Update app.py

app.py

@@ -15,7 +15,12 @@ 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")  # Ensure it's in RGB format
-    img_tensor = midas_transform(image).unsqueeze(0).to(device)
+
+    # Convert PIL image to a NumPy array and normalize it
+    img_np = np.array(image, dtype=np.float32) / 255.0  # Normalize to [0, 1]
+
+    # Convert NumPy array to a Torch tensor
+    img_tensor = torch.tensor(img_np).permute(2, 0, 1).unsqueeze(0).to(device)
 
     # Ensure tensor shape is [1, 3, H, W]
     if img_tensor.dim() == 5:  # If an extra batch dimension is present
@@ -23,7 +28,7 @@ def estimate_depth(image):
 
     with torch.no_grad():
         depth = midas_model(img_tensor).squeeze().cpu().numpy()
-    
+
     depth = cv2.resize(depth, (image.size[0], image.size[1]))
     depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255
     return depth.astype(np.uint8)