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from functools import lru_cache |
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from typing import List, Tuple |
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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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import facefusion.choices |
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from facefusion import inference_manager, state_manager |
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from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url |
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from facefusion.filesystem import resolve_relative_path |
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from facefusion.thread_helper import conditional_thread_semaphore |
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from facefusion.types import DownloadScope, DownloadSet, FaceLandmark68, FaceMaskRegion, InferencePool, Mask, ModelSet, Padding, VisionFrame |
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@lru_cache(maxsize = None) |
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def create_static_model_set(download_scope : DownloadScope) -> ModelSet: |
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return\ |
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{ |
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'xseg_1': |
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{ |
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'hashes': |
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{ |
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'face_occluder': |
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{ |
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'url': resolve_download_url('models-3.1.0', 'xseg_1.hash'), |
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'path': resolve_relative_path('../.assets/models/xseg_1.hash') |
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} |
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}, |
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'sources': |
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{ |
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'face_occluder': |
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{ |
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'url': resolve_download_url('models-3.1.0', 'xseg_1.onnx'), |
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'path': resolve_relative_path('../.assets/models/xseg_1.onnx') |
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} |
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}, |
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'size': (256, 256) |
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}, |
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'xseg_2': |
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{ |
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'hashes': |
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{ |
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'face_occluder': |
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{ |
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'url': resolve_download_url('models-3.1.0', 'xseg_2.hash'), |
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'path': resolve_relative_path('../.assets/models/xseg_2.hash') |
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} |
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}, |
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'sources': |
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{ |
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'face_occluder': |
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{ |
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'url': resolve_download_url('models-3.1.0', 'xseg_2.onnx'), |
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'path': resolve_relative_path('../.assets/models/xseg_2.onnx') |
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} |
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}, |
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'size': (256, 256) |
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}, |
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'xseg_3': |
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{ |
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'hashes': |
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{ |
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'face_occluder': |
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{ |
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'url': resolve_download_url('models-3.2.0', 'xseg_3.hash'), |
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'path': resolve_relative_path('../.assets/models/xseg_3.hash') |
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} |
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}, |
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'sources': |
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{ |
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'face_occluder': |
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{ |
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'url': resolve_download_url('models-3.2.0', 'xseg_3.onnx'), |
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'path': resolve_relative_path('../.assets/models/xseg_3.onnx') |
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} |
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}, |
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'size': (256, 256) |
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}, |
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'bisenet_resnet_18': |
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{ |
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'hashes': |
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{ |
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'face_parser': |
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{ |
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'url': resolve_download_url('models-3.1.0', 'bisenet_resnet_18.hash'), |
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'path': resolve_relative_path('../.assets/models/bisenet_resnet_18.hash') |
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} |
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}, |
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'sources': |
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{ |
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'face_parser': |
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{ |
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'url': resolve_download_url('models-3.1.0', 'bisenet_resnet_18.onnx'), |
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'path': resolve_relative_path('../.assets/models/bisenet_resnet_18.onnx') |
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} |
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}, |
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'size': (512, 512) |
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}, |
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'bisenet_resnet_34': |
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{ |
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'hashes': |
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{ |
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'face_parser': |
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{ |
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'url': resolve_download_url('models-3.0.0', 'bisenet_resnet_34.hash'), |
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'path': resolve_relative_path('../.assets/models/bisenet_resnet_34.hash') |
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} |
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}, |
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'sources': |
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{ |
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'face_parser': |
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{ |
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'url': resolve_download_url('models-3.0.0', 'bisenet_resnet_34.onnx'), |
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'path': resolve_relative_path('../.assets/models/bisenet_resnet_34.onnx') |
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} |
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}, |
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'size': (512, 512) |
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} |
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} |
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def get_inference_pool() -> InferencePool: |
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model_names = [state_manager.get_item('face_occluder_model'), state_manager.get_item('face_parser_model')] |
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_, model_source_set = collect_model_downloads() |
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return inference_manager.get_inference_pool(__name__, model_names, model_source_set) |
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def clear_inference_pool() -> None: |
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model_names = [ state_manager.get_item('face_occluder_model'), state_manager.get_item('face_parser_model') ] |
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inference_manager.clear_inference_pool(__name__, model_names) |
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def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]: |
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model_set = create_static_model_set('full') |
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model_hash_set = {} |
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model_source_set = {} |
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for face_occluder_model in [ 'xseg_1', 'xseg_2', 'xseg_3' ]: |
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if state_manager.get_item('face_occluder_model') == face_occluder_model: |
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model_hash_set[face_occluder_model] = model_set.get(face_occluder_model).get('hashes').get('face_occluder') |
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model_source_set[face_occluder_model] = model_set.get(face_occluder_model).get('sources').get('face_occluder') |
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for face_parser_model in [ 'bisenet_resnet_18', 'bisenet_resnet_34' ]: |
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if state_manager.get_item('face_parser_model') == face_parser_model: |
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model_hash_set[face_parser_model] = model_set.get(face_parser_model).get('hashes').get('face_parser') |
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model_source_set[face_parser_model] = model_set.get(face_parser_model).get('sources').get('face_parser') |
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return model_hash_set, model_source_set |
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def pre_check() -> bool: |
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model_hash_set, model_source_set = collect_model_downloads() |
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return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set) |
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@lru_cache(maxsize = None) |
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def create_static_box_mask(crop_size : Size, face_mask_blur : float, face_mask_padding : Padding) -> Mask: |
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blur_amount = int(crop_size[0] * 0.5 * face_mask_blur) |
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blur_area = max(blur_amount // 2, 1) |
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box_mask : Mask = numpy.ones(crop_size).astype(numpy.float32) |
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box_mask[:max(blur_area, int(crop_size[1] * face_mask_padding[0] / 100)), :] = 0 |
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box_mask[-max(blur_area, int(crop_size[1] * face_mask_padding[2] / 100)):, :] = 0 |
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box_mask[:, :max(blur_area, int(crop_size[0] * face_mask_padding[3] / 100))] = 0 |
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box_mask[:, -max(blur_area, int(crop_size[0] * face_mask_padding[1] / 100)):] = 0 |
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if blur_amount > 0: |
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box_mask = cv2.GaussianBlur(box_mask, (0, 0), blur_amount * 0.25) |
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return box_mask |
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def create_occlusion_mask(crop_vision_frame : VisionFrame) -> Mask: |
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face_occluder_model = state_manager.get_item('face_occluder_model') |
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model_size = create_static_model_set('full').get(face_occluder_model).get('size') |
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prepare_vision_frame = cv2.resize(crop_vision_frame, model_size) |
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prepare_vision_frame = numpy.expand_dims(prepare_vision_frame, axis = 0).astype(numpy.float32) / 255.0 |
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prepare_vision_frame = prepare_vision_frame.transpose(0, 1, 2, 3) |
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occlusion_mask = forward_occlude_face(prepare_vision_frame) |
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occlusion_mask = occlusion_mask.transpose(0, 1, 2).clip(0, 1).astype(numpy.float32) |
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occlusion_mask = cv2.resize(occlusion_mask, crop_vision_frame.shape[:2][::-1]) |
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occlusion_mask = (cv2.GaussianBlur(occlusion_mask.clip(0, 1), (0, 0), 5).clip(0.5, 1) - 0.5) * 2 |
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return occlusion_mask |
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def create_region_mask(crop_vision_frame : VisionFrame, face_mask_regions : List[FaceMaskRegion]) -> Mask: |
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face_parser_model = state_manager.get_item('face_parser_model') |
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model_size = create_static_model_set('full').get(face_parser_model).get('size') |
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prepare_vision_frame = cv2.resize(crop_vision_frame, model_size) |
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prepare_vision_frame = prepare_vision_frame[:, :, ::-1].astype(numpy.float32) / 255.0 |
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prepare_vision_frame = numpy.subtract(prepare_vision_frame, numpy.array([ 0.485, 0.456, 0.406 ]).astype(numpy.float32)) |
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prepare_vision_frame = numpy.divide(prepare_vision_frame, numpy.array([ 0.229, 0.224, 0.225 ]).astype(numpy.float32)) |
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prepare_vision_frame = numpy.expand_dims(prepare_vision_frame, axis = 0) |
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prepare_vision_frame = prepare_vision_frame.transpose(0, 3, 1, 2) |
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region_mask = forward_parse_face(prepare_vision_frame) |
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region_mask = numpy.isin(region_mask.argmax(0), [ facefusion.choices.face_mask_region_set.get(face_mask_region) for face_mask_region in face_mask_regions ]) |
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region_mask = cv2.resize(region_mask.astype(numpy.float32), crop_vision_frame.shape[:2][::-1]) |
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region_mask = (cv2.GaussianBlur(region_mask.clip(0, 1), (0, 0), 5).clip(0.5, 1) - 0.5) * 2 |
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return region_mask |
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def create_mouth_mask(face_landmark_68 : FaceLandmark68) -> Mask: |
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convex_hull = cv2.convexHull(face_landmark_68[numpy.r_[3:14, 31:36]].astype(numpy.int32)) |
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mouth_mask : Mask = numpy.zeros((512, 512)).astype(numpy.float32) |
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mouth_mask = cv2.fillConvexPoly(mouth_mask, convex_hull, 1.0) |
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mouth_mask = cv2.erode(mouth_mask.clip(0, 1), numpy.ones((21, 3))) |
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mouth_mask = cv2.GaussianBlur(mouth_mask, (0, 0), sigmaX = 1, sigmaY = 15) |
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return mouth_mask |
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def forward_occlude_face(prepare_vision_frame : VisionFrame) -> Mask: |
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face_occluder_model = state_manager.get_item('face_occluder_model') |
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face_occluder = get_inference_pool().get(face_occluder_model) |
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with conditional_thread_semaphore(): |
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occlusion_mask : Mask = face_occluder.run(None, |
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{ |
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'input': prepare_vision_frame |
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})[0][0] |
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return occlusion_mask |
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def forward_parse_face(prepare_vision_frame : VisionFrame) -> Mask: |
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face_parser_model = state_manager.get_item('face_parser_model') |
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face_parser = get_inference_pool().get(face_parser_model) |
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with conditional_thread_semaphore(): |
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region_mask : Mask = face_parser.run(None, |
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{ |
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'input': prepare_vision_frame |
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})[0][0] |
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return region_mask |
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