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Hu Chuqi
2023-01-06 09:34:28 +08:00
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69
examples/vision/headpose/fsanet/cpp/README.md
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# FSANet C++部署示例
English | [简体中文](README_CN.md)
# FSANet C++ Deployment Example
本目录下提供`infer.cc`快速完成FSANet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 FSANet 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1.0.2以上(x.x.x>=1.0.2), 或使用nightly built版本
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 1.0.2 or above (x.x.x>=1.0.2), or the nightly built version 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 FSANet 模型文件和测试图片
# Download the official converted FSANet model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/fsanet-var.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/headpose_input.png
# CPU推理
# CPU inference
./infer_demo --model fsanet-var.onnx --image headpose_input.png --device cpu
# GPU推理
# GPU inference
./infer_demo --model fsanet-var.onnx --image headpose_input.png --device gpu
# GPU上TensorRT推理
# TensorRT Inference on GPU
./infer_demo --model fsanet-var.onnx --image headpose_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="520">
<img width="500" height="514" float="left" src="https://user-images.githubusercontent.com/19977378/198279932-3eee424e-98a2-4249-bdeb-0f79127cbc9d.png">
</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)
## FSANet C++接口
## FSANet C++ Interface
### FSANet 类
### FSANet Class
```c++
fastdeploy::vision::headpose::FSANet(
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const RuntimeOption& runtime_option = RuntimeOption(),
const ModelFormat& model_format = ModelFormat::ONNX)
```
FSANet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
FSANet model loading and initialization, among which model_file is the exported ONNX model format.
**Parameter**
> * **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 Function
> ```c++
> FSANet::Predict(cv::Mat* im, HeadPoseResult* result)
> ```
>
> 模型预测接口，输入图像直接输出头部姿态预测结果。
> Model prediction interface. Input images and output head pose prediction results.
>
> **参数**
> **Parameter**
>
> > * **im**: 输入图像，注意需为HWC，BGR格式
> > * **result**: 头部姿态预测结果, HeadPoseResult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[112, 112]
> > * **im**: Input images in HWC or BGR format
> > * **result**: Head pose prediction results. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of HeadPoseResult
### Class Member Variable
Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
> > * **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 [112, 112]
- [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)