det.py

import cv2

# Load the pre-trained Haar Cascade classifier for cars
car_cascade = cv2.CascadeClassifier('haarcascade_cars.xml')

# Function to detect cars in a frame
def detect_cars(frame):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    cars = car_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=1, minSize=(30, 30))

    for (x, y, w, h) in cars:
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
    
    return frame

def main():
    # Open a connection to the webcam
    cap = cv2.VideoCapture(0)
    
    if not cap.isOpened():
        print("Error: Could not open webcam.")
        return

    while True:
        ret, frame = cap.read()
        if not ret:
            print("Error: Could not read frame.")
            break

        # Detect cars in the frame
        frame = detect_cars(frame)

        # Display the frame with detected cars
        cv2.imshow('Car Detection', frame)

        # Break the loop on 'q' key press
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    # Release the webcam and close all OpenCV windows
    cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    main()