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from typing import List, Optional
import numpy
from facefusion import state_manager
from facefusion.common_helper import get_first
from facefusion.face_classifier import detect_gender_age
from facefusion.face_detector import detect_faces, detect_rotated_faces
from facefusion.face_helper import apply_nms, convert_to_face_landmark_5, estimate_face_angle, get_nms_threshold
from facefusion.face_landmarker import detect_face_landmarks, estimate_face_landmark_68_5
from facefusion.face_recognizer import calc_embedding
from facefusion.face_store import get_static_faces, set_static_faces
from facefusion.typing import BoundingBox, Face, FaceLandmark5, FaceLandmarkSet, FaceScoreSet, Score, VisionFrame
def create_faces(vision_frame : VisionFrame, bounding_boxes : List[BoundingBox], face_scores : List[Score], face_landmarks_5 : List[FaceLandmark5]) -> List[Face]:
faces = []
nms_threshold = get_nms_threshold(state_manager.get_item('face_detector_model'), state_manager.get_item('face_detector_angles'))
keep_indices = apply_nms(bounding_boxes, face_scores, state_manager.get_item('face_detector_score'), nms_threshold)
for index in keep_indices:
bounding_box = bounding_boxes[index]
face_score = face_scores[index]
face_landmark_5 = face_landmarks_5[index]
face_landmark_5_68 = face_landmark_5
face_landmark_68_5 = estimate_face_landmark_68_5(face_landmark_5_68)
face_landmark_68 = face_landmark_68_5
face_landmark_score_68 = 0.0
face_angle = estimate_face_angle(face_landmark_68_5)
if state_manager.get_item('face_landmarker_score') > 0:
face_landmark_68, face_landmark_score_68 = detect_face_landmarks(vision_frame, bounding_box, face_angle)
if face_landmark_score_68 > state_manager.get_item('face_landmarker_score'):
face_landmark_5_68 = convert_to_face_landmark_5(face_landmark_68)
face_landmark_set : FaceLandmarkSet =\
{
'5': face_landmark_5,
'5/68': face_landmark_5_68,
'68': face_landmark_68,
'68/5': face_landmark_68_5
}
face_score_set : FaceScoreSet =\
{
'detector': face_score,
'landmarker': face_landmark_score_68
}
embedding, normed_embedding = calc_embedding(vision_frame, face_landmark_set.get('5/68'))
gender, age = detect_gender_age(vision_frame, bounding_box)
faces.append(Face(
bounding_box = bounding_box,
score_set = face_score_set,
landmark_set = face_landmark_set,
angle = face_angle,
embedding = embedding,
normed_embedding = normed_embedding,
gender = gender,
age = age
))
return faces
def get_one_face(faces : List[Face], position : int = 0) -> Optional[Face]:
if faces:
position = min(position, len(faces) - 1)
return faces[position]
return None
def get_average_face(faces : List[Face]) -> Optional[Face]:
embeddings = []
normed_embeddings = []
if faces:
first_face = get_first(faces)
for face in faces:
embeddings.append(face.embedding)
normed_embeddings.append(face.normed_embedding)
return Face(
bounding_box = first_face.bounding_box,
score_set = first_face.score_set,
landmark_set = first_face.landmark_set,
angle = first_face.angle,
embedding = numpy.mean(embeddings, axis = 0),
normed_embedding = numpy.mean(normed_embeddings, axis = 0),
gender = first_face.gender,
age = first_face.age
)
return None
def get_many_faces(vision_frames : List[VisionFrame]) -> List[Face]:
many_faces : List[Face] = []
for vision_frame in vision_frames:
if numpy.any(vision_frame):
static_faces = get_static_faces(vision_frame)
if static_faces:
many_faces.extend(static_faces)
else:
all_bounding_boxes = []
all_face_scores = []
all_face_landmarks_5 = []
for face_detector_angle in state_manager.get_item('face_detector_angles'):
if face_detector_angle == 0:
bounding_boxes, face_scores, face_landmarks_5 = detect_faces(vision_frame)
else:
bounding_boxes, face_scores, face_landmarks_5 = detect_rotated_faces(vision_frame, face_detector_angle)
all_bounding_boxes.extend(bounding_boxes)
all_face_scores.extend(face_scores)
all_face_landmarks_5.extend(face_landmarks_5)
if all_bounding_boxes and all_face_scores and all_face_landmarks_5 and state_manager.get_item('face_detector_score') > 0:
faces = create_faces(vision_frame, all_bounding_boxes, all_face_scores, all_face_landmarks_5)
if faces:
many_faces.extend(faces)
set_static_faces(vision_frame, faces)
return many_faces
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