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Live Vehicle Detection using Python
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Live Vehicle Detection using Python

Overview

In this tutorial, we will learn how to detect a vehicl from a video using the Haar Cascades classifier. For the well-known tasks, the classifiers / detectors already exists, for example: detecting things like faces, cars, smiles, eyes and license plates.

Installing the modules

To install the OpenCV module and some other associated dependencies, we can use the pip command:

pip install opencv-python

Download the Haar Cascades

  1. Follow the URL:
    https://github.com/AdityaPai2398/Vehicle-And-Pedestrian-Detection-Using-Haar-Cascades/tree/master/Main%20Project/Main%20Project/Car%20Detection
  2. Click on cars.xml
  3. Click on Raw and then press Ctrl + S. This will help you save the Haar Cascade file for vehicle detection.

The Code

import cv2

car_cascade = cv2.CascadeClassifier("haarcascades_car.xml")

def detect_cars(frame):
    cars = car_cascade.detectMultiScale(frame, 1.15, 4)
    for (x, y, w, h) in cars:
        cv2.rectangle(frame, (x, y), (x+w, y+h), color=(155, 155, 0), thickness=2)
    return frame

def Simulator():
    car_video = cv2.VideoCapture("video.mp4")
    while car_video.isOpened():
        ret, frame = car_video.read()
        control_key = cv2.waitKey(1)

        if ret:
            cars_frame = detect_cars(frame)
            cv2.imshow('Frame', cars_frame)
        else:
            break

        if control_key == ord('q'):
            break

if __name__ == '__main__':
    Simulator()

The Ouput

Live Vehicle Detection using Python