def random_class(category_name: str, _add_errors: bool = False) -> Optional[str]: # Alter 10% of the valid labels. class_names = sorted(TRACKING_NAMES) tmp = category_to_tracking_name(category_name) if tmp is None: return None else: if not _add_errors or np.random.rand() < .9: return tmp else: return class_names[np.random.randint(0, len(class_names) - 1)]
def load_gt(nusc: NuScenes, eval_split: str, box_cls, verbose: bool = False) -> EvalBoxes: """ Loads ground truth boxes from DB. :param nusc: A NuScenes instance. :param eval_split: The evaluation split for which we load GT boxes. :param box_cls: Type of box to load, e.g. DetectionBox or TrackingBox. :param verbose: Whether to print messages to stdout. :return: The GT boxes. """ # Init. if box_cls == DetectionBox: attribute_map = {a['token']: a['name'] for a in nusc.attribute} if verbose: print('Loading annotations for {} split from nuScenes version: {}'. format(eval_split, nusc.version)) # Read out all sample_tokens in DB. sample_tokens_all = [s['token'] for s in nusc.sample] assert len(sample_tokens_all) > 0, "Error: Database has no samples!" # Only keep samples from this split. splits = create_splits_scenes() # Check compatibility of split with nusc_version. version = nusc.version if eval_split in {'train', 'val', 'train_detect', 'train_track'}: assert version.endswith('trainval'), \ 'Error: Requested split {} which is not compatible with NuScenes version {}'.format(eval_split, version) elif eval_split in {'mini_train', 'mini_val'}: assert version.endswith('mini'), \ 'Error: Requested split {} which is not compatible with NuScenes version {}'.format(eval_split, version) elif eval_split == 'test': assert version.endswith('test'), \ 'Error: Requested split {} which is not compatible with NuScenes version {}'.format(eval_split, version) else: raise ValueError( 'Error: Requested split {} which this function cannot map to the correct NuScenes version.' .format(eval_split)) if eval_split == 'test': # Check that you aren't trying to cheat :). assert len(nusc.sample_annotation) > 0, \ 'Error: You are trying to evaluate on the test set but you do not have the annotations!' sample_tokens = [] for sample_token in sample_tokens_all: scene_token = nusc.get('sample', sample_token)['scene_token'] scene_record = nusc.get('scene', scene_token) if scene_record['name'] in splits[eval_split]: sample_tokens.append(sample_token) all_annotations = EvalBoxes() # Load annotations and filter predictions and annotations. tracking_id_set = set() for sample_token in tqdm.tqdm(sample_tokens, leave=verbose): sample = nusc.get('sample', sample_token) sample_annotation_tokens = sample['anns'] sample_boxes = [] for sample_annotation_token in sample_annotation_tokens: sample_annotation = nusc.get('sample_annotation', sample_annotation_token) if box_cls == DetectionBox: # Get label name in detection task and filter unused labels. detection_name = category_to_detection_name( sample_annotation['category_name']) if detection_name is None: continue # Get attribute_name. attr_tokens = sample_annotation['attribute_tokens'] attr_count = len(attr_tokens) if attr_count == 0: attribute_name = '' elif attr_count == 1: attribute_name = attribute_map[attr_tokens[0]] else: raise Exception( 'Error: GT annotations must not have more than one attribute!' ) sample_boxes.append( box_cls( sample_token=sample_token, translation=sample_annotation['translation'], size=sample_annotation['size'], rotation=sample_annotation['rotation'], velocity=nusc.box_velocity( sample_annotation['token'])[:2], num_pts=sample_annotation['num_lidar_pts'] + sample_annotation['num_radar_pts'], detection_name=detection_name, detection_score=-1.0, # GT samples do not have a score. attribute_name=attribute_name)) elif box_cls == TrackingBox: # Use nuScenes token as tracking id. tracking_id = sample_annotation['instance_token'] tracking_id_set.add(tracking_id) # Get label name in detection task and filter unused labels. tracking_name = category_to_tracking_name( sample_annotation['category_name']) if tracking_name is None: continue sample_boxes.append( box_cls( sample_token=sample_token, translation=sample_annotation['translation'], size=sample_annotation['size'], rotation=sample_annotation['rotation'], velocity=nusc.box_velocity( sample_annotation['token'])[:2], num_pts=sample_annotation['num_lidar_pts'] + sample_annotation['num_radar_pts'], tracking_id=tracking_id, tracking_name=tracking_name, tracking_score=-1.0 # GT samples do not have a score. )) else: raise NotImplementedError('Error: Invalid box_cls %s!' % box_cls) all_annotations.add_boxes(sample_token, sample_boxes) if verbose: print("Loaded ground truth annotations for {} samples.".format( len(all_annotations.sample_tokens))) return all_annotations