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https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-25 09:31:38 +08:00
[Model] Add tinypose single && pipeline model (#177)
* Add tinypose model * Add PPTinypose python API * Fix picodet preprocess bug && Add Tinypose examples * Update tinypose example code * Update ppseg preprocess if condition * Update ppseg backend support type * Update permute.h * Update README.md * Update code with comments * Move files dir * Delete premute.cc * Add single model pptinypose * Delete pptinypose old code in ppdet * Code format * Add ppdet + pptinypose pipeline model * Fix bug for posedetpipeline * Change Frontend to ModelFormat * Change Frontend to ModelFormat in __init__.py * Add python posedetpipeline/ * Update pptinypose example dir name * Update README.md * Update README.md * Update README.md * Update README.md * Create keypointdetection_result.md * Create README.md * Create README.md * Create README.md * Update README.md * Update README.md * Create README.md * Fix det_keypoint_unite_infer.py bug * Create README.md * Update PP-Tinypose by comment * Update by comment * Add pipeline directory * Add pptinypose dir * Update pptinypose to align accuracy * Addd warpAffine processor * Update GetCpuMat to GetOpenCVMat * Add comment for pptinypose && pipline * Update docs/main_page.md * Add README.md for pptinypose * Add README for det_keypoint_unite * Remove ENABLE_PIPELINE option * Remove ENABLE_PIPELINE option * Change pptinypose default backend * PP-TinyPose Pipeline support multi PP-Detection models * Update pp-tinypose comment * Update by comments * Add single test example Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
huangjianhui
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
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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}/det_keypoint_unite_infer.cc)
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# 添加FastDeploy库依赖
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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# PP-PicoDet + PP-TinyPose (Pipeline) C++部署示例
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本目录下提供`det_keypoint_unite_infer.cc`快速完成多人模型配置 PP-PicoDet + PP-TinyPose 在CPU/GPU，以及GPU上通过TensorRT加速部署的`单图多人关键点检测`示例。执行如下脚本即可完成
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>> **注意**: PP-TinyPose单模型独立部署，请参考[PP-TinyPose 单模型](../../tiny_pose/cpp/README.md)
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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以Linux上推理为例，在本目录执行如下命令即可完成编译测试
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```bash
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.3.0.tgz
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tar xvf fastdeploy-linux-x64-gpu-0.3.0.tgz
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cd fastdeploy-linux-x64-gpu-0.3.0/examples/vision/keypointdetection/tiny_pose/cpp/
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mkdir build
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cd build
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.3.0
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make -j
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# 下载PP-TinyPose和PP-PicoDet模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_TinyPose_256x192_infer.tgz
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tar -xvf PP_TinyPose_256x192_infer.tgz
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_PicoDet_V2_S_Pedestrian_320x320_infer.tgz
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tar -xvf PP_PicoDet_V2_S_Pedestrian_320x320_infer.tgz
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wget https://bj.bcebos.com/paddlehub/fastdeploy/000000018491.jpg
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# CPU推理
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./infer_demo PP_PicoDet_V2_S_Pedestrian_320x320_infer PP_TinyPose_256x192_infer 000000018491.jpg 0
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# GPU推理
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./infer_demo PP_PicoDet_V2_S_Pedestrian_320x320_infer PP_TinyPose_256x192_infer 000000018491.jpg 1
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# GPU上TensorRT推理
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./infer_demo PP_PicoDet_V2_S_Pedestrian_320x320_infer PP_TinyPose_256x192_infer 000000018491.jpg 2
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```
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运行完成可视化结果如下图所示
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<div align="center">
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<img src="https://user-images.githubusercontent.com/16222477/196393343-eeb6b68f-0bc6-4927-871f-5ac610da7293.jpeg", width=359px, height=423px />
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</div>
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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-TinyPose C++接口
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### PP-TinyPose类
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```c++
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fastdeploy::pipeline::PPTinyPose(
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fastdeploy::vision::detection::PPYOLOE* det_model,
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fastdeploy::vision::keypointdetection::PPTinyPose* pptinypose_model)
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```
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PPTinyPose Pipeline模型加载和初始化。
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**参数**
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> * **model_det_modelfile**(fastdeploy::vision::detection): 初始化后的检测模型，参考[PP-TinyPose](../../tiny_pose/README.md)
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> * **pptinypose_model**(fastdeploy::vision::keypointdetection): 初始化后的检测模型[Detection](../../../detection/paddledetection/README.md)，暂时只提供PaddleDetection系列
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#### Predict函数
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> ```c++
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> PPTinyPose::Predict(cv::Mat* im, KeyPointDetectionResult* result)
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> ```
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>
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> 模型预测接口，输入图像直接输出关键点检测结果。
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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**: 关键点检测结果，包括关键点的坐标以及关键点对应的概率值, KeyPointDetectionResult说明参考[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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#### 后处理参数
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> > * **detection_model_score_threshold**(bool):
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输入PP-TinyPose模型前，Detectin模型过滤检测框的分数阈值
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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 +1,196 @@
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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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#include "fastdeploy/pipeline.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& det_model_dir,
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const std::string& tinypose_model_dir,
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const std::string& image_file) {
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auto det_model_file = det_model_dir + sep + "model.pdmodel";
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auto det_params_file = det_model_dir + sep + "model.pdiparams";
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auto det_config_file = det_model_dir + sep + "infer_cfg.yml";
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auto det_model = fastdeploy::vision::detection::PicoDet(
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det_model_file, det_params_file, det_config_file);
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if (!det_model.Initialized()) {
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std::cerr << "Detection Model Failed to initialize." << std::endl;
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return;
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}
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auto tinypose_model_file = tinypose_model_dir + sep + "model.pdmodel";
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auto tinypose_params_file = tinypose_model_dir + sep + "model.pdiparams";
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auto tinypose_config_file = tinypose_model_dir + sep + "infer_cfg.yml";
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auto tinypose_model = fastdeploy::vision::keypointdetection::PPTinyPose(
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tinypose_model_file, tinypose_params_file, tinypose_config_file);
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if (!tinypose_model.Initialized()) {
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std::cerr << "TinyPose Model Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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fastdeploy::vision::KeyPointDetectionResult res;
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auto pipeline =fastdeploy::pipeline::PPTinyPose(&det_model, &tinypose_model);
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pipeline.detection_model_score_threshold = 0.5;
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if (!pipeline.Predict(&im, &res)) {
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std::cerr << "TinyPose Prediction Failed." << std::endl;
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return;
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} else {
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std::cout << "TinyPose Prediction Done!" << std::endl;
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}
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// 输出预测框结果
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std::cout << res.Str() << std::endl;
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// 可视化预测结果
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auto vis_im =
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fastdeploy::vision::VisKeypointDetection(im, res, 0.2);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "TinyPose visualized result saved in ./vis_result.jpg"
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<< std::endl;
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}
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void GpuInfer(const std::string& det_model_dir,
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const std::string& tinypose_model_dir,
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const std::string& image_file) {
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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auto det_model_file = det_model_dir + sep + "model.pdmodel";
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auto det_params_file = det_model_dir + sep + "model.pdiparams";
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auto det_config_file = det_model_dir + sep + "infer_cfg.yml";
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auto det_model = fastdeploy::vision::detection::PicoDet(
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det_model_file, det_params_file, det_config_file, option);
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if (!det_model.Initialized()) {
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std::cerr << "Detection Model Failed to initialize." << std::endl;
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return;
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}
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auto tinypose_model_file = tinypose_model_dir + sep + "model.pdmodel";
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auto tinypose_params_file = tinypose_model_dir + sep + "model.pdiparams";
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auto tinypose_config_file = tinypose_model_dir + sep + "infer_cfg.yml";
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auto tinypose_model = fastdeploy::vision::keypointdetection::PPTinyPose(
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tinypose_model_file, tinypose_params_file, tinypose_config_file, option);
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if (!tinypose_model.Initialized()) {
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std::cerr << "TinyPose Model Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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fastdeploy::vision::KeyPointDetectionResult res;
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auto pipeline =
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fastdeploy::pipeline::PPTinyPose(
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&det_model, &tinypose_model);
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pipeline.detection_model_score_threshold = 0.5;
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if (!pipeline.Predict(&im, &res)) {
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std::cerr << "TinyPose Prediction Failed." << std::endl;
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return;
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} else {
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std::cout << "TinyPose Prediction Done!" << std::endl;
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}
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// 输出预测框结果
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std::cout << res.Str() << std::endl;
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// 可视化预测结果
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auto vis_im =
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fastdeploy::vision::VisKeypointDetection(im, res, 0.2);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "TinyPose visualized result saved in ./vis_result.jpg"
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<< std::endl;
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}
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void TrtInfer(const std::string& det_model_dir,
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const std::string& tinypose_model_dir,
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const std::string& image_file) {
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auto det_model_file = det_model_dir + sep + "model.pdmodel";
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auto det_params_file = det_model_dir + sep + "model.pdiparams";
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auto det_config_file = det_model_dir + sep + "infer_cfg.yml";
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auto det_option = fastdeploy::RuntimeOption();
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det_option.UseGpu();
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det_option.UseTrtBackend();
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det_option.SetTrtInputShape("image", {1, 3, 320, 320});
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det_option.SetTrtInputShape("scale_factor", {1, 2});
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auto det_model = fastdeploy::vision::detection::PicoDet(
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det_model_file, det_params_file, det_config_file, det_option);
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if (!det_model.Initialized()) {
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std::cerr << "Detection Model Failed to initialize." << std::endl;
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return;
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}
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auto tinypose_model_file = tinypose_model_dir + sep + "model.pdmodel";
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auto tinypose_params_file = tinypose_model_dir + sep + "model.pdiparams";
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auto tinypose_config_file = tinypose_model_dir + sep + "infer_cfg.yml";
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auto tinypose_option = fastdeploy::RuntimeOption();
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tinypose_option.UseGpu();
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tinypose_option.UseTrtBackend();
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auto tinypose_model = fastdeploy::vision::keypointdetection::PPTinyPose(
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tinypose_model_file, tinypose_params_file, tinypose_config_file,
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tinypose_option);
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if (!tinypose_model.Initialized()) {
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std::cerr << "TinyPose Model Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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fastdeploy::vision::KeyPointDetectionResult res;
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auto pipeline =
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fastdeploy::pipeline::PPTinyPose(
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&det_model, &tinypose_model);
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pipeline.detection_model_score_threshold = 0.5;
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if (!pipeline.Predict(&im, &res)) {
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std::cerr << "TinyPose Prediction Failed." << std::endl;
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return;
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} else {
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std::cout << "TinyPose Prediction Done!" << std::endl;
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}
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// 输出预测关键点结果
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std::cout << res.Str() << std::endl;
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// 可视化预测结果
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auto vis_im =
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fastdeploy::vision::VisKeypointDetection(im, res, 0.2);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "TinyPose visualized result saved in ./vis_result.jpg"
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<< std::endl;
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}
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int main(int argc, char* argv[]) {
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if (argc < 5) {
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std::cout << "Usage: infer_demo path/to/detection_model_dir "
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"path/to/pptinypose_model_dir path/to/image run_option, "
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"e.g ./infer_model ./picodet_model_dir ./pptinypose_model_dir "
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"./test.jpeg 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[4]) == 0) {
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CpuInfer(argv[1], argv[2], argv[3]);
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} else if (std::atoi(argv[4]) == 1) {
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GpuInfer(argv[1], argv[2], argv[3]);
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} else if (std::atoi(argv[4]) == 2) {
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TrtInfer(argv[1], argv[2], argv[3]);
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}
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return 0;
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}
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