[Model] add pptracking model (#357)

* add override mark

* delete some

* recovery

* recovery

* add tracking

* add tracking py_bind and example

* add pptracking

* add pptracking

* iomanip head file

* add opencv_video lib

* add python libs package

Signed-off-by: ChaoII <849453582@qq.com>

* complete comments

Signed-off-by: ChaoII <849453582@qq.com>

* add jdeTracker_ member variable

Signed-off-by: ChaoII <849453582@qq.com>

* add 'FASTDEPLOY_DECL' macro

Signed-off-by: ChaoII <849453582@qq.com>

* remove kwargs params

Signed-off-by: ChaoII <849453582@qq.com>

* [Doc]update pptracking docs

* delete 'ENABLE_PADDLE_FRONTEND' switch

* add pptracking unit test

* update pptracking unit test

Signed-off-by: ChaoII <849453582@qq.com>

* modify test video file path and remove trt test

* update unit test model url

* remove 'FASTDEPLOY_DECL' macro

Signed-off-by: ChaoII <849453582@qq.com>

* fix build python packages about pptracking on win32

Signed-off-by: ChaoII <849453582@qq.com>

Signed-off-by: ChaoII <849453582@qq.com>
Co-authored-by: Jason <jiangjiajun@baidu.com>

examples/vision/tracking/pptracking/cpp/CMakeLists.txt

@@ -0,0 +1,14 @@
+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})

examples/vision/tracking/pptracking/cpp/README.md

@@ -0,0 +1,79 @@
+# PP-Tracking C++部署示例
+
+本目录下提供`infer.cc`快速完成PP-Tracking在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+
+在部署前，需确认以下两个步骤
+
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
+- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+
+以Linux上 PP-Tracking 推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md/CPP_prebuilt_libraries.md)下载CPU推理库）
+
+```bash
+#下载SDK，编译模型examples代码（SDK中包含了examples代码）
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.3.0.tgz
+tar xvf fastdeploy-linux-x64-gpu-0.3.0.tgz
+cd fastdeploy-linux-x64-gpu-0.3.0/examples/vision/tracking/pptracking/cpp/
+mkdir build && cd build
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.3.0
+make -j
+
+# 下载PP-Tracking模型文件和测试视频
+wget https://bj.bcebos.com/paddlehub/fastdeploy/fairmot_hrnetv2_w18_dlafpn_30e_576x320.tgz
+tar -xvf fairmot_hrnetv2_w18_dlafpn_30e_576x320.tgz
+wget https://bj.bcebos.com/paddlehub/fastdeploy/person.mp4
+wget https://bj.bcebos.com/paddlehub/fastdeploy/person.mp4
+
+
+# CPU推理
+./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 0
+# GPU推理
+./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 1
+# GPU上TensorRT推理
+./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 2
+```
+
+以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:  
+- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
+
+## PP-Tracking C++接口
+
+### PPTracking类
+
+```c++
+fastdeploy::vision::tracking::PPTracking(
+        const string& model_file,
+        const string& params_file = "",
+        const string& config_file,
+        const RuntimeOption& runtime_option = RuntimeOption(),
+        const ModelFormat& model_format = ModelFormat::PADDLE)
+```
+
+PP-Tracking模型加载和初始化，其中model_file为导出的Paddle模型格式。
+
+**参数**
+
+> * **model_file**(str): 模型文件路径
+> * **params_file**(str): 参数文件路径
+> * **config_file**(str): 推理部署配置文件
+> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
+> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
+
+#### Predict函数
+
+> ```c++
+> PPTracking::Predict(cv::Mat* im, MOTResult* result)
+> ```
+>
+> 模型预测接口，输入图像直接输出检测结果。
+>
+> **参数**
+>
+> > * **im**: 输入图像，注意需为HWC，BGR格式
+> > * **result**: 检测结果，包括检测框，跟踪id，各个框的置信度，对象类别id，MOTResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
+
+
+- [模型介绍](../../)
+- [Python部署](../python)
+- [视觉模型预测结果](../../../../../docs/api/vision_results/)
+- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)

examples/vision/tracking/pptracking/cpp/infer.cc

@@ -0,0 +1,158 @@
+// Copyright (c) 2022 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 "fastdeploy/vision.h"
+
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void CpuInfer(const std::string& model_dir, const std::string& video_file) {
+  auto model_file = model_dir + sep + "model.pdmodel";
+  auto params_file = model_dir + sep + "model.pdiparams";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+  auto model = fastdeploy::vision::tracking::PPTracking(
+      model_file, params_file, config_file);
+
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  fastdeploy::vision::MOTResult result;
+  cv::Mat frame;
+  int frame_id=0;
+  cv::VideoCapture capture(video_file);
+  // according to the time of prediction to calculate fps
+  float fps= 0.0f;
+  while (capture.read(frame)) {
+    if (frame.empty()) {
+        break;
+    }
+    if (!model.Predict(&frame, &result)) {
+        std::cerr << "Failed to predict." << std::endl;
+        return;
+    }
+    // std::cout << result.Str() << std::endl;
+    cv::Mat out_img = fastdeploy::vision::VisMOT(frame, result, fps , frame_id);
+    cv::imshow("mot",out_img);
+    cv::waitKey(30);
+    frame_id++;
+  }
+  capture.release();
+  cv::destroyAllWindows();
+}
+
+void GpuInfer(const std::string& model_dir, const std::string& video_file) {
+  auto model_file = model_dir + sep + "model.pdmodel";
+  auto params_file = model_dir + sep + "model.pdiparams";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  auto model = fastdeploy::vision::tracking::PPTracking(
+      model_file, params_file, config_file, option);
+
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  fastdeploy::vision::MOTResult result;
+  cv::Mat frame;
+  int frame_id=0;
+  cv::VideoCapture capture(video_file);
+  // according to the time of prediction to calculate fps
+  float fps= 0.0f;
+  while (capture.read(frame)) {
+    if (frame.empty()) {
+        break;
+    }
+    if (!model.Predict(&frame, &result)) {
+        std::cerr << "Failed to predict." << std::endl;
+        return;
+    }
+    // std::cout << result.Str() << std::endl;
+    cv::Mat out_img = fastdeploy::vision::VisMOT(frame, result, fps , frame_id);
+    cv::imshow("mot",out_img);
+    cv::waitKey(30);
+    frame_id++;
+  }
+  capture.release();
+  cv::destroyAllWindows();
+}
+
+void TrtInfer(const std::string& model_dir, const std::string& video_file) {
+  auto model_file = model_dir + sep + "model.pdmodel";
+  auto params_file = model_dir + sep + "model.pdiparams";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  option.UseTrtBackend();
+  auto model = fastdeploy::vision::tracking::PPTracking(
+      model_file, params_file, config_file, option);
+
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  fastdeploy::vision::MOTResult result;
+  cv::Mat frame;
+  int frame_id=0;
+  cv::VideoCapture capture(video_file);
+  // according to the time of prediction to calculate fps
+  float fps= 0.0f;
+  while (capture.read(frame)) {
+    if (frame.empty()) {
+        break;
+    }
+    if (!model.Predict(&frame, &result)) {
+        std::cerr << "Failed to predict." << std::endl;
+        return;
+    }
+    // std::cout << result.Str() << std::endl;
+    cv::Mat out_img = fastdeploy::vision::VisMOT(frame, result, fps , frame_id);
+    cv::imshow("mot",out_img);
+    cv::waitKey(30);
+    frame_id++;
+  }
+  capture.release();
+  cv::destroyAllWindows();
+}
+
+int main(int argc, char* argv[]) {
+  if (argc < 4) {
+    std::cout
+        << "Usage: infer_demo path/to/model_dir path/to/video run_option, "
+           "e.g ./infer_model ./pptracking_model_dir ./person.mp4 0"
+        << std::endl;
+    std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
+                 "with gpu; 2: run with gpu and use tensorrt backend."
+              << std::endl;
+    return -1;
+  }
+
+  if (std::atoi(argv[3]) == 0) {
+    CpuInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 1) {
+    GpuInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 2) {
+    TrtInfer(argv[1], argv[2]);
+  }
+  return 0;
+}