import cv2
import mediapipe as mp
import numpy as np

mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
cap = cv2.VideoCapture(0)
hands = mp_hands.Hands(
		model_complexity=0,
		min_detection_confidence=0.5,
		min_tracking_confidence=0.5)
BUFFER_LENGTH = 30
TH_FRACTION = 3/4
resultBuffer = []

def reconnaissancePouce(handLandmarks):
		etatDuPouce=["neutre","thumbs_down","thumbs_up"]
		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]


def getThumbState():
	if cap.isOpened():
		success, image = cap.read()
		if not success:
			print("Ignoring empty camera frame.")
			# If loading a video, use 'break' instead of 'continue'.
			return False

		# To improve performance, optionally mark the image as not writeable to
		# pass by reference.
		image.flags.writeable = False
		results = hands.process(image)
		# print(results)
		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])

			thumbState = reconnaissancePouce(handLandmarks)

			resultBuffer.append(thumbState)
			if(len(resultBuffer) > BUFFER_LENGTH):
				resultBuffer.pop(0)
			
			thumbsUpCount = sum(map(lambda x : x == "thumbs_up", resultBuffer))			
			thumbsDownCount = sum(map(lambda x : x == "thumbs_down", resultBuffer))

			print(thumbsUpCount,thumbsDownCount)

			if(thumbsUpCount > TH_FRACTION * BUFFER_LENGTH):
				result = "thumbs_up"
			elif(thumbsDownCount > TH_FRACTION * BUFFER_LENGTH):
				result = "thumbs_down"
			else:
				result = False

			if(thumbState != "neutre"):
				return thumbState, handLandmarks[9], np.linalg.norm(np.array(handLandmarks[9]) - np.array(handLandmarks[0])), result
	return False