mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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[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>
This commit is contained in:
ChaoII
@@ -0,0 +1,14 @@
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
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# 指定下载解压后的fastdeploy库路径
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# 添加FastDeploy依赖头文件
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include_directories(${FASTDEPLOY_INCS})
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
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# 添加FastDeploy库依赖
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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@@ -0,0 +1,79 @@
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# PP-Tracking C++部署示例
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本目录下提供`infer.cc`快速完成PP-Tracking在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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以Linux上 PP-Tracking 推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md/CPP_prebuilt_libraries.md)下载CPU推理库）
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```bash
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#下载SDK，编译模型examples代码（SDK中包含了examples代码）
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.3.0.tgz
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tar xvf fastdeploy-linux-x64-gpu-0.3.0.tgz
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cd fastdeploy-linux-x64-gpu-0.3.0/examples/vision/tracking/pptracking/cpp/
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mkdir build && cd build
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.3.0
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make -j
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# 下载PP-Tracking模型文件和测试视频
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wget https://bj.bcebos.com/paddlehub/fastdeploy/fairmot_hrnetv2_w18_dlafpn_30e_576x320.tgz
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tar -xvf fairmot_hrnetv2_w18_dlafpn_30e_576x320.tgz
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wget https://bj.bcebos.com/paddlehub/fastdeploy/person.mp4
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wget https://bj.bcebos.com/paddlehub/fastdeploy/person.mp4
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# CPU推理
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./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 0
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# GPU推理
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./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 1
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# GPU上TensorRT推理
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./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 2
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```
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以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
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- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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## PP-Tracking C++接口
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### PPTracking类
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```c++
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fastdeploy::vision::tracking::PPTracking(
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const string& model_file,
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const string& params_file = "",
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const string& config_file,
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE)
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```
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PP-Tracking模型加载和初始化，其中model_file为导出的Paddle模型格式。
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径
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> * **config_file**(str): 推理部署配置文件
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
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#### Predict函数
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> ```c++
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> PPTracking::Predict(cv::Mat* im, MOTResult* result)
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> ```
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>
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> 模型预测接口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **im**: 输入图像，注意需为HWC，BGR格式
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> > * **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/)
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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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@@ -0,0 +1,158 @@
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/vision.h"
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#ifdef WIN32
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const char sep = '\\';
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#else
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const char sep = '/';
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#endif
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void CpuInfer(const std::string& model_dir, const std::string& video_file) {
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auto model_file = model_dir + sep + "model.pdmodel";
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auto params_file = model_dir + sep + "model.pdiparams";
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auto config_file = model_dir + sep + "infer_cfg.yml";
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auto model = fastdeploy::vision::tracking::PPTracking(
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model_file, params_file, config_file);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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fastdeploy::vision::MOTResult result;
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cv::Mat frame;
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int frame_id=0;
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cv::VideoCapture capture(video_file);
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// according to the time of prediction to calculate fps
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float fps= 0.0f;
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while (capture.read(frame)) {
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if (frame.empty()) {
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break;
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}
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if (!model.Predict(&frame, &result)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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// std::cout << result.Str() << std::endl;
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cv::Mat out_img = fastdeploy::vision::VisMOT(frame, result, fps , frame_id);
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cv::imshow("mot",out_img);
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cv::waitKey(30);
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frame_id++;
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}
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capture.release();
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cv::destroyAllWindows();
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}
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void GpuInfer(const std::string& model_dir, const std::string& video_file) {
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auto model_file = model_dir + sep + "model.pdmodel";
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auto params_file = model_dir + sep + "model.pdiparams";
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auto config_file = model_dir + sep + "infer_cfg.yml";
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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auto model = fastdeploy::vision::tracking::PPTracking(
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model_file, params_file, config_file, option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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fastdeploy::vision::MOTResult result;
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cv::Mat frame;
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int frame_id=0;
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cv::VideoCapture capture(video_file);
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// according to the time of prediction to calculate fps
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float fps= 0.0f;
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while (capture.read(frame)) {
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if (frame.empty()) {
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break;
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}
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if (!model.Predict(&frame, &result)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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// std::cout << result.Str() << std::endl;
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cv::Mat out_img = fastdeploy::vision::VisMOT(frame, result, fps , frame_id);
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cv::imshow("mot",out_img);
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cv::waitKey(30);
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frame_id++;
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}
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capture.release();
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cv::destroyAllWindows();
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}
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void TrtInfer(const std::string& model_dir, const std::string& video_file) {
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auto model_file = model_dir + sep + "model.pdmodel";
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auto params_file = model_dir + sep + "model.pdiparams";
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auto config_file = model_dir + sep + "infer_cfg.yml";
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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option.UseTrtBackend();
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auto model = fastdeploy::vision::tracking::PPTracking(
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model_file, params_file, config_file, option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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fastdeploy::vision::MOTResult result;
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cv::Mat frame;
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int frame_id=0;
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cv::VideoCapture capture(video_file);
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// according to the time of prediction to calculate fps
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float fps= 0.0f;
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while (capture.read(frame)) {
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if (frame.empty()) {
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break;
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}
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if (!model.Predict(&frame, &result)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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// std::cout << result.Str() << std::endl;
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cv::Mat out_img = fastdeploy::vision::VisMOT(frame, result, fps , frame_id);
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cv::imshow("mot",out_img);
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cv::waitKey(30);
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frame_id++;
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}
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capture.release();
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cv::destroyAllWindows();
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}
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int main(int argc, char* argv[]) {
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if (argc < 4) {
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std::cout
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<< "Usage: infer_demo path/to/model_dir path/to/video run_option, "
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"e.g ./infer_model ./pptracking_model_dir ./person.mp4 0"
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<< std::endl;
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std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
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"with gpu; 2: run with gpu and use tensorrt backend."
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<< std::endl;
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return -1;
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}
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if (std::atoi(argv[3]) == 0) {
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CpuInfer(argv[1], argv[2]);
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} else if (std::atoi(argv[3]) == 1) {
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GpuInfer(argv[1], argv[2]);
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} else if (std::atoi(argv[3]) == 2) {
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TrtInfer(argv[1], argv[2]);
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}
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return 0;
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}
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@@ -0,0 +1,70 @@
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# PP-Tracking Python部署示例
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在部署前，需确认以下两个步骤
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- 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)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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本目录下提供`infer.py`快速完成PP-Tracking在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/tracking/pptracking/python
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# 下载PP-Tracking模型文件和测试视频
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wget https://bj.bcebos.com/paddlehub/fastdeploy/fairmot_hrnetv2_w18_dlafpn_30e_576x320.tgz
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tar -xvf fairmot_hrnetv2_w18_dlafpn_30e_576x320.tgz
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wget https://bj.bcebos.com/paddlehub/fastdeploy/person.mp4
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# CPU推理
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python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp4 --device cpu
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# GPU推理
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python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp4 --device gpu
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# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
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python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp4 --device gpu --use_trt True
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```
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## PP-Tracking Python接口
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```python
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fd.vision.tracking.PPTracking(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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PP-Tracking模型加载和初始化，其中model_file, params_file以及config_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting)
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径
|
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> * **config_file**(str): 推理部署配置文件
|
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
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### predict函数
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> ```python
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> PPTracking.predict(frame)
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> ```
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>
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> 模型预测结口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **frame**(np.ndarray): 输入数据，注意需为HWC，BGR格式,frame为视频帧如：_,frame=cap.read()得到
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> **返回**
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>
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> > 返回`fastdeploy.vision.MOTResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
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### 类成员属性
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#### 预处理参数
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用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
|
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## 其它文档
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- [PP-Tracking 模型介绍](..)
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- [PP-Tracking C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
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@@ -0,0 +1,79 @@
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# 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.
|
||||
|
||||
import fastdeploy as fd
|
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import cv2
|
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import time
|
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import os
|
||||
|
||||
|
||||
def parse_arguments():
|
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import argparse
|
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import ast
|
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parser = argparse.ArgumentParser()
|
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parser.add_argument(
|
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"--model", required=True, help="Path of PaddleSeg model.")
|
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parser.add_argument(
|
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"--video", type=str, required=True, help="Path of test video file.")
|
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parser.add_argument(
|
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"--device",
|
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type=str,
|
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default='cpu',
|
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help="Type of inference device, support 'cpu' or 'gpu'.")
|
||||
parser.add_argument(
|
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"--use_trt",
|
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type=ast.literal_eval,
|
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default=False,
|
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help="Wether to use tensorrt.")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
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option = fd.RuntimeOption()
|
||||
|
||||
if args.device.lower() == "gpu":
|
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option.use_gpu()
|
||||
|
||||
if args.use_trt:
|
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option.use_trt_backend()
|
||||
return option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
# 配置runtime，加载模型
|
||||
runtime_option = build_option(args)
|
||||
model_file = os.path.join(args.model, "model.pdmodel")
|
||||
params_file = os.path.join(args.model, "model.pdiparams")
|
||||
config_file = os.path.join(args.model, "infer_cfg.yml")
|
||||
model = fd.vision.tracking.PPTracking(
|
||||
model_file, params_file, config_file, runtime_option=runtime_option)
|
||||
|
||||
# 预测图片分割结果
|
||||
cap = cv2.VideoCapture(args.video)
|
||||
frame_id = 0
|
||||
while True:
|
||||
start_time = time.time()
|
||||
frame_id = frame_id+1
|
||||
_, frame = cap.read()
|
||||
if frame is None:
|
||||
break
|
||||
result = model.predict(frame)
|
||||
end_time = time.time()
|
||||
fps = 1.0/(end_time-start_time)
|
||||
img = fd.vision.vis_mot(frame, result, fps, frame_id)
|
||||
cv2.imshow("video", img)
|
||||
cv2.waitKey(30)
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
Reference in New Issue
Block a user