285 lines
13 KiB
Python
Executable File
285 lines
13 KiB
Python
Executable File
from argparse import ArgumentParser
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from typing import Any, List
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import cv2
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import numpy
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from cv2.typing import Size
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from numpy.typing import NDArray
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import facefusion.jobs.job_manager
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import facefusion.jobs.job_store
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import facefusion.processors.core as processors
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from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, process_manager, state_manager, wording
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from facefusion.common_helper import create_int_metavar
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from facefusion.download import conditional_download_hashes, conditional_download_sources
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from facefusion.face_analyser import get_many_faces, get_one_face
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from facefusion.face_helper import merge_matrix, paste_back, warp_face_by_face_landmark_5
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from facefusion.face_masker import create_occlusion_mask, create_static_box_mask
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from facefusion.face_selector import find_similar_faces, sort_and_filter_faces
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from facefusion.face_store import get_reference_faces
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from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
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from facefusion.processors import choices as processors_choices
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from facefusion.processors.typing import AgeModifierInputs
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from facefusion.program_helper import find_argument_group
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from facefusion.thread_helper import thread_semaphore
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from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, Mask, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
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from facefusion.vision import read_image, read_static_image, write_image
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MODEL_SET : ModelSet =\
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{
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'styleganex_age':
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{
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'hashes':
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{
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'age_modifier':
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{
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'url': 'https://huggingface.co/bluefoxcreation/StyleGANEX-AGE/resolve/main/styleganex_age_opt.hash',
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'path': resolve_relative_path('../.assets/models/styleganex_age_opt.hash')
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}
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},
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'sources':
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{
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'age_modifier':
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{
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'url': 'https://huggingface.co/bluefoxcreation/StyleGANEX-AGE/resolve/main/styleganex_age_opt.onnx',
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'path': resolve_relative_path('../.assets/models/styleganex_age_opt.onnx')
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}
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},
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'templates':
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{
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'target': 'ffhq_512',
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'target_with_background': 'styleganex_384'
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},
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'sizes':
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{
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'target': (256, 256),
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'target_with_background': (384, 384)
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}
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}
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}
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def get_inference_pool() -> InferencePool:
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model_sources = get_model_options().get('sources')
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model_context = __name__ + '.' + state_manager.get_item('age_modifier_model')
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return inference_manager.get_inference_pool(model_context, model_sources)
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def clear_inference_pool() -> None:
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model_context = __name__ + '.' + state_manager.get_item('age_modifier_model')
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inference_manager.clear_inference_pool(model_context)
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def get_model_options() -> ModelOptions:
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age_modifier_model = state_manager.get_item('age_modifier_model')
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return MODEL_SET.get(age_modifier_model)
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def register_args(program : ArgumentParser) -> None:
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group_processors = find_argument_group(program, 'processors')
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if group_processors:
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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)
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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))
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facefusion.jobs.job_store.register_step_keys([ 'age_modifier_model', 'age_modifier_direction' ])
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def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
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apply_state_item('age_modifier_model', args.get('age_modifier_model'))
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apply_state_item('age_modifier_direction', args.get('age_modifier_direction'))
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def pre_check() -> bool:
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download_directory_path = resolve_relative_path('../.assets/models')
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model_hashes = get_model_options().get('hashes')
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model_sources = get_model_options().get('sources')
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return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
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def pre_process(mode : ProcessMode) -> bool:
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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')):
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logger.error(wording.get('choose_image_or_video_target') + wording.get('exclamation_mark'), __name__)
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return False
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if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
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logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
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return False
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if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
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logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
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return False
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return True
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def post_process() -> None:
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read_static_image.cache_clear()
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if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]:
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clear_inference_pool()
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if state_manager.get_item('video_memory_strategy') == 'strict':
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content_analyser.clear_inference_pool()
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face_classifier.clear_inference_pool()
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face_detector.clear_inference_pool()
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face_landmarker.clear_inference_pool()
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face_masker.clear_inference_pool()
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face_recognizer.clear_inference_pool()
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def modify_age(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
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model_template = get_model_options().get('templates').get('target')
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model_size = get_model_options().get('sizes').get('target')
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extend_crop_template = get_model_options().get('templates').get('target_with_background')
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extend_crop_size = get_model_options().get('sizes').get('target_with_background')
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face_landmark_5 = target_face.landmark_set.get('5/68').copy()
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crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, face_landmark_5, model_template, model_size)
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extend_vision_frame, extend_affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, face_landmark_5, extend_crop_template, extend_crop_size)
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extend_vision_frame_raw = extend_vision_frame.copy()
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box_mask = create_static_box_mask(model_size, state_manager.get_item('face_mask_blur'), (0, 0, 0, 0))
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crop_masks =\
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[
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box_mask
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]
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if 'occlusion' in state_manager.get_item('face_mask_types'):
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occlusion_mask = create_occlusion_mask(crop_vision_frame)
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combined_matrix = merge_matrix([ extend_affine_matrix, cv2.invertAffineTransform(affine_matrix) ])
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occlusion_mask = cv2.warpAffine(occlusion_mask, combined_matrix, model_size)
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crop_masks.append(occlusion_mask)
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crop_vision_frame = prepare_vision_frame(crop_vision_frame)
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extend_vision_frame = prepare_vision_frame(extend_vision_frame)
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extend_vision_frame = forward(crop_vision_frame, extend_vision_frame)
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extend_vision_frame = normalize_extend_frame(extend_vision_frame)
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extend_vision_frame = fix_color(extend_vision_frame_raw, extend_vision_frame)
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extend_crop_mask = prepare_crop_masks(crop_masks)
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extend_affine_matrix *= (model_size[0] * 4) / extend_crop_size[0]
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paste_vision_frame = paste_back(temp_vision_frame, extend_vision_frame, extend_crop_mask, extend_affine_matrix)
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return paste_vision_frame
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def forward(crop_vision_frame : VisionFrame, extend_vision_frame : VisionFrame) -> VisionFrame:
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age_modifier = get_inference_pool().get('age_modifier')
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age_modifier_inputs = {}
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for age_modifier_input in age_modifier.get_inputs():
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if age_modifier_input.name == 'target':
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age_modifier_inputs[age_modifier_input.name] = crop_vision_frame
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if age_modifier_input.name == 'target_with_background':
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age_modifier_inputs[age_modifier_input.name] = extend_vision_frame
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if age_modifier_input.name == 'direction':
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age_modifier_inputs[age_modifier_input.name] = prepare_direction(state_manager.get_item('age_modifier_direction'))
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with thread_semaphore():
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crop_vision_frame = age_modifier.run(None, age_modifier_inputs)[0]
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return crop_vision_frame
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def fix_color(extend_vision_frame_raw : VisionFrame, extend_vision_frame : VisionFrame) -> VisionFrame:
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color_difference = compute_color_difference(extend_vision_frame_raw, extend_vision_frame, (48, 48))
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color_difference_mask = create_static_box_mask(extend_vision_frame.shape[:2][::-1], 1.0, (0, 0, 0, 0))
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color_difference_mask = numpy.stack((color_difference_mask, ) * 3, axis = -1)
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extend_vision_frame = normalize_color_difference(color_difference, color_difference_mask, extend_vision_frame)
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return extend_vision_frame
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def compute_color_difference(extend_vision_frame_raw : VisionFrame, extend_vision_frame : VisionFrame, size : Size) -> VisionFrame:
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extend_vision_frame_raw = extend_vision_frame_raw.astype(numpy.float32) / 255
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extend_vision_frame_raw = cv2.resize(extend_vision_frame_raw, size, interpolation = cv2.INTER_AREA)
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extend_vision_frame = extend_vision_frame.astype(numpy.float32) / 255
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extend_vision_frame = cv2.resize(extend_vision_frame, size, interpolation = cv2.INTER_AREA)
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color_difference = extend_vision_frame_raw - extend_vision_frame
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return color_difference
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def normalize_color_difference(color_difference : VisionFrame, color_difference_mask : Mask, extend_vision_frame : VisionFrame) -> VisionFrame:
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color_difference = cv2.resize(color_difference, extend_vision_frame.shape[:2][::-1], interpolation = cv2.INTER_CUBIC)
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color_difference_mask = 1 - color_difference_mask.clip(0, 0.75)
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extend_vision_frame = extend_vision_frame.astype(numpy.float32) / 255
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extend_vision_frame += color_difference * color_difference_mask
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extend_vision_frame = extend_vision_frame.clip(0, 1)
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extend_vision_frame = numpy.multiply(extend_vision_frame, 255).astype(numpy.uint8)
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return extend_vision_frame
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def prepare_direction(direction : int) -> NDArray[Any]:
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direction = numpy.interp(float(direction), [ -100, 100 ], [ 2.5, -2.5 ]) #type:ignore[assignment]
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return numpy.array(direction).astype(numpy.float32)
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def prepare_vision_frame(vision_frame : VisionFrame) -> VisionFrame:
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vision_frame = vision_frame[:, :, ::-1] / 255.0
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vision_frame = (vision_frame - 0.5) / 0.5
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vision_frame = numpy.expand_dims(vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
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return vision_frame
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def prepare_crop_masks(crop_masks : List[Mask]) -> Mask:
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model_size = get_model_options().get('sizes').get('target')
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crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1)
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crop_mask = cv2.resize(crop_mask, (model_size[0] * 4, model_size[1] * 4))
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return crop_mask
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def normalize_extend_frame(extend_vision_frame : VisionFrame) -> VisionFrame:
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model_size = get_model_options().get('sizes').get('target')
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extend_vision_frame = numpy.clip(extend_vision_frame, -1, 1)
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extend_vision_frame = (extend_vision_frame + 1) / 2
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extend_vision_frame = extend_vision_frame[0].transpose(1, 2, 0).clip(0, 255)
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extend_vision_frame = (extend_vision_frame * 255.0)
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extend_vision_frame = extend_vision_frame.astype(numpy.uint8)[:, :, ::-1]
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extend_vision_frame = cv2.resize(extend_vision_frame, (model_size[0] * 4, model_size[1] * 4))
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return extend_vision_frame
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def get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
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return modify_age(target_face, temp_vision_frame)
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def process_frame(inputs : AgeModifierInputs) -> VisionFrame:
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reference_faces = inputs.get('reference_faces')
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target_vision_frame = inputs.get('target_vision_frame')
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many_faces = sort_and_filter_faces(get_many_faces([ target_vision_frame ]))
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if state_manager.get_item('face_selector_mode') == 'many':
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if many_faces:
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for target_face in many_faces:
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target_vision_frame = modify_age(target_face, target_vision_frame)
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if state_manager.get_item('face_selector_mode') == 'one':
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target_face = get_one_face(many_faces)
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if target_face:
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target_vision_frame = modify_age(target_face, target_vision_frame)
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if state_manager.get_item('face_selector_mode') == 'reference':
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similar_faces = find_similar_faces(many_faces, reference_faces, state_manager.get_item('reference_face_distance'))
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if similar_faces:
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for similar_face in similar_faces:
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target_vision_frame = modify_age(similar_face, target_vision_frame)
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return target_vision_frame
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def process_frames(source_path : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
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reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else None
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for queue_payload in process_manager.manage(queue_payloads):
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target_vision_path = queue_payload['frame_path']
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target_vision_frame = read_image(target_vision_path)
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output_vision_frame = process_frame(
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{
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'reference_faces': reference_faces,
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'target_vision_frame': target_vision_frame
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})
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write_image(target_vision_path, output_vision_frame)
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update_progress(1)
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def process_image(source_path : str, target_path : str, output_path : str) -> None:
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reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else None
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target_vision_frame = read_static_image(target_path)
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output_vision_frame = process_frame(
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{
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'reference_faces': reference_faces,
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'target_vision_frame': target_vision_frame
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})
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write_image(output_path, output_vision_frame)
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def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
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processors.multi_process_frames(None, temp_frame_paths, process_frames)
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