cv2.rectangle(frame, (startX, startY), (endX, endY), COLOR, 2)

mask_session = tf.Session(graph=mask_detection_graph)

general_session = tf.Session(graph=general_detection_graph)

pos_dict = dict()
coordinates = dict()
ret, image_np = cap.read()
#image_np = cv2.resize(image_np, (640, 360))
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# # Each box represents a part of the image where a particular object was detected.
# boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# # Each score represent how level of confidence for each of the objects.
# # Score is shown on the result image, together with the class label.
# scores = detection_graph.get_tensor_by_name('detection_scores:0')
# classes = detection_graph.get_tensor_by_name('detection_classes:0')
# num_detections = detection_graph.get_tensor_by_name('num_detections:0')
if cap.isOpened():
    # get vcap property
    width = cap.get(3)  # float
    height = cap.get(4)  # float

    # it gives me 0.0 :/
    fps = cap.get(7)

#im = Image.fromarray(image_np)
#width, height = im.size

# Actual detection.
(mboxes, mscores, mclasses, mnum) = mask_session.run(
                 [mask_detection_boxes, mask_detection_scores,
                    mask_detection_classes, mask_num_detections],
                 feed_dict={mask_image_tensor: image_np_expanded})

# ----------Predictions
(pred_boxes, pred_scores, pred_classes, pred_num) = general_session.run(
                 [general_detection_boxes, general_detection_scores,
                     general_detection_classes, general_num_detections],
                 feed_dict={general_image_tensor: image_np_expanded})

coordinates = vis_util.return_coordinates(
        image_np,
        np.squeeze(pred_boxes),
        np.squeeze(pred_scores).astype(np.int32),
        np.squeeze(pred_classes),
        general_category_index,
        use_normalized_coordinates=True,
        line_thickness=10,
        min_score_thresh=0.85)
#ymin = int(coordinates[0])
#ymax = int(coordinates[1])
#xmin = int(coordinates[2])
#xmax = int(coordinates[3])

for coordinate in coordinates:
    (ymin, ymax, xmin, xmax, acc) = coordinate
    x_mid = round((xmin + xmax) / 2, 4)
    y_mid = round((ymin + ymax) / 2, 4)

    height = round(ymax - ymin, 4)
    distance = (165 * 600) / height
    x_mid_ = (x_mid * distance) / 600
    y_mid = (y_mid * distance) / 600
for i in range():
    pos_dict[i] = (x_mid, y_mid,distance)
    coordinates[i] = (xmin, ymin, xmax, ymax)

close_objects = set()
for i in pos_dict.keys():
    for j in pos_dict.keys():
        if i < j:
            dist = math.sqrt(math.pow(pos_dict[i][0] - pos_dict[j][0], 2) + math.pow(pos_dict[i][1] - pos_dict[j][1], 2) + math.pow(
                    pos_dict[i][2] - pos_dict[j][2], 2))

        # Check if distance less than 2 metres or 200 centimetres
            if dist < 200:
                close_objects.add(i)
                close_objects.add(j)

for i in pos_dict.keys():
    if i in close_objects:
         COLOR = (0, 0, 255)
    else:
        COLOR = (0, 255, 0)
    (startX, startY, endX, endY) = coordinates[i]


    cv2.rectangle(image_np, (startX, startY), (endX, endY), COLOR, 5)
        # vis_util.visualize_boxes_and_labels_on_image_array(
        #     image_np,
        #     np.squeeze(pred_boxes[i]),
        #     np.squeeze(pred_classes).astype(np.int32),
        #     np.squeeze(pred_scores),
        #     general_category_index,
        #     use_normalized_coordinates=True,
        #     line_thickness=8,
        #     groundtruth_box_visualization_color=COLOR)
    y = startY - 15 if startY - 15 > 15 else startY + 15
    cv2.putText(image_np, 'Depth: {i} ft'.format(i=round(pos_dict[i][2] / 30.48, 4)), (startX, y),cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLOR, 2)

vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(mboxes),
np.squeeze(mclasses).astype(np.int32),
np.squeeze(mscores),
mask_category_index,
use_normalized_coordinates=True,
line_thickness=8)

cv2.imshow('Detected Tools', cv2.resize(image_np, (1000, 850)))
#       cv2.imshow('Tools Name',cv2.resize(cv2.putText(img,'OpenCV',(10,500), font, 4,(255,255,255),2,2),(100,150)))
if cv2.waitKey(25) & 0xFF == ord('q'):
    cv2.destroyAllWindows()
    cap.release()
    break