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This commit is contained in:
Hu Chuqi
2023-01-06 09:34:28 +08:00
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69
examples/vision/keypointdetection/det_keypoint_unite/cpp/README.md
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@@ -1,53 +1,53 @@
# PP-PicoDet + PP-TinyPose (Pipeline) C++部署示例
English | [简体中文](README_CN.md)
# PP-PicoDet + PP-TinyPose (Pipeline) C++ Deployment Example
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](../../tiny_pose/cpp/README.md)
This directory provides the `Multi-person keypoint detection in a single image` example that `det_keypoint_unite_infer.cc` fast finishes the deployment of multi-person detection model PP-PicoDet + PP-TinyPose on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
在部署前，需确认以下两个步骤
Before deployment, two steps require confirmation
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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0.7.0以上(x.x.x>=0.7.0)
Taking the inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.
```bash
mkdir build
cd build
# 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
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j
# 下载PP-TinyPose和PP-PicoDet模型文件和测试图片
# Download PP-TinyPose+PP-PicoDet model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_TinyPose_256x192_infer.tgz
tar -xvf PP_TinyPose_256x192_infer.tgz
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_PicoDet_V2_S_Pedestrian_320x320_infer.tgz
tar -xvf PP_PicoDet_V2_S_Pedestrian_320x320_infer.tgz
wget https://bj.bcebos.com/paddlehub/fastdeploy/000000018491.jpg
# CPU推理
# CPU inference
./infer_demo PP_PicoDet_V2_S_Pedestrian_320x320_infer PP_TinyPose_256x192_infer 000000018491.jpg 0
# GPU推理
# GPU inference
./infer_demo PP_PicoDet_V2_S_Pedestrian_320x320_infer PP_TinyPose_256x192_infer 000000018491.jpg 1
# GPU上TensorRT推理
# TensorRT inference on GPU
./infer_demo PP_PicoDet_V2_S_Pedestrian_320x320_infer PP_TinyPose_256x192_infer 000000018491.jpg 2
# 昆仑芯XPU推理
# kunlunxin XPU inference
./infer_demo PP_PicoDet_V2_S_Pedestrian_320x320_infer PP_TinyPose_256x192_infer 000000018491.jpg 3
```
运行完成可视化结果如下图所示
The visualized result after running is as follows
<div align="center">
<img src="https://user-images.githubusercontent.com/16222477/196393343-eeb6b68f-0bc6-4927-871f-5ac610da7293.jpeg", width=359px, height=423px />
</div>
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:
- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/cn/faq/use_sdk_on_windows.md)
## PP-TinyPose C++接口
## PP-TinyPose C++ Interface
### PP-TinyPose类
### PP-TinyPose Class
```c++
fastdeploy::pipeline::PPTinyPose(
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fastdeploy::vision::keypointdetection::PPTinyPose* pptinypose_model)
```
PPTinyPose Pipeline模型加载和初始化。
PPTinyPose Pipeline model loading and initialization.
**参数**
**Parameter**
> * **model_det_modelfile**(fastdeploy::vision::detection): 初始化后的检测模型，参考[PP-TinyPose](../../tiny_pose/README.md)
> * **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
> * **model_det_modelfile**(fastdeploy::vision::detection): Initialized detection model. Refer to [PP-TinyPose](../../tiny_pose/README.md)
> * **pptinypose_model**(fastdeploy::vision::keypointdetection): Initialized detection model [Detection](../../../detection/paddledetection/README.md). Currently only PaddleDetection series is available.
#### Predict函数
#### Predict Function
> ```c++
> PPTinyPose::Predict(cv::Mat* im, KeyPointDetectionResult* result)
> ```
>
> 模型预测接口，输入图像直接输出关键点检测结果。
> Model prediction interface. Input images and output keypoint detection results.
>
> **参数**
> **Parameter**
>
> > * **im**: 输入图像，注意需为HWC，BGR格式
> > * **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/)
> > * **im**: Input images in HWC or BGR format
> > * **result**: Keypoint detection results, including coordinates and the corresponding probability value. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of KeyPointDetectionResult
### 类成员属性
#### 后处理参数
> > * **detection_model_score_threshold**(bool):
输入PP-TinyPose模型前，Detectin模型过滤检测框的分数阈值
### Class Member Property
#### Post-processing Parameter
> > * **detection_model_score_threshold**(bool): Score threshold of the Detectin model for filtering detection boxes before entering the PP-TinyPose model
- [模型介绍](../../)
- [Python部署](../python)
- [视觉模型预测结果](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
- [Model Description](../../)
- [Python Deployment](../python)
- [Vision Model Prediction Results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)