def _filter_crowd_proposals(roidb, crowd_thresh):
"""Finds proposals that are inside crowd regions and marks them as
overlap = -1 with each ground-truth rois, which means they will be excluded
from training.
"""
for entry in roidb:
gt_overlaps = entry['gt_overlaps'].toarray()
crowd_inds = np.where(entry['is_crowd'] == 1)[0]
non_gt_inds = np.where(entry['gt_classes'] == 0)[0]
if len(crowd_inds) == 0 or len(non_gt_inds) == 0:
continue
crowd_boxes = box_utils.xyxy_to_xywh(entry['boxes'][crowd_inds, :])
non_gt_boxes = box_utils.xyxy_to_xywh(entry['boxes'][non_gt_inds, :])
iscrowd_flags = [int(True)] * len(crowd_inds)
ious = COCOmask.iou(non_gt_boxes, crowd_boxes, iscrowd_flags)
bad_inds = np.where(ious.max(axis=1) > crowd_thresh)[0]
gt_overlaps[non_gt_inds[bad_inds], :] = -1
entry['gt_overlaps'] = scipy.sparse.csr_matrix(gt_overlaps)
def bbox_results_one_category(self, boxes, cat_id):
results = []
image_ids = self.dataset.COCO.getImgIds()
image_ids.sort()
assert len(boxes) == len(image_ids)
for i, image_id in enumerate(image_ids):
dets = boxes[i]
if isinstance(dets, list) and len(dets) == 0:
continue
dets = dets.astype(np.float)
scores = dets[:, -1]
xywh_dets = bbox_transform.xyxy_to_xywh(dets[:, 0:4])
xs = xywh_dets[:, 0]
ys = xywh_dets[:, 1]
ws = xywh_dets[:, 2]
hs = xywh_dets[:, 3]
results.extend(
[{'image_id': image_id,
'category_id': cat_id,
'bbox': [xs[k], ys[k], ws[k], hs[k]],
'score': scores[k]} for k in range(dets.shape[0])])
return results