Implémentation du produit scalaire

afin de reconnaitre la pose d'un pouce
Pour ameliorer le resultat il faudra ajouter
une borne de fiabilite

code/Interface_Lounes/reconnaissancePouce.py

@@ -0,0 +1,91 @@
+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=j+np.dot(V1,V2)
+
+    print(j)
+    
+    if(j==-4):#round(produitScalaire()) remplacer ça plus tard pour plus de précision
+      
+      V1=handLandmarks[4]-handLandmarks[0]
+      V2=handLandmarks[3]-handLandmarks[0]
+
+      if((np.dot(V1,V2))==1):i=2 
+      elif((np.dot(V1,V2))==-1):i=1
+      return etatDuPouce[i]
+    else: return"" 
+  
+
+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)
+ """