如何从Google Colab访问笔记本电脑摄像头，以便在直播视频中进行对象检测？

import cv2
from google.colab import output
from base64 import b64decode

!pip install pyngrok
from pyngrok import ngrok

ngrok_tunnel = ngrok.connect(8888)
url = ngrok_tunnel.public_url

html = """
<script src="https://webrtc.github.io/adapter/adapter-latest.js"></script>
<script>
  var video = document.querySelector("#videoElement");
  
  navigator.mediaDevices.getUserMedia({ video: true })
    .then(function(stream) {
      video.srcObject = stream;
    })
    .catch(function(err) {
      console.log(err);
    });
</script>
<video autoplay="true" id="videoElement"></video>
"""

output.eval_js('new Response(`{html}`).text()', {'html': html})

# 加载对象检测模型
net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 捕获摄像头的视频流
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break
    
    # 对视频帧进行对象检测
    blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
    net.setInput(blob)
    outs = net.forward(output_layers)
    
    # 处理检测结果
    class_ids = []
    confidences = []
    boxes = []
    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5:
                # 检测到对象
                # 获取对象的位置信息
                center_x = int(detection[0] * frame.shape[1])
                center_y = int(detection[1] * frame.shape[0])
                w = int(detection[2] * frame.shape[1])
                h = int(detection[3] * frame.shape[0])
                x = int(center_x - w / 2)
                y = int(center_y - h / 2)
                
                # 保存对象的相关信息
                class_ids.append(class_id)
                confidences.append(float(confidence))
                boxes.append([x, y, w, h])
    
    # 绘制检测结果
    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
    font = cv2.FONT_HERSHEY_SIMPLEX
    for i in range(len(boxes)):
        if i in indexes:
            x, y, w, h = boxes[i]
            label = str(class_ids[i])
            confidence = confidences[i]
            color = (0, 255, 0)
            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
            cv2.putText(frame, label, (x, y - 10), font, 0.5, color, 2)
    
    # 显示检测结果
    cv2.imshow('Object Detection', frame)
    
    # 按下'q'键退出循环
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像头和窗口
cap.release()
cv2.destroyAllWindows()