Update app.py

app.py

@@ -68,7 +68,7 @@ def recolor_dress(image_np, dress_mask, target_color):
 
     mean_L, mean_A, mean_B = np.mean(dress_pixels, axis=0)
 
-    # Apply histogram-based color transfer with topological adjustment
+    # Apply LAB shift
     a_shift = target_color_lab[1] - mean_A
     b_shift = target_color_lab[2] - mean_B
     img_lab[..., 1] = np.clip(img_lab[..., 1] + (dress_mask / 255.0) * a_shift, 0, 255)
@@ -78,19 +78,20 @@ def recolor_dress(image_np, dress_mask, target_color):
     img_recolored = cv2.cvtColor(img_lab.astype(np.uint8), cv2.COLOR_LAB2RGB)
 
     # Create feathered mask for smooth blending
+    lightness_mask = (img_lab[..., 0] / 255.0)
     feathered_mask = cv2.GaussianBlur(dress_mask, (15, 15), 5)
+    adaptive_feather = (feathered_mask * lightness_mask).astype(np.uint8)
 
     # Blend the recolored dress with the original image
-    img_final = (image_np * (1 - feathered_mask[..., None] / 255) + img_recolored * (feathered_mask[..., None] / 255)).astype(np.uint8)
+    img_final = (image_np * (1 - adaptive_feather[..., None] / 255) + img_recolored * (adaptive_feather[..., None] / 255)).astype(np.uint8)
 
     return img_final
 
-def change_dress_color(image_path, color):
+def change_dress_color(img, color):
     """Main function to change dress color naturally"""
-    if image_path is None:
+    if img is None:
         return None
 
-    img = Image.open(image_path).convert("RGB")
     img_np = np.array(img)
 
     # Get dress segmentation mask
@@ -119,7 +120,7 @@ def change_dress_color(image_path, color):
 demo = gr.Interface(
     fn=change_dress_color,
     inputs=[
-        gr.Image(type="filepath", label="Upload Dress Image"),
+        gr.Image(type="pil", label="Upload Dress Image"),
         gr.Radio(["Red", "Blue", "Green", "Yellow", "Purple", "Orange", "Cyan", "Magenta", "White", "Black"], label="Choose New Dress Color")
     ],
     outputs=gr.Image(type="pil", label="Color Changed Dress"),