import cv2
import mediapipe as mp
import numpy as np
import os
from dotenv import load_dotenv
load_dotenv()


class HandDetector():
	def __init__(self):
		self.camera_id = int(os.getenv("CAMERA_ID"))
		self.mp_drawing = mp.solutions.drawing_utils
		self.mp_drawing_styles = mp.solutions.drawing_styles
		self.mp_hands = mp.solutions.hands
		self.cap = cv2.VideoCapture(self.camera_id)
		self.hands = self.mp_hands.Hands(
				model_complexity=0,
				min_detection_confidence=0.5,
				min_tracking_confidence=0.5)
		#Paramètres
		self.BUFFER_LENGTH = 60
		self.DETECTION_THRESHOLD = 3/4

		self.resultBuffer = []
				
	def reset(self):
		self.resultBuffer = []

	def reconnaissancePouce(self,handLandmarks):
			etatDuPouce=["neutre","thumbs_down","thumbs_up"]
			i=0
			j=0
			for cpt in range (0,4):
				V1=[handLandmarks[(4*cpt)+5][0]-handLandmarks[(4*cpt)+0][0],handLandmarks[(4*cpt)+5][1]-handLandmarks[(4*cpt)+0][1]]
				V2=[handLandmarks[(4*cpt)+8][0]-handLandmarks[(4*cpt)+5][0],handLandmarks[(4*cpt)+8][1]-handLandmarks[(4*cpt)+5][1]]
				j1=np.dot(V1,V2)
				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]]
				j2=np.dot(V1,V2)
				if (j1>0 and j2>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 detect(self):
		if self.cap.isOpened():
			success, image = self.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
			image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
			results = self.hands.process(image)

			if results.multi_hand_landmarks:
				handsPositions = []
				for hand_landmarks in results.multi_hand_landmarks:
					handLandmarks = []
					# Fill list with x and y positions of each landmark
					for landmarks in hand_landmarks.landmark:
						handLandmarks.append([landmarks.x, landmarks.y])
					#On ajoute la position de chaque mains a une liste
					handsPositions.append([self.reconnaissancePouce(handLandmarks), handLandmarks])
				
				#On calcule le résultat suivant la position des deux mains
				if(len(handsPositions) == 2):
					if(handsPositions[0][0] == handsPositions[1][0]):
						thumbState = handsPositions[0]
						handLandmarks = handsPositions[0][1]
					elif(handsPositions[0][0] == "neutre"):
						thumbState = handsPositions[1]
						handLandmarks = handsPositions[1][1]
					elif(handsPositions[1][0] == "neutre"):
						thumbState = handsPositions[0][0]
						handLandmarks = handsPositions[0][1]
					else:
						thumbState = "neutre"
				else:
					thumbState = handsPositions[0][0]
					handsLandmarks = handsPositions[0][1]

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

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

				progress = 0
				if thumbState  == "thumbs_up":
					progress = thumbsUpCount / (self.BUFFER_LENGTH * self.DETECTION_THRESHOLD)
				elif thumbState == "thumbs_down":
					progress = thumbsDownCount / (self.BUFFER_LENGTH * self.DETECTION_THRESHOLD)
				
				if(thumbState != "neutre"):
					return thumbState, handLandmarks[9], np.linalg.norm(np.array(handLandmarks[9]) - np.array(handLandmarks[0])), result, progress
		return False


if __name__ == "__main__":
	h = HandDetector()
	while(1):
		print(h.detect())