def evaluate_recall(self, candidate_boxes=None, thresholds=None,
                      area='all', limit=None):
    """Evaluate detection proposal recall metrics.

    Returns:
        results: dictionary of results with keys
            'ar': average recall
            'recalls': vector recalls at each IoU overlap threshold
            'thresholds': vector of IoU overlap thresholds
            'gt_overlaps': vector of all ground-truth overlaps
    """
    # Record max overlap value for each gt box
    # Return vector of overlap values
    areas = {'all': 0, 'small': 1, 'medium': 2, 'large': 3,
             '96-128': 4, '128-256': 5, '256-512': 6, '512-inf': 7}
    area_ranges = [[0 ** 2, 1e5 ** 2],  # all
                   [0 ** 2, 32 ** 2],  # small
                   [32 ** 2, 96 ** 2],  # medium
                   [96 ** 2, 1e5 ** 2],  # large
                   [96 ** 2, 128 ** 2],  # 96-128
                   [128 ** 2, 256 ** 2],  # 128-256
                   [256 ** 2, 512 ** 2],  # 256-512
                   [512 ** 2, 1e5 ** 2],  # 512-inf
                   ]
    assert area in areas, 'unknown area range: {}'.format(area)
    area_range = area_ranges[areas[area]]
    gt_overlaps = np.zeros(0)
    num_pos = 0
    for i in range(self.num_images):
      # Checking for max_overlaps == 1 avoids including crowd annotations
      # (...pretty hacking :/)
      max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(axis=1)
      gt_inds = np.where((self.roidb[i]['gt_classes'] > 0) &
                         (max_gt_overlaps == 1))[0]
      gt_boxes = self.roidb[i]['boxes'][gt_inds, :]
      gt_areas = self.roidb[i]['seg_areas'][gt_inds]
      valid_gt_inds = np.where((gt_areas >= area_range[0]) &
                               (gt_areas <= area_range[1]))[0]
      gt_boxes = gt_boxes[valid_gt_inds, :]
      num_pos += len(valid_gt_inds)

      if candidate_boxes is None:
        # If candidate_boxes is not supplied, the default is to use the
        # non-ground-truth boxes from this roidb
        non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0]
        boxes = self.roidb[i]['boxes'][non_gt_inds, :]
      else:
        boxes = candidate_boxes[i]
      if boxes.shape[0] == 0:
        continue
      if limit is not None and boxes.shape[0] > limit:
        boxes = boxes[:limit, :]

      overlaps = bbox_overlaps(boxes.astype(np.float),
                               gt_boxes.astype(np.float))

      _gt_overlaps = np.zeros((gt_boxes.shape[0]))
      for j in range(gt_boxes.shape[0]):
        # find which proposal box maximally covers each gt box
        argmax_overlaps = overlaps.argmax(axis=0)
        # and get the iou amount of coverage for each gt box
        max_overlaps = overlaps.max(axis=0)
        # find which gt box is 'best' covered (i.e. 'best' = most iou)
        gt_ind = max_overlaps.argmax()
        gt_ovr = max_overlaps.max()
        assert (gt_ovr >= 0)
        # find the proposal box that covers the best covered gt box
        box_ind = argmax_overlaps[gt_ind]
        # record the iou coverage of this gt box
        _gt_overlaps[j] = overlaps[box_ind, gt_ind]
        assert (_gt_overlaps[j] == gt_ovr)
        # mark the proposal box and the gt box as used
        overlaps[box_ind, :] = -1
        overlaps[:, gt_ind] = -1
      # append recorded iou coverage level
      gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))

    gt_overlaps = np.sort(gt_overlaps)
    if thresholds is None:
      step = 0.05
      thresholds = np.arange(0.5, 0.95 + 1e-5, step)
    recalls = np.zeros_like(thresholds)
    # compute recall for each iou threshold
    for i, t in enumerate(thresholds):
      recalls[i] = (gt_overlaps >= t).sum() / float(num_pos)
    # ar = 2 * np.trapz(recalls, thresholds)
    ar = recalls.mean()
    return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds,
            'gt_overlaps': gt_overlaps}
Evaluate detection proposal recall metrics.

    Returns:
        results: dictionary of results with keys
            'ar': average recall
            'recalls': vector recalls at each IoU overlap threshold
            'thresholds': vector of IoU overlap thresholds
            'gt_overlaps': vector of all ground-truth overlaps
    
lib/datasets/imdb.py