code: def analyze_individual_category(
      k, cocoDt, cocoGt, catId, iou_type, areas=None
  ):
      nm = cocoGt.loadCats(catId)[0]
      print(
          f'--------------analyzing {k + 1}-{nm["name"]}---------------'
      )
      ps_ = {}
      dt = copy.deepcopy(cocoDt)
      nm = cocoGt.loadCats(catId)[0]
      imgIds = cocoGt.getImgIds()
      dt_anns = dt.dataset["annotations"]
      select_dt_anns = []
      for ann in dt_anns:
          if ann["category_id"] == catId:
              select_dt_anns.append(ann)
      dt.dataset["annotations"] = select_dt_anns
      dt.createIndex()
      # compute precision but ignore superclass confusion
      gt = copy.deepcopy(cocoGt)
      child_catIds = gt.getCatIds(supNms=[nm["supercategory"]])
      for idx, ann in enumerate(gt.dataset["annotations"]):
          if (
              ann["category_id"] in child_catIds
              and ann["category_id"] != catId
          ):
              gt.dataset["annotations"][idx]["ignore"] = 1
              gt.dataset["annotations"][idx]["iscrowd"] = 1
              gt.dataset["annotations"][idx]["category_id"] = catId
      cocoEval = COCOeval(gt, copy.deepcopy(dt), iou_type)
      cocoEval.params.imgIds = imgIds
      cocoEval.params.maxDets = [100]
      cocoEval.params.iouThrs = [0.1]
      cocoEval.params.useCats = 1
      if areas:
          cocoEval.params.areaRng = [
              [0**2, areas[2]],
              [0**2, areas[0]],
              [areas[0], areas[1]],
              [areas[1], areas[2]],
          ]
      cocoEval.evaluate()
      cocoEval.accumulate()
      ps_supercategory = cocoEval.eval["precision"][0, :, k, :, :]
      ps_["ps_supercategory"] = ps_supercategory
      # compute precision but ignore any class confusion
      gt = copy.deepcopy(cocoGt)
      for idx, ann in enumerate(gt.dataset["annotations"]):
          if ann["category_id"] != catId:
              gt.dataset["annotations"][idx]["ignore"] = 1
              gt.dataset["annotations"][idx]["iscrowd"] = 1
              gt.dataset["annotations"][idx]["category_id"] = catId
      cocoEval = COCOeval(gt, copy.deepcopy(dt), iou_type)
      cocoEval.params.imgIds = imgIds
      cocoEval.params.maxDets = [100]
      cocoEval.params.iouThrs = [0.1]
      cocoEval.params.useCats = 1
      if areas:
          cocoEval.params.areaRng = [
              [0**2, areas[2]],
              [0**2, areas[0]],
              [areas[0], areas[1]],
              [areas[1], areas[2]],
          ]
      cocoEval.evaluate()
      cocoEval.accumulate()
      ps_allcategory = cocoEval.eval["precision"][0, :, k, :, :]
      ps_["ps_allcategory"] = ps_allcategory
      return k, ps_
  
file_path: mmdetection/tools/analysis_tools/coco_error_analysis.py
imports: import copy
  import os
  from argparse import ArgumentParser
  from multiprocessing import Pool
  import matplotlib.pyplot
  import numpy
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
label_desc: Use image node_id 7 for calling a global function with example usage: analyze_individual_category(k, cocoDt, cocoGt, catId, iou_type, areas) and returns: k,  ps_