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[Doc]Add English version of documents in examples (#1070)
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Hu Chuqi
@@ -1,70 +1,71 @@
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# PFLD Python部署示例
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English | [简体中文](README_CN.md)
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# PFLD Python Deployment Example
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在部署前，需确认以下两个步骤
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Before deployment, two steps require confirmation
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- 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)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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本目录下提供`infer.py`快速完成PFLD在CPU/GPU，以及GPU上通过TensorRT加速部署的示例，保证 FastDeploy 版本 >= 0.6.0 支持PFLD模型。执行如下脚本即可完成
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This directory provides examples that `infer.py` fast finishes the deployment of PFLD on CPU/GPU and GPU accelerated by TensorRT. FastDeploy version 0.6.0 or above is required to support this model. The script is as follows
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```bash
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#下载部署示例代码
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# Download deployment example code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/facealign/pfld/python
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# 下载PFLD模型文件和测试图片以及视频
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## 原版ONNX模型
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# Download the PFLD model files, test images, and videos
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## Original ONNX Model
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wget https://bj.bcebos.com/paddlehub/fastdeploy/pfld-106-lite.onnx
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wget https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png
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# CPU推理
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# CPU inference
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python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device cpu
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# GPU推理
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# GPU inference
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python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device gpu
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# TRT推理
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# TRT inference
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python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device gpu --backend trt
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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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The visualized result after running is as follows
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<div width="500">
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<img width="470" height="384" float="left" src="https://user-images.githubusercontent.com/19977378/197931737-c2d8e760-a76d-478a-a6c9-4574fb5c70eb.png">
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</div>
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## PFLD Python接口
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## PFLD Python Interface
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```python
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fd.vision.facealign.PFLD(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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PFLD模型加载和初始化，其中model_file为导出的ONNX模型格式
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PFLD model loading and initialization, among which model_file is the exported ONNX model format
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **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
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为ONNX
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path. No need to set when the model is in ONNX format
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> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
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> * **model_format**(ModelFormat): Model format. ONNX format by default
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### predict函数
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### predict Parameter
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> ```python
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> PFLD.predict(input_image)
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> ```
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>
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> 模型预测结口，输入图像直接输出landmarks坐标结果。
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> Model prediction interface. Input images and output landmarks results directly
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>
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> **参数**
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> **Parameter**
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>
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> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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> > * **input_image**(np.ndarray): Input data in HWC or BGR format
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> **返回**
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> **Return**
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>
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> > 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/)
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> > Return `fastdeploy.vision.FaceAlignmentResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
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## 其它文档
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## Other Documents
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- [PFLD 模型介绍](..)
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- [PFLD C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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- [PFLD Model Description](..)
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- [PFLD C++ Deployment](../cpp)
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- [Model Prediction Results](../../../../../docs/api/vision_results/)
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- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)
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71
examples/vision/facealign/pfld/python/README_CN.md
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71
examples/vision/facealign/pfld/python/README_CN.md
Normal file
@@ -0,0 +1,71 @@
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[English](README.md) | 简体中文
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# PFLD Python部署示例
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在部署前，需确认以下两个步骤
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- 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)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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本目录下提供`infer.py`快速完成PFLD在CPU/GPU，以及GPU上通过TensorRT加速部署的示例，保证 FastDeploy 版本 >= 0.6.0 支持PFLD模型。执行如下脚本即可完成
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/facealign/pfld/python
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# 下载PFLD模型文件和测试图片以及视频
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## 原版ONNX模型
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wget https://bj.bcebos.com/paddlehub/fastdeploy/pfld-106-lite.onnx
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wget https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png
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# CPU推理
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python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device cpu
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# GPU推理
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python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device gpu
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# TRT推理
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python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device gpu --backend trt
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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 width="500">
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<img width="470" height="384" float="left" src="https://user-images.githubusercontent.com/19977378/197931737-c2d8e760-a76d-478a-a6c9-4574fb5c70eb.png">
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</div>
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## PFLD Python接口
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```python
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fd.vision.facealign.PFLD(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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PFLD模型加载和初始化，其中model_file为导出的ONNX模型格式
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **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
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为ONNX
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### predict函数
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> ```python
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> PFLD.predict(input_image)
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> ```
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>
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> 模型预测结口，输入图像直接输出landmarks坐标结果。
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>
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> **参数**
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>
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> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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> **返回**
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>
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> > 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/)
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## 其它文档
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- [PFLD 模型介绍](..)
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- [PFLD C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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