以下是否为您提供非零大小：

print(frame.shape)

如果没有，则说明图像没有正确加载。Notetype表示没有任何内容存储在名为frame的变量中

我也面临着同样的问题。我想用我的网络摄像头用yolov 4检测一些物体。然后我发现了（The AI Guy）。
他使用了Camera Capture的代码片段，该代码片段运行JavaScript代码来利用他的计算机的摄像头。代码片段将拍摄一张摄像头照片，然后他将其传递到YOLOv 4模型中进行对象检测。
下面是一个使用JavaScript拍摄网络摄像头图片然后运行YOLOv 4的帮助函数。
注：-“js = Javascript（“后面的这三个单撇号（'''）在StackOverflow中用作注解，但在协作代码shell中它将用作代码。

通过网络摄像头拍摄照片：

def take_photo(filename='photo.jpg', quality=0.8):
  js = Javascript('''       
    async function takePhoto(quality) {
      const div = document.createElement('div');
      const capture = document.createElement('button');
      capture.textContent = 'Capture';
      div.appendChild(capture);

      const video = document.createElement('video');
      video.style.display = 'block';
      const stream = await navigator.mediaDevices.getUserMedia({video: true});

      document.body.appendChild(div);
      div.appendChild(video);
      video.srcObject = stream;
      await video.play();

      // Resize the output to fit the video element.
      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);

      // Wait for Capture to be clicked.
      await new Promise((resolve) => capture.onclick = resolve);

      const canvas = document.createElement('canvas');
      canvas.width = video.videoWidth;
      canvas.height = video.videoHeight;
      canvas.getContext('2d').drawImage(video, 0, 0);
      stream.getVideoTracks()[0].stop();
      div.remove();
      return canvas.toDataURL('image/jpeg', quality);
    }
    ''')
  display(js)

  # get photo data
  data = eval_js('takePhoto({})'.format(quality))
  # get OpenCV format image
  img = js_to_image(data) 
  
  # call our darknet helper on webcam image
  detections, width_ratio, height_ratio = darknet_helper(img, width, height)

  # loop through detections and draw them on webcam image
  for label, confidence, bbox in detections:
    left, top, right, bottom = bbox2points(bbox)
    left, top, right, bottom = int(left * width_ratio), int(top * height_ratio), int(right * width_ratio), int(bottom * height_ratio)
    cv2.rectangle(img, (left, top), (right, bottom), class_colors[label], 2)
    cv2.putText(img, "{} [{:.2f}]".format(label, float(confidence)),
                      (left, top - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                      class_colors[label], 2)
  # save image
  cv2.imwrite(filename, img)

  return filename
try:
  filename = take_photo('photo.jpg')
  print('Saved to {}'.format(filename))
  
  # Show the image which was just taken.
  display(Image(filename))
except Exception as err:
  # Errors will be thrown if the user does not have a webcam or if they do not
  # grant the page permission to access it.
  print(str(err))

下面是另一个帮助函数，使用类似于图像的JavaScript启动视频流。视频流帧作为输入馈送到YOLOv 4。

# JavaScript to properly create our live video stream using our webcam as input
def video_stream():
  js = Javascript('''
    var video;
    var div = null;
    var stream;
    var captureCanvas;
    var imgElement;
    var labelElement;
    
    var pendingResolve = null;
    var shutdown = false;
    
    function removeDom() {
       stream.getVideoTracks()[0].stop();
       video.remove();
       div.remove();
       video = null;
       div = null;
       stream = null;
       imgElement = null;
       captureCanvas = null;
       labelElement = null;
    }
    
    function onAnimationFrame() {
      if (!shutdown) {
        window.requestAnimationFrame(onAnimationFrame);
      }
      if (pendingResolve) {
        var result = "";
        if (!shutdown) {
          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);
          result = captureCanvas.toDataURL('image/jpeg', 0.8)
        }
        var lp = pendingResolve;
        pendingResolve = null;
        lp(result);
      }
    }
    
    async function createDom() {
      if (div !== null) {
        return stream;
      }

      div = document.createElement('div');
      div.style.border = '2px solid black';
      div.style.padding = '3px';
      div.style.width = '100%';
      div.style.maxWidth = '600px';
      document.body.appendChild(div);
      
      const modelOut = document.createElement('div');
      modelOut.innerHTML = "<span>Status:</span>";
      labelElement = document.createElement('span');
      labelElement.innerText = 'No data';
      labelElement.style.fontWeight = 'bold';
      modelOut.appendChild(labelElement);
      div.appendChild(modelOut);
           
      video = document.createElement('video');
      video.style.display = 'block';
      video.width = div.clientWidth - 6;
      video.setAttribute('playsinline', '');
      video.onclick = () => { shutdown = true; };
      stream = await navigator.mediaDevices.getUserMedia(
          {video: { facingMode: "environment"}});
      div.appendChild(video);

      imgElement = document.createElement('img');
      imgElement.style.position = 'absolute';
      imgElement.style.zIndex = 1;
      imgElement.onclick = () => { shutdown = true; };
      div.appendChild(imgElement);
      
      const instruction = document.createElement('div');
      instruction.innerHTML = 
          '<span style="color: red; font-weight: bold;">' +
          'When finished, click here or on the video to stop this demo</span>';
      div.appendChild(instruction);
      instruction.onclick = () => { shutdown = true; };
      
      video.srcObject = stream;
      await video.play();

      captureCanvas = document.createElement('canvas');
      captureCanvas.width = 640; //video.videoWidth;
      captureCanvas.height = 480; //video.videoHeight;
      window.requestAnimationFrame(onAnimationFrame);
      
      return stream;
    }
    async function stream_frame(label, imgData) {
      if (shutdown) {
        removeDom();
        shutdown = false;
        return '';
      }

      var preCreate = Date.now();
      stream = await createDom();
      
      var preShow = Date.now();
      if (label != "") {
        labelElement.innerHTML = label;
      }
            
      if (imgData != "") {
        var videoRect = video.getClientRects()[0];
        imgElement.style.top = videoRect.top + "px";
        imgElement.style.left = videoRect.left + "px";
        imgElement.style.width = videoRect.width + "px";
        imgElement.style.height = videoRect.height + "px";
        imgElement.src = imgData;
      }
      
      var preCapture = Date.now();
      var result = await new Promise(function(resolve, reject) {
        pendingResolve = resolve;
      });
      shutdown = false;
      
      return {'create': preShow - preCreate, 
              'show': preCapture - preShow, 
              'capture': Date.now() - preCapture,
              'img': result};
    }
    ''')

  display(js)
  
def video_frame(label, bbox):
  data = eval_js('stream_frame("{}", "{}")'.format(label, bbox))
  return data

# 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"])

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

    # call our darknet helper on video frame
    detections, width_ratio, height_ratio = darknet_helper(frame, width, height)

    # loop through detections and draw them on transparent overlay image
    for label, confidence, bbox in detections:
      left, top, right, bottom = bbox2points(bbox)
      left, top, right, bottom = int(left * width_ratio), int(top * height_ratio), int(right * width_ratio), int(bottom * height_ratio)
      bbox_array = cv2.rectangle(bbox_array, (left, top), (right, bottom), class_colors[label], 2)
      bbox_array = cv2.putText(bbox_array, "{} [{:.2f}]".format(label, float(confidence)),
                        (left, top - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                        class_colors[label], 2)

    bbox_array[:,:,3] = (bbox_array.max(axis = 2) > 0 ).astype(int) * 255
    # convert overlay of bbox into bytes
    bbox_bytes = bbox_to_bytes(bbox_array)
    # update bbox so next frame gets new overlay
    bbox = bbox_bytes

如果你没有从上面的代码中得到任何好处，那么请检查我的collab，我在那里成功地执行了这段代码。
collab link

使用Google colab中opencv-python包中的以下两个函数：
1.从google.colab.补丁导入cv 2和cv2_imshow，如下所示：
导入cv 2
从google.colab.patches导入cv2_imshow
1.使用cv 2读取图像，并使用cv2_imshow显示相同图像，如下所示：
img = cv2.imread（“文件夹名称/图像. jpg”）
cv2_imshow（图片）