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examples/vision/keypointdetection/det_keypoint_unite/python/README.md
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# PP-PicoDet + PP-TinyPose (Pipeline) Python部署示例
English | [简体中文](README_CN.md)
# PP-PicoDet + PP-TinyPose (Pipeline) Python Deployment Example
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)
本目录下提供`det_keypoint_unite_infer.py`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//python/README.md)
This directory provides the `Multi-person keypoint detection in a single image` example that `det_keypoint_unite_infer.py` 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
>> **Attention**: For standalone deployment of PP-TinyPose single model, refer to [PP-TinyPose Single Model](../../tiny_pose//python/README.md)
```bash
#下载部署示例代码
# Download the deployment example code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/keypointdetection/det_keypoint_unite/python
# 下载PP-TinyPose模型文件和测试图片
# Download PP-TinyPose 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
python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device cpu
# GPU推理
# GPU inference
python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device gpu
# 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）
# TensorRT inference on GPU （Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device gpu --use_trt True
# 昆仑芯XPU推理
# kunlunxin XPU inference
python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device kunlunxin
```
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=640px, height=427px />
</div>
## PPTinyPosePipeline Python接口
## PPTinyPosePipeline Python Interface
```python
fd.pipeline.PPTinyPose(det_model=None, pptinypose_model=None)
```
PPTinyPosePipeline模型加载和初始化，其中det_model是使用`fd.vision.detection.PicoDet`[参考Detection文档](../../../detection/paddledetection/python/)初始化的检测模型，pptinypose_model是使用`fd.vision.keypointdetection.PPTinyPose`[参考PP-TinyPose文档](../../tiny_pose/python/)初始化的检测模型
PPTinyPosePipeline model loading and initialization, among which the det_model is the detection model initialized by `fd.vision.detection.PicoDet`[Refer to Detection Document](../../../detection/paddledetection/python/) and pptinypose_model is the detection model initialized by `fd.vision.keypointdetection.PPTinyPose`[Refer to PP-TinyPose Document](../../tiny_pose/python/)
**参数**
**Parameter**
> * **det_model**(str): 初始化后的检测模型
> * **pptinypose_model**(str): 初始化后的PP-TinyPose模型
> * **det_model**(str): Initialized detection model
> * **pptinypose_model**(str): Initialized PP-TinyPose model
### predict函数
### predict function
> ```python
> PPTinyPosePipeline.predict(input_image)
> ```
>
> 模型预测结口，输入图像直接输出检测结果。
> Model prediction interface. Input images and output keypoint detection results.
>
> **参数**
> **Parameter**
>
> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
> > * **input_image**(np.ndarray): Input data in HWC or BGR format
> **返回**
> **Return**
>
> > 返回`fastdeploy.vision.KeyPointDetectionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
> > Return `fastdeploy.vision.KeyPointDetectionResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
### 类成员属性
#### 后处理参数
### Class Member Property
#### Post-processing Parameter
> > * **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
Score threshold of the Detectin model for filtering detection boxes before entering the PP-TinyPose model
## 其它文档
## Other Documents
- [Pipeline 模型介绍](..)
- [Pipeline C++部署](../cpp)
- [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)
- [Pipeline Model Description](..)
- [Pipeline C++ Deployment](../cpp)
- [Model Prediction Results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)

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[English](README.md) | 简体中文
# PP-PicoDet + PP-TinyPose (Pipeline) Python部署示例
在部署前，需确认以下两个步骤
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
本目录下提供`det_keypoint_unite_infer.py`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//python/README.md)
```bash
#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/keypointdetection/det_keypoint_unite/python
# 下载PP-TinyPose模型文件和测试图片
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
python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device cpu
# GPU推理
python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device gpu
# 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）
python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device gpu --use_trt True
# 昆仑芯XPU推理
python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device kunlunxin
```
运行完成可视化结果如下图所示
<div align="center">
<img src="https://user-images.githubusercontent.com/16222477/196393343-eeb6b68f-0bc6-4927-871f-5ac610da7293.jpeg", width=640px, height=427px />
</div>
## PPTinyPosePipeline Python接口
```python
fd.pipeline.PPTinyPose(det_model=None, pptinypose_model=None)
```
PPTinyPosePipeline模型加载和初始化，其中det_model是使用`fd.vision.detection.PicoDet`[参考Detection文档](../../../detection/paddledetection/python/)初始化的检测模型，pptinypose_model是使用`fd.vision.keypointdetection.PPTinyPose`[参考PP-TinyPose文档](../../tiny_pose/python/)初始化的检测模型
**参数**
> * **det_model**(str): 初始化后的检测模型
> * **pptinypose_model**(str): 初始化后的PP-TinyPose模型
### predict函数
> ```python
> PPTinyPosePipeline.predict(input_image)
> ```
>
> 模型预测结口，输入图像直接输出检测结果。
>
> **参数**
>
> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
> **返回**
>
> > 返回`fastdeploy.vision.KeyPointDetectionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
### 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
## 其它文档
- [Pipeline 模型介绍](..)
- [Pipeline C++部署](../cpp)
- [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)