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| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import cv2 | |
| from PIL import Image | |
| from torchvision import transforms | |
| from cloth_segmentation.networks.u2net import U2NET # Import U²-Net | |
| # Load U²-Net model | |
| model_path = "cloth_segmentation/networks/u2net.pth" # Ensure this path is correct | |
| model = U2NET(3, 1) | |
| # Load the state dictionary | |
| state_dict = torch.load(model_path, map_location=torch.device('cpu')) | |
| 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 segment_dress(image_np): | |
| """Segment the dress from the image using U²-Net and refine the mask.""" | |
| 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() | |
| mask = (output > 0.5).astype(np.uint8) * 255 # Binary mask | |
| # Resize mask to original image size | |
| mask = cv2.resize(mask, (image_np.shape[1], image_np.shape[0]), interpolation=cv2.INTER_NEAREST) | |
| # Apply morphological operations for better segmentation | |
| kernel = np.ones((7, 7), np.uint8) | |
| mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) # Close small gaps | |
| mask = cv2.dilate(mask, kernel, iterations=2) # Expand the detected dress area | |
| return mask | |
| def change_dress_color(image_path, color): | |
| """Change the dress color naturally while keeping textures.""" | |
| if image_path is None: | |
| return None | |
| img = Image.open(image_path).convert("RGB") | |
| img_np = np.array(img) | |
| mask = segment_dress(img_np) | |
| if mask is None: | |
| return img # No dress detected | |
| # Convert the selected color to BGR | |
| color_map = { | |
| "Red": (0, 0, 255), | |
| "Blue": (255, 0, 0), | |
| "Green": (0, 255, 0), | |
| "Yellow": (0, 255, 255), | |
| "Purple": (128, 0, 128) | |
| } | |
| 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] | |
| # Preserve texture by only modifying the A & B channels | |
| img_lab[..., 1] = np.where(mask == 255, new_color_lab[1], img_lab[..., 1]) # Modify A-channel | |
| img_lab[..., 2] = np.where(mask == 255, new_color_lab[2], img_lab[..., 2]) # Modify B-channel | |
| # Convert back to RGB | |
| img_recolored = cv2.cvtColor(img_lab, cv2.COLOR_LAB2RGB) | |
| # Apply Poisson blending for realistic color application | |
| img_recolored = cv2.seamlessClone(img_recolored, img_np, mask, (img_np.shape[1]//2, img_np.shape[0]//2), cv2.NORMAL_CLONE) | |
| return Image.fromarray(img_recolored) | |
| # Gradio Interface | |
| demo = gr.Interface( | |
| fn=change_dress_color, | |
| inputs=[ | |
| gr.Image(type="filepath", label="Upload Dress Image"), | |
| gr.Radio(["Red", "Blue", "Green", "Yellow", "Purple"], label="Choose New Dress Color") | |
| ], | |
| outputs=gr.Image(type="pil", label="Color Changed Dress"), | |
| title="Dress Color Changer", | |
| description="Upload an image of a dress and select a new color to change its appearance naturally." | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |