text: # --------------------------------------------------------
  # Fast/er R-CNN
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Bharath Hariharan
  # --------------------------------------------------------
  
  import os,sys
  #import cPickle
  import _pickle as cPickle
  import numpy as np
  import shutil
  sys.path.append("stats")
  from eval_ap import parse_rec
  from eval_all import get_image_txt_name
  from utils import load_class_names, read_data_cfg
  # from utils import get_all_boxes, bbox_iou, nms, read_data_cfg, load_class_names
  
  
  
  def compute_ap(rec, prec, use_07_metric=False):
      """ ap = compute_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 my_eval(detpath, imagesetfile, classname, cachedir, classlist,
               ovthresh=0.5, use_07_metric=False):
      """rec, prec, ap = my_eval(detpath,
                                  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):
              imagekey = os.path.basename(imagename).split('.')[0]    
              # recs[imagekey] = parse_rec(get_image_xml_name(imagename))
              recs[imagekey] = parse_rec(get_image_txt_name(imagename),classlist)
              # 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:
              cPickle.dump(recs, f)
      else:
          # load
          with open(cachefile, 'rb') as f:
              recs = cPickle.load(f)
  
      # extract gt objects for this class
      class_recs = {}
      npos = 0
      for imagename in imagenames:
          imagekey = os.path.basename(imagename).split('.')[0]
          # print('image name: ',imagename)
          # print('image key: ',imagekey)
          try:
              R = [obj for obj in recs[imagekey] if obj['name'] == classname]
          except:
              print('Error because exits cache folder')
              print("%s %s" % (imagename, imagekey))
              exit(0)
  
          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[imagekey] = {'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)
      if len(sorted_ind) > 0:
          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 = compute_ap(rec, prec, use_07_metric)
      print("fp:", fp)
      print("tp:", tp)
      #print('class: {:<10s} \t num occurrence: {:4d}'.format(classname, npos))
  
      return rec, prec, ap, npos
      
  
  
  def _do_python_eval(res_prefix, imagesetfile, classesfile, output_dir = 'output'):
      
      filename = res_prefix + '{:s}.txt'
  
      if os.path.exists(output_dir):
          shutil.rmtree(output_dir)
      os.mkdir(output_dir)
  
      cachedir = os.path.join(output_dir, 'annotations_cache')
      aps = []
      # The PASCAL VOC metric changed in 2010
      use_07_metric = False
      #print ('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
  
          
      _classes = load_class_names(classesfile)
      total = 0
      for i, cls in enumerate(_classes):
          if cls == '__background__':
              continue
        
          rec, prec, ap, noccur = my_eval(
              filename, imagesetfile, cls, cachedir, _classes, ovthresh=0.5,
              use_07_metric=use_07_metric)
          aps += [ap]
          total += noccur
          # print('AP for {:<10s} = {:.4f} with {:4d} views'.format(cls, ap, noccur))
          with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
              cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
  
      # print('Mean AP = {:.4f} with total {:4d} views'.format(np.mean(aps), total))
      #
      # print('~'*30)
      # print(' '*10, 'Results:')
      # print('-'*30)
      # for i, ap in enumerate(aps):
      #     print('{:<10s}\t{:.3f}'.format(_classes[i], ap))
      # print('='*30)
      mAP = np.mean(aps)
      # print('{:^10s}\t{:.3f}'.format('Average', mAP))
      # print('~'*30)
      # print('done')
      return mAP
  
  
  def Evaluation_from_Valid(res_prefix, imagesetfile, classesfile, output_dir='output'):
      import sys
      log = open("myprog.log", "a")
      sys.stdout = log
      filename = res_prefix + '{:s}.txt'
  
      if os.path.exists(output_dir):
          shutil.rmtree(output_dir)
      os.mkdir(output_dir)
  
      cachedir = os.path.join(output_dir, 'annotations_cache')
      aps = []
      # The PASCAL VOC metric changed in 2010
      use_07_metric = False
      # print ('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
      #recs=[]
      _classes = load_class_names(classesfile)
      total = 0
      for i, cls in enumerate(_classes):
          if cls == '__background__':
              continue
  
          rec, prec, ap, noccur = my_eval(
              filename, imagesetfile, cls, cachedir, _classes, ovthresh=0.5,
              use_07_metric=use_07_metric)
          #recs.append(rec)
          aps += [ap]
          total += noccur
          print('AP for {:<10s} = {:.4f} with {:4d} views'.format(cls, ap, noccur))
          with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
              cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
  
      print('Mean AP = {:.4f} with total {:4d} views'.format(np.mean(aps), total))
      print()
      print('~' * 30)
      print(' ' * 10, 'Results:')
      print('-' * 30)
      for i, ap in enumerate(aps):
          print('{:<10s}\t{:.3f}'.format(_classes[i], ap))
      print('=' * 30)
      mAP = np.mean(aps)
      #print(recs)
      print('{:^10s}\t{:.3f}'.format('Average', mAP))
      # print('~' * 30)
      # print('done')
      # print('precision: {:.3f}'.format(np.mean(prec)))
      # print('recall: {:.3f} '.format(np.mean(rec)))
      return mAP
  
  if __name__ == '__main__':
      try:
          if len(sys.argv) == 1:
              datacfg = 'data/kaist.data'
          elif len(sys.argv) == 2:
              datacfg = sys.argv[1]
          options = read_data_cfg(datacfg)
          test_file = options['valid']
          res_prefix = 'results/det_test_'
          class_names = options['names']
          _map = Evaluation_from_Valid(res_prefix, test_file, class_names, output_dir = 'output')
      except:
          print('Using: python my_eval.py [data/file.data]')
  
  
  