bdd100k.py

import os
import json
import numpy as np
from scipy.optimize import linear_sum_assignment
from ..utils import TrackEvalException
from ._base_dataset import _BaseDataset
from .. import utils
from .. import _timing


class BDD100K(_BaseDataset):
    """Dataset class for BDD100K 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/bdd100k/bdd100k_val'),  # Location of GT data
            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/bdd100k/bdd100k_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': ['pedestrian', 'rider', 'car', 'bus', 'truck', 'train', 'motorcycle', 'bicycle'],
            # Valid: ['pedestrian', 'rider', 'car', 'bus', 'truck', 'train', 'motorcycle', 'bicycle']
            '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
        }
        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 = True
        self.use_super_categories = True

        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 = ['pedestrian', 'rider', 'car', 'bus', 'truck', 'train', 'motorcycle', 'bicycle']
        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 [pedestrian, rider, car, '
                                     'bus, truck, train, motorcycle, bicycle] are valid.')
        self.super_categories = {"HUMAN": [cls for cls in ["pedestrian", "rider"] if cls in self.class_list],
                                 "VEHICLE": [cls for cls in ["car", "truck", "bus", "train"] if cls in self.class_list],
                                 "BIKE": [cls for cls in ["motorcycle", "bicycle"] if cls in self.class_list]}
        self.distractor_classes = ['other person', 'trailer', 'other vehicle']
        self.class_name_to_class_id = {'pedestrian': 1, 'rider': 2, 'other person': 3, 'car': 4, 'bus': 5, 'truck': 6,
                                       'train': 7, 'trailer': 8, 'other vehicle': 9, 'motorcycle': 10, 'bicycle': 11}

        # Get sequences to eval
        self.seq_list = []
        self.seq_lengths = {}

        self.seq_list = [seq_file.replace('.json', '') for seq_file in os.listdir(self.gt_fol)]

        # 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:
            for seq in self.seq_list:
                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.json')
                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 _load_raw_file(self, tracker, seq, is_gt):
        """Load a file (gt or tracker) in the BDD100K 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.
        """
        # File location
        if is_gt:
            file = os.path.join(self.gt_fol, seq + '.json')
        else:
            file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.json')

        with open(file) as f:
            data = json.load(f)

        # sort data by frame index
        data = sorted(data, key=lambda x: x['index'])

        # check sequence length
        if is_gt:
            self.seq_lengths[seq] = len(data)
            num_timesteps = len(data)
        else:
            num_timesteps = self.seq_lengths[seq]
            if num_timesteps != len(data):
                raise TrackEvalException('Number of ground truth and tracker timesteps do not match for sequence %s'
                                         % seq)

        # Convert data to required format
        data_keys = ['ids', 'classes', 'dets']
        if is_gt:
            data_keys += ['gt_crowd_ignore_regions']
        raw_data = {key: [None] * num_timesteps for key in data_keys}
        for t in range(num_timesteps):
            ig_ids = []
            keep_ids = []
            for i in range(len(data[t]['labels'])):
                ann = data[t]['labels'][i]
                if is_gt and (ann['category'] in self.distractor_classes or 'attributes' in ann.keys()
                              and ann['attributes']['Crowd']):
                    ig_ids.append(i)
                else:
                    keep_ids.append(i)

            if keep_ids:
                raw_data['dets'][t] = np.atleast_2d([[data[t]['labels'][i]['box2d']['x1'],
                                                      data[t]['labels'][i]['box2d']['y1'],
                                                      data[t]['labels'][i]['box2d']['x2'],
                                                      data[t]['labels'][i]['box2d']['y2']
                                                      ] for i in keep_ids]).astype(float)
                raw_data['ids'][t] = np.atleast_1d([data[t]['labels'][i]['id'] for i in keep_ids]).astype(int)
                raw_data['classes'][t] = np.atleast_1d([self.class_name_to_class_id[data[t]['labels'][i]['category']]
                                                        for i in keep_ids]).astype(int)
            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 is_gt:
                if ig_ids:
                    raw_data['gt_crowd_ignore_regions'][t] = np.atleast_2d([[data[t]['labels'][i]['box2d']['x1'],
                                                                             data[t]['labels'][i]['box2d']['y1'],
                                                                             data[t]['labels'][i]['box2d']['x2'],
                                                                             data[t]['labels'][i]['box2d']['y2']
                                                                             ] for i in ig_ids]).astype(float)
                else:
                    raw_data['gt_crowd_ignore_regions'][t] = np.empty((0, 4)).astype(float)

        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
        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.

        BDD100K:
            In BDD100K, the 4 preproc steps are as follow:
                1) There are eight classes (pedestrian, rider, car, bus, truck, train, motorcycle, bicycle)
                    which are evaluated separately.
                2) For BDD100K there is no removal of matched tracker dets.
                3) Crowd ignore regions are used to remove unmatched detections.
                4) No removal of gt dets.
        """
        cls_id = 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][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]
            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] = 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_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[unmatched_indices, :]
            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 unwanted tracker dets.
            to_remove_tracker = unmatched_indices[is_within_crowd_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)

            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(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(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']

        # 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