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examples/vision/keypointdetection/tiny_pose/python/README.md
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# PP-TinyPose Python部署示例
English | [简体中文](README_CN.md)
# PP-TinyPose 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`pptinypose_infer.py`快速完成PP-TinyPose在CPU/GPU，以及GPU上通过TensorRT加速部署的`单图单人关键点检测`示例。执行如下脚本即可完成
>> **注意**: PP-Tinypose单模型目前只支持单图单人关键点检测，因此输入的图片应只包含一个人或者进行过裁剪的图像。多人关键点检测请参考[PP-TinyPose Pipeline](../../det_keypoint_unite/python/README.md)
This directory provides the `Multi-person keypoint detection in a single image` example that `pptinypose_infer.py` fast finishes the deployment of PP-TinyPose on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
>> **Attention**: single model currently only supports single-person keypoint detection in a single image. Therefore, the input image should contain one person only or should be cropped. For multi-person keypoint detection, refer to [PP-TinyPose Pipeline](../../det_keypoint_unite/python/README.md)
```bash
#下载部署示例代码
# Download the example code for deployment
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/keypointdetection/tiny_pose/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/hrnet_demo.jpg
# CPU推理
# CPU inference
python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device cpu
# GPU推理
# GPU inference
python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.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 pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device gpu --use_trt True
# 昆仑芯XPU推理
# KunlunXin XPU inference
python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.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/16222477/196386764-dd51ad56-c410-4c54-9580-643f282f5a83.jpeg", width=359px, height=423px />
</div>
## PP-TinyPose Python接口
## PP-TinyPose Python Interface
```python
fd.vision.keypointdetection.PPTinyPose(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
```
PP-TinyPose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/PaddleDetection/blob/release/2.5/deploy/EXPORT_MODEL.md)
PP-TinyPose model loading and initialization, among which model_file, params_file, and config_file are the Paddle inference files exported from the training model. Refer to [Model Export](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/deploy/EXPORT_MODEL.md) for more information
**参数**
**Parameter**
> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径
> * **config_file**(str): 推理部署配置文件
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
> * **model_file**(str): Model file path
> * **params_file**(str): Parameter file path
> * **config_file**(str): Inference deployment configuration file
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
> * **model_format**(ModelFormat): Model format. Paddle format by default
### predict函数
### predict function
> ```python
> PPTinyPose.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 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
用户可按照自己的实际需求，修改下列后处理参数，从而影响最终的推理和部署效果
### Class Member Property
#### Post-processing Parameter
Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
> > * **use_dark**(bool): 是否使用DARK进行后处理[参考论文](https://arxiv.org/abs/1910.06278)
> > * **use_dark**(bool): • Whether to use DARK for post-processing. Refer to [Reference Paper](https://arxiv.org/abs/1910.06278)
## 其它文档
## Other Documents
- [PP-TinyPose 模型介绍](..)
- [PP-TinyPose 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)
- [PP-TinyPose Model Description](..)
- [PP-TinyPose 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-TinyPose 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`pptinypose_infer.py`快速完成PP-TinyPose在CPU/GPU，以及GPU上通过TensorRT加速部署的`单图单人关键点检测`示例。执行如下脚本即可完成
>> **注意**: PP-Tinypose单模型目前只支持单图单人关键点检测，因此输入的图片应只包含一个人或者进行过裁剪的图像。多人关键点检测请参考[PP-TinyPose Pipeline](../../det_keypoint_unite/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/tiny_pose/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/hrnet_demo.jpg
# CPU推理
python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device cpu
# GPU推理
python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.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 pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device gpu --use_trt True
# 昆仑芯XPU推理
python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.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/16222477/196386764-dd51ad56-c410-4c54-9580-643f282f5a83.jpeg", width=359px, height=423px />
</div>
## PP-TinyPose Python接口
```python
fd.vision.keypointdetection.PPTinyPose(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
```
PP-TinyPose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/PaddleDetection/blob/release/2.5/deploy/EXPORT_MODEL.md)
**参数**
> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径
> * **config_file**(str): 推理部署配置文件
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
### predict函数
> ```python
> PPTinyPose.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
用户可按照自己的实际需求，修改下列后处理参数，从而影响最终的推理和部署效果
> > * **use_dark**(bool): 是否使用DARK进行后处理[参考论文](https://arxiv.org/abs/1910.06278)
## 其它文档
- [PP-TinyPose 模型介绍](..)
- [PP-TinyPose 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)