def _load_image_set_index(self):
        """
        Load the indexes listed in this dataset's image set file.
        """

        kitti_train_nums = [154, 447, 233, 144, 314, 297, 270, 800, 390, 803, 294, \
                            373, 78, 340, 106, 376, 209, 145, 339, 1059, 837]

        kitti_test_nums = [465, 147, 243, 257, 421, 809, 114, 215, 165, 349, 1176, \
                           774, 694, 152, 850, 701, 510, 305, 180, 404, 173, 203, \
                           436, 430, 316, 176, 170, 85, 175]

        if self._seq_name == 'train' or self._seq_name == 'trainval':

            assert self._image_set == 'training', 'Use train set or trainval set in testing'

            if self._seq_name == 'train':
                seq_index = [0, 1, 2, 3, 4, 5, 12, 13, 14, 15, 16]
            else:
                seq_index = range(0, 21)

            # for each sequence
            image_index = []
            for i in xrange(len(seq_index)):
                seq_idx = seq_index[i]
                num = kitti_train_nums[seq_idx]
                for j in xrange(num):
                    image_index.append('{:04d}/{:06d}'.format(seq_idx, j))
        else:
            # a single sequence
            seq_num = int(self._seq_name)
            if self._image_set == 'training':
                num = kitti_train_nums[seq_num]
            else:
                num = kitti_test_nums[seq_num]
            image_index = []
            for i in xrange(num):
                image_index.append('{:04d}/{:06d}'.format(seq_num, i))

        return image_index

        Load the indexes listed in this dataset's image set file.
        
lib/datasets/kitti_tracking.py