Improve fallback colorization with better color hints and saturation

app/main_fastai.py

@@ -215,8 +215,9 @@ async def health_check():
 
 def simple_colorize_fallback(image: Image.Image) -> Image.Image:
     """
-    Simple fallback colorization using LAB color space
+    Enhanced fallback colorization using LAB color space with better color hints
     This provides basic colorization when the model doesn't load
+    Note: This is a simple heuristic-based approach and won't match trained models
     """
     # Convert to LAB color space
     if image.mode != "RGB":
@@ -224,26 +225,43 @@ def simple_colorize_fallback(image: Image.Image) -> Image.Image:
     
     # Convert to numpy array
     img_array = np.array(image)
+    original_shape = img_array.shape
     
     # Convert RGB to LAB
     lab = cv2.cvtColor(img_array, cv2.COLOR_RGB2LAB)
     
-    # Apply simple colorization: enhance the L channel and add some color hints
+    # Split channels
     l, a, b = cv2.split(lab)
     
-    # Enhance lightness
-    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
-    l = clahe.apply(l)
+    # Enhance lightness with CLAHE (Contrast Limited Adaptive Histogram Equalization)
+    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+    l_enhanced = clahe.apply(l)
     
-    # Add subtle color hints to a and b channels
-    # This is a very basic approach - just adds some warm tones
-    a = np.clip(a.astype(np.float32) + 5, 0, 255).astype(np.uint8)
-    b = np.clip(b.astype(np.float32) + 10, 0, 255).astype(np.uint8)
+    # Add intelligent color hints based on image characteristics
+    # Analyze the grayscale image to determine color hints
+    l_normalized = l.astype(np.float32) / 255.0
+    
+    # Create color hints: warmer tones for mid-brightness areas
+    # a channel: green-red axis (positive = red, negative = green)
+    # b channel: blue-yellow axis (positive = yellow, negative = blue)
+    
+    # Add warm tones (slight red and yellow bias) based on brightness
+    # Darker areas get cooler tones, mid-brightness gets warmer
+    brightness_mask = np.clip((l_normalized - 0.3) * 2, 0, 1)  # Emphasize mid-brightness
+    
+    # Add color hints: warm tones for skin/faces, cooler for shadows
+    a_hint = np.clip(a.astype(np.float32) + brightness_mask * 8 + (1 - brightness_mask) * 2, 0, 255).astype(np.uint8)
+    b_hint = np.clip(b.astype(np.float32) + brightness_mask * 12 + (1 - brightness_mask) * 3, 0, 255).astype(np.uint8)
     
     # Merge channels and convert back to RGB
-    lab_colored = cv2.merge([l, a, b])
+    lab_colored = cv2.merge([l_enhanced, a_hint, b_hint])
     colored_rgb = cv2.cvtColor(lab_colored, cv2.COLOR_LAB2RGB)
     
+    # Apply slight saturation boost
+    hsv = cv2.cvtColor(colored_rgb, cv2.COLOR_RGB2HSV)
+    hsv[:, :, 1] = np.clip(hsv[:, :, 1].astype(np.float32) * 1.2, 0, 255).astype(np.uint8)
+    colored_rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
+    
     return Image.fromarray(colored_rgb)