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  # Import U²-Net

# 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()}  # Remove 'module.' prefix
model.load_state_dict(state_dict)
model.eval()

def detect_design(image_np):
    """Detects if a design exists on the dress using edge detection & clustering."""
    gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
    edges = cv2.Canny(gray, 50, 150)

    # Dilation to highlight patterns
    kernel = np.ones((3, 3), np.uint8)
    edges = cv2.dilate(edges, kernel, iterations=1)

    # Count edge density
    design_ratio = np.sum(edges > 0) / (image_np.shape[0] * image_np.shape[1])

    return design_ratio > 0.02, edges  # If edge density is high, assume a design is present

def segment_dress(image_np):
    """Segment the dress using U²-Net & refine with Lab color space."""
    
    # Convert to Lab space
    img_lab = cv2.cvtColor(image_np, cv2.COLOR_RGB2LAB)
    L, A, B = cv2.split(img_lab)

    # Use K-means clustering to detect dominant dress region
    pixel_values = img_lab.reshape((-1, 3)).astype(np.float32)
    k = 3  # Three clusters: background, skin, dress
    _, labels, centers = cv2.kmeans(pixel_values, k, None, (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0), 10, cv2.KMEANS_RANDOM_CENTERS)
    labels = labels.reshape(image_np.shape[:2])
    
    # Assume dress is the largest non-background cluster
    unique_labels, counts = np.unique(labels, return_counts=True)
    dress_label = unique_labels[np.argmax(counts[1:]) + 1]  # Avoid background
    
    # Create dress mask
    mask = (labels == dress_label).astype(np.uint8) * 255

    # Use U²-Net prediction to refine segmentation
    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)
    
    # Combine K-means and U²-Net masks
    refined_mask = cv2.bitwise_and(mask, u2net_mask)

    # Morphological operations for smoothness
    kernel = np.ones((5, 5), np.uint8)
    refined_mask = cv2.morphologyEx(refined_mask, cv2.MORPH_CLOSE, kernel)
    refined_mask = cv2.GaussianBlur(refined_mask, (15, 15), 5)
    
    return refined_mask

def recolor_dress(image_np, mask, target_color, edges):
    """Change dress color while preserving texture, shadows, and designs."""
    
    img_lab = cv2.cvtColor(image_np, cv2.COLOR_RGB2LAB)
    target_color_lab = cv2.cvtColor(np.uint8([[target_color]]), cv2.COLOR_BGR2LAB)[0][0]
    
    # Exclude design from recoloring
    design_mask = (edges > 0).astype(np.uint8) * 255
    mask = cv2.bitwise_and(mask, cv2.bitwise_not(design_mask))

    # Preserve lightness (L) and change only chromatic channels (A & B)
    blend_factor = 0.7
    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_recolored = cv2.cvtColor(img_lab, cv2.COLOR_LAB2RGB)
    return img_recolored

def change_dress_color(image_path, color):
    """Change the dress color naturally while keeping designs intact."""
    if image_path is None:
        return None

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

    # Detect if a design is present
    design_present, edges = detect_design(img_np)

    # 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
    if design_present:
        print("Design detected! Coloring only non-design areas.")
        img_recolored = recolor_dress(img_np, mask, new_color_bgr, edges)
    else:
        print("No design detected. Coloring entire dress.")
        img_recolored = recolor_dress(img_np, mask, new_color_bgr, np.zeros_like(mask))  # No design mask

    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()