Computer vision: detect , track and count car and moto in video using openCV  computer vision 

import cv2

from tracker import *

import os

os.chdir("D:\object traking\object_tracking")

result = cv2.VideoWriter('ahmedd.mp4', 

                         cv2.VideoWriter_fourcc(*'XVID'),

                         20, (250,250))

# Create tracker object

tracker = EuclideanDistTracker()

cap = cv2.VideoCapture("highway.mp4")

# Object detection from Stable camera

object_detector = cv2.createBackgroundSubtractorMOG2(history=100, varThreshold=50) # if history is big number it will be hight 

while True:

    ret, frame = cap.read()        

    if ret is not True:        

        break              

    height, width, _ = frame.shape

    # Extract Region of interest

    roi = frame[340: 720,500: 800]

    # 1. Object Detection

    mask = object_detector.apply(roi)

    _, mask = cv2.threshold(mask, 254, 255, cv2.THRESH_BINARY)

    contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    detections = []

    for cnt in contours:

        # Calculate area and remove small elements       

        area = cv2.contourArea(cnt)       

        if area > 100:       

            #cv2.drawContours(roi, [cnt], -1, (0, 255, 0), 2)              

            x, y, w, h = cv2.boundingRect(cnt)               

            detections.append([x, y, w, h])              

    # 2. Object Tracking

    boxes_ids = tracker.update(detections)

    for box_id in boxes_ids:

        x, y, w, h, id = box_id

        cv2.putText(roi, str(id), (x, y), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2)

        cv2.rectangle(roi, (x, y), (x + w, y + h), (0, 255, 0), 3)

    cv2.imshow("roi", roi)

    cv2.imshow("Frame", frame)

    cv2.imshow("Mask", mask)

    result.write(frame)

  

    key = cv2.waitKey(30)

    if key == 27:

        break        

result.release()

cap.release()

cv2.destroyAllWindows()

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Click here to get the tracker file and the Video

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