text: __author__ = 'tsungyi'
  
  import numpy as np
  import datetime
  import time
  from collections import defaultdict
  from faster_rcnn.pycocotools import mask
  #import mask
  import copy
  
  class COCOeval:
      # Interface for evaluating detection on the Microsoft COCO dataset.
      #
      # The usage for CocoEval is as follows:
      #  cocoGt=..., cocoDt=...       # load dataset and results
      #  E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object
      #  E.params.recThrs = ...;      # set parameters as desired
      #  E.evaluate();                # run per image evaluation
      #  E.accumulate();              # accumulate per image results
      #  E.summarize();               # display summary metrics of results
      # For example usage see evalDemo.m and http://mscoco.org/.
      #
      # The evaluation parameters are as follows (defaults in brackets):
      #  imgIds     - [all] N img ids to use for evaluation
      #  catIds     - [all] K cat ids to use for evaluation
      #  iouThrs    - [.5:.05:.95] T=10 IoU thresholds for evaluation
      #  recThrs    - [0:.01:1] R=101 recall thresholds for evaluation
      #  areaRng    - [...] A=4 object area ranges for evaluation
      #  maxDets    - [1 10 100] M=3 thresholds on max detections per image
      #  useSegm    - [1] if true evaluate against ground-truth segments
      #  useCats    - [1] if true use category labels for evaluation    # Note: if useSegm=0 the evaluation is run on bounding boxes.
      # Note: if useCats=0 category labels are ignored as in proposal scoring.
      # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified.
      #
      # evaluate(): evaluates detections on every image and every category and
      # concats the results into the "evalImgs" with fields:
      #  dtIds      - [1xD] id for each of the D detections (dt)
      #  gtIds      - [1xG] id for each of the G ground truths (gt)
      #  dtMatches  - [TxD] matching gt id at each IoU or 0
      #  gtMatches  - [TxG] matching dt id at each IoU or 0
      #  dtScores   - [1xD] confidence of each dt
      #  gtIgnore   - [1xG] ignore flag for each gt
      #  dtIgnore   - [TxD] ignore flag for each dt at each IoU
      #
      # accumulate(): accumulates the per-image, per-category evaluation
      # results in "evalImgs" into the dictionary "eval" with fields:
      #  params     - parameters used for evaluation
      #  date       - date evaluation was performed
      #  counts     - [T,R,K,A,M] parameter dimensions (see above)
      #  precision  - [TxRxKxAxM] precision for every evaluation setting
      #  recall     - [TxKxAxM] max recall for every evaluation setting
      # Note: precision and recall==-1 for settings with no gt objects.
      #
      # See also coco, mask, pycocoDemo, pycocoEvalDemo
      #
      # Microsoft COCO Toolbox.      version 2.0
      # Data, paper, and tutorials available at:  http://mscoco.org/
      # Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
      # Licensed under the Simplified BSD License [see coco/license.txt]
      def __init__(self, cocoGt=None, cocoDt=None):
          '''
          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
          '''
          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()              # 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())
  
  
      def _prepare(self):
          '''
          Prepare ._gts and ._dts for evaluation based on params
          :return: None
          '''
          #
          def _toMask(objs, coco):
              # modify segmentation by reference
              for obj in objs:
                  t = coco.imgs[obj['image_id']]
                  if type(obj['segmentation']) == list:
                      if type(obj['segmentation'][0]) == dict:
                          print( 'debug')
                      obj['segmentation'] = mask.frPyObjects(obj['segmentation'],t['height'],t['width'])
                      if len(obj['segmentation']) == 1:
                          obj['segmentation'] = obj['segmentation'][0]
                      else:
                          # an object can have multiple polygon regions
                          # merge them into one RLE mask
                          obj['segmentation'] = mask.merge(obj['segmentation'])
                  elif type(obj['segmentation']) == dict and type(obj['segmentation']['counts']) == list:
                      obj['segmentation'] = mask.frPyObjects([obj['segmentation']],t['height'],t['width'])[0]
                  elif type(obj['segmentation']) == dict and \
                       type(type(obj['segmentation']['counts']) == str):
                      pass
                  else:
                      raise Exception('segmentation format not supported.')
          p = self.params
          if p.useCats:
              gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
              dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
          else:
              gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))
              dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))
  
          if p.useSegm:
              _toMask(gts, self.cocoGt)
              _toMask(dts, self.cocoDt)
          self._gts = defaultdict(list)       # gt for evaluation
          self._dts = defaultdict(list)       # dt for evaluation
          for gt in gts:
              self._gts[gt['image_id'], gt['category_id']].append(gt)
          for dt in dts:
              self._dts[dt['image_id'], dt['category_id']].append(dt)
          self.evalImgs = defaultdict(list)   # per-image per-category evaluation results
          self.eval     = {}                  # accumulated evaluation results
  
      def evaluate(self):
          '''
          Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
          :return: None
          '''
          tic = time.time()
          print( 'Running per image evaluation...      ')
          p = self.params
          p.imgIds = list(np.unique(p.imgIds))
          if p.useCats:
              p.catIds = list(np.unique(p.catIds))
          p.maxDets = sorted(p.maxDets)
          self.params=p
  
          self._prepare()
          # loop through images, area range, max detection number
          catIds = p.catIds if p.useCats else [-1]
  
          computeIoU = self.computeIoU
          self.ious = {(imgId, catId): computeIoU(imgId, catId) \
                          for imgId in p.imgIds
                          for catId in catIds}
  
          evaluateImg = self.evaluateImg
          maxDet = p.maxDets[-1]
          self.evalImgs = [evaluateImg(imgId, catId, areaRng, maxDet)
                   for catId in catIds
                   for areaRng in p.areaRng
                   for imgId in p.imgIds
               ]
          self._paramsEval = copy.deepcopy(self.params)
          toc = time.time()
          print( 'DONE (t=%0.2fs).'%(toc-tic))
  
      def computeIoU(self, imgId, catId):
          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 []
          dt = sorted(dt, key=lambda x: -x['score'])
          if len(dt) > p.maxDets[-1]:
              dt=dt[0:p.maxDets[-1]]
  
          if p.useSegm:
              g = [g['segmentation'] for g in gt]
              d = [d['segmentation'] for d in dt]
          else:
              g = [g['bbox'] for g in gt]
              d = [d['bbox'] for d in dt]
  
          # compute iou between each dt and gt region
          iscrowd = [int(o['iscrowd']) for o in gt]
          ious = mask.iou(d,g,iscrowd)
          return ious
  
      def evaluateImg(self, imgId, catId, aRng, maxDet):
          '''
          perform evaluation for single category and image
          :return: dict (single image results)
          '''
          #
          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:
              if 'ignore' not in g:
                  g['ignore'] = 0
              if g['iscrowd'] == 1 or g['ignore'] or (g['area']<aRng[0] or g['area']>aRng[1]):
                  g['_ignore'] = 1
              else:
                  g['_ignore'] = 0
  
          # sort dt highest score first, sort gt ignore last
          # gt = sorted(gt, key=lambda x: x['_ignore'])
          gtind = [ind for (ind, g) in sorted(enumerate(gt), key=lambda ind_g: ind_g[1]['_ignore']) ]
  
          gt = [gt[ind] for ind in gtind]
          dt = sorted(dt, key=lambda x: -x['score'])[0:maxDet]
          iscrowd = [int(o['iscrowd']) for o in gt]
          # load computed ious
          N_iou = len(self.ious[imgId, catId])
          ious = self.ious[imgId, catId][0:maxDet, np.array(gtind)] if N_iou >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 not len(ious)==0:
              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 ious[dind,gind] < iou:
                              continue
                          # match successful and best so far, store appropriately
                          iou=ious[dind,gind]
                          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']
          # 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
          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,
              }
  
      def accumulate(self, p = None):
          '''
          Accumulate per image evaluation results and store the result in self.eval
          :param p: input params for evaluation
          :return: None
          '''
          print( 'Accumulating evaluation results...   ')
          tic = time.time()
          if not self.evalImgs:
              print( 'Please run evaluate() first')
          # allows input customized parameters
          if p is None:
              p = self.params
          p.catIds = p.catIds if p.useCats == 1 else [-1]
          T           = len(p.iouThrs)
          R           = len(p.recThrs)
          K           = len(p.catIds) if p.useCats else 1
          A           = len(p.areaRng)
          M           = len(p.maxDets)
          precision   = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories
          recall      = -np.ones((T,K,A,M))
  
          # create dictionary for future indexing
          _pe = self._paramsEval
          catIds = _pe.catIds if _pe.useCats else [-1]
          setK = set(catIds)
          setA = set(map(tuple, _pe.areaRng))
          setM = set(_pe.maxDets)
          setI = set(_pe.imgIds)
          # get inds to evaluate
          k_list = [n for n, k in enumerate(p.catIds)  if k in setK]
          m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
          a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA]
          i_list = [n for n, i in enumerate(p.imgIds)  if i in setI]
          # K0 = len(_pe.catIds)
          I0 = len(_pe.imgIds)
          A0 = len(_pe.areaRng)
          # retrieve E at each category, area range, and max number of detections
          for k, k0 in enumerate(k_list):
              Nk = k0*A0*I0
              for a, a0 in enumerate(a_list):
                  Na = a0*I0
                  for m, maxDet in enumerate(m_list):
                      E = [self.evalImgs[Nk+Na+i] for i in i_list]
                      E = filter(None, E)
                      if len(E) == 0:
                          continue
                      dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E])
  
                      # different sorting method generates slightly different results.
                      # mergesort is used to be consistent as Matlab implementation.
                      inds = np.argsort(-dtScores, kind='mergesort')
  
                      dtm  = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds]
                      dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet]  for e in E], axis=1)[:,inds]
                      gtIg = np.concatenate([e['gtIgnore']  for e in E])
                      npig = len([ig for ig in gtIg if ig == 0])
                      if npig == 0:
                          continue
                      tps = np.logical_and(               dtm,  np.logical_not(dtIg) )
                      fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) )
  
                      tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
                      fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
                      for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
                          tp = np.array(tp)
                          fp = np.array(fp)
                          nd = len(tp)
                          rc = tp / npig
                          pr = tp / (fp+tp+np.spacing(1))
                          q  = np.zeros((R,))
  
                          if nd:
                              recall[t,k,a,m] = rc[-1]
                          else:
                              recall[t,k,a,m] = 0
  
                          # numpy is slow without cython optimization for accessing elements
                          # use python array gets significant speed improvement
                          pr = pr.tolist(); q = q.tolist()
  
                          for i in range(nd-1, 0, -1):
                              if pr[i] > pr[i-1]:
                                  pr[i-1] = pr[i]
  
                          inds = np.searchsorted(rc, p.recThrs)
                          try:
                              for ri, pi in enumerate(inds):
                                  q[ri] = pr[pi]
                          except:
                              pass
                          precision[t,:,k,a,m] = np.array(q)
          self.eval = {
              'params': p,
              'counts': [T, R, K, A, M],
              'date': datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
              'precision': precision,
              'recall':   recall,
          }
          toc = time.time()
          print( 'DONE (t=%0.2fs).'%( toc-tic ))
  
      def summarize(self):
          '''
          Compute and display summary metrics for evaluation results.
          Note this functin can *only* be applied on the default parameter setting
          '''
          def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ):
              p = self.params
              iStr        = ' {:<18} {} @[ IoU={:<9} | area={:>6} | maxDets={:>3} ] = {}'
              titleStr    = 'Average Precision' if ap == 1 else 'Average Recall'
              typeStr     = '(AP)' if ap==1 else '(AR)'
              iouStr      = '%0.2f:%0.2f'%(p.iouThrs[0], p.iouThrs[-1]) if iouThr is None else '%0.2f'%(iouThr)
              areaStr     = areaRng
              maxDetsStr  = '%d'%(maxDets)
  
              aind = [i for i, aRng in enumerate(['all', 'small', 'medium', 'large']) if aRng == areaRng]
              mind = [i for i, mDet in enumerate([1, 10, 100]) if mDet == maxDets]
              if ap == 1:
                  # dimension of precision: [TxRxKxAxM]
                  s = self.eval['precision']
                  # IoU
                  if iouThr is not None:
                      t = np.where(iouThr == p.iouThrs)[0]
                      s = s[t]
                  # areaRng
                  s = s[:,:,:,aind,mind]
              else:
                  # dimension of recall: [TxKxAxM]
                  s = self.eval['recall']
                  s = s[:,:,aind,mind]
              if len(s[s>-1])==0:
                  mean_s = -1
              else:
                  mean_s = np.mean(s[s>-1])
              print( iStr.format(titleStr, typeStr, iouStr, areaStr, maxDetsStr, '%.3f'%(float(mean_s))))
              return mean_s
  
          if not self.eval:
              raise Exception('Please run accumulate() first')
          self.stats = np.zeros((12,))
          self.stats[0] = _summarize(1)
          self.stats[1] = _summarize(1,iouThr=.5)
          self.stats[2] = _summarize(1,iouThr=.75)
          self.stats[3] = _summarize(1,areaRng='small')
          self.stats[4] = _summarize(1,areaRng='medium')
          self.stats[5] = _summarize(1,areaRng='large')
          self.stats[6] = _summarize(0,maxDets=1)
          self.stats[7] = _summarize(0,maxDets=10)
          self.stats[8] = _summarize(0,maxDets=100)
          self.stats[9]  = _summarize(0,areaRng='small')
          self.stats[10] = _summarize(0,areaRng='medium')
          self.stats[11] = _summarize(0,areaRng='large')
  
      def __str__(self):
          self.summarize()
  
  class Params:
      '''
      Params for coco evaluation api
      '''
      def __init__(self):
          self.imgIds = []
          self.catIds = []
          # np.arange causes trouble.  the data point on arange is slightly larger than the true value
          self.iouThrs = np.linspace(.5, 0.95, np.round((0.95-.5)/.05)+1, endpoint=True)
          self.recThrs = np.linspace(.0, 1.00, np.round((1.00-.0)/.01)+1, endpoint=True)
          self.maxDets = [1,10,100]
          self.areaRng = [ [0**2,1e5**2], [0**2, 32**2], [32**2, 96**2], [96**2, 1e5**2] ]
          self.useSegm = 0
          self.useCats = 1