imdb2.py 文件源码

def evaluate_recall(self, candidate_boxes, ar_thresh=0.5):
        # Record max overlap value for each gt box
        # Return vector of overlap values
        gt_overlaps = np.zeros(0)
        for i in xrange(self.num_images):
            gt_inds = np.where(self.roidb[i]['gt_classes'] > 0)[0]
            gt_boxes = self.roidb[i]['boxes'][gt_inds, :]

            boxes = candidate_boxes[i]
            if boxes.shape[0] == 0:
                continue
            overlaps = bbox_overlaps(boxes.astype(np.float),
                                     gt_boxes.astype(np.float))

            # gt_overlaps = np.hstack((gt_overlaps, overlaps.max(axis=0)))
            _gt_overlaps = np.zeros((gt_boxes.shape[0]))
            for j in xrange(gt_boxes.shape[0]):
                argmax_overlaps = overlaps.argmax(axis=0)
                max_overlaps = overlaps.max(axis=0)
                gt_ind = max_overlaps.argmax()
                gt_ovr = max_overlaps.max()
                assert(gt_ovr >= 0)
                box_ind = argmax_overlaps[gt_ind]
                _gt_overlaps[j] = overlaps[box_ind, gt_ind]
                assert(_gt_overlaps[j] == gt_ovr)
                overlaps[box_ind, :] = -1
                overlaps[:, gt_ind] = -1

            gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))

        num_pos = gt_overlaps.size
        gt_overlaps = np.sort(gt_overlaps)
        step = 0.001
        thresholds = np.minimum(np.arange(0.5, 1.0 + step, step), 1.0)
        recalls = np.zeros_like(thresholds)
        for i, t in enumerate(thresholds):
            recalls[i] = (gt_overlaps >= t).sum() / float(num_pos)
        ar = 2 * np.trapz(recalls, thresholds)

        return ar, gt_overlaps, recalls, thresholds