<filename>src/object_detection_webcam_new.py
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
import time
import cv2


sys.path.append("..")
from object_detection.utils import ops as utils_ops

# ## Object detection imports
# Here are the imports from the object detection module.

from utils import label_map_util
from utils import visualization_utils as vis_util


#MODEL_NAME = 'faster_rcnn_inception_v2_coco_2018_01_28'
#MODEL_NAME = ' '
#MODEL_NAME = ' '
#MODEL_NAME = ' '
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
#MODEL_NAME = 'ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03'

MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

## Download Model
#opener = urllib.request.URLopener()
#opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
  file_name = os.path.basename(file.name)
  if 'frozen_inference_graph.pb' in file_name:
    tar_file.extract(file, os.getcwd())

graph = tf.Graph()
with 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)

new=[]
count=0

with graph.as_default():
    sess = tf.Session()
    cap=cv2.VideoCapture(0)
    while True:
      ret, image=cap.read()  
      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image, axis=0)
      st=time.time() 
      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)
        # Follow the convention by adding back the batch dimension
        tensor_dict['detection_masks'] = tf.expand_dims(
            detection_masks_reframed, 0)
      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

      # Run inference
      output_dict = sess.run(tensor_dict,
                             feed_dict={image_tensor: np.expand_dims(image, 0)})
      print("--- %s seconds ---" % (time.time() - st))   
      # all outputs are float32 numpy arrays, so convert types as appropriate
      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]
      # Visualization of the results of a detection.
      vis_util.visualize_boxes_and_labels_on_image_array(
      image,
      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=12)
      classes =  graph.get_tensor_by_name('detection_classes:0')
      new.append(output_dict['detection_classes'].tolist())
      tf.InteractiveSession()
      classes = graph.get_tensor_by_name('detection_classes:0')
      #print(classes)
      #print([category_index.get(i) for i in classes[0]])
      count+=1
      cv2.imshow('object detection',cv2.resize(image,(640,480)))
      if cv2.waitKey(1) & 0xFF==ord('q'):
        break
new2=[]
x=0
y=0
d={}
for x in range(len(new)):
  for y in range(len(new[x])):
    if new[x][y] in new2:
      if new[x][y] in d:
        a=d[new[x][y]]
        a+=1
        d[new[x][y]]=a
      else:
        d[new[x][y]]=1
    else:
      new2.append(new[x][y])

x=0
while True:
  if x==len(new2):
    break
  x+=1

#print(category_index)
#print(d)
th=max(d.values())*0.15
for x in d.keys():
  if d[x]>th:
    print(category_index[x]['name'])
