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Merge pull request #72 from faceshiftlabs/feat/random-color-change

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Feat/random color change
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Jeremy Hummel 2019-09-17 10:25:58 -07:00 • committed by GitHub
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3 changed files with 29 additions and 2 deletions
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16
imagelib/color_transfer.py
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@ -6,6 +6,8 @@ import numpy as np
import scipy as sp
import scipy.sparse
from scipy.sparse.linalg import spsolve
from scipy.stats import special_ortho_group
class ColorTransferMode(IntEnum):
NONE = 0
@ -145,6 +147,20 @@ def seamless_clone(source, target, mask):
return np.clip( np.dstack(result), 0, 1 )
def random_color_transform(image, seed=None):
image = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
M = np.eye(3)
M[1:, 1:] = special_ortho_group.rvs(2, 1, seed)
image = image.dot(M)
l, a, b = cv2.split(image)
l = np.clip(l, 0, 100)
a = np.clip(a, -127, 127)
b = np.clip(b, -127, 127)
image = cv2.merge([l, a, b])
image = cv2.cvtColor(image.astype(np.float32), cv2.COLOR_LAB2BGR)
np.clip(image, 0, 1, out=image)
return image
def reinhard_color_transfer(source, target, clip=False, preserve_paper=False, source_mask=None, target_mask=None):
"""
Transfers the color distribution from the target to the source

9
models/Model_SAE/Model.py
View file

@ -142,6 +142,11 @@ class SAEModel(ModelBase):
"but the training time may be longer, due to the src faceset is becoming more diverse."),
ColorTransferMode.NONE, ColorTransferMode.MASKED_RCT_PAPER_CLIP)
default_random_color_change = False if is_first_run else self.options.get('random_color_change', False)
self.options['random_color_change'] = io.input_bool(
"Enable random color change? (y/n, ?:help skip:%s) : " % (yn_str[default_random_color_change]), default_random_color_change,
help_message="")
if nnlib.device.backend != 'plaidML': # todo https://github.com/plaidml/plaidml/issues/301
default_clipgrad = False if is_first_run else self.options.get('clipgrad', False)
self.options['clipgrad'] = io.input_bool(
@ -464,7 +469,9 @@ class SAEModel(ModelBase):
face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF
global t_mode_bgr
t_mode_bgr = t.MODE_BGR if not self.pretrain else t.MODE_BGR_SHUFFLE
t_mode_bgr = t.MODE_BGR if not self.pretrain else t.MODE_LAB_RAND_TRANSFORM
if self.options['random_color_change']:
t_mode_bgr = t.MODE_LAB_RAND_TRANSFORM
global training_data_src_path
training_data_src_path = self.training_data_src_path

6
samplelib/SampleProcessor.py
View file

@ -6,7 +6,7 @@ import numpy as np
import imagelib
from facelib import FaceType, LandmarksProcessor
from imagelib.color_transfer import ColorTransferMode
from imagelib.color_transfer import ColorTransferMode, random_color_transform
"""
output_sample_types = [
@ -72,6 +72,7 @@ class SampleProcessor(object):
MODE_GGG = 42 #3xGrayscale
MODE_M = 43 #mask only
MODE_BGR_SHUFFLE = 44 #BGR shuffle
MODE_LAB_RAND_TRANSFORM = 45 #Random transform in LAB space
MODE_END = 50
class Options(object):
@ -281,6 +282,9 @@ class SampleProcessor(object):
elif mode_type == SPTF.MODE_BGR_SHUFFLE:
rnd_state = np.random.RandomState (sample_rnd_seed)
img = np.take (img_bgr, rnd_state.permutation(img_bgr.shape[-1]), axis=-1)
elif mode_type == SPTF.MODE_LAB_RAND_TRANSFORM:
rnd_state = np.random.RandomState (sample_rnd_seed)
img = random_color_transform(img_bgr, rnd_state)
elif mode_type == SPTF.MODE_G:
img = np.concatenate ( (np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1),img_mask) , -1 )
elif mode_type == SPTF.MODE_GGG: