Update app.py

app.py

@@ -1,95 +1,88 @@
+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
-from PIL import Image, ImageDraw, ImageFont
 import gradio as gr
-import torch
-import torchvision.transforms as transforms
-from skimage.filters import sobel
-from skimage.restoration import denoise_tv_chambolle
-from scipy.interpolate import Rbf
-
-
-# Function to estimate a normal map from cloth texture
-def estimate_normal_map(image):
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    sobel_x = sobel(gray)
-    sobel_y = sobel(gray)
+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
+        )
     
-    normal_map = np.stack([sobel_x, sobel_y, np.ones_like(sobel_x)], axis=-1)
-    normal_map /= np.linalg.norm(normal_map, axis=-1, keepdims=True)
-
-    return (normal_map * 255).astype(np.uint8)
-
-
-def apply_tps_warping(design, normal_map):
-    # Resize normal map to match the design size
-    normal_map = cv2.resize(normal_map, (design.shape[1], design.shape[0]))
-
-    h, w = design.shape[:2]
-    x, y = np.meshgrid(np.arange(w), np.arange(h))
-
-    # Generate warp offsets from normal map
-    control_x = x + (normal_map[:, :, 0] - 128) * 0.5
-    control_y = y + (normal_map[:, :, 1] - 128) * 0.5
-
-    # Apply Radial Basis Function (RBF) interpolation
-    rbf_x = Rbf(x.flatten(), y.flatten(), control_x.flatten(), function='thin_plate')
-    rbf_y = Rbf(x.flatten(), y.flatten(), control_y.flatten(), function='thin_plate')
-
-    warped_x = rbf_x(x, y).astype(np.float32)
-    warped_y = rbf_y(x, y).astype(np.float32)
-
-    # Warp the design
-    warped_design = cv2.remap(design, warped_x, warped_y, interpolation=cv2.INTER_LINEAR)
-
-    return warped_design
-
-
-# Function to blend design onto the cloth using Poisson Editing
-def blend_design_cloth(cloth, design, x=50, y=50):
-    cloth_bgr = np.array(cloth)
-    design_bgr = np.array(design)
-
-    normal_map = estimate_normal_map(cloth_bgr)
-
-    # Resize design to fit the center of the cloth
-    design_resized = cv2.resize(design_bgr, (cloth_bgr.shape[1] // 2, cloth_bgr.shape[0] // 5))
+    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()
     
-    # Convert to grayscale and create a mask
-    design_gray = cv2.cvtColor(design_resized, cv2.COLOR_BGR2GRAY)
-    _, mask = cv2.threshold(design_gray, 1, 255, cv2.THRESH_BINARY)
-
-    # Warp design using normal map
-    warped_design = apply_tps_warping(design_resized, normal_map)
+    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)
 
-    # Blend using Poisson seamless cloning
-    center = (x + design_resized.shape[1] // 2, y + design_resized.shape[0] // 2)
-    blended = cv2.seamlessClone(warped_design, cloth_bgr, mask, center, cv2.MIXED_CLONE)
-
-    return Image.fromarray(blended)
-
-
-# Gradio function
-def process_image(cloth_image, design_image, x=50, y=50):
-    # Blend design onto cloth
-    result = blend_design_cloth(cloth_image, design_image, x, y)
+# 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
 
-
-# Gradio Interface
-interface = gr.Interface(
+iface = gr.Interface(
     fn=process_image,
-    inputs=[
-        gr.Image(type="pil", label="Upload Cloth Image"),
-        gr.Image(type="pil", label="Upload Design"),
-        gr.Slider(0, 1000, step=10, label="X Coordinate", value=50),
-        gr.Slider(0, 1000, step=10, label="Y Coordinate", value=50),
-    ],
-    outputs=gr.Image(type="pil", label="Blended Output"),
-    title="Advanced Cloth Design Blending",
-    description="Upload a cloth image and a design to blend them naturally using advanced warping & Poisson blending.",
+    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."
 )
 
-# Launch the app
+# Run Gradio app
 if __name__ == "__main__":
-    interface.launch(server_name="0.0.0.0", server_port=7860)
+    train_model()
+    iface.launch()