docstr: Initialize CocoEval using coco APIs for gt and dt
  :param cocoGt: coco object with ground truth annotations
  :param cocoDt: coco object with detection results
  :return: None
docstr_tok: ["Initialize","CocoEval","using","coco","APIs","for","gt","and","dt",":","param","cocoGt",":","coco","object","with","ground","truth","annotations",":","param","cocoDt",":","coco","object","with","detection","results",":","return",":","None"]
code: def __init__(
      self,
      evalDataDir,
      cocoGt=None,
      cocoDt=None,
      iouType="segm",
      sigma=1.0,
  ):
      """
      Initialize CocoEval using coco APIs for gt and dt
      :param cocoGt: coco object with ground truth annotations
      :param cocoDt: coco object with detection results
      :return: None
      """
      if not iouType:
          print("iouType not specified. use default iouType segm")
      self.evalDataDir = evalDataDir
      self.cocoGt = cocoGt  # ground truth COCO API
      self.cocoDt = cocoDt  # detections COCO API
      self.params = {}  # evaluation parameters
      self.evalImgs = defaultdict(
          list
      )  # per-image per-category evaluation results [KxAxI] elements
      self.eval = {}  # accumulated evaluation results
      self._gts = defaultdict(list)  # gt for evaluation
      self._dts = defaultdict(list)  # dt for evaluation
      self.params = Params(iouType=iouType)  # parameters
      self._paramsEval = {}  # parameters for evaluation
      self.stats = []  # result summarization
      self.ious = {}  # ious between all gts and dts
      if not cocoGt is None:
          self.params.imgIds = sorted(cocoGt.getImgIds())
          self.params.catIds = sorted(cocoGt.getCatIds())
      if iouType == "uv":
          self.sigma = sigma
      self.ignoreThrBB = 0.7
      self.ignoreThrUV = 0.9
  
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 1 to create a new denseposeCOCOeval object  with example: obj = denseposeCOCOeval(evalDataDir, cocoGt, cocoDt, iouType, sigma)