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examples/vision/facealign/pipnet/python/README.md
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@@ -3,8 +3,8 @@ English | [简体中文](README_CN.md)
Before deployment, two steps require confirmation
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
This directory provides examples that `infer.py` fast finishes the deployment of PIPNet on CPU/GPU and GPU accelerated by TensorRT. FastDeploy version 0.7.0 or above is required to support this model. The script is as follows
@@ -69,4 +69,4 @@ PIPNet model loading and initialization, among which model_file is the exported
- [PIPNet Model Description](..)
- [PIPNet 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)
- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)

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examples/vision/facealign/pipnet/python/README_CN.md
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[English](README.md) | 简体中文
# PIPNet 模型部署
## 模型版本说明
# PIPNet Python部署示例
- [PIPNet](https://github.com/jhb86253817/PIPNet/tree/b9eab58)
在部署前，需确认以下两个步骤
## 支持模型列表
- 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. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
目前FastDeploy支持如下模型的部署
本目录下提供`infer.py`快速完成PIPNet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例，保证 FastDeploy 版本 >= 0.7.0 支持PIPNet模型。执行如下脚本即可完成
- [PIPNet 模型](https://github.com/jhb86253817/PIPNet)
```bash
#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/facealign/pipnet/python
## 下载预训练模型
# 下载PIPNet模型文件和测试图片以及视频
## 原版ONNX模型
wget https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet18_10x19x32x256_aflw.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png
为了方便开发者的测试，下面提供了PIPNet导出的各系列模型，开发者可直接下载使用。
# CPU推理
python infer.py --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device cpu
# GPU推理
python infer.py --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device gpu
# TRT推理
python infer.py --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device gpu --backend trt
```
| 模型 | 参数大小 | 精度 | 备注 |
|:---------------------------------------------------------------- |:----- |:----- | :------ |
| [PIPNet19_ResNet18_AFLW](https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet18_10x19x32x256_aflw.onnx) | 45.6M | - |
| [PIPNet29_ResNet18_COFW](https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet18_10x29x32x256_cofw.onnx) | 46.1M | - |
| [PIPNet68_ResNet18_300W](https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet18_10x68x32x256_300w.onnx) | 47.9M | - |
| [PIPNet98_ResNet18_WFLW](https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet18_10x98x32x256_wflw.onnx) | 49.3M | - |
| [PIPNet19_ResNet101_AFLW](https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet101_10x19x32x256_aflw.onnx) | 173.4M | - |
| [PIPNet29_ResNet101_COFW](https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet101_10x29x32x256_cofw.onnx) | 175.3M | - |
| [PIPNet68_ResNet101_300W](https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet101_10x68x32x256_300w.onnx) | 182.6M | - |
| [PIPNet98_ResNet101_WFLW](https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet101_10x98x32x256_wflw.onnx) | 188.3M | - |
运行完成可视化结果如下图所示
<div width="500">
<img width="470" height="384" float="left" src="https://user-images.githubusercontent.com/67993288/200761400-08491112-56c3-470f-87ac-87be805d5658.jpg">
</div>
## PIPNet Python接口
```python
fd.vision.facealign.PIPNet(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
```
PIPNet模型加载和初始化，其中model_file为导出的ONNX模型格式
**参数**
> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径，当模型格式为ONNX格式时，此参数无需设定
> * **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ONNX
### predict函数
> ```python
> PIPNet.predict(input_image)
> ```
>
> 模型预测结口，输入图像直接输出landmarks坐标结果。
>
> **参数**
>
> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
> **返回**
>
> > 返回`fastdeploy.vision.FaceAlignmentResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
## 其它文档
## 详细部署文档
- [Python部署](python)
- [C++部署](cpp)
- [PIPNet 模型介绍](..)
- [PIPNet 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)