How to use the datasets.ds_utils.xyxy_to_xywh function in datasets

def _filter_crowd_proposals(roidb, crowd_thresh):
    """
    Finds proposals that are inside crowd regions and marks them with
    overlap = -1 (for all gt rois), which means they will be excluded from
    training.
    """
    for ix, entry in enumerate(roidb):
        overlaps = entry['gt_overlaps'].toarray()
        crowd_inds = np.where(overlaps.max(axis=1) == -1)[0]
        non_gt_inds = np.where(entry['gt_classes'] == 0)[0]
        if len(crowd_inds) == 0 or len(non_gt_inds) == 0:
            continue
        iscrowd = [int(True) for _ in xrange(len(crowd_inds))]
        crowd_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][crowd_inds, :])
        non_gt_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][non_gt_inds, :])
        ious = COCOmask.iou(non_gt_boxes, crowd_boxes, iscrowd)
        bad_inds = np.where(ious.max(axis=1) > crowd_thresh)[0]
        overlaps[non_gt_inds[bad_inds], :] = -1
        roidb[ix]['gt_overlaps'] = scipy.sparse.csr_matrix(overlaps)
    return roidb

def _filter_crowd_proposals(roidb, crowd_thresh):
  """
  Finds proposals that are inside crowd regions and marks them with
  overlap = -1 (for all gt rois), which means they will be excluded from
  training.
  """
  for ix, entry in enumerate(roidb):
    overlaps = entry['gt_overlaps'].toarray()
    crowd_inds = np.where(overlaps.max(axis=1) == -1)[0]
    non_gt_inds = np.where(entry['gt_classes'] == 0)[0]
    if len(crowd_inds) == 0 or len(non_gt_inds) == 0:
      continue
    iscrowd = [int(True) for _ in range(len(crowd_inds))]
    crowd_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][crowd_inds, :])
    non_gt_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][non_gt_inds, :])
    ious = COCOmask.iou(non_gt_boxes, crowd_boxes, iscrowd)
    bad_inds = np.where(ious.max(axis=1) > crowd_thresh)[0]
    overlaps[non_gt_inds[bad_inds], :] = -1
    roidb[ix]['gt_overlaps'] = scipy.sparse.csr_matrix(overlaps)
  return roidb

def _filter_crowd_proposals(roidb, crowd_thresh):
    """
    Finds proposals that are inside crowd regions and marks them with
    overlap = -1 (for all gt rois), which means they will be excluded from
    training.
    """
    for ix, entry in enumerate(roidb):
        overlaps = entry['gt_overlaps'].toarray()
        crowd_inds = np.where(overlaps.max(axis=1) == -1)[0]
        non_gt_inds = np.where(entry['gt_classes'] == 0)[0]
        if len(crowd_inds) == 0 or len(non_gt_inds) == 0:
            continue
        iscrowd = [int(True) for _ in xrange(len(crowd_inds))]
        crowd_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][crowd_inds, :])
        non_gt_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][non_gt_inds, :])
        ious = COCOmask.iou(non_gt_boxes, crowd_boxes, iscrowd)
        bad_inds = np.where(ious.max(axis=1) > crowd_thresh)[0]
        overlaps[non_gt_inds[bad_inds], :] = -1
        roidb[ix]['gt_overlaps'] = scipy.sparse.csr_matrix(overlaps)
    return roidb

def _filter_crowd_proposals(roidb, crowd_thresh):
    """
    Finds proposals that are inside crowd regions and marks them with
    overlap = -1 (for all gt rois), which means they will be excluded from
    training.
    """
    for ix, entry in enumerate(roidb):
        overlaps = entry['gt_overlaps'].toarray()
        crowd_inds = np.where(overlaps.max(axis=1) == -1)[0]
        non_gt_inds = np.where(entry['gt_classes'] == 0)[0]
        if len(crowd_inds) == 0 or len(non_gt_inds) == 0:
            continue
        iscrowd = [int(True) for _ in range(len(crowd_inds))]
        crowd_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][crowd_inds, :])
        non_gt_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][non_gt_inds, :])
        ious = COCOmask.iou(non_gt_boxes, crowd_boxes, iscrowd)
        bad_inds = np.where(ious.max(axis=1) > crowd_thresh)[0]
        overlaps[non_gt_inds[bad_inds], :] = -1
        roidb[ix]['gt_overlaps'] = scipy.sparse.csr_matrix(overlaps)
    return roidb

def _filter_crowd_proposals(roidb, crowd_thresh):
    """
    Finds proposals that are inside crowd regions and marks them with
    overlap = -1 (for all gt rois), which means they will be excluded from
    training.
    """
    for ix, entry in enumerate(roidb):
        overlaps = entry['gt_overlaps'].toarray()
        crowd_inds = np.where(overlaps.max(axis=1) == -1)[0]
        non_gt_inds = np.where(entry['gt_classes'] == 0)[0]
        if len(crowd_inds) == 0 or len(non_gt_inds) == 0:
            continue
        iscrowd = [int(True) for _ in range(len(crowd_inds))]
        crowd_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][crowd_inds, :])
        non_gt_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][non_gt_inds, :])
        ious = COCOmask.iou(non_gt_boxes, crowd_boxes, iscrowd)
        bad_inds = np.where(ious.max(axis=1) > crowd_thresh)[0]
        overlaps[non_gt_inds[bad_inds], :] = -1
        roidb[ix]['gt_overlaps'] = scipy.sparse.csr_matrix(overlaps)
    return roidb