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https://github.com/facefusion/facefusion.git
synced 2026-04-24 10:26:05 +02:00
Move everything to face analyser
This commit is contained in:
henryruhs
+6
-6
@@ -1,7 +1,7 @@
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[general]
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source_paths =
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target_path =
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output_path =
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source_paths = /home/henry/PycharmProjects/facefusion/.assets/examples/source.jpg
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target_path = /home/henry/PycharmProjects/facefusion/.assets/examples/target-240p.mp4
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output_path = .
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[misc]
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force_download =
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@@ -10,9 +10,9 @@ headless =
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log_level =
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[execution]
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execution_providers =
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execution_thread_count =
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execution_queue_count =
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execution_providers = cuda
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execution_thread_count = 32
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execution_queue_count = 2
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[memory]
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video_memory_strategy =
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+35
-15
@@ -8,7 +8,7 @@ import onnxruntime
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import facefusion.globals
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from facefusion import process_manager
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from facefusion.common_helper import get_first
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from facefusion.face_helper import warp_face_by_face_landmark_5, warp_face_by_translation, create_static_anchors, distance_to_face_landmark_5, distance_to_bounding_box, convert_face_landmark_68_to_5, convert_face_landmark_5_to_68, apply_nms, categorize_age, categorize_gender
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from facefusion.face_helper import warp_face_by_face_landmark_5, warp_face_by_translation, create_static_anchors, distance_to_face_landmark_5, distance_to_bounding_box, convert_face_landmark_68_to_5, apply_nms, categorize_age, categorize_gender, WARP_TEMPLATES
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from facefusion.face_store import get_static_faces, set_static_faces
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from facefusion.execution import apply_execution_provider_options
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from facefusion.download import conditional_download
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@@ -61,11 +61,16 @@ MODELS : ModelSet =\
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/arcface_w600k_r50.onnx',
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'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.onnx')
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},
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'face_landmarker':
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'face_landmarker_68':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/2dfan4.onnx',
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'path': resolve_relative_path('../.assets/models/2dfan4.onnx')
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},
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'face_landmarker_68_5':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/face_landmarker_68_5.onnx',
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'path': resolve_relative_path('../.assets/models/face_landmarker_68_5.onnx')
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},
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'gender_age':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gender_age.onnx',
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@@ -78,22 +83,20 @@ def get_face_analyser() -> Any:
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global FACE_ANALYSER
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face_detectors = {}
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face_landmarkers = {}
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with THREAD_LOCK:
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while process_manager.is_checking():
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sleep(0.5)
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if FACE_ANALYSER is None:
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if facefusion.globals.face_detector_model in [ 'many', 'retinaface' ]:
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face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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face_detectors['retinaface'] = face_detector
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face_detectors['retinaface'] = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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if facefusion.globals.face_detector_model in [ 'many', 'scrfd' ]:
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face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_scrfd').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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face_detectors['scrfd'] = face_detector
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face_detectors['scrfd'] = onnxruntime.InferenceSession(MODELS.get('face_detector_scrfd').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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if facefusion.globals.face_detector_model in [ 'many', 'yoloface' ]:
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face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_yoloface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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face_detectors['yoloface'] = face_detector
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face_detectors['yoloface'] = onnxruntime.InferenceSession(MODELS.get('face_detector_yoloface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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if facefusion.globals.face_detector_model in [ 'yunet' ]:
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face_detector = cv2.FaceDetectorYN.create(MODELS.get('face_detector_yunet').get('path'), '', (0, 0))
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face_detectors['yunet'] = face_detector
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face_detectors['yunet'] = cv2.FaceDetectorYN.create(MODELS.get('face_detector_yunet').get('path'), '', (0, 0))
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if facefusion.globals.face_recognizer_model == 'arcface_blendswap':
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face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_blendswap').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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if facefusion.globals.face_recognizer_model == 'arcface_inswapper':
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@@ -102,13 +105,14 @@ def get_face_analyser() -> Any:
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face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_simswap').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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if facefusion.globals.face_recognizer_model == 'arcface_uniface':
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face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_uniface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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face_landmarker = onnxruntime.InferenceSession(MODELS.get('face_landmarker').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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face_landmarkers['68'] = onnxruntime.InferenceSession(MODELS.get('face_landmarker_68').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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face_landmarkers['68_5'] = onnxruntime.InferenceSession(MODELS.get('face_landmarker_68_5').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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gender_age = onnxruntime.InferenceSession(MODELS.get('gender_age').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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FACE_ANALYSER =\
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{
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'face_detectors': face_detectors,
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'face_recognizer': face_recognizer,
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'face_landmarker': face_landmarker,
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'face_landmarkers': face_landmarkers,
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'gender_age': gender_age
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}
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return FACE_ANALYSER
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@@ -125,7 +129,8 @@ def pre_check() -> bool:
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download_directory_path = resolve_relative_path('../.assets/models')
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model_urls =\
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[
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MODELS.get('face_landmarker').get('url'),
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MODELS.get('face_landmarker_68').get('url'),
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MODELS.get('face_landmarker_68_5').get('url'),
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MODELS.get('gender_age').get('url')
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]
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@@ -321,7 +326,7 @@ def create_faces(vision_frame : VisionFrame, bounding_box_list : List[BoundingBo
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for index in keep_indices:
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bounding_box = bounding_box_list[index]
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face_landmark_5_68 = face_landmark_5_list[index]
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face_landmark_68_5 = convert_face_landmark_5_to_68(face_landmark_5_68)
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face_landmark_68_5 = expand_face_landmark_68_from_5(face_landmark_5_68)
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face_landmark_68 = face_landmark_68_5
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face_landmark_68_score = 0.0
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if facefusion.globals.face_landmarker_score > 0:
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@@ -370,7 +375,7 @@ def calc_embedding(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandma
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def detect_face_landmark_68(temp_vision_frame : VisionFrame, bounding_box : BoundingBox) -> Tuple[FaceLandmark68, Score]:
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face_landmarker = get_face_analyser().get('face_landmarker')
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face_landmarker = get_face_analyser().get('face_landmarkers').get('68')
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scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max()
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translation = (256 - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5
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crop_vision_frame, affine_matrix = warp_face_by_translation(temp_vision_frame, translation, scale, (256, 256))
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@@ -392,6 +397,21 @@ def detect_face_landmark_68(temp_vision_frame : VisionFrame, bounding_box : Boun
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return face_landmark_68, face_landmark_68_score
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def expand_face_landmark_68_from_5(face_landmark_5 : FaceLandmark5) -> FaceLandmark68:
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face_landmarker = get_face_analyser().get('face_landmarkers').get('68_5')
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normed_warp_template = WARP_TEMPLATES.get('ffhq_512') * 512
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affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
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face_landmark_5 = cv2.transform(face_landmark_5.reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
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face_landmark_5 = face_landmark_5 / 512
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face_landmark_68_5 = face_landmarker.run(None,
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{
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face_landmarker.get_inputs()[0].name: [ face_landmark_5 ]
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})[0][0]
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face_landmark_68_5 = (face_landmark_68_5 * 512).reshape(68, 2)
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face_landmark_68_5 = cv2.transform(face_landmark_68_5.reshape(1, -1, 2), cv2.invertAffineTransform(affine_matrix)).reshape(-1, 2)
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return face_landmark_68_5
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def detect_gender_age(temp_vision_frame : VisionFrame, bounding_box : BoundingBox) -> Tuple[int, int]:
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gender_age = get_face_analyser().get('gender_age')
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bounding_box = bounding_box.reshape(2, -1)
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@@ -1,30 +1,10 @@
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import threading
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from time import sleep
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from typing import Any, Tuple, List
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from cv2.typing import Size
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from functools import lru_cache
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import cv2
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import numpy
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import onnxruntime
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import facefusion.globals
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from facefusion import process_manager
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from facefusion.typing import BoundingBox, FaceLandmark5, FaceLandmark68, VisionFrame, Mask, Matrix, Translation, WarpTemplate, WarpTemplateSet, FaceAnalyserAge, FaceAnalyserGender, ModelSet
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from facefusion.filesystem import resolve_relative_path
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from facefusion.download import conditional_download
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FACE_LANDMARK_CONVERTER = None
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THREAD_LOCK : threading.Lock = threading.Lock()
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MODELS : ModelSet =\
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{
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'face_landmark_converter':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/face_landmarker_5_68.onnx',
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'path': resolve_relative_path('../.assets/models/face_landmarker_5_68.onnx')
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}
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}
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from facefusion.typing import BoundingBox, FaceLandmark5, FaceLandmark68, VisionFrame, Mask, Matrix, Translation, WarpTemplate, WarpTemplateSet, FaceAnalyserAge, FaceAnalyserGender
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WARP_TEMPLATES : WarpTemplateSet =\
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{
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@@ -63,34 +43,6 @@ WARP_TEMPLATES : WarpTemplateSet =\
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}
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def get_face_landmark_converter() -> Any:
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global FACE_LANDMARK_CONVERTER
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with THREAD_LOCK:
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while process_manager.is_checking():
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sleep(0.5)
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if FACE_LANDMARK_CONVERTER is None:
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model_path = MODELS.get('face_landmark_converter').get('path')
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FACE_LANDMARK_CONVERTER = onnxruntime.InferenceSession(model_path, providers = [ 'CPUExecutionProvider' ])
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return FACE_LANDMARK_CONVERTER
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def clear_face_landmark_converter() -> None:
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global FACE_LANDMARK_CONVERTER
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FACE_LANDMARK_CONVERTER = None
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def pre_check() -> bool:
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if not facefusion.globals.skip_download:
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download_directory_path = resolve_relative_path('../.assets/models')
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model_url = MODELS.get('face_landmark_converter').get('url')
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process_manager.check()
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conditional_download(download_directory_path, [ model_url ])
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process_manager.end()
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return True
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def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
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normed_warp_template = WARP_TEMPLATES.get(warp_template) * crop_size
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affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
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@@ -172,21 +124,6 @@ def convert_face_landmark_68_to_5(face_landmark_68 : FaceLandmark68) -> FaceLand
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return face_landmark_5
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def convert_face_landmark_5_to_68(face_landmark_5 : FaceLandmark5) -> FaceLandmark68:
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face_landmarker_converter = get_face_landmark_converter()
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normed_warp_template = WARP_TEMPLATES.get('ffhq_512') * 512
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affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
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face_landmark_5 = cv2.transform(face_landmark_5.reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
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face_landmark_5 = face_landmark_5 / 512
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face_landmark_68 = face_landmarker_converter.run(None,
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{
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face_landmarker_converter.get_inputs()[0].name: [ face_landmark_5 ]
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})[0][0]
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face_landmark_68 = (face_landmark_68 * 512).reshape(68, 2)
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face_landmark_68 = cv2.transform(face_landmark_68.reshape(1, -1, 2), cv2.invertAffineTransform(affine_matrix)).reshape(-1, 2)
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return face_landmark_68
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def apply_nms(bounding_box_list : List[BoundingBox], iou_threshold : float) -> List[int]:
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keep_indices = []
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dimension_list = numpy.reshape(bounding_box_list, (-1, 4))
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