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

def prodScalaire(V1,V2):
  return V1[0]*V2[0]+V1[1]*V2[1]

def reconnaissancePouce(handLandmarks):
    etatDuPouce=["neutre","baissé","levé"]
    i=0
    j=0
    for cpt in range (0,4):
      V1=[handLandmarks[(4*cpt)+6][0]-handLandmarks[(4*cpt)+5][0],handLandmarks[(4*cpt)+6][1]-handLandmarks[(4*cpt)+5][1]]
      V2=[handLandmarks[(4*cpt)+8][0]-handLandmarks[(4*cpt)+6][0],handLandmarks[(4*cpt)+8][1]-handLandmarks[(4*cpt)+6][1]]
      j=np.dot(V1,V2)
      if (j>0):
        return etatDuPouce[0]      
    V1=[handLandmarks[4][0]-handLandmarks[1][0],handLandmarks[4][1]-handLandmarks[1][1]]
    V2=[handLandmarks[2][0]-handLandmarks[1][0],handLandmarks[2][1]-handLandmarks[1][1]]
    if((np.dot(V1,V2))>0 and (handLandmarks[4][1]>handLandmarks[2][1])):
      i=1
    elif(np.dot(V1,V2)>0 and handLandmarks[4][1]<handLandmarks[2][1]):
      i=2
    return etatDuPouce[i]


cap = cv2.VideoCapture(0)
with mp_hands.Hands(
    model_complexity=0,
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5) as hands:
  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:
        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())

    # Set variable to keep landmarks positions (x and y)
    handLandmarks = []
    if results.multi_hand_landmarks:
      for hand_landmarks in results.multi_hand_landmarks:
        # Fill list with x and y positions of each landmark
        for landmarks in hand_landmarks.landmark:
          handLandmarks.append([landmarks.x, landmarks.y])

        cv2.putText(image, reconnaissancePouce(handLandmarks), (50, 450), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 0, 0), 10)

    # Flip the image horizontally for a selfie-view display.
    cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))
    if cv2.waitKey(5) & 0xFF == 27:
      break 
cap.release()




""" etatDuPouce=["neutre","baissé","levé"]
    i=0

    if results.multi_hand_landmarks:
        
        if(results.multi_hand_landmarks.gestures.categories[0].categoryName==Thumb_Up):
            cv2.putText(image, str(results.multi_hand_landmarks.gestures.categories[0].categoryName), (50, 450), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 0, 0), 10)
        else:
            cv2.putText(image, "raté", (50, 450), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 0, 0), 10)
 """