parameters: (rois, overlaps, labels)
return_statement: return targets
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(rois, overlaps, labels):
      """Compute bounding-box regression targets for an image."""
      # Indices of ground-truth ROIs
      gt_inds = np.where(overlaps == 1)[0]
      if len(gt_inds) == 0:
          # Bail if the image has no ground-truth ROIs
          return np.zeros((rois.shape[0], 5), dtype=np.float32)
      # Indices of examples for which we try to make predictions
      ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
  
      # Get IoU overlap between each ex ROI and gt ROI
      ex_gt_overlaps = bbox_overlaps(
          np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
          np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
  
      # Find which gt ROI each ex ROI has max overlap with:
      # this will be the ex ROI's gt target
      gt_assignment = ex_gt_overlaps.argmax(axis=1)
      gt_rois = rois[gt_inds[gt_assignment], :]
      ex_rois = rois[ex_inds, :]
  
      targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
      targets[ex_inds, 0] = labels[ex_inds]
      targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
      return targets
function_tokens: def _compute_targets ( rois , overlaps , labels ) : # Indices of ground-truth ROIs gt_inds = np . where ( overlaps == 1 ) [ 0 ] if len ( gt_inds ) == 0 : # Bail if the image has no ground-truth ROIs return np . zeros ( ( rois . shape [ 0 ] , 5 ) , dtype = np . float32 ) # Indices of examples for which we try to make predictions ex_inds = np . where ( overlaps >= cfg . TRAIN . BBOX_THRESH ) [ 0 ] # Get IoU overlap between each ex ROI and gt ROI ex_gt_overlaps = bbox_overlaps ( np . ascontiguousarray ( rois [ ex_inds , : ] , dtype = np . float ) , np . ascontiguousarray ( rois [ gt_inds , : ] , dtype = np . float ) ) # Find which gt ROI each ex ROI has max overlap with: # this will be the ex ROI's gt target gt_assignment = ex_gt_overlaps . argmax ( axis = 1 ) gt_rois = rois [ gt_inds [ gt_assignment ] , : ] ex_rois = rois [ ex_inds , : ] targets = np . zeros ( ( rois . shape [ 0 ] , 5 ) , dtype = np . float32 ) targets [ ex_inds , 0 ] = labels [ ex_inds ] targets [ ex_inds , 1 : ] = bbox_transform ( ex_rois , gt_rois ) return targets