Convert a list of bounding boxes to roi format. Args: bbox_list (list[torch.Tensor]): A list of bounding boxes corresponding to a batch of images. Returns: torch.Tensor: Region of interests in shape (n, c), where the channels are in order of [batch_i
(bbox_list)
| 25 | |
| 26 | |
| 27 | def bbox3d2roi(bbox_list): |
| 28 | """Convert a list of bounding boxes to roi format. |
| 29 | |
| 30 | Args: |
| 31 | bbox_list (list[torch.Tensor]): A list of bounding boxes |
| 32 | corresponding to a batch of images. |
| 33 | |
| 34 | Returns: |
| 35 | torch.Tensor: Region of interests in shape (n, c), where |
| 36 | the channels are in order of [batch_ind, x, y ...]. |
| 37 | """ |
| 38 | rois_list = [] |
| 39 | for img_id, bboxes in enumerate(bbox_list): |
| 40 | if bboxes.size(0) > 0: |
| 41 | img_inds = bboxes.new_full((bboxes.size(0), 1), img_id) |
| 42 | rois = torch.cat([img_inds, bboxes], dim=-1) |
| 43 | else: |
| 44 | rois = torch.zeros_like(bboxes) |
| 45 | rois_list.append(rois) |
| 46 | rois = torch.cat(rois_list, 0) |
| 47 | return rois |
| 48 | |
| 49 | |
| 50 | # TODO delete this |