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| from typing import Any, List, Dict, Literal, Optional | |
| from argparse import ArgumentParser | |
| import threading | |
| import numpy | |
| import onnx | |
| import onnxruntime | |
| from onnx import numpy_helper | |
| import cv2 | |
| import facefusion.globals | |
| import facefusion.processors.frame.core as frame_processors | |
| from facefusion import wording | |
| from facefusion.face_analyser import get_one_face, get_many_faces, find_similar_faces, clear_face_analyser | |
| from facefusion.face_helper import warp_face, paste_back_ellipse | |
| from facefusion.face_reference import get_face_reference | |
| from facefusion.content_analyser import clear_content_analyser | |
| from facefusion.typing import Face, Frame, Update_Process, ProcessMode, ModelValue, OptionsWithModel, Embedding | |
| from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video, is_file, is_download_done, update_status | |
| from facefusion.vision import read_image, read_static_image, write_image | |
| from facefusion.processors.frame import globals as frame_processors_globals | |
| from facefusion.processors.frame import choices as frame_processors_choices | |
| FRAME_PROCESSOR = None | |
| MODEL_MATRIX = None | |
| THREAD_LOCK : threading.Lock = threading.Lock() | |
| NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_BLUR' | |
| MODELS : Dict[str, ModelValue] =\ | |
| { | |
| 'blendface_256': | |
| { | |
| 'type': 'blendface', | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/blendface_256.onnx', | |
| 'path': resolve_relative_path('../.assets/models/blendface_256.onnx'), | |
| 'template': 'ffhq', | |
| 'size': (512, 256), | |
| 'mean': [ 0.0, 0.0, 0.0 ], | |
| 'standard_deviation': [ 1.0, 1.0, 1.0 ] | |
| }, | |
| 'inswapper_128': | |
| { | |
| 'type': 'inswapper', | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx', | |
| 'path': resolve_relative_path('../.assets/models/inswapper_128.onnx'), | |
| 'template': 'arcface_v2', | |
| 'size': (128, 128), | |
| 'mean': [ 0.0, 0.0, 0.0 ], | |
| 'standard_deviation': [ 1.0, 1.0, 1.0 ] | |
| }, | |
| 'inswapper_128_fp16': | |
| { | |
| 'type': 'inswapper', | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx', | |
| 'path': resolve_relative_path('../.assets/models/inswapper_128_fp16.onnx'), | |
| 'template': 'arcface_v2', | |
| 'size': (128, 128), | |
| 'mean': [ 0.0, 0.0, 0.0 ], | |
| 'standard_deviation': [ 1.0, 1.0, 1.0 ] | |
| }, | |
| 'simswap_256': | |
| { | |
| 'type': 'simswap', | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/simswap_256.onnx', | |
| 'path': resolve_relative_path('../.assets/models/simswap_256.onnx'), | |
| 'template': 'arcface_v1', | |
| 'size': (112, 256), | |
| 'mean': [ 0.485, 0.456, 0.406 ], | |
| 'standard_deviation': [ 0.229, 0.224, 0.225 ] | |
| }, | |
| 'simswap_512_unofficial': | |
| { | |
| 'type': 'simswap', | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/simswap_512_unofficial.onnx', | |
| 'path': resolve_relative_path('../.assets/models/simswap_512_unofficial.onnx'), | |
| 'template': 'arcface_v1', | |
| 'size': (112, 512), | |
| 'mean': [ 0.0, 0.0, 0.0 ], | |
| 'standard_deviation': [ 1.0, 1.0, 1.0 ] | |
| } | |
| } | |
| OPTIONS : Optional[OptionsWithModel] = None | |
| def get_frame_processor() -> Any: | |
| global FRAME_PROCESSOR | |
| with THREAD_LOCK: | |
| if FRAME_PROCESSOR is None: | |
| model_path = get_options('model').get('path') | |
| FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers) | |
| return FRAME_PROCESSOR | |
| def clear_frame_processor() -> None: | |
| global FRAME_PROCESSOR | |
| FRAME_PROCESSOR = None | |
| def get_model_matrix() -> Any: | |
| global MODEL_MATRIX | |
| with THREAD_LOCK: | |
| if MODEL_MATRIX is None: | |
| model_path = get_options('model').get('path') | |
| model = onnx.load(model_path) | |
| MODEL_MATRIX = numpy_helper.to_array(model.graph.initializer[-1]) | |
| return MODEL_MATRIX | |
| def clear_model_matrix() -> None: | |
| global MODEL_MATRIX | |
| MODEL_MATRIX = None | |
| def get_options(key : Literal['model']) -> Any: | |
| global OPTIONS | |
| if OPTIONS is None: | |
| OPTIONS =\ | |
| { | |
| 'model': MODELS[frame_processors_globals.face_swapper_model] | |
| } | |
| return OPTIONS.get(key) | |
| def set_options(key : Literal['model'], value : Any) -> None: | |
| global OPTIONS | |
| OPTIONS[key] = value | |
| def register_args(program : ArgumentParser) -> None: | |
| pass | |
| def apply_args(program : ArgumentParser) -> None: | |
| args = program.parse_args() | |
| frame_processors_globals.face_swapper_model = args.face_swapper_model | |
| if args.face_swapper_model == 'blendface_256': | |
| facefusion.globals.face_recognizer_model = 'arcface_blendface' | |
| if args.face_swapper_model == 'inswapper_128' or args.face_swapper_model == 'inswapper_128_fp16': | |
| facefusion.globals.face_recognizer_model = 'arcface_inswapper' | |
| if args.face_swapper_model == 'simswap_256' or args.face_swapper_model == 'simswap_512_unofficial': | |
| facefusion.globals.face_recognizer_model = 'arcface_simswap' | |
| def pre_check() -> bool: | |
| if not facefusion.globals.skip_download: | |
| download_directory_path = resolve_relative_path('../.assets/models') | |
| model_url = get_options('model').get('url') | |
| conditional_download(download_directory_path, [ model_url ]) | |
| return True | |
| def pre_process(mode : ProcessMode) -> bool: | |
| model_url = get_options('model').get('url') | |
| model_path = get_options('model').get('path') | |
| if not facefusion.globals.skip_download and not is_download_done(model_url, model_path): | |
| update_status(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME) | |
| return False | |
| elif not is_file(model_path): | |
| update_status(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME) | |
| return False | |
| if not is_image(facefusion.globals.source_path): | |
| update_status(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME) | |
| return False | |
| elif not get_one_face(read_static_image(facefusion.globals.source_path)): | |
| update_status(wording.get('no_source_face_detected') + wording.get('exclamation_mark'), NAME) | |
| return False | |
| if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path): | |
| update_status(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME) | |
| return False | |
| if mode == 'output' and not facefusion.globals.output_path: | |
| update_status(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME) | |
| return False | |
| return True | |
| def post_process() -> None: | |
| clear_frame_processor() | |
| clear_model_matrix() | |
| clear_face_analyser() | |
| clear_content_analyser() | |
| read_static_image.cache_clear() | |
| def apply_blur_to_face(target_face: Face, temp_frame: Frame) -> Frame: | |
| print('apply_blur_to_face') | |
| model_template = get_options('model').get('template') | |
| model_size = get_options('model').get('size') | |
| crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, model_template, model_size) | |
| blurred_face = apply_blur(crop_frame) | |
| temp_frame = paste_back_ellipse(temp_frame, blurred_face, affine_matrix, facefusion.globals.face_mask_blur, facefusion.globals.face_mask_padding) | |
| return temp_frame | |
| def apply_blur(crop_frame: Frame) -> Frame: | |
| blurred_frame = cv2.GaussianBlur(crop_frame, (45, 45), 0) | |
| return blurred_frame | |
| def prepare_source_frame(source_face : Face) -> numpy.ndarray[Any, Any]: | |
| source_frame = read_static_image(facefusion.globals.source_path) | |
| source_frame, _ = warp_face(source_frame, source_face.kps, 'arcface_v2', (112, 112)) | |
| source_frame = source_frame[:, :, ::-1] / 255.0 | |
| source_frame = source_frame.transpose(2, 0, 1) | |
| source_frame = numpy.expand_dims(source_frame, axis = 0).astype(numpy.float32) | |
| return source_frame | |
| def prepare_source_embedding(source_face : Face) -> Embedding: | |
| model_type = get_options('model').get('type') | |
| if model_type == 'inswapper': | |
| model_matrix = get_model_matrix() | |
| source_embedding = source_face.embedding.reshape((1, -1)) | |
| source_embedding = numpy.dot(source_embedding, model_matrix) / numpy.linalg.norm(source_embedding) | |
| else: | |
| source_embedding = source_face.normed_embedding.reshape(1, -1) | |
| return source_embedding | |
| def prepare_crop_frame(crop_frame : Frame) -> Frame: | |
| model_mean = get_options('model').get('mean') | |
| model_standard_deviation = get_options('model').get('standard_deviation') | |
| crop_frame = crop_frame[:, :, ::-1] / 255.0 | |
| crop_frame = (crop_frame - model_mean) / model_standard_deviation | |
| crop_frame = crop_frame.transpose(2, 0, 1) | |
| crop_frame = numpy.expand_dims(crop_frame, axis = 0).astype(numpy.float32) | |
| return crop_frame | |
| def normalize_crop_frame(crop_frame : Frame) -> Frame: | |
| crop_frame = crop_frame.transpose(1, 2, 0) | |
| crop_frame = (crop_frame * 255.0).round() | |
| crop_frame = crop_frame[:, :, ::-1].astype(numpy.uint8) | |
| return crop_frame | |
| def process_frame(source_face: Face, reference_face: Face, temp_frame: Frame) -> Frame: | |
| if 'reference' in facefusion.globals.face_selector_mode: | |
| similar_faces = find_similar_faces(temp_frame, reference_face, facefusion.globals.reference_face_distance) | |
| if similar_faces: | |
| for similar_face in similar_faces: | |
| temp_frame = apply_blur_to_face(similar_face, temp_frame) | |
| if 'one' in facefusion.globals.face_selector_mode: | |
| target_face = get_one_face(temp_frame) | |
| if target_face: | |
| temp_frame = apply_blur_to_face(target_face, temp_frame) | |
| if 'many' in facefusion.globals.face_selector_mode: | |
| many_faces = get_many_faces(temp_frame) | |
| if many_faces: | |
| for target_face in many_faces: | |
| temp_frame = apply_blur_to_face(target_face, temp_frame) | |
| return temp_frame | |
| def process_frames(source_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None: | |
| source_face = get_one_face(read_static_image(source_path)) | |
| reference_face = get_face_reference() if 'reference' in facefusion.globals.face_selector_mode else None | |
| for temp_frame_path in temp_frame_paths: | |
| temp_frame = read_image(temp_frame_path) | |
| result_frame = process_frame(source_face, reference_face, temp_frame) | |
| write_image(temp_frame_path, result_frame) | |
| update_progress() | |
| def process_image(source_path : str, target_path : str, output_path : str) -> None: | |
| source_face = get_one_face(read_static_image(source_path)) | |
| target_frame = read_static_image(target_path) | |
| reference_face = get_one_face(target_frame, facefusion.globals.reference_face_position) if 'reference' in facefusion.globals.face_selector_mode else None | |
| result_frame = process_frame(source_face, reference_face, target_frame) | |
| write_image(output_path, result_frame) | |
| def process_video(source_path : str, temp_frame_paths : List[str]) -> None: | |
| frame_processors.multi_process_frames(source_path, temp_frame_paths, process_frames) | |