#!/usr/bin/python3

import cv2

from picamera2 import Picamera2
import mediapipe as mp

mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands


# Grab images as numpy arrays and leave everything else to OpenCV.

cv2.startWindowThread()

picam2 = Picamera2()
picam2.configure(picam2.create_preview_configuration(main={"format": 'XRGB8888', "size": (1280, 720)}))
picam2.start()

cv2.namedWindow("Camera", cv2.WND_PROP_FULLSCREEN)
cv2.setWindowProperty("Camera", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)

with mp_hands.Hands(
    model_complexity=0,
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5,
    max_num_hands=20) as hands:

  while True:
      image = picam2.capture_array()


      image.flags.writeable = False
      image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
      results = hands.process(image)

      # Draw the hand annotations on the image.
      image.flags.writeable = True
      image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

    
 
      if results.multi_hand_landmarks:
        for hand_landmarks in results.multi_hand_landmarks:
          x = hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x
          y = hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y

          mp_drawing.draw_landmarks(
              image,
              hand_landmarks,
              mp_hands.HAND_CONNECTIONS,
              mp_drawing_styles.get_default_hand_landmarks_style(),
              mp_drawing_styles.get_default_hand_connections_style())
      # Flip the image horizontally for a selfie-view display.
      cv2.imshow("Camera", image)
      if cv2.waitKey(1) & 0xFF == ord('q'):
        break