Yolov5: Is there any way I can use yolov5 with opencv dnn

[chaUAV]

🚀 Feature

Is there any way I can use yolov5 with opencv dnn

[edurenye]

Yes @chaUAV it is possible, you need to export it using https://github.com/ultralytics/yolov5/blob/master/models/export.py, inside the file there is an usage example, then the model will be exported as an ONNX model and it can be imported in OpenCV using cv2.dnn.readNetFromONNX(model_path)

Or at least this is the supposed way, I found this issue doing that: #250

[glenn-jocher]

@chaUAV @edurenye I've added a pinned documentation issue now at the top of https://github.com/ultralytics/yolov5/issues for this, hopefully this will help everyone to understand the basic functionality.

The INT64's remain one mystery among many in the export process though.

[edurenye]

Thanks @glenn-jocher I think it's the labels but I need to test, and I'm having some problems with Docker and me trying to update the nvidia drivers to 450, so it might take me a while.

[chaUAV]

Thanks you guys, I had already export to onnx and use it with opencv but I got the same error as #250 is there anythings i could do to fix it? @glenn-jocher

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[MohamedAliRashad]

This issue needs to be reopened @glenn-jocher
we need an alternative way to the onnx method.

[glenn-jocher]

@MohamedAliRashad sure. Are you trying to export an official YOLOv5 model for use with opencv? I can provide versions of these in ONNX format with outputs structured correctly, but they will lack NMS functionality. Is there a way to append an NMS module in ONNX?

[MohamedAliRashad]

@glenn-jocher
I was thinking about readNetFromDarkNet like the previous versions of YOLO

[glenn-jocher]

@MohamedAliRashad sorry I've just never used opencv dnn. Can you provide demo code for how this would work ideally? As I said I can provide fully functional exports in all supported formats for COCO and VOC trained YOLOv5 models. What format do you need it in exactly, and how is NMS handled?

[MohamedAliRashad]

@glenn-jocher it's quite simple actually.
First, you read the model weights and configuration to construct the network
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)

then, we infer an input

blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
detections = net.forward(ln)

And finally we run thresholds for filtration with a code like this

boxes = []
confidences = []
classIDs = []
for output in detections:
    for detection in output:
        scores = detection[5:]
        classID = np.argmax(scores)
        confidence = scores[classID]
        if confidence > args["confidence"]:
            # W, H are the dimensions of the input image
            box = detection[0:4] * np.array([W, H, W, H])
            (centerX, centerY, width, height) = box.astype("int")
            x = int(centerX - (width / 2))
            y = int(centerY - (height / 2))
            boxes.append([x, y, int(width), int(height)])
            confidences.append(float(confidence))
            classIDs.append(classID)
idxs = cv2.dnn.NMSBoxes(boxes, confidences, confidence, threshold)