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random noise

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Jeremy Hummel 2021-05-23 01:10:40 -07:00
commit de11c2ed2e
2 changed files with 31 additions and 3 deletions
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15
models/Model_SAEHD/Model.py
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@ -57,6 +57,7 @@ class SAEHDModel(ModelBase):
default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True) 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_random_downsample = self.options['random_downsample'] = self.load_or_def_option('random_downsample', False)
default_random_noise = self.options['random_noise'] = self.load_or_def_option('random_noise', False)
default_background_power = self.options['background_power'] = self.load_or_def_option('background_power', 0.0) 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_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) default_face_style_power = self.options['face_style_power'] = self.load_or_def_option('face_style_power', 0.0)
@ -161,7 +162,7 @@ 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_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_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_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_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['random_jpeg'] = io.input_bool("Enable random jpeg compression of samples", False, help_message="")
@ -751,7 +752,11 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
self.set_training_data_generators ([ 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(), 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), sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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}, output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp,
'random_downsample': self.options['random_downsample'],
'random_noise': self.options['random_noise'],
'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_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, '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}, {'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},
@ -761,7 +766,11 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(), 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), sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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}, output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp,
'random_downsample': self.options['random_downsample'],
'random_noise': self.options['random_noise'],
'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_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, '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}, {'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},

19
samplelib/SampleProcessor.py
View file

@ -113,6 +113,7 @@ class SampleProcessor(object):
warp = opts.get('warp', False) warp = opts.get('warp', False)
transform = opts.get('transform', False) transform = opts.get('transform', False)
random_downsample = opts.get('random_downsample', False) random_downsample = opts.get('random_downsample', False)
random_noise = opts.get('random_noise', False)
motion_blur = opts.get('motion_blur', None) motion_blur = opts.get('motion_blur', None)
gaussian_blur = opts.get('gaussian_blur', None) gaussian_blur = opts.get('gaussian_blur', None)
random_bilinear_resize = opts.get('random_bilinear_resize', None) random_bilinear_resize = opts.get('random_bilinear_resize', None)
@ -220,6 +221,24 @@ class SampleProcessor(object):
img = cv2.resize(img, (down_res, down_res), interpolation=cv2.INTER_CUBIC) img = cv2.resize(img, (down_res, down_res), interpolation=cv2.INTER_CUBIC)
img = cv2.resize(img, (resolution, resolution), interpolation=cv2.INTER_CUBIC) img = cv2.resize(img, (resolution, resolution), interpolation=cv2.INTER_CUBIC)
# Apply random noise
if random_noise:
noise_type = np.random.choice(['gaussian', 'laplace', 'poisson'])
noise_scale = (20 * np.random.random() + 20) / 255.0
if noise_type == 'gaussian':
noise = np.random.normal(scale=noise_scale, size=img.shape)
img += noise
elif noise_type == 'laplace':
# noise = np.random.laplace(scale=noise_scale, size=img.shape)
# img += noise
pass
elif noise_type == 'poisson':
# noise_lam = (15 * np.random.random() + 15)
# noise = np.random.poisson(lam=noise_lam, size=img.shape)
# img += noise
pass
img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate) 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) img = np.clip(img.astype(np.float32), 0, 1)