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import os
import cv2
import numpy as np
import tensorflow as tf
import tensorflow.compat.v1 as tf
import sys

# This is needed since the notebook is stored in the object_detection folder.

# Import utilites

from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

# Name of the directory containing the object detection module we're using
MODEL_NAME = 'inference_graph'
VIDEO_NAME = 'testvideo.mp4'

# Grab path to current working directory
CWD_PATH = os.getcwd()

# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')

# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')

# Path to video

# Number of classes the object detector can identify

# Load the label map.
# Label maps map indices to category names, so that when our convolution
# network predicts `5`, we know that this corresponds to `king`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
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)

# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with, 'rb') as fid:
        serialized_graph =
        tf.import_graph_def(od_graph_def, name='')

    sess = tf.Session(graph=detection_graph)

# Define input and output tensors (i.e. data) for the object detection classifier

# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

# Output tensors are the detection boxes, scores, and classes
# 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 represents level of confidence for each of the objects.
# The 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')

# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')

# Open video file
video = cv2.VideoCapture(PATH_TO_VIDEO)


    # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
    # i.e. a single-column array, where each item in the column has the pixel RGB value
    ret, frame =
    frame_expanded = np.expand_dims(frame, axis=0)

    # Perform the actual detection by running the model with the image as input
    (boxes, scores, classes, num) =
        [detection_boxes, detection_scores, detection_classes, num_detections],
        feed_dict={image_tensor: frame_expanded})

    # Draw the results of the detection (aka 'visulaize the results')

    # All the results have been drawn on the frame, so it's time to display it.
    cv2.imshow('Object detector', frame)

    # Press 'q' to quit
    if cv2.waitKey(1) == ord('q'):

# Clean up

What I have tried:

i have tried running the code its run but the detection system or model simply slows down the video for the detection of accidents as it's for detecting accident.I cant figure out how to increase the frame speed so that detection happens faster.

iam refereing this github project, please check this GitHub - snehal2841/Accident-detection-system[^]

dataset for above code
Accdet.rar - Google Drive[^]
Gerry Schmitz 3-Oct-22 11:58am    
How many frames do you need? Every one? Every 2nd one? Check your "sampling rate"; maybe you're over-sampling.
ASHIN KUNJUMON 3-Oct-22 12:34pm    
Bro i need to run the video at normal speed at the same time detection must happen
ASHIN KUNJUMON 3-Oct-22 12:35pm    
Also can u tell how the print the object detection in this case "accident detected " in terminal since i need to perform other functions based on it

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