content: # --------------------------------------------------------
  # Fast R-CNN
  # Copyright (c) 2015 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick
  # --------------------------------------------------------
  
  """Transform a roidb into a trainable roidb by adding a bunch of metadata."""
  
  import numpy as np
  
  import PIL
  
  # TODO: make fast_rcnn irrelevant
  # >>>> obsolete, because it depends on sth outside of this project
  from ..fast_rcnn.config import cfg
  from ..fast_rcnn.bbox_transform import bbox_transform
  # <<<< obsolete
  from ..utils.cython_bbox import bbox_overlaps
  
  def prepare_roidb(imdb):
      """Enrich the imdb's roidb by adding some derived quantities that
      are useful for training. This function precomputes the maximum
      overlap, taken over ground-truth boxes, between each ROI and
      each ground-truth box. The class with maximum overlap is also
      recorded.
      """
      sizes = [PIL.Image.open(imdb.image_path_at(i)).size
               for i in xrange(imdb.num_images)]
      roidb = imdb.roidb
      for i in xrange(len(imdb.image_index)):
          roidb[i]['image'] = imdb.image_path_at(i)
          roidb[i]['width'] = sizes[i][0]
          roidb[i]['height'] = sizes[i][1]
          # need gt_overlaps as a dense array for argmax
          gt_overlaps = roidb[i]['gt_overlaps'].toarray()
          # max overlap with gt over classes (columns)
          max_overlaps = gt_overlaps.max(axis=1)
          # gt class that had the max overlap
          max_classes = gt_overlaps.argmax(axis=1)
          roidb[i]['max_classes'] = max_classes
          roidb[i]['max_overlaps'] = max_overlaps
          # sanity checks
          # max overlap of 0 => class should be zero (background)
          zero_inds = np.where(max_overlaps == 0)[0]
          assert all(max_classes[zero_inds] == 0)
          # max overlap > 0 => class should not be zero (must be a fg class)
          nonzero_inds = np.where(max_overlaps > 0)[0]
          assert all(max_classes[nonzero_inds] != 0)
  
  def add_bbox_regression_targets(roidb):
      """
      Add information needed to train bounding-box regressors.
      For each roi find the corresponding gt box, and compute the distance.
      then normalize the distance into Gaussian by minus mean and divided by std
      """
      assert len(roidb) > 0
      assert 'max_classes' in roidb[0], 'Did you call prepare_roidb first?'
  
      num_images = len(roidb)
      # Infer number of classes from the number of columns in gt_overlaps
      num_classes = roidb[0]['gt_overlaps'].shape[1]
      for im_i in xrange(num_images):
          rois = roidb[im_i]['boxes']
          max_overlaps = roidb[im_i]['max_overlaps']
          max_classes = roidb[im_i]['max_classes']
          roidb[im_i]['bbox_targets'] = \
                  _compute_targets(rois, max_overlaps, max_classes)
  
      if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
          # Use fixed / precomputed "means" and "stds" instead of empirical values
          means = np.tile(
                  np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS), (num_classes, 1))
          stds = np.tile(
                  np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS), (num_classes, 1))
      else:
          # Compute values needed for means and stds
          # var(x) = E(x^2) - E(x)^2
          class_counts = np.zeros((num_classes, 1)) + cfg.EPS
          sums = np.zeros((num_classes, 4))
          squared_sums = np.zeros((num_classes, 4))
          for im_i in xrange(num_images):
              targets = roidb[im_i]['bbox_targets']
              for cls in xrange(1, num_classes):
                  cls_inds = np.where(targets[:, 0] == cls)[0]
                  if cls_inds.size > 0:
                      class_counts[cls] += cls_inds.size
                      sums[cls, :] += targets[cls_inds, 1:].sum(axis=0)
                      squared_sums[cls, :] += \
                              (targets[cls_inds, 1:] ** 2).sum(axis=0)
  
          means = sums / class_counts
          stds = np.sqrt(squared_sums / class_counts - means ** 2)
          # too small number will cause nan error
          assert np.min(stds) < 0.01, \
              'Boxes std is too small, std:{}'.format(stds)
  
      print 'bbox target means:'
      print means
      print means[1:, :].mean(axis=0) # ignore bg class
      print 'bbox target stdevs:'
      print stds
      print stds[1:, :].mean(axis=0) # ignore bg class
  
      # Normalize targets
      if cfg.TRAIN.BBOX_NORMALIZE_TARGETS:
          print "Normalizing targets"
          for im_i in xrange(num_images):
              targets = roidb[im_i]['bbox_targets']
              for cls in xrange(1, num_classes):
                  cls_inds = np.where(targets[:, 0] == cls)[0]
                  roidb[im_i]['bbox_targets'][cls_inds, 1:] -= means[cls, :]
                  roidb[im_i]['bbox_targets'][cls_inds, 1:] /= stds[cls, :]
      else:
          print "NOT normalizing targets"
  
      # These values will be needed for making predictions
      # (the predicts will need to be unnormalized and uncentered)
      return means.ravel(), stds.ravel()
  
  def _compute_targets(rois, overlaps, labels):
      """
      Compute bounding-box regression targets for an image.
      for each roi find the corresponding gt_box, then compute the distance.
      """
      # 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
  