restructuration du backend

préparation de l'intégration des modules suivants

code/backend_reconnaissance/compteurDoigt.py

@@ -1,98 +0,0 @@
-import cv2
-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])/((V1[0]**2+V1[1]**2)**(1/2)*(V2[0]**2+V2[1]**2)**(1/2)) #produit scalaire normalisé
-
-# initialisation de la caméra
-cap = cv2.VideoCapture(0)
-
-# initialisation de Mediapipe Hands
-with mp_hands.Hands( static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5, min_tracking_confidence=0.5) as hands:
-
-    while cap.isOpened():
-
-        # lecture de la vidéo
-        ret, frame = cap.read()
-
-        # conversion de l'image en RGB
-        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-
-        # détection des mains
-        results = hands.process(image)
-
-         # Draw the hand annotations on the image.
-        image.flags.writeable = True
-        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())
-
-            # détection du nombre de doigts levés
-            hand_landmarks = [0, 0]
-            finger_count = 0
-            if len(results.multi_hand_landmarks) >0 :
-                hand_landmarks[0] = results.multi_hand_landmarks[0] 
-                V0= [hand_landmarks[0].landmark[2].x - hand_landmarks[0].landmark[0].x, hand_landmarks[0].landmark[2].y - hand_landmarks[0].landmark[0].y]
-                V1= [hand_landmarks[0].landmark[4].x - hand_landmarks[0].landmark[2].x, hand_landmarks[0].landmark[4].y - hand_landmarks[0].landmark[2].y]
-                if prodScalaire(V0,V1) > 0.905135675:
-                    finger_count += 1
-                V0= [hand_landmarks[0].landmark[8].x - hand_landmarks[0].landmark[6].x, hand_landmarks[0].landmark[8].y - hand_landmarks[0].landmark[6].y]
-                V1= [hand_landmarks[0].landmark[6].x - hand_landmarks[0].landmark[0].x, hand_landmarks[0].landmark[6].y - hand_landmarks[0].landmark[0].y]
-                if prodScalaire(V0,V1) > 0:
-                    finger_count += 1
-                V0= [hand_landmarks[0].landmark[12].x - hand_landmarks[0].landmark[10].x, hand_landmarks[0].landmark[12].y - hand_landmarks[0].landmark[10].y]
-                V1= [hand_landmarks[0].landmark[10].x - hand_landmarks[0].landmark[0].x, hand_landmarks[0].landmark[10].y - hand_landmarks[0].landmark[0].y]
-                if prodScalaire(V0,V1) > 0:
-                    finger_count += 1
-                V0= [hand_landmarks[0].landmark[20].x - hand_landmarks[0].landmark[18].x, hand_landmarks[0].landmark[20].y - hand_landmarks[0].landmark[18].y]
-                V1= [hand_landmarks[0].landmark[18].x - hand_landmarks[0].landmark[0].x, hand_landmarks[0].landmark[18].y - hand_landmarks[0].landmark[0].y]
-                if prodScalaire(V0,V1) > 0:
-                    finger_count += 1
-                V0= [hand_landmarks[0].landmark[16].x - hand_landmarks[0].landmark[14].x, hand_landmarks[0].landmark[16].y - hand_landmarks[0].landmark[14].y] 
-                V1= [hand_landmarks[0].landmark[14].x - hand_landmarks[0].landmark[0].x, hand_landmarks[0].landmark[14].y - hand_landmarks[0].landmark[0].y]
-                if prodScalaire(V0,V1) > 0:
-                    finger_count += 1
-
-                if len(results.multi_hand_landmarks) >1 :
-                    hand_landmarks[1] = results.multi_hand_landmarks[1]
-                    V0= [hand_landmarks[1].landmark[2].x - hand_landmarks[1].landmark[0].x, hand_landmarks[1].landmark[2].y - hand_landmarks[1].landmark[0].y]
-                    V1= [hand_landmarks[1].landmark[4].x - hand_landmarks[1].landmark[2].x, hand_landmarks[1].landmark[4].y - hand_landmarks[1].landmark[2].y]
-                    if prodScalaire(V0,V1) > 0.905135675:
-                        finger_count += 1
-                    V0= [hand_landmarks[1].landmark[8].x - hand_landmarks[1].landmark[6].x, hand_landmarks[1].landmark[8].y - hand_landmarks[1].landmark[6].y]
-                    V1= [hand_landmarks[1].landmark[6].x - hand_landmarks[1].landmark[0].x, hand_landmarks[1].landmark[6].y - hand_landmarks[1].landmark[0].y]
-                    if prodScalaire(V0,V1) > 0:
-                        finger_count += 1
-                    V0= [hand_landmarks[1].landmark[12].x - hand_landmarks[1].landmark[10].x, hand_landmarks[1].landmark[12].y - hand_landmarks[1].landmark[10].y]
-                    V1= [hand_landmarks[1].landmark[10].x - hand_landmarks[1].landmark[0].x, hand_landmarks[1].landmark[10].y - hand_landmarks[1].landmark[0].y]
-                    if prodScalaire(V0,V1) > 0:
-                        finger_count += 1
-                    V0= [hand_landmarks[1].landmark[20].x - hand_landmarks[1].landmark[18].x, hand_landmarks[1].landmark[20].y - hand_landmarks[1].landmark[18].y]
-                    V1= [hand_landmarks[1].landmark[18].x - hand_landmarks[1].landmark[0].x, hand_landmarks[1].landmark[18].y - hand_landmarks[1].landmark[0].y]
-                    if prodScalaire(V0,V1) > 0:
-                        finger_count += 1
-                    V0= [hand_landmarks[1].landmark[16].x - hand_landmarks[1].landmark[14].x, hand_landmarks[1].landmark[16].y - hand_landmarks[1].landmark[14].y] 
-                    V1= [hand_landmarks[1].landmark[14].x - hand_landmarks[1].landmark[0].x, hand_landmarks[1].landmark[14].y - hand_landmarks[1].landmark[0].y]
-                    if prodScalaire(V0,V1) > 0:
-                        finger_count += 1
-
-            # affichage du nombre de doigts levés
-            cv2.putText(image, f"Finger count: {finger_count}", (10, 50),
-                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-
-        # affichage de la vidéo
-        cv2.imshow('Video', cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
-        if cv2.waitKey(10) & 0xFF == ord('q'):
-            break
-
-# libération de la caméra et des ressources
-cap.release()
-cv2.destroyAllWindows()

code/backend_reconnaissance/exemple_utilisation.py

@@ -1,29 +0,0 @@
-import requests
-#Exemple ajout d'un commentaire depuis la borne (site ou geste)
-avis = {
-    "note": 8,
-    "source": "borne",
-    #Optionel
-    "auteur_age": 20,
-    "notes_autre": '{"proprete":8,"calme":10}',
-    "auteur_sexe": 'f',
-    "commentaire": "Commentaire"
-}
-
-res = requests.post("http://localhost:8080/add_review", data=avis)
-# print(res.text)
-
-#Exemple ajout d'un commentaire trouvé sur les réseaux sociaux
-avis = {
-    "auteur_nom": "michel",
-    "source": "instagram",
-    "note": 8,
-    "date": "2022-12-24",
-    #Optionel
-    "commentaire": "J'ai beaucoup aimé !",
-    "lien": "https://instagram.com/si_insta_avait_des_liens_vers_des_commentaires_faudrait_le_mettre_ici",
-    "auteur_lien": "https://instagram.com/michel",
-}
-
-# res = requests.post("http://localhost:8080/add_social_review", data=avis)
-print(res.text)

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
+
+

code/backend_reconnaissance/hands.py

@@ -1,78 +0,0 @@
-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
-
-

code/backend_reconnaissance/main.py

@@ -1,45 +1,5 @@
-import asyncio
+from manager import Manager
-import json
+if __name__ == "__main__":
-import math
+    print("backend started")
-import websockets
+    m = Manager()
-import random
+    m.loop()
-import os
-import time
-from hands import getThumbState 
-
-
-class WebsocketServer:
-    def __init__(self,getEffects,port=os.getenv("PORT"),host=os.getenv("HOST")) -> None:
-        self.thumbResult = None
-        self.state = 0
-        self.host = host
-        self.port = port
-        self.getEffects = getEffects
-    
-    async def run(self):
-        async with websockets.serve(self.handler, self.host, self.port):
-            await asyncio.Future()
-
-
-    async def handler(self,websocket):
-        while True:
-            if(self.state == 0):
-                messages, result = self.getEffects()
-                if(messages != False):
-                    if(result == False):
-                        await websocket.send(json.dumps(messages))
-                    else:
-                        self.thumbResult = result
-                        self.state = 1
-                        await websocket.send('{"type":"state","state":2}')
-
-def getEffects():
-    res = getThumbState()
-    if(res != False):
-        state, coords, size, result = res
-        return {"type": "effects", "effects": [{"type": state, "x":coords[0], "y": coords[1], "width": size, "height": size}]}, result
-    else:
-        return False,False
-
-server = WebsocketServer(getEffects)
-asyncio.run(server.run())

code/backend_reconnaissance/manager.py

@@ -0,0 +1,44 @@
+from hand_detector import HandDetector
+from network import WebsocketServer
+import time
+
+class Manager():
+    def __init__(self):
+        self.state = 0
+        self.avis = {
+            "note": None,
+            "commentaire": None,
+            "notes_autres": {}
+        }
+        self.server = WebsocketServer(None)
+        self.server.start()
+        self.handDetector = HandDetector()
+        print("Backend ready")
+
+    def loop(self):
+        while(True):
+            if(self.state == 0):
+                self.sleep()
+            if(self.state == 1):
+                self.camera()
+
+            time.sleep(0.01)
+
+    def sleep(self):
+        res = self.handDetector.detect()
+        if(res != False):
+            self.state = 1
+            self.server.sendMessage({"type": "state", "state": 1})
+
+    def camera(self):
+        res = self.handDetector.detect()
+        if(res != False):
+            state, coords, size, finalDecision = res
+            self.server.sendMessage({"type": "effects", "effects": [{"type": state, "x":coords[0], "y": coords[1], "width": size, "height": size}]})
+            if(finalDecision != False):
+                self.avis["note"] = 10 if finalDecision == "thumbs_up" else 0
+                self.state = 2
+                self.server.sendMessage({"type": "state", "state": 2})
+
+        
+

code/backend_reconnaissance/network.py

@@ -0,0 +1,31 @@
+import asyncio
+import json
+import os
+import threading
+import websockets
+
+class WebsocketServer(threading.Thread):
+    def __init__(self, onMessage, port=os.getenv("PORT"), host=os.getenv("HOST")):
+        threading.Thread.__init__(self)
+        self.host = host
+        self.port = port 
+        self.messageQueue = []
+        self.onMessage = onMessage
+
+    def run(self):
+        print("server thread started")
+        asyncio.run(self.runServer())
+
+    async def runServer(self):
+        async with websockets.serve(self.handler, self.host, self.port):
+            await asyncio.Future()
+    
+    async def handler(self,websocket):
+        while True:
+            for msg in self.messageQueue:
+                await websocket.send(json.dumps(msg))
+                self.messageQueue.pop(0)
+            await asyncio.sleep(0.01)
+
+    def sendMessage(self,message):
+        self.messageQueue.append(message)

code/backend_reconnaissance/reconnaissancePouce.py

@@ -1,88 +0,0 @@
-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)
- """