def _load_proposals(self, method, gt_roidb):
        """
        Load pre-computed proposals in the format provided by Jan Hosang:
        http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-
          computing/research/object-recognition-and-scene-understanding/how-
          good-are-detection-proposals-really/
        For MCG, use boxes from http://www.eecs.berkeley.edu/Research/Projects/
          CS/vision/grouping/mcg/ and convert the file layout using
        lib/datasets/tools/mcg_munge.py.
        """
        box_list = []
        top_k = self.config['top_k']
        valid_methods = [
            'MCG',
            'selective_search',
            'edge_boxes_AR',
            'edge_boxes_70']
        assert method in valid_methods

        print 'Loading {} boxes'.format(method)
        for i, index in enumerate(self._image_index):
            if i % 1000 == 0:
                print '{:d} / {:d}'.format(i + 1, len(self._image_index))

            box_file = osp.join(
                cfg.DATA_DIR, 'coco_proposals', method, 'mat',
                self._get_box_file(index))

            raw_data = sio.loadmat(box_file)['boxes']
            boxes = np.maximum(raw_data - 1, 0).astype(np.uint16)
            if method == 'MCG':
                # Boxes from the MCG website are in (y1, x1, y2, x2) order
                boxes = boxes[:, (1, 0, 3, 2)]
            # Remove duplicate boxes and very small boxes and then take top k
            keep = ds_utils.unique_boxes(boxes)
            boxes = boxes[keep, :]
            keep = ds_utils.filter_small_boxes(boxes, self.config['min_size'])
            boxes = boxes[keep, :]
            boxes = boxes[:top_k, :]
            box_list.append(boxes)
            # Sanity check
            im_ann = self._COCO.loadImgs(index)[0]
            width = im_ann['width']
            height = im_ann['height']
            ds_utils.validate_boxes(boxes, width=width, height=height)
        return self.create_roidb_from_box_list(box_list, gt_roidb)

        Load pre-computed proposals in the format provided by Jan Hosang:
        http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-
          computing/research/object-recognition-and-scene-understanding/how-
          good-are-detection-proposals-really/
        For MCG, use boxes from http://www.eecs.berkeley.edu/Research/Projects/
          CS/vision/grouping/mcg/ and convert the file layout using
        lib/datasets/tools/mcg_munge.py.
        
lib/datasets/coco.py