parameters: (roidb, crowd_thresh)
return_statement: return roidb
docstring: 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.
docstring_summary: 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.
docstring_tokens: 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 .
function: 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
function_tokens: def _filter_crowd_proposals ( roidb , crowd_thresh ) : 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