text: # --------------------------------------------------------
  # Fast/er R-CNN
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Bharath Hariharan
  # --------------------------------------------------------
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import xml.etree.ElementTree as ET
  import os
  import pickle
  import numpy as np
  
  def parse_rec(filename):
    """ Parse a PASCAL VOC xml file """
    tree = ET.parse(filename)
    objects = []
    for obj in tree.findall('object'):
      obj_struct = {}
      obj_struct['name'] = obj.find('name').text
      # city xml files don't have pose attribute
      #obj_struct['pose'] = obj.find('pose').text
      obj_struct['truncated'] = int(obj.find('truncated').text)
      obj_struct['difficult'] = int(obj.find('difficult').text)
      bbox = obj.find('bndbox')
      obj_struct['bbox'] = [int(bbox.find('xmin').text),
                            int(bbox.find('ymin').text),
                            int(bbox.find('xmax').text),
                            int(bbox.find('ymax').text)]
      objects.append(obj_struct)
  
    return objects
  
  
  def voc_ap(rec, prec, use_07_metric=False):
    """ ap = voc_ap(rec, prec, [use_07_metric])
    Compute VOC AP given precision and recall.
    If use_07_metric is true, uses the
    VOC 07 11 point method (default:False).
    """
    if use_07_metric:
      # 11 point metric
      ap = 0.
      for t in np.arange(0., 1.1, 0.1):
        if np.sum(rec >= t) == 0:
          p = 0
        else:
          p = np.max(prec[rec >= t])
        ap = ap + p / 11.
    else:
      # correct AP calculation
      # first append sentinel values at the end
      mrec = np.concatenate(([0.], rec, [1.]))
      mpre = np.concatenate(([0.], prec, [0.]))
  
      # compute the precision envelope
      for i in range(mpre.size - 1, 0, -1):
        mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
  
      # to calculate area under PR curve, look for points
      # where X axis (recall) changes value
      i = np.where(mrec[1:] != mrec[:-1])[0]
  
      # and sum (\Delta recall) * prec
      ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
    return ap
  
  
  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, '%s_annots.pkl' % imagesetfile)
    # 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 annotations
      recs = {}
      for i, imagename in enumerate(imagenames):
        print('imagename', i, imagename)
        print('formated annopath', annopath.format(imagename))
        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, 'wb') as f:
        pickle.dump(recs, f)
    else:
      # load
      with open(cachefile, 'rb') as f:
        try:
          recs = pickle.load(f)
        except:
          recs = pickle.load(f, encoding='bytes')
  
    # 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])
  
    nd = len(image_ids)
    tp = np.zeros(nd)
    fp = np.zeros(nd)
  
    if BB.shape[0] > 0:
      # 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
      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)
    # 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)
  
    return rec, prec, ap
  