code: def analyze_results(
      res_file,
      ann_file,
      res_types,
      out_dir,
      extraplots=None,
      areas=None,
      score_thr=None,
  ):
      for res_type in res_types:
          assert res_type in ["bbox", "segm"]
      if areas:
          assert (
              len(areas) == 3
          ), "3 integers should be specified as areas, \
              representing 3 area regions"
  
      if score_thr:
          assert score_thr >= 0, "score_thr should be bigger than 0"
  
      directory = os.path.dirname(out_dir + "/")
      if not os.path.exists(directory):
          print(f"-------------create {out_dir}-----------------")
          os.makedirs(directory)
  
      cocoGt = COCO(ann_file)
      cocoDt = cocoGt.loadRes(res_file)
      imgIds = cocoGt.getImgIds()
  
      if score_thr:
          cocoDt.dataset["annotations"] = list(
              filter(
                  lambda ann: ann["score"] >= score_thr,
                  cocoDt.dataset["annotations"],
              )
          )
          cocoDt.createIndex()
  
      for res_type in res_types:
          res_out_dir = out_dir + "/" + res_type + "/"
          res_directory = os.path.dirname(res_out_dir)
          if not os.path.exists(res_directory):
              print(
                  f"-------------create {res_out_dir}-----------------"
              )
              os.makedirs(res_directory)
          iou_type = res_type
          cocoEval = COCOeval(
              copy.deepcopy(cocoGt), copy.deepcopy(cocoDt), iou_type
          )
          cocoEval.params.imgIds = imgIds
          cocoEval.params.iouThrs = [0.75, 0.5, 0.1]
          cocoEval.params.maxDets = [100]
          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 = cocoEval.eval["precision"]
          ps = np.vstack([ps, np.zeros((4, *ps.shape[1:]))])
          catIds = cocoGt.getCatIds()
          recThrs = cocoEval.params.recThrs
          with Pool(processes=48) as pool:
              args = [
                  (k, cocoDt, cocoGt, catId, iou_type, areas)
                  for k, catId in enumerate(catIds)
              ]
              analyze_results = pool.starmap(
                  analyze_individual_category, args
              )
          for k, catId in enumerate(catIds):
              nm = cocoGt.loadCats(catId)[0]
              print(
                  f'--------------saving {k + 1}-{nm["name"]}---------------'
              )
              analyze_result = analyze_results[k]
              assert k == analyze_result[0]
              ps_supercategory = analyze_result[1]["ps_supercategory"]
              ps_allcategory = analyze_result[1]["ps_allcategory"]
              # compute precision but ignore superclass confusion
              ps[3, :, k, :, :] = ps_supercategory
              # compute precision but ignore any class confusion
              ps[4, :, k, :, :] = ps_allcategory
              # fill in background and false negative errors and plot
              ps[ps == -1] = 0
              ps[5, :, k, :, :] = ps[4, :, k, :, :] > 0
              ps[6, :, k, :, :] = 1.0
              makeplot(
                  recThrs,
                  ps[:, :, k],
                  res_out_dir,
                  nm["name"],
                  iou_type,
              )
              if extraplots:
                  makebarplot(
                      recThrs,
                      ps[:, :, k],
                      res_out_dir,
                      nm["name"],
                      iou_type,
                  )
          makeplot(recThrs, ps, res_out_dir, "allclass", iou_type)
          if extraplots:
              makebarplot(
                  recThrs, ps, res_out_dir, "allclass", iou_type
              )
              make_gt_area_group_numbers_plot(
                  cocoEval=cocoEval, outDir=res_out_dir, verbose=True
              )
              make_gt_area_histogram_plot(
                  cocoEval=cocoEval, outDir=res_out_dir
              )
  
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 8 for calling a global function with example usage: analyze_results(res_file, ann_file, res_types, out_dir, extraplots, areas, score_thr) without return types