parameters: (detpath, annopath, imageset_file, classname, annocache, ovthresh=0.5, use_07_metric=False)
return_statement: return rec, prec, ap
docstring: pascal voc evaluation
      :param detpath: detection results detpath.format(classname)
      :param annopath: annotations annopath.format(classname)
      :param imageset_file: text file containing list of images
      :param classname: category name
      :param annocache: caching annotations
      :param ovthresh: overlap threshold
      :param use_07_metric: whether to use voc07's 11 point ap computation
      :return: rec, prec, ap
docstring_summary: pascal voc evaluation
      :param detpath: detection results detpath.format(classname)
      :param annopath: annotations annopath.format(classname)
      :param imageset_file: text file containing list of images
      :param classname: category name
      :param annocache: caching annotations
      :param ovthresh: overlap threshold
      :param use_07_metric: whether to use voc07's 11 point ap computation
      :return: rec, prec, ap
docstring_tokens: pascal voc evaluation : param detpath : detection results detpath . format ( classname ) : param annopath : annotations annopath . format ( classname ) : param imageset_file : text file containing list of images : param classname : category name : param annocache : caching annotations : param ovthresh : overlap threshold : param use_07_metric : whether to use voc07 s 11 point ap computation : return : rec prec ap
function: def voc_eval(detpath, annopath, imageset_file, classname, annocache, ovthresh=0.5, use_07_metric=False):
      """
      pascal voc evaluation
      :param detpath: detection results detpath.format(classname)
      :param annopath: annotations annopath.format(classname)
      :param imageset_file: text file containing list of images
      :param classname: category name
      :param annocache: caching annotations
      :param ovthresh: overlap threshold
      :param use_07_metric: whether to use voc07's 11 point ap computation
      :return: rec, prec, ap
      """
      with open(imageset_file, 'r') as f:
          lines = f.readlines()
      image_filenames = [x.strip() for x in lines]
  
      # load annotations from cache
      if not os.path.isfile(annocache):
          recs = {}
          for ind, image_filename in enumerate(image_filenames):
              recs[image_filename] = parse_voc_rec(annopath.format(image_filename))
              if ind % 100 == 0:
                  logger.info('reading annotations for %d/%d' % (ind + 1, len(image_filenames)))
          logger.info('saving annotations cache to %s' % annocache)
          with open(annocache, 'wb') as f:
              cPickle.dump(recs, f, protocol=cPickle.HIGHEST_PROTOCOL)
      else:
          with open(annocache, 'rb') as f:
              recs = cPickle.load(f)
  
      # extract objects in :param classname:
      class_recs = {}
      npos = 0
      for image_filename in image_filenames:
          objects = [obj for obj in recs[image_filename] if obj['name'] == classname]
          bbox = np.array([x['bbox'] for x in objects])
          difficult = np.array([x['difficult'] for x in objects]).astype(np.bool)
          det = [False] * len(objects)  # stand for detected
          npos = npos + sum(~difficult)
          class_recs[image_filename] = {'bbox': bbox,
                                        'difficult': difficult,
                                        'det': det}
  
      # read detections
      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])
      bbox = np.array([[float(z) for z in x[2:]] for x in splitlines])
  
      # sort by confidence
      if bbox.shape[0] > 0:
          sorted_inds = np.argsort(-confidence)
          sorted_scores = np.sort(-confidence)
          bbox = bbox[sorted_inds, :]
          image_ids = [image_ids[x] for x in sorted_inds]
  
      # go down detections and mark true positives and false positives
      nd = len(image_ids)
      tp = np.zeros(nd)
      fp = np.zeros(nd)
      for d in range(nd):
          r = class_recs[image_ids[d]]
          bb = bbox[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)
      # avoid division by zero in case first detection matches a difficult ground ruth
      prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
      ap = voc_ap(rec, prec, use_07_metric)
  
      return rec, prec, ap
function_tokens: def voc_eval ( detpath , annopath , imageset_file , classname , annocache , ovthresh = 0.5 , use_07_metric = False ) : with open ( imageset_file , 'r' ) as f : lines = f . readlines ( ) image_filenames = [ x . strip ( ) for x in lines ] # load annotations from cache if not os . path . isfile ( annocache ) : recs = { } for ind , image_filename in enumerate ( image_filenames ) : recs [ image_filename ] = parse_voc_rec ( annopath . format ( image_filename ) ) if ind % 100 == 0 : logger . info ( 'reading annotations for %d/%d' % ( ind + 1 , len ( image_filenames ) ) ) logger . info ( 'saving annotations cache to %s' % annocache ) with open ( annocache , 'wb' ) as f : cPickle . dump ( recs , f , protocol = cPickle . HIGHEST_PROTOCOL ) else : with open ( annocache , 'rb' ) as f : recs = cPickle . load ( f ) # extract objects in :param classname: class_recs = { } npos = 0 for image_filename in image_filenames : objects = [ obj for obj in recs [ image_filename ] if obj [ 'name' ] == classname ] bbox = np . array ( [ x [ 'bbox' ] for x in objects ] ) difficult = np . array ( [ x [ 'difficult' ] for x in objects ] ) . astype ( np . bool ) det = [ False ] * len ( objects ) # stand for detected npos = npos + sum ( ~ difficult ) class_recs [ image_filename ] = { 'bbox' : bbox , 'difficult' : difficult , 'det' : det } # read detections 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 ] ) bbox = np . array ( [ [ float ( z ) for z in x [ 2 : ] ] for x in splitlines ] ) # sort by confidence if bbox . shape [ 0 ] > 0 : sorted_inds = np . argsort ( - confidence ) sorted_scores = np . sort ( - confidence ) bbox = bbox [ sorted_inds , : ] image_ids = [ image_ids [ x ] for x in sorted_inds ] # go down detections and mark true positives and false positives nd = len ( image_ids ) tp = np . zeros ( nd ) fp = np . zeros ( nd ) for d in range ( nd ) : r = class_recs [ image_ids [ d ] ] bb = bbox [ 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 ) # avoid division by zero in case first detection matches a difficult ground ruth prec = tp / np . maximum ( tp + fp , np . finfo ( np . float64 ) . eps ) ap = voc_ap ( rec , prec , use_07_metric ) return rec , prec , ap