video_detection_continue.py

# ----------------------------------------------
# --- Author         : Pushpraj Katiyar
# --- Mail           : pushprajkatiyar@gmail.com
# --- Date           : 23th April 2019
# ----------------------------------------------
import numpy as np
import math
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import json
import argparse

from collections import defaultdict
from io import StringIO
import cv2

if tf.__version__ < '1.13.0':
    raise ImportError(
        'Please upgrade your tensorflow installation to v1.13.* or later!')

sys.path.insert(0, 'utils')
import label_map_util
import people_class_util as class_utils
import visualization_utils as vis_util

# parser = argparse.ArgumentParser()
# parser.add_argument('--path', required=True,
#                     help='Path to the video')
#
# opt = parser.parse_args()

# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_2018_01_28'
# MODEL_NAME = 'faster_rcnn_nas_lowproposals_coco_2017_11_08'
#MODEL_NAME = 'faster_rcnn_resnet50_coco_2018_01_28'
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_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = 'utils/person_label_map.pbtxt'

NUM_CLASSES = 90
cap = cv2.VideoCapture(0)
#cap = cv2.VideoCapture('http://devimages.apple.com/iphone/samples/bipbop/bipbopall.m3u8')
# checking if file exist or not and if yes then downloading specified model
if not os.path.exists(MODEL_FILE):
    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())

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='')

# loading specified class/category description
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)


# some helper code
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)


# start detection here
# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
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(1, 3) ]

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
i = 0
success = True
#detection of video
with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    while success:
      success, image_np = cap.read()
      if not success:
          print ('end of the video file...')
          break
      # 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')
      # Actual detection.
      (boxes, scores, classes, num_detections) = sess.run(
          [boxes, scores, classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
      # Visualization of the results of a detection.
      vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,
          np.squeeze(boxes),
          np.squeeze(classes).astype(np.int32),
          np.squeeze(scores),
          category_index,
          use_normalized_coordinates=True,
          line_thickness=8)

     # cv2.imshow('object detection', cv2.resize(image_np, (800,600)))
      cv2.imshow('object detection', image_np)
      print(">>>>framenumber>>>>>>>>>" + str(i))
      if cv2.waitKey(25) & 0xFF == ord('q'):
        cv2.destroyAllWindows()
        break