text: #!/usr/bin/env python3
  # -*- coding:utf-8 -*-
  # Code are based on
  # https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py
  # Copyright (c) Bharath Hariharan.
  # Copyright (c) Megvii, Inc. and its affiliates.
  
  import os
  import pickle
  import xml.etree.ElementTree as ET
  
  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 = 0.0
          for t in np.arange(0.0, 1.1, 0.1):
              if np.sum(rec >= t) == 0:
                  p = 0
              else:
                  p = np.max(prec[rec >= t])
              ap = ap + p / 11.0
      else:
          # correct AP calculation
          # first append sentinel values at the end
          mrec = np.concatenate(([0.0], rec, [1.0]))
          mpre = np.concatenate(([0.0], prec, [0.0]))
  
          # 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,
  ):
      # 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, "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 len(lines) == 0:
          return 0, 0, 0
  
      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)
      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, 0.0)
              ih = np.maximum(iymax - iymin + 1.0, 0.0)
              inters = iw * ih
  
              # union
              uni = (
                  (bb[2] - bb[0] + 1.0) * (bb[3] - bb[1] + 1.0)
                  + (BBGT[:, 2] - BBGT[:, 0] + 1.0) * (BBGT[:, 3] - BBGT[:, 1] + 1.0)
                  - 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] = 1.0
                      R["det"][jmax] = 1
                  else:
                      fp[d] = 1.0
          else:
              fp[d] = 1.0
  
          # 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
  