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[Doc] Add English version of some documents (#1221)
* Update README_CN.md * Create README.md * Update README.md * Create README_CN.md * Update README.md * Update README_CN.md * Update README_CN.md * Create README.md * Update README.md * Update README_CN.md * Create README.md * Update README.md * Update README_CN.md * Rename examples/vision/faceid/insightface/rknpu2/cpp/README.md to examples/vision/faceid/insightface/rknpu2/README_EN.md * Rename README_CN.md to README.md * Rename README.md to README_EN.md * Rename README.md to README_CN.md * Rename README_EN.md to README.md * Create build.md * Create environment.md * Create issues.md * Update build.md * Update environment.md * Update issues.md * Update build.md * Update environment.md * Update issues.md
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Hu Chuqi
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examples/vision/detection/yolov8/cpp/README.md
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English | [简体中文](README_CN.md)
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# YOLOv8 C++ Deployment Example
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This directory provides the example that `infer.cc` fast finishes the deployment of YOLOv8 on CPU/GPU and GPU through TensorRT.
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Two steps before deployment
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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. Download the precompiled deployment library and samples code based on your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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Taking the CPU inference on Linux as an example, FastDeploy version 1.0.3 or above (x.x.x>=1.0.3) is required to support this model.
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```bash
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mkdir build
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cd build
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# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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# 1. Download the official converted YOLOv8 ONNX model files and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov8s.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 yolov8s.onnx 000000014439.jpg 0
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# GPU inference
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./infer_demo yolov8s.onnx 000000014439.jpg 1
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# TensorRT inference on GPU
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./infer_demo yolov8s.onnx 000000014439.jpg 2
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```
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The visualized result is as follows
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<img width="640" src="https://user-images.githubusercontent.com/67993288/184309358-d803347a-8981-44b6-b589-4608021ad0f4.jpg">
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he above command works for Linux or MacOS. For SDK in Windows, refer to:
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- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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If you use Huawei Ascend NPU deployment, refer to the following document to initialize the deployment environment:
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- [How to use Huawei Ascend NPU deployment](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
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## YOLOv8 C++ Interface
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### YOLOv8
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```c++
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fastdeploy::vision::detection::YOLOv8(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::ONNX)
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```
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YOLOv8 model loading and initialization, among which model_file is the exported ONNX model format
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**Parameter**
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path. Merely passing an empty string 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 function
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> ```c++
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> YOLOv8::Predict(cv::Mat* im, DetectionResult* result)
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> ```
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>
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> Model prediction interface. Input images and output detection results
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>
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> **Parameter**
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>
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> > * **im**: Input images in HWC or BGR format
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> > * **result**: Detection results, including detection box and confidence of each box. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for DetectionResult.
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### Class Member Variable
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#### Pre-processing Parameter
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Users can modify the following preprocessing parameters based on actual needs to change the final inference and deployment results
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> > * **size**(vector<int>): This parameter changes the resize used during preprocessing, containing two integer elements for [width, height] with default value [640, 640]
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> > * **padding_value**(vector<float>): This parameter is used to change the padding value of images during resize, containing three floating-point elements that represent the value of three channels. Default value [114, 114, 114]
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> > * **is_no_pad**(bool): Specify whether to resize the image through padding. `is_no_pad=ture` represents no paddling. Default `is_no_pad=false`
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> > * **is_mini_pad**(bool): This parameter sets the width and height of the image after resize to the value nearest to the `size` member variable and to the point where the padded pixel size is divisible by the `stride` member variable. Default `is_mini_pad=false`
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> > * **stride**(int): Used with the `stris_mini_pad` member variable. Default `stride=32`
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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 backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)
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@@ -81,7 +81,7 @@ YOLOv8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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> > * **size**(vector<int>): 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[640, 640]
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> > * **padding_value**(vector<float>): 通过此参数可以修改图片在resize时候做填充(padding)的值, 包含三个浮点型元素, 分别表示三个通道的值, 默认值为[114, 114, 114]
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> > * **is_no_pad**(bool): 通过此参数让图片是否通过填充的方式进行resize, `is_no_pad=ture` 表示不使用填充的方式，默认值为`is_no_pad=false`
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> > * **is_mini_pad**(bool): 通过此参数可以将resize之后图像的宽高这是为最接近`size`成员变量的值, 并且满足填充的像素大小是可以被`stride`成员变量整除的。默认值为`is_mini_pad=false`
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> > * **is_mini_pad**(bool): 通过此参数可以将resize之后图像的宽高设置为最接近`size`成员变量的值, 并且满足填充的像素大小是可以被`stride`成员变量整除的。默认值为`is_mini_pad=false`
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> > * **stride**(int): 配合`stris_mini_pad`成员变量使用, 默认值为`stride=32`
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- [模型介绍](../../)
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