Update app.py

app.py

@@ -15,10 +15,11 @@ midas_transform = torch.hub.load("intel-isl/MiDaS", "transforms").default_transf
 def estimate_depth(image):
     """Estimate depth map using MiDaS v3."""
     image = image.convert("RGB")
+    image = np.array(image) / 255.0  # Normalize and convert to NumPy array
     input_tensor = midas_transform(image).unsqueeze(0).to(device)
     with torch.no_grad():
         depth = midas_model(input_tensor).squeeze().cpu().numpy()
-    depth = cv2.resize(depth, (image.size[0], image.size[1]))
+    depth = cv2.resize(depth, (image.shape[1], image.shape[0]))
     depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255
     return depth.astype(np.uint8)