text: # --------------------------------------------------------
  # Fast/er R-CNN
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Bharath Hariharan
  # --------------------------------------------------------
  
  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
          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, '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))
              recs[imagename] = parse_rec(annopath+'/'+imagename.split('.')[0]+'.xml')
              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()
  
      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)
      # 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
  