mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-08-19 13:09:56 -07:00
Merge pull request #144 from faceshiftlabs/feat/image-degradation-random-downsample
Feat/image degradation random downsample
This commit is contained in:
Jeremy Hummel
GitHub
commit
75bad1ba0c
2 changed files with 37 additions and 2 deletions
|
|
@ -56,6 +56,7 @@ class SAEHDModel(ModelBase):
|
|||
default_loss_function = self.options['loss_function'] = self.load_or_def_option('loss_function', 'SSIM')
|
||||
|
||||
default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
|
||||
default_random_downsample = self.options['random_downsample'] = self.load_or_def_option('random_downsample', False)
|
||||
default_background_power = self.options['background_power'] = self.load_or_def_option('background_power', 0.0)
|
||||
default_true_face_power = self.options['true_face_power'] = self.load_or_def_option('true_face_power', 0.0)
|
||||
default_face_style_power = self.options['face_style_power'] = self.load_or_def_option('face_style_power', 0.0)
|
||||
|
|
@ -159,6 +160,11 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
|
|||
|
||||
self.options['random_warp'] = io.input_bool ("Enable random warp of samples", default_random_warp, help_message="Random warp is required to generalize facial expressions of both faces. When the face is trained enough, you can disable it to get extra sharpness and reduce subpixel shake for less amount of iterations.")
|
||||
|
||||
self.options['random_downsample'] = io.input_bool("Enable random downsample of samples", default_random_downsample, help_message="")
|
||||
# self.options['random_noise'] = io.input_bool("Enable random noise added to samples", False, help_message="")
|
||||
# self.options['random_blur'] = io.input_bool("Enable random blur of samples", False, help_message="")
|
||||
# self.options['random_jpeg'] = io.input_bool("Enable random jpeg compression of samples", False, help_message="")
|
||||
|
||||
self.options['gan_version'] = np.clip (io.input_int("GAN version", default_gan_version, add_info="2 or 3", help_message="Choose GAN version (v2: 7/16/2020, v3: 1/3/2021):"), 2, 3)
|
||||
|
||||
if self.options['gan_version'] == 2:
|
||||
|
|
@ -745,7 +751,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
|
|||
self.set_training_data_generators ([
|
||||
SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
|
||||
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : channel_type, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'random_downsample': self.options['random_downsample'], 'transform':True, 'channel_type' : channel_type, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : channel_type, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE_EYES, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
|
|
@ -755,7 +761,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
|
|||
|
||||
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
|
||||
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : channel_type, 'ct_mode': fs_aug, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'random_downsample': self.options['random_downsample'], 'transform':True, 'channel_type' : channel_type, 'ct_mode': fs_aug, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : channel_type, 'ct_mode': fs_aug, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE_EYES, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
|
|
@ -830,6 +836,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
|
|||
(warped_dst, target_dst, target_dstm, target_dstm_em) ) = samples
|
||||
|
||||
S, D, SS, SSM, DD, DDM, SD, SDM = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ]
|
||||
SW, DW = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([warped_src,warped_dst]) ]
|
||||
SSM, DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [SSM, DDM, SDM] ]
|
||||
|
||||
target_srcm, target_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm, target_dstm] )]
|
||||
|
|
@ -845,6 +852,11 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
|
|||
st.append ( np.concatenate ( ar, axis=1) )
|
||||
result += [ ('SAEHD', np.concatenate (st, axis=0 )), ]
|
||||
|
||||
wt = []
|
||||
for i in range(n_samples):
|
||||
ar = SW[i], SS[i], DW[i], DD[i], SD[i]
|
||||
wt.append ( np.concatenate ( ar, axis=1) )
|
||||
result += [ ('SAEHD warped', np.concatenate (wt, axis=0 )), ]
|
||||
|
||||
st_m = []
|
||||
for i in range(n_samples):
|
||||
|
|
@ -875,6 +887,23 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
|
|||
st.append ( np.concatenate ( ar, axis=1) )
|
||||
result += [ ('SAEHD pred', np.concatenate (st, axis=0 )), ]
|
||||
|
||||
wt = []
|
||||
for i in range(n_samples):
|
||||
ar = SW[i], SS[i]
|
||||
wt.append ( np.concatenate ( ar, axis=1) )
|
||||
result += [ ('SAEHD warped src-src', np.concatenate (wt, axis=0 )), ]
|
||||
|
||||
wt = []
|
||||
for i in range(n_samples):
|
||||
ar = DW[i], DD[i]
|
||||
wt.append ( np.concatenate ( ar, axis=1) )
|
||||
result += [ ('SAEHD warped dst-dst', np.concatenate (wt, axis=0 )), ]
|
||||
|
||||
wt = []
|
||||
for i in range(n_samples):
|
||||
ar = DW[i], SD[i]
|
||||
wt.append ( np.concatenate ( ar, axis=1) )
|
||||
result += [ ('SAEHD warped pred', np.concatenate (wt, axis=0 )), ]
|
||||
|
||||
st_m = []
|
||||
for i in range(n_samples):
|
||||
|
|
|
|||
|
|
@ -112,6 +112,7 @@ class SampleProcessor(object):
|
|||
nearest_resize_to = opts.get('nearest_resize_to', None)
|
||||
warp = opts.get('warp', False)
|
||||
transform = opts.get('transform', False)
|
||||
random_downsample = opts.get('random_downsample', False)
|
||||
motion_blur = opts.get('motion_blur', None)
|
||||
gaussian_blur = opts.get('gaussian_blur', None)
|
||||
random_bilinear_resize = opts.get('random_bilinear_resize', None)
|
||||
|
|
@ -213,6 +214,11 @@ class SampleProcessor(object):
|
|||
ct_sample_bgr = ct_sample.load_bgr()
|
||||
img = imagelib.color_transfer (ct_mode, img, cv2.resize( ct_sample_bgr, (resolution,resolution), interpolation=cv2.INTER_LINEAR ) )
|
||||
|
||||
# Apply random downsampling
|
||||
if random_downsample:
|
||||
down_res = np.random.randint(int(0.125*resolution), int(0.25*resolution))
|
||||
img = cv2.resize(img, (down_res, down_res), interpolation=cv2.INTER_CUBIC)
|
||||
img = cv2.resize(img, (resolution, resolution), interpolation=cv2.INTER_CUBIC)
|
||||
|
||||
img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate)
|
||||
img = np.clip(img.astype(np.float32), 0, 1)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue