How to use the esper.embed_google_images.name_to_embedding function in esper

def face_search():
    from esper.embed_google_images import name_to_embedding
    from esper.face_embeddings import knn
    emb = name_to_embedding('Wolf Blitzer')
    face_ids = [x for x, _ in knn(targets=[emb], max_threshold=0.4)][::10]
    return qs_to_result(
        Face.objects.filter(id__in=face_ids), custom_order_by_id=face_ids, limit=len(face_ids))

def exclude_faces(face_ids, exclude_ids, exclude_thresh):
        excluded_face_ids = set()
        for exclude_id in exclude_ids:
            excluded_face_ids.update([x for x, _ in knn(ids=[exclude_id], max_threshold=exclude_thresh)])
        face_ids = set(face_ids)
        return face_ids - excluded_face_ids, face_ids & excluded_face_ids

    # Some params
    exclude_labeled = False
    show_excluded = False

    face_qs = UnlabeledFace.objects if exclude_labeled else Face.objects

    name = 'Wolf Blitzer'

    emb = name_to_embedding(name)
    face_ids = [x for x, _ in knn(features=emb, max_threshold=0.6)]

    kept_ids, excluded_ids = exclude_faces(
        face_ids,
        [1634585, 531076, 3273872, 2586010, 921211, 3176879, 3344886, 3660089, 249499, 2236580],
        0.4)

    if show_excluded:
        # Show the furthest faces that we kept and the faces that were excluded
        kept_results = qs_to_result(face_qs.filter(id__in=kept_ids, shot__in_commercial=False),
                                    custom_order_by_id=face_ids[::-1])
        excluded_results = qs_to_result(face_qs.filter(id__in=excluded_ids, shot__in_commercial=False))

        return group_results([('excluded', excluded_results), (name, kept_results)])
    else:
        # Show all of the faces that were kept

def face_search():
    from esper.embed_google_images import name_to_embedding
    from esper.face_embeddings import knn
    emb = name_to_embedding('Wolf Blitzer')
    face_ids = [x for x, _ in knn(targets=[emb], max_threshold=0.4)][::10]
    return qs_to_result(
        Face.objects.filter(id__in=face_ids), custom_order_by_id=face_ids, limit=len(face_ids))

def exclude_faces(face_ids, exclude_ids, exclude_thresh):
        excluded_face_ids = set()
        for exclude_id in exclude_ids:
            excluded_face_ids.update([x for x, _ in knn(ids=[exclude_id], max_threshold=exclude_thresh)])
        face_ids = set(face_ids)
        return face_ids - excluded_face_ids, face_ids & excluded_face_ids

    # Some params
    exclude_labeled = False
    show_excluded = False

    face_qs = UnlabeledFace.objects if exclude_labeled else Face.objects

    name = 'Wolf Blitzer'

    emb = name_to_embedding(name)
    face_ids = [x for x, _ in knn(features=emb, max_threshold=0.6)]

    kept_ids, excluded_ids = exclude_faces(
        face_ids,
        [1634585, 531076, 3273872, 2586010, 921211, 3176879, 3344886, 3660089, 249499, 2236580],
        0.4)

    if show_excluded:
        # Show the furthest faces that we kept and the faces that were excluded
        kept_results = qs_to_result(face_qs.filter(id__in=kept_ids, shot__in_commercial=False),
                                    custom_order_by_id=face_ids[::-1])
        excluded_results = qs_to_result(face_qs.filter(id__in=excluded_ids, shot__in_commercial=False))

        return group_results([('excluded', excluded_results), (name, kept_results)])
    else:
        # Show all of the faces that were kept

def groups_of_faces_by_distance_threshold():
    from esper.embed_google_images import name_to_embedding
    from esper.face_embeddings import knn
    emb = name_to_embedding('Wolf Blitzer')

    increment = 0.05
    max_thresh = 1.0
    max_results_per_group = 50
    exclude_labeled = False

    face_qs = UnlabeledFace.objects if exclude_labeled else Face.objects

    face_sims = knn(targets=[emb], max_threshold=max_thresh)

    results_by_bucket = {}
    for t in frange(min_thresh, max_thresh, increment):
        face_ids = [x for x, _ in filter(lambda z: z[1] >= t and z[1] < t + increment, face_sims)]
        if len(face_ids) != 0:
            faces = face_qs.filter(
                id__in=random.sample(face_ids, k=min(len(face_ids), max_results_per_group))

def groups_of_faces_by_distance_threshold():
    from esper.embed_google_images import name_to_embedding
    from esper.face_embeddings import knn
    emb = name_to_embedding('Wolf Blitzer')

    increment = 0.05
    max_thresh = 1.0
    max_results_per_group = 50
    exclude_labeled = False

    face_qs = UnlabeledFace.objects if exclude_labeled else Face.objects

    face_sims = knn(targets=[emb], max_threshold=max_thresh)

    results_by_bucket = {}
    for t in frange(min_thresh, max_thresh, increment):
        face_ids = [x for x, _ in filter(lambda z: z[1] >= t and z[1] < t + increment, face_sims)]
        if len(face_ids) != 0:
            faces = face_qs.filter(
                id__in=random.sample(face_ids, k=min(len(face_ids), max_results_per_group))