parameters: (self)
docstring: Compute and display summary metrics for evaluation results.
          Note this functin can *only* be applied on the default parameter setting
docstring_summary: Compute and display summary metrics for evaluation results.
          Note this functin can *only* be applied on the default parameter setting
docstring_tokens: Compute and display summary metrics for evaluation results . Note this functin can * only * be applied on the default parameter setting
function: 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')
function_tokens: def summarize ( self ) : 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' )