parameters: (ex_rois, gt_rois, labels)
return_statement: return np.hstack(
              (labels[:, np.newaxis], targets)).astype(np.float32, copy=False)
docstring: Compute bounding-box regression targets for an image.
docstring_summary: Compute bounding-box regression targets for an image.
docstring_tokens: Compute bounding - box regression targets for an image .
function: def _compute_targets(ex_rois, gt_rois, labels):
      """Compute bounding-box regression targets for an image."""
  
      assert ex_rois.shape[0] == gt_rois.shape[0]
      assert ex_rois.shape[1] == 4
      assert gt_rois.shape[1] == 4
  
      targets = bbox_transform(ex_rois, gt_rois)
      if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
          # Optionally normalize targets by a precomputed mean and stdev
          targets = ((targets - np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS))
                  / np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS))
      return np.hstack(
              (labels[:, np.newaxis], targets)).astype(np.float32, copy=False)
function_tokens: def _compute_targets ( ex_rois , gt_rois , labels ) : assert ex_rois . shape [ 0 ] == gt_rois . shape [ 0 ] assert ex_rois . shape [ 1 ] == 4 assert gt_rois . shape [ 1 ] == 4 targets = bbox_transform ( ex_rois , gt_rois ) if cfg . TRAIN . BBOX_NORMALIZE_TARGETS_PRECOMPUTED : # Optionally normalize targets by a precomputed mean and stdev targets = ( ( targets - np . array ( cfg . TRAIN . BBOX_NORMALIZE_MEANS ) ) / np . array ( cfg . TRAIN . BBOX_NORMALIZE_STDS ) ) return np . hstack ( ( labels [ : , np . newaxis ] , targets ) ) . astype ( np . float32 , copy = False )