Update app.py

app.py

@@ -1026,11 +1026,15 @@ def predict_with_paper(image, paper_size, offset, offset_unit, finger_clearance=
         
         # Resize mask to match cropped region and place back in original image space
         full_mask = np.zeros((orig_size[0], orig_size[1]), dtype=np.uint8)
+        x_min, y_min = crop_offset  # Use crop_offset to ensure defined variables
         if cropped_image.shape[:2] != processed_size:
             resized_mask = cv2.resize(objects_mask, (cropped_image.shape[1], cropped_image.shape[0]))
         else:
             resized_mask = objects_mask
-        full_mask[y_min:y_min+resized_mask.shape[0], x_min:x_min+resized_mask.shape[1]] = resized_mask
+        if x_min == 0 and y_min == 0 and cropped_image.shape[:2] == masked_input_image.shape[:2]:
+            full_mask = resized_mask  # No cropping occurred, use mask directly
+        else:
+            full_mask[y_min:y_min+resized_mask.shape[0], x_min:x_min+resized_mask.shape[1]] = resized_mask
         
         # Remove paper area from mask to focus only on objects
         objects_mask = exclude_paper_area(full_mask, paper_contour)
@@ -1079,8 +1083,8 @@ def predict_with_paper(image, paper_size, offset, offset_unit, finger_clearance=
         # Generate DXF - scaling_factor should be in mm/px for proper DXF units
         dxf, finger_polygons, original_polygons = save_dxf_spline(
             contours,
-            scaling_factor,  # This should mm/px
-            orig_size[0],  # Use original image height instead of processed_size
+            scaling_factor,  # This should be mm/px
+            orig_size[0],  # Use original image height
             finger_clearance=(finger_clearance == "On")
         )
     except FingerCutOverlapError as e: