Add C++ support for pyramidbox_lite_mobile/server_mask model deployment

b8730bce · sjtubinlong · 381424bd · b8730bce · b8730bce · b8730bce
10 changed file
--- a/deploy/demo/mask_detector/CMakeLists.txt
+++ b/deploy/demo/mask_detector/CMakeLists.txt
+cmake_minimum_required(VERSION 3.0)
+project(PaddleMaskDetector 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_STATIC_LIB "Compile demo with static/shared library, default use static."   ON)
+option(USE_TENSORRT "Compile demo with TensorRT."   OFF)
+
+SET(PADDLE_DIR "" CACHE PATH "Location of libraries")
+SET(OPENCV_DIR "" CACHE PATH "Location of libraries")
+SET(CUDA_LIB "" CACHE PATH "Location of libraries")
+
+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()
+
+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/install/zlib/include")
+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("${PADDLE_DIR}/third_party/install/zlib/lib")
+link_directories("${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")
+  include_directories("${OPENCV_DIR}/build/include")
+  include_directories("${OPENCV_DIR}/opencv/build/include")
+  link_directories("${OPENCV_DIR}/build/x64/vc14/lib")
+else ()
+  include_directories("${PADDLE_DIR}/paddle/include")
+  link_directories("${PADDLE_DIR}/paddle/lib")
+  include_directories("${OPENCV_DIR}/include")
+  link_directories("${OPENCV_DIR}/lib64")
+endif ()
+
+if (WIN32)
+    add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
+    set(CMAKE_C_FLAGS_DEBUG   "${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   "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
+    if (WITH_STATIC_LIB)
+        safe_set_static_flag()
+        add_definitions(-DSTATIC_LIB)
+    endif()
+else()
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -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 ${CUDA_LIB} STREQUAL "")
+        message(FATAL_ERROR "please set CUDA_LIB with -DCUDA_LIB=/path/cuda-8.0/lib64")
+    endif()
+    if (NOT WIN32)
+        if (NOT DEFINED CUDNN_LIB)
+            message(FATAL_ERROR "please set CUDNN_LIB with -DCUDNN_LIB=/path/cudnn_v7.4/cuda/lib64")
+        endif()
+    endif(NOT WIN32)
+endif()
+
+
+if (NOT WIN32)
+  if (USE_TENSORRT AND WITH_GPU)
+      include_directories("${PADDLE_DIR}/third_party/install/tensorrt/include")
+      link_directories("${PADDLE_DIR}/third_party/install/tensorrt/lib")
+  endif()
+endif(NOT WIN32)
+
+if (NOT WIN32)
+    set(NGRAPH_PATH "${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 "${PADDLE_DIR}/paddle/fluid/inference/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX}")
+        set(DEPS
+            ${PADDLE_DIR}/paddle/fluid/inference/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
+    else()
+        set(DEPS
+            ${PADDLE_DIR}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
+    endif()
+endif()
+
+if(WITH_STATIC_LIB)
+    set(DEPS
+        ${PADDLE_DIR}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
+else()
+    set(DEPS
+        ${PADDLE_DIR}/paddle/lib/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
+endif()
+
+if (NOT WIN32)
+    set(DEPS ${DEPS}
+        ${MATH_LIB} ${MKLDNN_LIB}
+        glog gflags protobuf z xxhash
+        )
+    if(EXISTS "${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}
+        opencv_world346 glog gflags_static libprotobuf zlibstatic xxhash)
+    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 (USE_TENSORRT)
+      set(DEPS ${DEPS} ${PADDLE_DIR}/third_party/install/tensorrt/lib/libnvinfer${CMAKE_STATIC_LIBRARY_SUFFIX})
+      set(DEPS ${DEPS} ${PADDLE_DIR}/third_party/install/tensorrt/lib/libnvinfer_plugin${CMAKE_STATIC_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} ${CUDA_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX})
+  endif()
+endif()
+
+if (NOT WIN32)
+    set(DEPS ${DEPS} ${OPENCV_DIR}/lib64/libopencv_imgcodecs${CMAKE_STATIC_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${OPENCV_DIR}/lib64/libopencv_imgproc${CMAKE_STATIC_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${OPENCV_DIR}/lib64/libopencv_core${CMAKE_STATIC_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${OPENCV_DIR}/lib64/libopencv_highgui${CMAKE_STATIC_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${OPENCV_DIR}/share/OpenCV/3rdparty/lib64/libIlmImf${CMAKE_STATIC_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${OPENCV_DIR}/share/OpenCV/3rdparty/lib64/liblibjasper${CMAKE_STATIC_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${OPENCV_DIR}/share/OpenCV/3rdparty/lib64/liblibpng${CMAKE_STATIC_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${OPENCV_DIR}/share/OpenCV/3rdparty/lib64/liblibtiff${CMAKE_STATIC_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${OPENCV_DIR}/share/OpenCV/3rdparty/lib64/libittnotify${CMAKE_STATIC_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${OPENCV_DIR}/share/OpenCV/3rdparty/lib64/liblibjpeg-turbo${CMAKE_STATIC_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${OPENCV_DIR}/share/OpenCV/3rdparty/lib64/liblibwebp${CMAKE_STATIC_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${OPENCV_DIR}/share/OpenCV/3rdparty/lib64/libzlib${CMAKE_STATIC_LIBRARY_SUFFIX})
+endif()
+
+if (NOT WIN32)
+    set(EXTERNAL_LIB "-ldl -lrt -lpthread")
+    set(DEPS ${DEPS} ${EXTERNAL_LIB})
+endif()
+
+add_executable(mask_detector main.cc mask_detector.cc)
+target_link_libraries(mask_detector ${DEPS})
+
+if (WIN32)
+    add_custom_command(TARGET mask_detector 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 ${PADDLE_DIR}/third_party/install/mkldnn/lib/mkldnn.dll ./release/mkldnn.dll
+    )
+endif()
--- a/deploy/demo/mask_detector/CMakeSettings.json
+++ b/deploy/demo/mask_detector/CMakeSettings.json
+{
+  "configurations": [
+    {
+      "name": "x64-Release",
+      "generator": "Ninja",
+      "configurationType": "RelWithDebInfo",
+      "inheritEnvironments": [ "msvc_x64_x64" ],
+      "buildRoot": "${projectDir}\\out\\build\\${name}",
+      "installRoot": "${projectDir}\\out\\install\\${name}",
+      "cmakeCommandArgs": "",
+      "buildCommandArgs": "-v",
+      "ctestCommandArgs": "",
+      "variables": [
+        {
+          "name": "CUDA_LIB",
+          "value": "D:/projects/packages/cuda10_0/lib64",
+          "type": "PATH"
+        },
+        {
+          "name": "CUDNN_LIB",
+          "value": "D:/projects/packages/cuda10_0/lib64",
+          "type": "PATH"
+        },
+        {
+          "name": "OPENCV_DIR",
+          "value": "D:/projects/packages/opencv3_4_6",
+          "type": "PATH"
+        },
+        {
+          "name": "PADDLE_DIR",
+          "value": "D:/projects/packages/fluid_inference1_6_1",
+          "type": "PATH"
+        },
+        {
+          "name": "CMAKE_BUILD_TYPE",
+          "value": "Release",
+          "type": "STRING"
+        }
+      ]
+    }
+  ]
+}
--- a/deploy/demo/mask_detector/README.md
+++ b/deploy/demo/mask_detector/README.md
+# PaddleHub口罩人脸识别及分类模型C++预测部署
+
+百度通过 `PaddleHub` 开源了业界首个口罩人脸检测及人类模型，该模型可以有效检测在密集人类区域中携带和未携带口罩的所有人脸，同时判断出是否有佩戴口罩。开发者可以通过 `PaddleHub` 快速体验模型效果、搭建在线服务，还可以导出模型集成到`Windows`和`Linux`等不同平台的`C++`开发项目中。
+
+本文档主要介绍如何把模型在`Windows`和`Linux`上完成基于`C++`的预测部署。
+
+主要包含两个步骤：
+- [1. PaddleHub导出预测模型](#1PaddleHub导出预测模型)
+- [2. C++预测部署编译](#2C预测部署编译)
+
+## 1. PaddleHub导出预测模型
+
+#### 1.1 安装 `PaddlePaddle` 和 `PaddleHub`
+  - `PaddlePaddle`的安装:
+    请点击[官方安装文档](https://paddlepaddle.org.cn/install/quick) 选择适合的方式
+  - `PaddleHub`的安装: `pip install paddlehub`
+
+#### 1.2 从`PaddleHub`导出预测模型
+
+在有网络访问条件下，执行`python export_model.py`导出两个可用于推理部署的口罩模型
+其中`pyramidbox_lite_mobile_mask`为移动版模型, 模型更小，计算量低；
+`pyramidbox_lite_server_mask`为服务器版模型，在此推荐该版本模型，精度相对移动版本更高。
+
+成功执行代码后导出的模型路径结构：
+```
+pyramidbox_lite_server_mask
+|
+├── mask_detector   # 口罩人脸分类模型
+|   ├── __model__   # 模型文件
+│   └── __params__  # 参数文件
+|
+└── pyramidbox_lite # 口罩人脸检测模型
+    ├── __model__   # 模型文件
+    └── __params__  # 参数文件
+
+```
+
+## 2. C++预测部署编译
+
+本项目支持在Windows和Linux上编译并部署C++项目，不同平台的编译请参考：
+- [Linux 编译](./docs/linux_build.md)
+- [Windows 使用 Visual Studio 2019编译](./docs/windows_build.md)
--- a/deploy/demo/mask_detector/docs/linux_build.md
+++ b/deploy/demo/mask_detector/docs/linux_build.md
+# Linux平台口罩人脸检测及分类模型C++预测部署
+
+## 1. 系统和软件依赖
+
+### 1.1 操作系统及硬件要求
+
+- Ubuntu 14.04 或者 16.04 (其它平台未测试)
+- GCC版本4.8.5 ~ 4.9.2
+- 支持Intel MKL-DNN的CPU
+- NOTE: 如需在Nvidia GPU运行，请自行安装CUDA 9.0 / 10.0 + CUDNN 7.3+ (不支持9.1/10.1版本的CUDA)
+
+### 1.2 下载PaddlePaddle C++预测库
+
+PaddlePaddle C++ 预测库主要分为CPU版本和GPU版本。
+
+其中，GPU 版本支持`CUDA 10.0` 和 `CUDA 9.0`:
+
+以下为各版本C++预测库的下载链接：
+
+|  版本   | 链接  |
+|  ----  | ----  |
+| CPU+MKL版  | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.6.3-cpu-avx-mkl/fluid_inference.tgz) |
+| CUDA9.0+MKL 版  | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.6.3-gpu-cuda9-cudnn7-avx-mkl/fluid_inference.tgz) |
+| CUDA10.0+MKL 版 | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.6.3-gpu-cuda10-cudnn7-avx-mkl/fluid_inference.tgz) |
+
+更多可用预测库版本，请点击以下链接下载:[C++预测库下载列表](https://paddlepaddle.org.cn/documentation/docs/zh/advanced_usage/deploy/inference/build_and_install_lib_cn.html)
+
+
+下载并解压, 解压后的 `fluid_inference`目录包含的内容：
+```
+fluid_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:** 请把解压后的目录放到合适的路径，**该目录路径后续会作为编译依赖**使用。
+
+### 1.2 编译安装 OpenCV
+
+```shell
+# 1. 下载OpenCV3.4.6版本源代码
+wget -c https://paddleseg.bj.bcebos.com/inference/opencv-3.4.6.zip
+# 2. 解压
+unzip opencv-3.4.6.zip && cd opencv-3.4.6
+# 3. 创建build目录并编译, 这里安装到/root/projects/opencv3目录
+mkdir build && cd build
+cmake .. -DCMAKE_INSTALL_PREFIX=$HOME/opencv3 -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=OFF -DWITH_IPP=OFF -DBUILD_IPP_IW=OFF -DWITH_LAPACK=OFF -DWITH_EIGEN=OFF -DCMAKE_INSTALL_LIBDIR=lib64 -DWITH_ZLIB=ON -DBUILD_ZLIB=ON -DWITH_JPEG=ON -DBUILD_JPEG=ON -DWITH_PNG=ON -DBUILD_PNG=ON -DWITH_TIFF=ON -DBUILD_TIFF=ON
+make -j4
+make install
+```
+
+其中参数 `CMAKE_INSTALL_PREFIX` 参数指定了安装路径, 上述操作完成后，`opencv` 被安装在 `$HOME/opencv3` 目录(用户也可选择其他路径)，**该目录后续作为编译依赖**。
+
+## 2. 编译与运行
+
+### 2.1 配置编译脚本
+
+cd `PaddleHub/deploy/demo/mask_detector/`
+
+打开文件`linux_build.sh`, 看到以下内容:
+```shell
+# Paddle 预测库路径
+PADDLE_DIR=/PATH/TO/fluid_inference/
+# OpenCV 库路径
+OPENCV_DIR=/PATH/TO/opencv3gcc4.8/
+# 是否使用GPU
+WITH_GPU=ON
+# CUDA库路径, 仅 WITH_GPU=ON 时设置
+CUDA_LIB=/PATH/TO/CUDA_LIB64/
+# CUDNN库路径，仅 WITH_GPU=ON 且 CUDA_LIB有效时设置
+CUDNN_LIB=/PATH/TO/CUDNN_LIB64/
+
+cd build
+cmake .. \
+    -DWITH_GPU=${WITH_GPU} \
+    -DPADDLE_DIR=${PADDLE_DIR} \
+    -DCUDA_LIB=${CUDA_LIB} \
+    -DCUDNN_LIB=${CUDNN_LIB} \
+    -DOPENCV_DIR=${OPENCV_DIR} \
+    -DWITH_STATIC_LIB=OFF
+make -j4
+```
+
+把上述参数根据实际情况做修改后，运行脚本编译程序：
+```shell
+sh linux_build.sh
+```
+
+### 2.2. 运行和可视化
+
+可执行文件有 **2** 个参数，第一个是前面导出的`inference_model`路径，第二个是需要预测的图片路径。
+
+示例:
+```shell
+./build/main /PATH/TO/pyramidbox_lite_server_mask/ /PATH/TO/TEST_IMAGE
+```
+
+执行程序时会打印检测框的位置与口罩是否佩戴的结果，另外result.jpg文件为检测的可视化结果。
+
+**预测结果示例:**
+
+![output_image](https://paddlehub.bj.bcebos.com/deploy/result.jpg)
--- a/deploy/demo/mask_detector/docs/windows_build.md
+++ b/deploy/demo/mask_detector/docs/windows_build.md
+# Windows平台口罩人脸检测及分类模型C++预测部署
+
+## 1. 系统和软件依赖
+
+### 1.1 基础依赖
+
+- Windows 10 / Windows Server 2016+ (其它平台未测试)
+- Visual Studio 2019 (社区版或专业版均可)
+- CUDA 9.0 / 10.0 + CUDNN 7.3+ (不支持9.1/10.1版本的CUDA)
+
+### 1.2 下载OpenCV并设置环境变量
+
+- 在OpenCV官网下载适用于Windows平台的3.4.6版本: [点击下载](https://sourceforge.net/projects/opencvlibrary/files/3.4.6/opencv-3.4.6-vc14_vc15.exe/download)
+- 运行下载的可执行文件，将OpenCV解压至合适目录，这里以解压到`D:\projects\opencv`为例
+- 把OpenCV动态库加入到系统环境变量
+   - 此电脑(我的电脑)->属性->高级系统设置->环境变量
+   - 在系统变量中找到Path（如没有，自行创建），并双击编辑
+   - 新建，将opencv路径填入并保存，如D:\projects\opencv\build\x64\vc14\bin
+
+**注意:** `OpenCV`的解压目录后续将做为编译配置项使用，所以请放置合适的目录中。
+
+### 1.3 下载PaddlePaddle C++ 预测库
+
+`PaddlePaddle` **C++ 预测库** 主要分为`CPU`和`GPU`版本， 其中`GPU版本`提供`CUDA 9.0` 和 `CUDA 10.0` 支持。
+
+常用的版本如下：
+
+|  版本   | 链接  |
+|  ----  | ----  |
+| CPU+MKL版  | [fluid_inference_install_dir.zip](https://paddle-wheel.bj.bcebos.com/1.6.3/win-infer/mkl/cpu/fluid_inference_install_dir.zip) |
+| CUDA9.0+MKL 版  | [fluid_inference_install_dir.zip](https://paddle-wheel.bj.bcebos.com/1.6.3/win-infer/mkl/post97/fluid_inference_install_dir.zip) |
+| CUDA10.0+MKL 版 | [fluid_inference_install_dir.zip](https://paddle-wheel.bj.bcebos.com/1.6.3/win-infer/mkl/post107/fluid_inference_install_dir.zip) |
+
+更多不同平台的可用预测库版本，请[点击查看](https://paddlepaddle.org.cn/documentation/docs/zh/advanced_usage/deploy/inference/windows_cpp_inference.html) 选择适合你的版本。
+
+
+下载并解压, 解压后的 `fluid_inference`目录包含的内容：
+```
+fluid_inference_install_dir
+├── 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：** 这里的`fluid_inference_install_dir` 目录所在路径，将用于后面的编译参数设置，请放置在合适的位置。
+
+## 2. Visual Studio 2019 编译
+
+- 2.1 打开Visual Studio 2019 Community，点击`继续但无需代码`， 如下图：
+![step2.1](https://paddleseg.bj.bcebos.com/inference/vs2019_step1.png)
+
+- 2.2 点击 `文件`->`打开`->`CMake`, 如下图：
+![step2.2](https://paddleseg.bj.bcebos.com/inference/vs2019_step2.png)  
+
+- 2.3 选择本项目根目录`CMakeList.txt`文件打开， 如下图：
+![step2.3](https://paddleseg.bj.bcebos.com/deploy/docs/vs2019_step2.3.png)
+
+- 2.4 点击：`项目`->`PaddleMaskDetector的CMake设置`
+![step2.4](https://paddleseg.bj.bcebos.com/deploy/docs/vs2019_step2.4.png)
+
+- 2.5 点击浏览设置`OPENCV_DIR`, `CUDA_LIB` 和 `PADDLE_DIR` 3个编译依赖库的位置, 设置完成后点击`保存并生成CMake缓存并加载变量`
+![step2.5](https://paddleseg.bj.bcebos.com/inference/vs2019_step5.png)
+
+- 2.6 点击`生成`->`全部生成` 编译项目
+![step2.6](https://paddleseg.bj.bcebos.com/inference/vs2019_step6.png)
+
+## 3. 运行程序
+
+成功编译后， 产出的可执行文件在项目子目录`out\build\x64-Release`目录， 按以下步骤运行代码：
+
+- 打开`cmd`切换至该目录
+- 运行以下命令传入口罩识别模型路径与测试图片
+
+```shell
+main.exe ./pyramidbox_lite_server_mask/ ./images/mask_input.png
+```
+第一个参数即`PaddleHub`导出的预测模型，第二个参数即要预测的图片。
+
+运行后，预测结果保存在文件`result.jpg`中。
+
+**预测结果示例:**
+
+![output_image](https://paddlehub.bj.bcebos.com/deploy/result.jpg)
--- a/deploy/demo/mask_detector/export_model.py
+++ b/deploy/demo/mask_detector/export_model.py
+#   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.
+
+import paddlehub as hub
+
+# Load mask detector module from PaddleHub
+module = hub.Module(name="pyramidbox_lite_server_mask")
+# Export inference model for deployment
+module.processor.save_inference_model("./pyramidbox_lite_server_mask")
+print("pyramidbox_lite_server_mask module export done!")
+
+# Load mask detector (mobile version) module from PaddleHub
+module = hub.Module(name="pyramidbox_lite_mobile_mask")
+# Export inference model for deployment
+module.processor.save_inference_model("./pyramidbox_lite_mobile_mask")
+print("pyramidbox_lite_mobile_mask module export done!")
--- a/deploy/demo/mask_detector/linux_build.sh
+++ b/deploy/demo/mask_detector/linux_build.sh
+
+WITH_GPU=ON
+
+PADDLE_DIR=/ssd3/chenzeyu01/PaddleMaskDetector/fluid_inference
+CUDA_LIB=/home/work/cuda-10.1/lib64/
+CUDNN_LIB=/home/work/cudnn/cudnn_v7.4/cuda/lib64/
+OPENCV_DIR=/ssd3/chenzeyu01/PaddleMaskDetector/opencv3gcc4.8/
+
+rm -rf build
+mkdir -p build
+cd build
+
+cmake .. \
+    -DWITH_GPU=${WITH_GPU} \
+    -DPADDLE_DIR=${PADDLE_DIR} \
+    -DCUDA_LIB=${CUDA_LIB} \
+    -DCUDNN_LIB=${CUDNN_LIB} \
+    -DOPENCV_DIR=${OPENCV_DIR} \
+    -DWITH_STATIC_LIB=OFF
+make clean
+make -j12
--- a/deploy/demo/mask_detector/main.cc
+++ b/deploy/demo/mask_detector/main.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 <iostream>
+#include <string>
+
+#include "mask_detector.h" // NOLINT
+
+int main(int argc, char* argv[]) {
+  if (argc < 3 || argc > 4) {
+    std::cout << "Usage:"
+              << "./mask_detector ./models/ ./images/test.png"
+              << std::endl;
+    return -1;
+  }
+
+  bool use_gpu = (argc == 4 ? std::stoi(argv[3]) : false);
+  auto det_model_dir = std::string(argv[1]) + "/pyramidbox_lite";
+  auto cls_model_dir = std::string(argv[1]) + "/mask_detector";
+  auto image_path = argv[2];
+
+  // Init Detection Model
+  float det_shrink = 0.6;
+  float det_threshold = 0.7;
+  std::vector<float> det_means = {104, 177, 123};
+  std::vector<float> det_scale = {0.007843, 0.007843, 0.007843};
+  FaceDetector detector(
+      det_model_dir,
+      det_means,
+      det_scale,
+      use_gpu,
+      det_threshold);
+
+  // Init Classification Model
+  std::vector<float> cls_means = {0.5, 0.5, 0.5};
+  std::vector<float> cls_scale = {1.0, 1.0, 1.0};
+  MaskClassifier classifier(
+      cls_model_dir,
+      cls_means,
+      cls_scale,
+      use_gpu);
+
+  // Load image
+  cv::Mat img = imread(image_path, cv::IMREAD_COLOR);
+  // Prediction result
+  std::vector<FaceResult> results;
+  // Stage1: Face detection
+  detector.Predict(img, &results, det_shrink);
+  // Stage2: Mask wearing classification
+  classifier.Predict(&results);
+
+  for (const FaceResult& item : results) {
+    printf("{left=%d, right=%d, top=%d, bottom=%d},"
+           " class_id=%d, confidence=%.5f\n",
+           item.rect[0],
+           item.rect[1],
+           item.rect[2],
+           item.rect[3],
+           item.class_id,
+           item.score);
+  }
+
+  // Visualization result
+  cv::Mat vis_img;
+  VisualizeResult(img, results, &vis_img);
+  cv::imwrite("result.jpg", vis_img);
+
+  return 0;
+}
--- a/deploy/demo/mask_detector/mask_detector.cc
+++ b/deploy/demo/mask_detector/mask_detector.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 "mask_detector.h"
+
+// Normalize the image by (pix - mean) * scale
+void NormalizeImage(
+    const std::vector<float> &mean,
+    const std::vector<float> &scale,
+    cv::Mat& im, // NOLINT
+    float* input_buffer) {
+  int height = im.rows;
+  int width = im.cols;
+  int stride = width * height;
+  for (int h = 0; h < height; h++) {
+    for (int w = 0; w < width; w++) {
+      int base = h * width + w;
+      input_buffer[base + 0 * stride] =
+          (im.at<cv::Vec3f>(h, w)[0] - mean[0]) * scale[0];
+      input_buffer[base + 1 * stride] =
+          (im.at<cv::Vec3f>(h, w)[1] - mean[1]) * scale[1];
+      input_buffer[base + 2 * stride] =
+          (im.at<cv::Vec3f>(h, w)[2] - mean[2]) * scale[2];
+    }
+  }
+}
+
+// Load Model and return model predictor
+void LoadModel(
+    const std::string& model_dir,
+    bool use_gpu,
+    std::unique_ptr<paddle::PaddlePredictor>* predictor) {
+  // Config the model info
+  paddle::AnalysisConfig config;
+  config.SetModel(model_dir + "/__model__",
+                  model_dir + "/__params__");
+  if (use_gpu) {
+      config.EnableUseGpu(100, 0);
+  } else {
+      config.DisableGpu();
+  }
+  config.SwitchUseFeedFetchOps(false);
+  config.SwitchSpecifyInputNames(true);
+  // Memory optimization
+  config.EnableMemoryOptim();
+  *predictor = std::move(CreatePaddlePredictor(config));
+}
+
+
+// Visualiztion MaskDetector results
+void VisualizeResult(const cv::Mat& img,
+                     const std::vector<FaceResult>& results,
+                     cv::Mat* vis_img) {
+  for (int i = 0; i < results.size(); ++i) {
+    int w = results[i].rect[1] - results[i].rect[0];
+    int h = results[i].rect[3] - results[i].rect[2];
+    cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[2], w, h);
+
+    // Configure color and text size
+    cv::Scalar roi_color;
+    std::string text;
+    if (results[i].class_id == 1) {
+      text = "MASK:  ";
+      roi_color = cv::Scalar(0, 255, 0);
+    } else {
+      text = "NO MASK:  ";
+      roi_color = cv::Scalar(0, 0, 255);
+    }
+    text += std::to_string(static_cast<int>(results[i].score*100)) + "%";
+    int font_face = cv::FONT_HERSHEY_TRIPLEX;
+    double font_scale = 1.f;
+    float thickness = 1;
+    cv::Size text_size = cv::getTextSize(text,
+                                         font_face,
+                                         font_scale,
+                                         thickness,
+                                         nullptr);
+    float new_font_scale = roi.width * font_scale / text_size.width;
+    text_size = cv::getTextSize(text,
+                               font_face,
+                               new_font_scale,
+                               thickness,
+                               nullptr);
+    cv::Point origin;
+    origin.x = roi.x;
+    origin.y = roi.y;
+
+    // Configure text background
+    cv::Rect text_back = cv::Rect(results[i].rect[0],
+    results[i].rect[2] - text_size.height,
+    text_size.width,
+    text_size.height);
+
+    // Draw roi object, text, and background
+    *vis_img = img;
+    cv::rectangle(*vis_img, roi, roi_color, 2);
+    cv::rectangle(*vis_img, text_back, cv::Scalar(225, 225, 225), -1);
+    cv::putText(*vis_img,
+                text,
+                origin,
+                font_face,
+                new_font_scale,
+                cv::Scalar(0, 0, 0),
+                thickness);
+  }
+}
+
+
+
+void FaceDetector::Preprocess(const cv::Mat& image_mat, float shrink) {
+  // Clone the image : keep the original mat for postprocess
+  cv::Mat im = image_mat.clone();
+  cv::resize(im, im, cv::Size(), shrink, shrink, cv::INTER_CUBIC);
+  im.convertTo(im, CV_32FC3, 1.0);
+  int rc = im.channels();
+  int rh = im.rows;
+  int rw = im.cols;
+  input_shape_ = {1, rc, rh, rw};
+  input_data_.resize(1 * rc * rh * rw);
+  float* buffer = input_data_.data();
+  NormalizeImage(mean_, scale_, im, input_data_.data());
+}
+
+void FaceDetector::Postprocess(
+    const cv::Mat& raw_mat,
+    float shrink,
+    std::vector<FaceResult>* result) {
+  result->clear();
+  int rect_num = 0;
+  int rh = input_shape_[2];
+  int rw = input_shape_[3];
+  int total_size = output_data_.size() / 6;
+  for (int j = 0; j < total_size; ++j) {
+    // Class id
+    int class_id = static_cast<int>(round(output_data_[0 + j * 6]));
+    // Confidence score
+    float score = output_data_[1 + j * 6];
+    int xmin = (output_data_[2 + j * 6] * rw) / shrink;
+    int ymin = (output_data_[3 + j * 6] * rh) / shrink;
+    int xmax = (output_data_[4 + j * 6] * rw) / shrink;
+    int ymax = (output_data_[5 + j * 6] * rh) / shrink;
+    int wd = xmax - xmin;
+    int hd = ymax - ymin;
+    if (score > threshold_) {
+      auto roi = cv::Rect(xmin, ymin, wd, hd) &
+                  cv::Rect(0, 0, rw / shrink, rh / shrink);
+      // A view ref to original mat
+      cv::Mat roi_ref(raw_mat, roi);
+      FaceResult result_item;
+      result_item.rect = {xmin, xmax, ymin, ymax};
+      result_item.roi_rect = roi_ref;
+      result->push_back(result_item);
+    }
+  }
+}
+
+void FaceDetector::Predict(const cv::Mat& im,
+                                  std::vector<FaceResult>* result,
+                                  float shrink) {
+  // Preprocess image
+  Preprocess(im, shrink);
+  // Prepare input tensor
+  auto input_names = predictor_->GetInputNames();
+  auto in_tensor = predictor_->GetInputTensor(input_names[0]);
+  in_tensor->Reshape(input_shape_);
+  in_tensor->copy_from_cpu(input_data_.data());
+  // Run predictor
+  predictor_->ZeroCopyRun();
+  // Get output tensor
+  auto output_names = predictor_->GetOutputNames();
+  auto out_tensor = predictor_->GetOutputTensor(output_names[0]);
+  std::vector<int> output_shape = out_tensor->shape();
+  // Calculate output length
+  int output_size = 1;
+  for (int j = 0; j < output_shape.size(); ++j) {
+      output_size *= output_shape[j];
+  }
+  output_data_.resize(output_size);
+  out_tensor->copy_to_cpu(output_data_.data());
+  // Postprocessing result
+  Postprocess(im, shrink, result);
+}
+
+inline void MaskClassifier::Preprocess(std::vector<FaceResult>* faces) {
+  int batch_size = faces->size();
+  input_shape_ = {
+      batch_size,
+      EVAL_CROP_SIZE_[0],
+      EVAL_CROP_SIZE_[1],
+      EVAL_CROP_SIZE_[2]
+  };
+  // Reallocate input buffer
+  int input_size = 1;
+  for (int x : input_shape_) {
+    input_size *= x;
+  }
+  input_data_.resize(input_size);
+  auto buffer_base = input_data_.data();
+  for (int i = 0; i < batch_size; ++i) {
+    cv::Mat im = (*faces)[i].roi_rect;
+    // Resize
+    int rc = im.channels();
+    int rw = im.cols;
+    int rh = im.rows;
+    cv::Size resize_size(input_shape_[3], input_shape_[2]);
+    if (rw != input_shape_[3] || rh != input_shape_[2]) {
+      cv::resize(im, im, resize_size, 0.f, 0.f, cv::INTER_CUBIC);
+    }
+    im.convertTo(im, CV_32FC3, 1.0 / 256.0);
+    rc = im.channels();
+    rw = im.cols;
+    rh = im.rows;
+    float* buffer_i = buffer_base + i * rc * rw * rh;
+    NormalizeImage(mean_, scale_, im, buffer_i);
+  }
+}
+
+void MaskClassifier::Postprocess(std::vector<FaceResult>* faces) {
+  float* data = output_data_.data();
+  int batch_size = faces->size();
+  int out_num = output_data_.size();
+  for (int i = 0; i < batch_size; ++i) {
+    auto out_addr = data + (out_num / batch_size) * i;
+    int best_class_id = 0;
+    float best_class_score = *(best_class_id + out_addr);
+    for (int j = 0; j < (out_num / batch_size); ++j) {
+      auto infer_class = j;
+      auto score = *(j + out_addr);
+      if (score > best_class_score) {
+        best_class_id = infer_class;
+        best_class_score = score;
+      }
+    }
+    (*faces)[i].class_id = best_class_id;
+    (*faces)[i].score = best_class_score;
+  }
+}
+
+void MaskClassifier::Predict(std::vector<FaceResult>* faces) {
+  Preprocess(faces);
+  // Prepare input tensor
+  auto input_names = predictor_->GetInputNames();
+  auto in_tensor = predictor_->GetInputTensor(input_names[0]);
+  in_tensor->Reshape(input_shape_);
+  in_tensor->copy_from_cpu(input_data_.data());
+  // Run predictor
+  predictor_->ZeroCopyRun();
+  // Get output tensor
+  auto output_names = predictor_->GetOutputNames();
+  auto out_tensor = predictor_->GetOutputTensor(output_names[1]);
+  std::vector<int> output_shape = out_tensor->shape();
+  // Calculate output length
+  int output_size = 1;
+  for (int j = 0; j < output_shape.size(); ++j) {
+      output_size *= output_shape[j];
+  }
+  output_data_.resize(output_size);
+  out_tensor->copy_to_cpu(output_data_.data());
+  Postprocess(faces);
+}
--- a/deploy/demo/mask_detector/mask_detector.h
+++ b/deploy/demo/mask_detector/mask_detector.h
+//   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.
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include <memory>
+#include <utility>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/highgui/highgui.hpp>
+
+#include "paddle_inference_api.h" // NOLINT
+
+// MaskDetector Result
+struct FaceResult {
+  // Detection result: face rectangle
+  std::vector<int> rect;
+  // Detection result: cv::Mat of face rectange
+  cv::Mat roi_rect;
+  // Classification result: confidence
+  float score;
+  // Classification result : class id
+  int class_id;
+};
+
+// Load Paddle Inference Model
+void LoadModel(
+    const std::string& model_dir,
+    bool use_gpu,
+    std::unique_ptr<paddle::PaddlePredictor>* predictor);
+
+// Visualiztion MaskDetector results
+void VisualizeResult(const cv::Mat& img,
+                     const std::vector<FaceResult>& results,
+                     cv::Mat* vis_img);
+
+class FaceDetector {
+ public:
+  explicit FaceDetector(const std::string& model_dir,
+                        const std::vector<float>& mean,
+                        const std::vector<float>& scale,
+                        bool use_gpu = false,
+                        float threshold = 0.7) :
+      mean_(mean),
+      scale_(scale),
+      threshold_(threshold) {
+    LoadModel(model_dir, use_gpu, &predictor_);
+  }
+
+  // Run predictor
+  void Predict(
+      const cv::Mat& img,
+      std::vector<FaceResult>* result,
+      float shrink);
+
+ private:
+  // Preprocess image and copy data to input buffer
+  void Preprocess(const cv::Mat& image_mat, float shrink);
+  // Postprocess result
+  void Postprocess(
+      const cv::Mat& raw_mat,
+      float shrink,
+      std::vector<FaceResult>* result);
+
+  std::unique_ptr<paddle::PaddlePredictor> predictor_;
+  std::vector<float> input_data_;
+  std::vector<float> output_data_;
+  std::vector<int> input_shape_;
+  std::vector<float> mean_;
+  std::vector<float> scale_;
+  float threshold_;
+};
+
+class MaskClassifier {
+ public:
+  explicit MaskClassifier(const std::string& model_dir,
+                      const std::vector<float>& mean,
+                      const std::vector<float>& scale,
+                      bool use_gpu = false) :
+  mean_(mean),
+  scale_(scale) {
+    LoadModel(model_dir, use_gpu, &predictor_);
+  }
+
+  void Predict(std::vector<FaceResult>* faces);
+
+ private:
+  void Preprocess(std::vector<FaceResult>* faces);
+
+  void Postprocess(std::vector<FaceResult>* faces);
+
+  std::unique_ptr<paddle::PaddlePredictor> predictor_;
+  std::vector<float> input_data_;
+  std::vector<int> input_shape_;
+  std::vector<float> output_data_;
+  const std::vector<int> EVAL_CROP_SIZE_ = {3, 128, 128};
+  std::vector<float> mean_;
+  std::vector<float> scale_;
+};