title: OpenCV TypeError: Expected Ptr<cv::UMat> for argument 'img' - What is this?
body: <p>I am not able to understand what type of error this is..i have written a python script which accepts image input from the user and runs emotion detection.
  here's the code:</p>
  
  <pre><code>import numpy as np
  import os
  import sys
  import tensorflow as tf
  import json
  from PIL import Image
  
  sys.path.append("..")
  from object_detection.utils import ops as utils_ops
  
  from utils import label_map_util
  from utils import visualization_utils as vis_util
  
  def Image_tensorflow(xa,ya):
      PATH_TO_FROZEN_GRAPH = 'frozen_inference_graph.pb'
      PATH_TO_LABELS = 'object-detection.pbtxt'
      NUM_CLASSES = 4
  
      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='')
  
      label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
      categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
                                                                  use_display_name=True)
      category_index = label_map_util.create_category_index(categories)
  
  
      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)
  
  
      def image_url(xa, ya):
          file_path = 'images/'
          file_name = ya
          image = xa
          f = open((file_path + str(file_name) + ".json"), "w")
          f.close
          return_dict = {'image': image, 'file': f};
          return return_dict
  
  
      get_image_data = image_url(xa,ya)
      image_path= get_image_data['image']
  
      IMAGE_SIZE = (12, 8)
  
  
      def run_inference_for_single_image(image, graph):
          with graph.as_default():
              with tf.Session() as sess:
                  # Get handles to input and output tensors
                  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:
                      # The following processing is only for single image
                      detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
                      detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
                      # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
                      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[0], image.shape[1])
                      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: np.expand_dims(image, 0)})
  
                  output_dict['num_detections'] = int(output_dict['num_detections'][0])
                  output_dict['detection_classes'] = output_dict[
                      'detection_classes'][0].astype(np.uint8)
                  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
  
  
      for img in xa:
          image = Image.open(img)
      image_np = load_image_into_numpy_array(image)
      image_np_expanded = np.expand_dims(image_np, axis=0)
      # Actual detection.
      output_dict = run_inference_for_single_image(image_np, 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)
  
      # get_image_data = image_url(sys.argv[1],sys.argv[2])
      # image_file = get_image_data['image']
  
  
      # pass values
  
  
      import cv2 as cv
  
      image_file = image_path
      img = cv.imread('image_file')
      i = 0
      j = 0
      limiter = 0.3
  
      while (i &lt; 100):
          if (output_dict['detection_scores'][i] &gt; limiter):
              j = j + 1
          i = i + 1
  
      # In[17]:
  
  
      # store the pass values in lists
      i = 0
      detection_classes = []
      detection_boxes = [[]] * j
      detection_scores = []
      while (i &lt; j):
          detection_classes.append(output_dict['detection_classes'][i])
          detection_scores.append(output_dict['detection_scores'][i])
          detection_boxes[i].append(output_dict['detection_boxes'][i])
          i = i + 1
  
      list1 = []
      for items in detection_classes:
          if items == 1:
              list1.append("Angry")
          elif items == 2:
              list1.append("Sad")
          elif items == 3:
              list1.append("Neutral")
          elif items == 4:
              list1.append("Happy")
  
      final_dict = {'DETECTION': list1}
  
      file_to_write_to = get_image_data['file'].name
      file_to_write_to = str(file_to_write_to)
      text_file = open(file_to_write_to, "w")
      text_file.write(json.dumps(final_dict))
      text_file.close()
      final_path = "images/" + str(ya) + "_annotated" + ".jpg"
  
      # draw bounding boxes
      img = cv.imread('xa')
      i = 0
      for item in detection_classes:
          width, height = image.size
          ymin = int(detection_boxes[0][i][0] * height)
          xmin = int(detection_boxes[0][i][1] * width)
          ymax = int(detection_boxes[0][i][2] * height)
          xmax = int(detection_boxes[0][i][3] * width)
          font = cv.FONT_HERSHEY_SIMPLEX
          panel_colour = (182, 182, 42)
          bumper_colour = (241, 239, 236)
          damage_colour = (0, 255, 0)
          text_colour = (255, 255, 255)
          bumper_text = (0, 0, 0)
          buffer = int(5 * width / 1000)
          if (detection_classes[i] == 1):
              img = cv.rectangle(img, (xmin, ymin), (xmax, ymax), panel_colour, int(2 * (height / 600)))
              cv.rectangle(img, (xmin, (ymin + (buffer * 8))), (xmax, ymin), panel_colour, -1)
              cv.putText(img, 'angry', (xmin, (ymin + (buffer * 6))), font, 0.8 * (height / 500), text_colour,
                         int(2 * (height / 400)), cv.LINE_AA)
          elif (detection_classes[i] == 2):
              img = cv.rectangle(img, (xmin, ymin), (xmax, ymax), panel_colour, int(2 * (height / 600)))
              cv.rectangle(img, (xmin, (ymin + (buffer * 8))), (xmax, ymin), panel_colour, -1)
              cv.putText(img, 'sad', (xmin, (ymin + (buffer * 6))), font, 0.8 * (height / 500), text_colour,
                         int(2 * (height / 400)), cv.LINE_AA)
          elif (detection_classes[i] == 3):
              img = cv.rectangle(img, (xmin, ymin), (xmax, ymax), bumper_colour, int(2 * (height / 600)))
              cv.rectangle(img, (xmin, (ymin + (buffer * 8))), (xmax, ymin), bumper_colour, -1)
              cv.putText(img, 'neutral', (xmin, (ymin + (buffer * 6))), font, 0.8 * (height / 500), bumper_text,
                         int(2 * (height / 400)), cv.LINE_AA)
          elif (detection_classes[i] == 4):
              img = cv.rectangle(img, (xmin, ymin), (xmax, ymax), panel_colour, int(2 * (height / 600)))
              cv.rectangle(img, (xmin, (ymin + (buffer * 8))), (xmax, ymin), panel_colour, -1)
              cv.putText(img, 'happy', (xmin, (ymin + (buffer * 6))), font, 0.8 * (height / 500), text_colour,
                         int(2 * (height / 400)), cv.LINE_AA)
          i = i + 1
  
      final_path = "/home/mayureshk/PycharmProjects/ImageDetection/venv/models/research/object_detection/images/" + str(ya) + "_annotated" + ".jpg"
      cv.imwrite(final_path, img)
  </code></pre>
  
  <p>Stuck on this problem since 2 days and i am unable to solve it by myself. Need help from an OpenCV expert.. What exactly am I doing wrong here?</p>