Spaces:
Build error
Build error
| 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) | |