Spaces:
Runtime error
Runtime error
File size: 3,778 Bytes
ec313eb |
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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
from typing import List
import numpy
from facefusion import state_manager
from facefusion.types import Face, FaceSelectorOrder, FaceSet, Gender, Race, Score
def find_similar_faces(faces : List[Face], reference_faces : FaceSet, face_distance : float) -> List[Face]:
similar_faces : List[Face] = []
if faces and reference_faces:
for reference_set in reference_faces:
if not similar_faces:
for reference_face in reference_faces[reference_set]:
for face in faces:
if compare_faces(face, reference_face, face_distance):
similar_faces.append(face)
return similar_faces
def compare_faces(face : Face, reference_face : Face, face_distance : float) -> bool:
current_face_distance = calc_face_distance(face, reference_face)
current_face_distance = float(numpy.interp(current_face_distance, [ 0, 2 ], [ 0, 1 ]))
return current_face_distance < face_distance
def calc_face_distance(face : Face, reference_face : Face) -> float:
if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'):
return 1 - numpy.dot(face.normed_embedding, reference_face.normed_embedding)
return 0
def sort_and_filter_faces(faces : List[Face]) -> List[Face]:
if faces:
if state_manager.get_item('face_selector_order'):
faces = sort_faces_by_order(faces, state_manager.get_item('face_selector_order'))
if state_manager.get_item('face_selector_gender'):
faces = filter_faces_by_gender(faces, state_manager.get_item('face_selector_gender'))
if state_manager.get_item('face_selector_race'):
faces = filter_faces_by_race(faces, state_manager.get_item('face_selector_race'))
if state_manager.get_item('face_selector_age_start') or state_manager.get_item('face_selector_age_end'):
faces = filter_faces_by_age(faces, state_manager.get_item('face_selector_age_start'), state_manager.get_item('face_selector_age_end'))
return faces
def sort_faces_by_order(faces : List[Face], order : FaceSelectorOrder) -> List[Face]:
if order == 'left-right':
return sorted(faces, key = get_bounding_box_left)
if order == 'right-left':
return sorted(faces, key = get_bounding_box_left, reverse = True)
if order == 'top-bottom':
return sorted(faces, key = get_bounding_box_top)
if order == 'bottom-top':
return sorted(faces, key = get_bounding_box_top, reverse = True)
if order == 'small-large':
return sorted(faces, key = get_bounding_box_area)
if order == 'large-small':
return sorted(faces, key = get_bounding_box_area, reverse = True)
if order == 'best-worst':
return sorted(faces, key = get_face_detector_score, reverse = True)
if order == 'worst-best':
return sorted(faces, key = get_face_detector_score)
return faces
def get_bounding_box_left(face : Face) -> float:
return face.bounding_box[0]
def get_bounding_box_top(face : Face) -> float:
return face.bounding_box[1]
def get_bounding_box_area(face : Face) -> float:
return (face.bounding_box[2] - face.bounding_box[0]) * (face.bounding_box[3] - face.bounding_box[1])
def get_face_detector_score(face : Face) -> Score:
return face.score_set.get('detector')
def filter_faces_by_gender(faces : List[Face], gender : Gender) -> List[Face]:
filter_faces = []
for face in faces:
if face.gender == gender:
filter_faces.append(face)
return filter_faces
def filter_faces_by_age(faces : List[Face], face_selector_age_start : int, face_selector_age_end : int) -> List[Face]:
filter_faces = []
age = range(face_selector_age_start, face_selector_age_end)
for face in faces:
if set(face.age) & set(age):
filter_faces.append(face)
return filter_faces
def filter_faces_by_race(faces : List[Face], race : Race) -> List[Face]:
filter_faces = []
for face in faces:
if face.race == race:
filter_faces.append(face)
return filter_faces
|