docs: def voc_eval(detpath,
               annopath,
               imagesetfile,
               classname,
               cachedir,
               ovthresh=0.5,
               use_07_metric=True):
      """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 True)
  """
  # 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 % (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, 'wb') as f:
              pickle.dump(recs, f)
      else:
          # load
          with open(cachefile, 'rb') as f:
              recs = pickle.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()
      if any(lines) == 1:
  
          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, 0.)
                  ih = np.maximum(iymax - iymin, 0.)
                  inters = iw * ih
                  uni = ((bb[2] - bb[0]) * (bb[3] - bb[1]) +
                         (BBGT[:, 2] - BBGT[:, 0]) *
                         (BBGT[:, 3] - BBGT[:, 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 divide by zero in case the first detection matches a difficult
          # ground truth
          prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
          ap = voc_ap(rec, prec, use_07_metric)
      else:
          rec = -1.
          prec = -1.
          ap = -1.
  
      return rec, prec, ap