Intégration de la reco d'image à l'interface borne

code/backend_reconnaissance/hands.py

@@ -1,44 +1,78 @@
 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
-
-# For webcam input:
 cap = cv2.VideoCapture(0)
 hands = mp_hands.Hands(
-    model_complexity=0,
-    min_detection_confidence=0.5,
-    min_tracking_confidence=0.5)
+		model_complexity=0,
+		min_detection_confidence=0.5,
+		min_tracking_confidence=0.5)
+BUFFER_LENGTH = 30
+TH_FRACTION = 3/4
+resultBuffer = []
 
-def frame():
-  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
+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]
 
-    # 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())
-    # Flip the image horizontally for a selfie-view display.
-    # cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))
-    if cv2.waitKey(5) & 0xFF == 27:
-      return
-# cap.release()
+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
+