kitti_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 .. import _timing
from ..utils import TrackEvalException


class KittiMOTS(_BaseDataset):
    """Dataset class for KITTI MOTS tracking"""

    @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/kitti/kitti_mots_val'),  # Location of GT data
            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/kitti/kitti_mots_val'),  # 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)
            'CLASSES_TO_EVAL': ['car', 'pedestrian'],  # Valid: ['car', 'pedestrian']
            'SPLIT_TO_EVAL': 'val',  # Valid: 'training', 'val'
            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped
            'PRINT_CONFIG': True,  # Whether to print current config
            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER
            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_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)
            'SEQMAP_FILE': None,    # Directly specify seqmap file (if none use seqmap_folder/split_to_eval.seqmap)
            'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps
            'GT_LOC_FORMAT': '{gt_folder}/label_02/{seq}.txt',  # format of gt localization
        }
        return default_config

    def __init__(self, config=None):
        """Initialise dataset, checking that all required files are present"""
        super().__init__()
        # Fill non-given config values with defaults
        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())
        self.gt_fol = self.config['GT_FOLDER']
        self.tracker_fol = self.config['TRACKERS_FOLDER']
        self.split_to_eval = self.config['SPLIT_TO_EVAL']
        self.should_classes_combine = False
        self.use_super_categories = False
        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 = self.config['OUTPUT_SUB_FOLDER']

        # Get classes to eval
        self.valid_classes = ['car', 'pedestrian']
        self.class_list = [cls.lower() if cls.lower() in self.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 [car, pedestrian] are valid.')
        self.class_name_to_class_id = {'car': '1', 'pedestrian': '2', 'ignore': '10'}

        # Get sequences to eval and check gt files exist
        self.seq_list, self.seq_lengths = self._get_seq_info()
        if len(self.seq_list) < 1:
            raise TrackEvalException('No sequences are selected to be evaluated.')

        # Check gt files exist
        for seq in self.seq_list:
            if not self.data_is_zipped:
                curr_file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
                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, '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, self.tracker_sub_fol + '.zip')
                if not os.path.isfile(curr_file):
                    print('Tracker file not found: ' + curr_file)
                    raise TrackEvalException('Tracker file not found: ' + tracker + '/' + 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, seq + '.txt')
                    if not os.path.isfile(curr_file):
                        print('Tracker file not found: ' + curr_file)
                        raise TrackEvalException(
                            'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(
                                curr_file))

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

    def _get_seq_info(self):
        seq_list = []
        seq_lengths = {}
        seqmap_name = 'evaluate_mots.seqmap.' + self.config['SPLIT_TO_EVAL']

        if self.config["SEQ_INFO"]:
            seq_list = list(self.config["SEQ_INFO"].keys())
            seq_lengths = self.config["SEQ_INFO"]
        else:
            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.config['GT_FOLDER'], seqmap_name)
                else:
                    seqmap_file = os.path.join(self.config["SEQMAP_FOLDER"], seqmap_name)
            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:
                reader = csv.reader(fp)
                for i, _ in enumerate(reader):
                    dialect = csv.Sniffer().sniff(fp.read(1024))
                    fp.seek(0)
                    reader = csv.reader(fp, dialect)
                    for row in reader:
                        if len(row) >= 4:
                            seq = "%04d" % int(row[0])
                            seq_list.append(seq)
                            seq_lengths[seq] = int(row[3]) + 1
        return seq_list, seq_lengths

    def _load_raw_file(self, tracker, seq, is_gt):
        """Load a file (gt or tracker) in the KITTI MOTS 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]: list (for each timestep) of lists of detections.
        [gt_ignore_region]: list (for each timestep) of masks for the ignore regions

        if not is_gt, this returns a dict which contains the fields:
        [tracker_ids, tracker_classes] : list (for each timestep) of 1D NDArrays (for each det).
        [tracker_dets]: list (for each timestep) of lists of detections.
        """

        # Only loaded when run 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, 'data.zip')
            else:
                zip_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
            file = seq + '.txt'
        else:
            zip_file = None
            if is_gt:
                file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
            else:
                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')

        # Ignore regions
        if is_gt:
            crowd_ignore_filter = {2: ['10']}
        else:
            crowd_ignore_filter = None

        # Load raw data from text file
        read_data, ignore_data = self._load_simple_text_file(file, crowd_ignore_filter=crowd_ignore_filter,
                                                             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 is_gt:
            data_keys += ['gt_ignore_region']
        raw_data = {key: [None] * num_timesteps for key in data_keys}

        # Check for any extra time keys
        extra_time_keys = [x for x in read_data.keys() if x not in [str(t) for t in range(num_timesteps)]]
        if len(extra_time_keys) > 0:
            if is_gt:
                text = 'Ground-truth'
            else:
                text = 'Tracking'
            raise TrackEvalException(
                text + ' data contains the following invalid timesteps in seq %s: ' % seq + ', '.join(
                    [str(x) + ', ' for x in extra_time_keys]))

        for t in range(num_timesteps):
            time_key = str(t)
            # list to collect all masks of a timestep to check for overlapping areas
            all_masks = []
            if time_key in read_data.keys():
                try:
                    raw_data['dets'][t] = [{'size': [int(region[3]), int(region[4])],
                                            'counts': region[5].encode(encoding='UTF-8')}
                                           for region in read_data[time_key]]
                    raw_data['ids'][t] = np.atleast_1d([region[1] for region in read_data[time_key]]).astype(int)
                    raw_data['classes'][t] = np.atleast_1d([region[2] for region in read_data[time_key]]).astype(int)
                    all_masks += raw_data['dets'][t]
                except IndexError:
                    self._raise_index_error(is_gt, tracker, seq)
                except ValueError:
                    self._raise_value_error(is_gt, tracker, seq)
            else:
                raw_data['dets'][t] = []
                raw_data['ids'][t] = np.empty(0).astype(int)
                raw_data['classes'][t] = np.empty(0).astype(int)
            if is_gt:
                if time_key in ignore_data.keys():
                    try:
                        time_ignore = [{'size': [int(region[3]), int(region[4])],
                                        'counts': region[5].encode(encoding='UTF-8')}
                                       for region in ignore_data[time_key]]
                        raw_data['gt_ignore_region'][t] = mask_utils.merge([mask for mask in time_ignore],
                                                                           intersect=False)
                        all_masks += [raw_data['gt_ignore_region'][t]]
                    except IndexError:
                        self._raise_index_error(is_gt, tracker, seq)
                    except ValueError:
                        self._raise_value_error(is_gt, tracker, seq)
                else:
                    raw_data['gt_ignore_region'][t] = mask_utils.merge([], intersect=False)

            # check for overlapping masks
            if all_masks:
                masks_merged = all_masks[0]
                for mask in all_masks[1:]:
                    if mask_utils.area(mask_utils.merge([masks_merged, mask], intersect=True)) != 0.0:
                        raise TrackEvalException(
                            'Tracker has overlapping masks. Tracker: ' + tracker + ' Seq: ' + seq + ' Timestep: ' + str(
                                t))
                    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['seq'] = seq
        return raw_data

    @_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]: list (for each timestep) of 1D NDArrays (for each det).
                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detection masks.
                    [similarity_scores]: list (for each timestep) of 2D NDArrays.
        Notes:
            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.
                1) Extract only detections relevant for the class to be evaluated (including distractor detections).
                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a
                    distractor class, or otherwise marked as to be removed.
                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain
                    other criteria (e.g. are too small).
                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.
            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.

        KITTI MOTS:
            In KITTI MOTS, the 4 preproc steps are as follow:
                1) There are two classes (car and pedestrian) which are evaluated separately.
                2) There are no ground truth detections marked as to be removed/distractor classes.
                    Therefore also no matched tracker detections are removed.
                3) Ignore regions are used to remove unmatched detections (at least 50% overlap with ignore region).
                4) There are no ground truth detections (e.g. those of distractor classes) to be removed.
        """
        # Check that input data has unique ids
        self._check_unique_ids(raw_data)

        cls_id = int(self.class_name_to_class_id[cls])

        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', '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 for preproc and eval (cls)
            gt_class_mask = np.atleast_1d(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]
            gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]

            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]]
            similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]

            # 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] = -10000
                match_rows, match_cols = linear_sum_assignment(-matching_scores)
                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps
                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 a crowd ignore region.
            unmatched_tracker_dets = [tracker_dets[i] for i in range(len(tracker_dets)) if i in unmatched_indices]
            ignore_region = raw_data['gt_ignore_region'][t]
            intersection_with_ignore_region = self._calculate_mask_ious(unmatched_tracker_dets, [ignore_region],
                                                                        is_encoded=True, do_ioa=True)
            is_within_ignore_region = np.any(intersection_with_ignore_region > 0.5 + np.finfo('float').eps, axis=1)

            # Apply preprocessing to remove unwanted tracker dets.
            to_remove_tracker = unmatched_indices[is_within_ignore_region]
            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)
            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['cls'] = cls

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

        return data

    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):
        similarity_scores = self._calculate_mask_ious(gt_dets_t, tracker_dets_t, is_encoded=True, do_ioa=False)
        return similarity_scores

    @staticmethod
    def _raise_index_error(is_gt, tracker, 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 tracker %s, sequence %s, because there are not enough ' \
                  'columns in the data.' % (tracker, seq)
            raise TrackEvalException(err)

    @staticmethod
    def _raise_value_error(is_gt, tracker, 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 tracker %s, sequence %s cannot be converted to the right format. '
                'Is data corrupted?' % (tracker, seq))