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SAE : WARNING, RETRAIN IS REQUIRED !

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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
parent 201b762541
commit dc11ec32be
26 changed files with 1308 additions and 250 deletions
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26
samplelib/SampleLoader.py
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@ -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()),