import cv2
import serial
import time

ml = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
webcam = cv2.VideoCapture(0)

#serial setup
ser = serial.Serial('COM4', 9600)
time.sleep(2)

crosshair_x, crosshair_y = None, None

while True:
    succes, frame = webcam.read()
    if not succes:
        print("Goruntu Basarisiz")
        break


    frame_height, frame_width = frame.shape[:2]


    crosshair_x = frame_width // 2
    crosshair_y = frame_height // 2
    gray_img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = ml.detectMultiScale(gray_img)


    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 3)

        face_center_x = x + w // 2
        face_center_y = y + h // 2

        cv2.circle(frame, (face_center_x, face_center_y), 5, (0, 0, 255), -1)

        faceY = f"{face_center_y}\n"
        crossY = f"{crosshair_y}\n"
        crossX = f"{crosshair_x}\n"
        faceX = f"{face_center_x}\n"
        ser.write(faceY.encode('utf-8'))
        ser.write(faceX.encode('utf-8'))
        ser.write(crossY.encode('utf-8'))
        ser.write(crossX.encode('utf-8'))
        print(faceY.encode('utf-8'))
        print(crossY.encode('utf-8'))


    crosshair_size = 10  # Size of the crosshair
    cv2.line(frame, (crosshair_x - crosshair_size, crosshair_y), (crosshair_x + crosshair_size, crosshair_y),
             (0, 255, 0), 2)  # Horizontal line (Red)
    cv2.line(frame, (crosshair_x, crosshair_y - crosshair_size), (crosshair_x, crosshair_y + crosshair_size),
             (0, 255, 0), 2)  # Vertical line (Red)

    cv2.imshow("video", frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break8


webcam.release()
cv2.destroyAllWindows()