restructuration du backend

préparation de l'intégration des modules suivants

code/backend_reconnaissance/hand_detector.py

@@ -0,0 +1,84 @@
+import cv2
+import mediapipe as mp
+import numpy as np
+
+class HandDetector():
+	def __init__(self):
+		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(0)
+		self.hands = self.mp_hands.Hands(
+				model_complexity=0,
+				min_detection_confidence=0.5,
+				min_tracking_confidence=0.5)
+		#Paramètres
+		self.BUFFER_LENGTH = 30
+		self.DETECTION_THRESHOLD = 3/4
+
+		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)+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 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
+			results = self.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 = self.reconnaissancePouce(handLandmarks)
+
+				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))
+
+				print(thumbsUpCount,thumbsDownCount)
+
+				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
+
+				if(thumbState != "neutre"):
+					return thumbState, handLandmarks[9], np.linalg.norm(np.array(handLandmarks[9]) - np.array(handLandmarks[0])), result
+		return False
+
+