[Feature] Support CopyPaste when mask is not available

mmdet/datasets/transforms/transforms.py

@@ -2938,11 +2938,13 @@ def __init__(
         bbox_occluded_thr: int = 10,
         mask_occluded_thr: int = 300,
         selected: bool = True,
+        paste_by_box: bool = False,
     ) -> None:
         self.max_num_pasted = max_num_pasted
         self.bbox_occluded_thr = bbox_occluded_thr
         self.mask_occluded_thr = mask_occluded_thr
         self.selected = selected
+        self.paste_by_box = paste_by_box
 
     @cache_randomness
     def get_indexes(self, dataset: BaseDataset) -> int:
@@ -2981,11 +2983,28 @@ def _get_selected_inds(self, num_bboxes: int) -> np.ndarray:
         num_pasted = np.random.randint(0, max_num_pasted)
         return np.random.choice(num_bboxes, size=num_pasted, replace=False)
 
+    def get_gt_masks(self, results):
+        """Get gt_masks originally or generated based on bboxes.
+
+        If gt_masks is not contained in results,
+        it will be generated based on gt_bboxes.
+        Args:
+            results (dict): Result dict.
+        Returns:
+            BitmapMasks: gt_masks, originally or generated based on bboxes.
+        """
+        if results.get('gt_masks', None) is not None:
+            return results['gt_masks']
+        else:
+            if not self.paste_by_box:
+                raise RuntimeError('results does not contain masks.')
+            return results['gt_bboxes'].create_masks(results['img'].shape[:2])
+
     def _select_object(self, results: dict) -> dict:
         """Select some objects from the source results."""
         bboxes = results['gt_bboxes']
         labels = results['gt_bboxes_labels']
-        masks = results['gt_masks']
+        masks = self.get_gt_masks(results)
         ignore_flags = results['gt_ignore_flags']
 
         selected_inds = self._get_selected_inds(bboxes.shape[0])
@@ -3013,7 +3032,7 @@ def _copy_paste(self, dst_results: dict, src_results: dict) -> dict:
         dst_img = dst_results['img']
         dst_bboxes = dst_results['gt_bboxes']
         dst_labels = dst_results['gt_bboxes_labels']
-        dst_masks = dst_results['gt_masks']
+        dst_masks = self.get_gt_masks(dst_results)
         dst_ignore_flags = dst_results['gt_ignore_flags']
 
         src_img = src_results['img']

mmdet/structures/bbox/base_boxes.py

@@ -509,6 +509,17 @@ def find_inside_points(self,
         """
         pass
 
+    @abstractmethod
+    def create_masks(self, img_shape: Tuple[int, int]) -> Tensor:
+        """
+        Args:
+            img_shape (Tuple[int, int]): A tuple of image height and width.
+
+        Returns:
+            :obj:`BitmapMasks`: Converted masks
+        """
+        pass
+
     @abstractstaticmethod
     def overlaps(boxes1: 'BaseBoxes',
                  boxes2: 'BaseBoxes',

mmdet/structures/bbox/horizontal_boxes.py

@@ -335,6 +335,26 @@ def find_inside_points(self,
         return (points[..., 0] >= x_min) & (points[..., 0] <= x_max) & \
             (points[..., 1] >= y_min) & (points[..., 1] <= y_max)
 
+    def create_masks(self, img_shape: Tuple[int, int]) -> BitmapMasks:
+        """
+        Args:
+            img_shape (Tuple[int, int]): A tuple of image height and width.
+
+        Returns:
+            :obj:`BitmapMasks`: Converted masks
+        """
+        img_h, img_w = img_shape
+        boxes = self.tensor
+
+        xmin, ymin = boxes[:, 0:1], boxes[:, 1:2]
+        xmax, ymax = boxes[:, 2:3], boxes[:, 3:4]
+        gt_masks = np.zeros((len(boxes), img_h, img_w), dtype=np.uint8)
+        for i in range(len(boxes)):
+            gt_masks[i,
+                     int(ymin[i]):int(ymax[i]),
+                     int(xmin[i]):int(xmax[i])] = 1
+        return BitmapMasks(gt_masks, img_h, img_w)
+
     @staticmethod
     def overlaps(boxes1: BaseBoxes,
                  boxes2: BaseBoxes,

tests/test_structures/test_bbox/utils.py

@@ -48,6 +48,9 @@ def is_inside(self, img_shape):
     def find_inside_points(self, points, is_aligned=False):
         pass
 
+    def create_masks(self, img_shape):
+        pass
+
     def overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e-6):
         pass