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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. | |
| import torch | |
| from .bounding_box import BoxList | |
| from maskrcnn_benchmark.layers import nms as _box_nms | |
| def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores"): | |
| """ | |
| Performs non-maximum suppression on a boxlist, with scores specified | |
| in a boxlist field via score_field. | |
| Arguments: | |
| boxlist(BoxList) | |
| nms_thresh (float) | |
| max_proposals (int): if > 0, then only the top max_proposals are kept | |
| after non-maximum suppression | |
| score_field (str) | |
| """ | |
| if nms_thresh <= 0: | |
| return boxlist | |
| mode = boxlist.mode | |
| boxlist = boxlist.convert("xyxy") | |
| boxes = boxlist.bbox | |
| score = boxlist.get_field(score_field) | |
| keep = _box_nms(boxes, score, nms_thresh) | |
| if max_proposals > 0: | |
| keep = keep[: max_proposals] | |
| boxlist = boxlist[keep] | |
| return boxlist.convert(mode) | |
| def remove_small_boxes(boxlist, min_size): | |
| """ | |
| Only keep boxes with both sides >= min_size | |
| Arguments: | |
| boxlist (Boxlist) | |
| min_size (int) | |
| """ | |
| # TODO maybe add an API for querying the ws / hs | |
| xywh_boxes = boxlist.convert("xywh").bbox | |
| _, _, ws, hs = xywh_boxes.unbind(dim=1) | |
| keep = ( | |
| (ws >= min_size) & (hs >= min_size) | |
| ).nonzero().squeeze(1) | |
| return boxlist[keep] | |
| # implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py | |
| # with slight modifications | |
| def boxlist_iou(boxlist1, boxlist2): | |
| """Compute the intersection over union of two set of boxes. | |
| The box order must be (xmin, ymin, xmax, ymax). | |
| Arguments: | |
| box1: (BoxList) bounding boxes, sized [N,4]. | |
| box2: (BoxList) bounding boxes, sized [M,4]. | |
| Returns: | |
| (tensor) iou, sized [N,M]. | |
| Reference: | |
| https://github.com/chainer/chainercv/blob/master/chainercv/utils/bbox/bbox_iou.py | |
| """ | |
| if boxlist1.size != boxlist2.size: | |
| raise RuntimeError( | |
| "boxlists should have same image size, got {}, {}".format(boxlist1, boxlist2)) | |
| N = len(boxlist1) | |
| M = len(boxlist2) | |
| area1 = boxlist1.area() | |
| area2 = boxlist2.area() | |
| box1, box2 = boxlist1.bbox, boxlist2.bbox | |
| lt = torch.max(box1[:, None, :2], box2[:, :2]) # [N,M,2] | |
| rb = torch.min(box1[:, None, 2:], box2[:, 2:]) # [N,M,2] | |
| TO_REMOVE = 1 | |
| wh = (rb - lt + TO_REMOVE).clamp(min=0) # [N,M,2] | |
| inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] | |
| iou = inter / (area1[:, None] + area2 - inter) | |
| return iou | |
| # TODO redundant, remove | |
| def _cat(tensors, dim=0): | |
| """ | |
| Efficient version of torch.cat that avoids a copy if there is only a single element in a list | |
| """ | |
| assert isinstance(tensors, (list, tuple)) | |
| if len(tensors) == 1: | |
| return tensors[0] | |
| return torch.cat(tensors, dim) | |
| def cat_boxlist(bboxes): | |
| """ | |
| Concatenates a list of BoxList (having the same image size) into a | |
| single BoxList | |
| Arguments: | |
| bboxes (list[BoxList]) | |
| """ | |
| assert isinstance(bboxes, (list, tuple)) | |
| assert all(isinstance(bbox, BoxList) for bbox in bboxes) | |
| size = bboxes[0].size | |
| assert all(bbox.size == size for bbox in bboxes) | |
| mode = bboxes[0].mode | |
| assert all(bbox.mode == mode for bbox in bboxes) | |
| fields = set(bboxes[0].fields()) | |
| assert all(set(bbox.fields()) == fields for bbox in bboxes) | |
| cat_boxes = BoxList(_cat([bbox.bbox for bbox in bboxes], dim=0), size, mode) | |
| for field in fields: | |
| data = _cat([bbox.get_field(field) for bbox in bboxes], dim=0) | |
| cat_boxes.add_field(field, data) | |
| return cat_boxes | |