text: # --------------------------------------------------------
  # Fast R-CNN
  # Copyright (c) 2015 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick
  #
  # Modified by Peng Tang for DPL
  # --------------------------------------------------------
  
  import datasets
  import datasets.pascal_voc
  import os
  import datasets.imdb
  import xml.etree.ElementTree as ET
  import numpy as np
  import scipy.sparse
  import scipy.io as sio
  import utils.cython_bbox
  import cPickle
  import subprocess
  
  class pascal_voc(datasets.imdb):
      def __init__(self, image_set, year, devkit_path=None):
          datasets.imdb.__init__(self, 'voc_' + year + '_' + image_set)
          self._year = year
          self._image_set = image_set
          self._devkit_path = self._get_default_path() if devkit_path is None \
                              else devkit_path
          self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year)
          self._classes = ('aeroplane', 'bicycle', 'bird', 'boat',
                           'bottle', 'bus', 'car', 'cat', 'chair',
                           'cow', 'diningtable', 'dog', 'horse',
                           'motorbike', 'person', 'pottedplant',
                           'sheep', 'sofa', 'train', 'tvmonitor')
          self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
          self._image_ext = '.jpg'
          self._image_index = self._load_image_set_index()
          # Default to roidb handler
          self._roidb_handler = self.selective_search_roidb
  
          # PASCAL specific config options
          self.config = {'cleanup'  : True,
                         'use_salt' : True,
                         'use_diff' : False,
                         'top_k'    : 2000}
  
          assert os.path.exists(self._devkit_path), \
                  'VOCdevkit path does not exist: {}'.format(self._devkit_path)
          assert os.path.exists(self._data_path), \
                  'Path does not exist: {}'.format(self._data_path)
  
      def image_path_at(self, i):
          """
          Return the absolute path to image i in the image sequence.
          """
          return self.image_path_from_index(self._image_index[i])
  
      def image_path_from_index(self, index):
          """
          Construct an image path from the image's "index" identifier.
          """
          image_path = os.path.join(self._data_path, 'JPEGImages',
                                    index + self._image_ext)
          assert os.path.exists(image_path), \
                  'Path does not exist: {}'.format(image_path)
          return image_path
  
      def _load_image_set_index(self):
          """
          Load the indexes listed in this dataset's image set file.
          """
          # Example path to image set file:
          # self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
          image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main',
                                        self._image_set + '.txt')
          assert os.path.exists(image_set_file), \
                  'Path does not exist: {}'.format(image_set_file)
          with open(image_set_file) as f:
              image_index = [x.strip() for x in f.readlines()]
          return image_index
  
      def _get_default_path(self):
          """
          Return the default path where PASCAL VOC is expected to be installed.
          """
          return os.path.join(datasets.ROOT_DIR, 'data', 'VOCdevkit' + self._year)
  
      def gt_roidb(self):
          """
          Return the database of ground-truth regions of interest.
  
          This function loads/saves from/to a cache file to speed up future calls.
          """
          cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
          if os.path.exists(cache_file):
              with open(cache_file, 'rb') as fid:
                  roidb = cPickle.load(fid)
              print '{} gt roidb loaded from {}'.format(self.name, cache_file)
              return roidb
  
          # gt_roidb = [self._load_pascal_annotation(index)
          gt_roidb = [self._load_pascal_labels(index)
                      for index in self.image_index]
          with open(cache_file, 'wb') as fid:
              cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
          print 'wrote gt roidb to {}'.format(cache_file)
  
          return gt_roidb
  
      def selective_search_roidb(self):
          """
          Return the database of selective search regions of interest.
          Ground-truth ROIs are also included.
  
          This function loads/saves from/to a cache file to speed up future calls.
          """
          cache_file = os.path.join(self.cache_path,
                                    self.name + '_selective_search_roidb.pkl')
  
          if os.path.exists(cache_file):
              with open(cache_file, 'rb') as fid:
                  roidb = cPickle.load(fid)
              print '{} ss roidb loaded from {}'.format(self.name, cache_file)
              return roidb
  
          if int(self._year) == 2007 or self._image_set != 'test':
              gt_roidb = self.gt_roidb()
              # ss_roidb = self._load_selective_search_roidb(gt_roidb)
              # roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
              roidb = self._load_selective_search_roidb(gt_roidb)
          else:
              roidb = self._load_selective_search_roidb(None)
          with open(cache_file, 'wb') as fid:
              cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
          print 'wrote ss roidb to {}'.format(cache_file)
  
          return roidb
  
      def _load_selective_search_roidb(self, gt_roidb):
          filename = os.path.abspath(os.path.join(self.cache_path, '..',
                                                  'selective_search_data',
                                                  self.name + '.mat'))
          assert os.path.exists(filename), \
                 'Selective search data not found at: {}'.format(filename)
          raw_data = sio.loadmat(filename)['boxes'].ravel()
  
          box_list = []
          for i in xrange(raw_data.shape[0]):
              box_list.append(raw_data[i][:, (1, 0, 3, 2)] - 1)
  
          return self.create_roidb_from_box_list(box_list, gt_roidb)
  
      def selective_search_IJCV_roidb(self):
          """
          Return the database of selective search regions of interest.
          Ground-truth ROIs are also included.
  
          This function loads/saves from/to a cache file to speed up future calls.
          """
          cache_file = os.path.join(self.cache_path,
                  '{:s}_selective_search_IJCV_top_{:d}_roidb.pkl'.
                  format(self.name, self.config['top_k']))
  
          if os.path.exists(cache_file):
              with open(cache_file, 'rb') as fid:
                  roidb = cPickle.load(fid)
              print '{} ss roidb loaded from {}'.format(self.name, cache_file)
              return roidb
  
          gt_roidb = self.gt_roidb()
          ss_roidb = self._load_selective_search_IJCV_roidb(gt_roidb)
          roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
          with open(cache_file, 'wb') as fid:
              cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
          print 'wrote ss roidb to {}'.format(cache_file)
  
          return roidb
  
      def _load_selective_search_IJCV_roidb(self, gt_roidb):
          IJCV_path = os.path.abspath(os.path.join(self.cache_path, '..',
                                                   'selective_search_IJCV_data',
                                                   'voc_' + self._year))
          assert os.path.exists(IJCV_path), \
                 'Selective search IJCV data not found at: {}'.format(IJCV_path)
  
          top_k = self.config['top_k']
          box_list = []
          for i in xrange(self.num_images):
              filename = os.path.join(IJCV_path, self.image_index[i] + '.mat')
              raw_data = sio.loadmat(filename)
              box_list.append((raw_data['boxes'][:top_k, :]-1).astype(np.uint16))
  
          return self.create_roidb_from_box_list(box_list, gt_roidb)
  
      def _load_pascal_labels(self, index):
          """
          Load image labels.
          """
          filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
          tree = ET.parse(filename)
          objs = tree.findall('object')
          objs = tree.findall('object')
          if not self.config['use_diff']:
              # Exclude the samples labeled as difficult
              non_diff_objs = [
                  obj for obj in objs if int(obj.find('difficult').text) == 0]
              # if len(non_diff_objs) != len(objs):
              #     print 'Removed {} difficult objects'.format(
              #         len(objs) - len(non_diff_objs))
              objs = non_diff_objs
          num_objs = len(objs)
  
          gt_classes = np.zeros((num_objs), dtype=np.int32)
  
          # Load object bounding boxes into a data frame.
          for ix, obj in enumerate(objs):
              cls = self._class_to_ind[obj.find('name').text.lower().strip()]
              gt_classes[ix] = cls
  
          real_label = np.zeros(self.num_classes).astype(np.float32)
          for label in gt_classes:
              real_label[label] = 1
  
          return {'labels' : real_label}
  
      def _write_voc_results_file(self, all_boxes):
          use_salt = self.config['use_salt']
          comp_id = 'comp4'
          if use_salt:
              comp_id += '-{}'.format(os.getpid())
  
          # VOCdevkit/results/VOC2007/Main/comp4-44503_det_test_aeroplane.txt
          path = os.path.join(self._devkit_path, 'results', 'VOC' + self._year,
                              'Main', comp_id + '_')
          for cls_ind, cls in enumerate(self.classes):
              if cls == '__background__':
                  continue
              print 'Writing {} VOC results file'.format(cls)
              filename = path + 'det_' + self._image_set + '_' + cls + '.txt'
              with open(filename, 'wt') as f:
                  for im_ind, index in enumerate(self.image_index):
                      dets = all_boxes[cls_ind][im_ind]
                      if dets == []:
                          continue
                      # the VOCdevkit expects 1-based indices
                      for k in xrange(dets.shape[0]):
                          f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
                                  format(index, dets[k, -1],
                                         dets[k, 0] + 1, dets[k, 1] + 1,
                                         dets[k, 2] + 1, dets[k, 3] + 1))
          return comp_id
  
      def _do_matlab_eval(self, comp_id, output_dir='output'):
          rm_results = self.config['cleanup']
  
          path = os.path.join(os.path.dirname(__file__),
                              'VOCdevkit-matlab-wrapper')
          cmd = 'cd {} && '.format(path)
          cmd += '{:s} -nodisplay -nodesktop '.format(datasets.MATLAB)
          cmd += '-r "dbstop if error; '
          cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',{:d}); quit;"' \
                 .format(self._devkit_path, comp_id,
                         self._image_set, output_dir, int(rm_results))
          print('Running:\n{}'.format(cmd))
          status = subprocess.call(cmd, shell=True)
  
      def evaluate_detections(self, all_boxes, output_dir):
          comp_id = self._write_voc_results_file(all_boxes)
          self._do_matlab_eval(comp_id, output_dir)
  
      def competition_mode(self, on):
          if on:
              self.config['use_salt'] = False
              self.config['cleanup'] = False
          else:
              self.config['use_salt'] = True
              self.config['cleanup'] = True
  
  if __name__ == '__main__':
      d = datasets.pascal_voc('trainval', '2007')
      res = d.roidb
      from IPython import embed; embed()
  