github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-07-07 13:32:09 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

removing trailing spaces

Browse source
This commit is contained in:
iperov 2019-03-19 23:53:27 +04:00
parent fa4e579b95
commit a3df04999c
61 changed files with 2110 additions and 2103 deletions
Download patch file Download diff file Expand all files Collapse all files

92
samples/SampleLoader.py
View file

@ -17,44 +17,44 @@ from interact import interact as io
class SampleLoader:
cache = dict()
@staticmethod
def load(sample_type, samples_path, target_samples_path=None):
cache = SampleLoader.cache
if str(samples_path) not in cache.keys():
cache[str(samples_path)] = [None]*SampleType.QTY
datas = cache[str(samples_path)]
if sample_type == SampleType.IMAGE:
if datas[sample_type] is None:
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:
if datas[sample_type] is None:
datas[sample_type] = SampleLoader.upgradeToFaceSamples( [ Sample(filename=filename) for filename in Path_utils.get_image_paths(samples_path) ] )
elif sample_type == SampleType.FACE_YAW_SORTED:
if datas[sample_type] is None:
datas[sample_type] = SampleLoader.upgradeToFaceYawSortedSamples( SampleLoader.load(SampleType.FACE, samples_path) )
elif sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
elif sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
if datas[sample_type] is None:
if target_samples_path is None:
raise Exception('target_samples_path is None for FACE_YAW_SORTED_AS_TARGET')
datas[sample_type] = SampleLoader.upgradeToFaceYawSortedAsTargetSamples( SampleLoader.load(SampleType.FACE_YAW_SORTED, samples_path), SampleLoader.load(SampleType.FACE_YAW_SORTED, target_samples_path) )
elif sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
elif sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
if datas[sample_type] is None:
datas[sample_type] = SampleLoader.upgradeToFaceCloseToSelfSamples( SampleLoader.load(SampleType.FACE, samples_path) )
return datas[sample_type]
@staticmethod
def upgradeToFaceSamples ( samples ):
sample_list = []
for s in io.progress_bar_generator(samples, "Loading"):
s_filename_path = Path(s.filename)
try:
@ -64,57 +64,57 @@ class SampleLoader:
dflimg = DFLJPG.load ( str(s_filename_path) )
else:
dflimg = None
if dflimg is None:
print ("%s is not a dfl image file required for training" % (s_filename_path.name) )
print ("%s is not a dfl image file required for training" % (s_filename_path.name) )
continue
pitch, yaw = LandmarksProcessor.estimate_pitch_yaw ( dflimg.get_landmarks() )
sample_list.append( s.copy_and_set(sample_type=SampleType.FACE,
face_type=FaceType.fromString (dflimg.get_face_type()),
shape=dflimg.get_shape(),
shape=dflimg.get_shape(),
landmarks=dflimg.get_landmarks(),
pitch=pitch,
yaw=yaw) )
except:
print ("Unable to load %s , error: %s" % (str(s_filename_path), traceback.format_exc() ) )
return sample_list
return sample_list
@staticmethod
def upgradeToFaceCloseToSelfSamples (samples):
yaw_samples = SampleLoader.upgradeToFaceYawSortedSamples(samples)
yaw_samples_len = len(yaw_samples)
sample_list = []
for i in io.progress_bar_generator( range(yaw_samples_len), "Sorting"):
if yaw_samples[i] is not None:
for s in yaw_samples[i]:
s_t = []
for n in range(2000):
for n in range(2000):
yaw_idx = np.clip ( i-10 +np.random.randint(20), 0, yaw_samples_len-1 )
if yaw_samples[yaw_idx] is None:
continue
yaw_idx_samples_len = len(yaw_samples[yaw_idx])
yaw_idx_sample = yaw_samples[yaw_idx][ np.random.randint(yaw_idx_samples_len) ]
if s.filename == yaw_idx_sample.filename:
continue
s_t.append ( yaw_idx_sample )
if len(s_t) >= 50:
break
if len(s_t) == 0:
s_t = [s]
sample_list.append( s.copy_and_set(close_target_list = s_t) )
return sample_list
@staticmethod
def upgradeToFaceYawSortedSamples( samples ):
@ -123,50 +123,50 @@ class SampleLoader:
diff_rot_per_grad = abs(highest_yaw-lowest_yaw) / gradations
yaws_sample_list = [None]*gradations
for i in io.progress_bar_generator(range(gradations), "Sorting"):
yaw = lowest_yaw + i*diff_rot_per_grad
next_yaw = lowest_yaw + (i+1)*diff_rot_per_grad
yaw_samples = []
for s in samples:
for s in samples:
s_yaw = s.yaw
if (i == 0 and s_yaw < next_yaw) or \
(i < gradations-1 and s_yaw >= yaw and s_yaw < next_yaw) or \
(i == gradations-1 and s_yaw >= yaw):
yaw_samples.append ( s.copy_and_set(sample_type=SampleType.FACE_YAW_SORTED) )
if len(yaw_samples) > 0:
yaws_sample_list[i] = yaw_samples
return yaws_sample_list
@staticmethod
def upgradeToFaceYawSortedAsTargetSamples (s, t):
l = len(s)
if l != len(t):
raise Exception('upgradeToFaceYawSortedAsTargetSamples() s_len != t_len')
b = l // 2
s_idxs = np.argwhere ( np.array ( [ 1 if x != None else 0 for x in s] ) == 1 )[:,0]
t_idxs = np.argwhere ( np.array ( [ 1 if x != None else 0 for x in t] ) == 1 )[:,0]
new_s = [None]*l
new_s = [None]*l
for t_idx in t_idxs:
search_idxs = []
search_idxs = []
for i in range(0,l):
search_idxs += [t_idx - i, (l-t_idx-1) - i, t_idx + i, (l-t_idx-1) + i]
for search_idx in search_idxs:
for search_idx in search_idxs:
if search_idx in s_idxs:
mirrored = ( t_idx != search_idx and ((t_idx < b and search_idx >= b) or (search_idx < b and t_idx >= b)) )
new_s[t_idx] = [ sample.copy_and_set(sample_type=SampleType.FACE_YAW_SORTED_AS_TARGET,
mirror=True,
yaw=-sample.yaw,
mirror=True,
yaw=-sample.yaw,
landmarks=LandmarksProcessor.mirror_landmarks (sample.landmarks, sample.shape[1] ))
for sample in s[search_idx]
] if mirrored else s[search_idx]
for sample in s[search_idx]
] if mirrored else s[search_idx]
break
return new_s
return new_s