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| from argparse import ArgumentParser | |
| from typing import Any, List | |
| import cv2 | |
| import numpy | |
| from cv2.typing import Size | |
| from numpy.typing import NDArray | |
| 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 merge_matrix, 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 AgeModifierInputs | |
| from facefusion.program_helper import find_argument_group | |
| from facefusion.thread_helper import thread_semaphore | |
| from facefusion.typing import Args, Face, InferencePool, Mask, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame | |
| from facefusion.vision import read_image, read_static_image, write_image | |
| MODEL_SET : ModelSet =\ | |
| { | |
| 'styleganex_age': | |
| { | |
| 'hashes': | |
| { | |
| 'age_modifier': | |
| { | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/styleganex_age.hash', | |
| 'path': resolve_relative_path('../.assets/models/styleganex_age.hash') | |
| } | |
| }, | |
| 'sources': | |
| { | |
| 'age_modifier': | |
| { | |
| 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/styleganex_age.onnx', | |
| 'path': resolve_relative_path('../.assets/models/styleganex_age.onnx') | |
| } | |
| }, | |
| 'template': 'ffhq_512', | |
| '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('age_modifier_model')] | |
| def register_args(program : ArgumentParser) -> None: | |
| group_processors = find_argument_group(program, 'processors') | |
| if group_processors: | |
| group_processors.add_argument('--age-modifier-model', help = wording.get('help.age_modifier_model'), default = config.get_str_value('processors.age_modifier_model', 'styleganex_age'), choices = processors_choices.age_modifier_models) | |
| group_processors.add_argument('--age-modifier-direction', help = wording.get('help.age_modifier_direction'), type = int, default = config.get_int_value('processors.age_modifier_direction', '0'), choices = processors_choices.age_modifier_direction_range, metavar = create_int_metavar(processors_choices.age_modifier_direction_range)) | |
| facefusion.jobs.job_store.register_step_keys([ 'age_modifier_model', 'age_modifier_direction' ]) | |
| def apply_args(args : Args) -> None: | |
| state_manager.init_item('age_modifier_model', args.get('age_modifier_model')) | |
| state_manager.init_item('age_modifier_direction', args.get('age_modifier_direction')) | |
| 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 modify_age(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: | |
| model_template = get_model_options().get('template') | |
| model_size = get_model_options().get('size') | |
| face_landmark_5 = target_face.landmark_set.get('5/68').copy() | |
| extend_face_landmark_5 = (face_landmark_5 - face_landmark_5[2]) * 2 + face_landmark_5[2] | |
| crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, face_landmark_5, model_template, (256, 256)) | |
| extend_vision_frame, extend_affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, extend_face_landmark_5, model_template, model_size) | |
| extend_vision_frame_raw = extend_vision_frame.copy() | |
| box_mask = create_static_box_mask(model_size, 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(crop_vision_frame) | |
| combined_matrix = merge_matrix([ extend_affine_matrix, cv2.invertAffineTransform(affine_matrix) ]) | |
| occlusion_mask = cv2.warpAffine(occlusion_mask, combined_matrix, model_size) | |
| crop_masks.append(occlusion_mask) | |
| crop_vision_frame = prepare_vision_frame(crop_vision_frame) | |
| extend_vision_frame = prepare_vision_frame(extend_vision_frame) | |
| extend_vision_frame = apply_modify(crop_vision_frame, extend_vision_frame) | |
| extend_vision_frame = normalize_extend_frame(extend_vision_frame) | |
| extend_vision_frame = fix_color(extend_vision_frame_raw, extend_vision_frame) | |
| extend_crop_mask = cv2.pyrUp(numpy.minimum.reduce(crop_masks).clip(0, 1)) | |
| extend_affine_matrix *= extend_vision_frame.shape[0] / 512 | |
| paste_vision_frame = paste_back(temp_vision_frame, extend_vision_frame, extend_crop_mask, extend_affine_matrix) | |
| return paste_vision_frame | |
| def apply_modify(crop_vision_frame : VisionFrame, crop_vision_frame_extended : VisionFrame) -> VisionFrame: | |
| age_modifier = get_inference_pool().get('age_modifier') | |
| age_modifier_inputs = {} | |
| for age_modifier_input in age_modifier.get_inputs(): | |
| if age_modifier_input.name == 'target': | |
| age_modifier_inputs[age_modifier_input.name] = crop_vision_frame | |
| if age_modifier_input.name == 'target_with_background': | |
| age_modifier_inputs[age_modifier_input.name] = crop_vision_frame_extended | |
| if age_modifier_input.name == 'direction': | |
| age_modifier_inputs[age_modifier_input.name] = prepare_direction(state_manager.get_item('age_modifier_direction')) | |
| with thread_semaphore(): | |
| crop_vision_frame = age_modifier.run(None, age_modifier_inputs)[0][0] | |
| return crop_vision_frame | |
| def fix_color(extend_vision_frame_raw : VisionFrame, extend_vision_frame : VisionFrame) -> VisionFrame: | |
| color_difference = compute_color_difference(extend_vision_frame_raw, extend_vision_frame, (48, 48)) | |
| color_difference_mask = create_static_box_mask(extend_vision_frame.shape[:2][::-1], 1.0, (0, 0, 0, 0)) | |
| color_difference_mask = numpy.stack((color_difference_mask, ) * 3, axis = -1) | |
| extend_vision_frame = normalize_color_difference(color_difference, color_difference_mask, extend_vision_frame) | |
| return extend_vision_frame | |
| def compute_color_difference(extend_vision_frame_raw : VisionFrame, extend_vision_frame : VisionFrame, size : Size) -> VisionFrame: | |
| extend_vision_frame_raw = extend_vision_frame_raw.astype(numpy.float32) / 255 | |
| extend_vision_frame_raw = cv2.resize(extend_vision_frame_raw, size, interpolation = cv2.INTER_AREA) | |
| extend_vision_frame = extend_vision_frame.astype(numpy.float32) / 255 | |
| extend_vision_frame = cv2.resize(extend_vision_frame, size, interpolation = cv2.INTER_AREA) | |
| color_difference = extend_vision_frame_raw - extend_vision_frame | |
| return color_difference | |
| def normalize_color_difference(color_difference : VisionFrame, color_difference_mask : Mask, extend_vision_frame : VisionFrame) -> VisionFrame: | |
| color_difference = cv2.resize(color_difference, extend_vision_frame.shape[:2][::-1], interpolation = cv2.INTER_CUBIC) | |
| color_difference_mask = 1 - color_difference_mask.clip(0, 0.75) | |
| extend_vision_frame = extend_vision_frame.astype(numpy.float32) / 255 | |
| extend_vision_frame += color_difference * color_difference_mask | |
| extend_vision_frame = extend_vision_frame.clip(0, 1) | |
| extend_vision_frame = numpy.multiply(extend_vision_frame, 255).astype(numpy.uint8) | |
| return extend_vision_frame | |
| def prepare_direction(direction : int) -> NDArray[Any]: | |
| direction = map_float(float(direction), -100, 100, 2.5, -2.5) #type:ignore[assignment] | |
| return numpy.array(direction).astype(numpy.float32) | |
| def prepare_vision_frame(vision_frame : VisionFrame) -> VisionFrame: | |
| vision_frame = vision_frame[:, :, ::-1] / 255.0 | |
| vision_frame = (vision_frame - 0.5) / 0.5 | |
| vision_frame = numpy.expand_dims(vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32) | |
| return vision_frame | |
| def normalize_extend_frame(extend_vision_frame : VisionFrame) -> VisionFrame: | |
| extend_vision_frame = numpy.clip(extend_vision_frame, -1, 1) | |
| extend_vision_frame = (extend_vision_frame + 1) / 2 | |
| extend_vision_frame = extend_vision_frame.transpose(1, 2, 0).clip(0, 255) | |
| extend_vision_frame = (extend_vision_frame * 255.0) | |
| extend_vision_frame = extend_vision_frame.astype(numpy.uint8)[:, :, ::-1] | |
| extend_vision_frame = cv2.pyrDown(extend_vision_frame) | |
| return extend_vision_frame | |
| def get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: | |
| return modify_age(target_face, temp_vision_frame) | |
| def process_frame(inputs : AgeModifierInputs) -> VisionFrame: | |
| reference_faces = inputs.get('reference_faces') | |
| 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 = modify_age(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 = modify_age(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 = modify_age(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): | |
| target_vision_path = queue_payload['frame_path'] | |
| target_vision_frame = read_image(target_vision_path) | |
| output_vision_frame = process_frame( | |
| { | |
| 'reference_faces': reference_faces, | |
| '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 | |
| target_vision_frame = read_static_image(target_path) | |
| output_vision_frame = process_frame( | |
| { | |
| 'reference_faces': reference_faces, | |
| '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) | |