Day10(objectdetection).py

import cv2

#  pre-trained Haar cascade for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

cap = cv2.VideoCapture(0)

prev_frame = None

while True:
    #frame-by-frame capturing
    ret, frame = cap.read()

    
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # face detection
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # draw rectangles around the faces
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)

  
    if prev_frame is not None:
       
        frame_diff = cv2.absdiff(prev_frame, gray)
        
       
        _, frame_diff_thresh = cv2.threshold(frame_diff, 30, 255, cv2.THRESH_BINARY)

        
        contours, _ = cv2.findContours(frame_diff_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

      
        for contour in contours:
            if cv2.contourArea(contour) > 50:
                x, y, w, h = cv2.boundingRect(contour)
                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

    cv2.imshow('Video', frame)

    # frame store for next iteration
    prev_frame = gray

    # exit with 'q'
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

#release
cap.release()
cv2.destroyAllWindows()