多目标跟踪-使用yolox+deepsort开发的多目标跟踪算法项目-优质项目实战-附完整流程教程.zip

# YOLOX-CPP-MegEngine Cpp file compile of YOLOX object detection base on [MegEngine](https://github.com/MegEngine/MegEngine). ## Tutorial ### Step1: install toolchain * host: sudo apt install gcc/g++ (gcc/g++, which version >= 6) build-essential git git-lfs gfortran libgfortran-6-dev autoconf gnupg flex bison gperf curl zlib1g-dev gcc-multilib g++-multilib cmake * cross build android: download [NDK](https://developer.android.com/ndk/downloads) * after unzip download NDK, then export NDK_ROOT="path of NDK" ### Step2: build MegEngine * git clone https://github.com/MegEngine/MegEngine.git * then init third_party * then export megengine_root="path of MegEngine" * cd $megengine_root && ./third_party/prepare.sh && ./third_party/install-mkl.sh * build example: * build host without cuda: ./scripts/cmake-build/host_build.sh * build host with cuda : ./scripts/cmake-build/host_build.sh -c * cross build for android aarch64: ./scripts/cmake-build/cross_build_android_arm_inference.sh * cross build for android aarch64(with V8.2+fp16): ./scripts/cmake-build/cross_build_android_arm_inference.sh -f * after build MegEngine, you need export the `MGE_INSTALL_PATH` * host without cuda: export MGE_INSTALL_PATH=${megengine_root}/build_dir/host/MGE_WITH_CUDA_OFF/MGE_INFERENCE_ONLY_ON/Release/install * host with cuda: export MGE_INSTALL_PATH=${megengine_root}/build_dir/host/MGE_WITH_CUDA_ON/MGE_INFERENCE_ONLY_ON/Release/install * cross build for android aarch64: export MGE_INSTALL_PATH=${megengine_root}/build_dir/android/arm64-v8a/Release/install * you can refs [build tutorial of MegEngine](https://github.com/MegEngine/MegEngine/blob/master/scripts/cmake-build/BUILD_README.md) to build other platform, eg, windows/macos/ etc! ### Step3: build OpenCV * git clone https://github.com/opencv/opencv.git * git checkout 3.4.15 (we test at 3.4.15, if test other version, may need modify some build) * patch diff for android: * ``` diff --git a/CMakeLists.txt b/CMakeLists.txt index f6a2da5310..10354312c9 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -643,7 +643,7 @@ if(UNIX) if(NOT APPLE) CHECK_INCLUDE_FILE(pthread.h HAVE_PTHREAD) if(ANDROID) - set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} dl m log) + set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} dl m log z) elseif(CMAKE_SYSTEM_NAME MATCHES "FreeBSD|NetBSD|DragonFly|OpenBSD|Haiku") set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} m pthread) elseif(EMSCRIPTEN) ``` * build for host * ``` cd root_dir_of_opencv mkdir -p build/install cd build cmake -DBUILD_JAVA=OFF -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=$PWD/install make install -j32 ``` * build for android-aarch64 * ``` cd root_dir_of_opencv mkdir -p build_android/install cd build_android cmake -DCMAKE_TOOLCHAIN_FILE="$NDK_ROOT/build/cmake/android.toolchain.cmake" -DANDROID_NDK="$NDK_ROOT" -DANDROID_ABI=arm64-v8a -DANDROID_NATIVE_API_LEVEL=21 -DBUILD_JAVA=OFF -DBUILD_ANDROID_PROJECTS=OFF -DBUILD_ANDROID_EXAMPLES=OFF -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=$PWD/install .. make install -j32 ``` * after build OpenCV, you need export `OPENCV_INSTALL_INCLUDE_PATH ` and `OPENCV_INSTALL_LIB_PATH` * host build: * export OPENCV_INSTALL_INCLUDE_PATH=${path of opencv}/build/install/include * export OPENCV_INSTALL_LIB_PATH=${path of opencv}/build/install/lib * cross build for android aarch64: * export OPENCV_INSTALL_INCLUDE_PATH=${path of opencv}/build_android/install/sdk/native/jni/include * export OPENCV_INSTALL_LIB_PATH=${path of opencv}/build_android/install/sdk/native/libs/arm64-v8a ### Step4: build test demo * run build.sh * host: * export CXX=g++ * ./build.sh * cross android aarch64 * export CXX=aarch64-linux-android21-clang++ * ./build.sh ### Step5: run demo > **Note**: two ways to get `yolox_s.mge` model file > > * reference to python demo's `dump.py` script. > * wget https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_s.mge * host: * LD_LIBRARY_PATH=$MGE_INSTALL_PATH/lib/:$OPENCV_INSTALL_LIB_PATH ./yolox yolox_s.mge ../../../assets/dog.jpg cuda/cpu/multithread <warmup_count> <thread_number> * cross android * adb push/scp $MGE_INSTALL_PATH/lib/libmegengine.so android_phone * adb push/scp $OPENCV_INSTALL_LIB_PATH/*.so android_phone * adb push/scp ./yolox yolox_s.mge android_phone * adb push/scp ../../../assets/dog.jpg android_phone * login in android_phone by adb or ssh * then run: LD_LIBRARY_PATH=. ./yolox yolox_s.mge dog.jpg cpu/multithread <warmup_count> <thread_number> <use_fast_run> <use_weight_preprocess> <run_with_fp16> * <warmup_count> means warmup count, valid number >=0 * <thread_number> means thread number, valid number >=1, only take effect `multithread` device * <use_fast_run> if >=1 , will use fastrun to choose best algo * <use_weight_preprocess> if >=1, will handle weight preprocess before exe * <run_with_fp16> if >=1, will run with fp16 mode ## Bechmark * model info: yolox-s @ input(1,3,640,640) * test devices * x86_64 -- Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz * aarch64 -- xiamo phone mi9 * cuda -- 1080TI @ cuda-10.1-cudnn-v7.6.3-TensorRT-6.0.1.5.sh @ Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz | megengine @ tag1.4(fastrun + weight\_preprocess)/sec | 1 thread | | ---------------------------------------------------- | -------- | | x86\_64 | 0.516245 | | aarch64(fp32+chw44) | 0.587857 | | CUDA @ 1080TI/sec | 1 batch | 2 batch | 4 batch | 8 batch | 16 batch | 32 batch | 64 batch | | ------------------- | ---------- | --------- | --------- | --------- | --------- | -------- | -------- | | megengine(fp32+chw) | 0.00813703 | 0.0132893 | 0.0236633 | 0.0444699 | 0.0864917 | 0.16895 | 0.334248 | ## Acknowledgement * [MegEngine](https://github.com/MegEngine/MegEngine) * [OpenCV](https://github.com/opencv/opencv) * [NDK](https://developer.android.com/ndk) * [CMAKE](https://cmake.org/)