ML Project.py

import cv2
import requests

# Load the classifiers
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
body_cascade = cv2.CascadeClassifier('haarcascade_fullbody.xml')

# Set up the camera
cap = cv2.VideoCapture(0)

while True:
    # Capture the video feed frame by frame
    ret, frame = cap.read()

    # Convert the frame to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces and bodies
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
    bodies = body_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)

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

    # Check if any faces or bodies were detected
    if len(faces) > 0 or len(bodies) > 0:
        
        print("Person detected!")
        
        # Save the frame with the rectangles drawn
        cv2.imwrite('detected_person.jpg', frame, [int(cv2.IMWRITE_JPEG_QUALITY), 90])
        
        # Send notification to LINE using LINE Notify API
        url = 'https://notify-api.line.me/api/notify'
        token = 'Your Token'
        headers = {'Authorization': f'Bearer {token}'}
        files = {'imageFile': open('detected_person.jpg', 'rb')}
        data = {'message': 'Person detected!'}
        res = requests.post(url, headers=headers, data=data, files=files)
    
    # Display the resulting frame
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) == ord('q'):
        break

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