title: Using cv2.imshow() in google Colab
body: <p>I am trying to conduct object detection for a video by inputting the video through</p>
  <p><code>cap = cv2.VideoCapture(&quot;video3.mp4&quot;)</code></p>
  <p>and after the processing part I want to display the video with real time object detection using</p>
  <pre><code>while True:
  ret, image_np = cap.read()
  
  # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
  image_np_expanded = np.expand_dims(image_np, axis=0)
  # Actual detection.
  output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)
  # Visualization of the results of a detection.
  vis_util.visualize_boxes_and_labels_on_image_array(
      image_np,
      output_dict['detection_boxes'],
      output_dict['detection_classes'],
      output_dict['detection_scores'],
      category_index,
      instance_masks=output_dict.get('detection_masks'),
      use_normalized_coordinates=True,
      line_thickness=8)
  
  cv2.imshow('object detection', cv2.resize(image_np, (800, 600)))
  if cv2.waitKey(25) &amp; 0XFF == ord('q'):
      cv2.destroyAllWindows()
      break
  </code></pre>
  <p>But the colab says that <code>cv2.imshow()</code> is disabled and to use <code>cv2_imshow()</code>. But it only renders images. [Frame by frame]. I want to get the video output as I did using <code>cv2.imshow()</code>. Please help me to solve this. Thanks in advance.</p>
  <p>My full code is attached</p>
  <pre><code>import numpy as np
  import os
  import six.moves.urllib as urllib
  import sys
  import tarfile
  import tensorflow as tf
  import zipfile
  
  from distutils.version import StrictVersion
  from collections import defaultdict
  from io import StringIO
  from matplotlib import pyplot as plt
  from PIL import Image
  
  import cv2
  from google.colab.patches import cv2_imshow
  
  cap = cv2.VideoCapture(&quot;video3.mp4&quot;)
  
  sys.path.append(&quot;..&quot;)
  from object_detection.utils import ops as utils_ops
  
  if StrictVersion(tf.__version__) &lt; StrictVersion('1.12.0'):
  raise ImportError('Please upgrade your TensorFlow installation to v1.12.*.')
  
  from object_detection.utils import label_map_util
  
  from object_detection.utils import visualization_utils as vis_util
  
  detection_graph = tf.Graph()
  with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
  serialized_graph = fid.read()
  od_graph_def.ParseFromString(serialized_graph)
  tf.import_graph_def(od_graph_def, name='')
  
  
  category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
  
  
  def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
    (im_height, im_width, 3)).astype(np.uint8)
  
  
  PATH_TO_TEST_IMAGES_DIR = 'test_images'
  TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(3, 8) ]
  
  IMAGE_SIZE = (12, 8)
  
  def run_inference_for_single_image(image, graph):
  with graph.as_default():
  with tf.Session() as sess:
   
    ops = tf.get_default_graph().get_operations()
    all_tensor_names = {output.name for op in ops for output in op.outputs}
    tensor_dict = {}
    for key in [
        'num_detections', 'detection_boxes', 'detection_scores',
        'detection_classes', 'detection_masks'
    ]:
      tensor_name = key + ':0'
      if tensor_name in all_tensor_names:
        tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
            tensor_name)
    if 'detection_masks' in tensor_dict:
     
      detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
      detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
     
      real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
      detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
      detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
      detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
          detection_masks, detection_boxes, image.shape[1], image.shape[2])
      detection_masks_reframed = tf.cast(
          tf.greater(detection_masks_reframed, 0.5), tf.uint8)
     
      tensor_dict['detection_masks'] = tf.expand_dims(
          detection_masks_reframed, 0)
    image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
  
    
    output_dict = sess.run(tensor_dict,
                           feed_dict={image_tensor: image})
  
    
    output_dict['num_detections'] = int(output_dict['num_detections'][0])
    output_dict['detection_classes'] = output_dict[
        'detection_classes'][0].astype(np.int64)
    output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
    output_dict['detection_scores'] = output_dict['detection_scores'][0]
    if 'detection_masks' in output_dict:
      output_dict['detection_masks'] = output_dict['detection_masks'][0]
  return output_dict
  
  while True:
      ret, image_np = cap.read()
  
      image_np_expanded = np.expand_dims(image_np, axis=0)
  
      output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)
  
      vis_util.visualize_boxes_and_labels_on_image_array(
      image_np,
      output_dict['detection_boxes'],
      output_dict['detection_classes'],
      output_dict['detection_scores'],
      category_index,
      instance_masks=output_dict.get('detection_masks'),
      use_normalized_coordinates=True,
      line_thickness=8)
  
  cv2_imshow(image_np)
  if cv2.waitKey(25) &amp; 0XFF == ord('q'):
      cv2.destroyAllWindows()
      break
  </code></pre>