def _add_gt_annotations(self, entry):
        """Add ground truth annotation metadata to an roidb entry."""
        ann_ids = self.COCO.getAnnIds(imgIds=entry['id'], iscrowd=None)
        objs = self.COCO.loadAnns(ann_ids)
        # Sanitize bboxes -- some are invalid
        valid_objs = []
        valid_segms = []
        width = entry['width']
        height = entry['height']
        for obj in objs:
            # crowd regions are RLE encoded
            if segm_utils.is_poly(obj['segmentation']):
                # Valid polygons have >= 3 points, so require >= 6 coordinates
                obj['segmentation'] = [
                    p for p in obj['segmentation'] if len(p) >= 6
                ]
            if obj['area'] < cfg.TRAIN.GT_MIN_AREA:
                continue
            if 'ignore' in obj and obj['ignore'] == 1:
                continue
            # Convert form (x1, y1, w, h) to (x1, y1, x2, y2)
            x1, y1, x2, y2 = box_utils.xywh_to_xyxy(obj['bbox'])
            x1, y1, x2, y2 = box_utils.clip_xyxy_to_image(
                x1, y1, x2, y2, height, width
            )
            # Require non-zero seg area and more than 1x1 box size
            if obj['area'] > 0 and x2 > x1 and y2 > y1:
                obj['clean_bbox'] = [x1, y1, x2, y2]
                valid_objs.append(obj)
                valid_segms.append(obj['segmentation'])
        num_valid_objs = len(valid_objs)

        boxes = np.zeros((num_valid_objs, 4), dtype=entry['boxes'].dtype)
        gt_classes = np.zeros((num_valid_objs), dtype=entry['gt_classes'].dtype)
        gt_overlaps = np.zeros(
            (num_valid_objs, self.num_classes),
            dtype=entry['gt_overlaps'].dtype
        )
        seg_areas = np.zeros((num_valid_objs), dtype=entry['seg_areas'].dtype)
        is_crowd = np.zeros((num_valid_objs), dtype=entry['is_crowd'].dtype)
        box_to_gt_ind_map = np.zeros(
            (num_valid_objs), dtype=entry['box_to_gt_ind_map'].dtype
        )
        if self.keypoints is not None:
            gt_keypoints = np.zeros(
                (num_valid_objs, 3, self.num_keypoints),
                dtype=entry['gt_keypoints'].dtype
            )

        im_has_visible_keypoints = False
        for ix, obj in enumerate(valid_objs):
            cls = self.json_category_id_to_contiguous_id[obj['category_id']]
            boxes[ix, :] = obj['clean_bbox']
            gt_classes[ix] = cls
            seg_areas[ix] = obj['area']
            is_crowd[ix] = obj['iscrowd']
            box_to_gt_ind_map[ix] = ix
            if self.keypoints is not None:
                gt_keypoints[ix, :, :] = self._get_gt_keypoints(obj)
                if np.sum(gt_keypoints[ix, 2, :]) > 0:
                    im_has_visible_keypoints = True
            if obj['iscrowd']:
                # Set overlap to -1 for all classes for crowd objects
                # so they will be excluded during training
                gt_overlaps[ix, :] = -1.0
            else:
                gt_overlaps[ix, cls] = 1.0
        entry['boxes'] = np.append(entry['boxes'], boxes, axis=0)
        entry['segms'].extend(valid_segms)
        # To match the original implementation:
        # entry['boxes'] = np.append(
        #     entry['boxes'], boxes.astype(int).astype(float), axis=0)
        entry['gt_classes'] = np.append(entry['gt_classes'], gt_classes)
        entry['seg_areas'] = np.append(entry['seg_areas'], seg_areas)
        entry['gt_overlaps'] = np.append(
            entry['gt_overlaps'].toarray(), gt_overlaps, axis=0
        )
        entry['gt_overlaps'] = scipy.sparse.csr_matrix(entry['gt_overlaps'])
        entry['is_crowd'] = np.append(entry['is_crowd'], is_crowd)
        entry['box_to_gt_ind_map'] = np.append(
            entry['box_to_gt_ind_map'], box_to_gt_ind_map
        )
        if self.keypoints is not None:
            entry['gt_keypoints'] = np.append(
                entry['gt_keypoints'], gt_keypoints, axis=0
            )
            entry['has_visible_keypoints'] = im_has_visible_keypoints
Add ground truth annotation metadata to an roidb entry.
detectron/datasets/json_dataset.py