安装好环境后，运行代码出错

[alittlenico]

outs = net.forward(getOutputsNames(net))

File "F:\刘毅\研究生\机器学习\python\yolov3_with_opencv\object_detection_yolo.py", line 42, in getOutputsNames return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()] File "F:\刘毅\研究生\机器学习\python\yolov3_with_opencv\object_detection_yolo.py", line 42, in return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()] IndexError: invalid index to scalar variable.

[iicannotdothis]

我也是，有什么解决办法没

[Edwin-Kevin]

There exists some issues in the code, here is the part fixed by me.

def getOutputsNames(net):
    # Get the names of all the layers in the network
    layersNames = net.getLayerNames()
    # Get the names of the output layers, i.e. the layers with unconnected outputs
    # return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    # In some OpenCV versions, getUnconnectedOutLayers() returns a list of int, in others, a list of numpy arrays
    unconnectedOutLayers = net.getUnconnectedOutLayers()
    if isinstance(unconnectedOutLayers[0], np.ndarray):
        # OpenCV version returns indices as numpy arrays
        return [layersNames[i[0] - 1] for i in unconnectedOutLayers]
    else:
        # OpenCV version returns indices as integers
        return [layersNames[i - 1] for i in unconnectedOutLayers]

and

# Remove the bounding boxes with low confidence using non-maxima suppression
def postprocess(frame, outs):
    frameHeight = frame.shape[0]
    frameWidth = frame.shape[1]

    classIds = []
    confidences = []
    boxes = []
    # Scan through all the bounding boxes output from the network and keep only the
    # ones with high confidence scores. Assign the box's class label as the class with the highest score.
    classIds = []
    confidences = []
    boxes = []
    for out in outs:
        for detection in out:
            scores = detection[5:]
            classId = np.argmax(scores)
            confidence = scores[classId]
            if confidence > confThreshold:
                center_x = int(detection[0] * frameWidth)
                center_y = int(detection[1] * frameHeight)
                width = int(detection[2] * frameWidth)
                height = int(detection[3] * frameHeight)
                left = int(center_x - width / 2)
                top = int(center_y - height / 2)
                classIds.append(classId)
                confidences.append(float(confidence))
                boxes.append([left, top, width, height])

    # Perform non maximum suppression to eliminate redundant overlapping boxes with
    # lower confidences.
    indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
    for i in indices:
        if isinstance(i, np.ndarray):
            i = i[0]
        box = boxes[i]
        left = box[0]
        top = box[1]
        width = box[2]
        height = box[3]
        drawPred(classIds[i], confidences[i], left, top, left + width, top + height)