rob_mots.py

import os
import csv
import numpy as np
from scipy.optimize import linear_sum_assignment
from ._base_dataset import _BaseDataset
from .. import utils
from ..utils import TrackEvalException
from .. import _timing
from ..datasets.rob_mots_classmap import cls_id_to_name


class RobMOTS(_BaseDataset):

    @staticmethod
    def get_default_dataset_config():
        """Default class config values"""
        code_path = utils.get_code_path()
        default_config = {
            'GT_FOLDER': os.path.join(code_path, 'data/gt/rob_mots'),  # Location of GT data
            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/rob_mots'),  # Trackers location
            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)
            'SUB_BENCHMARK': None,  # REQUIRED. Sub-benchmark to eval. If None, then error.
            # ['mots_challenge', 'kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'waymo', 'tao']
            'CLASSES_TO_EVAL': None,  # List of classes to eval. If None, then it does all COCO classes.
            'SPLIT_TO_EVAL': 'train',  # valid: ['train', 'val', 'test']
            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped
            'PRINT_CONFIG': True,  # Whether to print current config
            'OUTPUT_SUB_FOLDER': 'results',  # Output files are saved in OUTPUT_FOLDER/DATA_LOC_FORMAT/OUTPUT_SUB_FOLDER
            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/DATA_LOC_FORMAT/TRACKER_SUB_FOLDER
            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL
            'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/dataset_subfolder/seqmaps)
            'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use SEQMAP_FOLDER/BENCHMARK_SPLIT_TO_EVAL)
            'CLSMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/dataset_subfolder/clsmaps)
            'CLSMAP_FILE': None,  # Directly specify seqmap file (if none use CLSMAP_FOLDER/BENCHMARK_SPLIT_TO_EVAL)
        }
        return default_config

    def __init__(self, config=None):
        super().__init__()
        # Fill non-given config values with defaults
        self.config = utils.init_config(config, self.get_default_dataset_config())

        self.split = self.config['SPLIT_TO_EVAL']
        valid_benchmarks = ['mots_challenge', 'kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'waymo', 'tao']
        self.box_gt_benchmarks = ['waymo', 'tao']

        self.sub_benchmark = self.config['SUB_BENCHMARK']
        if not self.sub_benchmark:
            raise TrackEvalException('SUB_BENCHMARK config input is required (there is no default value)' +
                                     ', '.join(valid_benchmarks) + ' are valid.')
        if self.sub_benchmark not in valid_benchmarks:
            raise TrackEvalException('Attempted to evaluate an invalid benchmark: ' + self.sub_benchmark + '. Only benchmarks ' +
                                     ', '.join(valid_benchmarks) + ' are valid.')

        self.gt_fol = self.config['GT_FOLDER']
        self.tracker_fol = os.path.join(self.config['TRACKERS_FOLDER'], self.config['SPLIT_TO_EVAL'])
        self.data_is_zipped = self.config['INPUT_AS_ZIP']

        self.output_fol = self.config['OUTPUT_FOLDER']
        if self.output_fol is None:
            self.output_fol = self.tracker_fol

        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']
        self.output_sub_fol = os.path.join(self.config['OUTPUT_SUB_FOLDER'], self.sub_benchmark)

        # Loops through all sub-benchmarks, and reads in seqmaps to info on all sequences to eval.
        self._get_seq_info()

        if len(self.seq_list) < 1:
            raise TrackEvalException('No sequences are selected to be evaluated.')

        valid_class_ids = np.atleast_1d(np.genfromtxt(os.path.join(self.gt_fol, self.split, self.sub_benchmark,
                                                                   'clsmap.txt')))
        valid_classes = [cls_id_to_name[int(x)] for x in valid_class_ids] + ['all']
        self.valid_class_ids = valid_class_ids
        self.class_name_to_class_id = {cls_name: cls_id for cls_id, cls_name in cls_id_to_name.items()}
        self.class_name_to_class_id['all'] = -1
        if not self.config['CLASSES_TO_EVAL']:
            self.class_list = valid_classes
        else:
            self.class_list = [cls if cls in valid_classes else None
                               for cls in self.config['CLASSES_TO_EVAL']]
            if not all(self.class_list):
                raise TrackEvalException('Attempted to evaluate an invalid class. Only classes ' +
                                         ', '.join(valid_classes) + ' are valid.')

        # Check gt files exist
        for seq in self.seq_list:
            if not self.data_is_zipped:
                curr_file = os.path.join(self.gt_fol, self.split, self.sub_benchmark, 'data', seq + '.txt')
                if not os.path.isfile(curr_file):
                    print('GT file not found ' + curr_file)
                    raise TrackEvalException('GT file not found for sequence: ' + seq)
        if self.data_is_zipped:
            curr_file = os.path.join(self.gt_fol, self.split, self.sub_benchmark, 'data.zip')
            if not os.path.isfile(curr_file):
                raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))

        # Get trackers to eval
        if self.config['TRACKERS_TO_EVAL'] is None:
            self.tracker_list = os.listdir(self.tracker_fol)
        else:
            self.tracker_list = self.config['TRACKERS_TO_EVAL']

        if self.config['TRACKER_DISPLAY_NAMES'] is None:
            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))
        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (
                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):
            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))
        else:
            raise TrackEvalException('List of tracker files and tracker display names do not match.')

        for tracker in self.tracker_list:
            if self.data_is_zipped:
                curr_file = os.path.join(self.tracker_fol, tracker, 'data.zip')
                if not os.path.isfile(curr_file):
                    raise TrackEvalException('Tracker file not found: ' + os.path.basename(curr_file))
            else:
                for seq in self.seq_list:
                    curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, self.sub_benchmark, seq
                                             + '.txt')
                    if not os.path.isfile(curr_file):
                        print('Tracker file not found: ' + curr_file)
                        raise TrackEvalException(
                            'Tracker file not found: ' + self.sub_benchmark + '/' + os.path.basename(curr_file))

    def get_name(self):
        return self.get_class_name() + '.' + self.sub_benchmark

    def _get_seq_info(self):
        self.seq_list = []
        self.seq_lengths = {}
        self.seq_sizes = {}
        self.seq_ignore_class_ids = {}
        if self.config["SEQMAP_FILE"]:
            seqmap_file = self.config["SEQMAP_FILE"]
        else:
            if self.config["SEQMAP_FOLDER"] is None:
                seqmap_file = os.path.join(self.gt_fol, self.split, self.sub_benchmark, 'seqmap.txt')
            else:
                seqmap_file = os.path.join(self.config["SEQMAP_FOLDER"], self.split + '.seqmap')
        if not os.path.isfile(seqmap_file):
            print('no seqmap found: ' + seqmap_file)
            raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))
        with open(seqmap_file) as fp:
            dialect = csv.Sniffer().sniff(fp.readline(), delimiters=' ')
            fp.seek(0)
            reader = csv.reader(fp, dialect)
            for i, row in enumerate(reader):
                if len(row) >= 4:
                    # first col: sequence, second col: sequence length, third and fourth col: sequence height/width
                    # The rest of the columns list the 'sequence ignore class ids' which are classes not penalized as
                    # FPs for this sequence.
                    seq = row[0]
                    self.seq_list.append(seq)
                    self.seq_lengths[seq] = int(row[1])
                    self.seq_sizes[seq] = (int(row[2]), int(row[3]))
                    self.seq_ignore_class_ids[seq] = [int(row[x]) for x in range(4, len(row))]

    def get_display_name(self, tracker):
        return self.tracker_to_disp[tracker]

    def _load_raw_file(self, tracker, seq, is_gt):
        """Load a file (gt or tracker) in the unified RobMOTS format.

        If is_gt, this returns a dict which contains the fields:
        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).
        [gt_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.

        if not is_gt, this returns a dict which contains the fields:
        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).
        [tracker_dets]: list (for each timestep) of lists of detections.
        """
        # import to reduce minimum requirements
        from pycocotools import mask as mask_utils

        # File location
        if self.data_is_zipped:
            if is_gt:
                zip_file = os.path.join(self.gt_fol, self.split, self.sub_benchmark, 'data.zip')
            else:
                zip_file = os.path.join(self.tracker_fol, tracker, 'data.zip')
            file = seq + '.txt'
        else:
            zip_file = None
            if is_gt:
                file = os.path.join(self.gt_fol, self.split, self.sub_benchmark, 'data', seq + '.txt')
            else:
                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, self.sub_benchmark, seq + '.txt')

        # Load raw data from text file
        read_data, ignore_data = self._load_simple_text_file(file, is_zipped=self.data_is_zipped, zip_file=zip_file,
                                                             force_delimiters=' ')

        # Convert data to required format
        num_timesteps = self.seq_lengths[seq]
        data_keys = ['ids', 'classes', 'dets']
        if not is_gt:
            data_keys += ['tracker_confidences']
        raw_data = {key: [None] * num_timesteps for key in data_keys}
        for t in range(num_timesteps):
            time_key = str(t)
            # list to collect all masks of a timestep to check for overlapping areas (for segmentation datasets)
            all_valid_masks = []
            if time_key in read_data.keys():
                try:
                    raw_data['ids'][t] = np.atleast_1d([det[1] for det in read_data[time_key]]).astype(int)
                    raw_data['classes'][t] = np.atleast_1d([det[2] for det in read_data[time_key]]).astype(int)
                    if (not is_gt) or (self.sub_benchmark not in self.box_gt_benchmarks):
                        raw_data['dets'][t] = [{'size': [int(region[4]), int(region[5])],
                                                'counts': region[6].encode(encoding='UTF-8')}
                                               for region in read_data[time_key]]
                        all_valid_masks += [mask for mask, cls in zip(raw_data['dets'][t], raw_data['classes'][t]) if
                                      cls < 100]
                    else:
                        raw_data['dets'][t] = np.atleast_2d([det[4:8] for det in read_data[time_key]]).astype(float)

                    if not is_gt:
                        raw_data['tracker_confidences'][t] = np.atleast_1d([det[3] for det
                                                                            in read_data[time_key]]).astype(float)
                except IndexError:
                    self._raise_index_error(is_gt, self.sub_benchmark, seq)
                except ValueError:
                    self._raise_value_error(is_gt, self.sub_benchmark, seq)
            # no detection in this timestep
            else:
                if (not is_gt) or (self.sub_benchmark not in self.box_gt_benchmarks):
                    raw_data['dets'][t] = []
                else:
                    raw_data['dets'][t] = np.empty((0, 4)).astype(float)
                raw_data['ids'][t] = np.empty(0).astype(int)
                raw_data['classes'][t] = np.empty(0).astype(int)
                if not is_gt:
                    raw_data['tracker_confidences'][t] = np.empty(0).astype(float)

            # check for overlapping masks
            if all_valid_masks:
                masks_merged = all_valid_masks[0]
                for mask in all_valid_masks[1:]:
                    if mask_utils.area(mask_utils.merge([masks_merged, mask], intersect=True)) != 0.0:
                        err = 'Overlapping masks in frame %d' % t
                        raise TrackEvalException(err)
                    masks_merged = mask_utils.merge([masks_merged, mask], intersect=False)

        if is_gt:
            key_map = {'ids': 'gt_ids',
                       'classes': 'gt_classes',
                       'dets': 'gt_dets'}
        else:
            key_map = {'ids': 'tracker_ids',
                       'classes': 'tracker_classes',
                       'dets': 'tracker_dets'}

        for k, v in key_map.items():
            raw_data[v] = raw_data.pop(k)

        raw_data['num_timesteps'] = num_timesteps
        raw_data['frame_size'] = self.seq_sizes[seq]
        raw_data['seq'] = seq
        return raw_data

    @staticmethod
    def _raise_index_error(is_gt, sub_benchmark, seq):
        """
        Auxiliary method to raise an evaluation error in case of an index error while reading files.
        :param is_gt: whether gt or tracker data is read
        :param tracker: the name of the tracker
        :param seq: the name of the seq
        :return: None
        """
        if is_gt:
            err = 'Cannot load gt data from sequence %s, because there are not enough ' \
                  'columns in the data.' % seq
            raise TrackEvalException(err)
        else:
            err = 'Cannot load tracker data from benchmark %s, sequence %s, because there are not enough ' \
                  'columns in the data.' % (sub_benchmark, seq)
            raise TrackEvalException(err)

    @staticmethod
    def _raise_value_error(is_gt, sub_benchmark, seq):
        """
        Auxiliary method to raise an evaluation error in case of an value error while reading files.
        :param is_gt: whether gt or tracker data is read
        :param tracker: the name of the tracker
        :param seq: the name of the seq
        :return: None
        """
        if is_gt:
            raise TrackEvalException(
                'GT data for sequence %s cannot be converted to the right format. Is data corrupted?' % seq)
        else:
            raise TrackEvalException(
                'Tracking data from benchmark %s, sequence %s cannot be converted to the right format. '
                'Is data corrupted?' % (sub_benchmark, seq))

    @_timing.time
    def get_preprocessed_seq_data(self, raw_data, cls):
        """ Preprocess data for a single sequence for a single class ready for evaluation.
        Inputs:
             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().
             - cls is the class to be evaluated.
        Outputs:
             - data is a dict containing all of the information that metrics need to perform evaluation.
                It contains the following fields:
                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.
                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).
                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.
                    [similarity_scores]: list (for each timestep) of 2D NDArrays.
        Notes:
            Preprocessing (preproc) occurs in 3 steps.
                1) Extract only detections relevant for the class to be evaluated.
                2) Match gt dets and tracker dets. Tracker dets that are to a gt det (TPs) are marked as not to be
                    removed.
                3) Remove unmatched tracker dets if they fall within an ignore region or are too small, or if that class
                    is marked as an ignore class for that sequence.
            After the above preprocessing steps, this function also calculates the number of gt and tracker detections
                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are
                unique within each timestep.
            Note that there is a special 'all' class, which evaluates all of the COCO classes together in a
                'class agnostic' fashion.
        """
        # import to reduce minimum requirements
        from pycocotools import mask as mask_utils

        # Check that input data has unique ids
        self._check_unique_ids(raw_data)

        cls_id = self.class_name_to_class_id[cls]
        ignore_class_id = cls_id+100
        seq = raw_data['seq']

        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'tracker_confidences', 'similarity_scores']
        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}
        unique_gt_ids = []
        unique_tracker_ids = []
        num_gt_dets = 0
        num_tracker_dets = 0

        for t in range(raw_data['num_timesteps']):

            # Only extract relevant dets for this class
            if cls == 'all':
                gt_class_mask = raw_data['gt_classes'][t] < 100
            # For waymo, combine predictions for [car, truck, bus, motorcycle] into car, because they are all annotated
            # together as one 'vehicle' class.
            elif self.sub_benchmark == 'waymo' and cls == 'car':
                waymo_vehicle_classes = np.array([3, 4, 6, 8])
                gt_class_mask = np.isin(raw_data['gt_classes'][t], waymo_vehicle_classes)
            else:
                gt_class_mask = raw_data['gt_classes'][t] == cls_id
            gt_class_mask = gt_class_mask.astype(np.bool)
            gt_ids = raw_data['gt_ids'][t][gt_class_mask]
            if cls == 'all':
                ignore_regions_mask = raw_data['gt_classes'][t] >= 100
            else:
                ignore_regions_mask = raw_data['gt_classes'][t] == ignore_class_id
                ignore_regions_mask = np.logical_or(ignore_regions_mask, raw_data['gt_classes'][t] == 100)
            if self.sub_benchmark in self.box_gt_benchmarks:
                gt_dets = raw_data['gt_dets'][t][gt_class_mask]
                ignore_regions_box = raw_data['gt_dets'][t][ignore_regions_mask]
                if len(ignore_regions_box) > 0:
                    ignore_regions_box[:, 2] = ignore_regions_box[:, 2] - ignore_regions_box[:, 0]
                    ignore_regions_box[:, 3] = ignore_regions_box[:, 3] - ignore_regions_box[:, 1]
                    ignore_regions = mask_utils.frPyObjects(ignore_regions_box, self.seq_sizes[seq][0], self.seq_sizes[seq][1])
                else:
                    ignore_regions = []
            else:
                gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]
                ignore_regions = [raw_data['gt_dets'][t][ind] for ind in range(len(ignore_regions_mask)) if
                                  ignore_regions_mask[ind]]

            if cls == 'all':
                tracker_class_mask = np.ones_like(raw_data['tracker_classes'][t])
            else:
                tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)
            tracker_class_mask = tracker_class_mask.astype(np.bool)
            tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]
            tracker_dets = [raw_data['tracker_dets'][t][ind] for ind in range(len(tracker_class_mask)) if
                            tracker_class_mask[ind]]
            tracker_confidences = raw_data['tracker_confidences'][t][tracker_class_mask]
            similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]
            tracker_classes = raw_data['tracker_classes'][t][tracker_class_mask]

            # Only do preproc if there are ignore regions defined to remove
            if tracker_ids.shape[0] > 0:

                # Match tracker and gt dets (with hungarian algorithm)
                unmatched_indices = np.arange(tracker_ids.shape[0])
                if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:
                    matching_scores = similarity_scores.copy()
                    matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0
                    match_rows, match_cols = linear_sum_assignment(-matching_scores)
                    actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps
                    # match_rows = match_rows[actually_matched_mask]
                    match_cols = match_cols[actually_matched_mask]
                    unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)

                # For unmatched tracker dets remove those that are greater than 50% within an ignore region.
                # unmatched_tracker_dets = tracker_dets[unmatched_indices, :]
                # crowd_ignore_regions = raw_data['gt_ignore_regions'][t]
                # intersection_with_ignore_region = self. \
                #     _calculate_box_ious(unmatched_tracker_dets, crowd_ignore_regions, box_format='x0y0x1y1',
                #                         do_ioa=True)


                if cls_id in self.seq_ignore_class_ids[seq]:
                    # Remove unmatched detections for classes that are marked as 'ignore' for the whole sequence.
                    to_remove_tracker = unmatched_indices
                else:
                    unmatched_tracker_dets = [tracker_dets[i] for i in range(len(tracker_dets)) if
                                              i in unmatched_indices]

                    # For unmatched tracker dets remove those that are too small.
                    tracker_boxes_t = mask_utils.toBbox(unmatched_tracker_dets)
                    unmatched_widths = tracker_boxes_t[:, 2]
                    unmatched_heights = tracker_boxes_t[:, 3]
                    unmatched_size = np.maximum(unmatched_heights, unmatched_widths)
                    min_size = np.min(self.seq_sizes[seq])/8
                    is_too_small = unmatched_size <= min_size + np.finfo('float').eps

                    # For unmatched tracker dets remove those that are greater than 50% within an ignore region.
                    if ignore_regions:
                        ignore_region_merged = ignore_regions[0]
                        for mask in ignore_regions[1:]:
                            ignore_region_merged = mask_utils.merge([ignore_region_merged, mask], intersect=False)
                        intersection_with_ignore_region = self. \
                            _calculate_mask_ious(unmatched_tracker_dets, [ignore_region_merged], is_encoded=True, do_ioa=True)
                        is_within_ignore_region = np.any(intersection_with_ignore_region > 0.5 + np.finfo('float').eps, axis=1)
                        to_remove_tracker = unmatched_indices[np.logical_or(is_too_small, is_within_ignore_region)]
                    else:
                        to_remove_tracker = unmatched_indices[is_too_small]

                # For the special 'all' class, you need to remove unmatched detections from all ignore classes and
                #   non-evaluated classes.
                if cls == 'all':
                    unmatched_tracker_classes = [tracker_classes[i] for i in range(len(tracker_classes)) if
                                              i in unmatched_indices]
                    is_ignore_class = np.isin(unmatched_tracker_classes, self.seq_ignore_class_ids[seq])
                    is_not_evaled_class = np.logical_not(np.isin(unmatched_tracker_classes, self.valid_class_ids))
                    to_remove_all = unmatched_indices[np.logical_or(is_ignore_class, is_not_evaled_class)]
                    to_remove_tracker = np.concatenate([to_remove_tracker, to_remove_all], axis=0)
            else:
                to_remove_tracker = np.array([], dtype=np.int)

            # remove all unwanted tracker detections
            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)
            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)
            data['tracker_confidences'][t] = np.delete(tracker_confidences, to_remove_tracker, axis=0)
            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)

            # keep all ground truth detections
            data['gt_ids'][t] = gt_ids
            data['gt_dets'][t] = gt_dets
            data['similarity_scores'][t] = similarity_scores

            unique_gt_ids += list(np.unique(data['gt_ids'][t]))
            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))
            num_tracker_dets += len(data['tracker_ids'][t])
            num_gt_dets += len(data['gt_ids'][t])

        # Re-label IDs such that there are no empty IDs
        if len(unique_gt_ids) > 0:
            unique_gt_ids = np.unique(unique_gt_ids)
            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))
            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
            for t in range(raw_data['num_timesteps']):
                if len(data['gt_ids'][t]) > 0:
                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
        if len(unique_tracker_ids) > 0:
            unique_tracker_ids = np.unique(unique_tracker_ids)
            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
            for t in range(raw_data['num_timesteps']):
                if len(data['tracker_ids'][t]) > 0:
                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)

        # Record overview statistics.
        data['num_tracker_dets'] = num_tracker_dets
        data['num_gt_dets'] = num_gt_dets
        data['num_tracker_ids'] = len(unique_tracker_ids)
        data['num_gt_ids'] = len(unique_gt_ids)
        data['num_timesteps'] = raw_data['num_timesteps']
        data['seq'] = raw_data['seq']
        data['frame_size'] = raw_data['frame_size']

        # Ensure that ids are unique per timestep.
        self._check_unique_ids(data, after_preproc=True)

        return data

    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):

        # Only loaded when run to reduce minimum requirements
        from pycocotools import mask as mask_utils

        if self.sub_benchmark in self.box_gt_benchmarks:
            # Convert tracker masks to bboxes (for benchmarks with only bbox ground-truth),
            # and then convert to x0y0x1y1 format.
            tracker_boxes_t = mask_utils.toBbox(tracker_dets_t)
            tracker_boxes_t[:, 2] = tracker_boxes_t[:, 0] + tracker_boxes_t[:, 2]
            tracker_boxes_t[:, 3] = tracker_boxes_t[:, 1] + tracker_boxes_t[:, 3]
            similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_boxes_t, box_format='x0y0x1y1')
        else:
            similarity_scores = self._calculate_mask_ious(gt_dets_t, tracker_dets_t, is_encoded=True, do_ioa=False)
        return similarity_scores