text: # --------------------------------------------------------
  # dota_evaluation_task1
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Jian Ding, based on code from Bharath Hariharan
  # --------------------------------------------------------
  
  """
      To use the code, users should to config detpath, annopath and imagesetfile
      detpath is the path for 15 result files, for the format, you can refer to "http://captain.whu.edu.cn/DOTAweb/tasks.html"
      search for PATH_TO_BE_CONFIGURED to config the paths
      Note, the evaluation is on the large scale images
  """
  import xml.etree.ElementTree as ET
  import os
  #import cPickle
  import numpy as np
  import matplotlib.pyplot as plt
  
  def parse_gt(filename):
      objects = []
      with open(filename, 'r') as f:
          lines = f.readlines()
          splitlines = [x.strip().split(' ')  for x in lines]
          for splitline in splitlines:
              object_struct = {}
              object_struct['name'] = splitline[8]
              if (len(splitline) == 9):
                  object_struct['difficult'] = 0
              elif (len(splitline) == 10):
                  object_struct['difficult'] = int(splitline[9])
              # object_struct['difficult'] = 0
              object_struct['bbox'] = [int(float(splitline[0])),
                                           int(float(splitline[1])),
                                           int(float(splitline[4])),
                                           int(float(splitline[5]))]
              w = int(float(splitline[4])) - int(float(splitline[0]))
              h = int(float(splitline[5])) - int(float(splitline[1]))
              object_struct['area'] = w * h
              #print('area:', object_struct['area'])
              # if object_struct['area'] < (15 * 15):
              #     #print('area:', object_struct['area'])
              #     object_struct['difficult'] = 1
              objects.append(object_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]
      #print('imagenames: ', imagenames)
      #if not os.path.isfile(cachefile):
          # load annots
      recs = {}
      for i, imagename in enumerate(imagenames):
          #print('parse_files name: ', annopath.format(imagename))
          recs[imagename] = parse_gt(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, 'w') as f:
           #   cPickle.dump(recs, f)
      #else:
          # load
          #with open(cachefile, 'r') as f:
           #   recs = cPickle.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])
  
      #print('check confidence: ', confidence)
  
      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)
  
      #print('check sorted_scores: ', sorted_scores)
      #print('check sorted_ind: ', sorted_ind)
      BB = BB[sorted_ind, :]
      image_ids = [image_ids[x] for x in sorted_ind]
      #print('check imge_ids: ', image_ids)
      #print('imge_ids len:', len(image_ids))
      # 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)
              ## if there exist 2
              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.
                     # print('filename:', image_ids[d])
          else:
              fp[d] = 1.
  
      # compute precision recall
  
      print('check fp:', fp)
      print('check tp', tp)
  
  
      print('npos num:', npos)
      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
  
  def main():
      # detpath = r'E:\documentation\OneDrive\documentation\DotaEvaluation\evluation_task2\evluation_task2\faster-rcnn-nms_0.3_task2\nms_0.3_task\Task2_{:s}.txt'
      # annopath = r'I:\dota\testset\ReclabelTxt-utf-8\{:s}.txt'
      # imagesetfile = r'I:\dota\testset\va.txt'
  
      detpath = r'PATH_TO_BE_CONFIGURED/Task1_{:s}.txt'
      annopath = r'PATH_TO_BE_CONFIGURED/{:s}.txt'# change the directory to the path of val/labelTxt, if you want to do evaluation on the valset
      imagesetfile = r'PATH_TO_BE_CONFIGURED/valset.txt'
  
      classnames = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle', 'large-vehicle', 'ship', 'tennis-court',
                  'basketball-court', 'storage-tank',  'soccer-ball-field', 'roundabout', 'harbor', 'swimming-pool', 'helicopter']
      classaps = []
      map = 0
      for classname in classnames:
          print('classname:', classname)
          rec, prec, ap = voc_eval(detpath,
               annopath,
               imagesetfile,
               classname,
               ovthresh=0.5,
               use_07_metric=True)
          map = map + ap
          #print('rec: ', rec, 'prec: ', prec, 'ap: ', ap)
          print('ap: ', ap)
          classaps.append(ap)
  
          ## uncomment to plot p-r curve for each category
          # plt.figure(figsize=(8,4))
          # plt.xlabel('recall')
          # plt.ylabel('precision')
          # plt.plot(rec, prec)
          # plt.show()
      map = map/len(classnames)
      print('map:', map)
      classaps = 100*np.array(classaps)
      print('classaps: ', classaps)
  if __name__ == '__main__':
      main()