code: def evaluateImg(self, imgId, catId, aRng, maxDet, check_scores):
      p = self.params
      if p.useCats:
          gt = self._gts[imgId, catId]
          dt = self._dts[imgId, catId]
      else:
          gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]
          dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]
      if len(gt) == 0 and len(dt) == 0:
          return None
  
      for g in gt:
          # g['_ignore'] = g['ignore']
          if g["ignore"] or (
              g["area"] < aRng[0] or g["area"] > aRng[1]
          ):
              g["_ignore"] = True
          else:
              g["_ignore"] = False
  
      # sort dt highest score first, sort gt ignore last
      gtind = np.argsort([g["_ignore"] for g in gt], kind="mergesort")
      gt = [gt[i] for i in gtind]
      dtind = np.argsort([-d["score"] for d in dt], kind="mergesort")
      dt = [dt[i] for i in dtind[0:maxDet]]
      iscrowd = [int(o["iscrowd"]) for o in gt]
      # load computed ious
      if p.iouType == "uv":
          # print('Checking the length', len(self.ious[imgId, catId]))
          # if len(self.ious[imgId, catId]) == 0:
          #    print(self.ious[imgId, catId])
          ious = (
              self.ious[imgId, catId][0][:, gtind]
              if len(self.ious[imgId, catId]) > 0
              else self.ious[imgId, catId]
          )
          ioubs = (
              self.ious[imgId, catId][1][:, gtind]
              if len(self.ious[imgId, catId]) > 0
              else self.ious[imgId, catId]
          )
      else:
          ious = (
              self.ious[imgId, catId][:, gtind]
              if len(self.ious[imgId, catId]) > 0
              else self.ious[imgId, catId]
          )
  
      if self.do_gpsM:
          iousM = (
              self.real_ious[imgId, catId][:, gtind]
              if len(self.ious[imgId, catId]) > 0
              else self.ious[imgId, catId]
          )
  
      T = len(p.iouThrs)
      G = len(gt)
      D = len(dt)
      gtm = np.zeros((T, G))
      dtm = np.zeros((T, D))
      gtIg = np.array([g["_ignore"] for g in gt])
      dtIg = np.zeros((T, D))
      if np.all(gtIg) == True and p.iouType == "uv":
          dtIg = np.logical_or(dtIg, True)
  
      if len(ious) > 0:  # and not p.iouType == 'uv':
          for tind, t in enumerate(p.iouThrs):
              for dind, d in enumerate(dt):
                  # information about best match so far (m=-1 -> unmatched)
                  iou = min([t, 1 - 1e-10])
                  m = -1
                  for gind, g in enumerate(gt):
                      # if this gt already matched, and not a crowd, continue
                      if gtm[tind, gind] > 0 and not iscrowd[gind]:
                          continue
                      # if dt matched to reg gt, and on ignore gt, stop
                      if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1:
                          break
                      # continue to next gt unless better match made
                      if self.calc_mode == "GPSm":
                          new_iou = np.sqrt(
                              iousM[dind, gind] * ious[dind, gind]
                          )
                      elif self.calc_mode == "IOU":
                          new_iou = iousM[dind, gind]
                      elif self.calc_mode == "GPS":
                          new_iou = ious[dind, gind]
  
                      if new_iou < iou:
                          continue
                      if new_iou == 0.0:
                          continue
                      # if match successful and best so far, store appropriately
                      iou = new_iou
                      m = gind
                  # if match made store id of match for both dt and gt
                  if m == -1:
                      continue
                  dtIg[tind, dind] = gtIg[m]
                  dtm[tind, dind] = gt[m]["id"]
                  gtm[tind, m] = d["id"]
  
      if p.iouType == "uv":
          if not len(ioubs) == 0:
              for dind, d in enumerate(dt):
                  # information about best match so far (m=-1 -> unmatched)
                  if dtm[tind, dind] == 0:
                      ioub = 0.8
                      m = -1
                      for gind, g in enumerate(gt):
                          # if this gt already matched, and not a crowd, continue
                          if gtm[tind, gind] > 0 and not iscrowd[gind]:
                              continue
                          # continue to next gt unless better match made
                          if ioubs[dind, gind] < ioub:
                              continue
                          # if match successful and best so far, store appropriately
                          ioub = ioubs[dind, gind]
                          m = gind
                          # if match made store id of match for both dt and gt
                      if m > -1:
                          dtIg[:, dind] = gtIg[m]
                          if gtIg[m]:
                              dtm[tind, dind] = gt[m]["id"]
                              gtm[tind, m] = d["id"]
      # set unmatched detections outside of area range to ignore
      a = np.array(
          [d["area"] < aRng[0] or d["area"] > aRng[1] for d in dt]
      ).reshape((1, len(dt)))
      dtIg = np.logical_or(
          dtIg, np.logical_and(dtm == 0, np.repeat(a, T, 0))
      )
      # store results for given image and category
      # print('Done with the function', len(self.ious[imgId, catId]))
      return {
          "image_id": imgId,
          "category_id": catId,
          "aRng": aRng,
          "maxDet": maxDet,
          "dtIds": [d["id"] for d in dt],
          "gtIds": [g["id"] for g in gt],
          "dtMatches": dtm,
          "gtMatches": gtm,
          "dtScores": [d["score"] for d in dt],
          "gtIgnore": gtIg,
          "dtIgnore": dtIg,
      }
  
imports: import numpy
  import datetime
  import time
  from collections import defaultdict
  from pycocotools import mask
  import copy
  import h5py
  import pickle
  from scipy.io import loadmat
  import scipy.spatial.distance
  import os
  import itertools
  from scipy.optimize import linear_sum_assignment
  import numpy.ma
  import cv2
label_desc: Use image node_id 12 for calling the denseposeCOCOeval obj's underlying member method code with example usage: obj.evaluateImg(imgId, catId, aRng, maxDet, check_scores) and returns: dict, None