def _load_coco_annotation(self, index):
    """
    Loads COCO bounding-box instance annotations. Crowd instances are
    handled by marking their overlaps (with all categories) to -1. This
    overlap value means that crowd "instances" are excluded from training.
    """
    im_ann = self._COCO.loadImgs(index)[0]
    width = im_ann['width']
    height = im_ann['height']

    annIds = self._COCO.getAnnIds(imgIds=index, iscrowd=None)
    objs = self._COCO.loadAnns(annIds)
    # Sanitize bboxes -- some are invalid
    valid_objs = []
    for obj in objs:
      x1 = np.max((0, obj['bbox'][0]))
      y1 = np.max((0, obj['bbox'][1]))
      x2 = np.min((width - 1, x1 + np.max((0, obj['bbox'][2] - 1))))
      y2 = np.min((height - 1, y1 + np.max((0, obj['bbox'][3] - 1))))
      if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
        obj['clean_bbox'] = [x1, y1, x2, y2]
        valid_objs.append(obj)
    objs = valid_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_areas = np.zeros((num_objs), dtype=np.float32)

    # Lookup table to map from COCO category ids to our internal class
    # indices
    coco_cat_id_to_class_ind = dict([(self._class_to_coco_cat_id[cls],
                                      self._class_to_ind[cls])
                                     for cls in self._classes[1:]])

    for ix, obj in enumerate(objs):
      cls = coco_cat_id_to_class_ind[obj['category_id']]
      boxes[ix, :] = obj['clean_bbox']
      gt_classes[ix] = cls
      seg_areas[ix] = obj['area']
      if obj['iscrowd']:
        # Set overlap to -1 for all classes for crowd objects
        # so they will be excluded during training
        overlaps[ix, :] = -1.0
      else:
        overlaps[ix, cls] = 1.0

    ds_utils.validate_boxes(boxes, width=width, height=height)
    overlaps = scipy.sparse.csr_matrix(overlaps)
    return {'width': width,
            'height': height,
            'boxes': boxes,
            'gt_classes': gt_classes,
            'gt_overlaps': overlaps,
            'flipped': False,
            'seg_areas': seg_areas}

    Loads COCO bounding-box instance annotations. Crowd instances are
    handled by marking their overlaps (with all categories) to -1. This
    overlap value means that crowd "instances" are excluded from training.
    
lib/datasets/coco.py