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 areas.has_key(area), 'unknown area range: {}'.format(area)
        area_range = area_ranges[areas[area]]
        gt_overlaps = np.zeros(0)
        num_pos = 0
        for i in xrange(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 xrange(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