object_detection.py

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import datetime

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from utils import label_map_util
from EmailSender import SendEmail

sys.path.insert(0,r'C:\darknet')
from darknet import *
from utils import visualization_utils as vis_util
import cv2
def AccidentDetector(videofile):

    cap = cv2.VideoCapture(videofile)
    #MODEL_NAME = 'Accident_Detection25487-resnet'
    MODEL_NAME = 'Accident_Detection42214-resnet'
    #MODEL_NAME = 'Accident_Detection200000'
    MODEL_FILE = MODEL_NAME + '.tar.gz'
    DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
    PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
    PATH_TO_LABELS = os.path.join('data', 'object-detection.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)
    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) ]
    IMAGE_SIZE = (12, 8)


    check=0
    count=0
    prevdate=datetime.datetime.now()
    #currdate=datetime.datetime.now()
    with detection_graph.as_default():
      with tf.Session(graph=detection_graph) as sess:
        while True:
          ret, image_np = cap.read()
          imageOrg=image_np
          image_np_expanded = np.expand_dims(image_np, axis=0)
          image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
          boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
          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')

          (boxes, scores, classes, num_detections) = sess.run(
              [boxes, scores, classes, num_detections],
              feed_dict={image_tensor: image_np_expanded})
          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)
          for index,value in enumerate(classes[0]):
            if scores[0,index] > 0.5:
              list1 = [[category_index.get(value)]]
              for i in list1:
                for j in i:
                  if j['name'] == 'accident':
                    if(check == 0):
                        check=1
                        #SendEmail(currdate)
                        ts = datetime.datetime.now().timestamp()
                        file = "detections\\"+str(ts)+".jpg"
                        cv2.imwrite(file,imageOrg)
                        # 1576044803.3581
                        data,check = performDetect(imagePath=file)
                        print(data,check)
                    #name = "detections/frame%d.jpg"%count
                    count = count + 1

          cv2.imshow(videofile[0], cv2.resize(image_np, (600,600)))
          if cv2.waitKey(20) & 0xFF == ord('n'):
              current = int(cap.get(cv2.CAP_PROP_POS_FRAMES))
              print(current)
              cap.set(cv2.CAP_PROP_POS_FRAMES,current+50)

          if cv2.waitKey(20) & 0xFF == ord('p'):
              current = int(cap.get(cv2.CAP_PROP_POS_FRAMES))
              print(current)
              cap.set(cv2.CAP_PROP_POS_FRAMES,current-50)

          if cv2.waitKey(20) & 0xFF == ord('q'):
            cv2.destroyAllWindows()
            break