Update app.py

app.py

@@ -36,18 +36,25 @@ def segment_dress(image_np):
     # Apply morphological operations for better segmentation
     kernel = np.ones((5, 5), np.uint8)
     mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)  # Close small gaps
-    mask = cv2.GaussianBlur(mask, (21, 21), 10)  # Smooth edges for natural blending
+    mask = cv2.GaussianBlur(mask, (15, 15), 5)  # Smooth edges for natural blending
     
     return mask
 
-def get_ambient_light(img_np):
-    """Estimate ambient lighting from non-dress areas for realistic blending."""
-    lab = cv2.cvtColor(img_np, cv2.COLOR_RGB2LAB)
-    L_channel = lab[:, :, 0]  # Lightness channel
-    return np.median(L_channel)  # Median light level in the image
+def recolor_dress(image_np, mask, target_color):
+    """Change dress color while preserving texture and shadows."""
+    img_lab = cv2.cvtColor(image_np, cv2.COLOR_RGB2LAB)
+    target_color_lab = cv2.cvtColor(np.uint8([[target_color]]), cv2.COLOR_BGR2LAB)[0][0]
+    
+    # Preserve lightness (L) and change only chromatic channels (A & B)
+    blend_factor = 0.7
+    img_lab[..., 1] = np.where(mask > 128, img_lab[..., 1] * (1 - blend_factor) + target_color_lab[1] * blend_factor, img_lab[..., 1])
+    img_lab[..., 2] = np.where(mask > 128, img_lab[..., 2] * (1 - blend_factor) + target_color_lab[2] * blend_factor, img_lab[..., 2])
+
+    img_recolored = cv2.cvtColor(img_lab, cv2.COLOR_LAB2RGB)
+    return img_recolored
 
 def change_dress_color(image_path, color):
-    """Change the dress color naturally while keeping textures and adjusting to lighting."""
+    """Change the dress color naturally while keeping textures."""
     if image_path is None:
         return None
 
@@ -66,24 +73,8 @@ def change_dress_color(image_path, color):
     }
     new_color_bgr = np.array(color_map.get(color, (0, 0, 255)), dtype=np.uint8)  # Default to Red
     
-    # Convert image to LAB color space for better blending
-    img_lab = cv2.cvtColor(img_np, cv2.COLOR_RGB2LAB)
-    new_color_lab = cv2.cvtColor(np.uint8([[new_color_bgr]]), cv2.COLOR_BGR2LAB)[0][0]
-    
-    # Adjust color to match ambient lighting
-    ambient_light = get_ambient_light(img_np)
-    img_lab[..., 0] = np.clip(img_lab[..., 0] * (ambient_light / 128), 0, 255)  # Normalize lighting
-    
-    # Preserve texture by modifying only A & B channels
-    blend_factor = 0.6  # Controls intensity of color change
-    img_lab[..., 1] = np.where(mask > 128, img_lab[..., 1] * (1 - blend_factor) + new_color_lab[1] * blend_factor, img_lab[..., 1])
-    img_lab[..., 2] = np.where(mask > 128, img_lab[..., 2] * (1 - blend_factor) + new_color_lab[2] * blend_factor, img_lab[..., 2])
-
-    # Convert back to RGB
-    img_recolored = cv2.cvtColor(img_lab, cv2.COLOR_LAB2RGB)
-    
-    # Use Poisson blending for seamless integration with the environment
-    img_recolored = cv2.seamlessClone(img_recolored, img_np, mask, (img_np.shape[1]//2, img_np.shape[0]//2), cv2.MIXED_CLONE)
+    # Recolor the dress naturally
+    img_recolored = recolor_dress(img_np, mask, new_color_bgr)
 
     return Image.fromarray(img_recolored)