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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 2019-09-07 13:57:42 +04:00
parent 70dada42ea
commit 7ed38a8097
29 changed files with 768 additions and 314 deletions
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67
samplelib/SampleProcessor.py
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@ -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: