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[Doc] Add docs for ppocr ppseg examples (#1429)
* add docs for examples * add english doc * fix * fix docs
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chenjian
162
examples/vision/detection/yolov5/c/README.md
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examples/vision/detection/yolov5/c/README.md
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English | [简体中文](README_CN.md)
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# YOLOv5 C Deployment Example
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This directory provides `infer.c` to finish the deployment of YOLOv5 on CPU/GPU.
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Before deployment, two steps require confirmation
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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Taking inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.4 or above (x.x.x>=1.0.4) is required to support this model.
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```bash
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# 1. # Download the YOLOv5 model file and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# CPU inference
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./infer_demo yolov5s.onnx 000000014439.jpg 0
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# GPU inference
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./infer_demo yolov5s.onnx 000000014439.jpg 1
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```
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The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:
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- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/en/faq/use_sdk_on_windows.md)
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## YOLOv5 C Interface
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### RuntimeOption
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```c
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FD_C_RuntimeOptionWrapper* FD_C_CreateRuntimeOptionWrapper()
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```
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> Create a RuntimeOption object, and return a pointer to manipulate it.
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>
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> **Return**
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>
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> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
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```c
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void FD_C_RuntimeOptionWrapperUseCpu(
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FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper)
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```
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> Enable Cpu inference.
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>
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> **Params**
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>
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> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
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```c
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void FD_C_RuntimeOptionWrapperUseGpu(
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FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
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int gpu_id)
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```
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> Enable Gpu inference.
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>
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> **Params**
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>
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> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
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> * **gpu_id**(int): gpu id
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### Model
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```c
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FD_C_YOLOv5Wrapper* FD_C_CreateYOLOv5Wrapper(
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const char* model_file, const char* params_file, const char* config_file,
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FD_C_RuntimeOptionWrapper* runtime_option,
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const FD_C_ModelFormat model_format)
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```
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> Create a YOLOv5 model object, and return a pointer to manipulate it.
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>
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> **Params**
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>
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path，when model format is onnx，this can be empty string
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> * **runtime_option**(FD_C_RuntimeOptionWrapper*): Backend inference configuration. None by default, which is the default configuration
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> * **model_format**(FD_C_ModelFormat): Model format.
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>
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> **Return**
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> * **fd_c_yolov5_wrapper**(FD_C_YOLOv5Wrapper*): Pointer to manipulate YOLOv5 object.
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#### Read and write image
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```c
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FD_C_Mat FD_C_Imread(const char* imgpath)
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```
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> Read an image, and return a pointer to cv::Mat.
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>
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> **Params**
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>
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> * **imgpath**(const char*): image path
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>
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> **Return**
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>
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> * **imgmat**(FD_C_Mat): pointer to cv::Mat object which holds the image.
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```c
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FD_C_Bool FD_C_Imwrite(const char* savepath, FD_C_Mat img);
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```
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> Write image to a file.
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>
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> **Params**
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>
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> * **savepath**(const char*): save path
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> * **img**(FD_C_Mat): pointer to cv::Mat object
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>
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> **Return**
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>
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> * **result**(FD_C_Bool): bool to indicate success or failure
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#### Prediction
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```c
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FD_C_Bool FD_C_YOLOv5WrapperPredict(
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__fd_take FD_C_YOLOv5Wrapper* fd_c_yolov5_wrapper, FD_C_Mat img,
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FD_C_DetectionResult* fd_c_detection_result)
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```
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>
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> Predict an image, and generate detection result.
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>
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> **Params**
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> * **fd_c_yolov5_wrapper**(FD_C_YOLOv5Wrapper*): Pointer to manipulate YOLOv5 object.
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> * **img**（FD_C_Mat）: pointer to cv::Mat object, which can be obained by FD_C_Imread interface
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> * **fd_c_detection_result**FD_C_DetectionResult*): Detection result, including detection box and confidence of each box. Refer to [Vision Model Prediction Result](../../../../../docs/api/vision_results/) for DetectionResults
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#### Result
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```c
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FD_C_Mat FD_C_VisDetection(FD_C_Mat im, FD_C_DetectionResult* fd_detection_result,
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float score_threshold, int line_size, float font_size);
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```
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>
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> Visualize detection results and return visualization image.
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>
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> **Params**
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> * **im**(FD_C_Mat): pointer to input image
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> * **fd_detection_result**(FD_C_DetectionResult*): pointer to C DetectionResult structure
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> * **score_threshold**(float): score threshold
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> * **line_size**(int): line size
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> * **font_size**(float): font size
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>
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> **Return**
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> * **vis_im**(FD_C_Mat): pointer to visualization image.
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- [Model Description](../../)
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- [Python Deployment](../python)
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- [Vision Model prediction results](../../../../../docs/api/vision_results/)
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- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
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165
examples/vision/detection/yolov5/c/README_CN.md
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165
examples/vision/detection/yolov5/c/README_CN.md
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[English](README.md) | 简体中文
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# YOLOv5 C 部署示例
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本目录下提供`infer.c`来调用C API快速完成YOLOv5模型在CPU/GPU上部署的示例。
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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. 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)
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以Linux上推理为例，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1.0.4以上(x.x.x>=1.0.4)
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```bash
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# 1. 下载官方转换好的 yolov5 ONNX 模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# CPU推理
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./infer_demo yolov5s.onnx 000000014439.jpg 0
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# GPU推理
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./infer_demo yolov5s.onnx 000000014439.jpg 1
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```
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以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
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- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
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- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
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## YOLOv5 C API接口
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### 配置
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```c
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FD_C_RuntimeOptionWrapper* FD_C_CreateRuntimeOptionWrapper()
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```
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> 创建一个RuntimeOption的配置对象，并且返回操作它的指针。
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>
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> **返回**
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>
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> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption对象的指针
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```c
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void FD_C_RuntimeOptionWrapperUseCpu(
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FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper)
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```
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> 开启CPU推理
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>
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> **参数**
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>
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> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption对象的指针
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```c
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void FD_C_RuntimeOptionWrapperUseGpu(
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FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
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int gpu_id)
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```
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> 开启GPU推理
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>
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> **参数**
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>
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> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption对象的指针
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> * **gpu_id**(int): 显卡号
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### 模型
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```c
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FD_C_YOLOv5Wrapper* FD_C_CreateYOLOv5Wrapper(
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const char* model_file, const char* params_file, const char* config_file,
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FD_C_RuntimeOptionWrapper* runtime_option,
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const FD_C_ModelFormat model_format)
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```
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> 创建一个YOLOv5的模型，并且返回操作它的指针。
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>
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> **参数**
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>
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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时，此参数传入空字符串即可
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> * **runtime_option**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption的指针，表示后端推理配置
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> * **model_format**(FD_C_ModelFormat): 模型格式
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>
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> **返回**
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> * **fd_c_yolov5_wrapper**(FD_C_YOLOv5Wrapper*): 指向YOLOv5模型对象的指针
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#### 读写图像
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```c
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FD_C_Mat FD_C_Imread(const char* imgpath)
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```
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> 读取一个图像，并且返回cv::Mat的指针。
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>
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> **参数**
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>
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> * **imgpath**(const char*): 图像文件路径
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>
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> **返回**
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>
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> * **imgmat**(FD_C_Mat): 指向图像数据cv::Mat的指针。
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```c
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FD_C_Bool FD_C_Imwrite(const char* savepath, FD_C_Mat img);
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```
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> 将图像写入文件中。
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>
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> **参数**
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>
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> * **savepath**(const char*): 保存图像的路径
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> * **img**(FD_C_Mat): 指向图像数据的指针
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>
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> **返回**
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>
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> * **result**(FD_C_Bool): 表示操作是否成功
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#### Predict函数
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```c
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FD_C_Bool FD_C_YOLOv5WrapperPredict(
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__fd_take FD_C_YOLOv5Wrapper* fd_c_yolov5_wrapper, FD_C_Mat img,
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FD_C_DetectionResult* fd_c_detection_result)
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```
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>
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> 模型预测接口，输入图像直接并生成检测结果。
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>
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> **参数**
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> * **fd_c_yolov5_wrapper**(FD_C_YOLOv5Wrapper*): 指向YOLOv5模型的指针
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> * **img**（FD_C_Mat）: 输入图像的指针，指向cv::Mat对象，可以调用FD_C_Imread读取图像获取
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> * **fd_c_detection_result**FD_C_DetectionResult*): 指向检测结果的指针，检测结果包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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#### Predict结果
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```c
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FD_C_Mat FD_C_VisDetection(FD_C_Mat im, FD_C_DetectionResult* fd_detection_result,
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float score_threshold, int line_size, float font_size);
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```
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>
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> 对检测结果进行可视化，返回可视化的图像。
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>
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> **参数**
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> * **im**(FD_C_Mat): 指向输入图像的指针
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> * **fd_detection_result**(FD_C_DetectionResult*): 指向FD_C_DetectionResult结构的指针
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> * **score_threshold**(float): 检测阈值
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> * **line_size**(int): 检测框线大小
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> * **font_size**(float): 检测框字体大小
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>
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> **返回**
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> * **vis_im**(FD_C_Mat): 指向可视化图像的指针
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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../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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