将YoloV3输出转换为边界框，标签和置信度的坐标

Question

我运行YoloV3模型并获得检测-3个条目的字典：

1. “ detector / yolo-v3 / Conv_22 / BiasAdd / YoloRegion”：numpy.ndarray与 形状（1,255,52,52），
2. “ detector / yolo-v3 / Conv_6 / BiasAdd / YoloRegion”：numpy.ndarray与 形状（1,255,13,​​13），
3. “ detector / yolo-v3 / Conv_14 / BiasAdd / YoloRegion”：numpy.ndarray与 形状（1,255,26,26）。

我知道字典中的每个条目都是对象检测的其他大小。 Conv_22适用于小物体 Conv_14适用于中等对象 Conv_6适用于大物体

如何将字典输出转换为边界框，标签和置信度的坐标？

Answer 1

假设您使用python和opencv，

请在需要的地方找到以下带有注释的代码，以使用cv2.dnn模块提取输出。

net.setInput(blob)

layerOutputs = net.forward(ln)

boxes = []
confidences = []
classIDs = []
for output in layerOutputs:
# loop over each of the detections
    for detection in output:
        # extract the class ID and confidence (i.e., probability) of
        # the current object detection
        scores = detection[5:]
        classID = np.argmax(scores)
        confidence = scores[classID]

        # filter out weak predictions by ensuring the detected
        # probability is greater than the minimum probability
        if confidence > threshold:
            # scale the bounding box coordinates back relative to the
            # size of the image, keeping in mind that YOLO actually
            # returns the center (x, y)-coordinates of the bounding
            # box followed by the boxes' width and height
            box = detection[0:4] * np.array([W, H, W, H])
            (centerX, centerY, width, height) = box.astype("int")

            # use the center (x, y)-coordinates to derive the top and
            # and left corner of the bounding box
            x = int(centerX - (width / 2))
            y = int(centerY - (height / 2))

            # update our list of bounding box coordinates, confidences,
            # and class IDs
            boxes.append([x, y, int(width), int(height)])
            confidences.append(float(confidence))
            classIDs.append(classID)
idxs = cv2.dnn.NMSBoxes(boxes, confidences, confidence, threshold)
#results are stored in idxs,boxes,confidences,classIDs