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SAE : WARNING, RETRAIN IS REQUIRED !

Browse source 
fixed model sizes from previous update.
avoided bug in ML framework(keras) that forces to train the model on random noise.

Converter: added blur on the same keys as sharpness

Added new model 'TrueFace'. This is a GAN model ported from https://github.com/NVlabs/FUNIT
Model produces near zero morphing and high detail face.
Model has higher failure rate than other models.
Keep src and dst faceset in same lighting conditions.
This commit is contained in:
Colombo 2019-09-19 11:13:56 +04:00
commit dc11ec32be
26 changed files with 1308 additions and 250 deletions
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6
samplelib/Sample.py
View file

@ -22,9 +22,10 @@ class SampleType(IntEnum):
QTY = 5
class Sample(object):
def __init__(self, sample_type=None, filename=None, face_type=None, shape=None, landmarks=None, ie_polys=None, pitch_yaw_roll=None, eyebrows_expand_mod=None, source_filename=None, mirror=None, close_target_list=None, fanseg_mask_exist=False):
def __init__(self, sample_type=None, filename=None, person_id=None, face_type=None, shape=None, landmarks=None, ie_polys=None, pitch_yaw_roll=None, eyebrows_expand_mod=None, source_filename=None, mirror=None, close_target_list=None, fanseg_mask_exist=False):
self.sample_type = sample_type if sample_type is not None else SampleType.IMAGE
self.filename = filename
self.person_id = person_id
self.face_type = face_type
self.shape = shape
self.landmarks = np.array(landmarks) if landmarks is not None else None
@ -36,10 +37,11 @@ class Sample(object):
self.close_target_list = close_target_list
self.fanseg_mask_exist = fanseg_mask_exist
def copy_and_set(self, sample_type=None, filename=None, face_type=None, shape=None, landmarks=None, ie_polys=None, pitch_yaw_roll=None, eyebrows_expand_mod=None, source_filename=None, mirror=None, close_target_list=None, fanseg_mask=None, fanseg_mask_exist=None):
def copy_and_set(self, sample_type=None, filename=None, person_id=None, face_type=None, shape=None, landmarks=None, ie_polys=None, pitch_yaw_roll=None, eyebrows_expand_mod=None, source_filename=None, mirror=None, close_target_list=None, fanseg_mask=None, fanseg_mask_exist=None):
return Sample(
sample_type=sample_type if sample_type is not None else self.sample_type,
filename=filename if filename is not None else self.filename,
person_id=person_id if person_id is not None else self.person_id,
face_type=face_type if face_type is not None else self.face_type,
shape=shape if shape is not None else self.shape,
landmarks=landmarks if landmarks is not None else self.landmarks.copy(),

6
samplelib/SampleGeneratorBase.py
View file

@ -6,7 +6,7 @@ You can implement your own SampleGenerator
class SampleGeneratorBase(object):
def __init__ (self, samples_path, debug, batch_size):
def __init__ (self, samples_path, debug=False, batch_size=1):
if samples_path is None:
raise Exception('samples_path is None')
@ -25,6 +25,10 @@ class SampleGeneratorBase(object):
self.last_generation = next(self)
return self.last_generation
#overridable
def get_total_sample_count(self):
return 0
#overridable
def __iter__(self):
#implement your own iterator

43
samplelib/SampleGeneratorFace.py
View file

@ -18,11 +18,23 @@ output_sample_types = [
]
'''
class SampleGeneratorFace(SampleGeneratorBase):
def __init__ (self, samples_path, debug, batch_size, sort_by_yaw=False, sort_by_yaw_target_samples_path=None, random_ct_samples_path=None, sample_process_options=SampleProcessor.Options(), output_sample_types=[], add_sample_idx=False, generators_count=2, generators_random_seed=None, **kwargs):
def __init__ (self, samples_path, debug=False, batch_size=1,
sort_by_yaw=False,
sort_by_yaw_target_samples_path=None,
random_ct_samples_path=None,
sample_process_options=SampleProcessor.Options(),
output_sample_types=[],
person_id_mode=False,
add_sample_idx=False,
generators_count=2,
generators_random_seed=None,
**kwargs):
super().__init__(samples_path, debug, batch_size)
self.sample_process_options = sample_process_options
self.output_sample_types = output_sample_types
self.add_sample_idx = add_sample_idx
self.person_id_mode = person_id_mode
if sort_by_yaw_target_samples_path is not None:
self.sample_type = SampleType.FACE_YAW_SORTED_AS_TARGET
@ -35,9 +47,10 @@ class SampleGeneratorFace(SampleGeneratorBase):
raise ValueError("len(generators_random_seed) != generators_count")
self.generators_random_seed = generators_random_seed
samples = SampleLoader.load (self.sample_type, self.samples_path, sort_by_yaw_target_samples_path)
samples = SampleLoader.load (self.sample_type, self.samples_path, sort_by_yaw_target_samples_path, person_id_mode=person_id_mode)
self.total_samples_count = len(samples)
ct_samples = SampleLoader.load (SampleType.FACE, random_ct_samples_path) if random_ct_samples_path is not None else None
self.random_ct_sample_chance = 100
@ -49,7 +62,11 @@ class SampleGeneratorFace(SampleGeneratorBase):
self.generators = [iter_utils.SubprocessGenerator ( self.batch_func, (i, samples[i::self.generators_count], ct_samples ) ) for i in range(self.generators_count) ]
self.generator_counter = -1
#overridable
def get_total_sample_count(self):
return self.total_samples_count
def __iter__(self):
return self
@ -58,7 +75,7 @@ class SampleGeneratorFace(SampleGeneratorBase):
generator = self.generators[self.generator_counter % len(self.generators) ]
return next(generator)
def batch_func(self, param ):
def batch_func(self, param ):
generator_id, samples, ct_samples = param
if self.generators_random_seed is not None:
@ -82,7 +99,7 @@ class SampleGeneratorFace(SampleGeneratorBase):
shuffle_idxs = []
shuffle_idxs_2D = [[]]*samples_len
while True:
while True:
batches = None
for n_batch in range(self.batch_size):
while True:
@ -131,12 +148,24 @@ class SampleGeneratorFace(SampleGeneratorBase):
if self.add_sample_idx:
batches += [ [] ]
i_sample_idx = len(batches)-1
if self.person_id_mode:
batches += [ [] ]
i_person_id = len(batches)-1
for i in range(len(x)):
batches[i].append ( x[i] )
if self.add_sample_idx:
batches[i_sample_idx].append (idx)
if self.person_id_mode:
batches[i_person_id].append ( np.array([sample.person_id]) )
break
yield [ np.array(batch) for batch in batches]
@staticmethod
def get_person_id_max_count(samples_path):
return SampleLoader.get_person_id_max_count(samples_path)

26
samplelib/SampleLoader.py
View file

@ -19,7 +19,11 @@ class SampleLoader:
cache = dict()
@staticmethod
def load(sample_type, samples_path, target_samples_path=None):
def get_person_id_max_count(samples_path):
return len ( Path_utils.get_all_dir_names(samples_path) )
@staticmethod
def load(sample_type, samples_path, target_samples_path=None, person_id_mode=False):
cache = SampleLoader.cache
if str(samples_path) not in cache.keys():
@ -30,10 +34,16 @@ class SampleLoader:
if sample_type == SampleType.IMAGE:
if datas[sample_type] is None:
datas[sample_type] = [ Sample(filename=filename) for filename in io.progress_bar_generator( Path_utils.get_image_paths(samples_path), "Loading") ]
elif sample_type == SampleType.FACE:
if datas[sample_type] is None:
datas[sample_type] = SampleLoader.upgradeToFaceSamples( [ Sample(filename=filename) for filename in Path_utils.get_image_paths(samples_path) ] )
if person_id_mode:
dir_names = Path_utils.get_all_dir_names(samples_path)
all_samples = []
for i, dir_name in io.progress_bar_generator( [*enumerate(dir_names)] , "Loading"):
all_samples += SampleLoader.upgradeToFaceSamples( [ Sample(filename=filename, person_id=i) for filename in Path_utils.get_image_paths( samples_path / dir_name ) ], silent=True )
datas[sample_type] = all_samples
else:
datas[sample_type] = SampleLoader.upgradeToFaceSamples( [ Sample(filename=filename) for filename in Path_utils.get_image_paths(samples_path) ] )
elif sample_type == SampleType.FACE_TEMPORAL_SORTED:
if datas[sample_type] is None:
@ -52,10 +62,10 @@ class SampleLoader:
return datas[sample_type]
@staticmethod
def upgradeToFaceSamples ( samples ):
def upgradeToFaceSamples ( samples, silent=False ):
sample_list = []
for s in io.progress_bar_generator(samples, "Loading"):
for s in (samples if silent else io.progress_bar_generator(samples, "Loading")):
s_filename_path = Path(s.filename)
try:
if s_filename_path.suffix == '.png':
@ -68,13 +78,13 @@ class SampleLoader:
if dflimg is None:
print ("%s is not a dfl image file required for training" % (s_filename_path.name) )
continue
landmarks = dflimg.get_landmarks()
pitch_yaw_roll = dflimg.get_pitch_yaw_roll()
eyebrows_expand_mod = dflimg.get_eyebrows_expand_mod()
if pitch_yaw_roll is None:
pitch_yaw_roll = LandmarksProcessor.estimate_pitch_yaw_roll(landmarks)
pitch_yaw_roll = LandmarksProcessor.estimate_pitch_yaw_roll(landmarks)
sample_list.append( s.copy_and_set(sample_type=SampleType.FACE,
face_type=FaceType.fromString (dflimg.get_face_type()),