app.py

import gradio as gr
import numpy as np
import cv2
from PIL import Image
import torch
import torch.nn as nn
import torchvision.transforms as T
from skimage import color
from sklearn.cluster import KMeans
from cloth_segmentation.networks.u2net import U2NET

model = U2NET(3, 1)
model.load_state_dict(torch.load("cloth_segmentation/networks/u2net.pth", map_location=torch.device('cpu')))
model.eval()

# Preprocessing
transform = T.Compose([
    T.Resize((320, 320)),
    T.ToTensor(),
    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# Segmentation mask
@torch.no_grad()
def get_dress_mask(image_pil):
    img = transform(image_pil).unsqueeze(0)
    pred = model(img)[0]
    pred = pred.squeeze().cpu().numpy()
    mask = (pred > 0.5).astype(np.uint8)
    mask = cv2.resize(mask, image_pil.size[::-1])
    return mask

# Color parsing (extract target color from prompt)
def extract_target_color(prompt):
    # Basic keyword matching (can be replaced with NLP-based color detection)
    import re
    colors = ["red", "blue", "green", "yellow", "pink", "black", "white", "sky blue", "purple"]
    for c in colors:
        if re.search(c, prompt.lower()):
            return c
    return "red"  # default fallback

# Recoloring function
def recolor_dress(image_pil, prompt):
    image_np = np.array(image_pil.convert("RGB")) / 255.0
    lab = color.rgb2lab(image_np)

    mask = get_dress_mask(image_pil)

    # Get mean a, b values in masked region
    a_mean = lab[:, :, 1][mask == 1].mean()
    b_mean = lab[:, :, 2][mask == 1].mean()

    # Target a, b (from a small predefined palette)
    target_color_map = {
        "red": [60, 40],
        "blue": [20, -60],
        "green": [-60, 60],
        "yellow": [10, 70],
        "pink": [50, 10],
        "purple": [40, -40],
        "black": [0, 0],
        "white": [0, 0],
        "sky blue": [0, -50],
    }
    target = extract_target_color(prompt)
    target_a, target_b = target_color_map.get(target, [60, 40])

    # Apply color shift only to dress region
    lab_new = lab.copy()
    delta_a = target_a - a_mean
    delta_b = target_b - b_mean
    lab_new[:, :, 1][mask == 1] += delta_a
    lab_new[:, :, 2][mask == 1] += delta_b

    rgb_new = color.lab2rgb(lab_new)
    rgb_new = (rgb_new * 255).astype(np.uint8)
    return Image.fromarray(rgb_new)

# Gradio UI
def interface_fn(image, prompt):
    return recolor_dress(image, prompt)

interface = gr.Interface(
    fn=interface_fn,
    inputs=[
        gr.Image(label="Upload Image", type="pil"),
        gr.Textbox(label="Prompt", placeholder="Describe what to edit")
    ],
    outputs=gr.Image(label="Edited Image"),
    title="Image Editor",
    description="Uses Hugging Face model for real image editing based on prompt."
)

interface.launch(show_error = True)