更换文档检测模型
This commit is contained in:
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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include_directories(${FASTDEPLOY_INCS})
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
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add_executable(infer_tinypose_demo ${PROJECT_SOURCE_DIR}/pptinypose_infer.cc)
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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target_link_libraries(infer_tinypose_demo ${FASTDEPLOY_LIBS})
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142
paddle_detection/deploy/fastdeploy/cpu-gpu/cpp/README.md
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paddle_detection/deploy/fastdeploy/cpu-gpu/cpp/README.md
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[English](README.md) | 简体中文
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# PaddleDetection CPU-GPU C++部署示例
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本目录下提供`infer.cc`快速完成PPYOLOE模型包括PPYOLOE在CPU/GPU，以及GPU上通过Paddle-TensorRT加速部署的示例。
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## 1. 说明
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PaddleDetection支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署PaddleDetection模型。FastDeploy目前支持的模型系列，包括但不限于`PPYOLOE`, `PicoDet`, `PaddleYOLOX`, `PPYOLO`, `FasterRCNN`，`SSD`,`PaddleYOLOv5`,`PaddleYOLOv6`,`PaddleYOLOv7`,`RTMDet`,`CascadeRCNN`,`PSSDet`,`RetinaNet`,`PPYOLOESOD`,`FCOS`,`TTFNet`,`TOOD`,`GFL`所有类名的构造函数和预测函数在参数上完全一致。所有模型的调用，只需要参考PPYOLOE的示例，即可快速调用。
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## 2. 部署环境准备
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在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库。
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## 3. 部署模型准备
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在部署前，请准备好您所需要运行的推理模型，你可以选择使用[预导出的推理模型](../README.md)或者[自行导出PaddleDetection部署模型](../README.md)。
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## 4. 运行部署示例
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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，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>=1.0.4)
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### 4.1 目标检测示例
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```bash
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# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-gpu-x.x.x.tgz
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/PaddleDetection.git
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cd PaddleDetection/deploy/fastdeploy/cpu-gpu/cpp
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# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
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# git checkout develop
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# 编译部署示例
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mkdir build && cd build
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mv ../fastdeploy-linux-x64-gpu-x.x.x .
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-gpu-x.x.x
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make -j
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# 下载PPYOLOE模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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tar xvf ppyoloe_crn_l_300e_coco.tgz
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# 运行部署示例
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# CPU推理
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./infer_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 0
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# GPU推理
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./infer_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 1
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# GPU上Paddle-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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./infer_demo ./ppyoloe_crn_l_300e_coco 000000014439.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/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
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</div>
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### 4.2 关键点检测示例
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```bash
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# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-gpu-x.x.x.tgz
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/PaddleDetection.git
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cd PaddleDetection/deploy/fastdeploy/cpu-gpu/cpp
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# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
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# git checkout develop
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# 编译部署示例
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mkdir build && cd build
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mv ../fastdeploy-linux-x64-gpu-x.x.x .
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-gpu-x.x.x
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make -j
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# 下载PP-TinyPose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/hrnet_demo.jpg
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# 运行部署示例
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# CPU推理
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./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 0
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# GPU推理
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./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 1
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# GPU上Paddle-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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./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.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/16222477/196386764-dd51ad56-c410-4c54-9580-643f282f5a83.jpeg", width=359px, height=423px />
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</div>
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关于如何进行多人关键点检测，请参考[PPTinyPose Pipeline示例](./det_keypoint_unite/)
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- 注意，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](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/use_sdk_on_windows.md)
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- 关于如何通过FastDeploy使用更多不同的推理后端，以及如何使用不同的硬件，请参考文档：[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
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## 5. PaddleDetection C++接口
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FastDeploy目前支持的模型系列，包括但不限于`PPYOLOE`, `PicoDet`, `PaddleYOLOX`, `PPYOLO`, `FasterRCNN`，`SSD`,`PaddleYOLOv5`,`PaddleYOLOv6`,`PaddleYOLOv7`,`RTMDet`,`CascadeRCNN`,`PSSDet`,`RetinaNet`,`PPYOLOESOD`,`FCOS`,`TTFNet`,`TOOD`,`GFL`所有类名的构造函数和预测函数在参数上完全一致。所有模型的调用，只需要参考PPYOLOE的示例，即可快速调用。
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### 5.1 目标检测及实例分割模型
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```c++
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fastdeploy::vision::detection::PicoDet(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::SOLOv2(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::PPYOLOE(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::PPYOLO(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::YOLOv3(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::PaddleYOLOX(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::FasterRCNN(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::MaskRCNN(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::SSD(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::PaddleYOLOv5(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::PaddleYOLOv6(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::PaddleYOLOv7(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::PaddleYOLOv8(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::CascadeRCNN(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::PSSDet(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::RetinaNet(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::PPYOLOESOD(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::FCOS(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::TOOD(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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fastdeploy::vision::detection::GFL(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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```
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### 5.2 关键点检测模型
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```C++
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fastdeploy::vision::keypointdetection::PPTinyPose(const string& model_file, const string& params_file, const string& config_file, const RuntimeOption& runtime_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::PADDLE);
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```
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PaddleDetection模型加载和初始化，其中model_file， params_file为导出的Paddle部署模型格式, config_file为PaddleDetection同时导出的部署配置yaml文件
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## 6. 更多指南
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- [PaddleDetection C++ API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1detection.html)
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- [FastDeploy部署PaddleDetection模型概览](../../)
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- [Python部署](../python)
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## 7. 常见问题
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- [如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
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- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
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- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
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- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
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- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)
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@@ -0,0 +1,11 @@
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
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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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include_directories(${FASTDEPLOY_INCS})
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/det_keypoint_unite_infer.cc)
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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[English](README.md) | 简体中文
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# PP-PicoDet + PP-TinyPose (Pipeline) CPU-GPU C++部署示例
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本目录下提供`det_keypoint_unite_infer.cc`快速完成多人模型配置 PP-PicoDet + PP-TinyPose 在CPU/GPU，以及GPU上通过TensorRT加速部署的`单图多人关键点检测`示例。执行如下脚本即可完成。**注意**: PP-TinyPose单模型独立部署，请参考[PP-TinyPose 单模型](../README.md)
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## 1. 部署环境准备
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在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库。
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## 2. 部署模型准备
|
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在部署前，请准备好您所需要运行的推理模型，你可以选择使用[预导出的推理模型](../../README.md)或者[自行导出PaddleDetection部署模型](../../README.md)。
|
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## 3. 运行部署示例
|
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以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>=1.0.4)
|
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```bash
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mkdir build
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cd build
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# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
|
||||
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
|
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/PaddleDetection.git
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cd PaddleDetection/deploy/fastdeploy/cpu-gpu/cpp/det_keypoint_unite
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# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
|
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# git checkout develop
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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Paddle-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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./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 />
|
||||
</div>
|
||||
|
||||
- 注意，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](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/use_sdk_on_windows.md)
|
||||
- 关于如何通过FastDeploy使用更多不同的推理后端，以及如何使用不同的硬件，请参考文档：[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
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## 4. PP-TinyPose 模型串联 C++ 接口
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```c++
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fastdeploy::pipeline::PPTinyPose(
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fastdeploy::vision::detection::PicoDet* 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。det_model表示初始化后的检测模型，pptinypose_model表示初始化后的关键点检测模型。
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||||
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## 5. 更多指南
|
||||
- [PaddleDetection C++ API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1detection.html)
|
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- [FastDeploy部署PaddleDetection模型概览](../../../)
|
||||
- [Python部署](../../python/det_keypoint_unite/)
|
||||
|
||||
## 6. 常见问题
|
||||
- [如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
|
||||
- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
|
||||
- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
|
||||
- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
|
||||
- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)
|
||||
@@ -0,0 +1,205 @@
|
||||
// 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"
|
||||
#include "fastdeploy/pipeline.h"
|
||||
|
||||
#ifdef WIN32
|
||||
const char sep = '\\';
|
||||
#else
|
||||
const char sep = '/';
|
||||
#endif
|
||||
|
||||
void CpuInfer(const std::string& det_model_dir,
|
||||
const std::string& tinypose_model_dir,
|
||||
const std::string& image_file) {
|
||||
auto det_model_file = det_model_dir + sep + "model.pdmodel";
|
||||
auto det_params_file = det_model_dir + sep + "model.pdiparams";
|
||||
auto det_config_file = det_model_dir + sep + "infer_cfg.yml";
|
||||
auto det_model = fastdeploy::vision::detection::PicoDet(
|
||||
det_model_file, det_params_file, det_config_file);
|
||||
if (!det_model.Initialized()) {
|
||||
std::cerr << "Detection Model Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto tinypose_model_file = tinypose_model_dir + sep + "model.pdmodel";
|
||||
auto tinypose_params_file = tinypose_model_dir + sep + "model.pdiparams";
|
||||
auto tinypose_config_file = tinypose_model_dir + sep + "infer_cfg.yml";
|
||||
auto tinypose_model = fastdeploy::vision::keypointdetection::PPTinyPose(
|
||||
tinypose_model_file, tinypose_params_file, tinypose_config_file);
|
||||
if (!tinypose_model.Initialized()) {
|
||||
std::cerr << "TinyPose Model Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
fastdeploy::vision::KeyPointDetectionResult res;
|
||||
|
||||
auto pipeline =fastdeploy::pipeline::PPTinyPose(&det_model, &tinypose_model);
|
||||
pipeline.detection_model_score_threshold = 0.5;
|
||||
if (!pipeline.Predict(&im, &res)) {
|
||||
std::cerr << "TinyPose Prediction Failed." << std::endl;
|
||||
return;
|
||||
} else {
|
||||
std::cout << "TinyPose Prediction Done!" << std::endl;
|
||||
}
|
||||
|
||||
std::cout << res.Str() << std::endl;
|
||||
|
||||
auto vis_im =
|
||||
fastdeploy::vision::VisKeypointDetection(im, res, 0.2);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "TinyPose visualized result saved in ./vis_result.jpg"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
void GpuInfer(const std::string& det_model_dir,
|
||||
const std::string& tinypose_model_dir,
|
||||
const std::string& image_file) {
|
||||
auto option = fastdeploy::RuntimeOption();
|
||||
option.UseGpu();
|
||||
auto det_model_file = det_model_dir + sep + "model.pdmodel";
|
||||
auto det_params_file = det_model_dir + sep + "model.pdiparams";
|
||||
auto det_config_file = det_model_dir + sep + "infer_cfg.yml";
|
||||
auto det_model = fastdeploy::vision::detection::PicoDet(
|
||||
det_model_file, det_params_file, det_config_file, option);
|
||||
if (!det_model.Initialized()) {
|
||||
std::cerr << "Detection Model Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto tinypose_model_file = tinypose_model_dir + sep + "model.pdmodel";
|
||||
auto tinypose_params_file = tinypose_model_dir + sep + "model.pdiparams";
|
||||
auto tinypose_config_file = tinypose_model_dir + sep + "infer_cfg.yml";
|
||||
auto tinypose_model = fastdeploy::vision::keypointdetection::PPTinyPose(
|
||||
tinypose_model_file, tinypose_params_file, tinypose_config_file, option);
|
||||
if (!tinypose_model.Initialized()) {
|
||||
std::cerr << "TinyPose Model Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
fastdeploy::vision::KeyPointDetectionResult res;
|
||||
|
||||
auto pipeline =
|
||||
fastdeploy::pipeline::PPTinyPose(
|
||||
&det_model, &tinypose_model);
|
||||
pipeline.detection_model_score_threshold = 0.5;
|
||||
if (!pipeline.Predict(&im, &res)) {
|
||||
std::cerr << "TinyPose Prediction Failed." << std::endl;
|
||||
return;
|
||||
} else {
|
||||
std::cout << "TinyPose Prediction Done!" << std::endl;
|
||||
}
|
||||
|
||||
std::cout << res.Str() << std::endl;
|
||||
|
||||
auto vis_im =
|
||||
fastdeploy::vision::VisKeypointDetection(im, res, 0.2);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "TinyPose visualized result saved in ./vis_result.jpg"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
void TrtInfer(const std::string& det_model_dir,
|
||||
const std::string& tinypose_model_dir,
|
||||
const std::string& image_file) {
|
||||
auto det_model_file = det_model_dir + sep + "model.pdmodel";
|
||||
auto det_params_file = det_model_dir + sep + "model.pdiparams";
|
||||
auto det_config_file = det_model_dir + sep + "infer_cfg.yml";
|
||||
|
||||
auto det_option = fastdeploy::RuntimeOption();
|
||||
det_option.UseGpu();
|
||||
det_option.UsePaddleInferBackend();
|
||||
// If use original Tensorrt, not Paddle-TensorRT,
|
||||
// please try `option.UseTrtBackend()`
|
||||
det_option.paddle_infer_option.enable_trt = true;
|
||||
det_option.paddle_infer_option.collect_trt_shape = true;
|
||||
det_option.trt_option.SetShape("image", {1, 3, 320, 320}, {1, 3, 320, 320},
|
||||
{1, 3, 320, 320});
|
||||
det_option.trt_option.SetShape("scale_factor", {1, 2}, {1, 2}, {1, 2});
|
||||
auto det_model = fastdeploy::vision::detection::PicoDet(
|
||||
det_model_file, det_params_file, det_config_file, det_option);
|
||||
if (!det_model.Initialized()) {
|
||||
std::cerr << "Detection Model Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto tinypose_model_file = tinypose_model_dir + sep + "model.pdmodel";
|
||||
auto tinypose_params_file = tinypose_model_dir + sep + "model.pdiparams";
|
||||
auto tinypose_config_file = tinypose_model_dir + sep + "infer_cfg.yml";
|
||||
auto tinypose_option = fastdeploy::RuntimeOption();
|
||||
|
||||
tinypose_option.UseGpu();
|
||||
tinypose_option.UsePaddleInferBackend();
|
||||
// If use original Tensorrt, not Paddle-TensorRT,
|
||||
// please try `option.UseTrtBackend()`
|
||||
tinypose_option.paddle_infer_option.enable_trt = true;
|
||||
tinypose_option.paddle_infer_option.collect_trt_shape = true;
|
||||
tinypose_option.trt_option.SetShape("image", {1, 3, 256, 192}, {1, 3, 256, 192},
|
||||
{1, 3, 256, 192});
|
||||
auto tinypose_model = fastdeploy::vision::keypointdetection::PPTinyPose(
|
||||
tinypose_model_file, tinypose_params_file, tinypose_config_file,
|
||||
tinypose_option);
|
||||
if (!tinypose_model.Initialized()) {
|
||||
std::cerr << "TinyPose Model Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
fastdeploy::vision::KeyPointDetectionResult res;
|
||||
|
||||
auto pipeline =
|
||||
fastdeploy::pipeline::PPTinyPose(
|
||||
&det_model, &tinypose_model);
|
||||
pipeline.detection_model_score_threshold = 0.5;
|
||||
if (!pipeline.Predict(&im, &res)) {
|
||||
std::cerr << "TinyPose Prediction Failed." << std::endl;
|
||||
return;
|
||||
} else {
|
||||
std::cout << "TinyPose Prediction Done!" << std::endl;
|
||||
}
|
||||
|
||||
std::cout << res.Str() << std::endl;
|
||||
|
||||
auto vis_im =
|
||||
fastdeploy::vision::VisKeypointDetection(im, res, 0.2);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "TinyPose visualized result saved in ./vis_result.jpg"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
if (argc < 5) {
|
||||
std::cout << "Usage: infer_demo path/to/detection_model_dir "
|
||||
"path/to/pptinypose_model_dir path/to/image run_option, "
|
||||
"e.g ./infer_model ./picodet_model_dir ./pptinypose_model_dir "
|
||||
"./test.jpeg 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[4]) == 0) {
|
||||
CpuInfer(argv[1], argv[2], argv[3]);
|
||||
} else if (std::atoi(argv[4]) == 1) {
|
||||
GpuInfer(argv[1], argv[2], argv[3]);
|
||||
} else if (std::atoi(argv[4]) == 2) {
|
||||
TrtInfer(argv[1], argv[2], argv[3]);
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
134
paddle_detection/deploy/fastdeploy/cpu-gpu/cpp/infer.cc
Normal file
134
paddle_detection/deploy/fastdeploy/cpu-gpu/cpp/infer.cc
Normal file
@@ -0,0 +1,134 @@
|
||||
// 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& image_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.UseCpu();
|
||||
auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
|
||||
config_file, option);
|
||||
if (!model.Initialized()) {
|
||||
std::cerr << "Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
|
||||
fastdeploy::vision::DetectionResult res;
|
||||
if (!model.Predict(im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
std::cout << res.Str() << std::endl;
|
||||
auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
void GpuInfer(const std::string& model_dir, const std::string& image_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::detection::PPYOLOE(model_file, params_file,
|
||||
config_file, option);
|
||||
if (!model.Initialized()) {
|
||||
std::cerr << "Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
|
||||
fastdeploy::vision::DetectionResult res;
|
||||
if (!model.Predict(im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
std::cout << res.Str() << std::endl;
|
||||
auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
void TrtInfer(const std::string& model_dir, const std::string& image_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.UsePaddleInferBackend();
|
||||
// If use original Tensorrt, not Paddle-TensorRT,
|
||||
// please try `option.UseTrtBackend()`
|
||||
option.paddle_infer_option.enable_trt = true;
|
||||
option.paddle_infer_option.collect_trt_shape = true;
|
||||
option.trt_option.SetShape("image", {1, 3, 640, 640}, {1, 3, 640, 640},
|
||||
{1, 3, 640, 640});
|
||||
option.trt_option.SetShape("scale_factor", {1, 2}, {1, 2}, {1, 2});
|
||||
auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file,
|
||||
config_file, option);
|
||||
if (!model.Initialized()) {
|
||||
std::cerr << "Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
|
||||
fastdeploy::vision::DetectionResult res;
|
||||
if (!model.Predict(im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
std::cout << res.Str() << std::endl;
|
||||
auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
if (argc < 4) {
|
||||
std::cout
|
||||
<< "Usage: infer_demo path/to/model_dir path/to/image run_option, "
|
||||
"e.g ./infer_demo ./ppyoloe_model_dir ./test.jpeg 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;
|
||||
}
|
||||
@@ -0,0 +1,149 @@
|
||||
// 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& tinypose_model_dir,
|
||||
const std::string& image_file) {
|
||||
auto tinypose_model_file = tinypose_model_dir + sep + "model.pdmodel";
|
||||
auto tinypose_params_file = tinypose_model_dir + sep + "model.pdiparams";
|
||||
auto tinypose_config_file = tinypose_model_dir + sep + "infer_cfg.yml";
|
||||
auto option = fastdeploy::RuntimeOption();
|
||||
option.UseCpu();
|
||||
auto tinypose_model = fastdeploy::vision::keypointdetection::PPTinyPose(
|
||||
tinypose_model_file, tinypose_params_file, tinypose_config_file, option);
|
||||
if (!tinypose_model.Initialized()) {
|
||||
std::cerr << "TinyPose Model Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
fastdeploy::vision::KeyPointDetectionResult res;
|
||||
if (!tinypose_model.Predict(&im, &res)) {
|
||||
std::cerr << "TinyPose Prediction Failed." << std::endl;
|
||||
return;
|
||||
} else {
|
||||
std::cout << "TinyPose Prediction Done!" << std::endl;
|
||||
}
|
||||
|
||||
std::cout << res.Str() << std::endl;
|
||||
|
||||
auto tinypose_vis_im =
|
||||
fastdeploy::vision::VisKeypointDetection(im, res, 0.5);
|
||||
cv::imwrite("tinypose_vis_result.jpg", tinypose_vis_im);
|
||||
std::cout << "TinyPose visualized result saved in ./tinypose_vis_result.jpg"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
void GpuInfer(const std::string& tinypose_model_dir,
|
||||
const std::string& image_file) {
|
||||
auto option = fastdeploy::RuntimeOption();
|
||||
option.UseGpu();
|
||||
|
||||
auto tinypose_model_file = tinypose_model_dir + sep + "model.pdmodel";
|
||||
auto tinypose_params_file = tinypose_model_dir + sep + "model.pdiparams";
|
||||
auto tinypose_config_file = tinypose_model_dir + sep + "infer_cfg.yml";
|
||||
auto tinypose_model = fastdeploy::vision::keypointdetection::PPTinyPose(
|
||||
tinypose_model_file, tinypose_params_file, tinypose_config_file, option);
|
||||
if (!tinypose_model.Initialized()) {
|
||||
std::cerr << "TinyPose Model Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
fastdeploy::vision::KeyPointDetectionResult res;
|
||||
if (!tinypose_model.Predict(&im, &res)) {
|
||||
std::cerr << "TinyPose Prediction Failed." << std::endl;
|
||||
return;
|
||||
} else {
|
||||
std::cout << "TinyPose Prediction Done!" << std::endl;
|
||||
}
|
||||
|
||||
std::cout << res.Str() << std::endl;
|
||||
|
||||
auto tinypose_vis_im =
|
||||
fastdeploy::vision::VisKeypointDetection(im, res, 0.5);
|
||||
cv::imwrite("tinypose_vis_result.jpg", tinypose_vis_im);
|
||||
std::cout << "TinyPose visualized result saved in ./tinypose_vis_result.jpg"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
void TrtInfer(const std::string& tinypose_model_dir,
|
||||
const std::string& image_file) {
|
||||
auto tinypose_model_file = tinypose_model_dir + sep + "model.pdmodel";
|
||||
auto tinypose_params_file = tinypose_model_dir + sep + "model.pdiparams";
|
||||
auto tinypose_config_file = tinypose_model_dir + sep + "infer_cfg.yml";
|
||||
auto tinypose_option = fastdeploy::RuntimeOption();
|
||||
tinypose_option.UseGpu();
|
||||
tinypose_option.UsePaddleInferBackend();
|
||||
// If use original Tensorrt, not Paddle-TensorRT,
|
||||
// please try `option.UseTrtBackend()`
|
||||
tinypose_option.paddle_infer_option.enable_trt = true;
|
||||
tinypose_option.paddle_infer_option.collect_trt_shape = true;
|
||||
tinypose_option.trt_option.SetShape("image", {1, 3, 256, 192}, {1, 3, 256, 192},
|
||||
{1, 3, 256, 192});
|
||||
|
||||
auto tinypose_model = fastdeploy::vision::keypointdetection::PPTinyPose(
|
||||
tinypose_model_file, tinypose_params_file, tinypose_config_file,
|
||||
tinypose_option);
|
||||
if (!tinypose_model.Initialized()) {
|
||||
std::cerr << "TinyPose Model Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
fastdeploy::vision::KeyPointDetectionResult res;
|
||||
if (!tinypose_model.Predict(&im, &res)) {
|
||||
std::cerr << "TinyPose Prediction Failed." << std::endl;
|
||||
return;
|
||||
} else {
|
||||
std::cout << "TinyPose Prediction Done!" << std::endl;
|
||||
}
|
||||
|
||||
std::cout << res.Str() << std::endl;
|
||||
|
||||
auto tinypose_vis_im =
|
||||
fastdeploy::vision::VisKeypointDetection(im, res, 0.5);
|
||||
cv::imwrite("tinypose_vis_result.jpg", tinypose_vis_im);
|
||||
std::cout << "TinyPose visualized result saved in ./tinypose_vis_result.jpg"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
if (argc < 4) {
|
||||
std::cout << "Usage: infer_demo path/to/pptinypose_model_dir path/to/image "
|
||||
"run_option, "
|
||||
"e.g ./infer_demo ./pptinypose_model_dir ./test.jpeg 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;
|
||||
}
|
||||
Reference in New Issue
Block a user