mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-16 10:03:41 -07:00
Converter:
Session is now saved to the model folder. blur and erode ranges are increased to -400+400 hist-match-bw is now replaced with seamless2 mode. Added 'ebs' color transfer mode (works only on Windows). FANSEG model (used in FAN-x mask modes) is retrained with new model configuration and now produces better precision and less jitter
This commit is contained in:
Colombo
parent
70dada42ea
commit
7ed38a8097
29 changed files with 768 additions and 314 deletions
|
|
@ -22,7 +22,7 @@ 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, source_filename=None, mirror=None, close_target_list=None, fanseg_mask_exist=False):
|
||||
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):
|
||||
self.sample_type = sample_type if sample_type is not None else SampleType.IMAGE
|
||||
self.filename = filename
|
||||
self.face_type = face_type
|
||||
|
|
@ -30,12 +30,13 @@ class Sample(object):
|
|||
self.landmarks = np.array(landmarks) if landmarks is not None else None
|
||||
self.ie_polys = ie_polys
|
||||
self.pitch_yaw_roll = pitch_yaw_roll
|
||||
self.eyebrows_expand_mod = eyebrows_expand_mod
|
||||
self.source_filename = source_filename
|
||||
self.mirror = mirror
|
||||
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, 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, 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,
|
||||
|
|
@ -44,6 +45,7 @@ class Sample(object):
|
|||
landmarks=landmarks if landmarks is not None else self.landmarks.copy(),
|
||||
ie_polys=ie_polys if ie_polys is not None else self.ie_polys,
|
||||
pitch_yaw_roll=pitch_yaw_roll if pitch_yaw_roll is not None else self.pitch_yaw_roll,
|
||||
eyebrows_expand_mod=eyebrows_expand_mod if eyebrows_expand_mod is not None else self.eyebrows_expand_mod,
|
||||
source_filename=source_filename if source_filename is not None else self.source_filename,
|
||||
mirror=mirror if mirror is not None else self.mirror,
|
||||
close_target_list=close_target_list if close_target_list is not None else self.close_target_list,
|
||||
|
|
|
|||
|
|
@ -71,9 +71,10 @@ class SampleLoader:
|
|||
|
||||
landmarks = dflimg.get_landmarks()
|
||||
pitch_yaw_roll = dflimg.get_pitch_yaw_roll()
|
||||
if pitch_yaw_roll is None:
|
||||
pitch_yaw_roll = LandmarksProcessor.estimate_pitch_yaw_roll(landmarks)
|
||||
eyebrows_expand_mod = dflimg.get_eyebrows_expand_mod()
|
||||
|
||||
if pitch_yaw_roll is None:
|
||||
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()),
|
||||
|
|
@ -81,6 +82,7 @@ class SampleLoader:
|
|||
landmarks=landmarks,
|
||||
ie_polys=dflimg.get_ie_polys(),
|
||||
pitch_yaw_roll=pitch_yaw_roll,
|
||||
eyebrows_expand_mod=eyebrows_expand_mod,
|
||||
source_filename=dflimg.get_source_filename(),
|
||||
fanseg_mask_exist=dflimg.get_fanseg_mask() is not None, ) )
|
||||
except:
|
||||
|
|
|
|||
|
|
@ -36,7 +36,7 @@ opts:
|
|||
'MODE_BGR_SHUFFLE' #BGR shuffle
|
||||
|
||||
'resolution' : N
|
||||
'motion_blur' : (chance_int, range) - chance 0..100 to apply to face (not mask), and range [1..3] where 3 is highest power of motion blur
|
||||
'motion_blur' : (chance_int, range) - chance 0..100 to apply to face (not mask), and max_size of motion blur
|
||||
'apply_ct' : bool
|
||||
'normalize_tanh' : bool
|
||||
|
||||
|
|
@ -116,6 +116,7 @@ class SampleProcessor(object):
|
|||
resolution = opts.get('resolution', 0)
|
||||
types = opts.get('types', [] )
|
||||
|
||||
border_replicate = opts.get('border_replicate', True)
|
||||
random_sub_res = opts.get('random_sub_res', 0)
|
||||
normalize_std_dev = opts.get('normalize_std_dev', False)
|
||||
normalize_vgg = opts.get('normalize_vgg', False)
|
||||
|
|
@ -167,7 +168,7 @@ class SampleProcessor(object):
|
|||
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)
|
||||
img = imagelib.warp_by_params (params, img, warp, transform, flip, border_replicate)
|
||||
if mask is not None:
|
||||
mask = imagelib.warp_by_params (params, mask, warp, transform, flip, False)
|
||||
if len(mask.shape) == 2:
|
||||
|
|
@ -176,38 +177,30 @@ class SampleProcessor(object):
|
|||
img = np.concatenate( (img, mask ), -1 )
|
||||
return img
|
||||
|
||||
img = cached_images.get(img_type, None)
|
||||
if img is None:
|
||||
img = sample_bgr
|
||||
|
||||
### Prepare a mask
|
||||
mask = None
|
||||
if is_face_sample:
|
||||
mask = sample.load_fanseg_mask() #using fanseg_mask if exist
|
||||
|
||||
img = sample_bgr
|
||||
mask = None
|
||||
cur_sample = sample
|
||||
if mask is None:
|
||||
if sample.eyebrows_expand_mod is not None:
|
||||
mask = LandmarksProcessor.get_image_hull_mask (img.shape, sample.landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
|
||||
else:
|
||||
mask = LandmarksProcessor.get_image_hull_mask (img.shape, sample.landmarks)
|
||||
|
||||
if is_face_sample:
|
||||
if motion_blur is not None:
|
||||
chance, mb_range = motion_blur
|
||||
chance = np.clip(chance, 0, 100)
|
||||
if sample.ie_polys is not None:
|
||||
sample.ie_polys.overlay_mask(mask)
|
||||
##################
|
||||
|
||||
|
||||
if motion_blur is not None:
|
||||
chance, mb_max_size = motion_blur
|
||||
chance = np.clip(chance, 0, 100)
|
||||
|
||||
if np.random.randint(100) < chance:
|
||||
mb_range = [3,5,7,9][ : np.clip(mb_range, 0, 3)+1 ]
|
||||
dim = mb_range[ np.random.randint(len(mb_range) ) ]
|
||||
img = imagelib.LinearMotionBlur (img, dim, np.random.randint(180) )
|
||||
|
||||
mask = cur_sample.load_fanseg_mask() #using fanseg_mask if exist
|
||||
|
||||
if mask is None:
|
||||
mask = LandmarksProcessor.get_image_hull_mask (img.shape, cur_sample.landmarks)
|
||||
|
||||
if cur_sample.ie_polys is not None:
|
||||
cur_sample.ie_polys.overlay_mask(mask)
|
||||
|
||||
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
|
||||
if np.random.randint(100) < chance:
|
||||
img = imagelib.LinearMotionBlur (img, np.random.randint( mb_max_size )+1, np.random.randint(360) )
|
||||
|
||||
if is_face_sample and target_face_type != SPTF.NONE:
|
||||
target_ft = SampleProcessor.SPTF_FACETYPE_TO_FACETYPE[target_face_type]
|
||||
|
|
@ -215,16 +208,18 @@ class SampleProcessor(object):
|
|||
raise Exception ('sample %s type %s does not match model requirement %s. Consider extract necessary type of faces.' % (sample.filename, sample.face_type, target_ft) )
|
||||
|
||||
if sample.face_type == FaceType.MARK_ONLY:
|
||||
img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], target_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]
|
||||
#first warp to target facetype
|
||||
img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], target_ft), (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
|
||||
mask = cv2.warpAffine( mask, LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], target_ft), (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
|
||||
#then apply transforms
|
||||
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, target_ft), (resolution,resolution), borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
|
||||
img = do_transform (img, mask)
|
||||
img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, target_ft), (resolution,resolution), borderMode=(cv2.BORDER_REPLICATE if border_replicate else cv2.BORDER_CONSTANT), flags=cv2.INTER_CUBIC )
|
||||
|
||||
else:
|
||||
img = do_transform (img, mask)
|
||||
img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
|
||||
|
||||
if random_sub_res != 0:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue