kitti_2d_box.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


class Kitti2DBox(_BaseDataset):
    """Dataset class for KITTI 2D bounding box 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_2d_box_train'),  # Location of GT data
            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/kitti/kitti_2d_box_train/'),  # 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': 'training',  # Valid: 'training', 'val', 'training_minus_val', 'test'
            '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
        }
        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.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']

        self.max_occlusion = 2
        self.max_truncation = 0
        self.min_height = 25

        # 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, 'van': 2, 'truck': 3, 'pedestrian': 4, 'person': 5,  # person sitting
                                       'cyclist': 6, 'tram': 7, 'misc': 8, 'dontcare': 9, 'car_2': 1}

        # Get sequences to eval and check gt files exist
        self.seq_list = []
        self.seq_lengths = {}
        seqmap_name = 'evaluate_tracking.seqmap.' + self.config['SPLIT_TO_EVAL']
        seqmap_file = os.path.join(self.gt_fol, seqmap_name)
        if not os.path.isfile(seqmap_file):
            raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))
        with open(seqmap_file) as fp:
            dialect = csv.Sniffer().sniff(fp.read(1024))
            fp.seek(0)
            reader = csv.reader(fp, dialect)
            for row in reader:
                if len(row) >= 4:
                    seq = row[0]
                    self.seq_list.append(seq)
                    self.seq_lengths[seq] = int(row[3])
                    if not self.data_is_zipped:
                        curr_file = os.path.join(self.gt_fol, 'label_02', seq + '.txt')
                        if not os.path.isfile(curr_file):
                            raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))
            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):
                    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):
                        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 _load_raw_file(self, tracker, seq, is_gt):
        """Load a file (gt or tracker) in the kitti 2D box 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.
        [gt_extras] : list (for each timestep) of dicts (for each extra) of 1D NDArrays (for each det).

        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.
        """
        # 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 = os.path.join(self.gt_fol, 'label_02', seq + '.txt')
            else:
                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')

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

        # Valid classes
        valid_filter = {2: [x for x in self.class_list]}
        if is_gt:
            if 'car' in self.class_list:
                valid_filter[2].append('van')
            if 'pedestrian' in self.class_list:
                valid_filter[2] += ['person']

        # Convert kitti class strings to class ids
        convert_filter = {2: self.class_name_to_class_id}

        # Load raw data from text file
        read_data, ignore_data = self._load_simple_text_file(file, time_col=0, id_col=1, remove_negative_ids=True,
                                                             valid_filter=valid_filter,
                                                             crowd_ignore_filter=crowd_ignore_filter,
                                                             convert_filter=convert_filter,
                                                             is_zipped=self.data_is_zipped, zip_file=zip_file)
        # Convert data to required format
        num_timesteps = self.seq_lengths[seq]
        data_keys = ['ids', 'classes', 'dets']
        if is_gt:
            data_keys += ['gt_crowd_ignore_regions', 'gt_extras']
        else:
            data_keys += ['tracker_confidences']
        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)
            if time_key in read_data.keys():
                time_data = np.asarray(read_data[time_key], dtype=np.float)
                raw_data['dets'][t] = np.atleast_2d(time_data[:, 6:10])
                raw_data['ids'][t] = np.atleast_1d(time_data[:, 1]).astype(int)
                raw_data['classes'][t] = np.atleast_1d(time_data[:, 2]).astype(int)
                if is_gt:
                    gt_extras_dict = {'truncation': np.atleast_1d(time_data[:, 3].astype(int)),
                                      'occlusion': np.atleast_1d(time_data[:, 4].astype(int))}
                    raw_data['gt_extras'][t] = gt_extras_dict
                else:
                    if time_data.shape[1] > 17:
                        raw_data['tracker_confidences'][t] = np.atleast_1d(time_data[:, 17])
                    else:
                        raw_data['tracker_confidences'][t] = np.ones(time_data.shape[0])
            else:
                raw_data['dets'][t] = np.empty((0, 4))
                raw_data['ids'][t] = np.empty(0).astype(int)
                raw_data['classes'][t] = np.empty(0).astype(int)
                if is_gt:
                    gt_extras_dict = {'truncation': np.empty(0),
                                      'occlusion': np.empty(0)}
                    raw_data['gt_extras'][t] = gt_extras_dict
                else:
                    raw_data['tracker_confidences'][t] = np.empty(0)
            if is_gt:
                if time_key in ignore_data.keys():
                    time_ignore = np.asarray(ignore_data[time_key], dtype=np.float)
                    raw_data['gt_crowd_ignore_regions'][t] = np.atleast_2d(time_ignore[:, 6:10])
                else:
                    raw_data['gt_crowd_ignore_regions'][t] = np.empty((0, 4))

        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, 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:
            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:
            In KITTI, the 4 preproc steps are as follow:
                1) There are two classes (pedestrian and car) which are evaluated separately.
                2) For the pedestrian class, the 'person' class is distractor objects (people sitting).
                    For the car class, the 'van' class are distractor objects.
                    GT boxes marked as having occlusion level > 2 or truncation level > 0 are also treated as
                        distractors.
                3) Crowd ignore regions are used to remove unmatched detections. Also unmatched detections with
                    height <= 25 pixels are removed.
                4) Distractor gt dets (including truncated and occluded) are removed.
        """
        if cls == 'pedestrian':
            distractor_classes = [self.class_name_to_class_id['person']]
        elif cls == 'car':
            distractor_classes = [self.class_name_to_class_id['van']]
        else:
            raise (TrackEvalException('Class %s is not evaluatable' % cls))
        cls_id = self.class_name_to_class_id[cls]

        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 for preproc and eval (cls + distractor classes)
            gt_class_mask = np.sum([raw_data['gt_classes'][t] == c for c in [cls_id] + distractor_classes], axis=0)
            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][gt_class_mask]
            gt_classes = raw_data['gt_classes'][t][gt_class_mask]
            gt_occlusion = raw_data['gt_extras'][t]['occlusion'][gt_class_mask]
            gt_truncation = raw_data['gt_extras'][t]['truncation'][gt_class_mask]

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

            # Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets
            # which are labeled as truncated, occluded, or belonging to a distractor class.
            to_remove_matched = np.array([], np.int)
            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]

                is_distractor_class = np.isin(gt_classes[match_rows], distractor_classes)
                is_occluded_or_truncated = np.logical_or(
                    gt_occlusion[match_rows] > self.max_occlusion + np.finfo('float').eps,
                    gt_truncation[match_rows] > self.max_truncation + np.finfo('float').eps)
                to_remove_matched = np.logical_or(is_distractor_class, is_occluded_or_truncated)
                to_remove_matched = match_cols[to_remove_matched]
                unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)

            # For unmatched tracker dets, also remove those smaller than a minimum height.
            unmatched_tracker_dets = tracker_dets[unmatched_indices, :]
            unmatched_heights = unmatched_tracker_dets[:, 3] - unmatched_tracker_dets[:, 1]
            is_too_small = unmatched_heights <= self.min_height + np.finfo('float').eps

            # For unmatched tracker dets, also remove those that are greater than 50% within a crowd ignore region.
            crowd_ignore_regions = raw_data['gt_crowd_ignore_regions'][t]
            intersection_with_ignore_region = self._calculate_box_ious(unmatched_tracker_dets, crowd_ignore_regions,
                                                                       box_format='x0y0x1y1', do_ioa=True)
            is_within_crowd_ignore_region = np.any(intersection_with_ignore_region > 0.5 + np.finfo('float').eps, axis=1)

            # Apply preprocessing to remove all unwanted tracker dets.
            to_remove_unmatched = unmatched_indices[np.logical_or(is_too_small, is_within_crowd_ignore_region)]
            to_remove_tracker = np.concatenate((to_remove_matched, to_remove_unmatched), axis=0)
            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)

            # Also remove gt dets that were only useful for preprocessing and are not needed for evaluation.
            # These are those that are occluded, truncated and from distractor objects.
            gt_to_keep_mask = (np.less_equal(gt_occlusion, self.max_occlusion)) & \
                              (np.less_equal(gt_truncation, self.max_truncation)) & \
                              (np.equal(gt_classes, cls_id))
            data['gt_ids'][t] = gt_ids[gt_to_keep_mask]
            data['gt_dets'][t] = gt_dets[gt_to_keep_mask, :]
            data['similarity_scores'][t] = similarity_scores[gt_to_keep_mask]

            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']

        # Ensure that ids are unique per timestep.
        self._check_unique_ids(data)

        return data

    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):
        similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_dets_t, box_format='x0y0x1y1')
        return similarity_scores