几秒钟后 Google Colab 会话崩溃

Question

我的目标是将网络摄像头与 Google Colab 上的对象检测模型集成。 我有 JavaScript 代码来访问网络摄像头和 python 代码以与 Google Colab 上的模型集成。相机正在打开，几秒钟后会话崩溃。如何成功地将模型与网络摄像头集成？下面是代码片段：

# start streaming video from webcam
video_stream()
# label for video
label_html = 'Capturing...'
# initialze bounding box to empty

bbox = ''
count = 0

while True:
    js_reply = video_frame(label_html, bbox)
    if not js_reply:
        break

    # convert JS response to OpenCV Image
    frame = js_to_image(js_reply["img"])

    h, w = frame.shape[:2]

  


    # create transparent overlay for bounding box
    #bbox_array = np.zeros([480,640,4], dtype=np.uint8)

    # call our darknet helper on video frame

    blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
    network.setInput(blob)
    output_from_network = network.forward(layers_names_output)

    bounding_boxes = []
    confidences = []
    class_numbers = []

    # Going through all output layers after feed forward pass
    for result in output_from_network:
        # Going through all detections from current output layer
        for detected_objects in result:
            # Getting 80 classes' probabilities for current detected object
            scores = detected_objects[5:]
            # Getting index of the class with the maximum value of probability
            class_current = np.argmax(scores)
            # Getting value of probability for defined class
            confidence_current = scores[class_current]

            # # Check point
            # # Every 'detected_objects' numpy array has first 4 numbers with
            # # bounding box coordinates and rest 80 with probabilities
            # # for every class
            # print(detected_objects.shape)  # (85,)

            # Eliminating weak predictions with minimum probability
            if confidence_current > probability_minimum:
                # Scaling bounding box coordinates to the initial frame size
                # YOLO data format keeps coordinates for center of bounding box
                # and its current width and height
                # That is why we can just multiply them elementwise
                # to the width and height
                # of the original frame and in this way get coordinates for center
                # of bounding box, its width and height for original frame
                box_current = detected_objects[0:4] * np.array([w, h, w, h])

                # Now, from YOLO data format, we can get top left corner coordinates
                # that are x_min and y_min
                x_center, y_center, box_width, box_height = box_current
                x_min = int(x_center - (box_width / 2))
                y_min = int(y_center - (box_height / 2))

                # Adding results into prepared lists
                bounding_boxes.append([x_min, y_min,
                                       int(box_width), int(box_height)])
                confidences.append(float(confidence_current))
                class_numbers.append(class_current)
    results = cv2.dnn.NMSBoxes(bounding_boxes, confidences, probability_minimum, threshold)
    if len(results) > 0:
        # Going through indexes of results
        for i in results.flatten():
            # Getting current bounding box coordinates,
            # its width and height
            x_min, y_min = bounding_boxes[i][0], bounding_boxes[i][1]
            box_width, box_height = bounding_boxes[i][2], bounding_boxes[i][3]

            # Preparing colour for current bounding box
            # and converting from numpy array to list
            colour_box_current = colours[class_numbers[i]].tolist()

            # # # Check point
            # print(type(colour_box_current))  # <class 'list'>
            # print(colour_box_current)  # [172 , 10, 127]

            # Drawing bounding box on the original current frame
            cv2.rectangle(frame, (x_min, y_min),
                          (x_min + box_width, y_min + box_height),
                          colour_box_current, 2)

            # Preparing text with label and confidence for current bounding box
            text_box_current = '{}: {:.4f}'.format(labels[int(class_numbers[i])],
                                                   confidences[i])

            # Putting text with label and confidence on the original image
            cv2.putText(frame, text_box_current, (x_min, y_min - 5),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, colour_box_current, 2)
    cv2.namedWindow('YOLO v3 Real Time Detections', cv2.WINDOW_NORMAL)
    # Pay attention! 'cv2.imshow' takes images in BGR format
    cv2.imshow('YOLO v3 Real Time Detections', frame)

    # Breaking the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break