Update app.py

app.py

@@ -98,19 +98,13 @@ def process_image(image_path, depth_map_path=None):
 import cv2  # Make sure OpenCV is installed
 
 def create_3d_obj(foreground, depth_image, background, image_path, depth=10):
-    # Ensure depth_image is 2D and of type uint16
-    if depth_image.ndim != 2:
-        raise ValueError("Depth image must be a 2D array.")
-    if depth_image.dtype != np.uint16:
-        depth_image = depth_image.astype(np.uint16)
-
     # Step 1: Resize depth_image with PIL using bilinear interpolation for smoother resizing
     depth_image_resized = np.array(
-        Image.fromarray(depth_image, mode='I').resize(
+        Image.fromarray(depth_image.astype(np.uint16), mode='I;16').resize(
             foreground.shape[:2][::-1], Image.Resampling.BILINEAR
         )
     )
-    
+
     # Step 2: Apply a slight Gaussian blur to the depth map to smooth out artifacts
     depth_image_smoothed = gaussian_filter(depth_image_resized, sigma=1)
 
@@ -120,6 +114,14 @@ def create_3d_obj(foreground, depth_image, background, image_path, depth=10):
         (depth_image_smoothed.max() - depth_image_smoothed.min()) * 65535
     ).astype(np.uint16)
 
+    # Check for shape and dtype before creating PIL Image
+    if depth_image_normalized.ndim != 2 or depth_image_normalized.shape[0] == 0 or depth_image_normalized.shape[1] == 0:
+        raise ValueError("Normalized depth image is not valid. Shape: {}".format(depth_image_normalized.shape))
+    
+    # Check dtype
+    if depth_image_normalized.dtype != np.uint16:
+        raise ValueError("Depth image is not of type uint16, but rather: {}".format(depth_image_normalized.dtype))
+
     # Step 4: Convert the normalized depth image to Open3D Image object
     depth_o3d = o3d.geometry.Image(depth_image_normalized)
     image_o3d = o3d.geometry.Image(foreground)