DNN：Deep Neural Network 深度神经网络
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MatShape
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Backend（）
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Target（）
CPU/GPU
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blobFromImage(image,size,mean,scalefactor,swapRB,crop)
| image | input image (with 1-, 3- or 4-channels). |
| size | spatial size for output image |
| mean | scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true. |
| scalefactor | multiplier for image values. |
| swapRB | flag which indicates that swap first and last channels in 3-channel image is necessary. |
| crop | flag which indicates whether image will be cropped after resize or not |
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NMSBoxes(bboxes,scores,score_threshold,nms_threshold,indices,eta,top_k)
| bboxes | a set of bounding boxes to apply NMS. |
| scores | a set of corresponding confidences. |
| score_threshold | a threshold used to filter boxes by score. |
| nms_threshold | a threshold used in non maximum suppression. |
| indices | the kept indices of bboxes after NMS. |
| eta | a coefficient in adaptive threshold formula: nms_thresholdi+1=eta⋅nms_thresholdi. |
| top_k | if >0, keep at most top_k picked indices. |
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readNet()
| model | Binary file contains trained weights. The following file extensions are expected for models from different frameworks:
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| config | Text file contains network configuration. It could be a file with the following extensions:
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