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

code/Reconnaissance Image/Fingers5.py

@@ -1,75 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-from torchvision import datasets, transforms
-import matplotlib.pyplot as plt
-
-trainSet = datasets.ImageFolder(r'C:\Users\kesha\Desktop\TelecomParis\PACT\DownloadedDataset\train',
-                                transform = transforms.ToTensor())
-valSet = datasets.ImageFolder(r'C:\Users\kesha\Desktop\TelecomParis\PACT\DownloadedDataset\val',
-                              transform = transforms.ToTensor())
-
-trainloader = torch.utils.data.DataLoader(trainSet,
-                                          batch_size = 50,
-                                          shuffle = True)
-
-valloader = torch.utils.data.DataLoader(valSet,
-                                        batch_size = 50,
-                                        shuffle = True)
-
-class Net(nn.Module):
-    def __init__(self):
-        super().__init__()
-        #nn.Conv2d(channels_in, out_channels/number of filters, kernel size)
-        self.conv1 = nn.Conv2d(3, 16, 3)
-        self.pool = nn.MaxPool2d(2, 2)
-        self.conv2 = nn.Conv2d(16, 32, 3)
-        self.conv3 = nn.Conv2d(32, 64, 3)
-        self.fc1 = nn.Linear(64*14*14, 16)
-        self.fc2 = nn.Linear(16, 6)
-
-    def forward(self, x):
-        x = self.pool(F.relu(self.conv1(x)))
-        #size = 16*126*126 then 16*63*63
-        x = self.pool(F.relu(self.conv2(x)))
-        #size = 32*61*61 then 32*30*30
-        x = self.pool(F.relu(self.conv3(x)))
-        #size = 64*28*28 then 64*14*14
-        x = torch.flatten(x, 1)
-        x = F.relu(self.fc1(x))
-        x = self.fc2(x)
-        return x
-
-net = Net()
-print(net)
-
-criterion = nn.CrossEntropyLoss()
-optimizer = optim.RMSprop(net.parameters(), lr=0.001)
-
-device = torch.device('cuda')
-for epoch in range(1, 7):
-    print('Starting epoch ' + str(epoch))
-    current_loss = 0
-    Epoch = []
-    Loss = []
-    for i, data in enumerate(trainloader, 0):
-        inputs, labels = data
-
-        #très important
-        optimizer.zero_grad()
-
-        output = net(inputs)
-        loss = criterion(output, labels)
-        loss.backward()
-        optimizer.step()
-
-        current_loss += loss.item()
-    print('epoch: ', epoch, " loss: ", current_loss)
-    Loss.append(current_loss)
-    Epoch.append(epoch)
-    
-plt.plot(Epoch, Loss)
-plt.title('Valeur de la fonction cost en fonction de l\'epoch')
-plt.show()
-#to save a model: torch.save(net.state_dict(), file_location)

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)
+BUFFER_LENGTH = 30
+TH_FRACTION = 3/4
+resultBuffer = []
 
-def frame():
+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
+			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 = hands.process(image)
-
-    # Draw the hand annotations on the image.
-    image.flags.writeable = True
-    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+		# print(results)
+		handLandmarks = []
 		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()
+				# 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

@@ -4,12 +4,14 @@ import math
 import websockets
 import random
 import os
-import hands
 import time
+from hands import getThumbState 
+
 
-values = []
 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
@@ -21,19 +23,23 @@ class WebsocketServer:
 
     async def handler(self,websocket):
         while True:
-            start = time.time()
-            messages = self.getEffects()
-            hands.frame()
+            if(self.state == 0):
+                messages, result = self.getEffects()
+                if(messages != False):
+                    if(result == False):
                         await websocket.send(json.dumps(messages))
-            # await asyncio.sleep(1/30)
-            delay = time.time() - start
-            values.append(1/delay)
-            avg = sum(values) / len(values)
-            dev = [(v - avg) ** 2 for v in values]
-            print(avg, math.sqrt(sum(dev)/len(dev)))
-#Remplacer ça par la fonction qui récupère les effets (dans le module de reconnaissance de gestes)
+                    else:
+                        self.thumbResult = result
+                        self.state = 1
+                        await websocket.send('{"type":"state","state":2}')
+
 def getEffects():
-    return {"type": "effects", "effects": [{"type": "thumbs_up", "x":random.randint(0,100), "y": random.randint(0,100), "width": 50, "height": 50}]}
+    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/requirements.txt

@@ -2,3 +2,4 @@ websockets
 requests
 opencv-python
 mediapipe
+numpy

code/docker-compose.yaml

@@ -55,13 +55,13 @@ services:
     restart: always
 
   # Serveur web de l'interface de la borne
-  # interface_borne:
-  #   image: httpd:latest
-  #   volumes:
-  #     - ./interface_borne:/usr/local/apache2/htdocs/
-  #   container_name: interface_borne
-  #   ports:
-  #     - 8888:80
+  interface_borne:
+    image: httpd:latest
+    volumes:
+      - ./interface_borne:/usr/local/apache2/htdocs/
+    container_name: interface_borne
+    ports:
+      - 8888:80
   
   #Serveur web de l'interface admin
   interface_admin:
@@ -75,24 +75,24 @@ services:
   # #Backend de la borne : scripts pythons de reconnaissances video et audio
   # #Envoient les infos a l'interface de la borne par websocket pour mettre a jour l'interface rapidement
   # #Met a jour les avis en faisant des requêtes a l'API
-  # backend_reconnaissance:
-  #   build: ./backend_reconnaissance
-  #   container_name: backend_reconnaissance
-  #   restart: always
-  #   devices:
-  #     - /dev/video3:/dev/video0
-  #   environment:
-  #     - PORT=5000
-  #     - HOST=backend_reconnaissance
-  #   ports:
-  #     #Ce container est le serveur websocker dont le client est l'interface de la borne qui tourne dans le navigateur
-  #     - 5000:5000
+  backend_reconnaissance:
+    build: ./backend_reconnaissance
+    container_name: backend_reconnaissance
+    restart: always
+    devices:
+      - /dev/video3:/dev/video0
+    environment:
+      - PORT=5000
+      - HOST=backend_reconnaissance
+    ports:
+      #Ce container est le serveur websocker dont le client est l'interface de la borne qui tourne dans le navigateur
+      - 5000:5000
     
-  # video_loopback:
-  #   build: ./video_loopback
-  #   container_name: video_loopback
-  #   restart: always
-  #   devices:
-  #     - /dev/video0:/dev/video0 
-  #     - /dev/video2:/dev/video1 
-  #     - /dev/video3:/dev/video2 
+  video_loopback:
+    build: ./video_loopback
+    container_name: video_loopback
+    restart: always
+    devices:
+      - /dev/video0:/dev/video0 
+      - /dev/video2:/dev/video1 
+      - /dev/video3:/dev/video2 

code/interface_borne/assets/js/camera_page.js

@@ -84,7 +84,6 @@ class CameraPage {
     _frame() {
         if (this.streaming && this.enabled && this.width && this.height) {
             this.ctx.clearRect(0, 0, this.canvas.width, this.canvas.height);
-            // this.ctx.drawImage(this.video, 0, 0, this.width, this.height);
             this._drawEffects();
         }
         if (this.enabled) {
@@ -106,6 +105,11 @@ class CameraPage {
     _drawEffects() {
         for (let effect of this.activeEffects) {
             let { x, y, width, height } = this._scaleEffect(effect.x, effect.y, effect.width, effect.height);
+            width = width * this.videoWidth * 2;
+            height = height * this.videoHeight * 2;
+            x = x * this.videoWidth - width / 2;
+            y = y * this.videoHeight - height / 2;
+            console.log(width, height);
             if (effect.type == "thumbs_down") {
                 this._drawThumbsDown(x, y, width, height);
             }

code/interface_borne/assets/js/network.js

@@ -3,14 +3,16 @@ class WebsocketClient {
         this.socket = new WebSocket("ws://localhost:5000");
         this.socket.addEventListener("open", (event)  => {
             this.socket.send("connected");
+            console.log("connected")
         });
-        this.socket.addEventListener("message", (event) => {
+
+        this.socket.onmessage = (event) => {
             let msg = JSON.parse(event.data);
             if (msg.type == "effects") {
                 onNewEffects(msg.effects);
             }else if(msg.type == "state") {
                 onNewState(msg.state);
             }
-        });
+        };
     }
 }

code/interface_borne/assets/js/state_manager.js

@@ -14,8 +14,11 @@ class StateManager {
         this._thankYouPage = new ThankYouPage();
 
         this.wsClient = new WebsocketClient(
-            (effects) => this._cameraPage.setEffects(effects),
-            (state) => this.changeState(state)
+            (effects) =>  {
+                this.setState(STATE.video);
+                this._cameraPage.setEffects(effects)
+            },
+            (state) => this.setState(state)
         );
         
         this._sleepingPage.enabled = true;