Update app.py

app.py

@@ -21,9 +21,8 @@ def remove_background(img: Image.Image) -> Image.Image:
 
 def blur_faces_smooth(image: Image.Image) -> tuple[Image.Image, list]:
     """
-    Detects faces using Haar Cascades and applies a smooth, feathered Gaussian blur
-    strictly to the eyes/mouth area. It shifts the blur ellipse downwards so that
-    eyebrows (and forehead/hair) are left completely sharp and untouched.
+    Detects faces using multiple Haar Cascades (frontal, alt, profile) to ensure
+    all faces in group/couple photos are found, and applies a heavy Gaussian blur.
     """
     rgba = image.convert("RGBA")
     width, height = rgba.size
@@ -33,14 +32,20 @@ def blur_faces_smooth(image: Image.Image) -> tuple[Image.Image, list]:
     open_cv_image = open_cv_image[:, :, ::-1].copy() # Convert RGB to BGR
     gray = cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2GRAY)
     
-    # Load frontal face cascade
-    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-    faces = face_cascade.detectMultiScale(
-        gray, 
-        scaleFactor=1.1, 
-        minNeighbors=4, 
-        minSize=(25, 25)
-    )
+    # Load frontal and profile cascades for maximum coverage
+    face_cascade_default = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    face_cascade_alt = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_alt2.xml')
+    profile_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_profileface.xml')
+    
+    faces_default = face_cascade_default.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=3, minSize=(20, 20))
+    faces_alt = face_cascade_alt.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=3, minSize=(20, 20))
+    faces_profile = profile_cascade.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=3, minSize=(20, 20))
+    
+    # Combine detected faces
+    faces = []
+    if len(faces_default) > 0: faces.extend(faces_default)
+    if len(faces_alt) > 0: faces.extend(faces_alt)
+    if len(faces_profile) > 0: faces.extend(faces_profile)
     
     if len(faces) == 0:
         # Fallback: soft feathered blur on upper center if no faces detected
@@ -52,7 +57,7 @@ def blur_faces_smooth(image: Image.Image) -> tuple[Image.Image, list]:
         cv2.ellipse(mask_np, (cx, cy), (rx, ry), 0, 0, 360, 255, -1)
         mask = Image.fromarray(mask_np).filter(ImageFilter.GaussianBlur(radius=20))
         
-        blurred_img = rgba.filter(ImageFilter.GaussianBlur(radius=16))
+        blurred_img = rgba.filter(ImageFilter.GaussianBlur(radius=25))
         return Image.composite(blurred_img, rgba, mask), []
 
     # Create a smooth, feathered mask (0 = original, 255 = blurred)
@@ -75,8 +80,8 @@ def blur_faces_smooth(image: Image.Image) -> tuple[Image.Image, list]:
     # Heavy blur on mask to feather edges completely smoothly
     mask = mask.filter(ImageFilter.GaussianBlur(radius=10))
     
-    # Generate the blurred version of the face areas
-    blurred_img = rgba.filter(ImageFilter.GaussianBlur(radius=22))
+    # Generate the heavily blurred version of the face areas
+    blurred_img = rgba.filter(ImageFilter.GaussianBlur(radius=35))
     
     # Composite the blurred face areas onto the original image
     return Image.composite(blurred_img, rgba, mask), list(faces)