text: # Author : Anil Kumar (anil912001@gmail.com)
  # Modified From API
  # https://github.com/wagonhelm/TF_ObjectDetection_API/blob/master/ChessObjectDetection.ipynb
  
  import skimage
  import numpy as np
  from skimage import io, transform
  import os
  import shutil
  import glob
  import pandas as pd
  import xml.etree.ElementTree as ET
  import tensorflow as tf
  from collections import defaultdict
  from io import StringIO
  from matplotlib import pyplot as plt
  from PIL import Image
  import urllib.request
  import urllib.error
  import cv2
  
  from utils.app_utils import FPS, WebcamVideoStream
  from utils import label_map_util
  from utils import visualization_utils as vis_util
  
  root = os.getcwd()
  imagePath = os.path.join(root, 'images')
  labelsPath = os.path.join(root, 'labels')
  linksPath = os.path.join(imagePath, 'imageLinks')
  trainPath = os.path.join(imagePath, 'train')
  testPath = os.path.join(imagePath, 'test')
  
  # Path to frozen detection graph. This is the actual model that is used for the object detection.
  PATH_TO_CKPT = 'object_detection_graph/frozen_inference_graph.pb'
  
  # List of the strings that is used to add correct label for each box.
  PATH_TO_LABELS = 'data/label_map.pbtxt'
  
  NUM_CLASSES = 2
  
  detection_graph = tf.Graph()
  with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, '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)
  
  # Modified From API
  # https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
  
  with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
      # Definite input and output Tensors for detection_graph
      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.
      detection_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.
      detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
      detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
      num_detections = detection_graph.get_tensor_by_name('num_detections:0')
      
      #src=0 --> /dev/video0. change src number according to your video node
      video_capture = WebcamVideoStream(src=0, 
                                        width=720,
                                        height=480).start()
      fps = FPS().start()
      while(True):
        frame =  video_capture.read()
        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        image_np_expanded = np.expand_dims(frame_rgb, axis=0)
        # Actual detection.
        (boxes, scores, classes, num) = sess.run(
            [detection_boxes, detection_scores, detection_classes, num_detections],
            feed_dict={image_tensor: image_np_expanded})
  
        print('***************** Visualization ************')
        # Visualization of the results of a detection.
        vis_util.visualize_boxes_and_labels_on_image_array(
            frame_rgb,
            np.squeeze(boxes),
            np.squeeze(classes).astype(np.int32),
            np.squeeze(scores),
            category_index,
            use_normalized_coordinates=True,
            line_thickness=2)
        
        output_rgb = cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2BGR)
        cv2.imshow('Object Detection', output_rgb)
        fps.update()
        if cv2.waitKey(1) & 0xFF == ord('q'):
        	break
  
  
  