コード例 #1
0
ファイル: baseline.py プロジェクト: zjwbr/VidVRD-helper
def load_relation_feature():
    """
    Test loading precomputed relation features
    """
    dataset = VidVRD('../vidvrd-dataset', '../vidvrd-dataset/videos',
                     ['train', 'test'])
    extractor = feature.FeatureExtractor(dataset, prefetch_count=0)

    video_indices = dataset.get_index(split='train')
    for vid in video_indices:
        durations = set(
            rel_inst['duration']
            for rel_inst in dataset.get_relation_insts(vid, no_traj=True))
        for duration in durations:
            segs = segment_video(*duration)
            for fstart, fend in segs:
                extractor.extract_feature(dataset,
                                          vid,
                                          fstart,
                                          fend,
                                          verbose=True)

    video_indices = dataset.get_index(split='test')
    for vid in video_indices:
        anno = dataset.get_anno(vid)
        segs = segment_video(0, anno['frame_count'])
        for fstart, fend in segs:
            extractor.extract_feature(dataset, vid, fstart, fend, verbose=True)
コード例 #2
0
ファイル: baseline.py プロジェクト: zjwbr/VidVRD-helper
def load_object_trajectory_proposal():
    """
    Test loading precomputed object trajectory proposals
    """
    dataset = VidVRD('../vidvrd-dataset', '../vidvrd-dataset/videos',
                     ['train', 'test'])

    video_indices = dataset.get_index(split='train')
    for vid in video_indices:
        durations = set(
            rel_inst['duration']
            for rel_inst in dataset.get_relation_insts(vid, no_traj=True))
        for duration in durations:
            segs = segment_video(*duration)
            for fstart, fend in segs:
                trajs = trajectory.object_trajectory_proposal(dataset,
                                                              vid,
                                                              fstart,
                                                              fend,
                                                              gt=False,
                                                              verbose=True)
                trajs = trajectory.object_trajectory_proposal(dataset,
                                                              vid,
                                                              fstart,
                                                              fend,
                                                              gt=True,
                                                              verbose=True)

    video_indices = dataset.get_index(split='test')
    for vid in video_indices:
        anno = dataset.get_anno(vid)
        segs = segment_video(0, anno['frame_count'])
        for fstart, fend in segs:
            trajs = trajectory.object_trajectory_proposal(dataset,
                                                          vid,
                                                          fstart,
                                                          fend,
                                                          gt=False,
                                                          verbose=True)
            trajs = trajectory.object_trajectory_proposal(dataset,
                                                          vid,
                                                          fstart,
                                                          fend,
                                                          gt=True,
                                                          verbose=True)
コード例 #3
0
def eval_short_term_relation():
    """
    Evaluate short-term relation prediction
    """
    anno_rpath = 'baseline/vidvrd-dataset'
    video_rpath = 'baseline/vidvrd-dataset/videos'
    splits = ['train', 'test']
    st_prediction = 'baseline/vidvrd-dataset/vidvrd-baseline-output/short-term-predication.json'
    test_st_pred = '/home/daivd/Downloads/pad_result_24000_test_predicate_-1_pair_nms_0.4_rpn_nms_0.7_0.255_union.json'

    res_path = test_st_pred

    dataset = VidVRD(anno_rpath=anno_rpath,
                     video_rpath=video_rpath,
                     splits=splits)

    with open(os.path.join(get_model_path(), 'baseline_setting.json'), 'r') as fin:
        param = json.load(fin)

    if os.path.exists(res_path):
        with open(res_path, 'r') as fin:
            short_term_relations = json.load(fin)
    else:
        short_term_relations = model.predict(dataset, param)
        with open(res_path, 'w') as fout:
            json.dump(short_term_relations, fout)

    short_term_gt = dict()
    short_term_pred = dict()
    video_indices = dataset.get_index(split='test')
    for vid in video_indices:
        anno = dataset.get_anno(vid)
        segs = segment_video(0, anno['frame_count'])
        video_gts = dataset.get_relation_insts(vid)

        if 'results' in short_term_relations.keys():
            video_preds = short_term_relations['results'][vid]
        else:
            video_preds = short_term_relations[vid]

        for fstart, fend in segs:
            vsig = get_segment_signature(vid, fstart, fend)

            segment_gts = []
            for r in video_gts:
                s = max(r['duration'][0], fstart)
                e = min(r['duration'][1], fend)
                if s < e:
                    sub_trac = r['sub_traj'][s - r['duration'][0]: e - r['duration'][0]]
                    obj_trac = r['obj_traj'][s - r['duration'][0]: e - r['duration'][0]]
                    segment_gts.append({
                        "triplet": r['triplet'],
                        "subject_tid": r['subject_tid'],
                        "object_tid": r['object_tid'],
                        "duration": [s, e],
                        "sub_traj": sub_trac,
                        "obj_traj": obj_trac
                    })
            short_term_gt[vsig] = segment_gts

            segment_preds = []
            for r in video_preds:
                if fstart <= r['duration'][0] and r['duration'][1] <= fend:
                    s = max(r['duration'][0], fstart)
                    e = min(r['duration'][1], fend)
                    sub_trac = r['sub_traj'][s - r['duration'][0]: e - r['duration'][0]]
                    obj_trac = r['obj_traj'][s - r['duration'][0]: e - r['duration'][0]]
                    segment_preds.append({
                        "triplet": r['triplet'],
                        "score": r['score'],
                        "duration": [s, e],
                        "sub_traj": sub_trac,
                        "obj_traj": obj_trac
                    })
            short_term_pred[vsig] = segment_preds

    for each_vsig in short_term_gt.keys():
        if each_vsig not in short_term_pred.keys():
            short_term_pred[each_vsig] = []

    mean_ap, rec_at_n, mprec_at_n = eval_visual_relation(short_term_gt, short_term_pred)

    print('detection mean AP (used in challenge): {}'.format(mean_ap))
    print('detection recall@50: {}'.format(rec_at_n[50]))
    print('detection recall@100: {}'.format(rec_at_n[100]))
    print('tagging precision@1: {}'.format(mprec_at_n[1]))
    print('tagging precision@5: {}'.format(mprec_at_n[5]))
    print('tagging precision@10: {}'.format(mprec_at_n[10]))