#!/usr/bin/env python

import math
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands

# For webcam input:
# cv2.namedWindow("window", cv2.WND_PROP_FULLSCREEN)
# cv2.setWindowProperty("window",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
cap = cv2.VideoCapture(0)
count = 0
with mp_hands.Hands(
    model_complexity=0,
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5,
    max_num_hands=20) as hands:
  framecount = 0 
  previous_x = 0
  previous_y = 0
  timeout = 0
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      print("Ignoring empty camera frame.")
      # If loading a video, use 'break' instead of 'continue'.
      continue

    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    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
       
        
        if (framecount % 5) == 0:
          
          # print(f'{abs(previous_x - x)} {abs(previous_y - y)}')
          previous_x = x
          previous_y = y

          if abs(previous_x - x) < 0.01 and abs(previous_y - y) < 0.01 and timeout <= 0:
            print("Still")
            cv2.circle(image,(int(x),int(y)), 63, (0,0,255), -1)
            print(x, y)
            timeout = 5
          timeout -= 1
        framecount += 1
        

        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('window', cv2.flip(image, 1))
    if cv2.waitKey(5) & 0xFF == ord('q'):
      break
cap.release()