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import threading
from typing import Any, Optional, List
import insightface
import numpy
import DeepFakeAI.globals
from DeepFakeAI.typing import Frame, Face, FaceAnalyserDirection, FaceAnalyserAge, FaceAnalyserGender
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 = DeepFakeAI.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) -> List[Face]:
try:
faces = get_face_analyser().get(frame)
if DeepFakeAI.globals.face_analyser_direction:
faces = sort_by_direction(faces, DeepFakeAI.globals.face_analyser_direction)
if DeepFakeAI.globals.face_analyser_age:
faces = filter_by_age(faces, DeepFakeAI.globals.face_analyser_age)
if DeepFakeAI.globals.face_analyser_gender:
faces = filter_by_gender(faces, DeepFakeAI.globals.face_analyser_gender)
return faces
except (AttributeError, ValueError):
return []
def find_similar_faces(frame : Frame, reference_face : Face, face_distance : float) -> List[Face]:
many_faces = get_many_faces(frame)
similar_faces = []
if many_faces:
for face in many_faces:
if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'):
current_face_distance = numpy.sum(numpy.square(face.normed_embedding - reference_face.normed_embedding))
if current_face_distance < face_distance:
similar_faces.append(face)
return similar_faces
def sort_by_direction(faces : List[Face], direction : FaceAnalyserDirection) -> List[Face]:
if direction == 'left-right':
return sorted(faces, key = lambda face: face['bbox'][0])
if direction == 'right-left':
return sorted(faces, key = lambda face: face['bbox'][0], reverse = True)
if direction == 'top-bottom':
return sorted(faces, key = lambda face: face['bbox'][1])
if direction == 'bottom-top':
return sorted(faces, key = lambda face: face['bbox'][1], reverse = True)
if direction == 'small-large':
return sorted(faces, key = lambda face: (face['bbox'][2] - face['bbox'][0]) * (face['bbox'][3] - face['bbox'][1]))
if direction == 'large-small':
return sorted(faces, key = lambda face: (face['bbox'][2] - face['bbox'][0]) * (face['bbox'][3] - face['bbox'][1]), reverse = True)
return faces
def filter_by_age(faces : List[Face], age : FaceAnalyserAge) -> List[Face]:
filter_faces = []
for face in faces:
if face['age'] < 13 and age == 'child':
filter_faces.append(face)
elif face['age'] < 19 and age == 'teen':
filter_faces.append(face)
elif face['age'] < 60 and age == 'adult':
filter_faces.append(face)
elif face['age'] > 59 and age == 'senior':
filter_faces.append(face)
return filter_faces
def filter_by_gender(faces : List[Face], gender : FaceAnalyserGender) -> List[Face]:
filter_faces = []
for face in faces:
if face['gender'] == 1 and gender == 'male':
filter_faces.append(face)
if face['gender'] == 0 and gender == 'female':
filter_faces.append(face)
return filter_faces
def get_faces_total(frame : Frame) -> int:
return len(get_many_faces(frame))
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