myDeepFakeAI / DeepFakeAI /face_analyser.py
imseldrith's picture
Upload folder using huggingface_hub (#1)
21dcd64
raw
history blame
13.8 kB
from typing import Any, Optional, List, Tuple
import threading
import cv2
import numpy
import onnxruntime
import DeepFakeAI.globals
from DeepFakeAI.download import conditional_download
from DeepFakeAI.face_store import get_static_faces, set_static_faces
from DeepFakeAI.face_helper import warp_face, create_static_anchors, distance_to_kps, distance_to_bbox, apply_nms
from DeepFakeAI.filesystem import resolve_relative_path
from DeepFakeAI.typing import Frame, Face, FaceSet, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, ModelSet, Bbox, Kps, Score, Embedding
from DeepFakeAI.vision import resize_frame_dimension
FACE_ANALYSER = None
THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
THREAD_LOCK : threading.Lock = threading.Lock()
MODELS : ModelSet =\
{
'face_detector_retinaface':
{
'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/retinaface_10g.onnx',
'path': resolve_relative_path('../.assets/models/retinaface_10g.onnx')
},
'face_detector_yunet':
{
'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/yunet_2023mar.onnx',
'path': resolve_relative_path('../.assets/models/yunet_2023mar.onnx')
},
'face_recognizer_arcface_blendswap':
{
'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/arcface_w600k_r50.onnx',
'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.onnx')
},
'face_recognizer_arcface_inswapper':
{
'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/arcface_w600k_r50.onnx',
'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.onnx')
},
'face_recognizer_arcface_simswap':
{
'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/arcface_simswap.onnx',
'path': resolve_relative_path('../.assets/models/arcface_simswap.onnx')
},
'gender_age':
{
'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/gender_age.onnx',
'path': resolve_relative_path('../.assets/models/gender_age.onnx')
}
}
def get_face_analyser() -> Any:
global FACE_ANALYSER
with THREAD_LOCK:
if FACE_ANALYSER is None:
if DeepFakeAI.globals.face_detector_model == 'retinaface':
face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = DeepFakeAI.globals.execution_providers)
if DeepFakeAI.globals.face_detector_model == 'yunet':
face_detector = cv2.FaceDetectorYN.create(MODELS.get('face_detector_yunet').get('path'), '', (0, 0))
if DeepFakeAI.globals.face_recognizer_model == 'arcface_blendswap':
face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_blendswap').get('path'), providers = DeepFakeAI.globals.execution_providers)
if DeepFakeAI.globals.face_recognizer_model == 'arcface_inswapper':
face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_inswapper').get('path'), providers = DeepFakeAI.globals.execution_providers)
if DeepFakeAI.globals.face_recognizer_model == 'arcface_simswap':
face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_simswap').get('path'), providers = DeepFakeAI.globals.execution_providers)
gender_age = onnxruntime.InferenceSession(MODELS.get('gender_age').get('path'), providers = DeepFakeAI.globals.execution_providers)
FACE_ANALYSER =\
{
'face_detector': face_detector,
'face_recognizer': face_recognizer,
'gender_age': gender_age
}
return FACE_ANALYSER
def clear_face_analyser() -> Any:
global FACE_ANALYSER
FACE_ANALYSER = None
def pre_check() -> bool:
if not DeepFakeAI.globals.skip_download:
download_directory_path = resolve_relative_path('../.assets/models')
model_urls =\
[
MODELS.get('face_detector_retinaface').get('url'),
MODELS.get('face_detector_yunet').get('url'),
MODELS.get('face_recognizer_arcface_inswapper').get('url'),
MODELS.get('face_recognizer_arcface_simswap').get('url'),
MODELS.get('gender_age').get('url')
]
conditional_download(download_directory_path, model_urls)
return True
def extract_faces(frame: Frame) -> List[Face]:
face_detector_width, face_detector_height = map(int, DeepFakeAI.globals.face_detector_size.split('x'))
frame_height, frame_width, _ = frame.shape
temp_frame = resize_frame_dimension(frame, face_detector_width, face_detector_height)
temp_frame_height, temp_frame_width, _ = temp_frame.shape
ratio_height = frame_height / temp_frame_height
ratio_width = frame_width / temp_frame_width
if DeepFakeAI.globals.face_detector_model == 'retinaface':
bbox_list, kps_list, score_list = detect_with_retinaface(temp_frame, temp_frame_height, temp_frame_width, face_detector_height, face_detector_width, ratio_height, ratio_width)
return create_faces(frame, bbox_list, kps_list, score_list)
elif DeepFakeAI.globals.face_detector_model == 'yunet':
bbox_list, kps_list, score_list = detect_with_yunet(temp_frame, temp_frame_height, temp_frame_width, ratio_height, ratio_width)
return create_faces(frame, bbox_list, kps_list, score_list)
return []
def detect_with_retinaface(temp_frame : Frame, temp_frame_height : int, temp_frame_width : int, face_detector_height : int, face_detector_width : int, ratio_height : float, ratio_width : float) -> Tuple[List[Bbox], List[Kps], List[Score]]:
face_detector = get_face_analyser().get('face_detector')
bbox_list = []
kps_list = []
score_list = []
feature_strides = [ 8, 16, 32 ]
feature_map_channel = 3
anchor_total = 2
prepare_frame = numpy.zeros((face_detector_height, face_detector_width, 3))
prepare_frame[:temp_frame_height, :temp_frame_width, :] = temp_frame
temp_frame = (prepare_frame - 127.5) / 128.0
temp_frame = numpy.expand_dims(temp_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
with THREAD_SEMAPHORE:
detections = face_detector.run(None,
{
face_detector.get_inputs()[0].name: temp_frame
})
for index, feature_stride in enumerate(feature_strides):
keep_indices = numpy.where(detections[index] >= DeepFakeAI.globals.face_detector_score)[0]
if keep_indices.any():
stride_height = face_detector_height // feature_stride
stride_width = face_detector_width // feature_stride
anchors = create_static_anchors(feature_stride, anchor_total, stride_height, stride_width)
bbox_raw = (detections[index + feature_map_channel] * feature_stride)
kps_raw = detections[index + feature_map_channel * 2] * feature_stride
for bbox in distance_to_bbox(anchors, bbox_raw)[keep_indices]:
bbox_list.append(numpy.array(
[
bbox[0] * ratio_width,
bbox[1] * ratio_height,
bbox[2] * ratio_width,
bbox[3] * ratio_height
]))
for kps in distance_to_kps(anchors, kps_raw)[keep_indices]:
kps_list.append(kps * [ ratio_width, ratio_height ])
for score in detections[index][keep_indices]:
score_list.append(score[0])
return bbox_list, kps_list, score_list
def detect_with_yunet(temp_frame : Frame, temp_frame_height : int, temp_frame_width : int, ratio_height : float, ratio_width : float) -> Tuple[List[Bbox], List[Kps], List[Score]]:
face_detector = get_face_analyser().get('face_detector')
face_detector.setInputSize((temp_frame_width, temp_frame_height))
face_detector.setScoreThreshold(DeepFakeAI.globals.face_detector_score)
bbox_list = []
kps_list = []
score_list = []
with THREAD_SEMAPHORE:
_, detections = face_detector.detect(temp_frame)
if detections.any():
for detection in detections:
bbox_list.append(numpy.array(
[
detection[0] * ratio_width,
detection[1] * ratio_height,
(detection[0] + detection[2]) * ratio_width,
(detection[1] + detection[3]) * ratio_height
]))
kps_list.append(detection[4:14].reshape((5, 2)) * [ ratio_width, ratio_height])
score_list.append(detection[14])
return bbox_list, kps_list, score_list
def create_faces(frame : Frame, bbox_list : List[Bbox], kps_list : List[Kps], score_list : List[Score]) -> List[Face]:
faces = []
if DeepFakeAI.globals.face_detector_score > 0:
sort_indices = numpy.argsort(-numpy.array(score_list))
bbox_list = [ bbox_list[index] for index in sort_indices ]
kps_list = [ kps_list[index] for index in sort_indices ]
score_list = [ score_list[index] for index in sort_indices ]
keep_indices = apply_nms(bbox_list, 0.4)
for index in keep_indices:
bbox = bbox_list[index]
kps = kps_list[index]
score = score_list[index]
embedding, normed_embedding = calc_embedding(frame, kps)
gender, age = detect_gender_age(frame, kps)
faces.append(Face(
bbox = bbox,
kps = kps,
score = score,
embedding = embedding,
normed_embedding = normed_embedding,
gender = gender,
age = age
))
return faces
def calc_embedding(temp_frame : Frame, kps : Kps) -> Tuple[Embedding, Embedding]:
face_recognizer = get_face_analyser().get('face_recognizer')
crop_frame, matrix = warp_face(temp_frame, kps, 'arcface_112_v2', (112, 112))
crop_frame = crop_frame.astype(numpy.float32) / 127.5 - 1
crop_frame = crop_frame[:, :, ::-1].transpose(2, 0, 1)
crop_frame = numpy.expand_dims(crop_frame, axis = 0)
embedding = face_recognizer.run(None,
{
face_recognizer.get_inputs()[0].name: crop_frame
})[0]
embedding = embedding.ravel()
normed_embedding = embedding / numpy.linalg.norm(embedding)
return embedding, normed_embedding
def detect_gender_age(frame : Frame, kps : Kps) -> Tuple[int, int]:
gender_age = get_face_analyser().get('gender_age')
crop_frame, affine_matrix = warp_face(frame, kps, 'arcface_112_v2', (96, 96))
crop_frame = numpy.expand_dims(crop_frame, axis = 0).transpose(0, 3, 1, 2).astype(numpy.float32)
prediction = gender_age.run(None,
{
gender_age.get_inputs()[0].name: crop_frame
})[0][0]
gender = int(numpy.argmax(prediction[:2]))
age = int(numpy.round(prediction[2] * 100))
return gender, age
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_average_face(frames : List[Frame], position : int = 0) -> Optional[Face]:
average_face = None
faces = []
embedding_list = []
normed_embedding_list = []
for frame in frames:
face = get_one_face(frame, position)
if face:
faces.append(face)
embedding_list.append(face.embedding)
normed_embedding_list.append(face.normed_embedding)
if faces:
average_face = Face(
bbox = faces[0].bbox,
kps = faces[0].kps,
score = faces[0].score,
embedding = numpy.mean(embedding_list, axis = 0),
normed_embedding = numpy.mean(normed_embedding_list, axis = 0),
gender = faces[0].gender,
age = faces[0].age
)
return average_face
def get_many_faces(frame : Frame) -> List[Face]:
try:
faces_cache = get_static_faces(frame)
if faces_cache:
faces = faces_cache
else:
faces = extract_faces(frame)
set_static_faces(frame, faces)
if DeepFakeAI.globals.face_analyser_order:
faces = sort_by_order(faces, DeepFakeAI.globals.face_analyser_order)
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_faces : FaceSet, face_distance : float) -> List[Face]:
similar_faces : List[Face] = []
many_faces = get_many_faces(frame)
if reference_faces:
for reference_set in reference_faces:
if not similar_faces:
for reference_face in reference_faces[reference_set]:
for face in many_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:
if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'):
current_face_distance = 1 - numpy.dot(face.normed_embedding, reference_face.normed_embedding)
return current_face_distance < face_distance
return False
def sort_by_order(faces : List[Face], order : FaceAnalyserOrder) -> List[Face]:
if order == 'left-right':
return sorted(faces, key = lambda face: face.bbox[0])
if order == 'right-left':
return sorted(faces, key = lambda face: face.bbox[0], reverse = True)
if order == 'top-bottom':
return sorted(faces, key = lambda face: face.bbox[1])
if order == 'bottom-top':
return sorted(faces, key = lambda face: face.bbox[1], reverse = True)
if order == 'small-large':
return sorted(faces, key = lambda face: (face.bbox[2] - face.bbox[0]) * (face.bbox[3] - face.bbox[1]))
if order == 'large-small':
return sorted(faces, key = lambda face: (face.bbox[2] - face.bbox[0]) * (face.bbox[3] - face.bbox[1]), reverse = True)
if order == 'best-worst':
return sorted(faces, key = lambda face: face.score, reverse = True)
if order == 'worst-best':
return sorted(faces, key = lambda face: face.score)
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 == 0 and gender == 'female':
filter_faces.append(face)
if face.gender == 1 and gender == 'male':
filter_faces.append(face)
return filter_faces