Update app.py

app.py

@@ -979,14 +979,14 @@ def predict_with_paper(image, paper_size, offset, offset_unit, finger_clearance=
             
             if not results or len(results) == 0 or not hasattr(results[0], 'boxes') or len(results[0].boxes) == 0:
                 logger.warning("No objects detected by YOLOv8, proceeding with full image")
-                cropped_image = masked_input_image
+                cropped_image = image
                 crop_offset = (0, 0)
             else:
                 boxes = results[0].boxes.xyxy.cpu().numpy()
                 confidences = results[0].boxes.conf.cpu().numpy()
                 
                 # Filter out very large boxes (likely paper/background)
-                image_area = masked_input_image.shape[0] * masked_input_image.shape[1]
+                image_area = image.shape[0] * image.shape[1]
                 valid_boxes = []
                 
                 for i, box in enumerate(boxes):
@@ -998,7 +998,7 @@ def predict_with_paper(image, paper_size, offset, offset_unit, finger_clearance=
                 
                 if not valid_boxes:
                     logger.warning("No valid objects detected, proceeding with full image")
-                    cropped_image = masked_input_image
+                    cropped_image = image
                     crop_offset = (0, 0)
                 else:
                     # Get highest confidence valid box
@@ -1017,7 +1017,7 @@ def predict_with_paper(image, paper_size, offset, offset_unit, finger_clearance=
             resized_mask = cv2.resize(objects_mask, (cropped_image.shape[1], cropped_image.shape[0]))
         else:
             resized_mask = objects_mask
-        if x_min == 0 and y_min == 0 and cropped_image.shape[:2] == masked_input_image.shape[:2]:
+        if x_min == 0 and y_min == 0 and cropped_image.shape[:2] == 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