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(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'] # 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