Update app.py

app.py

@@ -974,62 +974,47 @@ def predict_with_paper(image, paper_size, offset, offset_unit, finger_clearance=
         raise gr.Error(f"Error processing image: {str(e)}")
 
     try:
-        # Get paper bounds with expansion
-        rect = cv2.boundingRect(paper_contour)
-        expansion = max(20, int(min(rect[2], rect[3]) * 0.05))  # 5% expansion
-        
-        x, y, w, h = rect
-        x_min = max(0, x - expansion)
-        y_min = max(0, y - expansion)
-        x_max = min(image.shape[1], x + w + expansion)
-        y_max = min(image.shape[0], y + h + expansion)
-        
-        # Process the expanded paper area
-        cropped_image = image[y_min:y_max, x_min:x_max]
-        crop_offset = (x_min, y_min)
-        
-        # Remove background
-        objects_mask = remove_bg(cropped_image)
-        
-        # Resize mask back to cropped image size
-        target_height = y_max - y_min
-        target_width = x_max - x_min
-        objects_mask_resized = cv2.resize(objects_mask, (target_width, target_height))
-        
-        # Place back in full image space
-        full_mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
-        full_mask[y_min:y_max, x_min:x_max] = objects_mask_resized
-
-        # Light filtering only - don't exclude paper area aggressively
-        # Just remove small noise
-        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
-        objects_mask = cv2.morphologyEx(full_mask, cv2.MORPH_OPEN, kernel)
-        
-        # Resize mask to match cropped region and place back in original image space
-        full_mask = np.zeros((image.shape[0], image.shape[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
-        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
-        
-        # Remove paper area from mask to focus only on objects
-        objects_mask = exclude_paper_area(full_mask, paper_contour)
-        
-        # Debug: Save intermediate masks
-        cv2.imwrite("./debug/objects_mask_after_yolo.jpg", objects_mask)
-        
-        # Check if we actually have object pixels after paper exclusion
-        object_pixels = np.count_nonzero(objects_mask)
-        if object_pixels < 300:  # Minimum threshold
-            raise NoObjectDetectedError("No significant object detected after excluding paper area")
-        
-        # Validate single object
-        validate_single_object(objects_mask, paper_contour)
+            # Get paper bounds with expansion
+            rect = cv2.boundingRect(paper_contour)
+            expansion = max(20, int(min(rect[2], rect[3]) * 0.05))  # 5% expansion
+            
+            x, y, w, h = rect
+            x_min = max(0, x - expansion)
+            y_min = max(0, y - expansion)
+            x_max = min(image.shape[1], x + w + expansion)
+            y_max = min(image.shape[0], y + h + expansion)
+            
+            # Process the expanded paper area
+            cropped_image = image[y_min:y_max, x_min:x_max]
+            crop_offset = (x_min, y_min)
+            
+            # Remove background
+            objects_mask = remove_bg(cropped_image)
+            
+            # Resize mask back to cropped image size
+            target_height = y_max - y_min
+            target_width = x_max - x_min
+            objects_mask_resized = cv2.resize(objects_mask, (target_width, target_height))
+            
+            # Place back in full image space
+            full_mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
+            full_mask[y_min:y_max, x_min:x_max] = objects_mask_resized
+    
+            # Light filtering only - don't exclude paper area aggressively
+            # Just remove small noise
+            kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
+            objects_mask = cv2.morphologyEx(full_mask, cv2.MORPH_OPEN, kernel)
+            
+            # Debug: Save intermediate masks
+            cv2.imwrite("./debug/objects_mask_after_processing.jpg", objects_mask)
+            
+            # Check if we actually have object pixels
+            object_pixels = np.count_nonzero(objects_mask)
+            if object_pixels < 300:  # Minimum threshold
+                raise NoObjectDetectedError("No significant object detected")
+            
+            # Validate single object
+            validate_single_object(objects_mask, paper_contour)
         
     except (MultipleObjectsError, NoObjectDetectedError) as e:
         return (
@@ -1065,7 +1050,7 @@ def predict_with_paper(image, paper_size, offset, offset_unit, finger_clearance=
         dxf, finger_polygons, original_polygons = save_dxf_spline(
             contours,
             scaling_factor,  # This should be mm/px
-            orig_size[0],  # Use original image height
+            image.shape[0],  # Use original image height
             finger_clearance=(finger_clearance == "On")
         )
     except FingerCutOverlapError as e:
@@ -1079,7 +1064,7 @@ def predict_with_paper(image, paper_size, offset, offset_unit, finger_clearance=
         for poly in finger_polygons:
             try:
                 coords = np.array([
-                    (int(x / scaling_factor), int(orig_size[0] - y / scaling_factor))
+                    (int(x / scaling_factor), int(image.shape[0] - y / scaling_factor))
                     for x, y in poly.exterior.coords
                 ], np.int32).reshape((-1, 1, 2))