def _load_pascal_annotation(self, index):
    """
    Load image and bounding boxes info from XML file in the PASCAL VOC
    format.
    """
    filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
    tree = ET.parse(filename)
    objs = tree.findall('object')
    if not self.config['use_diff']:
      # Exclude the samples labeled as difficult
      non_diff_objs = [
        obj for obj in objs if int(obj.find('difficult').text) == 0]
      # if len(non_diff_objs) != len(objs):
      #     print 'Removed {} difficult objects'.format(
      #         len(objs) - len(non_diff_objs))
      objs = non_diff_objs
    num_objs = len(objs)

    boxes = np.zeros((num_objs, 4), dtype=np.uint16)
    gt_classes = np.zeros((num_objs), dtype=np.int32)
    overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
    # "Seg" area for pascal is just the box area
    seg_areas = np.zeros((num_objs), dtype=np.float32)

    # Load object bounding boxes into a data frame.
    for ix, obj in enumerate(objs):
      bbox = obj.find('bndbox')
      # Make pixel indexes 0-based
      x1 = float(bbox.find('xmin').text) - 1
      y1 = float(bbox.find('ymin').text) - 1
      x2 = float(bbox.find('xmax').text) - 1
      y2 = float(bbox.find('ymax').text) - 1
      cls = self._class_to_ind[obj.find('name').text.lower().strip()]
      boxes[ix, :] = [x1, y1, x2, y2]
      gt_classes[ix] = cls
      overlaps[ix, cls] = 1.0
      seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)

    overlaps = scipy.sparse.csr_matrix(overlaps)

    return {'boxes': boxes,
            'gt_classes': gt_classes,
            'gt_overlaps': overlaps,
            'flipped': False,
            'seg_areas': seg_areas}

    Load image and bounding boxes info from XML file in the PASCAL VOC
    format.
    
lib/datasets/pascal_voc_rbg.py