Update app.py

app.py

@@ -5,81 +5,68 @@ import cv2
 from PIL import Image
 from torchvision import transforms
 from cloth_segmentation.networks.u2net import U2NET
+import matplotlib.colors as mcolors
 
-# Load U²-Net model
+# Load U²-Net
 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 = torch.load(model_path, map_location=torch.device("cpu"))
+state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
 model.load_state_dict(state_dict)
 model.eval()
 
+# Util to get BGR color from name
+def get_bgr_from_color_name(color_name):
+    try:
+        rgb = mcolors.to_rgb(color_name.lower())
+        return tuple(int(255 * c) for c in rgb[::-1])  # Convert to BGR
+    except:
+        return (0, 0, 255)  # Default to red
+
+# Mask refinement
 def refine_mask(mask):
-    """Enhanced mask refinement with erosion and morphological operations"""
-    # First closing to fill small holes
     close_kernel = np.ones((5, 5), np.uint8)
     mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, close_kernel)
-    
-    # Erosion to remove small protrusions and extra areas
     erode_kernel = np.ones((3, 3), np.uint8)
     mask = cv2.erode(mask, erode_kernel, iterations=1)
-    
-    # Second closing to refine edges after erosion
     mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, close_kernel)
-    
-    # Final blur to smooth edges while preserving shape
-    mask = cv2.GaussianBlur(mask, (5, 5), 1.5)
-    
-    return mask
+    return cv2.GaussianBlur(mask, (5, 5), 1.5)
 
+# U²-Net segmentation
 def segment_dress(image_np):
-    """Improved dress segmentation with adaptive thresholding"""
     transform_pipeline = transforms.Compose([
         transforms.ToTensor(),
         transforms.Resize((320, 320))
     ])
-
     image = Image.fromarray(image_np).convert("RGB")
     input_tensor = transform_pipeline(image).unsqueeze(0)
 
     with torch.no_grad():
         output = model(input_tensor)[0][0].squeeze().cpu().numpy()
 
-    # Adaptive threshold calculation
     output = (output - output.min()) / (output.max() - output.min() + 1e-8)
-    adaptive_thresh = np.mean(output) + 0.2  # Increased threshold for tighter mask
+    adaptive_thresh = np.mean(output) + 0.2
     dress_mask = (output > adaptive_thresh).astype(np.uint8) * 255
-    
-    # Preserve hard edges during resize
-    dress_mask = cv2.resize(dress_mask, (image_np.shape[1], image_np.shape[0]), 
-                          interpolation=cv2.INTER_NEAREST)
-
-    return refine_mask(dress_mask)
+    return refine_mask(cv2.resize(dress_mask, (image_np.shape[1], image_np.shape[0]), interpolation=cv2.INTER_NEAREST))
 
+# Optional GrabCut refinement
 def apply_grabcut(image_np, dress_mask):
-    """Mask refinement using GrabCut"""
     bgd_model = np.zeros((1, 65), np.float64)
     fgd_model = np.zeros((1, 65), np.float64)
-
     mask = np.where(dress_mask > 0, cv2.GC_PR_FGD, cv2.GC_BGD).astype('uint8')
-    
-    # Get bounding box coordinates
     coords = cv2.findNonZero(dress_mask)
     if coords is not None:
         x, y, w, h = cv2.boundingRect(coords)
         rect = (x, y, w, h)
         cv2.grabCut(image_np, mask, rect, bgd_model, fgd_model, 3, cv2.GC_INIT_WITH_MASK)
-    
-    refined_mask = np.where((mask == cv2.GC_FGD) | (mask == cv2.GC_PR_FGD), 255, 0).astype("uint8")
-    return refine_mask(refined_mask)
+    refined = np.where((mask == cv2.GC_FGD) | (mask == cv2.GC_PR_FGD), 255, 0).astype("uint8")
+    return refine_mask(refined)
 
+# LAB color recoloring
 def recolor_dress(image_np, dress_mask, target_color):
-    """Color transformation with improved blending"""
-    # Convert colors to LAB space
     target_color_lab = cv2.cvtColor(np.uint8([[target_color]]), cv2.COLOR_BGR2LAB)[0][0]
     img_lab = cv2.cvtColor(image_np, cv2.COLOR_RGB2LAB)
 
-    # Calculate color shifts
     dress_pixels = img_lab[dress_mask > 0]
     if len(dress_pixels) == 0:
         return image_np
@@ -88,71 +75,50 @@ def recolor_dress(image_np, dress_mask, target_color):
     a_shift = target_color_lab[1] - mean_A
     b_shift = target_color_lab[2] - mean_B
 
-    # Apply color transformation
     img_lab[..., 1] = np.clip(img_lab[..., 1] + (dress_mask / 255.0) * a_shift, 0, 255)
     img_lab[..., 2] = np.clip(img_lab[..., 2] + (dress_mask / 255.0) * b_shift, 0, 255)
 
-    # Create adaptive blending mask
     img_recolored = cv2.cvtColor(img_lab.astype(np.uint8), cv2.COLOR_LAB2RGB)
     feathered_mask = cv2.GaussianBlur(dress_mask, (21, 21), 7)
     lightness_mask = (img_lab[..., 0] / 255.0) ** 0.7
     adaptive_feather = (feathered_mask * lightness_mask).astype(np.uint8)
 
-    # Smooth blending
-    return (image_np * (1 - adaptive_feather[..., None]/255) + img_recolored * (adaptive_feather[..., None]/255)).astype(np.uint8)
-
-def change_dress_color(img, color):
-    """Main processing function with error handling"""
-    if img is None:
-        return None
-
-    color_map = {
-        "Red": (0, 0, 255), "Blue": (255, 0, 0), "Green": (0, 255, 0),
-        "Yellow": (0, 255, 255), "Purple": (128, 0, 128), "Orange": (0, 165, 255),
-        "Cyan": (255, 255, 0), "Magenta": (255, 0, 255), "White": (255, 255, 255),
-        "Black": (0, 0, 0)
-    }
-    
-    new_color_bgr = color_map.get(color, (0, 0, 255))
+    return (image_np * (1 - adaptive_feather[..., None] / 255) + img_recolored * (adaptive_feather[..., None] / 255)).astype(np.uint8)
+
+# Main function
+def change_dress_color(img, color_prompt):
+    if img is None or not color_prompt:
+        return img
+
     img_np = np.array(img)
-    
+    target_bgr = get_bgr_from_color_name(color_prompt)
+
     try:
         dress_mask = segment_dress(img_np)
-        if np.sum(dress_mask) < 1000:  # Minimum mask area threshold
+        if np.sum(dress_mask) < 1000:
             return img
-            
         dress_mask = apply_grabcut(img_np, dress_mask)
-        img_recolored = recolor_dress(img_np, dress_mask, new_color_bgr)
+        img_recolored = recolor_dress(img_np, dress_mask, target_bgr)
         return Image.fromarray(img_recolored)
-        
     except Exception as e:
-        print(f"Error processing image: {str(e)}")
+        print(f"Error: {e}")
         return img
 
-# Gradio Interface
+# Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("# AI Dress Color Changer")
-    gr.Markdown("Upload a dress image and select a new color for realistic recoloring")
-    
+    gr.Markdown("## 🎨 AI Dress Recolorer - Prompt Based")
+    gr.Markdown("Upload an image and type a color (e.g., 'lavender', 'light green', 'royal blue').")
+
     with gr.Row():
         with gr.Column():
-            input_image = gr.Image(type="pil", label="Input Image", image_mode = "RGB")
-            color_choice = gr.Dropdown(
-                choices=["Red", "Blue", "Green", "Yellow", "Purple", 
-                        "Orange", "Cyan", "Magenta", "White", "Black"],
-                value="Red",
-                label="Select New Color"
-            )
-            process_btn = gr.Button("Recolor Dress")
-        
+            input_image = gr.Image(type="pil", label="Upload Image")
+            color_input = gr.Textbox(label="Enter Dress Color", placeholder="e.g. crimson, lavender, sky blue")
+            recolor_btn = gr.Button("Apply New Color")
+
         with gr.Column():
-            output_image = gr.Image(type="pil", label="Result", image_mode = "RGB")
+            output_image = gr.Image(type="pil", label="Recolored Result")
 
-    process_btn.click(
-        fn=change_dress_color,
-        inputs=[input_image, color_choice],
-        outputs=output_image
-    )
+    recolor_btn.click(fn=change_dress_color, inputs=[input_image, color_input], outputs=output_image)
 
 if __name__ == "__main__":
     demo.launch()