app.py

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets
from torch.utils.data import DataLoader
import segmentation_models_pytorch as smp
import cv2
import numpy as np
import gradio as gr
from skimage.transform import warp, PiecewiseAffineTransform

# Define U-Net model for cloth fold segmentation
class ClothFoldUNet(nn.Module):
    def __init__(self):
        super(ClothFoldUNet, self).__init__()
        self.model = smp.Unet(
            encoder_name="resnet34",  # Pre-trained backbone
            encoder_weights="imagenet",
            in_channels=3,
            classes=1,  # Single channel output for segmentation
        )
    
    def forward(self, x):
        return self.model(x)

# Load dataset (placeholder, replace with real dataset)
def get_dataloader(batch_size=8):
    transform = transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
    ])
    dataset = datasets.FakeData(transform=transform)
    return DataLoader(dataset, batch_size=batch_size, shuffle=True)

# Train function
def train_model():
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = ClothFoldUNet().to(device)
    optimizer = optim.Adam(model.parameters(), lr=1e-4)
    criterion = nn.BCEWithLogitsLoss()
    dataloader = get_dataloader()
    
    for epoch in range(10):  # Placeholder epoch count
        for images, _ in dataloader:
            images = images.to(device)
            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, torch.ones_like(outputs))  # Placeholder loss
            loss.backward()
            optimizer.step()
        print(f"Epoch {epoch+1}: Loss {loss.item():.4f}")

# Function to apply design onto cloth using segmentation mask
def apply_design(image, design, mask):
    mask = cv2.resize(mask, (image.shape[1], image.shape[0]))
    design = cv2.resize(design, (image.shape[1], image.shape[0]))
    mask = np.expand_dims(mask, axis=-1)
    blended = (mask * design) + ((1 - mask) * image)
    return blended.astype(np.uint8)

# Gradio Interface
def process_image(image, design):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = ClothFoldUNet().to(device)
    model.eval()
    transform = transforms.Compose([
        transforms.ToPILImage(),
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
    ])
    img_tensor = transform(image).unsqueeze(0).to(device)
    with torch.no_grad():
        mask = model(img_tensor).squeeze().cpu().numpy()
    result = apply_design(np.array(image), np.array(design), mask)
    return result

iface = gr.Interface(
    fn=process_image,
    inputs=["image", "image"],
    outputs="image",
    title="AI Cloth Design Blending",
    description="Upload a cloth image and a design to blend the design onto the cloth while considering the folds."
)

# Run Gradio app
if __name__ == "__main__":
    train_model()
    iface.launch()