Add application file

wrinkles.py

@@ -0,0 +1,78 @@
+import gradio as gr
+import math
+import os
+import cv2
+import numpy as np
+import torch
+import segmentation_models_pytorch as smp
+
+def pad_to_divisible(img, div=32):
+    h, w, _ = img.shape
+    new_h = math.ceil(h / div) * div
+    new_w = math.ceil(w / div) * div
+    pad_bottom = new_h - h
+    pad_right = new_w - w
+    padded = cv2.copyMakeBorder(img, 0, pad_bottom, 0, pad_right, cv2.BORDER_CONSTANT, value=[0, 0, 0])
+    return padded
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print("Using device:", device)
+
+model_path = "best_unet_model_complete.pth"
+if os.path.exists(model_path):
+    loaded_model = torch.load(model_path, map_location=device)
+    loaded_model.eval()
+    print("Loaded complete model from", model_path)
+else:
+    raise FileNotFoundError(f"Model file not found at {model_path}")
+
+def predict(image):
+    """
+    Takes an input image (as a NumPy array), pads it, performs inference,
+    and returns: 
+    - the padded input image,
+    - the predicted mask (grayscale), and 
+    - the original image with a red overlay on the predicted regions.
+    """
+
+    if image.shape[2] == 4:
+        image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
+    
+    
+    external_image_rgb = image.copy()
+
+    external_image_padded = pad_to_divisible(external_image_rgb, div=32)
+    
+    external_image_norm = external_image_padded.astype(np.float32) / 255.0
+    external_tensor = torch.from_numpy(external_image_norm).permute(2, 0, 1)
+    external_tensor = external_tensor.unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        output = loaded_model(external_tensor)
+        pred_mask = (torch.sigmoid(output) > 0.3).float()  
+    pred_mask_np = pred_mask.cpu().squeeze().numpy()
+    
+
+    overlay_image = external_image_padded.astype(np.float32)
+    mask_bool = pred_mask_np > 0.5
+    red_color = np.array([255, 0, 0], dtype=np.float32)
+    alpha = 0.5  
+    overlay_image[mask_bool] = (1 - alpha) * overlay_image[mask_bool] + alpha * red_color
+    overlay_image = np.clip(overlay_image, 0, 255).astype(np.uint8)
+
+    return external_image_padded, pred_mask_np, overlay_image
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=[
+        gr.Image(label="Padded Image"),
+        gr.Image(label="Predicted Mask"),
+        gr.Image(label="Overlay (Predicted Regions)")
+    ],
+    title="Wrinkle Segmentation",
+    description="Upload an image to see wrinkle segmentation. The app displays the padded image, the predicted mask, and an overlay of the predicted regions in red."
+)
+
+demo.launch()