text: # -*- coding: utf-8 -*-
  # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  
  import numpy as np
  import os
  import xml.etree.ElementTree as ET
  from fvcore.common.file_io import PathManager
  
  from detectron2.data import DatasetCatalog, MetadataCatalog
  from detectron2.structures import BoxMode
  
  __all__ = ["register_pascal_voc"]
  
  
  # fmt: off
  CLASS_NAMES = [
      "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
      "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
      "pottedplant", "sheep", "sofa", "train", "tvmonitor",
  ]
  # fmt: on
  
  
  def load_voc_instances(dirname: str, split: str):
      """
      Load Pascal VOC detection annotations to Detectron2 format.
  
      Args:
          dirname: Contain "Annotations", "ImageSets", "JPEGImages"
          split (str): one of "train", "test", "val", "trainval"
      """
      with PathManager.open(os.path.join(dirname, "ImageSets", "Main", split + ".txt")) as f:
          fileids = np.loadtxt(f, dtype=np.str)
  
      dicts = []
      for fileid in fileids:
          anno_file = os.path.join(dirname, "Annotations", fileid + ".xml")
          jpeg_file = os.path.join(dirname, "JPEGImages", fileid + ".jpg")
  
          tree = ET.parse(anno_file)
  
          r = {
              "file_name": jpeg_file,
              "image_id": fileid,
              "height": int(tree.findall("./size/height")[0].text),
              "width": int(tree.findall("./size/width")[0].text),
          }
          instances = []
  
          for obj in tree.findall("object"):
              cls = obj.find("name").text
              # We include "difficult" samples in training.
              # Based on limited experiments, they don't hurt accuracy.
              # difficult = int(obj.find("difficult").text)
              # if difficult == 1:
              # continue
              bbox = obj.find("bndbox")
              bbox = [float(bbox.find(x).text) for x in ["xmin", "ymin", "xmax", "ymax"]]
              # Original annotations are integers in the range [1, W or H]
              # Assuming they mean 1-based pixel indices (inclusive),
              # a box with annotation (xmin=1, xmax=W) covers the whole image.
              # In coordinate space this is represented by (xmin=0, xmax=W)
              bbox[0] -= 1.0
              bbox[1] -= 1.0
              instances.append(
                  {"category_id": CLASS_NAMES.index(cls), "bbox": bbox, "bbox_mode": BoxMode.XYXY_ABS}
              )
          r["annotations"] = instances
          dicts.append(r)
      return dicts
  
  
  def register_pascal_voc(name, dirname, split, year):
      DatasetCatalog.register(name, lambda: load_voc_instances(dirname, split))
      MetadataCatalog.get(name).set(
          thing_classes=CLASS_NAMES, dirname=dirname, year=year, split=split
      )
  