code: import itertools
  import logging
  import os.path as osp
  import tempfile
  from collections import OrderedDict
  
  import mmcv
  import numpy as np
  import pycocotools
  from mmcv.utils import print_log
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
  from terminaltables import AsciiTable
  
  from mmdet.core import eval_recalls
  from .builder import DATASETS
  from .custom import CustomDataset
  
  
  @DATASETS.register_module()
  class CocoDataset(CustomDataset):
  
      CLASSES = ('schwarze_Schraube',)## check mark ##
  
      def load_annotations(self, ann_file):
          """Load annotation from COCO style annotation file.
  
          Args:
              ann_file (str): Path of annotation file.
  
          Returns:
              list[dict]: Annotation info from COCO api.
          """
          if not getattr(pycocotools, '__version__', '0') >= '12.0.2':
              raise AssertionError(
                  'Incompatible version of pycocotools is installed. '
                  'Run pip uninstall pycocotools first. Then run pip '
                  'install mmpycocotools to install open-mmlab forked '
                  'pycocotools.')
  
          self.coco = COCO(ann_file)
          self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
          self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
          self.img_ids = self.coco.get_img_ids()
          data_infos = []
          total_ann_ids = []
          for i in self.img_ids:
              info = self.coco.load_imgs([i])[0]
              info['filename'] = info['file_name']
              data_infos.append(info)
              ann_ids = self.coco.get_ann_ids(img_ids=[i])
              total_ann_ids.extend(ann_ids)
          assert len(set(total_ann_ids)) == len(
              total_ann_ids), f"Annotation ids in '{ann_file}' are not unique!"
          return data_infos
  
      def get_ann_info(self, idx):
          """Get COCO annotation by index.
  
          Args:
              idx (int): Index of data.
  
          Returns:
              dict: Annotation info of specified index.
          """
  
          img_id = self.data_infos[idx]['id']
          ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
          ann_info = self.coco.load_anns(ann_ids)
          return self._parse_ann_info(self.data_infos[idx], ann_info)
  
      def get_cat_ids(self, idx):
          """Get COCO category ids by index.
  
          Args:
              idx (int): Index of data.
  
          Returns:
              list[int]: All categories in the image of specified index.
          """
  
          img_id = self.data_infos[idx]['id']
          ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
          ann_info = self.coco.load_anns(ann_ids)
          return [ann['category_id'] for ann in ann_info]
  
      def _filter_imgs(self, min_size=32):
          """Filter images too small or without ground truths."""
          valid_inds = []
          # obtain images that contain annotation
          ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())
          # obtain images that contain annotations of the required categories
          ids_in_cat = set()
          for i, class_id in enumerate(self.cat_ids):
              ids_in_cat |= set(self.coco.cat_img_map[class_id])
          # merge the image id sets of the two conditions and use the merged set
          # to filter out images if self.filter_empty_gt=True
          ids_in_cat &= ids_with_ann
  
          valid_img_ids = []
          for i, img_info in enumerate(self.data_infos):
              img_id = self.img_ids[i]
              if self.filter_empty_gt and img_id not in ids_in_cat:
                  continue
              if min(img_info['width'], img_info['height']) >= min_size:
                  valid_inds.append(i)
                  valid_img_ids.append(img_id)
          self.img_ids = valid_img_ids
          return valid_inds
  
      def _parse_ann_info(self, img_info, ann_info):
          """Parse bbox and mask annotation.
  
          Args:
              ann_info (list[dict]): Annotation info of an image.
              with_mask (bool): Whether to parse mask annotations.
  
          Returns:
              dict: A dict containing the following keys: bboxes, bboxes_ignore,\
                  labels, masks, seg_map. "masks" are raw annotations and not \
                  decoded into binary masks.
          """
          gt_bboxes = []
          gt_labels = []
          gt_bboxes_ignore = []
          gt_masks_ann = []
          for i, ann in enumerate(ann_info):
              if ann.get('ignore', False):
                  continue
              x1, y1, w, h = ann['bbox']
              inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0))
              inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0))
              if inter_w * inter_h == 0:
                  continue
              if ann['area'] <= 0 or w < 1 or h < 1:
                  continue
              if ann['category_id'] not in self.cat_ids:
                  continue
              bbox = [x1, y1, x1 + w, y1 + h]
              if ann.get('iscrowd', False):
                  gt_bboxes_ignore.append(bbox)
              else:
                  gt_bboxes.append(bbox)
                  gt_labels.append(self.cat2label[ann['category_id']])
                  gt_masks_ann.append(ann.get('segmentation', None))
  
          if gt_bboxes:
              gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
              gt_labels = np.array(gt_labels, dtype=np.int64)
          else:
              gt_bboxes = np.zeros((0, 4), dtype=np.float32)
              gt_labels = np.array([], dtype=np.int64)
  
          if gt_bboxes_ignore:
              gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
          else:
              gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
  
          seg_map = img_info['filename'].replace('jpg', 'png')
  
          ann = dict(
              bboxes=gt_bboxes,
              labels=gt_labels,
              bboxes_ignore=gt_bboxes_ignore,
              masks=gt_masks_ann,
              seg_map=seg_map)
  
          return ann
  
      def xyxy2xywh(self, bbox):
          """Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO
          evaluation.
  
          Args:
              bbox (numpy.ndarray): The bounding boxes, shape (4, ), in
                  ``xyxy`` order.
  
          Returns:
              list[float]: The converted bounding boxes, in ``xywh`` order.
          """
  
          _bbox = bbox.tolist()
          return [
              _bbox[0],
              _bbox[1],
              _bbox[2] - _bbox[0],
              _bbox[3] - _bbox[1],
          ]
  
      def _proposal2json(self, results):
          """Convert proposal results to COCO json style."""
          json_results = []
          for idx in range(len(self)):
              img_id = self.img_ids[idx]
              bboxes = results[idx]
              for i in range(bboxes.shape[0]):
                  data = dict()
                  data['image_id'] = img_id
                  data['bbox'] = self.xyxy2xywh(bboxes[i])
                  data['score'] = float(bboxes[i][4])
                  data['category_id'] = 1
                  json_results.append(data)
          return json_results
  
      def _det2json(self, results):
          """Convert detection results to COCO json style."""
          json_results = []
          for idx in range(len(self)):
              img_id = self.img_ids[idx]
              result = results[idx]
              for label in range(len(result)):
                  bboxes = result[label]
                  for i in range(bboxes.shape[0]):
                      data = dict()
                      data['image_id'] = img_id
                      data['bbox'] = self.xyxy2xywh(bboxes[i])
                      data['score'] = float(bboxes[i][4])
                      data['category_id'] = self.cat_ids[label]
                      json_results.append(data)
          return json_results
  
      def _segm2json(self, results):
          """Convert instance segmentation results to COCO json style."""
          bbox_json_results = []
          segm_json_results = []
          for idx in range(len(self)):
              img_id = self.img_ids[idx]
              det, seg = results[idx]
              for label in range(len(det)):
                  # bbox results
                  bboxes = det[label]
                  for i in range(bboxes.shape[0]):
                      data = dict()
                      data['image_id'] = img_id
                      data['bbox'] = self.xyxy2xywh(bboxes[i])
                      data['score'] = float(bboxes[i][4])
                      data['category_id'] = self.cat_ids[label]
                      bbox_json_results.append(data)
  
                  # segm results
                  # some detectors use different scores for bbox and mask
                  if isinstance(seg, tuple):
                      segms = seg[0][label]
                      mask_score = seg[1][label]
                  else:
                      segms = seg[label]
                      mask_score = [bbox[4] for bbox in bboxes]
                  for i in range(bboxes.shape[0]):
                      data = dict()
                      data['image_id'] = img_id
                      data['bbox'] = self.xyxy2xywh(bboxes[i])
                      data['score'] = float(mask_score[i])
                      data['category_id'] = self.cat_ids[label]
                      if isinstance(segms[i]['counts'], bytes):
                          segms[i]['counts'] = segms[i]['counts'].decode()
                      data['segmentation'] = segms[i]
                      segm_json_results.append(data)
          return bbox_json_results, segm_json_results
  
      def results2json(self, results, outfile_prefix):
          """Dump the detection results to a COCO style json file.
  
          There are 3 types of results: proposals, bbox predictions, mask
          predictions, and they have different data types. This method will
          automatically recognize the type, and dump them to json files.
  
          Args:
              results (list[list | tuple | ndarray]): Testing results of the
                  dataset.
              outfile_prefix (str): The filename prefix of the json files. If the
                  prefix is "somepath/xxx", the json files will be named
                  "somepath/xxx.bbox.json", "somepath/xxx.segm.json",
                  "somepath/xxx.proposal.json".
  
          Returns:
              dict[str: str]: Possible keys are "bbox", "segm", "proposal", and \
                  values are corresponding filenames.
          """
          result_files = dict()
          if isinstance(results[0], list):
              json_results = self._det2json(results)
              result_files['bbox'] = f'{outfile_prefix}.bbox.json'
              result_files['proposal'] = f'{outfile_prefix}.bbox.json'
              mmcv.dump(json_results, result_files['bbox'])
          elif isinstance(results[0], tuple):
              json_results = self._segm2json(results)
              result_files['bbox'] = f'{outfile_prefix}.bbox.json'
              result_files['proposal'] = f'{outfile_prefix}.bbox.json'
              result_files['segm'] = f'{outfile_prefix}.segm.json'
              mmcv.dump(json_results[0], result_files['bbox'])
              mmcv.dump(json_results[1], result_files['segm'])
          elif isinstance(results[0], np.ndarray):
              json_results = self._proposal2json(results)
              result_files['proposal'] = f'{outfile_prefix}.proposal.json'
              mmcv.dump(json_results, result_files['proposal'])
          else:
              raise TypeError('invalid type of results')
          return result_files
  
      def fast_eval_recall(self, results, proposal_nums, iou_thrs, logger=None):
          gt_bboxes = []
          for i in range(len(self.img_ids)):
              ann_ids = self.coco.get_ann_ids(img_ids=self.img_ids[i])
              ann_info = self.coco.load_anns(ann_ids)
              if len(ann_info) == 0:
                  gt_bboxes.append(np.zeros((0, 4)))
                  continue
              bboxes = []
              for ann in ann_info:
                  if ann.get('ignore', False) or ann['iscrowd']:
                      continue
                  x1, y1, w, h = ann['bbox']
                  bboxes.append([x1, y1, x1 + w, y1 + h])
              bboxes = np.array(bboxes, dtype=np.float32)
              if bboxes.shape[0] == 0:
                  bboxes = np.zeros((0, 4))
              gt_bboxes.append(bboxes)
  
          recalls = eval_recalls(
              gt_bboxes, results, proposal_nums, iou_thrs, logger=logger)
          ar = recalls.mean(axis=1)
          return ar
  
      def format_results(self, results, jsonfile_prefix=None, **kwargs):
          """Format the results to json (standard format for COCO evaluation).
  
          Args:
              results (list[tuple | numpy.ndarray]): Testing results of the
                  dataset.
              jsonfile_prefix (str | None): The prefix of json files. It includes
                  the file path and the prefix of filename, e.g., "a/b/prefix".
                  If not specified, a temp file will be created. Default: None.
  
          Returns:
              tuple: (result_files, tmp_dir), result_files is a dict containing \
                  the json filepaths, tmp_dir is the temporal directory created \
                  for saving json files when jsonfile_prefix is not specified.
          """
          assert isinstance(results, list), 'results must be a list'
          assert len(results) == len(self), (
              'The length of results is not equal to the dataset len: {} != {}'.
              format(len(results), len(self)))
  
          if jsonfile_prefix is None:
              tmp_dir = tempfile.TemporaryDirectory()
              jsonfile_prefix = osp.join(tmp_dir.name, 'results')
          else:
              tmp_dir = None
          result_files = self.results2json(results, jsonfile_prefix)
          return result_files, tmp_dir
  
      def evaluate(self,
                   results,
                   metric='bbox',
                   logger=None,
                   jsonfile_prefix=None,
                   classwise=False,
                   proposal_nums=(100, 300, 1000),
                   iou_thrs=None,
                   metric_items=None):
          """Evaluation in COCO protocol.
  
          Args:
              results (list[list | tuple]): Testing results of the dataset.
              metric (str | list[str]): Metrics to be evaluated. Options are
                  'bbox', 'segm', 'proposal', 'proposal_fast'.
              logger (logging.Logger | str | None): Logger used for printing
                  related information during evaluation. Default: None.
              jsonfile_prefix (str | None): The prefix of json files. It includes
                  the file path and the prefix of filename, e.g., "a/b/prefix".
                  If not specified, a temp file will be created. Default: None.
              classwise (bool): Whether to evaluating the AP for each class.
              proposal_nums (Sequence[int]): Proposal number used for evaluating
                  recalls, such as recall@100, recall@1000.
                  Default: (100, 300, 1000).
              iou_thrs (Sequence[float], optional): IoU threshold used for
                  evaluating recalls/mAPs. If set to a list, the average of all
                  IoUs will also be computed. If not specified, [0.50, 0.55,
                  0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95] will be used.
                  Default: None.
              metric_items (list[str] | str, optional): Metric items that will
                  be returned. If not specified, ``['AR@100', 'AR@300',
                  'AR@1000', 'AR_s@1000', 'AR_m@1000', 'AR_l@1000' ]`` will be
                  used when ``metric=='proposal'``, ``['mAP', 'mAP_50', 'mAP_75',
                  'mAP_s', 'mAP_m', 'mAP_l']`` will be used when
                  ``metric=='bbox' or metric=='segm'``.
  
          Returns:
              dict[str, float]: COCO style evaluation metric.
          """
  
          metrics = metric if isinstance(metric, list) else [metric]
          allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
          for metric in metrics:
              if metric not in allowed_metrics:
                  raise KeyError(f'metric {metric} is not supported')
          if iou_thrs is None:
              iou_thrs = np.linspace(
                  .5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)
          if metric_items is not None:
              if not isinstance(metric_items, list):
                  metric_items = [metric_items]
  
          result_files, tmp_dir = self.format_results(results, jsonfile_prefix)
  
          eval_results = OrderedDict()
          cocoGt = self.coco
          for metric in metrics:
              msg = f'Evaluating {metric}...'
              if logger is None:
                  msg = '\n' + msg
              print_log(msg, logger=logger)
  
              if metric == 'proposal_fast':
                  ar = self.fast_eval_recall(
                      results, proposal_nums, iou_thrs, logger='silent')
                  log_msg = []
                  for i, num in enumerate(proposal_nums):
                      eval_results[f'AR@{num}'] = ar[i]
                      log_msg.append(f'\nAR@{num}\t{ar[i]:.4f}')
                  log_msg = ''.join(log_msg)
                  print_log(log_msg, logger=logger)
                  continue
  
              if metric not in result_files:
                  raise KeyError(f'{metric} is not in results')
              try:
                  cocoDt = cocoGt.loadRes(result_files[metric])
              except IndexError:
                  print_log(
                      'The testing results of the whole dataset is empty.',
                      logger=logger,
                      level=logging.ERROR)
                  break
  
              iou_type = 'bbox' if metric == 'proposal' else metric
              cocoEval = COCOeval(cocoGt, cocoDt, iou_type)
              cocoEval.params.catIds = self.cat_ids
              cocoEval.params.imgIds = self.img_ids
              cocoEval.params.maxDets = list(proposal_nums)
              cocoEval.params.iouThrs = iou_thrs
              # mapping of cocoEval.stats
              coco_metric_names = {
                  'mAP': 0,
                  'mAP_50': 1,
                  'mAP_75': 2,
                  'mAP_s': 3,
                  'mAP_m': 4,
                  'mAP_l': 5,
                  'AR@100': 6,
                  'AR@300': 7,
                  'AR@1000': 8,
                  'AR_s@1000': 9,
                  'AR_m@1000': 10,
                  'AR_l@1000': 11
              }
              if metric_items is not None:
                  for metric_item in metric_items:
                      if metric_item not in coco_metric_names:
                          raise KeyError(
                              f'metric item {metric_item} is not supported')
  
              if metric == 'proposal':
                  cocoEval.params.useCats = 0
                  cocoEval.evaluate()
                  cocoEval.accumulate()
                  cocoEval.summarize()
                  if metric_items is None:
                      metric_items = [
                          'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000',
                          'AR_m@1000', 'AR_l@1000'
                      ]
  
                  for item in metric_items:
                      val = float(
                          f'{cocoEval.stats[coco_metric_names[item]]:.3f}')
                      eval_results[item] = val
              else:
                  cocoEval.evaluate()
                  cocoEval.accumulate()
                  cocoEval.summarize()
                  if classwise:  # Compute per-category AP
                      # Compute per-category AP
                      # from https://github.com/facebookresearch/detectron2/
                      precisions = cocoEval.eval['precision']
                      # precision: (iou, recall, cls, area range, max dets)
                      assert len(self.cat_ids) == precisions.shape[2]
  
                      results_per_category = []
                      for idx, catId in enumerate(self.cat_ids):
                          # area range index 0: all area ranges
                          # max dets index -1: typically 100 per image
                          nm = self.coco.loadCats(catId)[0]
                          precision = precisions[:, :, idx, 0, -1]
                          precision = precision[precision > -1]
                          if precision.size:
                              ap = np.mean(precision)
                          else:
                              ap = float('nan')
                          results_per_category.append(
                              (f'{nm["name"]}', f'{float(ap):0.3f}'))
  
                      num_columns = min(6, len(results_per_category) * 2)
                      results_flatten = list(
                          itertools.chain(*results_per_category))
                      headers = ['category', 'AP'] * (num_columns // 2)
                      results_2d = itertools.zip_longest(*[
                          results_flatten[i::num_columns]
                          for i in range(num_columns)
                      ])
                      table_data = [headers]
                      table_data += [result for result in results_2d]
                      table = AsciiTable(table_data)
                      print_log('\n' + table.table, logger=logger)
  
                  if metric_items is None:
                      metric_items = [
                          'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'
                      ]
  
                  for metric_item in metric_items:
                      key = f'{metric}_{metric_item}'
                      val = float(
                          f'{cocoEval.stats[coco_metric_names[metric_item]]:.3f}'
                      )
                      eval_results[key] = val
                  ap = cocoEval.stats[:6]
                  eval_results[f'{metric}_mAP_copypaste'] = (
                      f'{ap[0]:.3f} {ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} '
                      f'{ap[4]:.3f} {ap[5]:.3f}')
          if tmp_dir is not None:
              tmp_dir.cleanup()
          return eval_results
  
apis: numpy.array numpy.mean numpy.round numpy.zeros