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