base_models.py 文件源码

def compute_distances(cls, inst_id):
        global feat_nn
        global feat_ids

        it = cls.objects.annotate(height=F('face__bbox_y2') - F('face__bbox_y1')).filter(
            height__gte=0.1).order_by('id')
        if feat_nn is None:
            _print('Loading features...')
            feats = list(it[::5])
            feat_ids = np.array([f.id for f in feats])
            feat_vectors = [f.load_features() for f in feats]
            X = np.vstack(feat_vectors)
            _print('Constructing KNN tree...')
            feat_nn = NearestNeighbors().fit(X)
            _print('Done!')

        # Erase distances from previous computation
        prev = list(cls.objects.filter(distto__isnull=False))
        for feat in prev:
            feat.distto = None
        cls.objects.bulk_update(prev)

        dists, indices = feat_nn.kneighbors([cls.objects.get(face=inst_id).load_features()], 1000)

        for dist, feat_id in zip(dists[0], feat_ids[indices[0]]):
            feat = cls.objects.get(id=feat_id)
            feat.distto = dist
            feat.save()