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examples/vision/facealign/pipnet/cpp/README.md
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# PIPNet C++部署示例
English | [简体中文](README_CN.md)
# PIPNet C++ Deployment Example
本目录下提供`infer.cc`快速完成PIPNet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
This directory provides examples that `infer.cc` fast finishes the deployment of PIPNet on CPU/GPU and GPU accelerated by TensorRT.
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CPU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 CPU 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
#下载官方转换好的 PIPNet 模型文件和测试图片
# Download the official converted PIPNet model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet18_10x19x32x256_aflw.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png
# CPU推理
# CPU inference
./infer_demo --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device cpu
# GPU推理
# GPU inference
./infer_demo --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device gpu
# GPU上TensorRT推理
# TensorRT inference on GPU
./infer_demo --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device gpu --backend trt
```
运行完成可视化结果如下图所示
The visualized result after running is as follows
<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>
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)
## PIPNet C++接口
## PIPNet C++ Interface
### PIPNet 类
### PIPNet Class
```c++
fastdeploy::vision::facealign::PIPNet(
@@ -51,35 +52,35 @@ fastdeploy::vision::facealign::PIPNet(
const ModelFormat& 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。
PIPNet model loading and initialization, among which model_file is the exported ONNX model format.
**参数**
**Parameter**
> * **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时，此参数传入空字符串即可
> * **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格式
> * **model_file**(str): Model file path
> * **params_file**(str): Parameter file path. Only passing an empty string when the model is in ONNX format
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
> * **model_format**(ModelFormat): Model format. ONNX format by default
#### Predict函数
#### Predict Function
> ```c++
> PIPNet::Predict(cv::Mat* im, FaceAlignmentResult* result)
> ```
>
> 模型预测接口，输入图像直接输出landmarks结果。
> Model prediction interface. Input images and output landmarks results.
>
> **参数**
> **Parameter**
>
> > * **im**: 输入图像，注意需为HWC，BGR格式
> > * **result**: landmarks结果, FaceAlignmentResult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**: landmarks result. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for FaceAlignmentResult
### 类成员变量
### Class Member Variable
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
> > * **size**(vector&lt;int&gt;): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[256, 256]
> > * **size**(vector&lt;int&gt;): This parameter changes the size of the resize used during preprocessing, containing two integer elements for [width, height] with default value [256, 256]
- [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)

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[English](README.md) | 简体中文
# PIPNet C++部署示例
本目录下提供`infer.cc`快速完成PIPNet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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)
以Linux上CPU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)
```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
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
#下载官方转换好的 PIPNet 模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/pipnet_resnet18_10x19x32x256_aflw.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png
# CPU推理
./infer_demo --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device cpu
# GPU推理
./infer_demo --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device gpu
# GPU上TensorRT推理
./infer_demo --model pipnet_resnet18_10x19x32x256_aflw.onnx --image facealign_input.png --device gpu --backend trt
```
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>
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)
## PIPNet C++接口
### PIPNet 类
```c++
fastdeploy::vision::facealign::PIPNet(
const string& model_file,
const string& params_file = "",
const RuntimeOption& runtime_option = RuntimeOption(),
const ModelFormat& 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时，此参数传入空字符串即可
> * **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格式
#### Predict函数
> ```c++
> PIPNet::Predict(cv::Mat* im, FaceAlignmentResult* result)
> ```
>
> 模型预测接口，输入图像直接输出landmarks结果。
>
> **参数**
>
> > * **im**: 输入图像，注意需为HWC，BGR格式
> > * **result**: landmarks结果, FaceAlignmentResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
### 类成员变量
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
> > * **size**(vector&lt;int&gt;): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[256, 256]
- [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)