class_docstr: Data augmentation method in `InstaBoost: Boosting Instance
  Segmentation Via Probability Map Guided Copy-Pasting
  <https://arxiv.org/abs/1908.07801>`_.
  
  Refer to https://github.com/GothicAi/Instaboost for implementation details.
code: def _parse_anns(self, results, anns, img):
      gt_bboxes = []
      gt_labels = []
      gt_masks_ann = []
      for ann in anns:
          x1, y1, w, h = ann["bbox"]
          # TODO: more essential bug need to be fixed in instaboost
          if w <= 0 or h <= 0:
              continue
          bbox = [x1, y1, x1 + w, y1 + h]
          gt_bboxes.append(bbox)
          gt_labels.append(ann["category_id"])
          gt_masks_ann.append(ann["segmentation"])
      gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
      gt_labels = np.array(gt_labels, dtype=np.int64)
      results["ann_info"]["labels"] = gt_labels
      results["ann_info"]["bboxes"] = gt_bboxes
      results["ann_info"]["masks"] = gt_masks_ann
      results["img"] = img
      return results
  
file_path: insightface/detection/scrfd/mmdet/datasets/pipelines/instaboost.py
imports: import numpy
  from ..builder import PIPELINES
label_desc: Use image node_id 3 for calling the InstaBoost obj's underlying member method code with example usage: obj._parse_anns(results, anns, img) and returns: results