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72 lines
2.4 KiB
Markdown
72 lines
2.4 KiB
Markdown
# YOLOv7 API说明
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## Python API
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### YOLOv7类
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```
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fastdeploy.vision.wongkinyiu.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=fd.Frontend.ONNX)
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```
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YOLOv7模型加载和初始化，当model_format为`fd.Frontend.ONNX`时，只需提供model_file，如`yolov7.onnx`；当model_format为`fd.Frontend.PADDLE`时，则需同时提供model_file和params_file。
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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
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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**(Frontend): 模型格式
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#### predict函数
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> ```
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> YOLOv7.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
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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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示例代码参考[yolov7.py](./yolov7.py)
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## C++ API
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### YOLOv7类
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```
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fastdeploy::vision::wongkinyiu::YOLOv7(
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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 Frontend& model_format = Frontend::ONNX)
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```
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YOLOv7模型加载和初始化，当model_format为`Frontend::ONNX`时，只需提供model_file，如`yolov7.onnx`；当model_format为`Frontend::PADDLE`时，则需同时提供model_file和params_file。
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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
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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**(Frontend): 模型格式
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#### Predict函数
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> ```
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> YOLOv7::Predict(cv::Mat* im, DetectionResult* result,
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> float conf_threshold = 0.25,
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> float nms_iou_threshold = 0.5)
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> ```
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> 模型预测接口，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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> > * **im**: 输入图像，注意需为HWC，BGR格式
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> > * **result**: 检测结果，包括检测框，各个框的置信度
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> > * **conf_threshold**: 检测框置信度过滤阈值
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> > * **nms_iou_threshold**: NMS处理过程中iou阈值
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示例代码参考[cpp/yolov7.cc](cpp/yolov7.cc)
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## 其它API使用
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- [模型部署RuntimeOption配置](../../../docs/api/runtime_option.md)
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