code: # coding: utf-8
  # --------------------------------------------------------
  # Fast R-CNN
  # Copyright (c) 2015 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick
  # --------------------------------------------------------
  from __future__ import print_function
  
  import xml.dom.minidom as minidom
  
  import os
  # import PIL
  import numpy as np
  import scipy.sparse
  import subprocess
  
  try:
      import cPickle
  except ImportError:
      import pickle as cPickle
  
  import math
  import glob
  import uuid
  import scipy.io as sio
  import xml.etree.ElementTree as ET
  
  from .imdb import imdb
  from .imdb import ROOT_DIR
  from . import ds_utils
  from .adas_eval import adas_eval
  
  # TODO: make fast_rcnn irrelevant
  # >>>> obsolete, because it depends on sth outside of this project
  from model.utils.config import cfg
  
  
  # <<<< obsolete
  
  
  class adas(imdb):
      def __init__(self, image_set, year, devkit_path=None, sub_type='car'):
          imdb.__init__(self, 'adas_' + 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, 'ADAS' + self._year)
  
          if sub_type == 'car':
              self._classes = ('__background__', #always index 0
                              'car',)
          elif sub_type == 'tired':
              self._classes = ('__background__', #always index 0
                              'o','s','w')
  
          self._class_to_ind = dict(zip(self.classes, range(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
          self._roidb_handler = self.gt_roidb
          self._salt = str(uuid.uuid4())
          self._comp_id = 'comp4'
  
          # PASCAL specific config options
          self.config = {'cleanup': True,
                         'use_salt': True,
                         'use_diff': False,
                         'matlab_eval': False,
                         'rpn_file': None,
                         'min_size': 2}
  
          assert os.path.exists(self._devkit_path), \
              'ADASdevkit 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_id_at(self, i):
          """
          Return the absolute path to image i in the image sequence.
          """
          return 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 + /ADASdevkit2007/ADAS2007/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 ADAS is expected to be installed.
          """
          return os.path.join(cfg.DATA_DIR, 'ADASdevkit' + 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):
              print(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)
                      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 = imdb.merge_roidbs(gt_roidb, ss_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 rpn_roidb(self):
          if int(self._year) == 2007 or self._image_set != 'test':
              gt_roidb = self.gt_roidb()
              rpn_roidb = self._load_rpn_roidb(gt_roidb)
              roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
          else:
              roidb = self._load_rpn_roidb(None)
  
          return roidb
  
      def _load_rpn_roidb(self, gt_roidb):
          filename = self.config['rpn_file']
          print('loading {}'.format(filename))
          assert os.path.exists(filename), \
              'rpn data not found at: {}'.format(filename)
          with open(filename, 'rb') as f:
              box_list = cPickle.load(f)
          return self.create_roidb_from_box_list(box_list, gt_roidb)
  
      def _load_selective_search_roidb(self, gt_roidb):
          filename = os.path.abspath(os.path.join(cfg.DATA_DIR,
                                                  '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 range(raw_data.shape[0]):
              boxes = raw_data[i][:, (1, 0, 3, 2)] - 1
              keep = ds_utils.unique_boxes(boxes)
              boxes = boxes[keep, :]
              keep = ds_utils.filter_small_boxes(boxes, self.config['min_size'])
              boxes = boxes[keep, :]
              box_list.append(boxes)
  
          return self.create_roidb_from_box_list(box_list, gt_roidb)
  
      def _load_pascal_annotation(self, index):
          """
          Load image and bounding boxes info from XML file in the PASCAL ADAS
          format.
          """
          filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
          tree = ET.parse(filename)
          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)
  
          boxes = np.zeros((num_objs, 4), dtype=np.uint16)
          gt_classes = np.zeros((num_objs), dtype=np.int32)
          overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
          # "Seg" area for pascal is just the box area
          seg_areas = np.zeros((num_objs), dtype=np.float32)
          ishards = np.zeros((num_objs), dtype=np.int32)
  
          # Load object bounding boxes into a data frame.
          for ix, obj in enumerate(objs):
              bbox = obj.find('bndbox')
              # Make pixel indexes 0-based
              x1 = float(bbox.find('xmin').text) - 1
              y1 = float(bbox.find('ymin').text) - 1
              x2 = float(bbox.find('xmax').text) - 1
              y2 = float(bbox.find('ymax').text) - 1
            
              diffc = obj.find('difficult')
              difficult = 0 if diffc == None else int(diffc.text)
              ishards[ix] = difficult
  
              cls = self._class_to_ind[obj.find('name').text.lower().strip()]
              boxes[ix, :] = [x1, y1, x2, y2]
              gt_classes[ix] = cls
              overlaps[ix, cls] = 1.0
              seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
  
          overlaps = scipy.sparse.csr_matrix(overlaps)
         
          return {'boxes': boxes,
                  'gt_classes': gt_classes,
                  'gt_ishard': ishards,
                  'gt_overlaps': overlaps,
                  'flipped': False,
                  'seg_areas': seg_areas}
  
      def _get_comp_id(self):
          comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
                     else self._comp_id)
          return comp_id
  
      def _get_adas_results_file_template(self):
          # ADASdevkit/results/ADAS2007/Main/<comp_id>_det_test_aeroplane.txt
          filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt'
          filedir = os.path.join(self._devkit_path, 'results', 'ADAS' + self._year, 'Main')
          if not os.path.exists(filedir):
              os.makedirs(filedir)
          path = os.path.join(filedir, filename)
          return path
  
      def _write_adas_results_file(self, all_boxes):
          for cls_ind, cls in enumerate(self.classes):
              if cls == '__background__':
                  continue
              print('Writing {} ADAS results file'.format(cls))
              filename = self._get_adas_results_file_template().format(cls)
              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 ADASdevkit expects 1-based indices
                      for k in range(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))
  
      def _do_python_eval(self, output_dir='output'):
          annopath = os.path.join(
              self._devkit_path,
              'ADAS' + self._year,
              'Annotations',
              '{:s}.xml')
          imagesetfile = os.path.join(
              self._devkit_path,
              'ADAS' + self._year,
              'ImageSets',
              'Main',
              self._image_set + '.txt')
          cachedir = os.path.join(self._devkit_path, 'annotations_cache')
          aps = []
  
          if not os.path.isdir(output_dir):
              os.mkdir(output_dir)
          for i, cls in enumerate(self._classes):
              if cls == '__background__':
                  continue
              filename = self._get_adas_results_file_template().format(cls)
              rec, prec, ap = adas_eval(
                  filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5)
              aps += [ap]
              print('AP for {} = {:.4f}'.format(cls, ap))
              with open(os.path.join(output_dir, cls + '_pr.pkl'), 'w') as f:
                  cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
          print('Mean AP = {:.4f}'.format(np.mean(aps)))
          print('~~~~~~~~')
          print('Results:')
          for ap in aps:
              print('{:.3f}'.format(ap))
          print('{:.3f}'.format(np.mean(aps)))
          print('~~~~~~~~')
          print('')
          print('--------------------------------------------------------------')
          print('Results computed with the **unofficial** Python eval code.')
          print('Results should be very close to the official MATLAB eval code.')
          print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
          print('-- Thanks, The Management')
          print('--------------------------------------------------------------')
  
      def _do_matlab_eval(self, output_dir='output'):
          print('-----------------------------------------------------')
          print('Computing results with the official MATLAB eval code.')
          print('-----------------------------------------------------')
          path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
                              'ADASdevkit-matlab-wrapper')
          cmd = 'cd {} && '.format(path)
          cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
          cmd += '-r "dbstop if error; '
          cmd += 'adas_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
              .format(self._devkit_path, self._get_comp_id(),
                      self._image_set, output_dir)
          print('Running:\n{}'.format(cmd))
          status = subprocess.call(cmd, shell=True)
  
      def evaluate_detections(self, all_boxes, output_dir):
          self._write_adas_results_file(all_boxes)
          self._do_python_eval(output_dir)
          if self.config['matlab_eval']:
              self._do_matlab_eval(output_dir)
          if self.config['cleanup']:
              for cls in self._classes:
                  if cls == '__background__':
                      continue
                  filename = self._get_adas_results_file_template().format(cls)
                  os.remove(filename)
  
      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 = adas('trainval', '2017')
      res = d.roidb
      from IPython import embed;
  
      embed()
  
apis: numpy.mean scipy.io.loadmat numpy.zeros