parameters: (detpath,
               annopath,
               imagesetfile,
               classname,
               cachedir,
               ovthresh=0.5,
               use_07_metric=False)
return_statement: return rec, prec, ap
docstring: rec, prec, ap = voc_eval(detpath,
                                  annopath,
                                  imagesetfile,
                                  classname,
                                  [ovthresh],
                                  [use_07_metric])
  
      Top level function that does the PASCAL VOC evaluation.
  
      detpath: Path to detections
          detpath.format(classname) should produce the detection results file.
      annopath: Path to annotations
          annopath.format(imagename) should be the xml annotations file.
      imagesetfile: Text file containing the list of images, one image per line.
      classname: Category name (duh)
      cachedir: Directory for caching the annotations
      [ovthresh]: Overlap threshold (default = 0.5)
      [use_07_metric]: Whether to use VOC07's 11 point AP computation
          (default False)
docstring_summary: rec, prec, ap = voc_eval(detpath,
                                  annopath,
                                  imagesetfile,
                                  classname,
                                  [ovthresh],
                                  [use_07_metric])
docstring_tokens: rec prec ap = voc_eval ( detpath annopath imagesetfile classname [ ovthresh ] [ use_07_metric ] )
function: def voc_eval(detpath,
               annopath,
               imagesetfile,
               classname,
               cachedir,
               ovthresh=0.5,
               use_07_metric=False):
      """rec, prec, ap = voc_eval(detpath,
                                  annopath,
                                  imagesetfile,
                                  classname,
                                  [ovthresh],
                                  [use_07_metric])
  
      Top level function that does the PASCAL VOC evaluation.
  
      detpath: Path to detections
          detpath.format(classname) should produce the detection results file.
      annopath: Path to annotations
          annopath.format(imagename) should be the xml annotations file.
      imagesetfile: Text file containing the list of images, one image per line.
      classname: Category name (duh)
      cachedir: Directory for caching the annotations
      [ovthresh]: Overlap threshold (default = 0.5)
      [use_07_metric]: Whether to use VOC07's 11 point AP computation
          (default False)
      """
      # assumes detections are in detpath.format(classname)
      # assumes annotations are in annopath.format(imagename)
      # assumes imagesetfile is a text file with each line an image name
      # cachedir caches the annotations in a pickle file
  
      # first load gt
      if not os.path.isdir(cachedir):
          os.mkdir(cachedir)
      cachefile = os.path.join(cachedir, 'annots.pkl')
      # read list of images
      with open(imagesetfile, 'r') as f:
          lines = f.readlines()
      imagenames = [x.strip() for x in lines]
  
      if not os.path.isfile(cachefile):
          # load annots
          recs = {}
          for i, imagename in enumerate(imagenames):
              recs[imagename] = parse_rec(annopath.format(imagename))
              if i % 100 == 0:
                  print 'Reading annotation for {:d}/{:d}'.format(
                      i + 1, len(imagenames))
          # save
          print 'Saving cached annotations to {:s}'.format(cachefile)
          with open(cachefile, 'w') as f:
              cPickle.dump(recs, f)
      else:
          # load
          with open(cachefile, 'r') as f:
              recs = cPickle.load(f)
  
      # extract gt objects for this class
      class_recs = {}
      npos = 0
      for imagename in imagenames:
          R = [obj for obj in recs[imagename] if obj['name'] == classname]
          bbox = np.array([x['bbox'] for x in R])
          difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
          det = [False] * len(R)
          npos = npos + sum(~difficult)
          class_recs[imagename] = {'bbox': bbox,
                                   'difficult': difficult,
                                   'det': det}
  
      # read dets
      detfile = detpath.format(classname)
      with open(detfile, 'r') as f:
          lines = f.readlines()
  
      splitlines = [x.strip().split(' ') for x in lines]
      image_ids = [x[0] for x in splitlines]
      confidence = np.array([float(x[1]) for x in splitlines])
      BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
  
      # sort by confidence
      sorted_ind = np.argsort(-confidence)
      sorted_scores = np.sort(-confidence)
      BB = BB[sorted_ind, :]
      image_ids = [image_ids[x] for x in sorted_ind]
  
      # go down dets and mark TPs and FPs
      nd = len(image_ids)
      tp = np.zeros(nd)
      fp = np.zeros(nd)
      for d in range(nd):
          R = class_recs[image_ids[d]]
          bb = BB[d, :].astype(float)
          ovmax = -np.inf
          BBGT = R['bbox'].astype(float)
  
          if BBGT.size > 0:
              # compute overlaps
              # intersection
              ixmin = np.maximum(BBGT[:, 0], bb[0])
              iymin = np.maximum(BBGT[:, 1], bb[1])
              ixmax = np.minimum(BBGT[:, 2], bb[2])
              iymax = np.minimum(BBGT[:, 3], bb[3])
              iw = np.maximum(ixmax - ixmin + 1., 0.)
              ih = np.maximum(iymax - iymin + 1., 0.)
              inters = iw * ih
  
              # union
              uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
                     (BBGT[:, 2] - BBGT[:, 0] + 1.) *
                     (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
  
              overlaps = inters / uni
              ovmax = np.max(overlaps)
              jmax = np.argmax(overlaps)
  
          if ovmax > ovthresh:
              if not R['difficult'][jmax]:
                  if not R['det'][jmax]:
                      tp[d] = 1.
                      R['det'][jmax] = 1
                  else:
                      fp[d] = 1.
          else:
              fp[d] = 1.
  
      # compute precision recall
      fp = np.cumsum(fp)
      tp = np.cumsum(tp)
      rec = tp / float(npos)
      prec = tp / (tp + fp)
      ap = voc_ap(rec, prec, use_07_metric)
  
      return rec, prec, ap
function_tokens: def voc_eval ( detpath , annopath , imagesetfile , classname , cachedir , ovthresh = 0.5 , use_07_metric = False ) : # assumes detections are in detpath.format(classname) # assumes annotations are in annopath.format(imagename) # assumes imagesetfile is a text file with each line an image name # cachedir caches the annotations in a pickle file # first load gt if not os . path . isdir ( cachedir ) : os . mkdir ( cachedir ) cachefile = os . path . join ( cachedir , 'annots.pkl' ) # read list of images with open ( imagesetfile , 'r' ) as f : lines = f . readlines ( ) imagenames = [ x . strip ( ) for x in lines ] if not os . path . isfile ( cachefile ) : # load annots recs = { } for i , imagename in enumerate ( imagenames ) : recs [ imagename ] = parse_rec ( annopath . format ( imagename ) ) if i % 100 == 0 : print 'Reading annotation for {:d}/{:d}' . format ( i + 1 , len ( imagenames ) ) # save print 'Saving cached annotations to {:s}' . format ( cachefile ) with open ( cachefile , 'w' ) as f : cPickle . dump ( recs , f ) else : # load with open ( cachefile , 'r' ) as f : recs = cPickle . load ( f ) # extract gt objects for this class class_recs = { } npos = 0 for imagename in imagenames : R = [ obj for obj in recs [ imagename ] if obj [ 'name' ] == classname ] bbox = np . array ( [ x [ 'bbox' ] for x in R ] ) difficult = np . array ( [ x [ 'difficult' ] for x in R ] ) . astype ( np . bool ) det = [ False ] * len ( R ) npos = npos + sum ( ~ difficult ) class_recs [ imagename ] = { 'bbox' : bbox , 'difficult' : difficult , 'det' : det } # read dets detfile = detpath . format ( classname ) with open ( detfile , 'r' ) as f : lines = f . readlines ( ) splitlines = [ x . strip ( ) . split ( ' ' ) for x in lines ] image_ids = [ x [ 0 ] for x in splitlines ] confidence = np . array ( [ float ( x [ 1 ] ) for x in splitlines ] ) BB = np . array ( [ [ float ( z ) for z in x [ 2 : ] ] for x in splitlines ] ) # sort by confidence sorted_ind = np . argsort ( - confidence ) sorted_scores = np . sort ( - confidence ) BB = BB [ sorted_ind , : ] image_ids = [ image_ids [ x ] for x in sorted_ind ] # go down dets and mark TPs and FPs nd = len ( image_ids ) tp = np . zeros ( nd ) fp = np . zeros ( nd ) for d in range ( nd ) : R = class_recs [ image_ids [ d ] ] bb = BB [ d , : ] . astype ( float ) ovmax = - np . inf BBGT = R [ 'bbox' ] . astype ( float ) if BBGT . size > 0 : # compute overlaps # intersection ixmin = np . maximum ( BBGT [ : , 0 ] , bb [ 0 ] ) iymin = np . maximum ( BBGT [ : , 1 ] , bb [ 1 ] ) ixmax = np . minimum ( BBGT [ : , 2 ] , bb [ 2 ] ) iymax = np . minimum ( BBGT [ : , 3 ] , bb [ 3 ] ) iw = np . maximum ( ixmax - ixmin + 1. , 0. ) ih = np . maximum ( iymax - iymin + 1. , 0. ) inters = iw * ih # union uni = ( ( bb [ 2 ] - bb [ 0 ] + 1. ) * ( bb [ 3 ] - bb [ 1 ] + 1. ) + ( BBGT [ : , 2 ] - BBGT [ : , 0 ] + 1. ) * ( BBGT [ : , 3 ] - BBGT [ : , 1 ] + 1. ) - inters ) overlaps = inters / uni ovmax = np . max ( overlaps ) jmax = np . argmax ( overlaps ) if ovmax > ovthresh : if not R [ 'difficult' ] [ jmax ] : if not R [ 'det' ] [ jmax ] : tp [ d ] = 1. R [ 'det' ] [ jmax ] = 1 else : fp [ d ] = 1. else : fp [ d ] = 1. # compute precision recall fp = np . cumsum ( fp ) tp = np . cumsum ( tp ) rec = tp / float ( npos ) prec = tp / ( tp + fp ) ap = voc_ap ( rec , prec , use_07_metric ) return rec , prec , ap