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[Doc]Add English version of documents in examples (#1070)

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This commit is contained in:
Hu Chuqi
2023-01-06 09:34:28 +08:00
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75
examples/vision/facedet/yolov7face/cpp/README.md
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@@ -1,55 +1,56 @@
# YOLOv7Face C++部署示例
English | [简体中文](README_CN.md)
# YOLOv7Face C++ Deployment Example
本目录下提供`infer.cc`快速完成YOLOv7Face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 YOLOv7Face 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
Taking the CPU inference on Linux as an example, the compilation test can be completed by executing the following command in this directory.
```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 # x.x.x > 1.0.2
tar xvf fastdeploy-linux-x64-x.x.x.tgz # x.x.x > 1.0.2
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x # x.x.x > 1.0.2
make -j
#下载官方转换好的YOLOv7Face模型文件和测试图片
# Download the official converted YOLOv7Face model files and test images
wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-lite-e.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-tiny-face.onnx
#使用yolov7-tiny-face.onnx模型
# CPU推理
# Use yolov7-tiny-face.onnx model
# CPU inference
./infer_demo yolov7-tiny-face.onnx test_lite_face_detector_3.jpg 0
# GPU推理
# GPU inference
./infer_demo yolov7-tiny-face.onnx test_lite_face_detector_3.jpg 1
# GPU上TensorRT推理
# TensorRT inference on GPU
./infer_demo yolov7-tiny-face.onnx test_lite_face_detector_3.jpg 2
#使用yolov7-lite-e.onnx模型
# CPU推理
# Use yolov7-lite-e.onnx model
# CPU inference
./infer_demo yolov7-lite-e.onnx test_lite_face_detector_3.jpg 0
# GPU推理
# GPU inference
./infer_demo yolov7-lite-e.onnx test_lite_face_detector_3.jpg 1
# GPU上TensorRT推理
# TensorRT inference on GPU
./infer_demo yolov7-lite-e.onnx test_lite_face_detector_3.jpg 2
```
运行完成可视化结果如下图所示
The visualized result after running is as follows
<img width="640" src="https://user-images.githubusercontent.com/49013063/206170111-843febb6-67d6-4c46-a121-d87d003bba21.jpg">
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)
## YOLOv7Face C++接口
## YOLOv7Face C++ Interface
### YOLOv7Face类
### YOLOv7Face Class
```c++
fastdeploy::vision::facedet::YOLOv7Face(
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const ModelFormat& model_format = ModelFormat::ONNX)
```
YOLOv7Face模型加载和初始化，其中model_file为导出的ONNX模型格式。
YOLOv7Face 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++
> YOLOv7Face::Predict(cv::Mat* im, FaceDetectionResult* result,
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> float nms_iou_threshold = 0.5)
> ```
>
> 模型预测接口，输入图像直接输出检测结果。
> Model prediction interface. Input images and output detection results.
>
> **参数**
> **Parameter**
>
> > * **im**: 输入图像，注意需为HWC，BGR格式
> > * **result**: 检测结果，包括检测框，各个框的置信度, FaceDetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
> > * **conf_threshold**: 检测框置信度过滤阈值
> > * **nms_iou_threshold**: NMS处理过程中iou阈值
> > * **im**: Input images in HWC or BGR format
> > * **result**: Detection results, including detection box and confidence of each box. Refer to [Vision Model Prediction Result](../../../../../docs/api/vision_results/) for FaceDetectionResult
> > * **conf_threshold**: Filtering threshold of detection box confidence
> > * **nms_iou_threshold**: iou threshold during NMS processing
- [模型介绍](../../)
- [Python部署](../python)
- [视觉模型预测结果](../../../../../docs/api/vision_results/)
- [Model Description](../../)
- [Python Deployment](../python)
- [Vision Model Prediction Results](../../../../../docs/api/vision_results/)