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Hu Chuqi
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examples/vision/detection/yolov5lite/python/README_CN.md
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examples/vision/detection/yolov5lite/python/README_CN.md
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[English](README.md) | 简体中文
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# YOLOv5Lite Python部署示例
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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. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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本目录下提供`infer.py`快速完成YOLOv5Lite在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vision/detection/yolov5lite/python/
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#下载YOLOv5Lite模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-g-sim-640.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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python infer.py --model v5Lite-g-sim-640.onnx --image 000000014439.jpg --device cpu
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# GPU推理
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python infer.py --model v5Lite-g-sim-640.onnx --image 000000014439.jpg --device gpu
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# GPU上使用TensorRT推理
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python infer.py --model v5Lite-g-sim-640.onnx --image 000000014439.jpg --device gpu --use_trt True
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```
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运行完成可视化结果如下图所示
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<img width="640" src="https://user-images.githubusercontent.com/67993288/184301943-263c8153-a52a-4533-a7c1-ee86d05d314b.jpg">
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## YOLOv5Lite Python接口
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```python
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fastdeploy.vision.detection.YOLOv5Lite(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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YOLOv5Lite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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**参数**
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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格式时，此参数无需设定
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为ONNX
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### predict函数
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> ```python
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> YOLOv5Lite.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
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> ```
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>
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> 模型预测结口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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> > * **conf_threshold**(float): 检测框置信度过滤阈值
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> > * **nms_iou_threshold**(float): NMS处理过程中iou阈值
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> **返回**
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>
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> > 返回`fastdeploy.vision.DetectionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
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### 类成员属性
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#### 预处理参数
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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
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> > * **size**(list[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**(list[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=True` 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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> > * **stride**(int): 配合`stris_mini_padide`成员变量使用, 默认值为`stride=32`
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## 其它文档
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- [YOLOv5Lite 模型介绍](..)
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- [YOLOv5Lite C++部署](../cpp)
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- [模型预测结果说明](../../../../../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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