Python cfg.MATLAB属性代码示例

# 需要导入模块: from model.config import cfg [as 别名]
# 或者: from model.config.cfg import MATLAB [as 别名]
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',
                        'VOCdevkit-matlab-wrapper')
    cmd = 'cd {} && '.format(path)
    cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
    cmd += '-r "dbstop if error; '
    cmd += 'voc_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) 

# 需要导入模块: from model.config import cfg [as 别名]
# 或者: from model.config.cfg import MATLAB [as 别名]
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',
                            'VOCdevkit-matlab-wrapper')
        cmd = 'cd {} && '.format(path)
        cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
        cmd += '-r "dbstop if error; '
        cmd += 'voc_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) 

# 需要导入模块: from model.config import cfg [as 别名]
# 或者: from model.config.cfg import MATLAB [as 别名]
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',
                        'VOCdevkit-matlab-wrapper')
    cmd = 'cd {} && '.format(path)
    cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
    cmd += '-r "dbstop if error; '
    cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
      .format(self._dist_path,
              self._image_set, output_dir)
    print(('Running:\n{}'.format(cmd)))
    status = subprocess.call(cmd, shell=True) 

# 需要导入模块: from model.config import cfg [as 别名]
# 或者: from model.config.cfg import MATLAB [as 别名]
def _do_matlab_eval(self, output_dir='output', suffix=''):
    print('-----------------------------------------------------')
    print('Computing results with the official MATLAB eval code.')
    print('-----------------------------------------------------')
    path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
                        'KAISTdevkit-matlab-wrapper')
    cmd = 'cd {} && '.format(path)
    cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
    cmd += '-r "dbstop if error; '
    cmd += 'kaist_eval_full(\'{:s}\',\'{:s}\'); quit;"' \
      .format(os.path.join(output_dir, 'det'+suffix), self._data_path)
    print(('Running:\n{}'.format(cmd)))
    status = subprocess.call(cmd, shell=True) 

# 需要导入模块: from model.config import cfg [as 别名]
# 或者: from model.config.cfg import MATLAB [as 别名]
def _do_python_eval(self, output_dir='output'):
    annopath = os.path.join(
      self._devkit_path,
      'VOC' + self._year,
      'Annotations',
      '{:s}.xml')
    imagesetfile = os.path.join(
      self._devkit_path,
      'VOC' + self._year,
      'ImageSets',
      'Main',
      self._image_set + '.txt')
    cachedir = os.path.join(self._devkit_path, 'annotations_cache')
    aps = []
    # The PASCAL VOC metric changed in 2010
    use_07_metric = True if int(self._year) < 2010 else False
    print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
    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_voc_results_file_template().format(cls)
      rec, prec, ap = voc_eval(
        filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
        use_07_metric=use_07_metric, use_diff=self.config['use_diff'])
      aps += [ap]
      print(('AP for {} = {:.4f}'.format(cls, ap)))
      with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
        pickle.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('--------------------------------------------------------------') 

# 需要导入模块: from model.config import cfg [as 别名]
# 或者: from model.config.cfg import MATLAB [as 别名]
def _do_python_eval(self, output_dir='output'):
        annopath = os.path.join(
            self._devkit_path,
            'VOC' + self._year,
            'Annotations',
            '{:s}.xml')
        imagesetfile = os.path.join(
            self._devkit_path,
            'VOC' + self._year,
            'ImageSets',
            'Main',
            self._image_set + '.txt')
        cachedir = os.path.join(self._devkit_path, 'annotations_cache')
        aps = []
        # The PASCAL VOC metric changed in 2010
        use_07_metric = True if int(self._year) < 2010 else False
        print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
        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_voc_results_file_template().format(cls)
            rec, prec, ap = voc_eval(
                filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
                use_07_metric=use_07_metric, use_diff=self.config['use_diff'])
            aps += [ap]
            print(('AP for {} = {:.4f}'.format(cls, ap)))
            with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
                pickle.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('--------------------------------------------------------------') 

# 需要导入模块: from model.config import cfg [as 别名]
# 或者: from model.config.cfg import MATLAB [as 别名]
def _do_python_eval(self, output_dir='output'):
    annopath = os.path.join(
      self._dist_path,
      'coco_multi' ,
      'Annotations',
      '{:s}.xml')
    imagesetfile = os.path.join(
      self._dist_path,
      self._image_set + '.txt')
    cachedir = os.path.join(self._dist_path, 'annotations_cache')
    aps = []
    # The PASCAL VOC metric changed in 2010
    #use_07_metric = True if int(self._year) < 2010 else False
    use_07_metric = False
    print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
    if not os.path.isdir(output_dir):
      os.mkdir(output_dir)
    for i, cls in enumerate(self._classes):
      if cls == '__background__' or cls == self.classes[0]:
        cls_ind=0
        continue
      else:
        cls_ind=self._class_to_ind[cls]
      #elif cls=='median_filtering':
        #cls_ind=3
        #continue
      filename = self._get_voc_results_file_template().format(cls)
      filename2 = self._get_voc_noise_results_file_template().format(cls)
      print(cls_ind)
      rec, prec, ap = voc_eval(
        filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
        use_07_metric=use_07_metric,fuse=False)
      aps += [ap]
      print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
      with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
        pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
      fig=plt.figure()
      plt.plot(rec,prec)
      fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
      plt.xlabel('recall',fontsize=15)
      plt.xlim((0,1.0))
      plt.ylim((0,1.0))
      plt.ylabel('precision',fontsize=15)
      fig.savefig('{}.jpg'.format(cls))

    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('--------------------------------------------------------------') 

# 需要导入模块: from model.config import cfg [as 别名]
# 或者: from model.config.cfg import MATLAB [as 别名]
def _do_python_eval(self, output_dir='output'):
    annopath = os.path.join(
      self._dist_path,
      'coco_multi' ,
      'Annotations',
      '{:s}.xml')
    imagesetfile = os.path.join(
      self._dist_path,
      self._image_set + '.txt')
    cachedir = os.path.join(self._dist_path, 'annotations_cache')
    aps = []
    # The PASCAL VOC metric changed in 2010
    #use_07_metric = True if int(self._year) < 2010 else False
    use_07_metric = False
    print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
    if not os.path.isdir(output_dir):
      os.mkdir(output_dir)
    for i, cls in enumerate(self._classes):
      if cls == '__background__' or cls == self.classes[0]:
        cls_ind=0
        continue
      else:
        cls_ind=self._class_to_ind[cls]
      #elif cls=='median_filtering':
        #cls_ind=3
        #continue
      filename = self._get_voc_results_file_template().format(cls)
      filename2 = self._get_voc_noise_results_file_template().format(cls)
      print(cls_ind)
      rec, prec, ap = voc_eval(
        filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
        use_07_metric=use_07_metric,fuse=False)
      aps += [ap]
      print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
      with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
        pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
      fig=plt.figure()
      plt.plot(rec,prec)
      fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
      plt.xlabel('recall',fontsize=15)
      plt.xlim((0,1.0))
      plt.ylim((0,1.0))
      plt.ylabel('precision',fontsize=15)
      fig.savefig('{}.png'.format(cls))

    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('--------------------------------------------------------------') 

# 需要导入模块: from model.config import cfg [as 别名]
# 或者: from model.config.cfg import MATLAB [as 别名]
def _do_python_eval(self, output_dir='output'):
    annopath = os.path.join(
      '/home-3/pengzhou@umd.edu/work/pengzhou/dataset',
      'coco_multi' ,
      'Annotations',
      '{:s}.xml')
    imagesetfile = os.path.join(
      '/home-3/pengzhou@umd.edu/work/pengzhou/dataset',
      self._image_set + '.txt')
    cachedir = os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset', 'annotations_cache')
    aps = []
    # The PASCAL VOC metric changed in 2010
    #use_07_metric = True if int(self._year) < 2010 else False
    use_07_metric = False
    print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
    if not os.path.isdir(output_dir):
      os.mkdir(output_dir)
    for i, cls in enumerate(self._classes):
      if cls == '__background__' or cls == self.classes[0]:
        cls_ind=0
        continue
      else:
        cls_ind=self._class_to_ind[cls]
      #elif cls=='median_filtering':
        #cls_ind=3
        #continue
      filename = self._get_voc_results_file_template().format(cls)
      filename2 = self._get_voc_noise_results_file_template().format(cls)
      print(cls_ind)
      rec, prec, ap = voc_eval(
        filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
        use_07_metric=use_07_metric,fuse=False)
      aps += [ap]
      print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
      with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
        pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
      fig=plt.figure()
      plt.plot(rec,prec)
      fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
      plt.xlabel('recall',fontsize=15)
      plt.xlim((0,1.0))
      plt.ylim((0,1.0))
      plt.ylabel('precision',fontsize=15)
      fig.savefig('{}.jpg'.format(cls))

    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('--------------------------------------------------------------') 

# 需要导入模块: from model.config import cfg [as 别名]
# 或者: from model.config.cfg import MATLAB [as 别名]
def _do_python_eval(self, output_dir='output'):
    annopath = os.path.join(
      self._devkit_path,
      'VOC' + self._year,
      'Annotations',
      '{:s}.xml')
    imagesetfile = os.path.join(
      self._devkit_path,
      'VOC' + self._year,
      'ImageSets',
      'Main',
      self._image_set + '.txt')
    cachedir = os.path.join(self._devkit_path, 'annotations_cache')
    aps = []
    # The PASCAL VOC metric changed in 2010
    use_07_metric = True if int(self._year) < 2010 else False
    print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
    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_voc_results_file_template().format(cls)
      rec, prec, ap = voc_eval(
        filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
        use_07_metric=use_07_metric)
      aps += [ap]
      print(('AP for {} = {:.4f}'.format(cls, ap)))
      with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
        pickle.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('--------------------------------------------------------------') 

# 需要导入模块: from model.config import cfg [as 别名]
# 或者: from model.config.cfg import MATLAB [as 别名]
def _do_python_eval(self, output_dir='output'):
    annopath = os.path.join(
      self._dist_path,
      'coco_multi' ,
      'Annotations',
      '{:s}.xml')
    imagesetfile = os.path.join(
      self._dist_path,
      self._image_set + '.txt')
    cachedir = os.path.join(self._dist_path, 'annotations_cache')
    aps = []
    # The PASCAL VOC metric changed in 2010
    #use_07_metric = True if int(self._year) < 2010 else False
    use_07_metric = False
    print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
    if not os.path.isdir(output_dir):
      os.mkdir(output_dir)
    for i, cls in enumerate(self._classes):
      if cls == '__background__' or cls == self.classes[0]:
        cls_ind=0
        continue
      else:
        cls_ind=self._class_to_ind[cls]
      #elif cls=='median_filtering':
        #cls_ind=3
        #continue
      filename = self._get_voc_results_file_template().format(cls)
      filename2 = self._get_voc_noise_results_file_template().format(cls)
      #print(cls_ind)
      rec, prec, ap = voc_eval(
        filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
        use_07_metric=use_07_metric,fuse=False)
      aps += [ap]
      print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
      with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
        pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
      fig=plt.figure()
      plt.plot(rec,prec)
      fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
      plt.xlabel('recall',fontsize=15)
      plt.xlim((0,1.0))
      plt.ylim((0,1.0))
      plt.ylabel('precision',fontsize=15)
      fig.savefig('{}.png'.format(cls))

    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('--------------------------------------------------------------') 

# 需要导入模块: from model.config import cfg [as 别名]
# 或者: from model.config.cfg import MATLAB [as 别名]
def _do_python_eval(self, output_dir=None):
    annopath = os.path.join(
      self._devkit_path,
      'VOC' + self._year,
      'Annotations',
      '{:s}.xml')
    imagesetfile = os.path.join(
      self._devkit_path,
      'VOC' + self._year,
      'ImageSets',
      'Main',
      self._image_set + '.txt')
    cachedir = os.path.join(self._devkit_path, 'annotations_cache')
    aps = []
    # The PASCAL VOC metric changed in 2010
    use_07_metric = True if int(self._year) < 2010 else False
    print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
    if output_dir is not None and not os.path.isdir(output_dir):
      os.mkdir(output_dir)
    for i, cls in enumerate(self._classes):
      if cls == '__background__':
        continue
      filename = self._get_voc_results_file_template().format(cls)
      rec, prec, ap = voc_eval(
        filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
        use_07_metric=use_07_metric, use_diff=self.config['use_diff'])
      aps += [ap]
      print(('AP for {} = {:.4f}'.format(cls, ap)))
      if output_dir is not None:
        with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
          pickle.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('--------------------------------------------------------------')
    '''