178 lines
6.2 KiB
Python
Executable File
178 lines
6.2 KiB
Python
Executable File
from functools import lru_cache
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from typing import Dict, 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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from facefusion import inference_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.typing import DownloadScope, DownloadSet, FaceLandmark68, FaceMaskRegion, InferencePool, Mask, ModelSet, Padding, VisionFrame
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FACE_MASK_REGIONS : Dict[FaceMaskRegion, int] =\
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{
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'skin': 1,
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'left-eyebrow': 2,
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'right-eyebrow': 3,
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'left-eye': 4,
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'right-eye': 5,
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'glasses': 6,
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'nose': 10,
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'mouth': 11,
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'upper-lip': 12,
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'lower-lip': 13
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}
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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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'face_occluder':
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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_groggy_5.hash'),
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'path': resolve_relative_path('../.assets/models/xseg_groggy_5.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_groggy_5.onnx'),
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'path': resolve_relative_path('../.assets/models/xseg_groggy_5.onnx')
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}
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},
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'size': (256, 256)
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},
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'face_parser':
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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_sources = collect_model_downloads()
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return inference_manager.get_inference_pool(__name__, model_sources)
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def clear_inference_pool() -> None:
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inference_manager.clear_inference_pool(__name__)
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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_hashes =\
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{
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'face_occluder': model_set.get('face_occluder').get('hashes').get('face_occluder'),
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'face_parser': model_set.get('face_parser').get('hashes').get('face_parser')
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}
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model_sources =\
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{
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'face_occluder': model_set.get('face_occluder').get('sources').get('face_occluder'),
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'face_parser': model_set.get('face_parser').get('sources').get('face_parser')
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}
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return model_hashes, model_sources
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def pre_check() -> bool:
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model_hashes, model_sources = collect_model_downloads()
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return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
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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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model_size = create_static_model_set('full').get('face_occluder').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
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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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model_size = create_static_model_set('full').get('face_parser').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
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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), [ FACE_MASK_REGIONS[region] for 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) #type:ignore[call-overload]
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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 = get_inference_pool().get('face_occluder')
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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 = get_inference_pool().get('face_parser')
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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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