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fixed error "Failed to get convolution algorithm" on some systems

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fixed error "dll load failed" on some systems
Expanded eyebrows line of face masks. It does not affect mask of FAN-x converter mode.
This commit is contained in:
iperov
2019-08-11 11:17:22 +04:00
parent 582c974851
commit b72d5a3f9a
15 changed files with 367 additions and 222 deletions
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13
samplelib/SampleGeneratorBase.py
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@@ -13,7 +13,18 @@ class SampleGeneratorBase(object):
self.samples_path = Path(samples_path)
self.debug = debug
self.batch_size = 1 if self.debug else batch_size
self.last_generation = None
self.active = True
def set_active(self, is_active):
self.active = is_active
def generate_next(self):
if not self.active and self.last_generation is not None:
return self.last_generation
self.last_generation = next(self)
return self.last_generation
#overridable
def __iter__(self):
#implement your own iterator

8
samplelib/SampleGeneratorFaceTemporal.py
View File

@@ -8,7 +8,7 @@ from samplelib import SampleType, SampleProcessor, SampleLoader, SampleGenerator
'''
output_sample_types = [
[SampleProcessor.TypeFlags, size, (optional)random_sub_size] ,
[SampleProcessor.TypeFlags, size, (optional) {} opts ] ,
...
]
'''
@@ -46,9 +46,9 @@ class SampleGeneratorFaceTemporal(SampleGeneratorBase):
raise ValueError('No training data provided.')
mult_max = 1
l = samples_len - (self.temporal_image_count-1)*mult_max + 1
l = samples_len - ( (self.temporal_image_count)*mult_max - (mult_max-1) )
samples_idxs = [ *range(l) ] [generator_id::self.generators_count]
samples_idxs = [ *range(l+1) ] [generator_id::self.generators_count]
if len(samples_idxs) - self.temporal_image_count < 0:
raise ValueError('Not enough samples to fit temporal line.')
@@ -67,7 +67,7 @@ class SampleGeneratorFaceTemporal(SampleGeneratorBase):
idx = shuffle_idxs.pop()
temporal_samples = []
mult = np.random.randint(mult_max)
mult = np.random.randint(mult_max)+1
for i in range( self.temporal_image_count ):
sample = samples[ idx+i*mult ]
try:

5
samplelib/SampleGeneratorImageTemporal.py
View File

@@ -43,7 +43,8 @@ class SampleGeneratorImageTemporal(SampleGeneratorBase):
raise ValueError('No training data provided.')
mult_max = 4
samples_sub_len = samples_len - (self.temporal_image_count-1)*mult_max
samples_sub_len = samples_len - ( (self.temporal_image_count)*mult_max - (mult_max-1) )
if samples_sub_len <= 0:
raise ValueError('Not enough samples to fit temporal line.')
@@ -61,7 +62,7 @@ class SampleGeneratorImageTemporal(SampleGeneratorBase):
idx = shuffle_idxs.pop()
temporal_samples = []
mult = np.random.randint(mult_max)
mult = np.random.randint(mult_max)+1
for i in range( self.temporal_image_count ):
sample = samples[ idx+i*mult ]
try:

44
samplelib/SampleProcessor.py
View File

@@ -61,6 +61,7 @@ class SampleProcessor(object):
FACE_TYPE_FULL = 11
FACE_TYPE_HEAD = 12 #currently unused
FACE_TYPE_AVATAR = 13 #currently unused
FACE_TYPE_FULL_NO_ALIGN = 14
FACE_TYPE_END = 20
MODE_BEGIN = 40
@@ -103,7 +104,7 @@ class SampleProcessor(object):
SPTF_FACETYPE_TO_FACETYPE = { SPTF.FACE_TYPE_HALF : FaceType.HALF,
SPTF.FACE_TYPE_FULL : FaceType.FULL,
SPTF.FACE_TYPE_HEAD : FaceType.HEAD,
SPTF.FACE_TYPE_AVATAR : FaceType.AVATAR }
SPTF.FACE_TYPE_FULL_NO_ALIGN : FaceType.FULL_NO_ALIGN }
outputs = []
for opts in output_sample_types:
@@ -157,6 +158,20 @@ class SampleProcessor(object):
if mode_type == SPTF.NONE:
raise ValueError ('expected MODE_ type')
def do_transform(img, mask):
warp = (img_type==SPTF.IMG_WARPED or img_type==SPTF.IMG_WARPED_TRANSFORMED)
transform = (img_type==SPTF.IMG_WARPED_TRANSFORMED or img_type==SPTF.IMG_TRANSFORMED)
flip = img_type != SPTF.IMG_WARPED
img = imagelib.warp_by_params (params, img, warp, transform, flip, True)
if mask is not None:
mask = imagelib.warp_by_params (params, mask, warp, transform, flip, False)
if len(mask.shape) == 2:
mask = mask[...,np.newaxis]
img = np.concatenate( (img, mask ), -1 )
return img
img = cached_images.get(img_type, None)
if img is None:
@@ -181,15 +196,12 @@ class SampleProcessor(object):
if cur_sample.ie_polys is not None:
cur_sample.ie_polys.overlay_mask(mask)
warp = (img_type==SPTF.IMG_WARPED or img_type==SPTF.IMG_WARPED_TRANSFORMED)
transform = (img_type==SPTF.IMG_WARPED_TRANSFORMED or img_type==SPTF.IMG_TRANSFORMED)
flip = img_type != SPTF.IMG_WARPED
img = imagelib.warp_by_params (params, img, warp, transform, flip, True)
if mask is not None:
mask = imagelib.warp_by_params (params, mask, warp, transform, flip, False)[...,np.newaxis]
img = np.concatenate( (img, mask ), -1 )
if sample.face_type == FaceType.MARK_ONLY:
if mask is not None:
img = np.concatenate( (img, mask), -1 )
else:
img = do_transform (img, mask)
cached_images[img_type] = img
@@ -197,7 +209,17 @@ class SampleProcessor(object):
ft = SPTF_FACETYPE_TO_FACETYPE[target_face_type]
if ft > sample.face_type:
raise Exception ('sample %s type %s does not match model requirement %s. Consider extract necessary type of faces.' % (sample.filename, sample.face_type, ft) )
img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, ft), (resolution,resolution), flags=cv2.INTER_CUBIC )
if sample.face_type == FaceType.MARK_ONLY:
img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], ft), (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
mask = img[...,3:4] if img.shape[2] > 3 else None
img = img[...,0:3]
img = do_transform (img, mask)
img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
else:
img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, ft), (resolution,resolution), flags=cv2.INTER_CUBIC )
else:
img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )