File size: 1,495 Bytes
c05c8fa
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
import threading
from typing import Any, Optional, List
import insightface
import numpy

import roop.globals
from roop.typing import Frame, Face

FACE_ANALYSER = None
THREAD_LOCK = threading.Lock()


def get_face_analyser() -> Any:
    global FACE_ANALYSER

    with THREAD_LOCK:
        if FACE_ANALYSER is None:
            FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=roop.globals.execution_providers)
            FACE_ANALYSER.prepare(ctx_id=0)
    return FACE_ANALYSER


def clear_face_analyser() -> Any:
    global FACE_ANALYSER

    FACE_ANALYSER = None


def get_one_face(frame: Frame, position: int = 0) -> Optional[Face]:
    many_faces = get_many_faces(frame)
    if many_faces:
        try:
            return many_faces[position]
        except IndexError:
            return many_faces[-1]
    return None


def get_many_faces(frame: Frame) -> Optional[List[Face]]:
    try:
        return get_face_analyser().get(frame)
    except ValueError:
        return None


def find_similar_face(frame: Frame, reference_face: Face) -> Optional[Face]:
    many_faces = get_many_faces(frame)
    if many_faces:
        for face in many_faces:
            if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'):
                distance = numpy.sum(numpy.square(face.normed_embedding - reference_face.normed_embedding))
                if distance < roop.globals.similar_face_distance:
                    return face
    return None