add pptracking (#4661)

deploy/pptracking/README.md

+# 实时跟踪系统PP-Tracking
+
+PP-Tracking是基于飞桨深度学习框架的业界首个开源实时跟踪系统。针对实际业务的难点痛点，PP-Tracking内置行人车辆跟踪、跨镜头跟踪、多类别跟踪、小目标跟踪及流量计数等能力与产业应用，同时提供可视化开发界面。模型集成多目标跟踪，目标检测，ReID轻量级算法，进一步提升PP-Tracking在服务器端部署性能。同时支持python，C++部署，适配Linux，Nvidia Jetson多平台环境。
+
+<div width="1000" align="center">
+  <img src="../../docs/images/pptracking.png"/>
+</div>
+
+<div width="1000" align="center">
+  <img src="../../docs/images/pptracking-demo.gif"/>
+  <br>
+  视频来源：VisDrone2021, BDD100K开源数据集</div>
+</div>
+
+### 一、快速开始
+
+PP-Tracking提供了简洁的可视化界面，无需开发即可实现多种跟踪功能，可以参考[PP-Tracking可视化界面使用文档]()快速上手体验
+
+### 二、算法介绍
+
+PP-Tracking集成了多目标跟踪，目标检测，ReID轻量级算法，提升跟踪系统实时性能。多目标跟踪算法基于FairMOT进行优化，实现了服务器端轻量级模型，同时基于不同应用场景提供了针对性的预训练模型。
+
+模型训练评估方法请参考[多目标跟踪快速开始](../../configs/mot/README_cn.md#快速开始)
+
+PP-Tracking中提供的多场景预训练模型及导出模型列表如下：
+
+| 场景  | 数据集 | 精度（MOTA） | NX模型预测速度（FPS） | 配置文件 | 模型权重 | 预测部署模型 |
+| :---------:|:--------------- | :-------:  | :------: | :------: |:---: | :---: |
+| 行人跟踪 | MOT17 | 65.3 | 23.9 | [配置文件](../../configs/mot/fairmot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.pdparams) | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.tar) |
+| 行人小目标跟踪 | VisDrone-pedestrian |  40.5 | 8.35 | [配置文件](../../configs/mot/pedestrian/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian.pdparams) | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian.tar) |
+| 车辆跟踪 | BDD100k-vehicle | 32.6 | 24.3 | [配置文件](../../configs/mot/vehicle/fairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100kmot_vehicle.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100kmot_vehicle.pdparams) | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100kmot_vehicle.tar) |
+| 车辆小目标跟踪 | VisDrone-vehicle | 39.8 | 22.8 | [配置文件](../../configs/mot/vehicle/fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_vehicle.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_vehicle.pdparams) | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_vehicle.tar)
+| 多类别跟踪 | BDD100k |  - | 12.5 | [配置文件]() | [下载链接]() | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100k_mcmot.tar) |
+| 多类别小目标跟踪 | VisDrone |  20.4 | 6.74 | [配置文件](../../configs/mot/mcfairmot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone.yml) | [下载链接](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone.pdparams) | [下载链接](https://bj.bcebos.com/v1/paddledet/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone.tar) |
+
+**注：**
+
+1. 模型预测速度为TensorRT FP16速度，测试环境为CUDA 10.2，JETPACK 4.5.1，TensorRT 7.1
+2. 更多跟踪模型请参考[多目标跟踪模型库](../../configs/mot/README_cn.md#模型库)
+
+检测模型使用轻量级特色模型PP-PicoDet，具体请参考[PP-PicoDet文档](../../configs/picodet)
+
+ReID模型使用超轻量骨干网络模型PP-LCNet, 具体请参考[PP-LCNet模型介绍](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/docs/zh_CN/models/PP-LCNet.md)
+
+### 三、Python端预测部署
+
+PP-Tracking 使用python预测部署教程请参考[PP-Tracking python部署文档](python/README.md)
+
+### 四、C++端预测部署
+
+PP-Tracking 使用c++预测部署教程请参考[PP-Tracking c++部署文档](cpp/README.md)

deploy/pptracking/cpp/CMakeLists.txt

+cmake_minimum_required(VERSION 3.0)
+project(PaddleObjectDetector CXX C)
+
+option(WITH_MKL        "Compile demo with MKL/OpenBlas support,defaultuseMKL."          ON)
+option(WITH_GPU        "Compile demo with GPU/CPU, default use CPU."                    ON)
+option(WITH_TENSORRT   "Compile demo with TensorRT."                                    OFF)
+
+SET(PADDLE_DIR "" CACHE PATH "Location of libraries")
+SET(PADDLE_LIB_NAME "" CACHE STRING "libpaddle_inference")
+SET(OPENCV_DIR "" CACHE PATH "Location of libraries")
+SET(CUDA_LIB "" CACHE PATH "Location of libraries")
+SET(CUDNN_LIB "" CACHE PATH "Location of libraries")
+SET(TENSORRT_INC_DIR "" CACHE PATH "Compile demo with TensorRT")
+SET(TENSORRT_LIB_DIR "" CACHE PATH "Compile demo with TensorRT")
+
+include(cmake/yaml-cpp.cmake)
+
+include_directories("${CMAKE_SOURCE_DIR}/")
+include_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/src/ext-yaml-cpp/include")
+link_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/lib")
+
+set(SRCS src/main.cc src/preprocess_op.cc src/pipeline.cc src/jde_predictor.cc src/sde_predictor.cc src/tracker.cc src/trajectory.cc src/lapjv.cpp src/postprocess.cc)
+
+macro(safe_set_static_flag)
+    foreach(flag_var
+        CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
+        CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
+      if(${flag_var} MATCHES "/MD")
+        string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
+      endif(${flag_var} MATCHES "/MD")
+    endforeach(flag_var)
+endmacro()
+
+if (WITH_MKL)
+    ADD_DEFINITIONS(-DUSE_MKL)
+endif()
+
+if (NOT DEFINED PADDLE_DIR OR ${PADDLE_DIR} STREQUAL "")
+    message(FATAL_ERROR "please set PADDLE_DIR with -DPADDLE_DIR=/path/paddle_influence_dir")
+endif()
+message("PADDLE_DIR IS:" ${PADDLE_DIR})
+
+if (NOT DEFINED OPENCV_DIR OR ${OPENCV_DIR} STREQUAL "")
+    message(FATAL_ERROR "please set OPENCV_DIR with -DOPENCV_DIR=/path/opencv")
+endif()
+
+include_directories("${CMAKE_SOURCE_DIR}/")
+include_directories("${PADDLE_DIR}/")
+include_directories("${PADDLE_DIR}/third_party/install/protobuf/include")
+include_directories("${PADDLE_DIR}/third_party/install/glog/include")
+include_directories("${PADDLE_DIR}/third_party/install/gflags/include")
+include_directories("${PADDLE_DIR}/third_party/install/xxhash/include")
+if (EXISTS "${PADDLE_DIR}/third_party/install/snappy/include")
+    include_directories("${PADDLE_DIR}/third_party/install/snappy/include")
+endif()
+if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/include")
+    include_directories("${PADDLE_DIR}/third_party/install/snappystream/include")
+endif()
+include_directories("${PADDLE_DIR}/third_party/boost")
+include_directories("${PADDLE_DIR}/third_party/eigen3")
+
+if (EXISTS "${PADDLE_DIR}/third_party/install/snappy/lib")
+    link_directories("${PADDLE_DIR}/third_party/install/snappy/lib")
+endif()
+if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/lib")
+    link_directories("${PADDLE_DIR}/third_party/install/snappystream/lib")
+endif()
+
+link_directories("${PADDLE_DIR}/third_party/install/protobuf/lib")
+link_directories("${PADDLE_DIR}/third_party/install/glog/lib")
+link_directories("${PADDLE_DIR}/third_party/install/gflags/lib")
+link_directories("${PADDLE_DIR}/third_party/install/xxhash/lib")
+link_directories("${PADDLE_DIR}/paddle/lib/")
+link_directories("${CMAKE_CURRENT_BINARY_DIR}")
+
+
+
+if (WIN32)
+  include_directories("${PADDLE_DIR}/paddle/fluid/inference")
+  include_directories("${PADDLE_DIR}/paddle/include")
+  link_directories("${PADDLE_DIR}/paddle/fluid/inference")
+  find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/build/ NO_DEFAULT_PATH)
+
+else ()
+  find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH)
+  include_directories("${PADDLE_DIR}/paddle/include")
+  link_directories("${PADDLE_DIR}/paddle/lib")
+endif ()
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+if (WIN32)
+    add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
+    set(CMAKE_C_FLAGS_DEBUG   "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd")
+    set(CMAKE_C_FLAGS_RELEASE  "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT")
+    set(CMAKE_CXX_FLAGS_DEBUG  "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd")
+    set(CMAKE_CXX_FLAGS_RELEASE   "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
+else()
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -o2 -fopenmp -std=c++11")
+    set(CMAKE_STATIC_LIBRARY_PREFIX "")
+endif()
+
+# TODO let users define cuda lib path
+if (WITH_GPU)
+    if (NOT DEFINED CUDA_LIB OR ${CUDA_LIB} STREQUAL "")
+        message(FATAL_ERROR "please set CUDA_LIB with -DCUDA_LIB=/path/cuda-8.0/lib64")
+    endif()
+    if (NOT WIN32)
+        if (NOT DEFINED CUDNN_LIB)
+            message(FATAL_ERROR "please set CUDNN_LIB with -DCUDNN_LIB=/path/cudnn_v7.4/cuda/lib64")
+        endif()
+    endif(NOT WIN32)
+endif()
+
+
+if (NOT WIN32)
+  if (WITH_TENSORRT AND WITH_GPU)
+	  include_directories("${TENSORRT_INC_DIR}/")
+	  link_directories("${TENSORRT_LIB_DIR}/")
+  endif()
+endif(NOT WIN32)
+
+if (NOT WIN32)
+    set(NGRAPH_PATH "${PADDLE_DIR}/third_party/install/ngraph")
+    if(EXISTS ${NGRAPH_PATH})
+        include(GNUInstallDirs)
+        include_directories("${NGRAPH_PATH}/include")
+        link_directories("${NGRAPH_PATH}/${CMAKE_INSTALL_LIBDIR}")
+        set(NGRAPH_LIB ${NGRAPH_PATH}/${CMAKE_INSTALL_LIBDIR}/libngraph${CMAKE_SHARED_LIBRARY_SUFFIX})
+    endif()
+endif()
+
+if(WITH_MKL)
+  include_directories("${PADDLE_DIR}/third_party/install/mklml/include")
+  if (WIN32)
+    set(MATH_LIB ${PADDLE_DIR}/third_party/install/mklml/lib/mklml.lib
+            ${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5md.lib)
+  else ()
+    set(MATH_LIB ${PADDLE_DIR}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
+            ${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
+    execute_process(COMMAND cp -r ${PADDLE_DIR}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX} /usr/lib)
+  endif ()
+  set(MKLDNN_PATH "${PADDLE_DIR}/third_party/install/mkldnn")
+  if(EXISTS ${MKLDNN_PATH})
+    include_directories("${MKLDNN_PATH}/include")
+    if (WIN32)
+      set(MKLDNN_LIB ${MKLDNN_PATH}/lib/mkldnn.lib)
+    else ()
+      set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
+    endif ()
+  endif()
+else()
+  set(MATH_LIB ${PADDLE_DIR}/third_party/install/openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX})
+endif()
+
+
+if (WIN32)
+    if(EXISTS "${PADDLE_DIR}/paddle/fluid/inference/${PADDLE_LIB_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX}")
+        set(DEPS
+            ${PADDLE_DIR}/paddle/fluid/inference/${PADDLE_LIB_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX})
+    else()
+        set(DEPS
+            ${PADDLE_DIR}/paddle/lib/${PADDLE_LIB_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX})
+    endif()
+endif()
+
+
+if (WIN32)
+    set(DEPS ${PADDLE_DIR}/paddle/lib/${PADDLE_LIB_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX})
+else()
+    set(DEPS ${PADDLE_DIR}/paddle/lib/${PADDLE_LIB_NAME}${CMAKE_SHARED_LIBRARY_SUFFIX})
+endif()
+
+message("PADDLE_LIB_NAME:" ${PADDLE_LIB_NAME})
+message("DEPS:" $DEPS)
+
+if (NOT WIN32)
+    set(DEPS ${DEPS}
+        ${MATH_LIB} ${MKLDNN_LIB}
+        glog gflags protobuf z xxhash yaml-cpp
+        )
+    if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/lib")
+        set(DEPS ${DEPS} snappystream)
+    endif()
+    if (EXISTS "${PADDLE_DIR}/third_party/install/snappy/lib")
+        set(DEPS ${DEPS} snappy)
+    endif()
+else()
+    set(DEPS ${DEPS}
+        ${MATH_LIB} ${MKLDNN_LIB}
+        glog gflags_static libprotobuf xxhash libyaml-cppmt)
+    set(DEPS ${DEPS} libcmt shlwapi)
+    if (EXISTS "${PADDLE_DIR}/third_party/install/snappy/lib")
+        set(DEPS ${DEPS} snappy)
+    endif()
+    if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/lib")
+        set(DEPS ${DEPS} snappystream)
+    endif()
+endif(NOT WIN32)
+
+if(WITH_GPU)
+  if(NOT WIN32)
+    if (WITH_TENSORRT)
+	    set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
+	    set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
+    endif()
+    set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX})
+  else()
+    set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX} )
+    set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX} )
+    set(DEPS ${DEPS} ${CUDNN_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX})
+  endif()
+endif()
+
+if (NOT WIN32)
+    set(EXTERNAL_LIB "-ldl -lrt -lgomp -lz -lm -lpthread")
+    set(DEPS ${DEPS} ${EXTERNAL_LIB})
+endif()
+
+set(DEPS ${DEPS} ${OpenCV_LIBS})
+add_executable(main ${SRCS})
+ADD_DEPENDENCIES(main ext-yaml-cpp)
+message("DEPS:" $DEPS)
+target_link_libraries(main ${DEPS})
+
+if (WIN32 AND WITH_MKL)
+    add_custom_command(TARGET main POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/mklml.dll ./mklml.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5md.dll ./libiomp5md.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mkldnn/lib/mkldnn.dll ./mkldnn.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/mklml.dll ./release/mklml.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5md.dll ./release/libiomp5md.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mkldnn/lib/mkldnn.dll ./release/mkldnn.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/paddle/lib/${PADDLE_LIB_NAME}.dll ./release/${PADDLE_LIB_NAME}.dll
+    )
+endif()
+
+if (WIN32)
+    add_custom_command(TARGET main POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/paddle/lib/${PADDLE_LIB_NAME}.dll ./release/${PADDLE_LIB_NAME}.dll
+    )
+endif()

deploy/pptracking/cpp/README.md

+# C++端预测部署
+
+在PaddlePaddle中预测引擎和训练引擎底层有着不同的优化方法, 预测引擎使用了AnalysisPredictor，专门针对推理进行了优化，该引擎可以对模型进行多项图优化，减少不必要的内存拷贝。如果用户在部署已训练模型的过程中对性能有较高的要求，我们提供了独立于PaddleDetection的预测脚本，方便用户直接集成部署。当前C++部署支持基于Fairmot的单镜头类别预测部署，并支持人流量统计、出入口计数功能。
+
+主要包含三个步骤：
+
+- 准备环境
+- 导出预测模型
+- C++预测
+
+## 一、准备环境
+
+环境要求：
+
+- GCC 8.2
+- CUDA 10.1/10.2/11.1; CUDNN 7.6/8.1
+- CMake 3.0+
+- TensorRT 6/7
+
+NVIDIA Jetson用户请参考[Jetson平台编译指南](../../cpp/Jetson_build.md#jetson环境搭建)完成JetPack安装
+
+### 1. 下载代码
+
+```
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
+# C++部署代码与其他目录代码独立
+cd deploy/pptracking/cpp
+```
+
+### 2. 下载或编译PaddlePaddle C++预测库
+
+请根据环境选择适当的预测库进行下载，参考[C++预测库下载列表](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html)
+
+下载并解压后`./paddle_inference`目录包含内容为：
+
+```
+paddle_inference
+├── paddle # paddle核心库和头文件
+|
+├── third_party # 第三方依赖库和头文件
+|
+└── version.txt # 版本和编译信息
+```
+
+**注意:** 如果用户环境与官网提供环境不一致（如cuda 、cudnn、tensorrt版本不一致等），或对飞桨源代码有修改需求，或希望进行定制化构建，可参考[文档](https://paddleinference.paddlepaddle.org.cn/user_guides/source_compile.html)自行源码编译预测库。
+
+### 3. 编译
+
+编译`cmake`的命令在`scripts/build.sh`中，请根据实际情况修改主要参数，其主要内容说明如下：
+
+```
+# 是否使用GPU(即是否使用 CUDA)
+WITH_GPU=ON
+
+# 是否使用MKL or openblas，TX2需要设置为OFF
+WITH_MKL=OFF
+
+# 是否集成 TensorRT(仅WITH_GPU=ON 有效)
+WITH_TENSORRT=ON
+
+# TensorRT 的include路径
+TENSORRT_INC_DIR=/path/to/TensorRT/include
+
+# TensorRT 的lib路径
+TENSORRT_LIB_DIR=/path/to/TensorRT/lib
+
+# Paddle 预测库路径
+PADDLE_DIR=/path/to/paddle_inference/
+
+# Paddle 预测库名称
+PADDLE_LIB_NAME=libpaddle_inference
+
+# CUDA 的 lib 路径
+CUDA_LIB=/path/to/cuda/lib
+
+# CUDNN 的 lib 路径
+CUDNN_LIB=/path/to/cudnn/lib
+
+# OPENCV路径
+OPENCV_DIR=/path/to/opencv
+```
+
+修改脚本设置好主要参数后，执行```build.sh```脚本：
+
+```
+sh ./scripts/build.sh
+```
+
+**注意：**
+
+1. `TX2`平台的`CUDA`、`CUDNN`需要通过`JetPack`安装。
+2. 已提供linux和tx2平台的opencv下载方式，其他环境请自行安装[opencv](https://opencv.org/)
+
+## 二、导出预测模型
+
+将训练保存的权重导出为预测库需要的模型格式，使用PaddleDetection下的```tools/export_model.py```导出模型
+
+```
+python tools/export_model.py -c configs/mot/fairmot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.pdparams
+```
+
+预测模型会默认导出到```output_inference/fairmot_hrnetv2_w18_dlafpn_30e_576x320```目录下，包括```infer_cfg.yml```, ```model.pdiparams```, ```model.pdiparams.info```, ```model.pdmodel```
+
+导出模型也可以通过[预测模型列表]()直接下载使用
+
+## 三、C++预测
+
+完成以上步骤后，可以通过```build/main```进行预测，参数列表如下:
+
+|  参数   | 说明  |
+|  ----  | ----  |
+| --track_model_dir  | 导出的跟踪预测模型所在路径 |
+| --video_file  | 要预测的视频文件路径 |
+| --device  | 运行时的设备，可选择`CPU/GPU/XPU`，默认为`CPU`|
+| --gpu_id  |  指定进行推理的GPU device id(默认值为0)|
+| --run_mode | 使用GPU时，默认为fluid, 可选（fluid/trt_fp32/trt_fp16/trt_int8）|
+| --output_dir | 输出图片所在的文件夹, 默认为output ｜
+| --use_mkldnn | CPU预测中是否开启MKLDNN加速 |
+| --cpu_threads | 设置cpu线程数，默认为1 |
+| --do_entrance_counting | 是否进行出入口流量统计，默认为否 |
+| --save_result | 是否保存跟踪结果 |
+
+样例一：
+
+```shell
+# 使用CPU测试视频 `test.mp4` , 模型和测试视频均移至`build`目录下
+
+./main --track_model_dir=./fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video_file=test.mp4
+
+# 视频可视化预测结果默认保存在当前目录下output/test.mp4文件中
+```
+
+
+样例二：
+
+```shell
+# 使用GPU测试视频 `test.mp4` , 模型和测试视频均移至`build`目录下，实现出入口计数功能，并保存跟踪结果
+
+./main -video_file=test.mp4 -track_model_dir=./fairmot_dla34_30e_1088x608/  --device=gpu --do_entrance_counting=True --save_result=True
+
+# 视频可视化预测结果默认保存在当前目录下`output/test.mp4`中
+# 跟踪结果保存在`output/mot_output.txt`中
+# 计数结果保存在`output/flow_statistic.txt`中
+```

deploy/pptracking/cpp/cmake/yaml-cpp.cmake

+
+find_package(Git REQUIRED)
+
+include(ExternalProject)
+
+message("${CMAKE_BUILD_TYPE}")
+
+ExternalProject_Add(
+        ext-yaml-cpp
+        URL https://bj.bcebos.com/paddlex/deploy/deps/yaml-cpp.zip
+        URL_MD5 9542d6de397d1fbd649ed468cb5850e6
+        CMAKE_ARGS
+        -DYAML_CPP_BUILD_TESTS=OFF
+		-DYAML_CPP_BUILD_TOOLS=OFF
+        -DYAML_CPP_INSTALL=OFF
+        -DYAML_CPP_BUILD_CONTRIB=OFF
+		-DMSVC_SHARED_RT=OFF
+		-DBUILD_SHARED_LIBS=OFF
+        -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
+        -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
+        -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
+        -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
+        -DCMAKE_LIBRARY_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
+        -DCMAKE_ARCHIVE_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
+        PREFIX "${CMAKE_BINARY_DIR}/ext/yaml-cpp"
+        # Disable install step
+        INSTALL_COMMAND ""
+	    LOG_DOWNLOAD ON
+        LOG_BUILD 1
+)

deploy/pptracking/cpp/include/config_parser.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <iostream>
+#include <map>
+#include <string>
+#include <vector>
+
+#include "yaml-cpp/yaml.h"
+
+#ifdef _WIN32
+#define OS_PATH_SEP "\\"
+#else
+#define OS_PATH_SEP "/"
+#endif
+
+namespace PaddleDetection {
+
+// Inference model configuration parser
+class ConfigPaser {
+ public:
+  ConfigPaser() {}
+
+  ~ConfigPaser() {}
+
+  bool load_config(const std::string& model_dir,
+                   const std::string& cfg = "infer_cfg.yml") {
+    // Load as a YAML::Node
+    YAML::Node config;
+    config = YAML::LoadFile(model_dir + OS_PATH_SEP + cfg);
+
+    // Get runtime mode : fluid, trt_fp16, trt_fp32
+    if (config["mode"].IsDefined()) {
+      mode_ = config["mode"].as<std::string>();
+    } else {
+      std::cerr << "Please set mode, "
+                << "support value : fluid/trt_fp16/trt_fp32." << std::endl;
+      return false;
+    }
+
+    // Get model arch: FairMot or YOLO/Picodet/LCNet for DeepSort
+    if (config["arch"].IsDefined()) {
+      arch_ = config["arch"].as<std::string>();
+    } else {
+      std::cerr << "Please set model arch,"
+                << "support value : FairMot, YOLO, PicoDet, LCNet etc"
+                << std::endl;
+      return false;
+    }
+
+    // Get min_subgraph_size for tensorrt
+    if (config["min_subgraph_size"].IsDefined()) {
+      min_subgraph_size_ = config["min_subgraph_size"].as<int>();
+    } else {
+      std::cerr << "Please set min_subgraph_size." << std::endl;
+      return false;
+    }
+    // Get draw_threshold for visualization
+    if (config["draw_threshold"].IsDefined()) {
+      draw_threshold_ = config["draw_threshold"].as<float>();
+    } else {
+      std::cerr << "Please set draw_threshold." << std::endl;
+      return false;
+    }
+    // Get Preprocess for preprocessing
+    if (config["Preprocess"].IsDefined()) {
+      preprocess_info_ = config["Preprocess"];
+    } else {
+      std::cerr << "Please set Preprocess." << std::endl;
+      return false;
+    }
+    // Get label_list for visualization
+    if (config["label_list"].IsDefined()) {
+      label_list_ = config["label_list"].as<std::vector<std::string>>();
+    } else {
+      std::cerr << "Please set label_list." << std::endl;
+      return false;
+    }
+
+    // Get use_dynamic_shape for TensorRT
+    if (config["use_dynamic_shape"].IsDefined()) {
+      use_dynamic_shape_ = config["use_dynamic_shape"].as<bool>();
+    } else {
+      std::cerr << "Please set use_dynamic_shape." << std::endl;
+      return false;
+    }
+
+    // Get conf_thresh for tracker
+    if (config["tracker"].IsDefined()) {
+      if (config["tracker"]["conf_thres"].IsDefined()) {
+        conf_thresh_ = config["tracker"]["conf_thres"].as<float>();
+      } else {
+        std::cerr << "Please set conf_thres in tracker." << std::endl;
+        return false;
+      }
+    }
+
+    // Get NMS for postprocess
+    if (config["NMS"].IsDefined()) {
+      nms_info_ = config["NMS"];
+    }
+    // Get fpn_stride in PicoDet
+    if (config["fpn_stride"].IsDefined()) {
+      fpn_stride_.clear();
+      for (auto item : config["fpn_stride"]) {
+        fpn_stride_.emplace_back(item.as<int>());
+      }
+    }
+
+    return true;
+  }
+  std::string mode_;
+  float draw_threshold_;
+  std::string arch_;
+  int min_subgraph_size_;
+  YAML::Node preprocess_info_;
+  YAML::Node nms_info_;
+  std::vector<std::string> label_list_;
+  std::vector<int> fpn_stride_;
+  bool use_dynamic_shape_;
+  float conf_thresh_;
+};
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/include/jde_predictor.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <ctime>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include "paddle_inference_api.h"  // NOLINT
+
+#include "include/config_parser.h"
+#include "include/preprocess_op.h"
+#include "include/utils.h"
+
+using namespace paddle_infer;  // NOLINT
+
+namespace PaddleDetection {
+
+class JDEPredictor {
+ public:
+  explicit JDEPredictor(const std::string& device = "CPU",
+                        const std::string& model_dir = "",
+                        const double threshold = -1.,
+                        const std::string& run_mode = "fluid",
+                        const int gpu_id = 0,
+                        const bool use_mkldnn = false,
+                        const int cpu_threads = 1,
+                        bool trt_calib_mode = false,
+                        const int min_box_area = 200) {
+    this->device_ = device;
+    this->gpu_id_ = gpu_id;
+    this->use_mkldnn_ = use_mkldnn;
+    this->cpu_math_library_num_threads_ = cpu_threads;
+    this->trt_calib_mode_ = trt_calib_mode;
+    this->min_box_area_ = min_box_area;
+
+    config_.load_config(model_dir);
+    this->min_subgraph_size_ = config_.min_subgraph_size_;
+    preprocessor_.Init(config_.preprocess_info_);
+    LoadModel(model_dir, run_mode);
+    this->conf_thresh_ = config_.conf_thresh_;
+  }
+
+  // Load Paddle inference model
+  void LoadModel(const std::string& model_dir,
+                 const std::string& run_mode = "fluid");
+
+  // Run predictor
+  void Predict(const std::vector<cv::Mat> imgs,
+               const double threshold = 0.5,
+               MOTResult* result = nullptr,
+               std::vector<double>* times = nullptr);
+
+ private:
+  std::string device_ = "CPU";
+  float threhold = 0.5;
+  int gpu_id_ = 0;
+  bool use_mkldnn_ = false;
+  int cpu_math_library_num_threads_ = 1;
+  int min_subgraph_size_ = 3;
+  bool trt_calib_mode_ = false;
+
+  // Preprocess image and copy data to input buffer
+  void Preprocess(const cv::Mat& image_mat);
+  // Postprocess result
+  void Postprocess(const cv::Mat dets, const cv::Mat emb, MOTResult* result);
+
+  std::shared_ptr<Predictor> predictor_;
+  Preprocessor preprocessor_;
+  ImageBlob inputs_;
+  std::vector<float> bbox_data_;
+  std::vector<float> emb_data_;
+  double threshold_;
+  ConfigPaser config_;
+  float min_box_area_;
+  float conf_thresh_;
+};
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/include/lapjv.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// The code is based on:
+// https://github.com/gatagat/lap/blob/master/lap/lapjv.h
+// Ths copyright of gatagat/lap is as follows:
+// MIT License
+
+#ifndef DEPLOY_PPTRACKING_CPP_INCLUDE_LAPJV_H_
+#define DEPLOY_PPTRACKING_CPP_INCLUDE_LAPJV_H_
+#define LARGE 1000000
+
+#if !defined TRUE
+#define TRUE 1
+#endif
+#if !defined FALSE
+#define FALSE 0
+#endif
+
+#define NEW(x, t, n)                                               \
+  if ((x = reinterpret_cast<t *>(malloc(sizeof(t) * (n)))) == 0) { \
+    return -1;                                                     \
+  }
+#define FREE(x) \
+  if (x != 0) { \
+    free(x);    \
+    x = 0;      \
+  }
+#define SWAP_INDICES(a, b) \
+  {                        \
+    int_t _temp_index = a; \
+    a = b;                 \
+    b = _temp_index;       \
+  }
+#include <opencv2/opencv.hpp>
+
+namespace PaddleDetection {
+
+typedef signed int int_t;
+typedef unsigned int uint_t;
+typedef double cost_t;
+typedef char boolean;
+typedef enum fp_t { FP_1 = 1, FP_2 = 2, FP_DYNAMIC = 3 } fp_t;
+
+int lapjv_internal(const cv::Mat &cost,
+                   const bool extend_cost,
+                   const float cost_limit,
+                   int *x,
+                   int *y);
+
+}  // namespace PaddleDetection
+
+#endif  // DEPLOY_PPTRACKING_CPP_INCLUDE_LAPJV_H_

deploy/pptracking/cpp/include/pipeline.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef DEPLOY_PPTRACKING_CPP_INCLUDE_PIPELINE_H_
+#define DEPLOY_PPTRACKING_CPP_INCLUDE_PIPELINE_H_
+
+#include <glog/logging.h>
+
+#include <math.h>
+#include <sys/types.h>
+#include <algorithm>
+#include <iostream>
+#include <numeric>
+#include <string>
+#include <vector>
+
+#ifdef _WIN32
+#include <direct.h>
+#include <io.h>
+#elif LINUX
+#include <stdarg.h>
+#include <sys/stat.h>
+#endif
+
+#include "include/jde_predictor.h"
+#include "include/sde_predictor.h"
+
+namespace PaddleDetection {
+
+class Pipeline {
+ public:
+  explicit Pipeline(const std::string& device,
+                    const double threshold,
+                    const std::string& output_dir,
+                    const std::string& run_mode = "fluid",
+                    const int gpu_id = 0,
+                    const bool use_mkldnn = false,
+                    const int cpu_threads = 1,
+                    const bool trt_calib_mode = false,
+                    const bool do_entrance_counting = false,
+                    const bool save_result = false,
+                    const std::string& scene = "pedestrian",
+                    const bool tiny_obj = false,
+                    const bool is_mtmct = false,
+                    const int secs_interval = 10,
+                    const std::string track_model_dir = "",
+                    const std::string det_model_dir = "",
+                    const std::string reid_model_dir = "") {
+    std::vector<std::string> input;
+    this->input_ = input;
+    this->device_ = device;
+    this->threshold_ = threshold;
+    this->output_dir_ = output_dir;
+    this->run_mode_ = run_mode;
+    this->gpu_id_ = gpu_id;
+    this->use_mkldnn_ = use_mkldnn;
+    this->cpu_threads_ = cpu_threads;
+    this->trt_calib_mode_ = trt_calib_mode;
+    this->do_entrance_counting_ = do_entrance_counting;
+    this->secs_interval_ = secs_interval_;
+    this->save_result_ = save_result;
+    SelectModel(scene,
+                tiny_obj,
+                is_mtmct,
+                track_model_dir,
+                det_model_dir,
+                reid_model_dir);
+    InitPredictor();
+  }
+
+  // Set input, it must execute before Run()
+  void SetInput(const std::string& input_video);
+  void ClearInput();
+
+  // Run pipeline in video
+  void Run();
+  void PredictMOT(const std::string& video_path);
+  void PredictMTMCT(const std::vector<std::string> video_inputs);
+
+  // Run pipeline in stream
+  void RunMOTStream(const cv::Mat img,
+                    const int frame_id,
+                    const int video_fps,
+                    const Rect entrance,
+                    cv::Mat out_img,
+                    std::vector<std::string>* records,
+                    std::set<int>* count_set,
+                    std::set<int>* interval_count_set,
+                    std::vector<int>* in_count_list,
+                    std::vector<int>* out_count_list,
+                    std::map<int, std::vector<float>>* prev_center,
+                    std::vector<std::string>* flow_records);
+  void RunMTMCTStream(const std::vector<cv::Mat> imgs,
+                      std::vector<std::string>* records);
+
+  void PrintBenchmarkLog(const std::vector<double> det_time, const int img_num);
+
+ private:
+  // Select model according to scenes, it must execute before Run()
+  void SelectModel(const std::string& scene = "pedestrian",
+                   const bool tiny_obj = false,
+                   const bool is_mtmct = false,
+                   const std::string track_model_dir = "",
+                   const std::string det_model_dir = "",
+                   const std::string reid_model_dir = "");
+  void InitPredictor();
+
+  std::shared_ptr<PaddleDetection::JDEPredictor> jde_sct_;
+  std::shared_ptr<PaddleDetection::SDEPredictor> sde_sct_;
+
+  std::vector<std::string> input_;
+  std::vector<cv::Mat> stream_;
+  std::string device_;
+  double threshold_;
+  std::string output_dir_;
+  std::string track_model_dir_;
+  std::string det_model_dir_;
+  std::string reid_model_dir_;
+  std::string run_mode_ = "fluid";
+  int gpu_id_ = 0;
+  bool use_mkldnn_ = false;
+  int cpu_threads_ = 1;
+  bool trt_calib_mode_ = false;
+  bool do_entrance_counting_ = false;
+  bool save_result_ = false;
+  int secs_interval_ = 10;
+};
+
+}  // namespace PaddleDetection
+
+#endif  // DEPLOY_PPTRACKING_CPP_INCLUDE_PIPELINE_H_

deploy/pptracking/cpp/include/postprocess.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <glog/logging.h>
+
+#include <ctime>
+#include <memory>
+#include <set>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include "include/utils.h"
+
+namespace PaddleDetection {
+
+// Generate visualization color
+cv::Scalar GetColor(int idx);
+
+// Visualize Tracking Results
+cv::Mat VisualizeTrackResult(const cv::Mat& img,
+                             const MOTResult& results,
+                             const float fps,
+                             const int frame_id);
+
+// Pedestrian/Vehicle Counting
+void FlowStatistic(const MOTResult& results,
+                   const int frame_id,
+                   const int secs_interval,
+                   const bool do_entrance_counting,
+                   const int video_fps,
+                   const Rect entrance,
+                   std::set<int>* id_set,
+                   std::set<int>* interval_id_set,
+                   std::vector<int>* in_id_list,
+                   std::vector<int>* out_id_list,
+                   std::map<int, std::vector<float>>* prev_center,
+                   std::vector<std::string>* records);
+
+// Save Tracking Results
+void SaveMOTResult(const MOTResult& results,
+                   const int frame_id,
+                   std::vector<std::string>* records);
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/include/predictor.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <ctime>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include "paddle_inference_api.h"  // NOLINT
+
+#include "include/config_parser.h"
+#include "include/jde_predictor.h"
+#include "include/preprocess_op.h"
+#include "include/sde_predictor.h"
+
+using namespace paddle_infer;  // NOLINT
+
+namespace PaddleDetection {
+
+class Predictor {
+ public:
+  explicit Predictor(const std::string& device = "CPU",
+                     const std::string& track_model_dir = "",
+                     const std::string& det_model_dir = "",
+                     const std::string& reid_model_dir = "",
+                     const double threshold = -1.,
+                     const std::string& run_mode = "fluid",
+                     const int gpu_id = 0,
+                     const bool use_mkldnn = false,
+                     const int cpu_threads = 1,
+                     bool trt_calib_mode = false,
+                     const int min_box_area = 200) {
+    if (track_model_dir.empty() && det_model_dir.empty()) {
+      throw "Predictor must receive track_model or det_model!";
+    }
+
+    if (!track_model_dir.empty() && !det_model_dir.empty()) {
+      throw "Predictor only receive one of track_model or det_model!";
+    }
+
+    if (!track_model_dir.empty()) {
+      jde_sct_ =
+          std::make_shared<PaddleDetection::JDEPredictor>(device,
+                                                          track_model_dir,
+                                                          threshold,
+                                                          run_mode,
+                                                          gpu_id,
+                                                          use_mkldnn,
+                                                          cpu_threads,
+                                                          trt_calib_mode,
+                                                          min_box_area);
+      use_jde_ = true;
+    }
+    if (!det_model_dir.empty()) {
+      sde_sct_ = std::make_shared<PaddleDetection::SDEPredictor>(device,
+                                                                 det_model_dir,
+                                                                 reid_model_dir,
+                                                                 threshold,
+                                                                 run_mode,
+                                                                 gpu_id,
+                                                                 use_mkldnn,
+                                                                 cpu_threads,
+                                                                 trt_calib_mode,
+                                                                 min_box_area);
+      use_jde_ = false;
+    }
+  }
+
+  // Run predictor
+  void Predict(const std::vector<cv::Mat> imgs,
+               const double threshold = 0.5,
+               MOTResult* result = nullptr,
+               std::vector<double>* times = nullptr);
+
+ private:
+  std::shared_ptr<PaddleDetection::JDEPredictor> jde_sct_;
+  std::shared_ptr<PaddleDetection::SDEPredictor> sde_sct_;
+  bool use_jde_ = true;
+};
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/include/preprocess_op.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <glog/logging.h>
+#include <yaml-cpp/yaml.h>
+
+#include <iostream>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+namespace PaddleDetection {
+
+// Object for storing all preprocessed data
+class ImageBlob {
+ public:
+  // image width and height
+  std::vector<float> im_shape_;
+  // Buffer for image data after preprocessing
+  std::vector<float> im_data_;
+  // in net data shape(after pad)
+  std::vector<float> in_net_shape_;
+  // Evaluation image width and height
+  // std::vector<float>  eval_im_size_f_;
+  // Scale factor for image size to origin image size
+  std::vector<float> scale_factor_;
+};
+
+// Abstraction of preprocessing opration class
+class PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) = 0;
+  virtual void Run(cv::Mat* im, ImageBlob* data) = 0;
+};
+
+class InitInfo : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {}
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+};
+
+class NormalizeImage : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    mean_ = item["mean"].as<std::vector<float>>();
+    scale_ = item["std"].as<std::vector<float>>();
+    is_scale_ = item["is_scale"].as<bool>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  // CHW or HWC
+  std::vector<float> mean_;
+  std::vector<float> scale_;
+  bool is_scale_;
+};
+
+class Permute : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {}
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+};
+
+class Resize : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    interp_ = item["interp"].as<int>();
+    keep_ratio_ = item["keep_ratio"].as<bool>();
+    target_size_ = item["target_size"].as<std::vector<int>>();
+  }
+
+  // Compute best resize scale for x-dimension, y-dimension
+  std::pair<float, float> GenerateScale(const cv::Mat& im);
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int interp_;
+  bool keep_ratio_;
+  std::vector<int> target_size_;
+  std::vector<int> in_net_shape_;
+};
+
+class LetterBoxResize : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    target_size_ = item["target_size"].as<std::vector<int>>();
+  }
+
+  float GenerateScale(const cv::Mat& im);
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  std::vector<int> target_size_;
+  std::vector<int> in_net_shape_;
+};
+// Models with FPN need input shape % stride == 0
+class PadStride : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    stride_ = item["stride"].as<int>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int stride_;
+};
+
+class Preprocessor {
+ public:
+  void Init(const YAML::Node& config_node) {
+    // initialize image info at first
+    ops_["InitInfo"] = std::make_shared<InitInfo>();
+    for (const auto& item : config_node) {
+      auto op_name = item["type"].as<std::string>();
+
+      ops_[op_name] = CreateOp(op_name);
+      ops_[op_name]->Init(item);
+    }
+  }
+
+  std::shared_ptr<PreprocessOp> CreateOp(const std::string& name) {
+    if (name == "Resize") {
+      return std::make_shared<Resize>();
+    } else if (name == "LetterBoxResize") {
+      return std::make_shared<LetterBoxResize>();
+    } else if (name == "Permute") {
+      return std::make_shared<Permute>();
+    } else if (name == "NormalizeImage") {
+      return std::make_shared<NormalizeImage>();
+    } else if (name == "PadStride") {
+      // use PadStride instead of PadBatch
+      return std::make_shared<PadStride>();
+    }
+    std::cerr << "can not find function of OP: " << name
+              << " and return: nullptr" << std::endl;
+    return nullptr;
+  }
+
+  void Run(cv::Mat* im, ImageBlob* data);
+
+ public:
+  static const std::vector<std::string> RUN_ORDER;
+
+ private:
+  std::unordered_map<std::string, std::shared_ptr<PreprocessOp>> ops_;
+};
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/include/sde_predictor.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <ctime>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include "paddle_inference_api.h"  // NOLINT
+
+#include "include/config_parser.h"
+#include "include/preprocess_op.h"
+#include "include/utils.h"
+
+using namespace paddle_infer;  // NOLINT
+
+namespace PaddleDetection {
+
+class SDEPredictor {
+ public:
+  explicit SDEPredictor(const std::string& device,
+                        const std::string& det_model_dir = "",
+                        const std::string& reid_model_dir = "",
+                        const double threshold = -1.,
+                        const std::string& run_mode = "fluid",
+                        const int gpu_id = 0,
+                        const bool use_mkldnn = false,
+                        const int cpu_threads = 1,
+                        bool trt_calib_mode = false,
+                        const int min_box_area = 200) {
+    this->device_ = device;
+    this->gpu_id_ = gpu_id;
+    this->use_mkldnn_ = use_mkldnn;
+    this->cpu_math_library_num_threads_ = cpu_threads;
+    this->trt_calib_mode_ = trt_calib_mode;
+    this->min_box_area_ = min_box_area;
+
+    det_config_.load_config(det_model_dir);
+    this->min_subgraph_size_ = det_config_.min_subgraph_size_;
+    det_preprocessor_.Init(det_config_.preprocess_info_);
+
+    reid_config_.load_config(reid_model_dir);
+    reid_preprocessor_.Init(reid_config_.preprocess_info_);
+
+    LoadModel(det_model_dir, reid_model_dir, run_mode);
+    this->conf_thresh_ = det_config_.conf_thresh_;
+  }
+
+  // Load Paddle inference model
+  void LoadModel(const std::string& det_model_dir,
+                 const std::string& reid_model_dir,
+                 const std::string& run_mode = "fluid");
+
+  // Run predictor
+  void Predict(const std::vector<cv::Mat> imgs,
+               const double threshold = 0.5,
+               MOTResult* result = nullptr,
+               std::vector<double>* times = nullptr);
+
+ private:
+  std::string device_ = "CPU";
+  float threhold = 0.5;
+  int gpu_id_ = 0;
+  bool use_mkldnn_ = false;
+  int cpu_math_library_num_threads_ = 1;
+  int min_subgraph_size_ = 3;
+  bool trt_calib_mode_ = false;
+
+  // Preprocess image and copy data to input buffer
+  void Preprocess(const cv::Mat& image_mat);
+  // Postprocess result
+  void Postprocess(const cv::Mat dets, const cv::Mat emb, MOTResult* result);
+
+  std::shared_ptr<Predictor> det_predictor_;
+  std::shared_ptr<Predictor> reid_predictor_;
+  Preprocessor det_preprocessor_;
+  Preprocessor reid_preprocessor_;
+  ImageBlob inputs_;
+  std::vector<float> bbox_data_;
+  std::vector<float> emb_data_;
+  double threshold_;
+  ConfigPaser det_config_;
+  ConfigPaser reid_config_;
+  float min_box_area_ = 200;
+  float conf_thresh_;
+};
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/include/tracker.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// The code is based on:
+// https://github.com/CnybTseng/JDE/blob/master/platforms/common/jdetracker.h
+// Ths copyright of CnybTseng/JDE is as follows:
+// MIT License
+
+#pragma once
+
+#include <map>
+#include <vector>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include "include/trajectory.h"
+
+namespace PaddleDetection {
+
+typedef std::map<int, int> Match;
+typedef std::map<int, int>::iterator MatchIterator;
+
+struct Track {
+  int id;
+  float score;
+  cv::Vec4f ltrb;
+};
+
+class JDETracker {
+ public:
+  static JDETracker *instance(void);
+  virtual bool update(const cv::Mat &dets,
+                      const cv::Mat &emb,
+                      std::vector<Track> *tracks);
+
+ private:
+  JDETracker(void);
+  virtual ~JDETracker(void) {}
+  cv::Mat motion_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b);
+  void linear_assignment(const cv::Mat &cost,
+                         float cost_limit,
+                         Match *matches,
+                         std::vector<int> *mismatch_row,
+                         std::vector<int> *mismatch_col);
+  void remove_duplicate_trajectory(TrajectoryPool *a,
+                                   TrajectoryPool *b,
+                                   float iou_thresh = 0.15f);
+
+ private:
+  static JDETracker *me;
+  int timestamp;
+  TrajectoryPool tracked_trajectories;
+  TrajectoryPool lost_trajectories;
+  TrajectoryPool removed_trajectories;
+  int max_lost_time;
+  float lambda;
+  float det_thresh;
+};
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/include/trajectory.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// The code is based on:
+// https://github.com/CnybTseng/JDE/blob/master/platforms/common/trajectory.h
+// Ths copyright of CnybTseng/JDE is as follows:
+// MIT License
+
+#pragma once
+
+#include <vector>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include "opencv2/video/tracking.hpp"
+
+namespace PaddleDetection {
+
+typedef enum { New = 0, Tracked = 1, Lost = 2, Removed = 3 } TrajectoryState;
+
+class Trajectory;
+typedef std::vector<Trajectory> TrajectoryPool;
+typedef std::vector<Trajectory>::iterator TrajectoryPoolIterator;
+typedef std::vector<Trajectory *> TrajectoryPtrPool;
+typedef std::vector<Trajectory *>::iterator TrajectoryPtrPoolIterator;
+
+class TKalmanFilter : public cv::KalmanFilter {
+ public:
+  TKalmanFilter(void);
+  virtual ~TKalmanFilter(void) {}
+  virtual void init(const cv::Mat &measurement);
+  virtual const cv::Mat &predict();
+  virtual const cv::Mat &correct(const cv::Mat &measurement);
+  virtual void project(cv::Mat *mean, cv::Mat *covariance) const;
+
+ private:
+  float std_weight_position;
+  float std_weight_velocity;
+};
+
+inline TKalmanFilter::TKalmanFilter(void) : cv::KalmanFilter(8, 4) {
+  cv::KalmanFilter::transitionMatrix = cv::Mat::eye(8, 8, CV_32F);
+  for (int i = 0; i < 4; ++i)
+    cv::KalmanFilter::transitionMatrix.at<float>(i, i + 4) = 1;
+  cv::KalmanFilter::measurementMatrix = cv::Mat::eye(4, 8, CV_32F);
+  std_weight_position = 1 / 20.f;
+  std_weight_velocity = 1 / 160.f;
+}
+
+class Trajectory : public TKalmanFilter {
+ public:
+  Trajectory();
+  Trajectory(const cv::Vec4f &ltrb, float score, const cv::Mat &embedding);
+  Trajectory(const Trajectory &other);
+  Trajectory &operator=(const Trajectory &rhs);
+  virtual ~Trajectory(void) {}
+
+  static int next_id();
+  virtual const cv::Mat &predict(void);
+  virtual void update(Trajectory *traj,
+                      int timestamp,
+                      bool update_embedding = true);
+  virtual void activate(int timestamp);
+  virtual void reactivate(Trajectory *traj, int timestamp, bool newid = false);
+  virtual void mark_lost(void);
+  virtual void mark_removed(void);
+
+  friend TrajectoryPool operator+(const TrajectoryPool &a,
+                                  const TrajectoryPool &b);
+  friend TrajectoryPool operator+(const TrajectoryPool &a,
+                                  const TrajectoryPtrPool &b);
+  friend TrajectoryPool &operator+=(TrajectoryPool &a,  // NOLINT
+                                    const TrajectoryPtrPool &b);
+  friend TrajectoryPool operator-(const TrajectoryPool &a,
+                                  const TrajectoryPool &b);
+  friend TrajectoryPool &operator-=(TrajectoryPool &a,  // NOLINT
+                                    const TrajectoryPool &b);
+  friend TrajectoryPtrPool operator+(const TrajectoryPtrPool &a,
+                                     const TrajectoryPtrPool &b);
+  friend TrajectoryPtrPool operator+(const TrajectoryPtrPool &a,
+                                     TrajectoryPool *b);
+  friend TrajectoryPtrPool operator-(const TrajectoryPtrPool &a,
+                                     const TrajectoryPtrPool &b);
+
+  friend cv::Mat embedding_distance(const TrajectoryPool &a,
+                                    const TrajectoryPool &b);
+  friend cv::Mat embedding_distance(const TrajectoryPtrPool &a,
+                                    const TrajectoryPtrPool &b);
+  friend cv::Mat embedding_distance(const TrajectoryPtrPool &a,
+                                    const TrajectoryPool &b);
+
+  friend cv::Mat mahalanobis_distance(const TrajectoryPool &a,
+                                      const TrajectoryPool &b);
+  friend cv::Mat mahalanobis_distance(const TrajectoryPtrPool &a,
+                                      const TrajectoryPtrPool &b);
+  friend cv::Mat mahalanobis_distance(const TrajectoryPtrPool &a,
+                                      const TrajectoryPool &b);
+
+  friend cv::Mat iou_distance(const TrajectoryPool &a, const TrajectoryPool &b);
+  friend cv::Mat iou_distance(const TrajectoryPtrPool &a,
+                              const TrajectoryPtrPool &b);
+  friend cv::Mat iou_distance(const TrajectoryPtrPool &a,
+                              const TrajectoryPool &b);
+
+ private:
+  void update_embedding(const cv::Mat &embedding);
+
+ public:
+  TrajectoryState state;
+  cv::Vec4f ltrb;
+  cv::Mat smooth_embedding;
+  int id;
+  bool is_activated;
+  int timestamp;
+  int starttime;
+  float score;
+
+ private:
+  static int count;
+  cv::Vec4f xyah;
+  cv::Mat current_embedding;
+  float eta;
+  int length;
+};
+
+inline cv::Vec4f ltrb2xyah(const cv::Vec4f &ltrb) {
+  cv::Vec4f xyah;
+  xyah[0] = (ltrb[0] + ltrb[2]) * 0.5f;
+  xyah[1] = (ltrb[1] + ltrb[3]) * 0.5f;
+  xyah[3] = ltrb[3] - ltrb[1];
+  xyah[2] = (ltrb[2] - ltrb[0]) / xyah[3];
+  return xyah;
+}
+
+inline Trajectory::Trajectory()
+    : state(New),
+      ltrb(cv::Vec4f()),
+      smooth_embedding(cv::Mat()),
+      id(0),
+      is_activated(false),
+      timestamp(0),
+      starttime(0),
+      score(0),
+      eta(0.9),
+      length(0) {}
+
+inline Trajectory::Trajectory(const cv::Vec4f &ltrb_,
+                              float score_,
+                              const cv::Mat &embedding)
+    : state(New),
+      ltrb(ltrb_),
+      smooth_embedding(cv::Mat()),
+      id(0),
+      is_activated(false),
+      timestamp(0),
+      starttime(0),
+      score(score_),
+      eta(0.9),
+      length(0) {
+  xyah = ltrb2xyah(ltrb);
+  update_embedding(embedding);
+}
+
+inline Trajectory::Trajectory(const Trajectory &other)
+    : state(other.state),
+      ltrb(other.ltrb),
+      id(other.id),
+      is_activated(other.is_activated),
+      timestamp(other.timestamp),
+      starttime(other.starttime),
+      xyah(other.xyah),
+      score(other.score),
+      eta(other.eta),
+      length(other.length) {
+  other.smooth_embedding.copyTo(smooth_embedding);
+  other.current_embedding.copyTo(current_embedding);
+  // copy state in KalmanFilter
+
+  other.statePre.copyTo(cv::KalmanFilter::statePre);
+  other.statePost.copyTo(cv::KalmanFilter::statePost);
+  other.errorCovPre.copyTo(cv::KalmanFilter::errorCovPre);
+  other.errorCovPost.copyTo(cv::KalmanFilter::errorCovPost);
+}
+
+inline Trajectory &Trajectory::operator=(const Trajectory &rhs) {
+  this->state = rhs.state;
+  this->ltrb = rhs.ltrb;
+  rhs.smooth_embedding.copyTo(this->smooth_embedding);
+  this->id = rhs.id;
+  this->is_activated = rhs.is_activated;
+  this->timestamp = rhs.timestamp;
+  this->starttime = rhs.starttime;
+  this->xyah = rhs.xyah;
+  this->score = rhs.score;
+  rhs.current_embedding.copyTo(this->current_embedding);
+  this->eta = rhs.eta;
+  this->length = rhs.length;
+
+  // copy state in KalmanFilter
+
+  rhs.statePre.copyTo(cv::KalmanFilter::statePre);
+  rhs.statePost.copyTo(cv::KalmanFilter::statePost);
+  rhs.errorCovPre.copyTo(cv::KalmanFilter::errorCovPre);
+  rhs.errorCovPost.copyTo(cv::KalmanFilter::errorCovPost);
+
+  return *this;
+}
+
+inline int Trajectory::next_id() {
+  ++count;
+  return count;
+}
+
+inline void Trajectory::mark_lost(void) { state = Lost; }
+
+inline void Trajectory::mark_removed(void) { state = Removed; }
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/include/utils.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <algorithm>
+#include <ctime>
+#include <numeric>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "include/tracker.h"
+
+namespace PaddleDetection {
+
+struct Rect {
+  float left;
+  float top;
+  float right;
+  float bottom;
+};
+
+struct MOTTrack {
+  int ids;
+  float score;
+  Rect rects;
+  int class_id = -1;
+};
+
+typedef std::vector<MOTTrack> MOTResult;
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/scripts/build.sh

+# 是否使用GPU(即是否使用 CUDA)
+WITH_GPU=OFF
+
+# 是否使用MKL or openblas，TX2需要设置为OFF
+WITH_MKL=ON
+
+# 是否集成 TensorRT(仅WITH_GPU=ON 有效)
+WITH_TENSORRT=OFF
+
+# paddle 预测库lib名称，由于不同平台不同版本预测库lib名称不同，请查看所下载的预测库中`paddle_inference/lib/`文件夹下`lib`的名称
+PADDLE_LIB_NAME=libpaddle_inference
+
+# TensorRT 的include路径
+TENSORRT_INC_DIR=/path/to/tensorrt/include
+
+# TensorRT 的lib路径
+TENSORRT_LIB_DIR=/path/to/tensorrt/lib
+
+# Paddle 预测库路径
+PADDLE_DIR=/path/to/paddle_inference
+
+# CUDA 的 lib 路径
+CUDA_LIB=/path/to/cuda/lib
+
+# CUDNN 的 lib 路径
+CUDNN_LIB=/path/to/cudnn/lib
+
+MACHINE_TYPE=`uname -m`
+echo "MACHINE_TYPE: "${MACHINE_TYPE}
+
+
+if [ "$MACHINE_TYPE" = "x86_64" ]
+then
+  echo "set OPENCV_DIR for x86_64"
+  # linux系统通过以下命令下载预编译的opencv
+  mkdir -p $(pwd)/deps && cd $(pwd)/deps
+  wget -c https://paddledet.bj.bcebos.com/data/opencv-3.4.16_gcc8.2_ffmpeg.tar.gz
+  tar -xvf opencv-3.4.16_gcc8.2_ffmpeg.tar.gz && cd ..
+
+  # set OPENCV_DIR
+  OPENCV_DIR=$(pwd)/deps/opencv-3.4.16_gcc8.2_ffmpeg
+
+elif [ "$MACHINE_TYPE" = "aarch64" ]
+then
+  echo "set OPENCV_DIR for aarch64"
+  # TX2平台通过以下命令下载预编译的opencv
+  mkdir -p $(pwd)/deps && cd $(pwd)/deps
+  wget -c https://bj.bcebos.com/v1/paddledet/data/TX2_JetPack4.3_opencv_3.4.6_gcc7.5.0.tar.gz
+  tar -xvf TX2_JetPack4.3_opencv_3.4.6_gcc7.5.0.tar.gz && cd ..
+
+  # set OPENCV_DIR
+  OPENCV_DIR=$(pwd)/deps/TX2_JetPack4.3_opencv_3.4.6_gcc7.5.0/
+
+else
+  echo "Please set OPENCV_DIR manually"
+fi
+
+echo "OPENCV_DIR: "$OPENCV_DIR
+
+# 以下无需改动
+rm -rf build
+mkdir -p build
+cd build
+cmake .. \
+    -DWITH_GPU=${WITH_GPU} \
+    -DWITH_MKL=${WITH_MKL} \
+    -DWITH_TENSORRT=${WITH_TENSORRT} \
+    -DTENSORRT_LIB_DIR=${TENSORRT_LIB_DIR} \
+    -DTENSORRT_INC_DIR=${TENSORRT_INC_DIR} \
+    -DPADDLE_DIR=${PADDLE_DIR} \
+    -DWITH_STATIC_LIB=${WITH_STATIC_LIB} \
+    -DCUDA_LIB=${CUDA_LIB} \
+    -DCUDNN_LIB=${CUDNN_LIB} \
+    -DOPENCV_DIR=${OPENCV_DIR} \
+    -DPADDLE_LIB_NAME=${PADDLE_LIB_NAME} \
+
+make
+echo "make finished!"

deploy/pptracking/cpp/src/jde_predictor.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include <sstream>
+// for setprecision
+#include <chrono>
+#include <iomanip>
+#include "include/jde_predictor.h"
+
+using namespace paddle_infer;  // NOLINT
+
+namespace PaddleDetection {
+
+// Load Model and create model predictor
+void JDEPredictor::LoadModel(const std::string& model_dir,
+                             const std::string& run_mode) {
+  paddle_infer::Config config;
+  std::string prog_file = model_dir + OS_PATH_SEP + "model.pdmodel";
+  std::string params_file = model_dir + OS_PATH_SEP + "model.pdiparams";
+  config.SetModel(prog_file, params_file);
+  if (this->device_ == "GPU") {
+    config.EnableUseGpu(200, this->gpu_id_);
+    config.SwitchIrOptim(true);
+    // use tensorrt
+    if (run_mode != "fluid") {
+      auto precision = paddle_infer::Config::Precision::kFloat32;
+      if (run_mode == "trt_fp32") {
+        precision = paddle_infer::Config::Precision::kFloat32;
+      } else if (run_mode == "trt_fp16") {
+        precision = paddle_infer::Config::Precision::kHalf;
+      } else if (run_mode == "trt_int8") {
+        precision = paddle_infer::Config::Precision::kInt8;
+      } else {
+        printf(
+            "run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or 'trt_int8'");
+      }
+      // set tensorrt
+      config.EnableTensorRtEngine(1 << 30,
+                                  1,
+                                  this->min_subgraph_size_,
+                                  precision,
+                                  false,
+                                  this->trt_calib_mode_);
+    }
+  } else if (this->device_ == "XPU") {
+    config.EnableXpu(10 * 1024 * 1024);
+  } else {
+    config.DisableGpu();
+    if (this->use_mkldnn_) {
+      config.EnableMKLDNN();
+      // cache 10 different shapes for mkldnn to avoid memory leak
+      config.SetMkldnnCacheCapacity(10);
+    }
+    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
+  }
+  config.SwitchUseFeedFetchOps(false);
+  config.SwitchIrOptim(true);
+  config.DisableGlogInfo();
+  // Memory optimization
+  config.EnableMemoryOptim();
+  predictor_ = std::move(CreatePredictor(config));
+}
+
+void FilterDets(const float conf_thresh,
+                const cv::Mat dets,
+                std::vector<int>* index) {
+  for (int i = 0; i < dets.rows; ++i) {
+    float score = *dets.ptr<float>(i, 4);
+    if (score > conf_thresh) {
+      index->push_back(i);
+    }
+  }
+}
+
+void JDEPredictor::Preprocess(const cv::Mat& ori_im) {
+  // Clone the image : keep the original mat for postprocess
+  cv::Mat im = ori_im.clone();
+  preprocessor_.Run(&im, &inputs_);
+}
+
+void JDEPredictor::Postprocess(const cv::Mat dets,
+                               const cv::Mat emb,
+                               MOTResult* result) {
+  result->clear();
+  std::vector<Track> tracks;
+  std::vector<int> valid;
+  FilterDets(conf_thresh_, dets, &valid);
+  cv::Mat new_dets, new_emb;
+  for (int i = 0; i < valid.size(); ++i) {
+    new_dets.push_back(dets.row(valid[i]));
+    new_emb.push_back(emb.row(valid[i]));
+  }
+  JDETracker::instance()->update(new_dets, new_emb, &tracks);
+  if (tracks.size() == 0) {
+    MOTTrack mot_track;
+    Rect ret = {*dets.ptr<float>(0, 0),
+                *dets.ptr<float>(0, 1),
+                *dets.ptr<float>(0, 2),
+                *dets.ptr<float>(0, 3)};
+    mot_track.ids = 1;
+    mot_track.score = *dets.ptr<float>(0, 4);
+    mot_track.rects = ret;
+    result->push_back(mot_track);
+  } else {
+    std::vector<Track>::iterator titer;
+    for (titer = tracks.begin(); titer != tracks.end(); ++titer) {
+      if (titer->score < threshold_) {
+        continue;
+      } else {
+        float w = titer->ltrb[2] - titer->ltrb[0];
+        float h = titer->ltrb[3] - titer->ltrb[1];
+        bool vertical = w / h > 1.6;
+        float area = w * h;
+        if (area > min_box_area_ && !vertical) {
+          MOTTrack mot_track;
+          Rect ret = {
+              titer->ltrb[0], titer->ltrb[1], titer->ltrb[2], titer->ltrb[3]};
+          mot_track.rects = ret;
+          mot_track.score = titer->score;
+          mot_track.ids = titer->id;
+          result->push_back(mot_track);
+        }
+      }
+    }
+  }
+}
+
+void JDEPredictor::Predict(const std::vector<cv::Mat> imgs,
+                           const double threshold,
+                           MOTResult* result,
+                           std::vector<double>* times) {
+  auto preprocess_start = std::chrono::steady_clock::now();
+  int batch_size = imgs.size();
+
+  // in_data_batch
+  std::vector<float> in_data_all;
+  std::vector<float> im_shape_all(batch_size * 2);
+  std::vector<float> scale_factor_all(batch_size * 2);
+
+  // Preprocess image
+  for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
+    cv::Mat im = imgs.at(bs_idx);
+    Preprocess(im);
+    im_shape_all[bs_idx * 2] = inputs_.im_shape_[0];
+    im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1];
+
+    scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0];
+    scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];
+
+    in_data_all.insert(
+        in_data_all.end(), inputs_.im_data_.begin(), inputs_.im_data_.end());
+  }
+
+  // Prepare input tensor
+  auto input_names = predictor_->GetInputNames();
+  for (const auto& tensor_name : input_names) {
+    auto in_tensor = predictor_->GetInputHandle(tensor_name);
+    if (tensor_name == "image") {
+      int rh = inputs_.in_net_shape_[0];
+      int rw = inputs_.in_net_shape_[1];
+      in_tensor->Reshape({batch_size, 3, rh, rw});
+      in_tensor->CopyFromCpu(in_data_all.data());
+    } else if (tensor_name == "im_shape") {
+      in_tensor->Reshape({batch_size, 2});
+      in_tensor->CopyFromCpu(im_shape_all.data());
+    } else if (tensor_name == "scale_factor") {
+      in_tensor->Reshape({batch_size, 2});
+      in_tensor->CopyFromCpu(scale_factor_all.data());
+    }
+  }
+
+  auto preprocess_end = std::chrono::steady_clock::now();
+  std::vector<int> bbox_shape;
+  std::vector<int> emb_shape;
+
+  // Run predictor
+  auto inference_start = std::chrono::steady_clock::now();
+  predictor_->Run();
+  // Get output tensor
+  auto output_names = predictor_->GetOutputNames();
+  auto bbox_tensor = predictor_->GetOutputHandle(output_names[0]);
+  bbox_shape = bbox_tensor->shape();
+  auto emb_tensor = predictor_->GetOutputHandle(output_names[1]);
+  emb_shape = emb_tensor->shape();
+  // Calculate bbox length
+  int bbox_size = 1;
+  for (int j = 0; j < bbox_shape.size(); ++j) {
+    bbox_size *= bbox_shape[j];
+  }
+  // Calculate emb length
+  int emb_size = 1;
+  for (int j = 0; j < emb_shape.size(); ++j) {
+    emb_size *= emb_shape[j];
+  }
+
+  bbox_data_.resize(bbox_size);
+  bbox_tensor->CopyToCpu(bbox_data_.data());
+
+  emb_data_.resize(emb_size);
+  emb_tensor->CopyToCpu(emb_data_.data());
+  auto inference_end = std::chrono::steady_clock::now();
+
+  // Postprocessing result
+  auto postprocess_start = std::chrono::steady_clock::now();
+  result->clear();
+
+  cv::Mat dets(bbox_shape[0], 6, CV_32FC1, bbox_data_.data());
+  cv::Mat emb(bbox_shape[0], emb_shape[1], CV_32FC1, emb_data_.data());
+
+  Postprocess(dets, emb, result);
+
+  auto postprocess_end = std::chrono::steady_clock::now();
+
+  std::chrono::duration<float> preprocess_diff =
+      preprocess_end - preprocess_start;
+  (*times)[0] += static_cast<double>(preprocess_diff.count() * 1000);
+  std::chrono::duration<float> inference_diff = inference_end - inference_start;
+  (*times)[1] += static_cast<double>(inference_diff.count() * 1000);
+  std::chrono::duration<float> postprocess_diff =
+      postprocess_end - postprocess_start;
+  (*times)[2] += static_cast<double>(postprocess_diff.count() * 1000);
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/src/lapjv.cpp

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// The code is based on:
+// https://github.com/gatagat/lap/blob/master/lap/lapjv.cpp
+// Ths copyright of gatagat/lap is as follows:
+// MIT License
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#include "include/lapjv.h"
+
+namespace PaddleDetection {
+
+/** Column-reduction and reduction transfer for a dense cost matrix.
+ */
+int _ccrrt_dense(
+    const int n, float *cost[], int *free_rows, int *x, int *y, float *v) {
+  int n_free_rows;
+  bool *unique;
+
+  for (int i = 0; i < n; i++) {
+    x[i] = -1;
+    v[i] = LARGE;
+    y[i] = 0;
+  }
+  for (int i = 0; i < n; i++) {
+    for (int j = 0; j < n; j++) {
+      const float c = cost[i][j];
+      if (c < v[j]) {
+        v[j] = c;
+        y[j] = i;
+      }
+    }
+  }
+  NEW(unique, bool, n);
+  memset(unique, TRUE, n);
+  {
+    int j = n;
+    do {
+      j--;
+      const int i = y[j];
+      if (x[i] < 0) {
+        x[i] = j;
+      } else {
+        unique[i] = FALSE;
+        y[j] = -1;
+      }
+    } while (j > 0);
+  }
+  n_free_rows = 0;
+  for (int i = 0; i < n; i++) {
+    if (x[i] < 0) {
+      free_rows[n_free_rows++] = i;
+    } else if (unique[i]) {
+      const int j = x[i];
+      float min = LARGE;
+      for (int j2 = 0; j2 < n; j2++) {
+        if (j2 == static_cast<int>(j)) {
+          continue;
+        }
+        const float c = cost[i][j2] - v[j2];
+        if (c < min) {
+          min = c;
+        }
+      }
+      v[j] -= min;
+    }
+  }
+  FREE(unique);
+  return n_free_rows;
+}
+
+/** Augmenting row reduction for a dense cost matrix.
+ */
+int _carr_dense(const int n,
+                float *cost[],
+                const int n_free_rows,
+                int *free_rows,
+                int *x,
+                int *y,
+                float *v) {
+  int current = 0;
+  int new_free_rows = 0;
+  int rr_cnt = 0;
+  while (current < n_free_rows) {
+    int i0;
+    int j1, j2;
+    float v1, v2, v1_new;
+    bool v1_lowers;
+
+    rr_cnt++;
+    const int free_i = free_rows[current++];
+    j1 = 0;
+    v1 = cost[free_i][0] - v[0];
+    j2 = -1;
+    v2 = LARGE;
+    for (int j = 1; j < n; j++) {
+      const float c = cost[free_i][j] - v[j];
+      if (c < v2) {
+        if (c >= v1) {
+          v2 = c;
+          j2 = j;
+        } else {
+          v2 = v1;
+          v1 = c;
+          j2 = j1;
+          j1 = j;
+        }
+      }
+    }
+    i0 = y[j1];
+    v1_new = v[j1] - (v2 - v1);
+    v1_lowers = v1_new < v[j1];
+    if (rr_cnt < current * n) {
+      if (v1_lowers) {
+        v[j1] = v1_new;
+      } else if (i0 >= 0 && j2 >= 0) {
+        j1 = j2;
+        i0 = y[j2];
+      }
+      if (i0 >= 0) {
+        if (v1_lowers) {
+          free_rows[--current] = i0;
+        } else {
+          free_rows[new_free_rows++] = i0;
+        }
+      }
+    } else {
+      if (i0 >= 0) {
+        free_rows[new_free_rows++] = i0;
+      }
+    }
+    x[free_i] = j1;
+    y[j1] = free_i;
+  }
+  return new_free_rows;
+}
+
+/** Find columns with minimum d[j] and put them on the SCAN list.
+ */
+int _find_dense(const int n, int lo, float *d, int *cols, int *y) {
+  int hi = lo + 1;
+  float mind = d[cols[lo]];
+  for (int k = hi; k < n; k++) {
+    int j = cols[k];
+    if (d[j] <= mind) {
+      if (d[j] < mind) {
+        hi = lo;
+        mind = d[j];
+      }
+      cols[k] = cols[hi];
+      cols[hi++] = j;
+    }
+  }
+  return hi;
+}
+
+// Scan all columns in TODO starting from arbitrary column in SCAN
+// and try to decrease d of the TODO columns using the SCAN column.
+int _scan_dense(const int n,
+                float *cost[],
+                int *plo,
+                int *phi,
+                float *d,
+                int *cols,
+                int *pred,
+                int *y,
+                float *v) {
+  int lo = *plo;
+  int hi = *phi;
+  float h, cred_ij;
+
+  while (lo != hi) {
+    int j = cols[lo++];
+    const int i = y[j];
+    const float mind = d[j];
+    h = cost[i][j] - v[j] - mind;
+    // For all columns in TODO
+    for (int k = hi; k < n; k++) {
+      j = cols[k];
+      cred_ij = cost[i][j] - v[j] - h;
+      if (cred_ij < d[j]) {
+        d[j] = cred_ij;
+        pred[j] = i;
+        if (cred_ij == mind) {
+          if (y[j] < 0) {
+            return j;
+          }
+          cols[k] = cols[hi];
+          cols[hi++] = j;
+        }
+      }
+    }
+  }
+  *plo = lo;
+  *phi = hi;
+  return -1;
+}
+
+/** Single iteration of modified Dijkstra shortest path algorithm as explained
+ * in the JV paper.
+ *
+ * This is a dense matrix version.
+ *
+ * \return The closest free column index.
+ */
+int find_path_dense(const int n,
+                    float *cost[],
+                    const int start_i,
+                    int *y,
+                    float *v,
+                    int *pred) {
+  int lo = 0, hi = 0;
+  int final_j = -1;
+  int n_ready = 0;
+  int *cols;
+  float *d;
+
+  NEW(cols, int, n);
+  NEW(d, float, n);
+
+  for (int i = 0; i < n; i++) {
+    cols[i] = i;
+    pred[i] = start_i;
+    d[i] = cost[start_i][i] - v[i];
+  }
+  while (final_j == -1) {
+    // No columns left on the SCAN list.
+    if (lo == hi) {
+      n_ready = lo;
+      hi = _find_dense(n, lo, d, cols, y);
+      for (int k = lo; k < hi; k++) {
+        const int j = cols[k];
+        if (y[j] < 0) {
+          final_j = j;
+        }
+      }
+    }
+    if (final_j == -1) {
+      final_j = _scan_dense(n, cost, &lo, &hi, d, cols, pred, y, v);
+    }
+  }
+
+  {
+    const float mind = d[cols[lo]];
+    for (int k = 0; k < n_ready; k++) {
+      const int j = cols[k];
+      v[j] += d[j] - mind;
+    }
+  }
+
+  FREE(cols);
+  FREE(d);
+
+  return final_j;
+}
+
+/** Augment for a dense cost matrix.
+ */
+int _ca_dense(const int n,
+              float *cost[],
+              const int n_free_rows,
+              int *free_rows,
+              int *x,
+              int *y,
+              float *v) {
+  int *pred;
+
+  NEW(pred, int, n);
+
+  for (int *pfree_i = free_rows; pfree_i < free_rows + n_free_rows; pfree_i++) {
+    int i = -1, j;
+    int k = 0;
+
+    j = find_path_dense(n, cost, *pfree_i, y, v, pred);
+    while (i != *pfree_i) {
+      i = pred[j];
+      y[j] = i;
+      SWAP_INDICES(j, x[i]);
+      k++;
+    }
+  }
+  FREE(pred);
+  return 0;
+}
+
+/** Solve dense sparse LAP.
+ */
+int lapjv_internal(const cv::Mat &cost,
+                   const bool extend_cost,
+                   const float cost_limit,
+                   int *x,
+                   int *y) {
+  int n_rows = cost.rows;
+  int n_cols = cost.cols;
+  int n;
+  if (n_rows == n_cols) {
+    n = n_rows;
+  } else if (!extend_cost) {
+    throw std::invalid_argument(
+        "Square cost array expected. If cost is intentionally non-square, pass "
+        "extend_cost=True.");
+  }
+
+  // Get extend cost
+  if (extend_cost || cost_limit < LARGE) {
+    n = n_rows + n_cols;
+  }
+  cv::Mat cost_expand(n, n, CV_32F);
+  float expand_value;
+  if (cost_limit < LARGE) {
+    expand_value = cost_limit / 2;
+  } else {
+    double max_v;
+    minMaxLoc(cost, nullptr, &max_v);
+    expand_value = static_cast<float>(max_v) + 1.;
+  }
+
+  for (int i = 0; i < n; ++i) {
+    for (int j = 0; j < n; ++j) {
+      cost_expand.at<float>(i, j) = expand_value;
+      if (i >= n_rows && j >= n_cols) {
+        cost_expand.at<float>(i, j) = 0;
+      } else if (i < n_rows && j < n_cols) {
+        cost_expand.at<float>(i, j) = cost.at<float>(i, j);
+      }
+    }
+  }
+
+  // Convert Mat to pointer array
+  float **cost_ptr;
+  NEW(cost_ptr, float *, n);
+  for (int i = 0; i < n; ++i) {
+    NEW(cost_ptr[i], float, n);
+  }
+  for (int i = 0; i < n; ++i) {
+    for (int j = 0; j < n; ++j) {
+      cost_ptr[i][j] = cost_expand.at<float>(i, j);
+    }
+  }
+
+  int ret;
+  int *free_rows;
+  float *v;
+  int *x_c;
+  int *y_c;
+
+  NEW(free_rows, int, n);
+  NEW(v, float, n);
+  NEW(x_c, int, n);
+  NEW(y_c, int, n);
+
+  ret = _ccrrt_dense(n, cost_ptr, free_rows, x_c, y_c, v);
+  int i = 0;
+  while (ret > 0 && i < 2) {
+    ret = _carr_dense(n, cost_ptr, ret, free_rows, x_c, y_c, v);
+    i++;
+  }
+  if (ret > 0) {
+    ret = _ca_dense(n, cost_ptr, ret, free_rows, x_c, y_c, v);
+  }
+  FREE(v);
+  FREE(free_rows);
+  for (int i = 0; i < n; ++i) {
+    FREE(cost_ptr[i]);
+  }
+  FREE(cost_ptr);
+  if (ret != 0) {
+    if (ret == -1) {
+      throw "Out of memory.";
+    }
+    throw "Unknown error (lapjv_internal)";
+  }
+  // Get output of x, y, opt
+  for (int i = 0; i < n; ++i) {
+    if (i < n_rows) {
+      x[i] = x_c[i];
+      if (x[i] >= n_cols) {
+        x[i] = -1;
+      }
+    }
+    if (i < n_cols) {
+      y[i] = y_c[i];
+      if (y[i] >= n_rows) {
+        y[i] = -1;
+      }
+    }
+  }
+
+  FREE(x_c);
+  FREE(y_c);
+  return ret;
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/src/main.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <glog/logging.h>
+
+#include <math.h>
+#include <sys/types.h>
+#include <algorithm>
+#include <iostream>
+#include <numeric>
+#include <string>
+#include <vector>
+
+#ifdef _WIN32
+#include <direct.h>
+#include <io.h>
+#else
+#include <stdarg.h>
+#include <sys/stat.h>
+#endif
+
+#include <gflags/gflags.h>
+#include "include/pipeline.h"
+
+DEFINE_string(video_file, "", "Path of input video.");
+DEFINE_string(video_other_file,
+              "",
+              "Path of other input video used for MTMCT.");
+DEFINE_string(device,
+              "CPU",
+              "Choose the device you want to run, it can be: CPU/GPU/XPU, "
+              "default is CPU.");
+DEFINE_double(threshold, 0.5, "Threshold of score.");
+DEFINE_string(output_dir, "output", "Directory of output visualization files.");
+DEFINE_string(run_mode,
+              "fluid",
+              "Mode of running(fluid/trt_fp32/trt_fp16/trt_int8)");
+DEFINE_int32(gpu_id, 0, "Device id of GPU to execute");
+DEFINE_bool(use_mkldnn, false, "Whether use mkldnn with CPU");
+DEFINE_int32(cpu_threads, 1, "Num of threads with CPU");
+DEFINE_bool(trt_calib_mode,
+            false,
+            "If the model is produced by TRT offline quantitative calibration, "
+            "trt_calib_mode need to set True");
+DEFINE_bool(tiny_obj, false, "Whether tracking tiny object");
+DEFINE_bool(do_entrance_counting,
+            false,
+            "Whether counting the numbers of identifiers entering "
+            "or getting out from the entrance.");
+DEFINE_int32(secs_interval, 10, "The seconds interval to count after tracking");
+DEFINE_bool(save_result, false, "Whether saving result after tracking");
+DEFINE_string(
+    scene,
+    "",
+    "scene of tracking system, it can be : pedestrian/vehicle/multiclass");
+DEFINE_bool(is_mtmct, false, "Whether use multi-target multi-camera tracking");
+DEFINE_string(track_model_dir, "", "Path of tracking model");
+DEFINE_string(det_model_dir, "", "Path of detection model");
+DEFINE_string(reid_model_dir, "", "Path of reid model");
+
+static std::string DirName(const std::string& filepath) {
+  auto pos = filepath.rfind(OS_PATH_SEP);
+  if (pos == std::string::npos) {
+    return "";
+  }
+  return filepath.substr(0, pos);
+}
+
+static bool PathExists(const std::string& path) {
+#ifdef _WIN32
+  struct _stat buffer;
+  return (_stat(path.c_str(), &buffer) == 0);
+#else
+  struct stat buffer;
+  return (stat(path.c_str(), &buffer) == 0);
+#endif  // !_WIN32
+}
+
+static void MkDir(const std::string& path) {
+  if (PathExists(path)) return;
+  int ret = 0;
+#ifdef _WIN32
+  ret = _mkdir(path.c_str());
+#else
+  ret = mkdir(path.c_str(), 0755);
+#endif  // !_WIN32
+  if (ret != 0) {
+    std::string path_error(path);
+    path_error += " mkdir failed!";
+    throw std::runtime_error(path_error);
+  }
+}
+
+static void MkDirs(const std::string& path) {
+  if (path.empty()) return;
+  if (PathExists(path)) return;
+
+  MkDirs(DirName(path));
+  MkDir(path);
+}
+
+int main(int argc, char** argv) {
+  // Parsing command-line
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  bool has_model_dir =
+      !(FLAGS_track_model_dir.empty() && FLAGS_det_model_dir.empty() &&
+        FLAGS_reid_model_dir.empty());
+  if (FLAGS_video_file.empty() || (FLAGS_scene.empty() && !has_model_dir)) {
+    LOG(ERROR) << "Usage: \n"
+               << "1. ./main -video_file=/PATH/TO/INPUT/IMAGE/ "
+               << "-scene=pedestrian/vehicle/multiclass\n"
+               << "2. ./main -video_file=/PATH/TO/INPUT/IMAGE/ "
+               << "-track_model_dir=/PATH/TO/MODEL_DIR" << std::endl;
+
+    return -1;
+  }
+  if (!(FLAGS_run_mode == "fluid" || FLAGS_run_mode == "trt_fp32" ||
+        FLAGS_run_mode == "trt_fp16" || FLAGS_run_mode == "trt_int8")) {
+    LOG(ERROR)
+        << "run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or 'trt_int8'.";
+    return -1;
+  }
+  transform(FLAGS_device.begin(),
+            FLAGS_device.end(),
+            FLAGS_device.begin(),
+            ::toupper);
+  if (!(FLAGS_device == "CPU" || FLAGS_device == "GPU" ||
+        FLAGS_device == "XPU")) {
+    LOG(ERROR) << "device should be 'CPU', 'GPU' or 'XPU'.";
+    return -1;
+  }
+
+  if (!PathExists(FLAGS_output_dir)) {
+    MkDirs(FLAGS_output_dir);
+  }
+
+  PaddleDetection::Pipeline pipeline(FLAGS_device,
+                                     FLAGS_threshold,
+                                     FLAGS_output_dir,
+                                     FLAGS_run_mode,
+                                     FLAGS_gpu_id,
+                                     FLAGS_use_mkldnn,
+                                     FLAGS_cpu_threads,
+                                     FLAGS_trt_calib_mode,
+                                     FLAGS_do_entrance_counting,
+                                     FLAGS_save_result,
+                                     FLAGS_scene,
+                                     FLAGS_tiny_obj,
+                                     FLAGS_is_mtmct,
+                                     FLAGS_secs_interval,
+                                     FLAGS_track_model_dir,
+                                     FLAGS_det_model_dir,
+                                     FLAGS_reid_model_dir);
+
+  pipeline.SetInput(FLAGS_video_file);
+  if (!FLAGS_video_other_file.empty()) {
+    pipeline.SetInput(FLAGS_video_other_file);
+  }
+  pipeline.Run();
+  return 0;
+}

deploy/pptracking/cpp/src/pipeline.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <sstream>
+// for setprecision
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <string>
+
+#include "include/pipeline.h"
+#include "include/postprocess.h"
+#include "include/predictor.h"
+
+namespace PaddleDetection {
+
+void Pipeline::SetInput(const std::string& input_video) {
+  input_.push_back(input_video);
+}
+
+void Pipeline::ClearInput() {
+  input_.clear();
+  stream_.clear();
+}
+
+void Pipeline::SelectModel(const std::string& scene,
+                           const bool tiny_obj,
+                           const bool is_mtmct,
+                           const std::string track_model_dir,
+                           const std::string det_model_dir,
+                           const std::string reid_model_dir) {
+  // model_dir has higher priority
+  if (!track_model_dir.empty()) {
+    track_model_dir_ = track_model_dir;
+    return;
+  }
+  if (!det_model_dir.empty() && !reid_model_dir.empty()) {
+    det_model_dir_ = det_model_dir;
+    reid_model_dir_ = reid_model_dir;
+    return;
+  }
+
+  // Single camera model, based on FairMot
+  if (scene == "pedestrian") {
+    if (tiny_obj) {
+      track_model_dir_ = "../pedestrian_track_tiny";
+    } else {
+      track_model_dir_ = "../pedestrian_track";
+    }
+  } else if (scene != "vehicle") {
+    if (tiny_obj) {
+      track_model_dir_ = "../vehicle_track_tiny";
+    } else {
+      track_model_dir_ = "../vehicle_track";
+    }
+  } else if (scene == "multiclass") {
+    if (tiny_obj) {
+      track_model_dir_ = "../multiclass_track_tiny";
+    } else {
+      track_model_dir_ = "../multiclass_track";
+    }
+  }
+
+  // Multi-camera model, based on PicoDet & LCNet
+  if (is_mtmct && scene == "pedestrian") {
+    det_model_dir_ = "../pedestrian_det";
+    reid_model_dir_ = "../pedestrian_reid";
+  } else if (is_mtmct && scene == "vehicle") {
+    det_model_dir_ = "../vehicle_det";
+    reid_model_dir_ = "../vehicle_reid";
+  } else if (is_mtmct && scene == "multiclass") {
+    throw "Multi-camera tracking is not supported in multiclass scene now.";
+  }
+}
+
+void Pipeline::InitPredictor() {
+  if (track_model_dir_.empty() && det_model_dir_.empty()) {
+    throw "Predictor must receive track_model or det_model!";
+  }
+
+  if (!track_model_dir_.empty()) {
+    jde_sct_ = std::make_shared<PaddleDetection::JDEPredictor>(device_,
+                                                               track_model_dir_,
+                                                               threshold_,
+                                                               run_mode_,
+                                                               gpu_id_,
+                                                               use_mkldnn_,
+                                                               cpu_threads_,
+                                                               trt_calib_mode_);
+  }
+  if (!det_model_dir_.empty()) {
+    sde_sct_ = std::make_shared<PaddleDetection::SDEPredictor>(device_,
+                                                               det_model_dir_,
+                                                               reid_model_dir_,
+                                                               threshold_,
+                                                               run_mode_,
+                                                               gpu_id_,
+                                                               use_mkldnn_,
+                                                               cpu_threads_,
+                                                               trt_calib_mode_);
+  }
+}
+
+void Pipeline::Run() {
+  if (track_model_dir_.empty() && det_model_dir_.empty()) {
+    LOG(ERROR) << "Pipeline must use SelectModel before Run";
+    return;
+  }
+  if (input_.size() == 0) {
+    LOG(ERROR) << "Pipeline must use SetInput before Run";
+    return;
+  }
+
+  if (!track_model_dir_.empty()) {
+    // single camera
+    if (input_.size() > 1) {
+      throw "Single camera tracking except single video, but received %d",
+          input_.size();
+    }
+    PredictMOT(input_[0]);
+  } else {
+    // multi cameras
+    if (input_.size() != 2) {
+      throw "Multi camera tracking except two videos, but received %d",
+          input_.size();
+    }
+    PredictMTMCT(input_);
+  }
+}
+
+void Pipeline::PredictMOT(const std::string& video_path) {
+  // Open video
+  cv::VideoCapture capture;
+  capture.open(video_path.c_str());
+  if (!capture.isOpened()) {
+    printf("can not open video : %s\n", video_path.c_str());
+    return;
+  }
+
+  // Get Video info : resolution, fps
+  int video_width = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_WIDTH));
+  int video_height = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_HEIGHT));
+  int video_fps = static_cast<int>(capture.get(CV_CAP_PROP_FPS));
+
+  LOG(INFO) << "----------------------- Input info -----------------------";
+  LOG(INFO) << "video_width: " << video_width;
+  LOG(INFO) << "video_height: " << video_height;
+  LOG(INFO) << "input fps: " << video_fps;
+
+  // Create VideoWriter for output
+  cv::VideoWriter video_out;
+  std::string video_out_path = output_dir_ + OS_PATH_SEP + "mot_output.mp4";
+  int fcc = cv::VideoWriter::fourcc('m', 'p', '4', 'v');
+  video_out.open(video_out_path.c_str(),
+                 fcc,  // 0x00000021,
+                 video_fps,
+                 cv::Size(video_width, video_height),
+                 true);
+  if (!video_out.isOpened()) {
+    printf("create video writer failed!\n");
+    return;
+  }
+
+  PaddleDetection::MOTResult result;
+  std::vector<double> det_times(3);
+  std::set<int> id_set;
+  std::set<int> interval_id_set;
+  std::vector<int> in_id_list;
+  std::vector<int> out_id_list;
+  std::map<int, std::vector<float>> prev_center;
+  Rect entrance = {0,
+                   static_cast<float>(video_height) / 2,
+                   static_cast<float>(video_width),
+                   static_cast<float>(video_height) / 2};
+  double times;
+  double total_time;
+  // Capture all frames and do inference
+  cv::Mat frame;
+  int frame_id = 0;
+
+  std::vector<std::string> records;
+  std::vector<std::string> flow_records;
+  records.push_back("result format: frame_id, track_id, x1, y1, w, h\n");
+
+  LOG(INFO) << "------------------- Predict info ------------------------";
+  while (capture.read(frame)) {
+    if (frame.empty()) {
+      break;
+    }
+    std::vector<cv::Mat> imgs;
+    imgs.push_back(frame);
+    jde_sct_->Predict(imgs, threshold_, &result, &det_times);
+    frame_id += 1;
+    total_time = std::accumulate(det_times.begin(), det_times.end(), 0.);
+    times = total_time / frame_id;
+
+    LOG(INFO) << "frame_id: " << frame_id
+              << " predict time(s): " << total_time / 1000;
+
+    cv::Mat out_img = PaddleDetection::VisualizeTrackResult(
+        frame, result, 1000. / times, frame_id);
+
+    // TODO(qianhui): the entrance line can be set by users
+    PaddleDetection::FlowStatistic(result,
+                                   frame_id,
+                                   secs_interval_,
+                                   do_entrance_counting_,
+                                   video_fps,
+                                   entrance,
+                                   &id_set,
+                                   &interval_id_set,
+                                   &in_id_list,
+                                   &out_id_list,
+                                   &prev_center,
+                                   &flow_records);
+
+    if (save_result_) {
+      PaddleDetection::SaveMOTResult(result, frame_id, &records);
+    }
+
+    // Draw the entrance line
+    if (do_entrance_counting_) {
+      float line_thickness = std::max(1, static_cast<int>(video_width / 500.));
+      cv::Point pt1 = cv::Point(entrance.left, entrance.top);
+      cv::Point pt2 = cv::Point(entrance.right, entrance.bottom);
+      cv::line(out_img, pt1, pt2, cv::Scalar(0, 255, 255), line_thickness);
+    }
+    video_out.write(out_img);
+  }
+  capture.release();
+  video_out.release();
+  PrintBenchmarkLog(det_times, frame_id);
+  LOG(INFO) << "-------------------- Final Output info -------------------";
+  LOG(INFO) << "Total frame: " << frame_id;
+  LOG(INFO) << "Visualized output saved as " << video_out_path.c_str();
+  if (save_result_) {
+    FILE* fp;
+
+    std::string result_output_path =
+        output_dir_ + OS_PATH_SEP + "mot_output.txt";
+    if ((fp = fopen(result_output_path.c_str(), "w+")) == NULL) {
+      printf("Open %s error.\n", result_output_path.c_str());
+      return;
+    }
+    for (int l; l < records.size(); ++l) {
+      fprintf(fp, records[l].c_str());
+    }
+
+    fclose(fp);
+    LOG(INFO) << "txt result output saved as " << result_output_path.c_str();
+
+    result_output_path = output_dir_ + OS_PATH_SEP + "flow_statistic.txt";
+    if ((fp = fopen(result_output_path.c_str(), "w+")) == NULL) {
+      printf("Open %s error.\n", result_output_path);
+      return;
+    }
+    for (int l; l < flow_records.size(); ++l) {
+      fprintf(fp, flow_records[l].c_str());
+    }
+    fclose(fp);
+    LOG(INFO) << "txt flow statistic saved as " << result_output_path.c_str();
+  }
+}
+
+void Pipeline::PredictMTMCT(const std::vector<std::string> video_path) {
+  throw "Not Implement!";
+}
+
+void Pipeline::RunMOTStream(const cv::Mat img,
+                            const int frame_id,
+                            const int video_fps,
+                            const Rect entrance,
+                            cv::Mat out_img,
+                            std::vector<std::string>* records,
+                            std::set<int>* id_set,
+                            std::set<int>* interval_id_set,
+                            std::vector<int>* in_id_list,
+                            std::vector<int>* out_id_list,
+                            std::map<int, std::vector<float>>* prev_center,
+                            std::vector<std::string>* flow_records) {
+  PaddleDetection::MOTResult result;
+  std::vector<double> det_times(3);
+  double times;
+  double total_time;
+
+  LOG(INFO) << "------------------- Predict info ------------------------";
+  std::vector<cv::Mat> imgs;
+  imgs.push_back(img);
+  jde_sct_->Predict(imgs, threshold_, &result, &det_times);
+  total_time = std::accumulate(det_times.begin(), det_times.end(), 0.);
+  times = total_time / frame_id;
+
+  LOG(INFO) << "frame_id: " << frame_id
+            << " predict time(s): " << total_time / 1000;
+
+  out_img = PaddleDetection::VisualizeTrackResult(
+      img, result, 1000. / times, frame_id);
+
+  // Count total number
+  // Count in & out number
+  PaddleDetection::FlowStatistic(result,
+                                 frame_id,
+                                 secs_interval_,
+                                 do_entrance_counting_,
+                                 video_fps,
+                                 entrance,
+                                 id_set,
+                                 interval_id_set,
+                                 in_id_list,
+                                 out_id_list,
+                                 prev_center,
+                                 flow_records);
+
+  PrintBenchmarkLog(det_times, frame_id);
+  if (save_result_) {
+    PaddleDetection::SaveMOTResult(result, frame_id, records);
+  }
+}
+
+void Pipeline::RunMTMCTStream(const std::vector<cv::Mat> imgs,
+                              std::vector<std::string>* records) {
+  throw "Not Implement!";
+}
+
+void Pipeline::PrintBenchmarkLog(const std::vector<double> det_time,
+                                 const int img_num) {
+  LOG(INFO) << "----------------------- Config info -----------------------";
+  LOG(INFO) << "runtime_device: " << device_;
+  LOG(INFO) << "ir_optim: "
+            << "True";
+  LOG(INFO) << "enable_memory_optim: "
+            << "True";
+  int has_trt = run_mode_.find("trt");
+  if (has_trt >= 0) {
+    LOG(INFO) << "enable_tensorrt: "
+              << "True";
+    std::string precision = run_mode_.substr(4, 8);
+    LOG(INFO) << "precision: " << precision;
+  } else {
+    LOG(INFO) << "enable_tensorrt: "
+              << "False";
+    LOG(INFO) << "precision: "
+              << "fp32";
+  }
+  LOG(INFO) << "enable_mkldnn: " << (use_mkldnn_ ? "True" : "False");
+  LOG(INFO) << "cpu_math_library_num_threads: " << cpu_threads_;
+  LOG(INFO) << "----------------------- Perf info ------------------------";
+  LOG(INFO) << "Total number of predicted data: " << img_num
+            << " and total time spent(s): "
+            << std::accumulate(det_time.begin(), det_time.end(), 0.) / 1000;
+  int num = std::max(1, img_num);
+  LOG(INFO) << "preproce_time(ms): " << det_time[0] / num
+            << ", inference_time(ms): " << det_time[1] / num
+            << ", postprocess_time(ms): " << det_time[2] / num;
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/src/postprocess.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <sstream>
+// for setprecision
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include "include/postprocess.h"
+
+namespace PaddleDetection {
+
+cv::Scalar GetColor(int idx) {
+  idx = idx * 3;
+  cv::Scalar color =
+      cv::Scalar((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255);
+  return color;
+}
+
+cv::Mat VisualizeTrackResult(const cv::Mat& img,
+                             const MOTResult& results,
+                             const float fps,
+                             const int frame_id) {
+  cv::Mat vis_img = img.clone();
+  int im_h = img.rows;
+  int im_w = img.cols;
+  float text_scale = std::max(1, static_cast<int>(im_w / 1600.));
+  float text_thickness = 2.;
+  float line_thickness = std::max(1, static_cast<int>(im_w / 500.));
+
+  std::ostringstream oss;
+  oss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
+  oss << "frame: " << frame_id << " ";
+  oss << "fps: " << fps << " ";
+  oss << "num: " << results.size();
+  std::string text = oss.str();
+
+  cv::Point origin;
+  origin.x = 0;
+  origin.y = static_cast<int>(15 * text_scale);
+  cv::putText(vis_img,
+              text,
+              origin,
+              cv::FONT_HERSHEY_PLAIN,
+              text_scale,
+              (0, 0, 255),
+              2);
+
+  for (int i = 0; i < results.size(); ++i) {
+    const int obj_id = results[i].ids;
+    const float score = results[i].score;
+
+    cv::Scalar color = GetColor(obj_id);
+
+    cv::Point pt1 = cv::Point(results[i].rects.left, results[i].rects.top);
+    cv::Point pt2 = cv::Point(results[i].rects.right, results[i].rects.bottom);
+    cv::Point id_pt =
+        cv::Point(results[i].rects.left, results[i].rects.top + 10);
+    cv::Point score_pt =
+        cv::Point(results[i].rects.left, results[i].rects.top - 10);
+    cv::rectangle(vis_img, pt1, pt2, color, line_thickness);
+
+    std::ostringstream idoss;
+    idoss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
+    idoss << obj_id;
+    std::string id_text = idoss.str();
+
+    cv::putText(vis_img,
+                id_text,
+                id_pt,
+                cv::FONT_HERSHEY_PLAIN,
+                text_scale,
+                cv::Scalar(0, 255, 255),
+                text_thickness);
+
+    std::ostringstream soss;
+    soss << std::setiosflags(std::ios::fixed) << std::setprecision(2);
+    soss << score;
+    std::string score_text = soss.str();
+
+    cv::putText(vis_img,
+                score_text,
+                score_pt,
+                cv::FONT_HERSHEY_PLAIN,
+                text_scale,
+                cv::Scalar(0, 255, 255),
+                text_thickness);
+  }
+  return vis_img;
+}
+
+void FlowStatistic(const MOTResult& results,
+                   const int frame_id,
+                   const int secs_interval,
+                   const bool do_entrance_counting,
+                   const int video_fps,
+                   const Rect entrance,
+                   std::set<int>* id_set,
+                   std::set<int>* interval_id_set,
+                   std::vector<int>* in_id_list,
+                   std::vector<int>* out_id_list,
+                   std::map<int, std::vector<float>>* prev_center,
+                   std::vector<std::string>* records) {
+  if (frame_id == 0) interval_id_set->clear();
+
+  if (do_entrance_counting) {
+    // Count in and out number:
+    // Use horizontal center line as the entrance just for simplification.
+    // If a person located in the above the horizontal center line
+    // at the previous frame and is in the below the line at the current frame,
+    // the in number is increased by one.
+    // If a person was in the below the horizontal center line
+    // at the previous frame and locates in the below the line at the current
+    // frame,
+    // the out number is increased by one.
+    // TODO(qianhui): if the entrance is not the horizontal center line,
+    // the counting method should be optimized.
+
+    float entrance_y = entrance.top;
+    for (const auto& result : results) {
+      float center_x = (result.rects.left + result.rects.right) / 2;
+      float center_y = (result.rects.top + result.rects.bottom) / 2;
+      int ids = result.ids;
+      std::map<int, std::vector<float>>::iterator iter;
+      iter = prev_center->find(ids);
+      if (iter != prev_center->end()) {
+        if (iter->second[1] <= entrance_y && center_y > entrance_y) {
+          in_id_list->push_back(ids);
+        }
+        if (iter->second[1] >= entrance_y && center_y < entrance_y) {
+          out_id_list->push_back(ids);
+        }
+        (*prev_center)[ids][0] = center_x;
+        (*prev_center)[ids][1] = center_y;
+      } else {
+        prev_center->insert(
+            std::pair<int, std::vector<float>>(ids, {center_x, center_y}));
+      }
+    }
+  }
+
+  // Count totol number, number at a manual-setting interval
+  for (const auto& result : results) {
+    id_set->insert(result.ids);
+    interval_id_set->insert(result.ids);
+  }
+
+  std::ostringstream os;
+  os << "Frame id: " << frame_id << ", Total count: " << id_set->size();
+  if (do_entrance_counting) {
+    os << ", In count: " << in_id_list->size()
+       << ", Out count: " << out_id_list->size();
+  }
+
+  // Reset counting at the interval beginning
+  int curr_interval_count = -1;
+  if (frame_id % video_fps == 0 && frame_id / video_fps % secs_interval == 0) {
+    curr_interval_count = interval_id_set->size();
+    os << ", Count during " << secs_interval
+       << " secs: " << curr_interval_count;
+    interval_id_set->clear();
+  }
+  os << "\n";
+  std::string record = os.str();
+  records->push_back(record);
+  LOG(INFO) << record;
+}
+
+void SaveMOTResult(const MOTResult& results,
+                   const int frame_id,
+                   std::vector<std::string>* records) {
+  // result format: frame_id, track_id, x1, y1, w, h
+  std::string record;
+  for (int i = 0; i < results.size(); ++i) {
+    MOTTrack mot_track = results[i];
+    int ids = mot_track.ids;
+    float score = mot_track.score;
+    Rect rects = mot_track.rects;
+    float x1 = rects.left;
+    float y1 = rects.top;
+    float x2 = rects.right;
+    float y2 = rects.bottom;
+    float w = x2 - x1;
+    float h = y2 - y1;
+    if (w == 0 || h == 0) {
+      continue;
+    }
+    std::ostringstream os;
+    os << frame_id << " " << ids << "" << x1 << " " << y1 << " " << w << " "
+       << h << "\n";
+    record = os.str();
+    records->push_back(record);
+  }
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/src/predictor.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include <sstream>
+// for setprecision
+#include <chrono>
+#include <iomanip>
+#include "include/predictor.h"
+
+using namespace paddle_infer;  // NOLINT
+
+namespace PaddleDetection {
+
+void Predictor::Predict(const std::vector<cv::Mat> imgs,
+                        const double threshold,
+                        MOTResult* result,
+                        std::vector<double>* times) {
+  if (use_jde_) {
+    jde_sct_->Predict(imgs, threshold, result, times);
+  } else {
+    sde_sct_->Predict(imgs, threshold, result, times);
+  }
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/src/preprocess_op.cc

+//   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <string>
+#include <thread>
+#include <vector>
+
+#include "include/preprocess_op.h"
+
+namespace PaddleDetection {
+
+void InitInfo::Run(cv::Mat* im, ImageBlob* data) {
+  data->im_shape_ = {static_cast<float>(im->rows),
+                     static_cast<float>(im->cols)};
+  data->scale_factor_ = {1., 1.};
+  data->in_net_shape_ = {static_cast<float>(im->rows),
+                         static_cast<float>(im->cols)};
+}
+
+void NormalizeImage::Run(cv::Mat* im, ImageBlob* data) {
+  double e = 1.0;
+  if (is_scale_) {
+    e /= 255.0;
+  }
+  (*im).convertTo(*im, CV_32FC3, e);
+  for (int h = 0; h < im->rows; h++) {
+    for (int w = 0; w < im->cols; w++) {
+      im->at<cv::Vec3f>(h, w)[0] =
+          (im->at<cv::Vec3f>(h, w)[0] - mean_[0]) / scale_[0];
+      im->at<cv::Vec3f>(h, w)[1] =
+          (im->at<cv::Vec3f>(h, w)[1] - mean_[1]) / scale_[1];
+      im->at<cv::Vec3f>(h, w)[2] =
+          (im->at<cv::Vec3f>(h, w)[2] - mean_[2]) / scale_[2];
+    }
+  }
+}
+
+void Permute::Run(cv::Mat* im, ImageBlob* data) {
+  (*im).convertTo(*im, CV_32FC3);
+  int rh = im->rows;
+  int rw = im->cols;
+  int rc = im->channels();
+  (data->im_data_).resize(rc * rh * rw);
+  float* base = (data->im_data_).data();
+  for (int i = 0; i < rc; ++i) {
+    cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, base + i * rh * rw), i);
+  }
+}
+
+void Resize::Run(cv::Mat* im, ImageBlob* data) {
+  auto resize_scale = GenerateScale(*im);
+  data->im_shape_ = {static_cast<float>(im->cols * resize_scale.first),
+                     static_cast<float>(im->rows * resize_scale.second)};
+  data->in_net_shape_ = {static_cast<float>(im->cols * resize_scale.first),
+                         static_cast<float>(im->rows * resize_scale.second)};
+  cv::resize(
+      *im, *im, cv::Size(), resize_scale.first, resize_scale.second, interp_);
+  data->im_shape_ = {
+      static_cast<float>(im->rows), static_cast<float>(im->cols),
+  };
+  data->scale_factor_ = {
+      resize_scale.second, resize_scale.first,
+  };
+}
+
+std::pair<float, float> Resize::GenerateScale(const cv::Mat& im) {
+  std::pair<float, float> resize_scale;
+  int origin_w = im.cols;
+  int origin_h = im.rows;
+
+  if (keep_ratio_) {
+    int im_size_max = std::max(origin_w, origin_h);
+    int im_size_min = std::min(origin_w, origin_h);
+    int target_size_max =
+        *std::max_element(target_size_.begin(), target_size_.end());
+    int target_size_min =
+        *std::min_element(target_size_.begin(), target_size_.end());
+    float scale_min =
+        static_cast<float>(target_size_min) / static_cast<float>(im_size_min);
+    float scale_max =
+        static_cast<float>(target_size_max) / static_cast<float>(im_size_max);
+    float scale_ratio = std::min(scale_min, scale_max);
+    resize_scale = {scale_ratio, scale_ratio};
+  } else {
+    resize_scale.first =
+        static_cast<float>(target_size_[1]) / static_cast<float>(origin_w);
+    resize_scale.second =
+        static_cast<float>(target_size_[0]) / static_cast<float>(origin_h);
+  }
+  return resize_scale;
+}
+
+void LetterBoxResize::Run(cv::Mat* im, ImageBlob* data) {
+  float resize_scale = GenerateScale(*im);
+  int new_shape_w = std::round(im->cols * resize_scale);
+  int new_shape_h = std::round(im->rows * resize_scale);
+  data->im_shape_ = {static_cast<float>(new_shape_h),
+                     static_cast<float>(new_shape_w)};
+  float padw = (target_size_[1] - new_shape_w) / 2.;
+  float padh = (target_size_[0] - new_shape_h) / 2.;
+
+  int top = std::round(padh - 0.1);
+  int bottom = std::round(padh + 0.1);
+  int left = std::round(padw - 0.1);
+  int right = std::round(padw + 0.1);
+
+  cv::resize(
+      *im, *im, cv::Size(new_shape_w, new_shape_h), 0, 0, cv::INTER_AREA);
+
+  data->in_net_shape_ = {
+      static_cast<float>(im->rows), static_cast<float>(im->cols),
+  };
+  cv::copyMakeBorder(*im,
+                     *im,
+                     top,
+                     bottom,
+                     left,
+                     right,
+                     cv::BORDER_CONSTANT,
+                     cv::Scalar(127.5));
+
+  data->in_net_shape_ = {
+      static_cast<float>(im->rows), static_cast<float>(im->cols),
+  };
+
+  data->scale_factor_ = {
+      resize_scale, resize_scale,
+  };
+}
+
+float LetterBoxResize::GenerateScale(const cv::Mat& im) {
+  int origin_w = im.cols;
+  int origin_h = im.rows;
+
+  int target_h = target_size_[0];
+  int target_w = target_size_[1];
+
+  float ratio_h = static_cast<float>(target_h) / static_cast<float>(origin_h);
+  float ratio_w = static_cast<float>(target_w) / static_cast<float>(origin_w);
+  float resize_scale = std::min(ratio_h, ratio_w);
+  return resize_scale;
+}
+
+void PadStride::Run(cv::Mat* im, ImageBlob* data) {
+  if (stride_ <= 0) {
+    return;
+  }
+  int rc = im->channels();
+  int rh = im->rows;
+  int rw = im->cols;
+  int nh = (rh / stride_) * stride_ + (rh % stride_ != 0) * stride_;
+  int nw = (rw / stride_) * stride_ + (rw % stride_ != 0) * stride_;
+  cv::copyMakeBorder(
+      *im, *im, 0, nh - rh, 0, nw - rw, cv::BORDER_CONSTANT, cv::Scalar(0));
+  data->in_net_shape_ = {
+      static_cast<float>(im->rows), static_cast<float>(im->cols),
+  };
+}
+
+// Preprocessor op running order
+const std::vector<std::string> Preprocessor::RUN_ORDER = {"InitInfo",
+                                                          "Resize",
+                                                          "LetterBoxResize",
+                                                          "NormalizeImage",
+                                                          "PadStride",
+                                                          "Permute"};
+
+void Preprocessor::Run(cv::Mat* im, ImageBlob* data) {
+  for (const auto& name : RUN_ORDER) {
+    if (ops_.find(name) != ops_.end()) {
+      ops_[name]->Run(im, data);
+    }
+  }
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/src/sde_predictor.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include <sstream>
+// for setprecision
+#include <chrono>
+#include <iomanip>
+#include "include/sde_predictor.h"
+
+using namespace paddle_infer;  // NOLINT
+
+namespace PaddleDetection {
+
+// Load Model and create model predictor
+void SDEPredictor::LoadModel(const std::string& det_model_dir,
+                             const std::string& reid_model_dir,
+                             const std::string& run_mode) {
+  throw "Not Implement";
+}
+
+void SDEPredictor::Preprocess(const cv::Mat& ori_im) { throw "Not Implement"; }
+
+void SDEPredictor::Postprocess(const cv::Mat dets,
+                               const cv::Mat emb,
+                               MOTResult* result) {
+  throw "Not Implement";
+}
+
+void SDEPredictor::Predict(const std::vector<cv::Mat> imgs,
+                           const double threshold,
+                           MOTResult* result,
+                           std::vector<double>* times) {
+  throw "Not Implement";
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/src/tracker.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// The code is based on:
+// https://github.com/CnybTseng/JDE/blob/master/platforms/common/jdetracker.cpp
+// Ths copyright of CnybTseng/JDE is as follows:
+// MIT License
+
+#include <limits.h>
+#include <stdio.h>
+#include <algorithm>
+#include <map>
+
+#include "include/lapjv.h"
+#include "include/tracker.h"
+
+#define mat2vec4f(m)             \
+  cv::Vec4f(*m.ptr<float>(0, 0), \
+            *m.ptr<float>(0, 1), \
+            *m.ptr<float>(0, 2), \
+            *m.ptr<float>(0, 3))
+
+namespace PaddleDetection {
+
+static std::map<int, float> chi2inv95 = {{1, 3.841459f},
+                                         {2, 5.991465f},
+                                         {3, 7.814728f},
+                                         {4, 9.487729f},
+                                         {5, 11.070498f},
+                                         {6, 12.591587f},
+                                         {7, 14.067140f},
+                                         {8, 15.507313f},
+                                         {9, 16.918978f}};
+
+JDETracker *JDETracker::me = new JDETracker;
+
+JDETracker *JDETracker::instance(void) { return me; }
+
+JDETracker::JDETracker(void)
+    : timestamp(0), max_lost_time(30), lambda(0.98f), det_thresh(0.3f) {}
+
+bool JDETracker::update(const cv::Mat &dets,
+                        const cv::Mat &emb,
+                        std::vector<Track> *tracks) {
+  ++timestamp;
+  TrajectoryPool candidates(dets.rows);
+  for (int i = 0; i < dets.rows; ++i) {
+    float score = *dets.ptr<float>(i, 4);
+    const cv::Mat &ltrb_ = dets(cv::Rect(0, i, 4, 1));
+    cv::Vec4f ltrb = mat2vec4f(ltrb_);
+    const cv::Mat &embedding = emb(cv::Rect(0, i, emb.cols, 1));
+    candidates[i] = Trajectory(ltrb, score, embedding);
+  }
+
+  TrajectoryPtrPool tracked_trajectories;
+  TrajectoryPtrPool unconfirmed_trajectories;
+  for (size_t i = 0; i < this->tracked_trajectories.size(); ++i) {
+    if (this->tracked_trajectories[i].is_activated)
+      tracked_trajectories.push_back(&this->tracked_trajectories[i]);
+    else
+      unconfirmed_trajectories.push_back(&this->tracked_trajectories[i]);
+  }
+
+  TrajectoryPtrPool trajectory_pool =
+      tracked_trajectories + &(this->lost_trajectories);
+
+  for (size_t i = 0; i < trajectory_pool.size(); ++i)
+    trajectory_pool[i]->predict();
+
+  Match matches;
+  std::vector<int> mismatch_row;
+  std::vector<int> mismatch_col;
+
+  cv::Mat cost = motion_distance(trajectory_pool, candidates);
+  linear_assignment(cost, 0.7f, &matches, &mismatch_row, &mismatch_col);
+
+  MatchIterator miter;
+  TrajectoryPtrPool activated_trajectories;
+  TrajectoryPtrPool retrieved_trajectories;
+
+  for (miter = matches.begin(); miter != matches.end(); miter++) {
+    Trajectory *pt = trajectory_pool[miter->first];
+    Trajectory &ct = candidates[miter->second];
+    if (pt->state == Tracked) {
+      pt->update(&ct, timestamp);
+      activated_trajectories.push_back(pt);
+    } else {
+      pt->reactivate(&ct, timestamp);
+      retrieved_trajectories.push_back(pt);
+    }
+  }
+
+  TrajectoryPtrPool next_candidates(mismatch_col.size());
+  for (size_t i = 0; i < mismatch_col.size(); ++i)
+    next_candidates[i] = &candidates[mismatch_col[i]];
+
+  TrajectoryPtrPool next_trajectory_pool;
+  for (size_t i = 0; i < mismatch_row.size(); ++i) {
+    int j = mismatch_row[i];
+    if (trajectory_pool[j]->state == Tracked)
+      next_trajectory_pool.push_back(trajectory_pool[j]);
+  }
+
+  cost = iou_distance(next_trajectory_pool, next_candidates);
+  linear_assignment(cost, 0.5f, &matches, &mismatch_row, &mismatch_col);
+
+  for (miter = matches.begin(); miter != matches.end(); miter++) {
+    Trajectory *pt = next_trajectory_pool[miter->first];
+    Trajectory *ct = next_candidates[miter->second];
+    if (pt->state == Tracked) {
+      pt->update(ct, timestamp);
+      activated_trajectories.push_back(pt);
+    } else {
+      pt->reactivate(ct, timestamp);
+      retrieved_trajectories.push_back(pt);
+    }
+  }
+
+  TrajectoryPtrPool lost_trajectories;
+  for (size_t i = 0; i < mismatch_row.size(); ++i) {
+    Trajectory *pt = next_trajectory_pool[mismatch_row[i]];
+    if (pt->state != Lost) {
+      pt->mark_lost();
+      lost_trajectories.push_back(pt);
+    }
+  }
+
+  TrajectoryPtrPool nnext_candidates(mismatch_col.size());
+  for (size_t i = 0; i < mismatch_col.size(); ++i)
+    nnext_candidates[i] = next_candidates[mismatch_col[i]];
+  cost = iou_distance(unconfirmed_trajectories, nnext_candidates);
+  linear_assignment(cost, 0.7f, &matches, &mismatch_row, &mismatch_col);
+
+  for (miter = matches.begin(); miter != matches.end(); miter++) {
+    unconfirmed_trajectories[miter->first]->update(
+        nnext_candidates[miter->second], timestamp);
+    activated_trajectories.push_back(unconfirmed_trajectories[miter->first]);
+  }
+
+  TrajectoryPtrPool removed_trajectories;
+
+  for (size_t i = 0; i < mismatch_row.size(); ++i) {
+    unconfirmed_trajectories[mismatch_row[i]]->mark_removed();
+    removed_trajectories.push_back(unconfirmed_trajectories[mismatch_row[i]]);
+  }
+
+  for (size_t i = 0; i < mismatch_col.size(); ++i) {
+    if (nnext_candidates[mismatch_col[i]]->score < det_thresh) continue;
+    nnext_candidates[mismatch_col[i]]->activate(timestamp);
+    activated_trajectories.push_back(nnext_candidates[mismatch_col[i]]);
+  }
+
+  for (size_t i = 0; i < this->lost_trajectories.size(); ++i) {
+    Trajectory &lt = this->lost_trajectories[i];
+    if (timestamp - lt.timestamp > max_lost_time) {
+      lt.mark_removed();
+      removed_trajectories.push_back(&lt);
+    }
+  }
+
+  TrajectoryPoolIterator piter;
+  for (piter = this->tracked_trajectories.begin();
+       piter != this->tracked_trajectories.end();) {
+    if (piter->state != Tracked)
+      piter = this->tracked_trajectories.erase(piter);
+    else
+      ++piter;
+  }
+
+  this->tracked_trajectories += activated_trajectories;
+  this->tracked_trajectories += retrieved_trajectories;
+
+  this->lost_trajectories -= this->tracked_trajectories;
+  this->lost_trajectories += lost_trajectories;
+  this->lost_trajectories -= this->removed_trajectories;
+  this->removed_trajectories += removed_trajectories;
+  remove_duplicate_trajectory(&this->tracked_trajectories,
+                              &this->lost_trajectories);
+
+  tracks->clear();
+  for (size_t i = 0; i < this->tracked_trajectories.size(); ++i) {
+    if (this->tracked_trajectories[i].is_activated) {
+      Track track = {.id = this->tracked_trajectories[i].id,
+                     .score = this->tracked_trajectories[i].score,
+                     .ltrb = this->tracked_trajectories[i].ltrb};
+      tracks->push_back(track);
+    }
+  }
+  return 0;
+}
+
+cv::Mat JDETracker::motion_distance(const TrajectoryPtrPool &a,
+                                    const TrajectoryPool &b) {
+  if (0 == a.size() || 0 == b.size())
+    return cv::Mat(a.size(), b.size(), CV_32F);
+
+  cv::Mat edists = embedding_distance(a, b);
+  cv::Mat mdists = mahalanobis_distance(a, b);
+  cv::Mat fdists = lambda * edists + (1 - lambda) * mdists;
+
+  const float gate_thresh = chi2inv95[4];
+  for (int i = 0; i < fdists.rows; ++i) {
+    for (int j = 0; j < fdists.cols; ++j) {
+      if (*mdists.ptr<float>(i, j) > gate_thresh)
+        *fdists.ptr<float>(i, j) = FLT_MAX;
+    }
+  }
+
+  return fdists;
+}
+
+void JDETracker::linear_assignment(const cv::Mat &cost,
+                                   float cost_limit,
+                                   Match *matches,
+                                   std::vector<int> *mismatch_row,
+                                   std::vector<int> *mismatch_col) {
+  matches->clear();
+  mismatch_row->clear();
+  mismatch_col->clear();
+  if (cost.empty()) {
+    for (int i = 0; i < cost.rows; ++i) mismatch_row->push_back(i);
+    for (int i = 0; i < cost.cols; ++i) mismatch_col->push_back(i);
+    return;
+  }
+
+  float opt = 0;
+  cv::Mat x(cost.rows, 1, CV_32S);
+  cv::Mat y(cost.cols, 1, CV_32S);
+
+  lapjv_internal(cost,
+                 true,
+                 cost_limit,
+                 reinterpret_cast<int *>(x.data),
+                 reinterpret_cast<int *>(y.data));
+
+  for (int i = 0; i < x.rows; ++i) {
+    int j = *x.ptr<int>(i);
+    if (j >= 0)
+      matches->insert({i, j});
+    else
+      mismatch_row->push_back(i);
+  }
+
+  for (int i = 0; i < y.rows; ++i) {
+    int j = *y.ptr<int>(i);
+    if (j < 0) mismatch_col->push_back(i);
+  }
+
+  return;
+}
+
+void JDETracker::remove_duplicate_trajectory(TrajectoryPool *a,
+                                             TrajectoryPool *b,
+                                             float iou_thresh) {
+  if (a->size() == 0 || b->size() == 0) return;
+
+  cv::Mat dist = iou_distance(*a, *b);
+  cv::Mat mask = dist < iou_thresh;
+  std::vector<cv::Point> idx;
+  cv::findNonZero(mask, idx);
+
+  std::vector<int> da;
+  std::vector<int> db;
+  for (size_t i = 0; i < idx.size(); ++i) {
+    int ta = (*a)[idx[i].y].timestamp - (*a)[idx[i].y].starttime;
+    int tb = (*b)[idx[i].x].timestamp - (*b)[idx[i].x].starttime;
+    if (ta > tb)
+      db.push_back(idx[i].x);
+    else
+      da.push_back(idx[i].y);
+  }
+
+  int id = 0;
+  TrajectoryPoolIterator piter;
+  for (piter = a->begin(); piter != a->end();) {
+    std::vector<int>::iterator iter = find(da.begin(), da.end(), id++);
+    if (iter != da.end())
+      piter = a->erase(piter);
+    else
+      ++piter;
+  }
+
+  id = 0;
+  for (piter = b->begin(); piter != b->end();) {
+    std::vector<int>::iterator iter = find(db.begin(), db.end(), id++);
+    if (iter != db.end())
+      piter = b->erase(piter);
+    else
+      ++piter;
+  }
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/cpp/src/trajectory.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// The code is based on:
+// https://github.com/CnybTseng/JDE/blob/master/platforms/common/trajectory.cpp
+// Ths copyright of CnybTseng/JDE is as follows:
+// MIT License
+
+#include "include/trajectory.h"
+#include <algorithm>
+
+namespace PaddleDetection {
+
+void TKalmanFilter::init(const cv::Mat &measurement) {
+  measurement.copyTo(statePost(cv::Rect(0, 0, 1, 4)));
+  statePost(cv::Rect(0, 4, 1, 4)).setTo(0);
+  statePost.copyTo(statePre);
+
+  float varpos = 2 * std_weight_position * (*measurement.ptr<float>(3));
+  varpos *= varpos;
+  float varvel = 10 * std_weight_velocity * (*measurement.ptr<float>(3));
+  varvel *= varvel;
+
+  errorCovPost.setTo(0);
+  *errorCovPost.ptr<float>(0, 0) = varpos;
+  *errorCovPost.ptr<float>(1, 1) = varpos;
+  *errorCovPost.ptr<float>(2, 2) = 1e-4f;
+  *errorCovPost.ptr<float>(3, 3) = varpos;
+  *errorCovPost.ptr<float>(4, 4) = varvel;
+  *errorCovPost.ptr<float>(5, 5) = varvel;
+  *errorCovPost.ptr<float>(6, 6) = 1e-10f;
+  *errorCovPost.ptr<float>(7, 7) = varvel;
+  errorCovPost.copyTo(errorCovPre);
+}
+
+const cv::Mat &TKalmanFilter::predict() {
+  float varpos = std_weight_position * (*statePre.ptr<float>(3));
+  varpos *= varpos;
+  float varvel = std_weight_velocity * (*statePre.ptr<float>(3));
+  varvel *= varvel;
+
+  processNoiseCov.setTo(0);
+  *processNoiseCov.ptr<float>(0, 0) = varpos;
+  *processNoiseCov.ptr<float>(1, 1) = varpos;
+  *processNoiseCov.ptr<float>(2, 2) = 1e-4f;
+  *processNoiseCov.ptr<float>(3, 3) = varpos;
+  *processNoiseCov.ptr<float>(4, 4) = varvel;
+  *processNoiseCov.ptr<float>(5, 5) = varvel;
+  *processNoiseCov.ptr<float>(6, 6) = 1e-10f;
+  *processNoiseCov.ptr<float>(7, 7) = varvel;
+
+  return cv::KalmanFilter::predict();
+}
+
+const cv::Mat &TKalmanFilter::correct(const cv::Mat &measurement) {
+  float varpos = std_weight_position * (*measurement.ptr<float>(3));
+  varpos *= varpos;
+
+  measurementNoiseCov.setTo(0);
+  *measurementNoiseCov.ptr<float>(0, 0) = varpos;
+  *measurementNoiseCov.ptr<float>(1, 1) = varpos;
+  *measurementNoiseCov.ptr<float>(2, 2) = 1e-2f;
+  *measurementNoiseCov.ptr<float>(3, 3) = varpos;
+
+  return cv::KalmanFilter::correct(measurement);
+}
+
+void TKalmanFilter::project(cv::Mat *mean, cv::Mat *covariance) const {
+  float varpos = std_weight_position * (*statePost.ptr<float>(3));
+  varpos *= varpos;
+
+  cv::Mat measurementNoiseCov_ = cv::Mat::eye(4, 4, CV_32F);
+  *measurementNoiseCov_.ptr<float>(0, 0) = varpos;
+  *measurementNoiseCov_.ptr<float>(1, 1) = varpos;
+  *measurementNoiseCov_.ptr<float>(2, 2) = 1e-2f;
+  *measurementNoiseCov_.ptr<float>(3, 3) = varpos;
+
+  *mean = measurementMatrix * statePost;
+  cv::Mat temp = measurementMatrix * errorCovPost;
+  gemm(temp,
+       measurementMatrix,
+       1,
+       measurementNoiseCov_,
+       1,
+       *covariance,
+       cv::GEMM_2_T);
+}
+
+int Trajectory::count = 0;
+
+const cv::Mat &Trajectory::predict(void) {
+  if (state != Tracked) *cv::KalmanFilter::statePost.ptr<float>(7) = 0;
+  return TKalmanFilter::predict();
+}
+
+void Trajectory::update(Trajectory *traj,
+                        int timestamp_,
+                        bool update_embedding_) {
+  timestamp = timestamp_;
+  ++length;
+  ltrb = traj->ltrb;
+  xyah = traj->xyah;
+  TKalmanFilter::correct(cv::Mat(traj->xyah));
+  state = Tracked;
+  is_activated = true;
+  score = traj->score;
+  if (update_embedding_) update_embedding(traj->current_embedding);
+}
+
+void Trajectory::activate(int timestamp_) {
+  id = next_id();
+  TKalmanFilter::init(cv::Mat(xyah));
+  length = 0;
+  state = Tracked;
+  if (timestamp_ == 1) {
+    is_activated = true;
+  }
+  timestamp = timestamp_;
+  starttime = timestamp_;
+}
+
+void Trajectory::reactivate(Trajectory *traj, int timestamp_, bool newid) {
+  TKalmanFilter::correct(cv::Mat(traj->xyah));
+  update_embedding(traj->current_embedding);
+  length = 0;
+  state = Tracked;
+  is_activated = true;
+  timestamp = timestamp_;
+  if (newid) id = next_id();
+}
+
+void Trajectory::update_embedding(const cv::Mat &embedding) {
+  current_embedding = embedding / cv::norm(embedding);
+  if (smooth_embedding.empty()) {
+    smooth_embedding = current_embedding;
+  } else {
+    smooth_embedding = eta * smooth_embedding + (1 - eta) * current_embedding;
+  }
+  smooth_embedding = smooth_embedding / cv::norm(smooth_embedding);
+}
+
+TrajectoryPool operator+(const TrajectoryPool &a, const TrajectoryPool &b) {
+  TrajectoryPool sum;
+  sum.insert(sum.end(), a.begin(), a.end());
+
+  std::vector<int> ids(a.size());
+  for (size_t i = 0; i < a.size(); ++i) ids[i] = a[i].id;
+
+  for (size_t i = 0; i < b.size(); ++i) {
+    std::vector<int>::iterator iter = find(ids.begin(), ids.end(), b[i].id);
+    if (iter == ids.end()) {
+      sum.push_back(b[i]);
+      ids.push_back(b[i].id);
+    }
+  }
+
+  return sum;
+}
+
+TrajectoryPool operator+(const TrajectoryPool &a, const TrajectoryPtrPool &b) {
+  TrajectoryPool sum;
+  sum.insert(sum.end(), a.begin(), a.end());
+
+  std::vector<int> ids(a.size());
+  for (size_t i = 0; i < a.size(); ++i) ids[i] = a[i].id;
+
+  for (size_t i = 0; i < b.size(); ++i) {
+    std::vector<int>::iterator iter = find(ids.begin(), ids.end(), b[i]->id);
+    if (iter == ids.end()) {
+      sum.push_back(*b[i]);
+      ids.push_back(b[i]->id);
+    }
+  }
+
+  return sum;
+}
+
+TrajectoryPool &operator+=(TrajectoryPool &a,  // NOLINT
+                           const TrajectoryPtrPool &b) {
+  std::vector<int> ids(a.size());
+  for (size_t i = 0; i < a.size(); ++i) ids[i] = a[i].id;
+
+  for (size_t i = 0; i < b.size(); ++i) {
+    if (b[i]->smooth_embedding.empty()) continue;
+    std::vector<int>::iterator iter = find(ids.begin(), ids.end(), b[i]->id);
+    if (iter == ids.end()) {
+      a.push_back(*b[i]);
+      ids.push_back(b[i]->id);
+    }
+  }
+
+  return a;
+}
+
+TrajectoryPool operator-(const TrajectoryPool &a, const TrajectoryPool &b) {
+  TrajectoryPool dif;
+  std::vector<int> ids(b.size());
+  for (size_t i = 0; i < b.size(); ++i) ids[i] = b[i].id;
+
+  for (size_t i = 0; i < a.size(); ++i) {
+    std::vector<int>::iterator iter = find(ids.begin(), ids.end(), a[i].id);
+    if (iter == ids.end()) dif.push_back(a[i]);
+  }
+
+  return dif;
+}
+
+TrajectoryPool &operator-=(TrajectoryPool &a,  // NOLINT
+                           const TrajectoryPool &b) {
+  std::vector<int> ids(b.size());
+  for (size_t i = 0; i < b.size(); ++i) ids[i] = b[i].id;
+
+  TrajectoryPoolIterator piter;
+  for (piter = a.begin(); piter != a.end();) {
+    std::vector<int>::iterator iter = find(ids.begin(), ids.end(), piter->id);
+    if (iter == ids.end())
+      ++piter;
+    else
+      piter = a.erase(piter);
+  }
+
+  return a;
+}
+
+TrajectoryPtrPool operator+(const TrajectoryPtrPool &a,
+                            const TrajectoryPtrPool &b) {
+  TrajectoryPtrPool sum;
+  sum.insert(sum.end(), a.begin(), a.end());
+
+  std::vector<int> ids(a.size());
+  for (size_t i = 0; i < a.size(); ++i) ids[i] = a[i]->id;
+
+  for (size_t i = 0; i < b.size(); ++i) {
+    std::vector<int>::iterator iter = find(ids.begin(), ids.end(), b[i]->id);
+    if (iter == ids.end()) {
+      sum.push_back(b[i]);
+      ids.push_back(b[i]->id);
+    }
+  }
+
+  return sum;
+}
+
+TrajectoryPtrPool operator+(const TrajectoryPtrPool &a, TrajectoryPool *b) {
+  TrajectoryPtrPool sum;
+  sum.insert(sum.end(), a.begin(), a.end());
+
+  std::vector<int> ids(a.size());
+  for (size_t i = 0; i < a.size(); ++i) ids[i] = a[i]->id;
+
+  for (size_t i = 0; i < b->size(); ++i) {
+    std::vector<int>::iterator iter = find(ids.begin(), ids.end(), (*b)[i].id);
+    if (iter == ids.end()) {
+      sum.push_back(&(*b)[i]);
+      ids.push_back((*b)[i].id);
+    }
+  }
+
+  return sum;
+}
+
+TrajectoryPtrPool operator-(const TrajectoryPtrPool &a,
+                            const TrajectoryPtrPool &b) {
+  TrajectoryPtrPool dif;
+  std::vector<int> ids(b.size());
+  for (size_t i = 0; i < b.size(); ++i) ids[i] = b[i]->id;
+
+  for (size_t i = 0; i < a.size(); ++i) {
+    std::vector<int>::iterator iter = find(ids.begin(), ids.end(), a[i]->id);
+    if (iter == ids.end()) dif.push_back(a[i]);
+  }
+
+  return dif;
+}
+
+cv::Mat embedding_distance(const TrajectoryPool &a, const TrajectoryPool &b) {
+  cv::Mat dists(a.size(), b.size(), CV_32F);
+  for (size_t i = 0; i < a.size(); ++i) {
+    float *distsi = dists.ptr<float>(i);
+    for (size_t j = 0; j < b.size(); ++j) {
+      cv::Mat u = a[i].smooth_embedding;
+      cv::Mat v = b[j].smooth_embedding;
+      double uv = u.dot(v);
+      double uu = u.dot(u);
+      double vv = v.dot(v);
+      double dist = std::abs(1. - uv / std::sqrt(uu * vv));
+      // double dist = cv::norm(a[i].smooth_embedding, b[j].smooth_embedding,
+      // cv::NORM_L2);
+      distsi[j] = static_cast<float>(std::max(std::min(dist, 2.), 0.));
+    }
+  }
+  return dists;
+}
+
+cv::Mat embedding_distance(const TrajectoryPtrPool &a,
+                           const TrajectoryPtrPool &b) {
+  cv::Mat dists(a.size(), b.size(), CV_32F);
+  for (size_t i = 0; i < a.size(); ++i) {
+    float *distsi = dists.ptr<float>(i);
+    for (size_t j = 0; j < b.size(); ++j) {
+      // double dist = cv::norm(a[i]->smooth_embedding, b[j]->smooth_embedding,
+      // cv::NORM_L2);
+      // distsi[j] = static_cast<float>(dist);
+      cv::Mat u = a[i]->smooth_embedding;
+      cv::Mat v = b[j]->smooth_embedding;
+      double uv = u.dot(v);
+      double uu = u.dot(u);
+      double vv = v.dot(v);
+      double dist = std::abs(1. - uv / std::sqrt(uu * vv));
+      distsi[j] = static_cast<float>(std::max(std::min(dist, 2.), 0.));
+    }
+  }
+
+  return dists;
+}
+
+cv::Mat embedding_distance(const TrajectoryPtrPool &a,
+                           const TrajectoryPool &b) {
+  cv::Mat dists(a.size(), b.size(), CV_32F);
+  for (size_t i = 0; i < a.size(); ++i) {
+    float *distsi = dists.ptr<float>(i);
+    for (size_t j = 0; j < b.size(); ++j) {
+      // double dist = cv::norm(a[i]->smooth_embedding, b[j].smooth_embedding,
+      // cv::NORM_L2);
+      // distsi[j] = static_cast<float>(dist);
+      cv::Mat u = a[i]->smooth_embedding;
+      cv::Mat v = b[j].smooth_embedding;
+      double uv = u.dot(v);
+      double uu = u.dot(u);
+      double vv = v.dot(v);
+      double dist = std::abs(1. - uv / std::sqrt(uu * vv));
+      distsi[j] = static_cast<float>(std::max(std::min(dist, 2.), 0.));
+    }
+  }
+
+  return dists;
+}
+
+cv::Mat mahalanobis_distance(const TrajectoryPool &a, const TrajectoryPool &b) {
+  std::vector<cv::Mat> means(a.size());
+  std::vector<cv::Mat> icovariances(a.size());
+  for (size_t i = 0; i < a.size(); ++i) {
+    cv::Mat covariance;
+    a[i].project(&means[i], &covariance);
+    cv::invert(covariance, icovariances[i]);
+  }
+
+  cv::Mat dists(a.size(), b.size(), CV_32F);
+  for (size_t i = 0; i < a.size(); ++i) {
+    float *distsi = dists.ptr<float>(i);
+    for (size_t j = 0; j < b.size(); ++j) {
+      const cv::Mat x(b[j].xyah);
+      float dist =
+          static_cast<float>(cv::Mahalanobis(x, means[i], icovariances[i]));
+      distsi[j] = dist * dist;
+    }
+  }
+
+  return dists;
+}
+
+cv::Mat mahalanobis_distance(const TrajectoryPtrPool &a,
+                             const TrajectoryPtrPool &b) {
+  std::vector<cv::Mat> means(a.size());
+  std::vector<cv::Mat> icovariances(a.size());
+  for (size_t i = 0; i < a.size(); ++i) {
+    cv::Mat covariance;
+    a[i]->project(&means[i], &covariance);
+    cv::invert(covariance, icovariances[i]);
+  }
+
+  cv::Mat dists(a.size(), b.size(), CV_32F);
+  for (size_t i = 0; i < a.size(); ++i) {
+    float *distsi = dists.ptr<float>(i);
+    for (size_t j = 0; j < b.size(); ++j) {
+      const cv::Mat x(b[j]->xyah);
+      float dist =
+          static_cast<float>(cv::Mahalanobis(x, means[i], icovariances[i]));
+      distsi[j] = dist * dist;
+    }
+  }
+
+  return dists;
+}
+
+cv::Mat mahalanobis_distance(const TrajectoryPtrPool &a,
+                             const TrajectoryPool &b) {
+  std::vector<cv::Mat> means(a.size());
+  std::vector<cv::Mat> icovariances(a.size());
+
+  for (size_t i = 0; i < a.size(); ++i) {
+    cv::Mat covariance;
+    a[i]->project(&means[i], &covariance);
+    cv::invert(covariance, icovariances[i]);
+  }
+
+  cv::Mat dists(a.size(), b.size(), CV_32F);
+  for (size_t i = 0; i < a.size(); ++i) {
+    float *distsi = dists.ptr<float>(i);
+    for (size_t j = 0; j < b.size(); ++j) {
+      const cv::Mat x(b[j].xyah);
+      float dist =
+          static_cast<float>(cv::Mahalanobis(x, means[i], icovariances[i]));
+      distsi[j] = dist * dist;
+    }
+  }
+
+  return dists;
+}
+
+static inline float calc_inter_area(const cv::Vec4f &a, const cv::Vec4f &b) {
+  if (a[2] < b[0] || a[0] > b[2] || a[3] < b[1] || a[1] > b[3]) return 0.f;
+
+  float w = std::min(a[2], b[2]) - std::max(a[0], b[0]);
+  float h = std::min(a[3], b[3]) - std::max(a[1], b[1]);
+  return w * h;
+}
+
+cv::Mat iou_distance(const TrajectoryPool &a, const TrajectoryPool &b) {
+  std::vector<float> areaa(a.size());
+  for (size_t i = 0; i < a.size(); ++i) {
+    float w = a[i].ltrb[2] - a[i].ltrb[0];
+    float h = a[i].ltrb[3] - a[i].ltrb[1];
+    areaa[i] = w * h;
+  }
+
+  std::vector<float> areab(b.size());
+  for (size_t j = 0; j < b.size(); ++j) {
+    float w = b[j].ltrb[2] - b[j].ltrb[0];
+    float h = b[j].ltrb[3] - b[j].ltrb[1];
+    areab[j] = w * h;
+  }
+
+  cv::Mat dists(a.size(), b.size(), CV_32F);
+  for (size_t i = 0; i < a.size(); ++i) {
+    const cv::Vec4f &boxa = a[i].ltrb;
+    float *distsi = dists.ptr<float>(i);
+    for (size_t j = 0; j < b.size(); ++j) {
+      const cv::Vec4f &boxb = b[j].ltrb;
+      float inters = calc_inter_area(boxa, boxb);
+      distsi[j] = 1.f - inters / (areaa[i] + areab[j] - inters);
+    }
+  }
+
+  return dists;
+}
+
+cv::Mat iou_distance(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b) {
+  std::vector<float> areaa(a.size());
+  for (size_t i = 0; i < a.size(); ++i) {
+    float w = a[i]->ltrb[2] - a[i]->ltrb[0];
+    float h = a[i]->ltrb[3] - a[i]->ltrb[1];
+    areaa[i] = w * h;
+  }
+
+  std::vector<float> areab(b.size());
+  for (size_t j = 0; j < b.size(); ++j) {
+    float w = b[j]->ltrb[2] - b[j]->ltrb[0];
+    float h = b[j]->ltrb[3] - b[j]->ltrb[1];
+    areab[j] = w * h;
+  }
+
+  cv::Mat dists(a.size(), b.size(), CV_32F);
+  for (size_t i = 0; i < a.size(); ++i) {
+    const cv::Vec4f &boxa = a[i]->ltrb;
+    float *distsi = dists.ptr<float>(i);
+    for (size_t j = 0; j < b.size(); ++j) {
+      const cv::Vec4f &boxb = b[j]->ltrb;
+      float inters = calc_inter_area(boxa, boxb);
+      distsi[j] = 1.f - inters / (areaa[i] + areab[j] - inters);
+    }
+  }
+
+  return dists;
+}
+
+cv::Mat iou_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b) {
+  std::vector<float> areaa(a.size());
+  for (size_t i = 0; i < a.size(); ++i) {
+    float w = a[i]->ltrb[2] - a[i]->ltrb[0];
+    float h = a[i]->ltrb[3] - a[i]->ltrb[1];
+    areaa[i] = w * h;
+  }
+
+  std::vector<float> areab(b.size());
+  for (size_t j = 0; j < b.size(); ++j) {
+    float w = b[j].ltrb[2] - b[j].ltrb[0];
+    float h = b[j].ltrb[3] - b[j].ltrb[1];
+    areab[j] = w * h;
+  }
+
+  cv::Mat dists(a.size(), b.size(), CV_32F);
+  for (size_t i = 0; i < a.size(); ++i) {
+    const cv::Vec4f &boxa = a[i]->ltrb;
+    float *distsi = dists.ptr<float>(i);
+    for (size_t j = 0; j < b.size(); ++j) {
+      const cv::Vec4f &boxb = b[j].ltrb;
+      float inters = calc_inter_area(boxa, boxb);
+      distsi[j] = 1.f - inters / (areaa[i] + areab[j] - inters);
+    }
+  }
+
+  return dists;
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/python/README.md

+# Python端预测部署
+
+在PaddlePaddle中预测引擎和训练引擎底层有着不同的优化方法, 预测引擎使用了AnalysisPredictor，专门针对推理进行了优化，是基于[C++预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/native_infer.html)的Python接口，该引擎可以对模型进行多项图优化，减少不必要的内存拷贝。如果用户在部署已训练模型的过程中对性能有较高的要求，我们提供了独立于PaddleDetection的预测脚本，方便用户直接集成部署。
+
+主要包含两个步骤：
+
+- 导出预测模型
+- 基于Python进行预测
+
+PaddleDetection在训练过程包括网络的前向和优化器相关参数，而在部署过程中，我们只需要前向参数，具体参考:[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/deploy/EXPORT_MODEL.md)
+导出后目录下，包括`infer_cfg.yml`, `model.pdiparams`,  `model.pdiparams.info`, `model.pdmodel`四个文件。
+
+## 1. 对FairMOT模型的导出和预测
+
+### 1.1 导出预测模型
+```bash
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/fairmot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.pdparams
+```
+
+### 1.2 用导出的模型基于Python去预测
+```bash
+python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video_file={your video name}.mp4 --device=GPU --save_mot_txts
+```
+**注意:**
+ - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。
+ - 对于多类别或车辆的FairMOT模型的导出和Python预测只需更改相应的config和模型权重即可。如：
+ ```
+ job_name=mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone
+ model_type=mot/mcfairmot
+ config=configs/${model_type}/${job_name}.yml
+
+ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=https://paddledet.bj.bcebos.com/models/mot/${job_name}.pdparams
+ python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/${job_name} --video_file={your video name}.mp4 --device=GPU --save_mot_txts
+ ```
+
+
+## 2. 对DeepSORT模型的导出和预测
+### 2.1 导出预测模型
+
+Step 1：导出检测模型
+```bash
+# 导出JDE YOLOv3行人检测模型
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/detector/jde_yolov3_darknet53_30e_1088x608_mix.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/jde_yolov3_darknet53_30e_1088x608_mix.pdparams
+
+# 或导出PPYOLOv2行人检测模型
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/detector/ppyolov2_r50vd_dcn_365e_640x640_mot17half.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/ppyolov2_r50vd_dcn_365e_640x640_mot17half.pdparams
+```
+
+Step 2：导出ReID模型
+```bash
+# 导出PCB Pyramid ReID模型
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/reid/deepsort_pcb_pyramid_r101.yml -o reid_weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pcb_pyramid_r101.pdparams
+# 或者导出PPLCNet ReID模型
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/reid/deepsort_pplcnet.yml -o reid_weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet.pdparams
+```
+
+### 2.2 用导出的模型基于Python去预测
+
+```bash
+# 用导出JDE YOLOv3行人检测模型和PCB Pyramid ReID模型
+python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/jde_yolov3_darknet53_30e_1088x608_mix/ --reid_model_dir=output_inference/deepsort_pcb_pyramid_r101/ --video_file={your video name}.mp4 --device=GPU --save_mot_txts
+
+# 或用导出的PPYOLOv2行人检测模型和PPLCNet ReID模型
+python deploy/pptracking/python/mot_sde_infer.py --model_dir=output_inference/ppyolov2_r50vd_dcn_365e_640x640_mot17half/ --reid_model_dir=output_inference/deepsort_pplcnet/ --video_file={your video name}.mp4 --device=GPU --scaled=True --save_mot_txts
+```
+**注意:**
+ - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`(对每个视频保存一个txt)或`--save_images`表示保存跟踪结果可视化图片。
+ - `--scaled`表示在模型输出结果的坐标是否已经是缩放回原图的，如果使用的检测模型是JDE的YOLOv3则为False，如果使用通用检测模型则为True。
+
+
+## 3. 跨境跟踪模型的导出和预测
+### 3.1 导出预测模型
+Step 1：下载导出的检测模型
+```bash
+wget https://paddledet.bj.bcebos.com/models/mot/deepsort/picodet_l_640_aic21mtmct_vehicle.tar
+tar -xvf picodet_l_640_aic21mtmct_vehicle.tar
+```
+Step 2：下载导出的ReID模型
+```bash
+wget https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet_vehicle.tar
+tar -xvf deepsort_pplcnet_vehicle.tar
+```
+
+### 3.2 用导出的模型基于Python去预测
+```bash
+# 用导出PicoDet车辆检测模型和PPLCNet车辆ReID模型
+python deploy/pptracking/python/mot_sde_infer.py --model_dir=picodet_l_640_aic21mtmct_vehicle/ --reid_model_dir=deepsort_pplcnet_vehicle/ --mtmct_dir={your mtmct scene video folder} --mtmct_cfg=mtmct_cfg --device=GPU --scaled=True --save_mot_txts --save_images
+```
+**注意:**
+  跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`(对每个视频保存一个txt)，或`--save_images`表示保存跟踪结果可视化图片。
+  `--scaled`表示在模型输出结果的坐标是否已经是缩放回原图的，如果使用的检测模型是JDE的YOLOv3则为False，如果使用通用检测模型则为True。
+  `--mtmct_dir`是MTMCT预测的某个场景的文件夹名字，里面包含该场景不同摄像头拍摄视频的图片文件夹，其数量至少为两个。
+
+
+## 参数说明:
+
+| 参数 | 是否必须|含义 |
+|-------|-------|----------|
+| --model_dir | Yes| 上述导出的模型路径 |
+| --image_file | Option | 需要预测的图片 |
+| --image_dir  | Option |  要预测的图片文件夹路径   |
+| --video_file | Option | 需要预测的视频 |
+| --camera_id | Option | 用来预测的摄像头ID，默认为-1(表示不使用摄像头预测，可设置为：0 - (摄像头数目-1) )，预测过程中在可视化界面按`q`退出输出预测结果到：output/output.mp4|
+| --device | Option | 运行时的设备，可选择`CPU/GPU/XPU`，默认为`CPU`|
+| --run_mode | Option |使用GPU时，默认为fluid, 可选（fluid/trt_fp32/trt_fp16/trt_int8）|
+| --batch_size | Option |预测时的batch size，在指定`image_dir`时有效，默认为1 |
+| --threshold | Option|预测得分的阈值，默认为0.5|
+| --output_dir | Option|可视化结果保存的根目录，默认为output/|
+| --run_benchmark | Option| 是否运行benchmark，同时需指定`--image_file`或`--image_dir`，默认为False |
+| --enable_mkldnn | Option | CPU预测中是否开启MKLDNN加速，默认为False |
+| --cpu_threads | Option| 设置cpu线程数，默认为1 |
+| --trt_calib_mode | Option| TensorRT是否使用校准功能，默认为False。使用TensorRT的int8功能时，需设置为True，使用PaddleSlim量化后的模型时需要设置为False |
+| --do_entrance_counting | Option | 是否统计出入口流量，默认为False |
+| --draw_center_traj | Option | 是否绘制跟踪轨迹，默认为False |
+| --mtmct_dir | Option | 需要进行MTMCT跨境头跟踪预测的图片文件夹路径，默认为None |
+| --mtmct_cfg | Option | 需要进行MTMCT跨境头跟踪预测的配置文件路径，默认为None |
+
+说明：
+
+- 参数优先级顺序：`camera_id` > `video_file` > `image_dir` > `image_file`。
+- run_mode：fluid代表使用AnalysisPredictor，精度float32来推理，其他参数指用AnalysisPredictor，TensorRT不同精度来推理。
+- 如果安装的PaddlePaddle不支持基于TensorRT进行预测，需要自行编译，详细可参考[预测库编译教程](https://paddleinference.paddlepaddle.org.cn/user_guides/source_compile.html)。
+- --run_benchmark如果设置为True，则需要安装依赖`pip install pynvml psutil GPUtil`。

deploy/pptracking/python/benchmark_utils.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import logging
+
+import paddle
+import paddle.inference as paddle_infer
+
+from pathlib import Path
+
+CUR_DIR = os.path.dirname(os.path.abspath(__file__))
+LOG_PATH_ROOT = f"{CUR_DIR}/../../output"
+
+
+class PaddleInferBenchmark(object):
+    def __init__(self,
+                 config,
+                 model_info: dict={},
+                 data_info: dict={},
+                 perf_info: dict={},
+                 resource_info: dict={},
+                 **kwargs):
+        """
+        Construct PaddleInferBenchmark Class to format logs.
+        args:
+            config(paddle.inference.Config): paddle inference config
+            model_info(dict): basic model info
+                {'model_name': 'resnet50'
+                 'precision': 'fp32'}
+            data_info(dict): input data info
+                {'batch_size': 1
+                 'shape': '3,224,224'
+                 'data_num': 1000}
+            perf_info(dict): performance result
+                {'preprocess_time_s': 1.0
+                'inference_time_s': 2.0
+                'postprocess_time_s': 1.0
+                'total_time_s': 4.0}
+            resource_info(dict): 
+                cpu and gpu resources
+                {'cpu_rss': 100
+                 'gpu_rss': 100
+                 'gpu_util': 60}
+        """
+        # PaddleInferBenchmark Log Version
+        self.log_version = "1.0.3"
+
+        # Paddle Version
+        self.paddle_version = paddle.__version__
+        self.paddle_commit = paddle.__git_commit__
+        paddle_infer_info = paddle_infer.get_version()
+        self.paddle_branch = paddle_infer_info.strip().split(': ')[-1]
+
+        # model info
+        self.model_info = model_info
+
+        # data info
+        self.data_info = data_info
+
+        # perf info
+        self.perf_info = perf_info
+
+        try:
+            # required value
+            self.model_name = model_info['model_name']
+            self.precision = model_info['precision']
+
+            self.batch_size = data_info['batch_size']
+            self.shape = data_info['shape']
+            self.data_num = data_info['data_num']
+
+            self.inference_time_s = round(perf_info['inference_time_s'], 4)
+        except:
+            self.print_help()
+            raise ValueError(
+                "Set argument wrong, please check input argument and its type")
+
+        self.preprocess_time_s = perf_info.get('preprocess_time_s', 0)
+        self.postprocess_time_s = perf_info.get('postprocess_time_s', 0)
+        self.total_time_s = perf_info.get('total_time_s', 0)
+
+        self.inference_time_s_90 = perf_info.get("inference_time_s_90", "")
+        self.inference_time_s_99 = perf_info.get("inference_time_s_99", "")
+        self.succ_rate = perf_info.get("succ_rate", "")
+        self.qps = perf_info.get("qps", "")
+
+        # conf info
+        self.config_status = self.parse_config(config)
+
+        # mem info
+        if isinstance(resource_info, dict):
+            self.cpu_rss_mb = int(resource_info.get('cpu_rss_mb', 0))
+            self.cpu_vms_mb = int(resource_info.get('cpu_vms_mb', 0))
+            self.cpu_shared_mb = int(resource_info.get('cpu_shared_mb', 0))
+            self.cpu_dirty_mb = int(resource_info.get('cpu_dirty_mb', 0))
+            self.cpu_util = round(resource_info.get('cpu_util', 0), 2)
+
+            self.gpu_rss_mb = int(resource_info.get('gpu_rss_mb', 0))
+            self.gpu_util = round(resource_info.get('gpu_util', 0), 2)
+            self.gpu_mem_util = round(resource_info.get('gpu_mem_util', 0), 2)
+        else:
+            self.cpu_rss_mb = 0
+            self.cpu_vms_mb = 0
+            self.cpu_shared_mb = 0
+            self.cpu_dirty_mb = 0
+            self.cpu_util = 0
+
+            self.gpu_rss_mb = 0
+            self.gpu_util = 0
+            self.gpu_mem_util = 0
+
+        # init benchmark logger
+        self.benchmark_logger()
+
+    def benchmark_logger(self):
+        """
+        benchmark logger
+        """
+        # remove other logging handler
+        for handler in logging.root.handlers[:]:
+            logging.root.removeHandler(handler)
+
+        # Init logger
+        FORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+        log_output = f"{LOG_PATH_ROOT}/{self.model_name}.log"
+        Path(f"{LOG_PATH_ROOT}").mkdir(parents=True, exist_ok=True)
+        logging.basicConfig(
+            level=logging.INFO,
+            format=FORMAT,
+            handlers=[
+                logging.FileHandler(
+                    filename=log_output, mode='w'),
+                logging.StreamHandler(),
+            ])
+        self.logger = logging.getLogger(__name__)
+        self.logger.info(
+            f"Paddle Inference benchmark log will be saved to {log_output}")
+
+    def parse_config(self, config) -> dict:
+        """
+        parse paddle predictor config
+        args:
+            config(paddle.inference.Config): paddle inference config
+        return:
+            config_status(dict): dict style config info
+        """
+        if isinstance(config, paddle_infer.Config):
+            config_status = {}
+            config_status['runtime_device'] = "gpu" if config.use_gpu(
+            ) else "cpu"
+            config_status['ir_optim'] = config.ir_optim()
+            config_status['enable_tensorrt'] = config.tensorrt_engine_enabled()
+            config_status['precision'] = self.precision
+            config_status['enable_mkldnn'] = config.mkldnn_enabled()
+            config_status[
+                'cpu_math_library_num_threads'] = config.cpu_math_library_num_threads(
+                )
+        elif isinstance(config, dict):
+            config_status['runtime_device'] = config.get('runtime_device', "")
+            config_status['ir_optim'] = config.get('ir_optim', "")
+            config_status['enable_tensorrt'] = config.get('enable_tensorrt', "")
+            config_status['precision'] = config.get('precision', "")
+            config_status['enable_mkldnn'] = config.get('enable_mkldnn', "")
+            config_status['cpu_math_library_num_threads'] = config.get(
+                'cpu_math_library_num_threads', "")
+        else:
+            self.print_help()
+            raise ValueError(
+                "Set argument config wrong, please check input argument and its type"
+            )
+        return config_status
+
+    def report(self, identifier=None):
+        """
+        print log report
+        args:
+            identifier(string): identify log
+        """
+        if identifier:
+            identifier = f"[{identifier}]"
+        else:
+            identifier = ""
+
+        self.logger.info("\n")
+        self.logger.info(
+            "---------------------- Paddle info ----------------------")
+        self.logger.info(f"{identifier} paddle_version: {self.paddle_version}")
+        self.logger.info(f"{identifier} paddle_commit: {self.paddle_commit}")
+        self.logger.info(f"{identifier} paddle_branch: {self.paddle_branch}")
+        self.logger.info(f"{identifier} log_api_version: {self.log_version}")
+        self.logger.info(
+            "----------------------- Conf info -----------------------")
+        self.logger.info(
+            f"{identifier} runtime_device: {self.config_status['runtime_device']}"
+        )
+        self.logger.info(
+            f"{identifier} ir_optim: {self.config_status['ir_optim']}")
+        self.logger.info(f"{identifier} enable_memory_optim: {True}")
+        self.logger.info(
+            f"{identifier} enable_tensorrt: {self.config_status['enable_tensorrt']}"
+        )
+        self.logger.info(
+            f"{identifier} enable_mkldnn: {self.config_status['enable_mkldnn']}")
+        self.logger.info(
+            f"{identifier} cpu_math_library_num_threads: {self.config_status['cpu_math_library_num_threads']}"
+        )
+        self.logger.info(
+            "----------------------- Model info ----------------------")
+        self.logger.info(f"{identifier} model_name: {self.model_name}")
+        self.logger.info(f"{identifier} precision: {self.precision}")
+        self.logger.info(
+            "----------------------- Data info -----------------------")
+        self.logger.info(f"{identifier} batch_size: {self.batch_size}")
+        self.logger.info(f"{identifier} input_shape: {self.shape}")
+        self.logger.info(f"{identifier} data_num: {self.data_num}")
+        self.logger.info(
+            "----------------------- Perf info -----------------------")
+        self.logger.info(
+            f"{identifier} cpu_rss(MB): {self.cpu_rss_mb}, cpu_vms: {self.cpu_vms_mb}, cpu_shared_mb: {self.cpu_shared_mb}, cpu_dirty_mb: {self.cpu_dirty_mb}, cpu_util: {self.cpu_util}%"
+        )
+        self.logger.info(
+            f"{identifier} gpu_rss(MB): {self.gpu_rss_mb}, gpu_util: {self.gpu_util}%, gpu_mem_util: {self.gpu_mem_util}%"
+        )
+        self.logger.info(
+            f"{identifier} total time spent(s): {self.total_time_s}")
+        self.logger.info(
+            f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, inference_time(ms): {round(self.inference_time_s*1000, 1)}, postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}"
+        )
+        if self.inference_time_s_90:
+            self.looger.info(
+                f"{identifier} 90%_cost: {self.inference_time_s_90}, 99%_cost: {self.inference_time_s_99}, succ_rate: {self.succ_rate}"
+            )
+        if self.qps:
+            self.logger.info(f"{identifier} QPS: {self.qps}")
+
+    def print_help(self):
+        """
+        print function help
+        """
+        print("""Usage: 
+            ==== Print inference benchmark logs. ====
+            config = paddle.inference.Config()
+            model_info = {'model_name': 'resnet50'
+                          'precision': 'fp32'}
+            data_info = {'batch_size': 1
+                         'shape': '3,224,224'
+                         'data_num': 1000}
+            perf_info = {'preprocess_time_s': 1.0
+                         'inference_time_s': 2.0
+                         'postprocess_time_s': 1.0
+                         'total_time_s': 4.0}
+            resource_info = {'cpu_rss_mb': 100
+                             'gpu_rss_mb': 100
+                             'gpu_util': 60}
+            log = PaddleInferBenchmark(config, model_info, data_info, perf_info, resource_info)
+            log('Test')
+            """)
+
+    def __call__(self, identifier=None):
+        """
+        __call__
+        args:
+            identifier(string): identify log
+        """
+        self.report(identifier)

deploy/pptracking/python/mot/__init__.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from . import matching
+from . import tracker
+from . import motion
+from . import utils
+from . import mtmct
+
+from .matching import *
+from .tracker import *
+from .motion import *
+from .utils import *
+from .mtmct import *

deploy/pptracking/python/mot/matching/__init__.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from . import jde_matching
+from . import deepsort_matching
+
+from .jde_matching import *
+from .deepsort_matching import *

deploy/pptracking/python/mot/matching/jde_matching.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is based on https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/tracker/matching.py
+"""
+
+import lap
+import scipy
+import numpy as np
+from scipy.spatial.distance import cdist
+from ..motion import kalman_filter
+
+__all__ = [
+    'merge_matches',
+    'linear_assignment',
+    'cython_bbox_ious',
+    'iou_distance',
+    'embedding_distance',
+    'fuse_motion',
+]
+
+
+def merge_matches(m1, m2, shape):
+    O, P, Q = shape
+    m1 = np.asarray(m1)
+    m2 = np.asarray(m2)
+
+    M1 = scipy.sparse.coo_matrix(
+        (np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P))
+    M2 = scipy.sparse.coo_matrix(
+        (np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q))
+
+    mask = M1 * M2
+    match = mask.nonzero()
+    match = list(zip(match[0], match[1]))
+    unmatched_O = tuple(set(range(O)) - set([i for i, j in match]))
+    unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match]))
+
+    return match, unmatched_O, unmatched_Q
+
+
+def linear_assignment(cost_matrix, thresh):
+    if cost_matrix.size == 0:
+        return np.empty(
+            (0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(
+                range(cost_matrix.shape[1]))
+    matches, unmatched_a, unmatched_b = [], [], []
+    cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
+    for ix, mx in enumerate(x):
+        if mx >= 0:
+            matches.append([ix, mx])
+    unmatched_a = np.where(x < 0)[0]
+    unmatched_b = np.where(y < 0)[0]
+    matches = np.asarray(matches)
+    return matches, unmatched_a, unmatched_b
+
+
+def cython_bbox_ious(atlbrs, btlbrs):
+    ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
+    if ious.size == 0:
+        return ious
+    try:
+        import cython_bbox
+    except Exception as e:
+        print('cython_bbox not found, please install cython_bbox.'
+              'for example: `pip install cython_bbox`.')
+        exit()
+
+    ious = cython_bbox.bbox_overlaps(
+        np.ascontiguousarray(
+            atlbrs, dtype=np.float),
+        np.ascontiguousarray(
+            btlbrs, dtype=np.float))
+    return ious
+
+
+def iou_distance(atracks, btracks):
+    """
+    Compute cost based on IoU between two list[STrack].
+    """
+    if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) or (
+            len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
+        atlbrs = atracks
+        btlbrs = btracks
+    else:
+        atlbrs = [track.tlbr for track in atracks]
+        btlbrs = [track.tlbr for track in btracks]
+    _ious = cython_bbox_ious(atlbrs, btlbrs)
+    cost_matrix = 1 - _ious
+
+    return cost_matrix
+
+
+def embedding_distance(tracks, detections, metric='euclidean'):
+    """
+    Compute cost based on features between two list[STrack].
+    """
+    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
+    if cost_matrix.size == 0:
+        return cost_matrix
+    det_features = np.asarray(
+        [track.curr_feat for track in detections], dtype=np.float)
+    track_features = np.asarray(
+        [track.smooth_feat for track in tracks], dtype=np.float)
+    cost_matrix = np.maximum(0.0, cdist(track_features, det_features,
+                                        metric))  # Nomalized features
+    return cost_matrix
+
+
+def fuse_motion(kf,
+                cost_matrix,
+                tracks,
+                detections,
+                only_position=False,
+                lambda_=0.98):
+    if cost_matrix.size == 0:
+        return cost_matrix
+    gating_dim = 2 if only_position else 4
+    gating_threshold = kalman_filter.chi2inv95[gating_dim]
+    measurements = np.asarray([det.to_xyah() for det in detections])
+    for row, track in enumerate(tracks):
+        gating_distance = kf.gating_distance(
+            track.mean,
+            track.covariance,
+            measurements,
+            only_position,
+            metric='maha')
+        cost_matrix[row, gating_distance > gating_threshold] = np.inf
+        cost_matrix[row] = lambda_ * cost_matrix[row] + (1 - lambda_
+                                                         ) * gating_distance
+    return cost_matrix

deploy/pptracking/python/mot/motion/__init__.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from . import kalman_filter
+
+from .kalman_filter import *

deploy/pptracking/python/mot/mtmct/__init__.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from . import utils
+from . import postprocess
+from .utils import *
+from .postprocess import *
+
+# The following codes are strongly related to zone and camera parameters
+from . import camera_utils
+from . import zone
+from .camera_utils import *
+from .zone import *

deploy/pptracking/python/mot/tracker/__init__.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from . import base_jde_tracker
+from . import base_sde_tracker
+from . import jde_tracker
+from . import deepsort_tracker
+
+from .base_jde_tracker import *
+from .base_sde_tracker import *
+from .jde_tracker import *
+from .deepsort_tracker import *

deploy/pptracking/python/mtmct_cfg.yml

+# config for MTMCT
+MTMCT: True
+cameras_bias:
+  c041: 0
+  c042: 0
+# 1.zone releated parameters
+use_zone: True
+zone_path: dataset/mot/aic21mtmct_vehicle/S06/zone
+# 2.tricks parameters, can be used for other mtmct dataset
+use_ff: True
+use_rerank: True
+# 3.camera releated parameters
+use_camera: True
+use_st_filter: False
+# 4.zone releated parameters
+use_roi: True
+roi_dir: dataset/mot/aic21mtmct_vehicle/S06