Update app.py

app.py

@@ -410,35 +410,83 @@ class NeuralNetworkSimulator:
 
 
 
-# Set up MediaPipe Pose for humanoid detection
-mp_pose = mp.solutions.pose
-pose = mp_pose.Pose(static_image_mode=True, min_detection_confidence=0.7)
+# Initialize MediaPipe Holistic
+mp_holistic = mp.solutions.holistic
+holistic = mp_holistic.Holistic(static_image_mode=True, min_detection_confidence=0.7)
 
-# Function to detect humanoid keypoints
-def detect_humanoid(image_path):
+# Lists of landmark names for pose, face, and hands
+pose_landmarks = [name for name in mp_holistic.PoseLandmark.__members__]
+face_landmarks = [name for name in mp_holistic.FaceLandmark.__members__]
+hand_landmarks = [name for name in mp_holistic.HandLandmark.__members__]
+
+def detect_landmarks(image_path):
     image = cv2.imread(image_path)
     image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    results = pose.process(image_rgb)
+    results = holistic.process(image_rgb)
+    
+    image_height, image_width, _ = image.shape
+    keypoints = []
     
+    # Pose landmarks
     if results.pose_landmarks:
-        landmarks = results.pose_landmarks.landmark
-        image_height, image_width, _ = image.shape
-        keypoints = [(int(landmark.x * image_width), int(landmark.y * image_height)) for landmark in landmarks]
-        return keypoints
-    return []
+        for idx, landmark in enumerate(results.pose_landmarks.landmark):
+            x = int(landmark.x * image_width)
+            y = int(landmark.y * image_height)
+            keypoints.append(('POSE_' + pose_landmarks[idx], (x, y)))
+        
+        # Compute chest point
+        nose = results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE]
+        left_shoulder = results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_SHOULDER]
+        right_shoulder = results.pose_landmarks.landmark[mp_holistic.PoseLandmark.RIGHT_SHOULDER]
+        left_hip = results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_HIP]
+        right_hip = results.pose_landmarks.landmark[mp_holistic.PoseLandmark.RIGHT_HIP]
+        
+        chest_x = int((nose.x + left_shoulder.x + right_shoulder.x + left_hip.x + right_hip.x) / 5 * image_width)
+        chest_y = int((nose.y + left_shoulder.y + right_shoulder.y + left_hip.y + right_hip.y) / 5 * image_height)
+        keypoints.append(('CHEST', (chest_x, chest_y)))
+    
+    # Face landmarks
+    if results.face_landmarks:
+        for idx, landmark in enumerate(results.face_landmarks.landmark):
+            x = int(landmark.x * image_width)
+            y = int(landmark.y * image_height)
+            keypoints.append(('FACE_' + face_landmarks[idx], (x, y)))
+    
+    # Left hand landmarks
+    if results.left_hand_landmarks:
+        for idx, landmark in enumerate(results.left_hand_landmarks.landmark):
+            x = int(landmark.x * image_width)
+            y = int(landmark.y * image_height)
+            keypoints.append(('LEFT_HAND_' + hand_landmarks[idx], (x, y)))
+    
+    # Right hand landmarks
+    if results.right_hand_landmarks:
+        for idx, landmark in enumerate(results.right_hand_landmarks.landmark):
+            x = int(landmark.x * image_width)
+            y = int(landmark.y * image_height)
+            keypoints.append(('RIGHT_HAND_' + hand_landmarks[idx], (x, y)))
+    
+    return keypoints
 
-# Function to apply touch points on detected humanoid keypoints
 def apply_touch_points(image_path, keypoints):
     image = cv2.imread(image_path)
     image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     image_pil = Image.fromarray(image_rgb)
     draw = ImageDraw.Draw(image_pil)
     
-    for point in keypoints:
-        draw.ellipse([point[0] - 5, point[1] - 5, point[0] + 5, point[1] + 5], fill='red')
+    font = ImageFont.load_default()  # Specify a better font if needed
+    
+    for name, point in keypoints:
+        if 'CHEST' in name:
+            draw.ellipse([point[0] - 10, point[1] - 10, point[0] + 10, point[1] + 10], fill='blue')
+            draw.text((point[0] + 15, point[1]), name, fill='blue', font=font)
+        else:
+            draw.ellipse([point[0] - 5, point[1] - 5, point[0] + 5, point[1] + 5], fill='red')
+            draw.text((point[0] + 10, point[1]), name, fill='red', font=font)
     
     return image_pil
 
+
 # Function to create a sensation map
 def create_sensation_map(width, height, keypoints):
     sensation_map = np.random.rand(height, width, 12) * 0.5 + 0.5