import os import numpy as np import json from ._base_dataset import _BaseDataset from ..utils import TrackEvalException from .. import utils from .. import _timing class YouTubeVIS(_BaseDataset): """Dataset class for YouTubeVIS 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/youtube_vis/'), # Location of GT data 'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/youtube_vis/'), # 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': None, # Classes to eval (if None, all classes) 'SPLIT_TO_EVAL': 'train_sub_split', # Valid: 'train', 'val', 'train_sub_split' 'PRINT_CONFIG': True, # Whether to print current config 'OUTPUT_SUB_FOLDER': '', # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER 'TRACKER_SUB_FOLDER': 'data', # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_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'] + 'youtube_vis_' + self.config['SPLIT_TO_EVAL'] self.tracker_fol = self.config['TRACKERS_FOLDER'] + 'youtube_vis_' + self.config['SPLIT_TO_EVAL'] self.use_super_categories = False self.should_classes_combine = True self.output_fol = self.config['OUTPUT_FOLDER'] if self.output_fol is None: self.output_fol = self.tracker_fol self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER'] self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER'] if not os.path.exists(self.gt_fol): print("GT folder not found: " + self.gt_fol) raise TrackEvalException("GT folder not found: " + os.path.basename(self.gt_fol)) gt_dir_files = [file for file in os.listdir(self.gt_fol) if file.endswith('.json')] if len(gt_dir_files) != 1: raise TrackEvalException(self.gt_fol + ' does not contain exactly one json file.') with open(os.path.join(self.gt_fol, gt_dir_files[0])) as f: self.gt_data = json.load(f) # Get classes to eval self.valid_classes = [cls['name'] for cls in self.gt_data['categories']] cls_name_to_cls_id_map = {cls['name']: cls['id'] for cls in self.gt_data['categories']} if self.config['CLASSES_TO_EVAL']: 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 ' + ', '.join(self.valid_classes) + ' are valid.') else: self.class_list = [cls['name'] for cls in self.gt_data['categories']] self.class_name_to_class_id = {k: v for k, v in cls_name_to_cls_id_map.items() if k in self.class_list} # Get sequences to eval and check gt files exist self.seq_list = [vid['file_names'][0].split('/')[0] for vid in self.gt_data['videos']] self.seq_name_to_seq_id = {vid['file_names'][0].split('/')[0]: vid['id'] for vid in self.gt_data['videos']} self.seq_lengths = {vid['id']: len(vid['file_names']) for vid in self.gt_data['videos']} # encode masks and compute track areas self._prepare_gt_annotations() # 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.') # counter for globally unique track IDs self.global_tid_counter = 0 self.tracker_data = dict() for tracker in self.tracker_list: tracker_dir_path = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol) tr_dir_files = [file for file in os.listdir(tracker_dir_path) if file.endswith('.json')] if len(tr_dir_files) != 1: raise TrackEvalException(tracker_dir_path + ' does not contain exactly one json file.') with open(os.path.join(tracker_dir_path, tr_dir_files[0])) as f: curr_data = json.load(f) self.tracker_data[tracker] = curr_data 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 YouTubeVIS 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. [classes_to_gt_tracks]: dictionary with class values as keys and list of dictionaries (with frame indices as keys and corresponding segmentations as values) for each track [classes_to_gt_track_ids, classes_to_gt_track_areas, classes_to_gt_track_iscrowd]: dictionary with class values as keys and lists (for each track) as values 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. [classes_to_dt_tracks]: dictionary with class values as keys and list of dictionaries (with frame indices as keys and corresponding segmentations as values) for each track [classes_to_dt_track_ids, classes_to_dt_track_areas]: dictionary with class values as keys and lists as values [classes_to_dt_track_scores]: dictionary with class values as keys and 1D numpy arrays as values """ # select sequence tracks seq_id = self.seq_name_to_seq_id[seq] if is_gt: tracks = [ann for ann in self.gt_data['annotations'] if ann['video_id'] == seq_id] else: tracks = self._get_tracker_seq_tracks(tracker, seq_id) # Convert data to required format num_timesteps = self.seq_lengths[seq_id] 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): raw_data['dets'][t] = [track['segmentations'][t] for track in tracks if track['segmentations'][t]] raw_data['ids'][t] = np.atleast_1d([track['id'] for track in tracks if track['segmentations'][t]]).astype(int) raw_data['classes'][t] = np.atleast_1d([track['category_id'] for track in tracks if track['segmentations'][t]]).astype(int) if not is_gt: raw_data['tracker_confidences'][t] = np.atleast_1d([track['score'] for track in tracks if track['segmentations'][t]]).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) all_cls_ids = {self.class_name_to_class_id[cls] for cls in self.class_list} classes_to_tracks = {cls: [track for track in tracks if track['category_id'] == cls] for cls in all_cls_ids} # mapping from classes to track representations and track information raw_data['classes_to_tracks'] = {cls: [{i: track['segmentations'][i] for i in range(len(track['segmentations']))} for track in tracks] for cls, tracks in classes_to_tracks.items()} raw_data['classes_to_track_ids'] = {cls: [track['id'] for track in tracks] for cls, tracks in classes_to_tracks.items()} raw_data['classes_to_track_areas'] = {cls: [track['area'] for track in tracks] for cls, tracks in classes_to_tracks.items()} if is_gt: raw_data['classes_to_gt_track_iscrowd'] = {cls: [track['iscrowd'] for track in tracks] for cls, tracks in classes_to_tracks.items()} else: raw_data['classes_to_dt_track_scores'] = {cls: np.array([track['score'] for track in tracks]) for cls, tracks in classes_to_tracks.items()} if is_gt: key_map = {'classes_to_tracks': 'classes_to_gt_tracks', 'classes_to_track_ids': 'classes_to_gt_track_ids', 'classes_to_track_areas': 'classes_to_gt_track_areas'} else: key_map = {'classes_to_tracks': 'classes_to_dt_tracks', 'classes_to_track_ids': 'classes_to_dt_track_ids', 'classes_to_track_areas': 'classes_to_dt_track_areas'} 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. YouTubeVIS: In YouTubeVIS, the 4 preproc steps are as follow: 1) There are 40 classes which are evaluated separately. 2) No matched tracker dets are removed. 3) No unmatched tracker dets are removed. 4) No gt dets are removed. Further, for TrackMAP computation track representations for the given class are accessed from a dictionary and the tracks from the tracker data are sorted according to the tracker confidence. """ 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 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] data['tracker_ids'][t] = tracker_ids data['tracker_dets'][t] = tracker_dets 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) # Ensure that ids are unique per timestep. self._check_unique_ids(data) # 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'] # get track representations data['gt_tracks'] = raw_data['classes_to_gt_tracks'][cls_id] data['gt_track_ids'] = raw_data['classes_to_gt_track_ids'][cls_id] data['gt_track_areas'] = raw_data['classes_to_gt_track_areas'][cls_id] data['gt_track_iscrowd'] = raw_data['classes_to_gt_track_iscrowd'][cls_id] data['dt_tracks'] = raw_data['classes_to_dt_tracks'][cls_id] data['dt_track_ids'] = raw_data['classes_to_dt_track_ids'][cls_id] data['dt_track_areas'] = raw_data['classes_to_dt_track_areas'][cls_id] data['dt_track_scores'] = raw_data['classes_to_dt_track_scores'][cls_id] data['iou_type'] = 'mask' # sort tracker data tracks by tracker confidence scores if data['dt_tracks']: idx = np.argsort([-score for score in data['dt_track_scores']], kind="mergesort") data['dt_track_scores'] = [data['dt_track_scores'][i] for i in idx] data['dt_tracks'] = [data['dt_tracks'][i] for i in idx] data['dt_track_ids'] = [data['dt_track_ids'][i] for i in idx] data['dt_track_areas'] = [data['dt_track_areas'][i] for i in idx] 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 def _prepare_gt_annotations(self): """ Prepares GT data by rle encoding segmentations and computing the average track area. :return: None """ # only loaded when needed to reduce minimum requirements from pycocotools import mask as mask_utils for track in self.gt_data['annotations']: h = track['height'] w = track['width'] for i, seg in enumerate(track['segmentations']): if seg: track['segmentations'][i] = mask_utils.frPyObjects(seg, h, w) areas = [a for a in track['areas'] if a] if len(areas) == 0: track['area'] = 0 else: track['area'] = np.array(areas).mean() def _get_tracker_seq_tracks(self, tracker, seq_id): """ Prepares tracker data for a given sequence. Extracts all annotations for given sequence ID, computes average track area and assigns a track ID. :param tracker: the given tracker :param seq_id: the sequence ID :return: the extracted tracks """ # only loaded when needed to reduce minimum requirements from pycocotools import mask as mask_utils tracks = [ann for ann in self.tracker_data[tracker] if ann['video_id'] == seq_id] for track in tracks: track['areas'] = [] for seg in track['segmentations']: if seg: track['areas'].append(mask_utils.area(seg)) else: track['areas'].append(None) areas = [a for a in track['areas'] if a] if len(areas) == 0: track['area'] = 0 else: track['area'] = np.array(areas).mean() track['id'] = self.global_tid_counter self.global_tid_counter += 1 return tracks