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https://github.com/facefusion/facefusion.git
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3.6.0 (#1062)
* mark as next * add fran model * add support for corridor key (#1060) * introduce despill color * simplify the apply dispill color * finalize naming for both fill and despill * follow vision_frame convension * patch fran model * adjust fran urls * Feat/dynamic env setup (#1061) * dynamic environment setup * dynamic environment setup * fix fran model * prevent directml using incompatible corridor_key model * fix environment setup for windows * switch to corridor_key_1024 and corridor_key_2048 * switch to corridor_key_1024 and corridor_key_2048 * mark it as 3.6.0 * rename environment to conda * rename environment to conda * fix testing for face analyser * some background remove cosmetics * some background remove cosmetics * some background remove cosmetics * update preview --------- Co-authored-by: harisreedhar <h4harisreedhar.s.s@gmail.com>
This commit is contained in:
Henry Ruhs
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+2
-1
@@ -67,7 +67,8 @@ processors =
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age_modifier_model =
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age_modifier_direction =
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background_remover_model =
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background_remover_color =
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background_remover_fill_color =
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background_remover_despill_color =
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deep_swapper_model =
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deep_swapper_morph =
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expression_restorer_model =
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+2
-1
@@ -4,7 +4,8 @@ import os
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os.environ['OMP_NUM_THREADS'] = '1'
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from facefusion import core
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from facefusion import conda, core
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if __name__ == '__main__':
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conda.setup()
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core.cli()
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@@ -0,0 +1,41 @@
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import os
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import sys
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from typing import List
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from facefusion.common_helper import is_linux, is_windows
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def setup() -> None:
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conda_prefix = os.getenv('CONDA_PREFIX')
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conda_ready = os.getenv('CONDA_READY')
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if conda_prefix and not conda_ready:
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if is_linux():
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python_id = 'python' + str(sys.version_info.major) + '.' + str(sys.version_info.minor)
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library_paths : List[str] =\
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[
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os.path.join(conda_prefix, 'lib'),
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os.path.join(conda_prefix, 'lib', python_id, 'site-packages', 'tensorrt_libs')
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]
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library_paths = list(filter(os.path.exists, library_paths))
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if library_paths:
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if os.getenv('LD_LIBRARY_PATH'):
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library_paths.append(os.getenv('LD_LIBRARY_PATH'))
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os.environ['LD_LIBRARY_PATH'] = os.pathsep.join(library_paths)
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os.environ['CONDA_READY'] = '1'
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os.execv(sys.executable, [ sys.executable ] + sys.argv)
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if is_windows():
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library_paths =\
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[
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os.path.join(conda_prefix, 'Lib'),
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os.path.join(conda_prefix, 'Lib', 'site-packages', 'tensorrt_libs')
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]
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library_paths = list(filter(os.path.exists, library_paths))
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if library_paths:
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if os.getenv('PATH'):
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library_paths.append(os.getenv('PATH'))
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os.environ['PATH'] = os.pathsep.join(library_paths)
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os.environ['CONDA_READY'] = '1'
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@@ -72,35 +72,3 @@ def run(program : ArgumentParser) -> None:
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subprocess.call([ shutil.which('pip'), 'uninstall', 'onnxruntime', onnxruntime_name, '-y', '-q' ])
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subprocess.call(commands)
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if args.onnxruntime == 'cuda' and has_conda:
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library_paths = []
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if is_linux():
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python_id = 'python' + str(sys.version_info.major) + '.' + str(sys.version_info.minor)
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library_paths.extend(
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[
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os.path.join(os.getenv('CONDA_PREFIX'), 'lib'),
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os.path.join(os.getenv('CONDA_PREFIX'), 'lib', python_id, 'site-packages', 'tensorrt_libs')
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])
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if os.getenv('LD_LIBRARY_PATH'):
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library_paths.extend(os.getenv('LD_LIBRARY_PATH').split(os.pathsep))
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library_paths = list(dict.fromkeys(filter(os.path.exists, library_paths)))
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subprocess.call([ shutil.which('conda'), 'env', 'config', 'vars', 'set', 'LD_LIBRARY_PATH=' + os.pathsep.join(library_paths) ])
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if is_windows():
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library_paths.extend(
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[
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os.path.join(os.getenv('CONDA_PREFIX'), 'Lib'),
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os.path.join(os.getenv('CONDA_PREFIX'), 'Lib', 'site-packages', 'tensorrt_libs')
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])
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if os.getenv('PATH'):
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library_paths.extend(os.getenv('PATH').split(os.pathsep))
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library_paths = list(dict.fromkeys(filter(os.path.exists, library_paths)))
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subprocess.call([ shutil.which('conda'), 'env', 'config', 'vars', 'set', 'PATH=' + os.pathsep.join(library_paths) ])
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@@ -4,7 +4,7 @@ METADATA =\
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{
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'name': 'FaceFusion',
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'description': 'Industry leading face manipulation platform',
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'version': '3.5.4',
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'version': '3.6.0',
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'license': 'OpenRAIL-AS',
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'author': 'Henry Ruhs',
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'url': 'https://facefusion.io'
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@@ -29,6 +29,41 @@ from facefusion.vision import match_frame_color, read_static_image, read_static_
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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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'fran':
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{
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'__metadata__':
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{
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'vendor': 'ry-lu',
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'license': 'mit',
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'year': 2024
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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': resolve_download_url('models-3.6.0', 'fran.hash'),
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'path': resolve_relative_path('../.assets/models/fran.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': resolve_download_url('models-3.6.0', 'fran.onnx'),
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'path': resolve_relative_path('../.assets/models/fran.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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},
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'sizes':
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{
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'target': (1024, 1024),
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},
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'mean': [ 0.0, 0.0, 0.0 ],
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'standard_deviation': [ 1.0, 1.0, 1.0 ]
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},
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'styleganex_age':
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{
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'__metadata__':
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@@ -62,7 +97,9 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
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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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'mean': [ 0.5, 0.5, 0.5 ],
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'standard_deviation': [ 0.5, 0.5, 0.5 ]
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}
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}
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@@ -87,7 +124,7 @@ def get_model_options() -> ModelOptions:
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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 = translator.get('help.model', __package__), default = config.get_str_value('processors', 'age_modifier_model', 'styleganex_age'), choices = age_modifier_choices.age_modifier_models)
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group_processors.add_argument('--age-modifier-model', help = translator.get('help.model', __package__), default = config.get_str_value('processors', 'age_modifier_model', 'fran'), choices = age_modifier_choices.age_modifier_models)
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group_processors.add_argument('--age-modifier-direction', help = translator.get('help.direction', __package__), type = int, default = config.get_int_value('processors', 'age_modifier_direction', '0'), choices = age_modifier_choices.age_modifier_direction_range, metavar = create_int_metavar(age_modifier_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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@@ -137,32 +174,57 @@ def modify_age(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFra
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model_sizes = get_model_options().get('sizes')
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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_templates.get('target'), model_sizes.get('target'))
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extend_face_landmark_5 = scale_face_landmark_5(face_landmark_5, 0.875)
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extend_vision_frame, extend_affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, extend_face_landmark_5, model_templates.get('target_with_background'), model_sizes.get('target_with_background'))
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extend_vision_frame_raw = extend_vision_frame.copy()
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box_mask = create_box_mask(extend_vision_frame, 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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temp_matrix = merge_matrix([ extend_affine_matrix, cv2.invertAffineTransform(affine_matrix) ])
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occlusion_mask = cv2.warpAffine(occlusion_mask, temp_matrix, model_sizes.get('target_with_background'))
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crop_masks.append(occlusion_mask)
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if state_manager.get_item('age_modifier_model') == 'fran':
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box_mask = create_box_mask(crop_vision_frame, 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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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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age_modifier_direction = numpy.array(numpy.interp(state_manager.get_item('age_modifier_direction'), [ -100, 100 ], [ 2.5, -2.5 ])).astype(numpy.float32)
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extend_vision_frame = forward(crop_vision_frame, extend_vision_frame, age_modifier_direction)
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extend_vision_frame = normalize_extend_frame(extend_vision_frame)
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extend_vision_frame = match_frame_color(extend_vision_frame_raw, extend_vision_frame)
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extend_affine_matrix *= (model_sizes.get('target')[0] * 4) / model_sizes.get('target_with_background')[0]
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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_sizes.get('target')[0] * 4, model_sizes.get('target')[1] * 4))
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paste_vision_frame = paste_back(temp_vision_frame, extend_vision_frame, crop_mask, extend_affine_matrix)
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return paste_vision_frame
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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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crop_masks.append(occlusion_mask)
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crop_vision_frame = prepare_vision_frame(crop_vision_frame)
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target_age = numpy.mean(target_face.age)
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age_modifier_direction = numpy.array([ target_age, target_age + state_manager.get_item('age_modifier_direction') ], dtype = numpy.float32) / 100
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age_modifier_direction = age_modifier_direction.clip(0, 1)
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crop_vision_frame = forward(crop_vision_frame, crop_vision_frame, age_modifier_direction)
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crop_vision_frame = normalize_vision_frame(crop_vision_frame)
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crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1)
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paste_vision_frame = paste_back(temp_vision_frame, crop_vision_frame, crop_mask, affine_matrix)
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return paste_vision_frame
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if state_manager.get_item('age_modifier_model') == 'styleganex_age':
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extend_face_landmark_5 = scale_face_landmark_5(face_landmark_5, 0.875)
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extend_vision_frame, extend_affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, extend_face_landmark_5, model_templates.get('target_with_background'), model_sizes.get('target_with_background'))
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extend_vision_frame_raw = extend_vision_frame.copy()
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box_mask = create_box_mask(extend_vision_frame, state_manager.get_item('face_mask_blur'), (0, 0, 0, 0))
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crop_masks =\
|
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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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temp_matrix = merge_matrix([ extend_affine_matrix, cv2.invertAffineTransform(affine_matrix) ])
|
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occlusion_mask = cv2.warpAffine(occlusion_mask, temp_matrix, model_sizes.get('target_with_background'))
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crop_masks.append(occlusion_mask)
|
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|
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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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age_modifier_direction = numpy.array(numpy.interp(state_manager.get_item('age_modifier_direction'), [ -100, 100 ], [ 2.5, -2.5 ])).astype(numpy.float32)
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extend_vision_frame = forward(crop_vision_frame, extend_vision_frame, age_modifier_direction)
|
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extend_vision_frame = normalize_extend_frame(extend_vision_frame)
|
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extend_vision_frame = match_frame_color(extend_vision_frame_raw, extend_vision_frame)
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extend_affine_matrix *= (model_sizes.get('target')[0] * 4) / model_sizes.get('target_with_background')[0]
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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_sizes.get('target')[0] * 4, model_sizes.get('target')[1] * 4))
|
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paste_vision_frame = paste_back(temp_vision_frame, extend_vision_frame, crop_mask, extend_affine_matrix)
|
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return paste_vision_frame
|
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|
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return temp_vision_frame
|
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|
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|
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def forward(crop_vision_frame : VisionFrame, extend_vision_frame : VisionFrame, age_modifier_direction : AgeModifierDirection) -> VisionFrame:
|
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@@ -187,12 +249,24 @@ def forward(crop_vision_frame : VisionFrame, extend_vision_frame : VisionFrame,
|
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|
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|
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def prepare_vision_frame(vision_frame : VisionFrame) -> VisionFrame:
|
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model_mean = get_model_options().get('mean')
|
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model_standard_deviation = get_model_options().get('standard_deviation')
|
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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 = (vision_frame - model_mean) / model_standard_deviation
|
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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
|
||||
|
||||
|
||||
def normalize_vision_frame(vision_frame : VisionFrame) -> VisionFrame:
|
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model_mean = get_model_options().get('mean')
|
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model_standard_deviation = get_model_options().get('standard_deviation')
|
||||
vision_frame = vision_frame.transpose(1, 2, 0)
|
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vision_frame = vision_frame * model_standard_deviation + model_mean
|
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vision_frame = vision_frame.clip(0, 1)
|
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vision_frame = vision_frame[:, :, ::-1] * 255
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return vision_frame
|
||||
|
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|
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def normalize_extend_frame(extend_vision_frame : VisionFrame) -> VisionFrame:
|
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model_sizes = get_model_options().get('sizes')
|
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extend_vision_frame = numpy.clip(extend_vision_frame, -1, 1)
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||||
@@ -12,6 +12,6 @@ AgeModifierInputs = TypedDict('AgeModifierInputs',
|
||||
'temp_vision_mask' : Mask
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||||
})
|
||||
|
||||
AgeModifierModel = Literal['styleganex_age']
|
||||
AgeModifierModel = Literal['fran', 'styleganex_age']
|
||||
|
||||
AgeModifierDirection : TypeAlias = NDArray[Any]
|
||||
|
||||
@@ -8,7 +8,7 @@ import numpy
|
||||
import facefusion.jobs.job_manager
|
||||
import facefusion.jobs.job_store
|
||||
from facefusion import config, content_analyser, inference_manager, logger, state_manager, translator, video_manager
|
||||
from facefusion.common_helper import is_macos
|
||||
from facefusion.common_helper import is_macos, is_windows
|
||||
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
|
||||
from facefusion.execution import has_execution_provider
|
||||
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
|
||||
@@ -51,6 +51,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/ben_2.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'ben',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.0, 0.0, 0.0 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
@@ -79,6 +80,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/birefnet_general.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'birefnet',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.0, 0.0, 0.0 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
@@ -107,10 +109,69 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/birefnet_portrait.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'birefnet',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.0, 0.0, 0.0 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
},
|
||||
'corridor_key_1024':
|
||||
{
|
||||
'__metadata__':
|
||||
{
|
||||
'vendor': 'nikopueringer',
|
||||
'license': 'Non-Commercial',
|
||||
'year': 2025
|
||||
},
|
||||
'hashes':
|
||||
{
|
||||
'background_remover':
|
||||
{
|
||||
'url': resolve_download_url('models-3.6.0', 'corridor_key_1024.hash'),
|
||||
'path': resolve_relative_path('../.assets/models/corridor_key_1024.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'background_remover':
|
||||
{
|
||||
'url': resolve_download_url('models-3.6.0', 'corridor_key_1024.onnx'),
|
||||
'path': resolve_relative_path('../.assets/models/corridor_key_1024.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'corridor_key',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
},
|
||||
'corridor_key_2048':
|
||||
{
|
||||
'__metadata__':
|
||||
{
|
||||
'vendor': 'nikopueringer',
|
||||
'license': 'Non-Commercial',
|
||||
'year': 2025
|
||||
},
|
||||
'hashes':
|
||||
{
|
||||
'background_remover':
|
||||
{
|
||||
'url': resolve_download_url('models-3.6.0', 'corridor_key_2048.hash'),
|
||||
'path': resolve_relative_path('../.assets/models/corridor_key_2048.hash')
|
||||
}
|
||||
},
|
||||
'sources':
|
||||
{
|
||||
'background_remover':
|
||||
{
|
||||
'url': resolve_download_url('models-3.6.0', 'corridor_key_2048.onnx'),
|
||||
'path': resolve_relative_path('../.assets/models/corridor_key_2048.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'corridor_key',
|
||||
'size': (2048, 2048),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
},
|
||||
'isnet_general':
|
||||
{
|
||||
'__metadata__':
|
||||
@@ -135,6 +196,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/isnet_general.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'isnet',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.5, 0.5, 0.5 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
@@ -163,6 +225,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/modnet.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'modnet',
|
||||
'size': (512, 512),
|
||||
'mean': [ 0.5, 0.5, 0.5 ],
|
||||
'standard_deviation': [ 0.5, 0.5, 0.5 ]
|
||||
@@ -191,6 +254,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/ormbg.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'ormbg',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.0, 0.0, 0.0 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
@@ -219,6 +283,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/rmbg_1.4.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'rmbg',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.5, 0.5, 0.5 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
@@ -247,6 +312,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/rmbg_2.0.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'rmbg',
|
||||
'size': (1024, 1024),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
@@ -275,6 +341,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/silueta.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'silueta',
|
||||
'size': (320, 320),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
@@ -303,6 +370,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/u2net_cloth.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'u2net_cloth',
|
||||
'size': (768, 768),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
@@ -331,6 +399,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/u2net_general.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'u2net',
|
||||
'size': (320, 320),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
@@ -359,6 +428,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/u2net_human.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'u2net',
|
||||
'size': (320, 320),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
@@ -387,6 +457,7 @@ def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
|
||||
'path': resolve_relative_path('../.assets/models/u2netp.onnx')
|
||||
}
|
||||
},
|
||||
'type': 'u2netp',
|
||||
'size': (320, 320),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
@@ -407,7 +478,9 @@ def clear_inference_pool() -> None:
|
||||
|
||||
|
||||
def resolve_execution_providers() -> List[ExecutionProvider]:
|
||||
if is_macos() and has_execution_provider('coreml'):
|
||||
model_type = get_model_options().get('type')
|
||||
|
||||
if is_macos() and has_execution_provider('coreml') or is_windows() and has_execution_provider('directml') and model_type == 'corridor_key':
|
||||
return [ 'cpu' ]
|
||||
return state_manager.get_item('execution_providers')
|
||||
|
||||
@@ -420,14 +493,16 @@ def get_model_options() -> ModelOptions:
|
||||
def register_args(program : ArgumentParser) -> None:
|
||||
group_processors = find_argument_group(program, 'processors')
|
||||
if group_processors:
|
||||
group_processors.add_argument('--background-remover-model', help = translator.get('help.model', __package__), default = config.get_str_value('processors', 'background_remover_model', 'rmbg_2.0'), choices = background_remover_choices.background_remover_models)
|
||||
group_processors.add_argument('--background-remover-color', help = translator.get('help.color', __package__), type = partial(sanitize_int_range, int_range = background_remover_choices.background_remover_color_range), default = config.get_int_list('processors', 'background_remover_color', '0 0 0 0'), nargs = '+')
|
||||
facefusion.jobs.job_store.register_step_keys([ 'background_remover_model', 'background_remover_color' ])
|
||||
group_processors.add_argument('--background-remover-model', help = translator.get('help.model', __package__), default = config.get_str_value('processors', 'background_remover_model', 'modnet'), choices = background_remover_choices.background_remover_models)
|
||||
group_processors.add_argument('--background-remover-fill-color', help = translator.get('help.fill_color', __package__), type = partial(sanitize_int_range, int_range = background_remover_choices.background_remover_color_range), default = config.get_int_list('processors', 'background_remover_fill_color', '0 0 0 0'), nargs = '+')
|
||||
group_processors.add_argument('--background-remover-despill-color', help = translator.get('help.despill_color', __package__), type = partial(sanitize_int_range, int_range = background_remover_choices.background_remover_color_range), default = config.get_int_list('processors', 'background_remover_despill_color', '0 0 0 0'), nargs = '+')
|
||||
facefusion.jobs.job_store.register_step_keys([ 'background_remover_model', 'background_remover_fill_color', 'background_remover_despill_color' ])
|
||||
|
||||
|
||||
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
||||
apply_state_item('background_remover_model', args.get('background_remover_model'))
|
||||
apply_state_item('background_remover_color', normalize_color(args.get('background_remover_color')))
|
||||
apply_state_item('background_remover_fill_color', normalize_color(args.get('background_remover_fill_color')))
|
||||
apply_state_item('background_remover_despill_color', normalize_color(args.get('background_remover_despill_color')))
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
@@ -461,16 +536,26 @@ def post_process() -> None:
|
||||
|
||||
|
||||
def remove_background(temp_vision_frame : VisionFrame) -> Tuple[VisionFrame, Mask]:
|
||||
temp_vision_mask = forward(prepare_temp_frame(temp_vision_frame))
|
||||
temp_vision_mask = normalize_vision_mask(temp_vision_mask)
|
||||
temp_vision_mask = cv2.resize(temp_vision_mask, temp_vision_frame.shape[:2][::-1])
|
||||
temp_vision_frame = apply_background_color(temp_vision_frame, temp_vision_mask)
|
||||
return temp_vision_frame, temp_vision_mask
|
||||
model_type = get_model_options().get('type')
|
||||
|
||||
if model_type == 'corridor_key':
|
||||
remove_vision_mask, remove_vision_frame = forward_corridor_key(prepare_temp_frame(temp_vision_frame))
|
||||
remove_vision_frame = numpy.squeeze(remove_vision_frame).transpose(1, 2, 0)
|
||||
remove_vision_frame = numpy.clip(remove_vision_frame * 255, 0, 255).astype(numpy.uint8)
|
||||
temp_vision_frame = cv2.resize(remove_vision_frame[:, :, ::-1], temp_vision_frame.shape[:2][::-1])
|
||||
else:
|
||||
remove_vision_mask = forward(prepare_temp_frame(temp_vision_frame))
|
||||
|
||||
remove_vision_mask = normalize_vision_mask(remove_vision_mask)
|
||||
remove_vision_mask = cv2.resize(remove_vision_mask, temp_vision_frame.shape[:2][::-1])
|
||||
temp_vision_frame = apply_despill_color(temp_vision_frame)
|
||||
temp_vision_frame = apply_fill_color(temp_vision_frame, remove_vision_mask)
|
||||
return temp_vision_frame, remove_vision_mask
|
||||
|
||||
|
||||
def forward(temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
background_remover = get_inference_pool().get('background_remover')
|
||||
model_name = state_manager.get_item('background_remover_model')
|
||||
model_type = get_model_options().get('type')
|
||||
|
||||
with thread_semaphore():
|
||||
remove_vision_frame = background_remover.run(None,
|
||||
@@ -478,20 +563,42 @@ def forward(temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
'input': temp_vision_frame
|
||||
})[0]
|
||||
|
||||
if model_name == 'u2net_cloth':
|
||||
if model_type == 'u2net_cloth':
|
||||
remove_vision_frame = numpy.argmax(remove_vision_frame, axis = 1)
|
||||
|
||||
return remove_vision_frame
|
||||
|
||||
|
||||
def forward_corridor_key(temp_vision_frame : VisionFrame) -> Tuple[Mask, VisionFrame]:
|
||||
background_remover = get_inference_pool().get('background_remover')
|
||||
|
||||
with thread_semaphore():
|
||||
remove_vision_mask, remove_vision_frame = background_remover.run(None,
|
||||
{
|
||||
'input': temp_vision_frame
|
||||
})
|
||||
|
||||
return remove_vision_mask, remove_vision_frame
|
||||
|
||||
|
||||
def prepare_temp_frame(temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
model_type = get_model_options().get('type')
|
||||
model_size = get_model_options().get('size')
|
||||
model_mean = get_model_options().get('mean')
|
||||
model_standard_deviation = get_model_options().get('standard_deviation')
|
||||
|
||||
if model_type == 'corridor_key':
|
||||
coarse_color = temp_vision_frame[:, :, ::-1].astype(numpy.float32) / 255.0
|
||||
coarse_bias = coarse_color[:, :, 1] - numpy.maximum(coarse_color[:, :, 0], coarse_color[:, :, 2])
|
||||
coarse_vision_mask = cv2.resize(1.0 - numpy.clip(coarse_bias * 2.0, 0, 1), model_size)[:, :, numpy.newaxis]
|
||||
|
||||
temp_vision_frame = cv2.resize(temp_vision_frame, model_size)
|
||||
temp_vision_frame = temp_vision_frame[:, :, ::-1] / 255.0
|
||||
temp_vision_frame = (temp_vision_frame - model_mean) / model_standard_deviation
|
||||
|
||||
if model_type == 'corridor_key':
|
||||
temp_vision_frame = numpy.concatenate([ temp_vision_frame, coarse_vision_mask ], axis = 2)
|
||||
|
||||
temp_vision_frame = temp_vision_frame.transpose(2, 0, 1)
|
||||
temp_vision_frame = numpy.expand_dims(temp_vision_frame, axis = 0).astype(numpy.float32)
|
||||
return temp_vision_frame
|
||||
@@ -503,16 +610,32 @@ def normalize_vision_mask(temp_vision_mask : Mask) -> Mask:
|
||||
return temp_vision_mask
|
||||
|
||||
|
||||
def apply_background_color(temp_vision_frame : VisionFrame, temp_vision_mask : Mask) -> VisionFrame:
|
||||
background_remover_color = state_manager.get_item('background_remover_color')
|
||||
def apply_fill_color(temp_vision_frame : VisionFrame, temp_vision_mask : Mask) -> VisionFrame:
|
||||
background_remover_fill_color = state_manager.get_item('background_remover_fill_color')
|
||||
temp_vision_mask = temp_vision_mask.astype(numpy.float32) / 255
|
||||
temp_vision_mask = numpy.expand_dims(temp_vision_mask, axis = 2)
|
||||
temp_vision_mask = (1 - temp_vision_mask) * background_remover_color[-1] / 255
|
||||
color_frame = numpy.zeros_like(temp_vision_frame)
|
||||
color_frame[:, :, 0] = background_remover_color[2]
|
||||
color_frame[:, :, 1] = background_remover_color[1]
|
||||
color_frame[:, :, 2] = background_remover_color[0]
|
||||
temp_vision_frame = temp_vision_frame * (1 - temp_vision_mask) + color_frame * temp_vision_mask
|
||||
temp_vision_mask = (1 - temp_vision_mask) * background_remover_fill_color[-1] / 255
|
||||
fill_vision_frame = numpy.zeros_like(temp_vision_frame)
|
||||
fill_vision_frame[:, :, 0] = background_remover_fill_color[2]
|
||||
fill_vision_frame[:, :, 1] = background_remover_fill_color[1]
|
||||
fill_vision_frame[:, :, 2] = background_remover_fill_color[0]
|
||||
temp_vision_frame = temp_vision_frame * (1 - temp_vision_mask) + fill_vision_frame * temp_vision_mask
|
||||
temp_vision_frame = temp_vision_frame.astype(numpy.uint8)
|
||||
return temp_vision_frame
|
||||
|
||||
|
||||
def apply_despill_color(temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
background_remover_despill_color = state_manager.get_item('background_remover_despill_color')
|
||||
temp_vision_frame = temp_vision_frame.astype(numpy.float32)
|
||||
color_alpha = background_remover_despill_color[3] / 255.0
|
||||
despill_vision_frame = numpy.zeros_like(temp_vision_frame)
|
||||
despill_vision_frame[:, :, 0] = background_remover_despill_color[2]
|
||||
despill_vision_frame[:, :, 1] = background_remover_despill_color[1]
|
||||
despill_vision_frame[:, :, 2] = background_remover_despill_color[0]
|
||||
color_weight = despill_vision_frame / numpy.maximum(numpy.max(background_remover_despill_color[:3]), 1)
|
||||
color_limit = numpy.roll(temp_vision_frame, 1, 2) + numpy.roll(temp_vision_frame, -1, 2)
|
||||
limit_vision_frame = numpy.minimum(temp_vision_frame, color_limit * 0.5)
|
||||
temp_vision_frame = temp_vision_frame + (limit_vision_frame - temp_vision_frame) * color_alpha * color_weight
|
||||
temp_vision_frame = temp_vision_frame.astype(numpy.uint8)
|
||||
return temp_vision_frame
|
||||
|
||||
|
||||
@@ -7,15 +7,20 @@ LOCALES : Locales =\
|
||||
'help':
|
||||
{
|
||||
'model': 'choose the model responsible for removing the background',
|
||||
'color': 'apply red, green blue and alpha values to the background'
|
||||
'fill_color': 'apply red, green, blue and alpha values to the background',
|
||||
'despill_color': 'remove red, green, blue and alpha values from the foreground'
|
||||
},
|
||||
'uis':
|
||||
{
|
||||
'model_dropdown': 'BACKGROUND REMOVER MODEL',
|
||||
'color_red_number': 'BACKGROUND COLOR RED',
|
||||
'color_green_number': 'BACKGROUND COLOR GREEN',
|
||||
'color_blue_number': 'BACKGROUND COLOR BLUE',
|
||||
'color_alpha_number': 'BACKGROUND COLOR ALPHA'
|
||||
'fill_color_red_number': 'FILL COLOR RED',
|
||||
'fill_color_green_number': 'FILL COLOR GREEN',
|
||||
'fill_color_blue_number': 'FILL COLOR BLUE',
|
||||
'fill_color_alpha_number': 'FILL COLOR ALPHA',
|
||||
'despill_color_red_number': 'DESPILL COLOR RED',
|
||||
'despill_color_green_number': 'DESPILL COLOR GREEN',
|
||||
'despill_color_blue_number': 'DESPILL COLOR BLUE',
|
||||
'despill_color_alpha_number': 'DESPILL COLOR ALPHA'
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -9,4 +9,4 @@ BackgroundRemoverInputs = TypedDict('BackgroundRemoverInputs',
|
||||
'temp_vision_mask' : Mask
|
||||
})
|
||||
|
||||
BackgroundRemoverModel = Literal['ben_2', 'birefnet_general', 'birefnet_portrait', 'isnet_general', 'modnet', 'ormbg', 'rmbg_1.4', 'rmbg_2.0', 'silueta', 'u2net_cloth', 'u2net_general', 'u2net_human', 'u2netp']
|
||||
BackgroundRemoverModel = Literal['ben_2', 'birefnet_general', 'birefnet_portrait', 'corridor_key_1024', 'corridor_key_2048', 'isnet_general', 'modnet', 'ormbg', 'rmbg_1.4', 'rmbg_2.0', 'silueta', 'u2net_cloth', 'u2net_general', 'u2net_human', 'u2netp']
|
||||
|
||||
@@ -540,8 +540,8 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
return False
|
||||
|
||||
source_image_paths = filter_image_paths(state_manager.get_item('source_paths'))
|
||||
source_frames = read_static_images(source_image_paths)
|
||||
source_faces = get_many_faces(source_frames)
|
||||
source_vision_frames = read_static_images(source_image_paths)
|
||||
source_faces = get_many_faces(source_vision_frames)
|
||||
|
||||
if not get_one_face(source_faces):
|
||||
logger.error(translator.get('no_source_face_detected') + translator.get('exclamation_mark'), __name__)
|
||||
|
||||
@@ -26,17 +26,17 @@ def multi_process_capture(camera_capture : cv2.VideoCapture, camera_fps : Fps) -
|
||||
futures = []
|
||||
|
||||
while camera_capture and camera_capture.isOpened():
|
||||
_, capture_frame = camera_capture.read()
|
||||
if analyse_stream(capture_frame, camera_fps):
|
||||
_, capture_vision_frame = camera_capture.read()
|
||||
if analyse_stream(capture_vision_frame, camera_fps):
|
||||
camera_capture.release()
|
||||
|
||||
if numpy.any(capture_frame):
|
||||
future = executor.submit(process_stream_frame, capture_frame)
|
||||
if numpy.any(capture_vision_frame):
|
||||
future = executor.submit(process_stream_frame, capture_vision_frame)
|
||||
futures.append(future)
|
||||
|
||||
for future_done in [ future for future in futures if future.done() ]:
|
||||
capture_frame = future_done.result()
|
||||
capture_deque.append(capture_frame)
|
||||
capture_vision_frame = future_done.result()
|
||||
capture_deque.append(capture_vision_frame)
|
||||
futures.remove(future_done)
|
||||
|
||||
while capture_deque:
|
||||
|
||||
@@ -14,7 +14,7 @@ preview_resolutions : List[str] = [ '512x512', '768x768', '1024x1024' ]
|
||||
webcam_modes : List[WebcamMode] = [ 'inline', 'udp', 'v4l2' ]
|
||||
webcam_resolutions : List[str] = [ '320x240', '640x480', '800x600', '1024x768', '1280x720', '1280x960', '1920x1080' ]
|
||||
|
||||
background_remover_colors : Dict[str, Color] =\
|
||||
background_remover_fill_colors : Dict[str, Color] =\
|
||||
{
|
||||
'red' : (255, 0, 0, 255),
|
||||
'green' : (0, 255, 0, 255),
|
||||
|
||||
@@ -11,82 +11,132 @@ from facefusion.sanitizer import sanitize_int_range
|
||||
from facefusion.uis.core import get_ui_component, register_ui_component
|
||||
|
||||
BACKGROUND_REMOVER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
|
||||
BACKGROUND_REMOVER_COLOR_WRAPPER : Optional[gradio.Group] = None
|
||||
BACKGROUND_REMOVER_COLOR_RED_NUMBER : Optional[gradio.Number] = None
|
||||
BACKGROUND_REMOVER_COLOR_GREEN_NUMBER : Optional[gradio.Number] = None
|
||||
BACKGROUND_REMOVER_COLOR_BLUE_NUMBER : Optional[gradio.Number] = None
|
||||
BACKGROUND_REMOVER_COLOR_ALPHA_NUMBER : Optional[gradio.Number] = None
|
||||
BACKGROUND_REMOVER_FILL_COLOR_WRAPPER : Optional[gradio.Group] = None
|
||||
BACKGROUND_REMOVER_FILL_COLOR_RED_NUMBER : Optional[gradio.Number] = None
|
||||
BACKGROUND_REMOVER_FILL_COLOR_GREEN_NUMBER : Optional[gradio.Number] = None
|
||||
BACKGROUND_REMOVER_FILL_COLOR_BLUE_NUMBER : Optional[gradio.Number] = None
|
||||
BACKGROUND_REMOVER_FILL_COLOR_ALPHA_NUMBER : Optional[gradio.Number] = None
|
||||
BACKGROUND_REMOVER_DESPILL_COLOR_WRAPPER : Optional[gradio.Group] = None
|
||||
BACKGROUND_REMOVER_DESPILL_COLOR_RED_NUMBER : Optional[gradio.Number] = None
|
||||
BACKGROUND_REMOVER_DESPILL_COLOR_GREEN_NUMBER : Optional[gradio.Number] = None
|
||||
BACKGROUND_REMOVER_DESPILL_COLOR_BLUE_NUMBER : Optional[gradio.Number] = None
|
||||
BACKGROUND_REMOVER_DESPILL_COLOR_ALPHA_NUMBER : Optional[gradio.Number] = None
|
||||
|
||||
|
||||
def render() -> None:
|
||||
global BACKGROUND_REMOVER_MODEL_DROPDOWN
|
||||
global BACKGROUND_REMOVER_COLOR_WRAPPER
|
||||
global BACKGROUND_REMOVER_COLOR_RED_NUMBER
|
||||
global BACKGROUND_REMOVER_COLOR_GREEN_NUMBER
|
||||
global BACKGROUND_REMOVER_COLOR_BLUE_NUMBER
|
||||
global BACKGROUND_REMOVER_COLOR_ALPHA_NUMBER
|
||||
global BACKGROUND_REMOVER_FILL_COLOR_WRAPPER
|
||||
global BACKGROUND_REMOVER_FILL_COLOR_RED_NUMBER
|
||||
global BACKGROUND_REMOVER_FILL_COLOR_GREEN_NUMBER
|
||||
global BACKGROUND_REMOVER_FILL_COLOR_BLUE_NUMBER
|
||||
global BACKGROUND_REMOVER_FILL_COLOR_ALPHA_NUMBER
|
||||
global BACKGROUND_REMOVER_DESPILL_COLOR_WRAPPER
|
||||
global BACKGROUND_REMOVER_DESPILL_COLOR_RED_NUMBER
|
||||
global BACKGROUND_REMOVER_DESPILL_COLOR_GREEN_NUMBER
|
||||
global BACKGROUND_REMOVER_DESPILL_COLOR_BLUE_NUMBER
|
||||
global BACKGROUND_REMOVER_DESPILL_COLOR_ALPHA_NUMBER
|
||||
|
||||
has_background_remover = 'background_remover' in state_manager.get_item('processors')
|
||||
background_remover_color = state_manager.get_item('background_remover_color')
|
||||
background_remover_fill_color = state_manager.get_item('background_remover_fill_color')
|
||||
background_remover_despill_color = state_manager.get_item('background_remover_despill_color')
|
||||
BACKGROUND_REMOVER_MODEL_DROPDOWN = gradio.Dropdown(
|
||||
label = translator.get('uis.model_dropdown', 'facefusion.processors.modules.background_remover'),
|
||||
choices = background_remover_choices.background_remover_models,
|
||||
value = state_manager.get_item('background_remover_model'),
|
||||
visible = has_background_remover
|
||||
)
|
||||
with gradio.Group(visible = has_background_remover) as BACKGROUND_REMOVER_COLOR_WRAPPER:
|
||||
with gradio.Group(visible = has_background_remover) as BACKGROUND_REMOVER_FILL_COLOR_WRAPPER:
|
||||
with gradio.Row():
|
||||
BACKGROUND_REMOVER_COLOR_RED_NUMBER = gradio.Number(
|
||||
label = translator.get('uis.color_red_number', 'facefusion.processors.modules.background_remover'),
|
||||
value = background_remover_color[0],
|
||||
BACKGROUND_REMOVER_FILL_COLOR_RED_NUMBER = gradio.Number(
|
||||
label = translator.get('uis.fill_color_red_number', 'facefusion.processors.modules.background_remover'),
|
||||
value = background_remover_fill_color[0],
|
||||
minimum = background_remover_choices.background_remover_color_range[0],
|
||||
maximum = background_remover_choices.background_remover_color_range[-1],
|
||||
step = calculate_int_step(background_remover_choices.background_remover_color_range)
|
||||
)
|
||||
BACKGROUND_REMOVER_COLOR_GREEN_NUMBER = gradio.Number(
|
||||
label = translator.get('uis.color_green_number', 'facefusion.processors.modules.background_remover'),
|
||||
value = background_remover_color[1],
|
||||
BACKGROUND_REMOVER_FILL_COLOR_GREEN_NUMBER = gradio.Number(
|
||||
label = translator.get('uis.fill_color_green_number', 'facefusion.processors.modules.background_remover'),
|
||||
value = background_remover_fill_color[1],
|
||||
minimum = background_remover_choices.background_remover_color_range[0],
|
||||
maximum = background_remover_choices.background_remover_color_range[-1],
|
||||
step = calculate_int_step(background_remover_choices.background_remover_color_range)
|
||||
)
|
||||
with gradio.Row():
|
||||
BACKGROUND_REMOVER_COLOR_BLUE_NUMBER = gradio.Number(
|
||||
label = translator.get('uis.color_blue_number', 'facefusion.processors.modules.background_remover'),
|
||||
value = background_remover_color[2],
|
||||
BACKGROUND_REMOVER_FILL_COLOR_BLUE_NUMBER = gradio.Number(
|
||||
label = translator.get('uis.fill_color_blue_number', 'facefusion.processors.modules.background_remover'),
|
||||
value = background_remover_fill_color[2],
|
||||
minimum = background_remover_choices.background_remover_color_range[0],
|
||||
maximum = background_remover_choices.background_remover_color_range[-1],
|
||||
step = calculate_int_step(background_remover_choices.background_remover_color_range)
|
||||
)
|
||||
BACKGROUND_REMOVER_COLOR_ALPHA_NUMBER = gradio.Number(
|
||||
label = translator.get('uis.color_alpha_number', 'facefusion.processors.modules.background_remover'),
|
||||
value = background_remover_color[3],
|
||||
BACKGROUND_REMOVER_FILL_COLOR_ALPHA_NUMBER = gradio.Number(
|
||||
label = translator.get('uis.fill_color_alpha_number', 'facefusion.processors.modules.background_remover'),
|
||||
value = background_remover_fill_color[3],
|
||||
minimum = background_remover_choices.background_remover_color_range[0],
|
||||
maximum = background_remover_choices.background_remover_color_range[-1],
|
||||
step = calculate_int_step(background_remover_choices.background_remover_color_range)
|
||||
)
|
||||
with gradio.Group(visible = has_background_remover) as BACKGROUND_REMOVER_DESPILL_COLOR_WRAPPER:
|
||||
with gradio.Row():
|
||||
BACKGROUND_REMOVER_DESPILL_COLOR_RED_NUMBER = gradio.Number(
|
||||
label = translator.get('uis.despill_color_red_number', 'facefusion.processors.modules.background_remover'),
|
||||
value = background_remover_despill_color[0],
|
||||
minimum = background_remover_choices.background_remover_color_range[0],
|
||||
maximum = background_remover_choices.background_remover_color_range[-1],
|
||||
step = calculate_int_step(background_remover_choices.background_remover_color_range)
|
||||
)
|
||||
BACKGROUND_REMOVER_DESPILL_COLOR_GREEN_NUMBER = gradio.Number(
|
||||
label = translator.get('uis.despill_color_green_number', 'facefusion.processors.modules.background_remover'),
|
||||
value = background_remover_despill_color[1],
|
||||
minimum = background_remover_choices.background_remover_color_range[0],
|
||||
maximum = background_remover_choices.background_remover_color_range[-1],
|
||||
step = calculate_int_step(background_remover_choices.background_remover_color_range)
|
||||
)
|
||||
with gradio.Row():
|
||||
BACKGROUND_REMOVER_DESPILL_COLOR_BLUE_NUMBER = gradio.Number(
|
||||
label = translator.get('uis.despill_color_blue_number', 'facefusion.processors.modules.background_remover'),
|
||||
value = background_remover_despill_color[2],
|
||||
minimum = background_remover_choices.background_remover_color_range[0],
|
||||
maximum = background_remover_choices.background_remover_color_range[-1],
|
||||
step = calculate_int_step(background_remover_choices.background_remover_color_range)
|
||||
)
|
||||
BACKGROUND_REMOVER_DESPILL_COLOR_ALPHA_NUMBER = gradio.Number(
|
||||
label = translator.get('uis.despill_color_alpha_number', 'facefusion.processors.modules.background_remover'),
|
||||
value = background_remover_despill_color[3],
|
||||
minimum = background_remover_choices.background_remover_color_range[0],
|
||||
maximum = background_remover_choices.background_remover_color_range[-1],
|
||||
step = calculate_int_step(background_remover_choices.background_remover_color_range)
|
||||
)
|
||||
register_ui_component('background_remover_model_dropdown', BACKGROUND_REMOVER_MODEL_DROPDOWN)
|
||||
register_ui_component('background_remover_color_red_number', BACKGROUND_REMOVER_COLOR_RED_NUMBER)
|
||||
register_ui_component('background_remover_color_green_number', BACKGROUND_REMOVER_COLOR_GREEN_NUMBER)
|
||||
register_ui_component('background_remover_color_blue_number', BACKGROUND_REMOVER_COLOR_BLUE_NUMBER)
|
||||
register_ui_component('background_remover_color_alpha_number', BACKGROUND_REMOVER_COLOR_ALPHA_NUMBER)
|
||||
register_ui_component('background_remover_fill_color_red_number', BACKGROUND_REMOVER_FILL_COLOR_RED_NUMBER)
|
||||
register_ui_component('background_remover_fill_color_green_number', BACKGROUND_REMOVER_FILL_COLOR_GREEN_NUMBER)
|
||||
register_ui_component('background_remover_fill_color_blue_number', BACKGROUND_REMOVER_FILL_COLOR_BLUE_NUMBER)
|
||||
register_ui_component('background_remover_fill_color_alpha_number', BACKGROUND_REMOVER_FILL_COLOR_ALPHA_NUMBER)
|
||||
register_ui_component('background_remover_despill_color_red_number', BACKGROUND_REMOVER_DESPILL_COLOR_RED_NUMBER)
|
||||
register_ui_component('background_remover_despill_color_green_number', BACKGROUND_REMOVER_DESPILL_COLOR_GREEN_NUMBER)
|
||||
register_ui_component('background_remover_despill_color_blue_number', BACKGROUND_REMOVER_DESPILL_COLOR_BLUE_NUMBER)
|
||||
register_ui_component('background_remover_despill_color_alpha_number', BACKGROUND_REMOVER_DESPILL_COLOR_ALPHA_NUMBER)
|
||||
|
||||
|
||||
def listen() -> None:
|
||||
BACKGROUND_REMOVER_MODEL_DROPDOWN.change(update_background_remover_model, inputs = BACKGROUND_REMOVER_MODEL_DROPDOWN, outputs = BACKGROUND_REMOVER_MODEL_DROPDOWN)
|
||||
background_remover_color_inputs = [ BACKGROUND_REMOVER_COLOR_RED_NUMBER, BACKGROUND_REMOVER_COLOR_GREEN_NUMBER, BACKGROUND_REMOVER_COLOR_BLUE_NUMBER, BACKGROUND_REMOVER_COLOR_ALPHA_NUMBER ]
|
||||
background_remover_fill_color_inputs = [ BACKGROUND_REMOVER_FILL_COLOR_RED_NUMBER, BACKGROUND_REMOVER_FILL_COLOR_GREEN_NUMBER, BACKGROUND_REMOVER_FILL_COLOR_BLUE_NUMBER, BACKGROUND_REMOVER_FILL_COLOR_ALPHA_NUMBER ]
|
||||
background_remover_despill_color_inputs = [ BACKGROUND_REMOVER_DESPILL_COLOR_RED_NUMBER, BACKGROUND_REMOVER_DESPILL_COLOR_GREEN_NUMBER, BACKGROUND_REMOVER_DESPILL_COLOR_BLUE_NUMBER, BACKGROUND_REMOVER_DESPILL_COLOR_ALPHA_NUMBER ]
|
||||
|
||||
for background_remover_color_input in background_remover_color_inputs:
|
||||
background_remover_color_input.change(update_background_remover_color, inputs = background_remover_color_inputs)
|
||||
for background_remover_fill_color_input in background_remover_fill_color_inputs:
|
||||
background_remover_fill_color_input.change(update_background_remover_fill_color, inputs = background_remover_fill_color_inputs)
|
||||
|
||||
for background_remover_despill_color_input in background_remover_despill_color_inputs:
|
||||
background_remover_despill_color_input.change(update_background_remover_despill_color, inputs = background_remover_despill_color_inputs)
|
||||
|
||||
processors_checkbox_group = get_ui_component('processors_checkbox_group')
|
||||
if processors_checkbox_group:
|
||||
processors_checkbox_group.change(remote_update, inputs = processors_checkbox_group, outputs = [ BACKGROUND_REMOVER_MODEL_DROPDOWN, BACKGROUND_REMOVER_COLOR_WRAPPER ])
|
||||
processors_checkbox_group.change(remote_update, inputs = processors_checkbox_group, outputs = [ BACKGROUND_REMOVER_MODEL_DROPDOWN, BACKGROUND_REMOVER_FILL_COLOR_WRAPPER, BACKGROUND_REMOVER_DESPILL_COLOR_WRAPPER ])
|
||||
|
||||
|
||||
def remote_update(processors : List[str]) -> Tuple[gradio.Dropdown, gradio.Group]:
|
||||
def remote_update(processors : List[str]) -> Tuple[gradio.Dropdown, gradio.Group, gradio.Group]:
|
||||
has_background_remover = 'background_remover' in processors
|
||||
return gradio.Dropdown(visible = has_background_remover), gradio.Group(visible = has_background_remover)
|
||||
return gradio.Dropdown(visible = has_background_remover), gradio.Group(visible = has_background_remover), gradio.Group(visible = has_background_remover)
|
||||
|
||||
|
||||
def update_background_remover_model(background_remover_model : BackgroundRemoverModel) -> gradio.Dropdown:
|
||||
@@ -99,9 +149,17 @@ def update_background_remover_model(background_remover_model : BackgroundRemover
|
||||
return gradio.Dropdown()
|
||||
|
||||
|
||||
def update_background_remover_color(red : int, green : int, blue : int, alpha : int) -> None:
|
||||
def update_background_remover_fill_color(red : int, green : int, blue : int, alpha : int) -> None:
|
||||
red = sanitize_int_range(red, background_remover_choices.background_remover_color_range)
|
||||
green = sanitize_int_range(green, background_remover_choices.background_remover_color_range)
|
||||
blue = sanitize_int_range(blue, background_remover_choices.background_remover_color_range)
|
||||
alpha = sanitize_int_range(alpha, background_remover_choices.background_remover_color_range)
|
||||
state_manager.set_item('background_remover_color', (red, green, blue, alpha))
|
||||
state_manager.set_item('background_remover_fill_color', (red, green, blue, alpha))
|
||||
|
||||
|
||||
def update_background_remover_despill_color(red : int, green : int, blue : int, alpha : int) -> None:
|
||||
red = sanitize_int_range(red, background_remover_choices.background_remover_color_range)
|
||||
green = sanitize_int_range(green, background_remover_choices.background_remover_color_range)
|
||||
blue = sanitize_int_range(blue, background_remover_choices.background_remover_color_range)
|
||||
alpha = sanitize_int_range(alpha, background_remover_choices.background_remover_color_range)
|
||||
state_manager.set_item('background_remover_despill_color', (red, green, blue, alpha))
|
||||
|
||||
@@ -90,10 +90,14 @@ def listen() -> None:
|
||||
|
||||
for ui_component in get_ui_components(
|
||||
[
|
||||
'background_remover_color_red_number',
|
||||
'background_remover_color_green_number',
|
||||
'background_remover_color_blue_number',
|
||||
'background_remover_color_alpha_number',
|
||||
'background_remover_fill_color_red_number',
|
||||
'background_remover_fill_color_green_number',
|
||||
'background_remover_fill_color_blue_number',
|
||||
'background_remover_fill_color_alpha_number',
|
||||
'background_remover_despill_color_red_number',
|
||||
'background_remover_despill_color_green_number',
|
||||
'background_remover_despill_color_blue_number',
|
||||
'background_remover_despill_color_alpha_number',
|
||||
'face_debugger_items_checkbox_group',
|
||||
'frame_colorizer_size_dropdown',
|
||||
'face_mask_types_checkbox_group',
|
||||
@@ -296,7 +300,7 @@ def extract_crop_frame(vision_frame : VisionFrame, face : Face) -> Optional[Visi
|
||||
|
||||
|
||||
def prepare_output_frame(target_vision_frame : VisionFrame, temp_vision_frame : VisionFrame, temp_vision_mask : Mask) -> VisionFrame:
|
||||
temp_vision_mask = temp_vision_mask.clip(state_manager.get_item('background_remover_color')[-1], 255)
|
||||
temp_vision_mask = temp_vision_mask.clip(state_manager.get_item('background_remover_fill_color')[-1], 255)
|
||||
temp_vision_frame = merge_vision_mask(temp_vision_frame, temp_vision_mask)
|
||||
temp_vision_frame = cv2.resize(temp_vision_frame, target_vision_frame.shape[1::-1])
|
||||
return temp_vision_frame
|
||||
|
||||
@@ -98,15 +98,15 @@ def start(webcam_device_id : int, webcam_mode : WebcamMode, webcam_resolution :
|
||||
camera_capture.set(cv2.CAP_PROP_FRAME_HEIGHT, webcam_height)
|
||||
camera_capture.set(cv2.CAP_PROP_FPS, webcam_fps)
|
||||
|
||||
for capture_frame in multi_process_capture(camera_capture, webcam_fps):
|
||||
capture_frame = cv2.cvtColor(capture_frame, cv2.COLOR_BGR2RGB)
|
||||
capture_frame = fit_cover_frame(capture_frame, (webcam_width, webcam_height))
|
||||
for capture_vision_frame in multi_process_capture(camera_capture, webcam_fps):
|
||||
capture_vision_frame = cv2.cvtColor(capture_vision_frame, cv2.COLOR_BGR2RGB)
|
||||
capture_vision_frame = fit_cover_frame(capture_vision_frame, (webcam_width, webcam_height))
|
||||
|
||||
if webcam_mode == 'inline':
|
||||
yield capture_frame
|
||||
yield capture_vision_frame
|
||||
if webcam_mode in [ 'udp', 'v4l2' ]:
|
||||
try:
|
||||
stream.stdin.write(capture_frame.tobytes())
|
||||
stream.stdin.write(capture_vision_frame.tobytes())
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
@@ -6,10 +6,14 @@ ComponentName = Literal\
|
||||
'age_modifier_direction_slider',
|
||||
'age_modifier_model_dropdown',
|
||||
'background_remover_model_dropdown',
|
||||
'background_remover_color_red_number',
|
||||
'background_remover_color_green_number',
|
||||
'background_remover_color_blue_number',
|
||||
'background_remover_color_alpha_number',
|
||||
'background_remover_fill_color_red_number',
|
||||
'background_remover_fill_color_green_number',
|
||||
'background_remover_fill_color_blue_number',
|
||||
'background_remover_fill_color_alpha_number',
|
||||
'background_remover_despill_color_red_number',
|
||||
'background_remover_despill_color_green_number',
|
||||
'background_remover_despill_color_blue_number',
|
||||
'background_remover_despill_color_alpha_number',
|
||||
'deep_swapper_model_dropdown',
|
||||
'deep_swapper_morph_slider',
|
||||
'expression_restorer_factor_slider',
|
||||
|
||||
@@ -5,6 +5,7 @@ import pytest
|
||||
from facefusion import face_classifier, face_detector, face_landmarker, face_recognizer, state_manager
|
||||
from facefusion.download import conditional_download
|
||||
from facefusion.face_analyser import get_many_faces
|
||||
from facefusion.face_store import clear_static_faces
|
||||
from facefusion.vision import read_static_image
|
||||
from .helper import get_test_example_file, get_test_examples_directory
|
||||
|
||||
@@ -37,6 +38,7 @@ def before_each() -> None:
|
||||
face_detector.clear_inference_pool()
|
||||
face_landmarker.clear_inference_pool()
|
||||
face_recognizer.clear_inference_pool()
|
||||
clear_static_faces()
|
||||
|
||||
|
||||
def test_get_one_face_with_retinaface() -> None:
|
||||
@@ -62,7 +64,7 @@ def test_get_one_face_with_retinaface() -> None:
|
||||
|
||||
def test_get_one_face_with_scrfd() -> None:
|
||||
state_manager.init_item('face_detector_model', 'scrfd')
|
||||
state_manager.init_item('face_detector_size', '640x640')
|
||||
state_manager.init_item('face_detector_size', '320x320')
|
||||
state_manager.init_item('face_detector_margin', (0, 0, 0, 0))
|
||||
face_detector.pre_check()
|
||||
|
||||
@@ -82,7 +84,7 @@ def test_get_one_face_with_scrfd() -> None:
|
||||
|
||||
|
||||
def test_get_one_face_with_yoloface() -> None:
|
||||
state_manager.init_item('face_detector_model', 'yoloface')
|
||||
state_manager.init_item('face_detector_model', 'yolo_face')
|
||||
state_manager.init_item('face_detector_size', '640x640')
|
||||
state_manager.init_item('face_detector_margin', (0, 0, 0, 0))
|
||||
face_detector.pre_check()
|
||||
|
||||
Reference in New Issue
Block a user