Merge branch 'Interface_Lounes' of gitlab.enst.fr:pact/2022-2023/pact71 into quentin

code/Interface_Lounes/determinationGenre.py

@@ -0,0 +1,59 @@
+import cv2
+import mediapipe as mp
+
+mp_drawing = mp.solutions.drawing_utils
+mp_face_mesh = mp.solutions.face_mesh
+
+drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
+cap = cv2.VideoCapture(0)
+with mp_face_mesh.FaceMesh(
+        max_num_faces=1,
+        refine_landmarks=True,
+        min_detection_confidence=0.5,
+        min_tracking_confidence=0.5) as face_mesh:
+    while cap.isOpened():
+        success, image = cap.read()
+        if not success:
+            print("Ignoring empty camera frame.")
+            continue
+        # Initialize the face mesh model
+        face_mesh = mp_face_mesh.FaceMesh(static_image_mode=False, max_num_faces=1, min_detection_confidence=0.5)
+
+        # Load the input image
+        # lecture de la vidéo
+        ret, frame = cap.read()
+        # conversion de l'image en RGB    
+        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        # Process the image and extract the landmarks
+        results = face_mesh.process(image)
+        if results.multi_face_landmarks:
+            landmarks = results.multi_face_landmarks[0]
+
+            # Define the landmark indices for the corners of the eyes and the tip of the nose
+            left_eye = [33, 133, 246, 161, 160, 159, 158, 157, 173, 133]
+            right_eye = [362, 263, 373, 380, 381, 382, 384, 385, 386, 362]
+            nose_tip = 4
+
+            # Calculate the distance between the eyes and the nose tip
+            left_eye_x = landmarks.landmark[left_eye[0]].x * image.shape[1]
+            right_eye_x = landmarks.landmark[right_eye[0]].x * image.shape[1]
+            nose_x = landmarks.landmark[nose_tip].x * image.shape[1]
+            eye_distance = abs(left_eye_x - right_eye_x)
+            nose_distance = abs(nose_x - (left_eye_x + right_eye_x) / 2)
+
+            # Determine the gender based on the eye and nose distances
+            if eye_distance > 1.5 * nose_distance:
+                gender = "Female"
+            else:
+                gender = "Male"
+
+            # Draw the landmarks on the image
+            cv2.putText(image, gender, (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/Interface_Lounes/reconnaissancePouce.py

@@ -0,0 +1,88 @@
+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].x-handLandmarks[(4*cpt)+5].x,handLandmarks[(4*cpt)+6].y-handLandmarks[(4*cpt)+5].y]
+      V2=[handLandmarks[(4*cpt)+8].x-handLandmarks[(4*cpt)+6].x,handLandmarks[(4*cpt)+8].y-handLandmarks[(4*cpt)+6].y]
+      j=np.dot(V1,V2)
+      if (j>0):
+        return etatDuPouce[0]      
+    V1=[handLandmarks[4].x-handLandmarks[1].x,handLandmarks[4].y-handLandmarks[1].y]
+    V2=[handLandmarks[2].x-handLandmarks[1].x,handLandmarks[2].y-handLandmarks[1].y]
+    if((np.dot(V1,V2))>0 and (handLandmarks[4].y>handLandmarks[2].y)):
+      i=1
+    elif(np.dot(V1,V2)>0 and handLandmarks[4].y<handLandmarks[2].y):
+      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)
+ """