# Copyright (c) 2025 Ye Liu. Licensed under the BSD-3-Clause License. import nncore from videomind.dataset.hybrid import DATASETS from videomind.dataset.wrappers import GroundingDataset from videomind.utils.parser import parse_query @DATASETS.register(name='qvhighlights') class QVHighlightsDataset(GroundingDataset): ANNO_PATH_TRAIN = 'data/qvhighlights/highlight_train_release.jsonl' ANNO_PATH_VALID = 'data/qvhighlights/highlight_val_release.jsonl' ANNO_PATH_TEST = 'data/qvhighlights/highlight_test_release.jsonl' VIDEO_ROOT = 'data/qvhighlights/videos_3fps_480_noaudio' UNIT = 2.0 @classmethod def load_annos(self, split='train'): if split == 'train': raw_annos = nncore.load(self.ANNO_PATH_TRAIN) elif split == 'valid': raw_annos = nncore.load(self.ANNO_PATH_VALID) else: print('WARNING: Test split does not have ground truth annotations') raw_annos = nncore.load(self.ANNO_PATH_TEST) annos = [] for raw_anno in raw_annos: vid = raw_anno['vid'] qid = raw_anno['qid'] anno = dict( source='qvhighlights', data_type='grounding', video_path=nncore.join(self.VIDEO_ROOT, vid + '.mp4'), duration=raw_anno['duration'], query=parse_query(raw_anno['query']), span=raw_anno.get('relevant_windows'), vid=vid, qid=qid) annos.append(anno) return annos @DATASETS.register(name='qvhighlights_single') class QVHighlightsSingleDataset(QVHighlightsDataset): @classmethod def load_annos(self, split='train'): assert split == 'train' raw_annos = nncore.load(self.ANNO_PATH_TRAIN) annos = [] for raw_anno in raw_annos: # skip samples with multiple moments if len(raw_anno['relevant_windows']) > 1: continue vid = raw_anno['vid'] anno = dict( source='qvhighlights_single', data_type='grounding', video_path=nncore.join(self.VIDEO_ROOT, vid + '.mp4'), duration=raw_anno['duration'], query=parse_query(raw_anno['query']), span=raw_anno.get('relevant_windows')) annos.append(anno) return annos