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| from typing import Any, List, Dict, Literal, Optional | |
| from argparse import ArgumentParser | |
| import cv2 | |
| import threading | |
| import numpy | |
| import onnxruntime | |
| import facefusion.globals | |
| import facefusion.processors.frame.core as frame_processors | |
| from facefusion import wording | |
| from facefusion.face_analyser import get_many_faces, clear_face_analyser | |
| from facefusion.face_helper import warp_face, paste_back | |
| from facefusion.content_analyser import clear_content_analyser | |
| from facefusion.typing import Face, Frame, Update_Process, ProcessMode, ModelValue, OptionsWithModel | |
| from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video, is_file, is_download_done, create_metavar, 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 | |
| THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore() | |
| THREAD_LOCK : threading.Lock = threading.Lock() | |
| NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_ENHANCER' | |
| MODELS : Dict[str, ModelValue] =\ | |
| { | |
| 'codeformer': | |
| { | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/codeformer.onnx', | |
| 'path': resolve_relative_path('../.assets/models/codeformer.onnx'), | |
| 'template': 'ffhq', | |
| 'size': (512, 512) | |
| }, | |
| 'gfpgan_1.2': | |
| { | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.2.onnx', | |
| 'path': resolve_relative_path('../.assets/models/gfpgan_1.2.onnx'), | |
| 'template': 'ffhq', | |
| 'size': (512, 512) | |
| }, | |
| 'gfpgan_1.3': | |
| { | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.3.onnx', | |
| 'path': resolve_relative_path('../.assets/models/gfpgan_1.3.onnx'), | |
| 'template': 'ffhq', | |
| 'size': (512, 512) | |
| }, | |
| 'gfpgan_1.4': | |
| { | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.4.onnx', | |
| 'path': resolve_relative_path('../.assets/models/gfpgan_1.4.onnx'), | |
| 'template': 'ffhq', | |
| 'size': (512, 512) | |
| }, | |
| 'gpen_bfr_256': | |
| { | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_256.onnx', | |
| 'path': resolve_relative_path('../.assets/models/gpen_bfr_256.onnx'), | |
| 'template': 'arcface_v2', | |
| 'size': (128, 256) | |
| }, | |
| 'gpen_bfr_512': | |
| { | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_512.onnx', | |
| 'path': resolve_relative_path('../.assets/models/gpen_bfr_512.onnx'), | |
| 'template': 'ffhq', | |
| 'size': (512, 512) | |
| }, | |
| 'restoreformer': | |
| { | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/restoreformer.onnx', | |
| 'path': resolve_relative_path('../.assets/models/restoreformer.onnx'), | |
| 'template': 'ffhq', | |
| 'size': (512, 512) | |
| } | |
| } | |
| 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 = [ 'CUDAExecutionProvider' ]) | |
| # FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers) | |
| model_path = get_options('model').get('path') | |
| FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = [ 'CPUExecutionProvider' ]) | |
| return FRAME_PROCESSOR | |
| def clear_frame_processor() -> None: | |
| global FRAME_PROCESSOR | |
| FRAME_PROCESSOR = None | |
| def get_options(key : Literal['model']) -> Any: | |
| global OPTIONS | |
| if OPTIONS is None: | |
| OPTIONS =\ | |
| { | |
| 'model': MODELS[frame_processors_globals.face_enhancer_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: | |
| program.add_argument('--face-enhancer-model', help = wording.get('frame_processor_model_help'), dest = 'face_enhancer_model', default = 'gfpgan_1.4', choices = frame_processors_choices.face_enhancer_models) | |
| program.add_argument('--face-enhancer-blend', help = wording.get('frame_processor_blend_help'), dest = 'face_enhancer_blend', type = int, default = 80, choices = frame_processors_choices.face_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.face_enhancer_blend_range)) | |
| def apply_args(program : ArgumentParser) -> None: | |
| args = program.parse_args() | |
| frame_processors_globals.face_enhancer_model = args.face_enhancer_model | |
| frame_processors_globals.face_enhancer_blend = args.face_enhancer_blend | |
| 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 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_face_analyser() | |
| clear_content_analyser() | |
| read_static_image.cache_clear() | |
| def enhance_face(target_face: Face, temp_frame: Frame) -> Frame: | |
| frame_processor = get_frame_processor() | |
| 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) | |
| crop_frame = prepare_crop_frame(crop_frame) | |
| frame_processor_inputs = {} | |
| for frame_processor_input in frame_processor.get_inputs(): | |
| if frame_processor_input.name == 'input': | |
| frame_processor_inputs[frame_processor_input.name] = crop_frame | |
| if frame_processor_input.name == 'weight': | |
| frame_processor_inputs[frame_processor_input.name] = numpy.array([ 1 ], dtype = numpy.double) | |
| with THREAD_SEMAPHORE: | |
| crop_frame = frame_processor.run(None, frame_processor_inputs)[0][0] | |
| crop_frame = normalize_crop_frame(crop_frame) | |
| paste_frame = paste_back(temp_frame, crop_frame, affine_matrix, facefusion.globals.face_mask_blur, (0, 0, 0, 0)) | |
| temp_frame = blend_frame(temp_frame, paste_frame) | |
| return temp_frame | |
| def prepare_crop_frame(crop_frame : Frame) -> Frame: | |
| crop_frame = crop_frame[:, :, ::-1] / 255.0 | |
| crop_frame = (crop_frame - 0.5) / 0.5 | |
| crop_frame = numpy.expand_dims(crop_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32) | |
| return crop_frame | |
| def normalize_crop_frame(crop_frame : Frame) -> Frame: | |
| crop_frame = numpy.clip(crop_frame, -1, 1) | |
| crop_frame = (crop_frame + 1) / 2 | |
| crop_frame = crop_frame.transpose(1, 2, 0) | |
| crop_frame = (crop_frame * 255.0).round() | |
| crop_frame = crop_frame.astype(numpy.uint8)[:, :, ::-1] | |
| return crop_frame | |
| def blend_frame(temp_frame : Frame, paste_frame : Frame) -> Frame: | |
| face_enhancer_blend = 1 - (frame_processors_globals.face_enhancer_blend / 100) | |
| temp_frame = cv2.addWeighted(temp_frame, face_enhancer_blend, paste_frame, 1 - face_enhancer_blend, 0) | |
| return temp_frame | |
| def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame: | |
| many_faces = get_many_faces(temp_frame) | |
| if many_faces: | |
| for target_face in many_faces: | |
| temp_frame = enhance_face(target_face, temp_frame) | |
| return temp_frame | |
| def process_frames(source_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None: | |
| for temp_frame_path in temp_frame_paths: | |
| temp_frame = read_image(temp_frame_path) | |
| result_frame = process_frame(None, None, temp_frame) | |
| write_image(temp_frame_path, result_frame) | |
| update_progress() | |
| def process_image(source_path : str, target_path : str, output_path : str) -> None: | |
| target_frame = read_static_image(target_path) | |
| result_frame = process_frame(None, None, 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(None, temp_frame_paths, process_frames) | |