def __init__(self, is_interactive, converter_config, frames, output_path):
if len(frames) == 0:
raise ValueError("len (frames) == 0")
super().__init__('Converter',
ConvertSubprocessor.Cli,
86400 if CONVERTER_DEBUG else 60,
io_loop_sleep_time=0.001,
initialize_subprocesses_in_serial=False
) # if debug == True else 60)
self.is_interactive = is_interactive
self.converter_config = converter_config
#dummy predict and sleep, tensorflow caching kernels. If remove it, sometime conversion speed can be x2 slower
self.converter_config.predictor_func(dummy_predict=True)
time.sleep(2)
self.predictor_func_host, self.predictor_func = SubprocessFunctionCaller.make_pair(
self.converter_config.predictor_func)
self.converter_config.predictor_func = None
self.dcscn = None
self.ranksrgan = None
def superres_func(mode, *args, **kwargs):
if mode == 1:
if self.ranksrgan is None:
self.ranksrgan = imagelib.RankSRGAN()
return self.ranksrgan.upscale(*args, **kwargs)
self.dcscn_host, self.superres_func = SubprocessFunctionCaller.make_pair(
superres_func)
self.frames = frames
self.output_path = output_path
self.prefetch_frame_count = self.process_count = min(
6, multiprocessing.cpu_count())
self.frames_idxs = [*range(len(self.frames))]
self.frames_done_idxs = []
digits = [str(i) for i in range(10)]
for i in range(len(self.frames)):
frame = self.frames[i]
frame.idx = i
inp_stem = Path(frame.frame_info.filename).stem
if len([True for symbol in inp_stem if symbol not in digits]) > 0:
frame.output_filename = self.output_path / ('%.5d.png' %
(i + 1))
else:
frame.output_filename = self.output_path / (inp_stem + '.png')
frames[0].cfg = self.converter_config.copy()
def __init__(self, is_interactive, converter_config, frames, output_path):
if len (frames) == 0:
raise ValueError ("len (frames) == 0")
super().__init__('Converter', ConvertSubprocessor.Cli, 86400 if CONVERTER_DEBUG else 60, io_loop_sleep_time=0.001, initialize_subprocesses_in_serial=False)# if debug == True else 60)
self.is_interactive = is_interactive
self.converter_config = converter_config
#dummy predict and sleep, tensorflow caching kernels. If remove it, sometime conversion speed can be x2 slower
self.converter_config.predictor_func (dummy_predict=True)
time.sleep(2)
self.predictor_func_host, self.predictor_func = SubprocessFunctionCaller.make_pair(self.converter_config.predictor_func)
self.converter_config.predictor_func = None
self.dcscn = None
def DCSCN_upscale(*args, **kwargs):
if self.dcscn is None:
self.dcscn = imagelib.DCSCN()
return self.dcscn.upscale(*args, **kwargs)
self.dcscn_host, self.dcscn_upscale_func = SubprocessFunctionCaller.make_pair(DCSCN_upscale)
self.frames = frames
self.output_path = output_path
self.prefetch_frame_count = self.process_count = min(6,multiprocessing.cpu_count())
self.frames_idxs = [ *range(len(self.frames)) ]
self.frames_done_idxs = []
for i in range( len(self.frames) ):
frame = self.frames[i]
frame.idx = i
frame.output_filename = self.output_path / ('%.5d.png' % i)
frames[0].cfg = self.converter_config.copy()
def __init__(self, predictor_func,
predictor_input_size=0):
super().__init__(predictor_func, Converter.TYPE_FACE_AVATAR)
self.predictor_input_size = predictor_input_size
#dummy predict and sleep, tensorflow caching kernels. If remove it, conversion speed will be x2 slower
predictor_func ( np.zeros ( (predictor_input_size,predictor_input_size,3), dtype=np.float32 ),
np.zeros ( (predictor_input_size,predictor_input_size,1), dtype=np.float32 ) )
time.sleep(2)
predictor_func_host, predictor_func = SubprocessFunctionCaller.make_pair(predictor_func)
self.predictor_func_host = AntiPickler(predictor_func_host)
self.predictor_func = predictor_func
def __init__(self,
converter,
input_path_image_paths,
output_path,
alignments,
debug=False):
super().__init__('Converter', ConvertSubprocessor.Cli,
86400 if debug == True else 60)
self.converter = converter
self.host_processor, self.cli_func = SubprocessFunctionCaller.make_pair(
self.converter.predictor_func)
self.process_converter = self.converter.copy_and_set_predictor(
self.cli_func)
self.input_data = self.input_path_image_paths = input_path_image_paths
self.output_path = output_path
self.alignments = alignments
self.debug = debug
self.files_processed = 0
self.faces_processed = 0
def __init__(self, is_interactive, converter_session_filepath, predictor_func, predictor_input_shape, converter_config, frames, output_path, model_iter):
if len (frames) == 0:
raise ValueError ("len (frames) == 0")
super().__init__('Converter', ConvertSubprocessor.Cli, 86400 if CONVERTER_DEBUG else 60, io_loop_sleep_time=0.001, initialize_subprocesses_in_serial=False)
self.is_interactive = is_interactive
self.converter_session_filepath = Path(converter_session_filepath)
self.converter_config = converter_config
#dummy predict and sleep, tensorflow caching kernels. If remove it, sometime conversion speed can be x2 slower
predictor_func (dummy_predict=True)
time.sleep(2)
self.predictor_func_host, self.predictor_func = SubprocessFunctionCaller.make_pair(predictor_func)
self.predictor_input_shape = predictor_input_shape
self.dcscn = None
self.ranksrgan = None
def superres_func(mode, *args, **kwargs):
if mode == 1:
if self.ranksrgan is None:
self.ranksrgan = imagelib.RankSRGAN()
return self.ranksrgan.upscale(*args, **kwargs)
self.dcscn_host, self.superres_func = SubprocessFunctionCaller.make_pair(superres_func)
self.output_path = output_path
self.model_iter = model_iter
self.prefetch_frame_count = self.process_count = min(6,multiprocessing.cpu_count())
session_data = None
if self.is_interactive and self.converter_session_filepath.exists():
if io.input_bool ("Use saved session? (y/n skip:y) : ", True):
try:
with open( str(self.converter_session_filepath), "rb") as f:
session_data = pickle.loads(f.read())
except Exception as e:
pass
self.frames = frames
self.frames_idxs = [ *range(len(self.frames)) ]
self.frames_done_idxs = []
if self.is_interactive and session_data is not None:
s_frames = session_data.get('frames', None)
s_frames_idxs = session_data.get('frames_idxs', None)
s_frames_done_idxs = session_data.get('frames_done_idxs', None)
s_model_iter = session_data.get('model_iter', None)
frames_equal = (s_frames is not None) and \
(s_frames_idxs is not None) and \
(s_frames_done_idxs is not None) and \
(s_model_iter is not None) and \
(len(frames) == len(s_frames))
if frames_equal:
for i in range(len(frames)):
frame = frames[i]
s_frame = s_frames[i]
if frame.frame_info.filename != s_frame.frame_info.filename:
frames_equal = False
if not frames_equal:
break
if frames_equal:
io.log_info ('Using saved session from ' + '/'.join (self.converter_session_filepath.parts[-2:]) )
self.frames = s_frames
self.frames_idxs = s_frames_idxs
self.frames_done_idxs = s_frames_done_idxs
if self.model_iter != s_model_iter:
#model is more trained, recompute all frames
for frame in self.frames:
frame.is_done = False
if self.model_iter != s_model_iter or \
len(self.frames_idxs) == 0:
#rewind to begin if model is more trained or all frames are done
while len(self.frames_done_idxs) > 0:
prev_frame = self.frames[self.frames_done_idxs.pop()]
self.frames_idxs.insert(0, prev_frame.idx)
if len(self.frames_idxs) != 0:
cur_frame = self.frames[self.frames_idxs[0]]
cur_frame.is_shown = False
if not frames_equal:
session_data = None
if session_data is None:
for filename in Path_utils.get_image_paths(self.output_path): #remove all images in output_path
Path(filename).unlink()
frames[0].cfg = self.converter_config.copy()
for i in range( len(self.frames) ):
frame = self.frames[i]
frame.idx = i
frame.output_filename = self.output_path / ( Path(frame.frame_info.filename).stem + '.png' )
def __init__(self,
predictor_func,
predictor_input_size=0,
predictor_masked=True,
face_type=FaceType.FULL,
default_mode=4,
base_erode_mask_modifier=0,
base_blur_mask_modifier=0,
default_erode_mask_modifier=0,
default_blur_mask_modifier=0,
clip_hborder_mask_per=0):
super().__init__(predictor_func, Converter.TYPE_FACE)
#dummy predict and sleep, tensorflow caching kernels. If remove it, conversion speed will be x2 slower
predictor_func(
np.zeros((predictor_input_size, predictor_input_size, 3),
dtype=np.float32))
time.sleep(2)
predictor_func_host, predictor_func = SubprocessFunctionCaller.make_pair(
predictor_func)
self.predictor_func_host = AntiPickler(predictor_func_host)
self.predictor_func = predictor_func
self.predictor_masked = predictor_masked
self.predictor_input_size = predictor_input_size
self.face_type = face_type
self.clip_hborder_mask_per = clip_hborder_mask_per
mode = io.input_int(
"Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless, (5) raw. Default - %d : "
% (default_mode), default_mode)
mode_dict = {
1: 'overlay',
2: 'hist-match',
3: 'hist-match-bw',
4: 'seamless',
5: 'raw'
}
self.mode = mode_dict.get(mode, mode_dict[default_mode])
if self.mode == 'raw':
mode = io.input_int(
"Choose raw mode: (1) rgb, (2) rgb+mask (default), (3) mask only, (4) predicted only : ",
2)
self.raw_mode = {
1: 'rgb',
2: 'rgb-mask',
3: 'mask-only',
4: 'predicted-only'
}.get(mode, 'rgb-mask')
if self.mode != 'raw':
if self.mode == 'seamless':
if io.input_bool("Seamless hist match? (y/n skip:n) : ",
False):
self.mode = 'seamless-hist-match'
if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
self.masked_hist_match = io.input_bool(
"Masked hist match? (y/n skip:y) : ", True)
if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match':
self.hist_match_threshold = np.clip(
io.input_int(
"Hist match threshold [0..255] (skip:255) : ", 255),
0, 255)
if face_type == FaceType.FULL:
self.mask_mode = np.clip(
io.input_int(
"Mask mode: (1) learned, (2) dst, (3) FAN-prd, (4) FAN-dst , (5) FAN-prd*FAN-dst (6) learned*FAN-prd*FAN-dst (?) help. Default - %d : "
% (1),
1,
help_message=
"If you learned mask, then option 1 should be choosed. 'dst' mask is raw shaky mask from dst aligned images. 'FAN-prd' - using super smooth mask by pretrained FAN-model from predicted face. 'FAN-dst' - using super smooth mask by pretrained FAN-model from dst face. 'FAN-prd*FAN-dst' or 'learned*FAN-prd*FAN-dst' - using multiplied masks."
), 1, 6)
else:
self.mask_mode = np.clip(
io.input_int(
"Mask mode: (1) learned, (2) dst . Default - %d : " % (1),
1), 1, 2)
if self.mask_mode >= 3 and self.mask_mode <= 6:
self.fan_seg = None
if self.mode != 'raw':
self.erode_mask_modifier = base_erode_mask_modifier + np.clip(
io.input_int(
"Choose erode mask modifier [-200..200] (skip:%d) : " %
(default_erode_mask_modifier),
default_erode_mask_modifier), -200, 200)
self.blur_mask_modifier = base_blur_mask_modifier + np.clip(
io.input_int(
"Choose blur mask modifier [-200..200] (skip:%d) : " %
(default_blur_mask_modifier), default_blur_mask_modifier),
-200, 200)
self.output_face_scale = np.clip(
1.0 + io.input_int(
"Choose output face scale modifier [-50..50] (skip:0) : ", 0) *
0.01, 0.5, 1.5)
if self.mode != 'raw':
self.color_transfer_mode = io.input_str(
"Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ",
None, ['rct', 'lct'])
self.super_resolution = io.input_bool(
"Apply super resolution? (y/n ?:help skip:n) : ",
False,
help_message="Enhance details by applying DCSCN network.")
if self.mode != 'raw':
self.final_image_color_degrade_power = np.clip(
io.input_int(
"Degrade color power of final image [0..100] (skip:0) : ",
0), 0, 100)
self.alpha = io.input_bool(
"Export png with alpha channel? (y/n skip:n) : ", False)
io.log_info("")
if self.super_resolution:
host_proc, dc_upscale = SubprocessFunctionCaller.make_pair(
imagelib.DCSCN().upscale)
self.dc_host = AntiPickler(host_proc)
self.dc_upscale = dc_upscale
else:
self.dc_host = None
def __init__(self,
predictor_func,
predictor_input_size=0,
predictor_masked=True,
face_type=FaceType.FULL,
default_mode=4,
base_erode_mask_modifier=0,
base_blur_mask_modifier=0,
default_erode_mask_modifier=0,
default_blur_mask_modifier=0,
clip_hborder_mask_per=0,
force_mask_mode=-1):
super().__init__(predictor_func, Converter.TYPE_FACE)
# dummy predict and sleep, tensorflow caching kernels. If remove it, conversion speed will be x2 slower
predictor_func(
np.zeros((predictor_input_size, predictor_input_size, 3),
dtype=np.float32))
time.sleep(2)
predictor_func_host, predictor_func = SubprocessFunctionCaller.make_pair(
predictor_func)
self.predictor_func_host = AntiPickler(predictor_func_host)
self.predictor_func = predictor_func
self.predictor_masked = predictor_masked
self.predictor_input_size = predictor_input_size
self.face_type = face_type
self.clip_hborder_mask_per = clip_hborder_mask_per
mode = io.input_int(
"选择模式: (1)覆盖，(2)直方图匹配，(3)直方图匹配白平衡，(4)无缝，(5)raw. 默认 - %d : " %
(default_mode), default_mode)
mode_dict = {
1: 'overlay',
2: 'hist-match',
3: 'hist-match-bw',
4: 'seamless',
5: 'raw'
}
self.mode = mode_dict.get(mode, mode_dict[default_mode])
if self.mode == 'raw':
mode = io.input_int(
"选择raw模式: (1) rgb， (2) rgb+掩码(默认)，(3)仅掩码，(4)仅预测 : ", 2)
self.raw_mode = {
1: 'rgb',
2: 'rgb-mask',
3: 'mask-only',
4: 'predicted-only'
}.get(mode, 'rgb-mask')
if self.mode != 'raw':
if self.mode == 'seamless':
if io.input_bool("无缝直方图匹配? (y/n 默认:n) : ", False):
self.mode = 'seamless-hist-match'
if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
self.masked_hist_match = io.input_bool(
"面部遮罩直方图匹配? (y/n 默认:y) : ", True)
if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match':
self.hist_match_threshold = np.clip(
io.input_int("直方图匹配阈值 [0..255] (skip:255) : ", 255), 0,
255)
if force_mask_mode != -1:
self.mask_mode = force_mask_mode
else:
if face_type == FaceType.FULL:
self.mask_mode = np.clip(
io.input_int(
"面部遮罩模式: (1) 学习, (2) dst原始视频, (3) FAN-prd, (4) FAN-dst , (5) FAN-prd*FAN-dst (6) learned*FAN-prd*FAN-dst (?) 帮助. 默认 - %d : "
% (1),
1,
help_message=
"如果你学过蒙版,那么选择选项1.“dst”遮罩是原始的抖动遮罩从dst对齐的图像.“扇-prd”-使用超光滑的面具,通过预先训练的扇模型从预测的脸.“风扇-dst”-使用超光滑的面具,由预先训练的风扇模型从dst的脸.“FAN-prd*FAN-dst”或“learned*FAN-prd*FAN-dst”——使用多个口罩."
), 1, 6)
else:
self.mask_mode = np.clip(
io.input_int("面部遮罩模式: (1) 学习, (2) dst . 默认 - %d : " % (1),
1), 1, 2)
if self.mask_mode >= 3 and self.mask_mode <= 6:
self.fan_seg = None
if self.mode != 'raw':
self.erode_mask_modifier = base_erode_mask_modifier + np.clip(
io.input_int(
"侵蚀遮罩 [-200..200] (默认:%d) : " %
(default_erode_mask_modifier),
default_erode_mask_modifier), -200, 200)
self.blur_mask_modifier = base_blur_mask_modifier + np.clip(
io.input_int(
"选择模糊遮罩边缘 [-200..200] (默认:%d) : " %
(default_blur_mask_modifier), default_blur_mask_modifier),
-200, 200)
self.output_face_scale = np.clip(
1.0 + io.input_int("选择输出脸部比例调整器 [-50..50] (默认:0) : ", 0) * 0.01,
0.5, 1.5)
if self.mode != 'raw':
self.color_transfer_mode = io.input_str(
"应用颜色转移到预测的脸? 选择模式 ( rct/lct 默认:None ) : ", None,
['rct', 'lct'])
self.super_resolution = io.input_bool("应用超分辨率? (y/n ?:帮助 默认:n) : ",
False,
help_message="通过应用DCSCN网络增强细节.")
if self.mode != 'raw':
self.final_image_color_degrade_power = np.clip(
io.input_int("降低最终图像色权 [0..100] (默认:0) : ", 0), 0, 100)
self.alpha = io.input_bool("使用alpha通道导出png? (y/n 默认:n) : ", False)
io.log_info("")
if self.super_resolution:
host_proc, dc_upscale = SubprocessFunctionCaller.make_pair(
imagelib.DCSCN().upscale)
self.dc_host = AntiPickler(host_proc)
self.dc_upscale = dc_upscale
else:
self.dc_host = None