Update utils.py

utils.py

@@ -1,24 +1,67 @@
 import cv2
 import mediapipe as mp
 import numpy as np
+from typing import Tuple, Optional
 
+# Initialize MediaPipe face detection
 mp_face_detection = mp.solutions.face_detection
+mp_face_mesh = mp.solutions.face_mesh  # For detailed landmarks
 
-def extract_features(image_path):
-    """Extract face shape, skin tone, and size from an image."""
+def classify_face_shape(landmarks) -> str:
+    """Determine face shape using geometric ratios."""
+    # Example: Calculate jaw-to-forehead ratio
+    jaw_width = landmarks[234].x - landmarks[454].x  # Left/right jaw points
+    forehead_height = landmarks[10].y - landmarks[152].y  # Forehead to chin
+    
+    ratio = jaw_width / forehead_height
+    if ratio > 1.1: return "Square"
+    elif ratio > 0.9: return "Round"
+    else: return "Oval"
+
+def estimate_skin_tone(image, face_roi) -> str:
+    """Analyze skin tone in the cheek region."""
+    x, y, w, h = face_roi
+    cheek_region = image[y:y+h//2, x:x+w//2]  # Get left cheek area
+    
+    # Convert to LAB color space for skin tone analysis
+    lab = cv2.cvtColor(cheek_region, cv2.COLOR_BGR2LAB)
+    l, a, b = np.mean(lab, axis=(0,1))
+    
+    if l > 160: return "Fair"
+    elif l > 120: return "Medium"
+    else: return "Dark"
+
+def extract_features(image_path: str) -> Tuple[Optional[str], Optional[str], Optional[str]]:
+    """Extract face attributes from an image."""
+    # Read and convert image
     image = cv2.imread(image_path)
+    if image is None:
+        return None, None, None
+        
     image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    return "Oval", "Medium", "Medium"  # Default fallback values
-    
-    # Detect face (simplified example)
-    with mp_face_detection.FaceDetection(min_detection_confidence=0.5) as face_detector:
-        results = face_detector.process(image_rgb)
+    height, width = image.shape[:2]
+
+    # Face detection
+    with mp_face_detection.FaceDetection(min_detection_confidence=0.5) as detector:
+        results = detector.process(image_rgb)
         if not results.detections:
             return None, None, None
+            
+        # Get face bounding box
+        bbox = results.detections[0].location_data.relative_bounding_box
+        x, y = int(bbox.xmin * width), int(bbox.ymin * height)
+        w, h = int(bbox.width * width), int(bbox.height * height)
         
-        # Mock logic (replace with actual calculations)
-        face_shape = "Oval"    # Use landmarks to classify shape
-        skin_tone = "Medium"  # Estimate from cheek region
-        face_size = "Medium"  # Measure bounding box
+        # Face size classification
+        face_size = "Large" if w > 300 else "Medium" if w > 200 else "Small"
         
-    return face_shape, skin_tone, face_size
+        # Face mesh for detailed landmarks
+        with mp_face_mesh.FaceMesh(static_image_mode=True) as mesh:
+            mesh_results = mesh.process(image_rgb)
+            if mesh_results.multi_face_landmarks:
+                landmarks = mesh_results.multi_face_landmarks[0].landmark
+                face_shape = classify_face_shape(landmarks)
+                skin_tone = estimate_skin_tone(image, (x, y, w, h))
+                return face_shape, skin_tone, face_size
+                
+    return None, None, None  # Fallback if mesh detection fails