app.py

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

# 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()}
model.load_state_dict(state_dict)
model.eval()

def segment_dress(image_np):
    """Segment the dress using U²-Net and GrabCut."""
    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()
    
    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

def recolor_dress(image_np, mask, target_color):
    """Recolor the dress while keeping texture, shadows, and 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)
    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])

    # 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."""
    if image_path is None:
        return None

    img = Image.open(image_path).convert("RGB")
    img_np = np.array(img)

    # Get dress segmentation mask
    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), "Orange": (0, 165, 255), "Cyan": (255, 255, 0), "Magenta": (255, 0, 255),
        "White": (255, 255, 255), "Black": (0, 0, 0)
    }
    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)

    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", "Orange", "Cyan", "Magenta", "White", "Black"], 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 while preserving designs."
)

if __name__ == "__main__":
    demo.launch()