Update app.py

app.py

@@ -4,18 +4,38 @@ import torch
 import cv2
 from PIL import Image
 from torchvision import transforms
-from cloth_segmentation.networks.u2net import U2NET
+from cloth_segmentation.networks.u2net import U2NET  # Import U²-Net
 
-# Load U²-Net Model
+# Load U²-Net model
 model_path = "cloth_segmentation/networks/u2net.pth"
 model = U2NET(3, 1)
 state_dict = torch.load(model_path, map_location=torch.device('cpu'))
-state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
+state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}  # Remove 'module.' prefix
 model.load_state_dict(state_dict)
 model.eval()
 
+def detect_design(image_np):
+    """Detects the design on the dress using edge detection and adaptive thresholding."""
+    gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
+    
+    # Use adaptive thresholding to segment the design
+    adaptive_thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
+                                            cv2.THRESH_BINARY_INV, 11, 2)
+
+    # Detect edges using Canny
+    edges = cv2.Canny(gray, 50, 150)
+
+    # Combine both masks
+    design_mask = cv2.bitwise_or(adaptive_thresh, edges)
+
+    # Morphological operations to remove noise
+    kernel = np.ones((3, 3), np.uint8)
+    design_mask = cv2.morphologyEx(design_mask, cv2.MORPH_CLOSE, kernel)
+
+    return design_mask
+
 def segment_dress(image_np):
-    """Segment the dress using U²-Net and GrabCut."""
+    """Segment the dress using U²-Net"""
     transform_pipeline = transforms.Compose([
         transforms.ToTensor(),
         transforms.Resize((320, 320))
@@ -26,42 +46,36 @@ def segment_dress(image_np):
 
     with torch.no_grad():
         output = model(input_tensor)[0][0].squeeze().cpu().numpy()
+
+    # Convert output to mask
+    dress_mask = (output > 0.5).astype(np.uint8) * 255
+    dress_mask = cv2.resize(dress_mask, (image_np.shape[1], image_np.shape[0]), interpolation=cv2.INTER_NEAREST)
+
+    # Morphological operations for smoothness
+    kernel = np.ones((5, 5), np.uint8)
+    dress_mask = cv2.morphologyEx(dress_mask, cv2.MORPH_CLOSE, kernel)
     
-    u2net_mask = (output > 0.5).astype(np.uint8) * 255
-    u2net_mask = cv2.resize(u2net_mask, (image_np.shape[1], image_np.shape[0]), interpolation=cv2.INTER_NEAREST)
-
-    # Apply GrabCut to refine the mask
-    mask = np.zeros(image_np.shape[:2], np.uint8)
-    mask[u2net_mask > 128] = cv2.GC_FGD
-    mask[u2net_mask <= 128] = cv2.GC_BGD
-    bg_model = np.zeros((1, 65), np.float64)
-    fg_model = np.zeros((1, 65), np.float64)
-    
-    cv2.grabCut(image_np, mask, None, bg_model, fg_model, 5, cv2.GC_INIT_WITH_MASK)
-    mask = np.where((mask == 2) | (mask == 0), 0, 255).astype(np.uint8)
-    
-    return mask
+    return dress_mask
 
-def recolor_dress(image_np, mask, target_color):
-    """Recolor the dress while keeping texture, shadows, and designs."""
+def recolor_dress(image_np, dress_mask, design_mask, target_color):
+    """Change dress color while preserving designs"""
     
-    # Convert to LAB color space
     img_lab = cv2.cvtColor(image_np, cv2.COLOR_RGB2LAB)
-    
-    # Target color in LAB
     target_color_lab = cv2.cvtColor(np.uint8([[target_color]]), cv2.COLOR_BGR2LAB)[0][0]
-    
-    # Preserve lightness (L) and change only chromatic channels (A & B)
+
+    # Ensure the design areas are NOT recolored
+    recolor_mask = cv2.bitwise_and(dress_mask, cv2.bitwise_not(design_mask))
+
+    # Apply color change only to the non-design dress areas
     blend_factor = 0.8
-    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_lab[..., 1] = np.where(recolor_mask > 128, img_lab[..., 1] * (1 - blend_factor) + target_color_lab[1] * blend_factor, img_lab[..., 1])
+    img_lab[..., 2] = np.where(recolor_mask > 128, img_lab[..., 2] * (1 - blend_factor) + target_color_lab[2] * blend_factor, img_lab[..., 2])
 
-    # Convert back to RGB
     img_recolored = cv2.cvtColor(img_lab, cv2.COLOR_LAB2RGB)
     return img_recolored
 
 def change_dress_color(image_path, color):
-    """Change the dress color while preserving texture and design details."""
+    """Change the dress color naturally while keeping designs intact."""
     if image_path is None:
         return None
 
@@ -69,11 +83,14 @@ def change_dress_color(image_path, color):
     img_np = np.array(img)
 
     # Get dress segmentation mask
-    mask = segment_dress(img_np)
+    dress_mask = segment_dress(img_np)
     
-    if mask is None:
+    if dress_mask is None:
         return img  # No dress detected
-    
+
+    # Detect design on the dress
+    design_mask = detect_design(img_np)
+
     # Convert the selected color to BGR
     color_map = {
         "Red": (0, 0, 255), "Blue": (255, 0, 0), "Green": (0, 255, 0), "Yellow": (0, 255, 255),
@@ -83,7 +100,7 @@ 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
     
     # Apply recoloring logic
-    img_recolored = recolor_dress(img_np, mask, new_color_bgr)
+    img_recolored = recolor_dress(img_np, dress_mask, design_mask, new_color_bgr)
 
     return Image.fromarray(img_recolored)