from argparse import ArgumentParser from typing import List import cv2 import numpy import scipy import facefusion.jobs.job_manager import facefusion.jobs.job_store import facefusion.processors.core as processors from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, process_manager, state_manager, wording from facefusion.common_helper import create_int_metavar, map_float from facefusion.download import conditional_download_hashes, conditional_download_sources from facefusion.face_analyser import get_many_faces, get_one_face from facefusion.face_helper import paste_back, warp_face_by_face_landmark_5 from facefusion.face_masker import create_occlusion_mask, create_static_box_mask from facefusion.face_selector import find_similar_faces, sort_and_filter_faces from facefusion.face_store import get_reference_faces from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension from facefusion.processors import choices as processors_choices from facefusion.processors.typing import ExpressionRestorerInputs from facefusion.program_helper import find_argument_group from facefusion.thread_helper import thread_semaphore from facefusion.typing import Args, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame from facefusion.vision import get_video_frame, read_image, read_static_image, write_image MODEL_SET : ModelSet =\ { 'live_portrait': { 'hashes': { 'feature_extractor': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_feature_extractor.hash', 'path': resolve_relative_path('../.assets/models/live_portrait_feature_extractor.hash') }, 'motion_extractor': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_motion_extractor.hash', 'path': resolve_relative_path('../.assets/models/live_portrait_motion_extractor.hash') }, 'generator': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_generator.hash', 'path': resolve_relative_path('../.assets/models/live_portrait_generator.hash') } }, 'sources': { 'feature_extractor': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_feature_extractor.onnx', 'path': resolve_relative_path('../.assets/models/live_portrait_feature_extractor.onnx') }, 'motion_extractor': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_motion_extractor.onnx', 'path': resolve_relative_path('../.assets/models/live_portrait_motion_extractor.onnx') }, 'generator': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/live_portrait_generator.onnx', 'path': resolve_relative_path('../.assets/models/live_portrait_generator.onnx') } }, 'template': 'arcface_128_v2', 'size': (512, 512) } } def get_inference_pool() -> InferencePool: model_sources = get_model_options().get('sources') return inference_manager.get_inference_pool(__name__, model_sources) def clear_inference_pool() -> None: inference_manager.clear_inference_pool(__name__) def get_model_options() -> ModelOptions: return MODEL_SET[state_manager.get_item('expression_restorer_model')] def register_args(program : ArgumentParser) -> None: group_processors = find_argument_group(program, 'processors') if group_processors: group_processors.add_argument('--expression-restorer-model', help = wording.get('help.expression_restorer_model'), default = config.get_str_value('processors.expression_restorer_model', 'live_portrait'), choices = processors_choices.expression_restorer_models) group_processors.add_argument('--expression-restorer-factor', help = wording.get('help.expression_restorer_factor'), type = int, default = config.get_int_value('processors.expression_restorer_factor', '100'), choices = processors_choices.expression_restorer_factor_range, metavar = create_int_metavar(processors_choices.expression_restorer_factor_range)) facefusion.jobs.job_store.register_step_keys([ 'expression_restorer_model','expression_restorer_factor' ]) def apply_args(args : Args) -> None: state_manager.init_item('expression_restorer_model', args.get('expression_restorer_model')) state_manager.init_item('expression_restorer_factor', args.get('expression_restorer_factor')) def pre_check() -> bool: download_directory_path = resolve_relative_path('../.assets/models') model_hashes = get_model_options().get('hashes') model_sources = get_model_options().get('sources') return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources) def pre_process(mode : ProcessMode) -> bool: if mode in [ 'output', 'preview' ] and not is_image(state_manager.get_item('target_path')) and not is_video(state_manager.get_item('target_path')): logger.error(wording.get('choose_image_or_video_target') + wording.get('exclamation_mark'), __name__.upper()) return False if mode == 'output' and not in_directory(state_manager.get_item('output_path')): logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__.upper()) return False if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]): logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__.upper()) return False return True def post_process() -> None: read_static_image.cache_clear() if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]: clear_inference_pool() if state_manager.get_item('video_memory_strategy') == 'strict': content_analyser.clear_inference_pool() face_classifier.clear_inference_pool() face_detector.clear_inference_pool() face_landmarker.clear_inference_pool() face_masker.clear_inference_pool() face_recognizer.clear_inference_pool() def restore_expression(source_vision_frame : VisionFrame, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: model_template = get_model_options().get('template') model_size = get_model_options().get('size') expression_restorer_factor = map_float(float(state_manager.get_item('expression_restorer_factor')), 0, 200, 0, 2) source_vision_frame = cv2.resize(source_vision_frame, temp_vision_frame.shape[:2][::-1]) source_crop_vision_frame, _ = warp_face_by_face_landmark_5(source_vision_frame, target_face.landmark_set.get('5/68'), model_template, model_size) target_crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmark_set.get('5/68'), model_template, model_size) box_mask = create_static_box_mask(target_crop_vision_frame.shape[:2][::-1], state_manager.get_item('face_mask_blur'), (0, 0, 0, 0)) crop_masks =\ [ box_mask ] if 'occlusion' in state_manager.get_item('face_mask_types'): occlusion_mask = create_occlusion_mask(target_crop_vision_frame) crop_masks.append(occlusion_mask) source_crop_vision_frame = prepare_crop_frame(source_crop_vision_frame) target_crop_vision_frame = prepare_crop_frame(target_crop_vision_frame) target_crop_vision_frame = apply_restore(source_crop_vision_frame, target_crop_vision_frame, expression_restorer_factor) target_crop_vision_frame = normalize_crop_frame(target_crop_vision_frame) crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1) temp_vision_frame = paste_back(temp_vision_frame, target_crop_vision_frame, crop_mask, affine_matrix) return temp_vision_frame def apply_restore(source_crop_vision_frame : VisionFrame, target_crop_vision_frame : VisionFrame, expression_restorer_factor : float) -> VisionFrame: feature_extractor = get_inference_pool().get('feature_extractor') motion_extractor = get_inference_pool().get('motion_extractor') generator = get_inference_pool().get('generator') with thread_semaphore(): feature_volume = feature_extractor.run(None, { 'input': target_crop_vision_frame })[0] with thread_semaphore(): source_expression = motion_extractor.run(None, { 'input': source_crop_vision_frame })[5] with thread_semaphore(): target_pitch, target_yaw, target_roll, target_scale, target_translation, target_expression, target_motion_points = motion_extractor.run(None, { 'input': target_crop_vision_frame }) target_rotation_matrix = scipy.spatial.transform.Rotation.from_euler('xyz', [ target_pitch, target_yaw, target_roll ], degrees = True).as_matrix() target_rotation_matrix = target_rotation_matrix.T.astype(numpy.float32) target_motion_points_transform = target_scale * (target_motion_points @ target_rotation_matrix + target_expression) + target_translation expression = source_expression * expression_restorer_factor + target_expression * (1 - expression_restorer_factor) expression[:, [ 0, 4, 5, 8, 9 ]] = target_expression[:, [ 0, 4, 5, 8, 9 ]] source_motion_points = target_scale * (target_motion_points @ target_rotation_matrix + expression) + target_translation with thread_semaphore(): crop_vision_frame = generator.run(None, { 'feature_volume': feature_volume, 'target': target_motion_points_transform, 'source': source_motion_points })[0][0] return crop_vision_frame def prepare_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame: crop_vision_frame = cv2.resize(crop_vision_frame, (256, 256), interpolation = cv2.INTER_AREA) crop_vision_frame = crop_vision_frame[:, :, ::-1] / 255.0 crop_vision_frame = numpy.expand_dims(crop_vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32) return crop_vision_frame def normalize_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame: crop_vision_frame = crop_vision_frame.transpose(1, 2, 0).clip(0, 1) crop_vision_frame = (crop_vision_frame * 255.0) crop_vision_frame = crop_vision_frame.astype(numpy.uint8)[:, :, ::-1] return crop_vision_frame def get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: pass def process_frame(inputs : ExpressionRestorerInputs) -> VisionFrame: reference_faces = inputs.get('reference_faces') source_vision_frame = inputs.get('source_vision_frame') target_vision_frame = inputs.get('target_vision_frame') many_faces = sort_and_filter_faces(get_many_faces([ target_vision_frame ])) if state_manager.get_item('face_selector_mode') == 'many': if many_faces: for target_face in many_faces: target_vision_frame = restore_expression(source_vision_frame, target_face, target_vision_frame) if state_manager.get_item('face_selector_mode') == 'one': target_face = get_one_face(many_faces) if target_face: target_vision_frame = restore_expression(source_vision_frame, target_face, target_vision_frame) if state_manager.get_item('face_selector_mode') == 'reference': similar_faces = find_similar_faces(many_faces, reference_faces, state_manager.get_item('reference_face_distance')) if similar_faces: for similar_face in similar_faces: target_vision_frame = restore_expression(source_vision_frame, similar_face, target_vision_frame) return target_vision_frame def process_frames(source_path : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None: reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else None for queue_payload in process_manager.manage(queue_payloads): frame_number = queue_payload.get('frame_number') if state_manager.get_item('trim_frame_start'): frame_number += state_manager.get_item('trim_frame_start') source_vision_frame = get_video_frame(state_manager.get_item('target_path'), frame_number) target_vision_path = queue_payload.get('frame_path') target_vision_frame = read_image(target_vision_path) output_vision_frame = process_frame( { 'reference_faces': reference_faces, 'source_vision_frame': source_vision_frame, 'target_vision_frame': target_vision_frame }) write_image(target_vision_path, output_vision_frame) update_progress(1) def process_image(source_path : str, target_path : str, output_path : str) -> None: reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else None source_vision_frame = read_static_image(state_manager.get_item('target_path')) target_vision_frame = read_static_image(target_path) output_vision_frame = process_frame( { 'reference_faces': reference_faces, 'source_vision_frame': source_vision_frame, 'target_vision_frame': target_vision_frame }) write_image(output_path, output_vision_frame) def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None: processors.multi_process_frames(None, temp_frame_paths, process_frames)