode modes[2]; + ASSERT_EQ(aiReturn_SUCCESS, mat->GetTexture(aiTextureType_DIFFUSE, 0, &path, nullptr, nullptr, nullptr, nullptr, modes)); + ASSERT_STREQ(path.C_Str(), "paper.png"); + + ASSERT_EQ(1, scene->mNumTextures); + ASSERT_TRUE(scene->mTextures[0]->pcData); + ASSERT_EQ(968029u, scene->mTextures[0]->mWidth) << "FBX ASCII base64 compression splits data by 512Kb, it should be two parts for this texture"; +} diff --git a/test/unit/utObjImportExport.cpp b/test/unit/utObjImportExport.cpp index 0f715af14..bb0d36b13 100644 --- a/test/unit/utObjImportExport.cpp +++ b/test/unit/utObjImportExport.cpp @@ -377,6 +377,53 @@ TEST_F(utObjImportExport, 0based_array_Test) { EXPECT_EQ(nullptr, scene); } +TEST_F(utObjImportExport, invalid_normals_uvs) { + static const char *ObjModel = + "v -0.500000 0.000000 0.400000\n" + "v -0.500000 0.000000 -0.800000\n" + "v -0.500000 1.000000 -0.800000\n" + "vt 0 0\n" + "vn 0 1 0\n" + "f 1/1/1 1/1/1 2/2/2\nB"; + + Assimp::Importer myImporter; + const aiScene *scene = myImporter.ReadFileFromMemory(ObjModel, strlen(ObjModel), 0); + EXPECT_NE(nullptr, scene); +} + +TEST_F(utObjImportExport, no_vt_just_vns) { + static const char *ObjModel = + "v 0 0 0\n" + "v 0 0 0\n" + "v 0 0 0\n" + "v 0 0 0\n" + "v 0 0 0\n" + "v 0 0 0\n" + "v 0 0 0\n" + "v 0 0 0\n" + "v 0 0 0\n" + "v 0 0 0\n" + "v 10 0 0\n" + "v 0 10 0\n" + "vn 0 0 1\n" + "vn 0 0 1\n" + "vn 0 0 1\n" + "vn 0 0 1\n" + "vn 0 0 1\n" + "vn 0 0 1\n" + "vn 0 0 1\n" + "vn 0 0 1\n" + "vn 0 0 1\n" + "vn 0 0 1\n" + "vn 0 0 1\n" + "vn 0 0 1\n" + "f 10/10 11/11 12/12\n"; + + Assimp::Importer myImporter; + const aiScene *scene = myImporter.ReadFileFromMemory(ObjModel, strlen(ObjModel), 0); + EXPECT_NE(nullptr, scene); +} + TEST_F( utObjImportExport, mtllib_after_g ) { ::Assimp::Importer importer; const aiScene *scene = importer.ReadFile( ASSIMP_TEST_MODELS_DIR "/OBJ/cube_mtllib_after_g.obj", aiProcess_ValidateDataStructure ); diff --git a/test/unit/utSTLImportExport.cpp b/test/unit/utSTLImportExport.cpp index b5518de23..0f90aacf9 100644 --- a/test/unit/utSTLImportExport.cpp +++ b/test/unit/utSTLImportExport.cpp @@ -152,10 +152,10 @@ TEST_F(utSTLImporterExporter, test_export_pointclouds) { auto pMesh = scene.mMeshes[0]; - long numValidPoints = points.size(); + size_t numValidPoints = points.size(); pMesh->mVertices = new aiVector3D[numValidPoints]; - pMesh->mNumVertices = numValidPoints; + pMesh->mNumVertices = static_cast( numValidPoints ); int i = 0; for (XYZ &p : points) { diff --git a/test/unit/utValidateDataStructure.cpp b/test/unit/utValidateDataStructure.cpp new file mode 100644 index 000000000..61fc93dd7 --- /dev/null +++ b/test/unit/utValidateDataStructure.cpp @@ -0,0 +1,199 @@ +/* +--------------------------------------------------------------------------- +Open Asset Import Library (assimp) +--------------------------------------------------------------------------- + +Copyright (c) 2006-2019, assimp team + + + +All rights reserved. + +Redistribution and use of this software in source and binary forms, +with or without modification, are permitted provided that the following +conditions are met: + +* Redistributions of source code must retain the above +copyright notice, this list of conditions and the +following disclaimer. + +* Redistributions in binary form must reproduce the above +copyright notice, this list of conditions and the +following disclaimer in the documentation and/or other +materials provided with the distribution. + +* Neither the name of the assimp team, nor the names of its +contributors may be used to endorse or promote products +derived from this software without specific prior +written permission of the assimp team. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +--------------------------------------------------------------------------- +*/ +#include "UnitTestPCH.h" + +#include +#include +#include + +using namespace std; +using namespace Assimp; + + +class ValidateDataStructureTest : public ::testing::Test +{ +public: + + virtual void SetUp(); + virtual void TearDown(); + +protected: + + + ValidateDSProcess* vds; + aiScene* scene; +}; + +// ------------------------------------------------------------------------------------------------ +void ValidateDataStructureTest::SetUp() +{ + // setup a dummy scene with a single node + scene = new aiScene(); + scene->mRootNode = new aiNode(); + scene->mRootNode->mName.Set(""); + + // add some translation + scene->mRootNode->mTransformation.a4 = 1.f; + scene->mRootNode->mTransformation.b4 = 2.f; + scene->mRootNode->mTransformation.c4 = 3.f; + + // and allocate a ScenePreprocessor to operate on the scene + vds = new ValidateDSProcess(); +} + +// ------------------------------------------------------------------------------------------------ +void ValidateDataStructureTest::TearDown() +{ + delete vds; + delete scene; +} + + + +// ------------------------------------------------------------------------------------------------ +//Template +//TEST_F(ScenePreprocessorTest, test) +//{ +//} +// TODO Conditions not yet checked: +//132: ReportError("aiScene::%s is NULL (aiScene::%s is %i)", +//139: ReportError("aiScene::%s[%i] is NULL (aiScene::%s is %i)", +//156: ReportError("aiScene::%s is NULL (aiScene::%s is %i)", +//163: ReportError("aiScene::%s[%i] is NULL (aiScene::%s is %i)", +//173: ReportError("aiScene::%s[%i] has the same name as " +//192: ReportError("aiScene::%s[%i] has no corresponding node in the scene graph (%s)", +//196: ReportError("aiScene::%s[%i]: there are more than one nodes with %s as name", +//217: ReportError("aiScene::mNumMeshes is 0. At least one mesh must be there"); +//220: ReportError("aiScene::mMeshes is non-null although there are no meshes"); +//229: ReportError("aiScene::mAnimations is non-null although there are no animations"); +//238: ReportError("aiScene::mCameras is non-null although there are no cameras"); +//247: ReportError("aiScene::mLights is non-null although there are no lights"); +//256: ReportError("aiScene::mTextures is non-null although there are no textures"); +//266: ReportError("aiScene::mNumMaterials is 0. At least one material must be there"); +//270: ReportError("aiScene::mMaterials is non-null although there are no materials"); +//281: ReportWarning("aiLight::mType is aiLightSource_UNDEFINED"); +//286: ReportWarning("aiLight::mAttenuationXXX - all are zero"); +//290: ReportError("aiLight::mAngleInnerCone is larger than aiLight::mAngleOuterCone"); +//295: ReportWarning("aiLight::mColorXXX - all are black and won't have any influence"); +//303: ReportError("aiCamera::mClipPlaneFar must be >= aiCamera::mClipPlaneNear"); +//308: ReportWarning("%f is not a valid value for aiCamera::mHorizontalFOV",pCamera->mHorizontalFOV); +//317: ReportError("aiMesh::mMaterialIndex is invalid (value: %i maximum: %i)", +//332: ReportError("aiMesh::mFaces[%i].mNumIndices is 0",i); +//336: ReportError("aiMesh::mFaces[%i] is a POINT but aiMesh::mPrimitiveTypes " +//337: "does not report the POINT flag",i); +//343: ReportError("aiMesh::mFaces[%i] is a LINE but aiMesh::mPrimitiveTypes " +//344: "does not report the LINE flag",i); +//350: ReportError("aiMesh::mFaces[%i] is a TRIANGLE but aiMesh::mPrimitiveTypes " +//351: "does not report the TRIANGLE flag",i); +//357: this->ReportError("aiMesh::mFaces[%i] is a POLYGON but aiMesh::mPrimitiveTypes " +//358: "does not report the POLYGON flag",i); +//365: ReportError("aiMesh::mFaces[%i].mIndices is NULL",i); +//370: ReportError("The mesh %s contains no vertices", pMesh->mName.C_Str()); +//374: ReportError("Mesh has too many vertices: %u, but the limit is %u",pMesh->mNumVertices,AI_MAX_VERTICES); +//377: ReportError("Mesh has too many faces: %u, but the limit is %u",pMesh->mNumFaces,AI_MAX_FACES); +//382: ReportError("If there are tangents, bitangent vectors must be present as well"); +//387: ReportError("Mesh %s contains no faces", pMesh->mName.C_Str()); +//398: ReportError("Face %u has too many faces: %u, but the limit is %u",i,face.mNumIndices,AI_MAX_FACE_INDICES); +//404: ReportError("aiMesh::mFaces[%i]::mIndices[%i] is out of range",i,a); +//412: ReportError("aiMesh::mVertices[%i] is referenced twice - second " +//426: ReportWarning("There are unreferenced vertices"); +//439: ReportError("Texture coordinate channel %i exists " +//453: ReportError("Vertex color channel %i is exists " +//464: ReportError("aiMesh::mBones is NULL (aiMesh::mNumBones is %i)", +//480: ReportError("Bone %u has too many weights: %u, but the limit is %u",i,bone->mNumWeights,AI_MAX_BONE_WEIGHTS); +//485: ReportError("aiMesh::mBones[%i] is NULL (aiMesh::mNumBones is %i)", +//498: ReportError("aiMesh::mBones[%i], name = \"%s\" has the same name as " +//507: ReportWarning("aiMesh::mVertices[%i]: bone weight sum != 1.0 (sum is %f)",i,afSum[i]); +//513: ReportError("aiMesh::mBones is non-null although there are no bones"); +//524: ReportError("aiBone::mNumWeights is zero"); +//531: ReportError("aiBone::mWeights[%i].mVertexId is out of range",i); +//534: ReportWarning("aiBone::mWeights[%i].mWeight has an invalid value",i); +//549: ReportError("aiAnimation::mChannels is NULL (aiAnimation::mNumChannels is %i)", +//556: ReportError("aiAnimation::mChannels[%i] is NULL (aiAnimation::mNumChannels is %i)", +//563: ReportError("aiAnimation::mNumChannels is 0. At least one node animation channel must be there."); +//567: // if (!pAnimation->mDuration)this->ReportError("aiAnimation::mDuration is zero"); +//592: ReportError("Material property %s is expected to be a string",prop->mKey.data); +//596: ReportError("%s #%i is set, but there are only %i %s textures", +//611: ReportError("Found texture property with index %i, although there " +//619: ReportError("Material property %s%i is expected to be an integer (size is %i)", +//627: ReportError("Material property %s%i is expected to be 5 floats large (size is %i)", +//635: ReportError("Material property %s%i is expected to be an integer (size is %i)", +//656: ReportWarning("Invalid UV index: %i (key %s). Mesh %i has only %i UV channels", +//676: ReportWarning("UV-mapped texture, but there are no UV coords"); +//690: ReportError("aiMaterial::mProperties[%i] is NULL (aiMaterial::mNumProperties is %i)", +//694: ReportError("aiMaterial::mProperties[%i].mDataLength or " +//702: ReportError("aiMaterial::mProperties[%i].mDataLength is " +//707: ReportError("Missing null-terminator in string material property"); +//713: ReportError("aiMaterial::mProperties[%i].mDataLength is " +//720: ReportError("aiMaterial::mProperties[%i].mDataLength is " +//739: ReportWarning("A specular shading model is specified but there is no " +//743: ReportWarning("A specular shading model is specified but the value of the " +//752: ReportWarning("Invalid opacity value (must be 0 < opacity < 1.0)"); +//776: ReportError("aiTexture::pcData is NULL"); +//781: ReportError("aiTexture::mWidth is zero (aiTexture::mHeight is %i, uncompressed texture)", +//788: ReportError("aiTexture::mWidth is zero (compressed texture)"); +//791: ReportWarning("aiTexture::achFormatHint must be zero-terminated"); +//794: ReportWarning("aiTexture::achFormatHint should contain a file extension " +//804: ReportError("aiTexture::achFormatHint contains non-lowercase letters"); +//815: ReportError("Empty node animation channel"); +//822: ReportError("aiNodeAnim::mPositionKeys is NULL (aiNodeAnim::mNumPositionKeys is %i)", +//833: ReportError("aiNodeAnim::mPositionKeys[%i].mTime (%.5f) is larger " +//840: ReportWarning("aiNodeAnim::mPositionKeys[%i].mTime (%.5f) is smaller " +//853: ReportError("aiNodeAnim::mRotationKeys is NULL (aiNodeAnim::mNumRotationKeys is %i)", +//861: ReportError("aiNodeAnim::mRotationKeys[%i].mTime (%.5f) is larger " +//868: ReportWarning("aiNodeAnim::mRotationKeys[%i].mTime (%.5f) is smaller " +//880: ReportError("aiNodeAnim::mScalingKeys is NULL (aiNodeAnim::mNumScalingKeys is %i)", +//888: ReportError("aiNodeAnim::mScalingKeys[%i].mTime (%.5f) is larger " +//895: ReportWarning("aiNodeAnim::mScalingKeys[%i].mTime (%.5f) is smaller " +//907: ReportError("A node animation channel must have at least one subtrack"); +//915: ReportError("A node of the scenegraph is NULL"); +//920: ReportError("Non-root node %s lacks a valid parent (aiNode::mParent is NULL) ",pNode->mName); +//928: ReportError("aiNode::mMeshes is NULL for node %s (aiNode::mNumMeshes is %i)", +//937: ReportError("aiNode::mMeshes[%i] is out of range for node %s (maximum is %i)", +//942: ReportError("aiNode::mMeshes[%i] is already referenced by this node %s (value: %i)", +//951: ReportError("aiNode::mChildren is NULL for node %s (aiNode::mNumChildren is %i)", +//965: ReportError("aiString::length is too large (%i, maximum is %lu)", +//974: ReportError("aiString::data is invalid: the terminal zero is at a wrong offset"); +//979: ReportError("aiString::data is invalid. There is no terminal character"); +} + diff --git a/test/unit/utglTF2ImportExport.cpp b/test/unit/utglTF2ImportExport.cpp index 3ad02645d..bac76473d 100644 --- a/test/unit/utglTF2ImportExport.cpp +++ b/test/unit/utglTF2ImportExport.cpp @@ -376,6 +376,12 @@ TEST_F(utglTF2ImportExport, importglTF2FromMemory) { EXPECT_EQ( nullptr, Scene );*/ } +TEST_F( utglTF2ImportExport, bug_import_simple_skin ) { + Assimp::Importer importer; + const aiScene *scene = importer.ReadFile( ASSIMP_TEST_MODELS_DIR "/glTF2/simple_skin/simple_skin.gltf", aiProcess_ValidateDataStructure ); + EXPECT_NE( nullptr, scene ); +} + #ifndef ASSIMP_BUILD_NO_EXPORT TEST_F( utglTF2ImportExport, exportglTF2FromFileTest ) { EXPECT_TRUE( exporterTest() ); diff --git a/tools/assimp_cmd/ImageExtractor.cpp b/tools/assimp_cmd/ImageExtractor.cpp index f9d55b8e8..a587ab434 100644 --- a/tools/assimp_cmd/ImageExtractor.cpp +++ b/tools/assimp_cmd/ImageExtractor.cpp @@ -228,7 +228,8 @@ int DoExport(const aiTexture* tx, FILE* p, const std::string& extension, // Implementation of the assimp extract utility int Assimp_Extract (const char* const* params, unsigned int num) { - const char* const invalid = "assimp extract: Invalid number of arguments. See \'assimp extract --help\'\n"; + const char* const invalid = "assimp extract: Invalid number of arguments. See \'assimp extract --help\'\n"; + // assimp extract in out [options] if (num < 1) { printf(invalid); return 1; @@ -240,11 +241,7 @@ int Assimp_Extract (const char* const* params, unsigned int num) return 0; } - // asssimp extract in out [options] - if (num < 1) { - printf(invalid); - return 1; - } + std::string in = std::string(params[0]); std::string out = (num > 1 ? std::string(params[1]) : "-"); diff --git a/tools/assimp_cmd/Info.cpp b/tools/assimp_cmd/Info.cpp index cb7ac86cf..d730b9308 100644 --- a/tools/assimp_cmd/Info.cpp +++ b/tools/assimp_cmd/Info.cpp @@ -51,27 +51,26 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include const char* AICMD_MSG_INFO_HELP_E = -"assimp info [-r] [-v]\n" -"\tPrint basic structure of a 3D model\n" -"\t-r,--raw: No postprocessing, do a raw import\n" -"\t-v,--verbose: Print verbose info such as node transform data\n" -"\t-s, --silent: Print only minimal info\n"; + "assimp info [-r] [-v]\n" + "\tPrint basic structure of a 3D model\n" + "\t-r,--raw: No postprocessing, do a raw import\n" + "\t-v,--verbose: Print verbose info such as node transform data\n" + "\t-s, --silent: Print only minimal info\n"; -const std::string TREE_BRANCH_ASCII = "|-"; -const std::string TREE_BRANCH_UTF8 = "\xe2\x94\x9c\xe2\x95\xb4"; -const std::string TREE_STOP_ASCII = "'-"; -const std::string TREE_STOP_UTF8 = "\xe2\x94\x94\xe2\x95\xb4"; -const std::string TREE_CONTINUE_ASCII = "| "; -const std::string TREE_CONTINUE_UTF8 = "\xe2\x94\x82 "; +const char *TREE_BRANCH_ASCII = "|-"; +const char *TREE_BRANCH_UTF8 = "\xe2\x94\x9c\xe2\x95\xb4"; +const char *TREE_STOP_ASCII = "'-"; +const char *TREE_STOP_UTF8 = "\xe2\x94\x94\xe2\x95\xb4"; +const char *TREE_CONTINUE_ASCII = "| "; +const char *TREE_CONTINUE_UTF8 = "\xe2\x94\x82 "; -// note: by default this is outputing utf-8 text. +// note: by default this is using utf-8 text. // this is well supported on pretty much any linux terminal. // if this causes problems on some platform, // put an #ifdef to use the ascii version for that platform. -const std::string TREE_BRANCH = TREE_BRANCH_UTF8; -const std::string TREE_STOP = TREE_STOP_UTF8; -const std::string TREE_CONTINUE = TREE_CONTINUE_UTF8; - +const char *TREE_BRANCH = TREE_BRANCH_UTF8; +const char *TREE_STOP = TREE_STOP_UTF8; +const char *TREE_CONTINUE = TREE_CONTINUE_UTF8; // ----------------------------------------------------------------------------------- unsigned int CountNodes(const aiNode* root) @@ -280,14 +279,7 @@ void PrintHierarchy( // ----------------------------------------------------------------------------------- // Implementation of the assimp info utility to print basic file info -int Assimp_Info (const char* const* params, unsigned int num) -{ - if (num < 1) { - printf("assimp info: Invalid number of arguments. " - "See \'assimp info --help\'\n"); - return 1; - } - +int Assimp_Info (const char* const* params, unsigned int num) { // --help if (!strcmp( params[0],"-h")||!strcmp( params[0],"--help")||!strcmp( params[0],"-?") ) { printf("%s",AICMD_MSG_INFO_HELP_E); @@ -325,10 +317,15 @@ int Assimp_Info (const char* const* params, unsigned int num) return 1; } - // do maximum post-processing unless -r was specified + // Parse post-processing flags unless -r was specified ImportData import; if (!raw) { - import.ppFlags = aiProcessPreset_TargetRealtime_MaxQuality; + // get import flags + ProcessStandardArguments(import, params + 1, num - 1); + + //No custom post process flags defined, we set all the post process flags active + if(import.ppFlags == 0) + import.ppFlags |= aiProcessPreset_TargetRealtime_MaxQuality; } // import the main model diff --git a/tools/assimp_cmd/WriteDumb.cpp b/tools/assimp_cmd/WriteDumb.cpp index 569749994..0d6f58c60 100644 --- a/tools/assimp_cmd/WriteDumb.cpp +++ b/tools/assimp_cmd/WriteDumb.cpp @@ -276,9 +276,12 @@ inline uint32_t WriteBounds(const T* in, unsigned int size) void ChangeInteger(uint32_t ofs,uint32_t n) { const uint32_t cur = ftell(out); - fseek(out,ofs,SEEK_SET); - fwrite(&n,4,1,out); - fseek(out,cur,SEEK_SET); + int retCode; + retCode = fseek(out, ofs, SEEK_SET); + ai_assert(0 == retCode); + fwrite(&n, 4, 1, out); + retCode = fseek(out, cur, SEEK_SET); + ai_assert(0 == retCode); } // ----------------------------------------------------------------------------------- @@ -1333,10 +1336,6 @@ int Assimp_Dump (const char* const* params, unsigned int num) { const char* fail = "assimp dump: Invalid number of arguments. " "See \'assimp dump --help\'\r\n"; - if (num < 1) { - printf("%s", fail); - return 1; - } // --help if (!strcmp( params[0], "-h") || !strcmp( params[0], "--help") || !strcmp( params[0], "-?") ) { diff --git a/tools/assimp_qt_viewer/CMakeLists.txt b/tools/assimp_qt_viewer/CMakeLists.txt index cd15372b5..d8d8e27a9 100644 --- a/tools/assimp_qt_viewer/CMakeLists.txt +++ b/tools/assimp_qt_viewer/CMakeLists.txt @@ -1,7 +1,8 @@ set(PROJECT_VERSION "") project(assimp_qt_viewer) -cmake_minimum_required(VERSION 3.0) +# Qt5 requires cmake 3.1 or newer +cmake_minimum_required(VERSION 3.1) FIND_PACKAGE(OpenGL QUIET) diff --git a/tools/assimp_view/CMakeLists.txt b/tools/assimp_view/CMakeLists.txt index f5365e11a..8112c19e8 100644 --- a/tools/assimp_view/CMakeLists.txt +++ b/tools/assimp_view/CMakeLists.txt @@ -88,6 +88,8 @@ SET_PROPERTY(TARGET assimp_viewer PROPERTY DEBUG_POSTFIX ${CMAKE_DEBUG_POSTFIX}) IF ( MSVC ) ADD_DEFINITIONS( -D_SCL_SECURE_NO_WARNINGS ) ADD_DEFINITIONS( -D_CRT_SECURE_NO_WARNINGS ) + # assimp_viewer is ANSI (MBCS) throughout + REMOVE_DEFINITIONS( -DUNICODE -D_UNICODE ) ENDIF ( MSVC ) diff --git a/tools/assimp_view/SceneAnimator.cpp b/tools/assimp_view/SceneAnimator.cpp index c2ebeacb3..86fe46a9c 100644 --- a/tools/assimp_view/SceneAnimator.cpp +++ b/tools/assimp_view/SceneAnimator.cpp @@ -90,7 +90,7 @@ void SceneAnimator::SetAnimIndex( size_t pAnimIndex) { delete mAnimEvaluator; mAnimEvaluator = nullptr; mNodesByName.clear(); - mCurrentAnimIndex = pAnimIndex; + mCurrentAnimIndex = static_cast( pAnimIndex ); // create the internal node tree. Do this even in case of invalid animation index // so that the transformation matrices are properly set up to mimic the current scene diff --git a/tools/assimp_view/assimp_view.cpp b/tools/assimp_view/assimp_view.cpp index 355287e0d..1bcdce967 100644 --- a/tools/assimp_view/assimp_view.cpp +++ b/tools/assimp_view/assimp_view.cpp @@ -508,19 +508,28 @@ int CreateAssetData() unsigned int nidx; switch (mesh->mPrimitiveTypes) { case aiPrimitiveType_POINT: - nidx = 1;break; + nidx = 1; + break; case aiPrimitiveType_LINE: - nidx = 2;break; + nidx = 2; + break; case aiPrimitiveType_TRIANGLE: - nidx = 3;break; - default: ai_assert(false); + nidx = 3; + break; + default: + ai_assert(false); + break; }; + unsigned int numIndices = mesh->mNumFaces * 3; + if (0 == numIndices && nidx == 1) { + numIndices = mesh->mNumVertices; + } // check whether we can use 16 bit indices - if (mesh->mNumFaces * 3 >= 65536) { + if (numIndices >= 65536) { // create 32 bit index buffer if(FAILED( g_piDevice->CreateIndexBuffer( 4 * - mesh->mNumFaces * nidx, + numIndices, D3DUSAGE_WRITEONLY | dwUsage, D3DFMT_INDEX32, D3DPOOL_DEFAULT, @@ -546,7 +555,7 @@ int CreateAssetData() else { // create 16 bit index buffer if(FAILED( g_piDevice->CreateIndexBuffer( 2 * - mesh->mNumFaces * nidx, + numIndices, D3DUSAGE_WRITEONLY | dwUsage, D3DFMT_INDEX16, D3DPOOL_DEFAULT, diff --git a/tools/assimp_view/stdafx.cpp b/tools/assimp_view/stdafx.cpp index 709f0fd9a..bbbb7e0c0 100644 --- a/tools/assimp_view/stdafx.cpp +++ b/tools/assimp_view/stdafx.cpp @@ -1,8 +1,8 @@ // stdafx.cpp : Quelldatei, die nur die Standard-Includes einbindet. // assimp_view.pch ist der vorkompilierte Header. -// stdafx.obj enthält die vorkompilierten Typinformationen. +// stdafx.obj enthält die vorkompilierten Typinformationen. #include "stdafx.h" -// TODO: Auf zusätzliche Header verweisen, die in STDAFX.H +// TODO: Auf zusätzliche Header verweisen, die in STDAFX.H // und nicht in dieser Datei erforderlich sind. diff --git a/tools/assimp_view/stdafx.h b/tools/assimp_view/stdafx.h index 35104d4b0..d3f4692cb 100644 --- a/tools/assimp_view/stdafx.h +++ b/tools/assimp_view/stdafx.h @@ -1,26 +1,26 @@ -// stdafx.h : Includedatei für Standardsystem-Includedateien -// oder häufig verwendete projektspezifische Includedateien, -// die nur in unregelmäßigen Abständen geändert werden. +// stdafx.h : Includedatei für Standardsystem-Includedateien +// oder häufig verwendete projektspezifische Includedateien, +// die nur in unregelmäßigen Abständen geändert werden. // #pragma once -// Ändern Sie folgende Definitionen für Plattformen, die älter als die unten angegebenen sind. -// In MSDN finden Sie die neuesten Informationen über die entsprechenden Werte für die unterschiedlichen Plattformen. -#ifndef WINVER // Lassen Sie die Verwendung spezifischer Features von Windows XP oder später zu. -# define WINVER 0x0501 // Ändern Sie dies in den geeigneten Wert für andere Versionen von Windows. +// Ändern Sie folgende Definitionen für Plattformen, die älter als die unten angegebenen sind. +// In MSDN finden Sie die neuesten Informationen über die entsprechenden Werte für die unterschiedlichen Plattformen. +#ifndef WINVER // Lassen Sie die Verwendung spezifischer Features von Windows XP oder später zu. +# define WINVER 0x0501 // Ändern Sie dies in den geeigneten Wert für andere Versionen von Windows. #endif -#ifndef _WIN32_WINNT // Lassen Sie die Verwendung spezifischer Features von Windows XP oder später zu. -# define _WIN32_WINNT 0x0501 // Ändern Sie dies in den geeigneten Wert für andere Versionen von Windows. +#ifndef _WIN32_WINNT // Lassen Sie die Verwendung spezifischer Features von Windows XP oder später zu. +# define _WIN32_WINNT 0x0501 // Ändern Sie dies in den geeigneten Wert für andere Versionen von Windows. #endif -#ifndef _WIN32_WINDOWS // Lassen Sie die Verwendung spezifischer Features von Windows 98 oder später zu. -# define _WIN32_WINDOWS 0x0410 // Ändern Sie dies in den geeigneten Wert für Windows Me oder höher. +#ifndef _WIN32_WINDOWS // Lassen Sie die Verwendung spezifischer Features von Windows 98 oder später zu. +# define _WIN32_WINDOWS 0x0410 // Ändern Sie dies in den geeigneten Wert für Windows Me oder höher. #endif -#ifndef _WIN32_IE // Lassen Sie die Verwendung spezifischer Features von IE 6.0 oder später zu. -#define _WIN32_IE 0x0600 // Ändern Sie dies in den geeigneten Wert für andere Versionen von IE. +#ifndef _WIN32_IE // Lassen Sie die Verwendung spezifischer Features von IE 6.0 oder später zu. +#define _WIN32_IE 0x0600 // Ändern Sie dies in den geeigneten Wert für andere Versionen von IE. #endif // Windows-Headerdateien: