def randomDataAugument(self, num_trans):
# 以下で定義する変換処理の内ランダムに幾つかの処理を選択
seq = iaa.SomeOf(num_trans, [
iaa.Affine(rotate=(-90, 90), order=1, mode="edge"),
iaa.Fliplr(1.0),
iaa.OneOf([
# 同じ系統の変換はどれか1つが起きるように 1つにまとめる
iaa.Affine(translate_percent={"x": (-0.125, 0.125)}, order=1, mode="edge"),
iaa.Affine(translate_percent={"y": (-0.125, 0.125)}, order=1, mode="edge")
]),
iaa.Affine(scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, order=1, mode="edge"),
iaa.OneOf([
iaa.AdditiveGaussianNoise(scale=[0.05 * 255, 0.2 * 255]),
iaa.AdditiveLaplaceNoise(scale=[0.05 * 255, 0.2 * 255]),
iaa.AdditivePoissonNoise(lam=(16.0, 48.0), per_channel=True)
]),
iaa.OneOf([
iaa.LogContrast((0.5, 1.5)),
iaa.LinearContrast((0.5, 2.0))
]),
iaa.OneOf([
iaa.GaussianBlur(sigma=(0.5, 1.0)),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0))
]),
iaa.Invert(1.0)
], random_order=True)
return seq
def data_aug(images):
seq = iaa.Sometimes(
0.5, iaa.Identity(),
iaa.Sometimes(
0.5,
iaa.Sequential([
iaa.Sometimes(
0.5,
iaa.OneOf([
iaa.AdditiveGaussianNoise(scale=(0, 0.1 * 255)),
iaa.AdditiveLaplaceNoise(scale=(0, 0.1 * 255)),
iaa.ReplaceElementwise(0.03, [0, 255]),
iaa.GaussianBlur(sigma=(0.0, 3.0)),
iaa.BilateralBlur(d=(3, 10),
sigma_color=(10, 250),
sigma_space=(10, 250))
])),
iaa.OneOf([
iaa.Add((-40, 40)),
iaa.AddElementwise((-20, 20)),
iaa.pillike.EnhanceBrightness()
]),
iaa.OneOf([
iaa.GammaContrast((0.2, 2.0)),
iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6)),
iaa.LogContrast(gain=(0.6, 1.4)),
iaa.AllChannelsCLAHE(),
iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0)),
])
])))
images = seq(images=images)
return images
def imgaugRGB(img):
print(img.shape)
seq = iaa.Sequential(
[
# blur
iaa.SomeOf((0, 2), [
iaa.GaussianBlur((0.0, 2.0)),
iaa.AverageBlur(k=(3, 7)),
iaa.MedianBlur(k=(3, 7)),
iaa.BilateralBlur(d=(1, 7)),
iaa.MotionBlur(k=(3, 7))
]),
#color
iaa.SomeOf(
(0, 2),
[
#iaa.WithColorspace(),
iaa.AddToHueAndSaturation((-20, 20)),
#iaa.ChangeColorspace(to_colorspace[], alpha=0.5),
iaa.Grayscale(alpha=(0.0, 0.2))
]),
#brightness
iaa.OneOf([
iaa.Sequential([
iaa.Add((-10, 10), per_channel=0.5),
iaa.Multiply((0.5, 1.5), per_channel=0.5)
]),
iaa.Add((-10, 10), per_channel=0.5),
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.FrequencyNoiseAlpha(exponent=(-4, 0),
first=iaa.Multiply(
(0.5, 1.5), per_channel=0.5),
second=iaa.ContrastNormalization(
(0.5, 2.0), per_channel=0.5))
]),
#contrast
iaa.SomeOf((0, 2), [
iaa.GammaContrast((0.5, 1.5), per_channel=0.5),
iaa.SigmoidContrast(
gain=(0, 10), cutoff=(0.25, 0.75), per_channel=0.5),
iaa.LogContrast(gain=(0.75, 1), per_channel=0.5),
iaa.LinearContrast(alpha=(0.7, 1.3), per_channel=0.5)
]),
#arithmetic
iaa.SomeOf((0, 3), [
iaa.AdditiveGaussianNoise(scale=(0, 0.05), per_channel=0.5),
iaa.AdditiveLaplaceNoise(scale=(0, 0.05), per_channel=0.5),
iaa.AdditivePoissonNoise(lam=(0, 8), per_channel=0.5),
iaa.Dropout(p=(0, 0.05), per_channel=0.5),
iaa.ImpulseNoise(p=(0, 0.05)),
iaa.SaltAndPepper(p=(0, 0.05)),
iaa.Salt(p=(0, 0.05)),
iaa.Pepper(p=(0, 0.05))
]),
#iaa.Sometimes(p=0.5, iaa.JpegCompression((0, 30)), None),
],
random_order=True)
return seq.augment_image(img)
def load_aug():
sometimes = lambda aug: iaa.Sometimes(0.3, aug)
seq[0] = iaa.Sequential(
[
# execute 0 to 5 of the following (less important) augmenters per image
# don't execute all of them, as that would often be way too strong
iaa.SomeOf(
(0, 5),
[
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode=ia.ALL,
pad_cval=(0, 255)),
iaa.Crop(percent=0.25, keep_size=True),
iaa.OneOf([
iaa.GaussianBlur(
(0, 0.2
)), # blur images with a sigma between 0 and 2.0
iaa.AverageBlur(k=(2,
3)), # blur image using local means
iaa.MedianBlur(
k=(1, 3)), # blur image using local medians
]),
iaa.Sharpen(alpha=(0, 0.5),
lightness=(0.75, 1.5)), # sharpen images
iaa.Emboss(alpha=(0, 0.5),
strength=(0, 0.5)), # emboss images
# search either for all edges or for directed edges,
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255),
per_channel=0.5), # add gaussian noise to images
iaa.Add(
(-10, 10), per_channel=0.5
), # change brightness of images (by -10 to 10 of original value)
iaa.AddToHueAndSaturation(
(-10, 10)), # change hue and saturation
# either change the brightness of the whole image (sometimes
# per channel) or change the brightness of subareas
iaa.contrast.LinearContrast(
(0.5, 2.0),
per_channel=0.5), # improve or worsen the contrast
iaa.Grayscale(alpha=(0.0, 0.5)),
iaa.AdditiveLaplaceNoise(scale=0.05 * 255),
iaa.AdditivePoissonNoise(lam=2),
iaa.Multiply(mul=(0.5, 1.5)),
iaa.Dropout(p=(0.1, 0.2)),
iaa.CoarseDropout(p=0.1, size_percent=0.05),
iaa.MotionBlur(k=3),
iaa.LinearContrast(),
iaa.AveragePooling(2)
],
random_order=True)
],
random_order=True)
def __init__(self):
sometimes = lambda aug: iaa.Sometimes(0.3, aug)
self.aug = iaa.Sequential(
iaa.SomeOf(
(1, 5),
[
# blur
sometimes(
iaa.OneOf([
iaa.GaussianBlur(sigma=(0, 1.0)),
iaa.MotionBlur(k=3),
iaa.AdditiveGaussianNoise(scale=(0, .1 * 255)),
iaa.AdditiveLaplaceNoise(scale=(0, .1 * 255))
])),
# color
sometimes(
iaa.AddToHueAndSaturation(value=(-10, 10),
per_channel=True)),
sometimes(
iaa.SigmoidContrast(gain=(3, 10),
cutoff=(0.4, 0.6),
per_channel=True)),
sometimes(iaa.Invert(0.25, per_channel=0.5)),
sometimes(iaa.Solarize(0.5, threshold=(32, 128))),
sometimes(iaa.Dropout2d(p=0.5)),
sometimes(iaa.Multiply((0.5, 1.5), per_channel=0.5)),
sometimes(iaa.Add((-40, 40), per_channel=0.5)),
sometimes(iaa.JpegCompression(compression=(5, 80))),
# distort
sometimes(
iaa.Crop(percent=(0.01, 0.05),
sample_independently=True)),
sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.01))),
sometimes(
iaa.Affine(
scale=(0.7, 1.3),
translate_percent=(-0.1, 0.1),
# rotate=(-5, 5), shear=(-5, 5),
order=[0, 1],
cval=(0, 255),
mode=ia.ALL)),
sometimes(iaa.ElasticTransformation(alpha=50, sigma=20)),
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.02))),
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.01))),
sometimes(
iaa.OneOf([
iaa.Dropout(p=(0, 0.1)),
iaa.CoarseDropout(p=(0, 0.1),
size_percent=(0.02, 0.25))
])),
],
random_order=True),
random_order=True)
def __call__(self, data):
tensor = data[self.type].astype(np.float32)
tensor = tensor[:, :, np.newaxis]
additive_guasian = iaa.AdditiveGaussianNoise(
scale=(0.01, 0.04)).augment_image
additive_laplacian = iaa.AdditiveLaplaceNoise(
scale=(0.01, 0.04)).augment_image
dropout = iaa.Dropout(p=(0.0, 0.1)).augment_image
random_blur = transforms.RandomChoice(
[additive_guasian, additive_laplacian, dropout])
data[self.type] = random_blur(tensor).squeeze(2)
return data
def __init__(self, ):
import imgaug.augmenters as ia
self.default_deg_templates = {
'sr4x':
ia.Sequential([
# It's almost like a 4x bicubic downsampling
ia.Resize((0.25000, 0.25001), cv2.INTER_AREA),
ia.Resize({
'height': 512,
'width': 512
}, cv2.INTER_CUBIC),
]),
'sr4x8x':
ia.Sequential([
ia.Resize((0.125, 0.25), cv2.INTER_AREA),
ia.Resize({
'height': 512,
'width': 512
}, cv2.INTER_CUBIC),
]),
'denoise':
ia.OneOf([
ia.AdditiveGaussianNoise(scale=(20, 40), per_channel=True),
ia.AdditiveLaplaceNoise(scale=(20, 40), per_channel=True),
ia.AdditivePoissonNoise(lam=(15, 30), per_channel=True),
]),
'deblur':
ia.OneOf([
ia.MotionBlur(k=(10, 20)),
ia.GaussianBlur((3.0, 8.0)),
]),
'jpeg':
ia.JpegCompression(compression=(50, 85)),
'16x':
Mosaic16x(),
}
rand_deg_list = [
self.default_deg_templates['deblur'],
self.default_deg_templates['denoise'],
self.default_deg_templates['jpeg'],
self.default_deg_templates['sr4x8x'],
]
self.default_deg_templates['face_renov'] = ia.Sequential(
rand_deg_list, random_order=True)
def do_all_aug(image):
do_aug(image, iaa.Noop(name="origin"))
do_aug(image, iaa.Crop((0, 10))) # 切边
do_aug(image, iaa.GaussianBlur((0, 3)))
do_aug(image, iaa.AverageBlur(1, 7))
do_aug(image, iaa.MedianBlur(1, 7))
do_aug(image, iaa.Sharpen())
do_aug(image, iaa.BilateralBlur()) # 既噪音又模糊，叫双边
do_aug(image, iaa.MotionBlur())
do_aug(image, iaa.MeanShiftBlur())
do_aug(image, iaa.GammaContrast())
do_aug(image, iaa.SigmoidContrast())
do_aug(image,
iaa.Affine(shear={
'x': (-10, 10),
'y': (-10, 10)
}, mode="edge")) # shear：x轴往左右偏离的像素书，(a,b)是a,b间随机值，[a,b]是二选一
do_aug(image,
iaa.Affine(shear={
'x': (-10, 10),
'y': (-10, 10)
}, mode="edge")) # shear：x轴往左右偏离的像素书，(a,b)是a,b间随机值，[a,b]是二选一
do_aug(image, iaa.Rotate(rotate=(-10, 10), mode="edge"))
do_aug(image, iaa.PiecewiseAffine()) # 局部点变形
do_aug(image, iaa.Fog())
do_aug(image, iaa.Clouds())
do_aug(image, iaa.Snowflakes(flake_size=(0.1, 0.2),
density=(0.005, 0.025)))
do_aug(
image,
iaa.Rain(
nb_iterations=1,
drop_size=(0.05, 0.1),
speed=(0.04, 0.08),
))
do_aug(
image,
iaa.ElasticTransformation(alpha=(0.0, 20.0),
sigma=(3.0, 5.0),
mode="nearest"))
do_aug(image, iaa.AdditiveGaussianNoise(scale=(0, 10)))
do_aug(image, iaa.AdditiveLaplaceNoise(scale=(0, 10)))
do_aug(image, iaa.AdditivePoissonNoise(lam=(0, 10)))
do_aug(image, iaa.Salt((0, 0.02)))
do_aug(image, iaa.Pepper((0, 0.02)))
def randomTestAugument(self, NUM_TRANS):
# 本番環境の危機から取得したデータにノイズが乗っていた状態を想定して変換
seq = iaa.SomeOf(
NUM_TRANS,
[
iaa.Affine(rotate=(-90, 90), order=1, mode="edge"),
iaa.Fliplr(1.0),
iaa.OneOf([
# 同じ系統の変換はどれか1つが起きるように 1つにまとめる
iaa.Affine(translate_percent={"x": (-0.125, 0.125)},
order=1,
mode="edge"),
iaa.Affine(translate_percent={"y": (-0.125, 0.125)},
order=1,
mode="edge")
]),
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
order=1,
mode="edge"),
iaa.OneOf([
iaa.AdditiveGaussianNoise(scale=[0.05 * 255, 0.2 * 255]),
iaa.AdditiveLaplaceNoise(scale=[0.05 * 255, 0.2 * 255]),
iaa.AdditivePoissonNoise(lam=(16.0, 48.0),
per_channel=True)
]),
iaa.OneOf([
iaa.LogContrast((0.5, 1.5)),
iaa.LinearContrast((0.5, 2.0))
]),
iaa.OneOf([
iaa.GaussianBlur(sigma=(0.5, 1.0)),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0))
]),
# iaa.Invert(1.0) # 色反転は故障しすぎでしょう..
],
random_order=True)
return seq
def get_augmenter():
sometimes = lambda aug: iaa.Sometimes(0.7, aug)
augmenter = iaa.Sequential([
sometimes(
iaa.OneOf([
iaa.GaussianBlur((0.8, 1.2)),
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5),
iaa.AveragePooling([2, 2]),
iaa.Sharpen(alpha=(0.0, 0.5), lightness=(0.75, 2.0)),
iaa.AdditiveLaplaceNoise(scale=0.05 * 255, per_channel=True),
iaa.LinearContrast((0.5, 2.0), per_channel=True),
iaa.Clouds(),
iaa.Fog(),
iaa.PiecewiseAffine(scale=0.02),
iaa.Affine(scale={
"x": (0.8, 1),
"y": (0.8, 1)
},
translate_percent={
"x": (-0.1, 0.1),
"y": (-0.1, 0.1)
},
rotate=(-10, 10),
shear=(-5, 5),
order=[0, 1],
cval=(0, 255),
mode='constant'),
])),
sometimes(
iaa.OneOf([
iaa.Crop(px=(2, 6)),
iaa.CoarseDropout((0.0, 0.01), size_percent=(0.02, 0.1)),
])),
sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.08),
keep_size=False))
],
random_order=True)
return augmenter
def __init__(self, train_list, test_list, database_root, store_memory=True, data_aug=False, new_aug=False):
"""Initialize the Dataset object
Args:
train_list: TXT file or list with the paths of the images to use for training (Images must be between 0 and 255)
test_list: TXT file or list with the paths of the images to use for testing (Images must be between 0 and 255)
database_root: Path to the root of the Database
store_memory: True stores all the training images, False loads at runtime the images
Returns:
"""
if not store_memory and data_aug:
sys.stderr.write('Online data augmentation not supported when the data is not stored in memory!')
sys.exit()
# Define types of data augmentation
data_aug_scales = [0.5, 0.8, 1]
data_aug_flip = True
#checking aug bool value
print('new augmentation value is : ')
print(new_aug)
if(new_aug):
#extra augmentation
data_aug_brightness = True
data_aug_addnoise = False
else:
data_aug_brightness = False
data_aug_addnoise = False
# Load training images (path) and labels
print('Started loading files...')
if not isinstance(train_list, list) and train_list is not None:
with open(train_list) as t:
train_paths = t.readlines()
elif isinstance(train_list, list):
train_paths = train_list
else:
train_paths = []
if not isinstance(test_list, list) and test_list is not None:
with open(test_list) as t:
test_paths = t.readlines()
elif isinstance(test_list, list):
test_paths = test_list
else:
test_paths = []
self.images_train = []
self.images_train_path = []
self.labels_train = []
self.labels_train_path = []
for idx, line in enumerate(train_paths):
if store_memory:
img = Image.open(os.path.join(database_root, str(line.split()[0])))
img.load()
label = Image.open(os.path.join(database_root, str(line.split()[1])))
label.load()
label = label.split()[0]
j = 0
if data_aug:
if idx == 0: sys.stdout.write('Performing the data augmentation')
for scale in data_aug_scales:
img_size = tuple([int(img.size[0] * scale), int(img.size[1] * scale)])
img_sc = img.resize(img_size)
label_sc = label.resize(img_size)
self.images_train.append(np.array(img_sc, dtype=np.uint8))
self.labels_train.append(np.array(label_sc, dtype=np.uint8))
if(data_aug_addnoise):
im_lap_noise = img_to_array(img_sc)
seq = iaa.Sequential([iaa.AdditiveLaplaceNoise(scale=0.9*255)]) #scale was 11 before
image_noise_np = seq.augment_image(im_lap_noise).astype('uint8')
# im_lap_noise_sq = Image.fromarray(seq.augment_image(im_lap_noise))
# im_lap_noise_sq.save('./aug_image/test_save/aug-im-noise' + str(j), "JPEG")
self.images_train.append(np.array(image_noise_np, dtype=np.uint8))
self.labels_train.append(np.array(label_sc, dtype=np.uint8))
if data_aug_flip:
img_sc_fl = img_sc.transpose(Image.FLIP_LEFT_RIGHT)
label_sc_fl = label_sc.transpose(Image.FLIP_LEFT_RIGHT)
self.images_train.append(np.array(img_sc_fl, dtype=np.uint8))
self.labels_train.append(np.array(label_sc_fl, dtype=np.uint8))
if data_aug_brightness:
data_img = img_to_array(img_sc)
br_samples = np.expand_dims(data_img, 0)
datagen = ImageDataGenerator(brightness_range=[0.2,1.7])
it = datagen.flow(br_samples, batch_size=1)
for i in range(9):
batch = it.next()
image_np = batch[0].astype('uint8')
aug_im = Image.fromarray(image_np)
aug_im.save('./aug_image/test_save/aug-im-' + str(j), "JPEG")
self.images_train.append(np.array(image_np, dtype=np.uint8))
self.labels_train.append(np.array(label_sc, dtype=np.uint8))
j = j+1
else:
if idx == 0: sys.stdout.write('Loading the data')
self.images_train.append(np.array(img, dtype=np.uint8))
self.labels_train.append(np.array(label, dtype=np.uint8))
if (idx + 1) % 50 == 0:
sys.stdout.write('.')
self.images_train_path.append(os.path.join(database_root, str(line.split()[0])))
self.labels_train_path.append(os.path.join(database_root, str(line.split()[1])))
sys.stdout.write('\n')
self.images_train_path = np.array(self.images_train_path)
self.labels_train_path = np.array(self.labels_train_path)
# Load testing images (path) and labels
self.images_test = []
self.images_test_path = []
for idx, line in enumerate(test_paths):
if store_memory:
self.images_test.append(np.array(Image.open(os.path.join(database_root, str(line.split()[0]))),
dtype=np.uint8))
if (idx + 1) % 1000 == 0:
print('Loaded ' + str(idx) + ' test images')
self.images_test_path.append(os.path.join(database_root, str(line.split()[0])))
print('Done initializing Dataset')
# Init parameters
self.train_ptr = 0
self.test_ptr = 0
self.train_size = max(len(self.images_train_path), len(self.images_train))
self.test_size = len(self.images_test_path)
self.train_idx = np.arange(self.train_size)
np.random.shuffle(self.train_idx)
self.store_memory = store_memory
def imgaug_augment(self,
TARGET_DIR,
INPUT_SIZE,
NORMALIZE=False,
AUGMENTATION='native'):
data, label = self.img2array(TARGET_DIR, INPUT_SIZE, NORMALIZE)
if AUGMENTATION == 'native':
return data, label
elif AUGMENTATION == 'rotation':
imgaug_aug = iaa.Affine(rotate=(-90, 90), order=1,
mode="edge") # 90度 "まで" 回転
elif AUGMENTATION == 'hflip':
imgaug_aug = iaa.Fliplr(1.0) # 左右反転
elif AUGMENTATION == 'width_shift':
imgaug_aug = iaa.Affine(translate_percent={"x": (-0.125, 0.125)},
order=1,
mode="edge") # 1/8 平行移動(左右)
elif AUGMENTATION == 'height_shift':
imgaug_aug = iaa.Affine(translate_percent={"y": (-0.125, 0.125)},
order=1,
mode="edge") # 1/8 平行移動(上下)
# imgaug_aug = iaa.Crop(px=(0, 40)) <= 平行移動ではなく、切り抜き
elif AUGMENTATION == 'zoom':
imgaug_aug = iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
order=1,
mode="edge") # 80~120% ズーム
# これも keras と仕様が違って、縦横独立に拡大・縮小されるようである。
elif AUGMENTATION == 'logcon':
imgaug_aug = iaa.LogContrast((0.5, 1.5))
elif AUGMENTATION == 'linecon':
imgaug_aug = iaa.LinearContrast((0.5, 2.0)) # 明度変換
elif AUGMENTATION == 'gnoise':
imgaug_aug = iaa.AdditiveGaussianNoise(
scale=[0.05 * 255, 0.2 * 255]) # Gaussian Noise
elif AUGMENTATION == 'lnoise':
imgaug_aug = iaa.AdditiveLaplaceNoise(
scale=[0.05 * 255, 0.2 * 255]) # LaplaceNoise
elif AUGMENTATION == 'pnoise':
imgaug_aug = iaa.AdditivePoissonNoise(
lam=(16.0, 48.0), per_channel=True) # PoissonNoise
elif AUGMENTATION == 'flatten':
imgaug_aug = iaa.GaussianBlur(sigma=(0.5, 1.0)) # blur: ぼかし (平滑化)
elif AUGMENTATION == 'sharpen':
imgaug_aug = iaa.Sharpen(alpha=(0, 1.0),
lightness=(0.75,
1.5)) # sharpen images (鮮鋭化)
elif AUGMENTATION == 'emboss':
imgaug_aug = iaa.Emboss(alpha=(0, 1.0),
strength=(0, 2.0)) # Edge 強調
elif AUGMENTATION == 'invert':
imgaug_aug = iaa.Invert(1.0) # 色反転 <= これがうまく行かないので自分で作った。
elif AUGMENTATION == 'someof': # 上記のうちのどれか1つ
imgaug_aug = iaa.SomeOf(
1,
[
iaa.Affine(rotate=(-90, 90), order=1, mode="edge"),
iaa.Fliplr(1.0),
iaa.Affine(translate_percent={"x": (-0.125, 0.125)},
order=1,
mode="edge"),
iaa.Affine(translate_percent={"y": (-0.125, 0.125)},
order=1,
mode="edge"),
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
order=1,
mode="edge"),
iaa.LogContrast((0.5, 1.5)),
iaa.LinearContrast((0.5, 2.0)),
iaa.AdditiveGaussianNoise(scale=[0.05 * 255, 0.25 * 255]),
iaa.AdditiveLaplaceNoise(scale=[0.05 * 255, 0.25 * 255]),
iaa.AdditivePoissonNoise(lam=(16.0, 48.0),
per_channel=True),
iaa.GaussianBlur(sigma=(0.5, 1.0)),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)),
iaa.Invert(1.0) # 14
])
elif AUGMENTATION == 'plural': # 異なる系統の変換を複数(1つの変換あとに画素値がマイナスになるとError..)
imgaug_aug = self.randomDataAugument(2)
elif AUGMENTATION == 'fortest': # plural - invert (色反転) (test 用)
imgaug_aug = self.randomTestAugument(2)
else:
print("現在 imgaug で選択できる DA のモードは以下の通りです。")
print(self.imgaug_aug_list, "\n")
raise ValueError("予期されないモードが選択されています。")
aug_data = imgaug_aug.augment_images(data)
aug_data = np.clip(aug_data, 0, 255)
return aug_data, label
# Blur and Noise
iaa.Sometimes(
0.2,
iaa.SomeOf((1, None), [
iaa.OneOf([
iaa.MotionBlur(k=3, name="motion-blur"),
iaa.GaussianBlur(sigma=(0.5, 1.0), name="gaus-blur")
]),
iaa.OneOf([
iaa.AddElementwise(
(-5, 5), per_channel=0.5, name="add-element"),
iaa.MultiplyElementwise(
(0.95, 1.05), per_channel=0.5, name="mul-element"),
iaa.AdditiveGaussianNoise(
scale=0.01 * 255, per_channel=0.5, name="guas-noise"),
iaa.AdditiveLaplaceNoise(
scale=(0, 0.01 * 255), per_channel=True, name="lap-noise"),
iaa.Sometimes(
1.0,
iaa.Dropout(
p=(0.003, 0.01), per_channel=0.5, name="dropout")),
]),
],
random_order=True)),
# Colored Blocks
iaa.Sometimes(
0.2,
iaa.CoarseDropout(0.02,
size_px=(4, 16),
per_channel=0.5,
name="cdropout")),
def imgaug_augment(self, target_dir='default', mode='native'):
if target_dir == 'default':
data, label = self.npzLoader(self.train_file)
else:
data, label = self.getStackedData(target_dir=target_dir)
if mode == 'native':
return data, label
elif mode == 'rotation':
imgaug_aug = iaa.Affine(rotate=(-90, 90), order=1, mode="edge") # 90度回転
# keras と仕様が異なることに注意
# keras は変化量 / imgaug は 変化の最大角を指定している
# 開いた部分の穴埋めができない..?? mode="edge" にするとそれなり..
elif mode == 'hflip':
imgaug_aug = iaa.Fliplr(1.0) # 左右反転
elif mode == 'width_shift':
imgaug_aug = iaa.Affine(translate_percent={"x": (-0.125, 0.125)}, order=1, mode="edge") # 1/8 平行移動(左右)
elif mode == 'height_shift':
imgaug_aug = iaa.Affine(translate_percent={"y": (-0.125, 0.125)}, order=1, mode="edge") # 1/8 平行移動(上下)
# imgaug_aug = iaa.Crop(px=(0, 40)) <= 平行移動ではなく、切り抜き
elif mode == 'zoom':
imgaug_aug = iaa.Affine(scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, order=1, mode="edge") # 80~120% ズーム
# これも keras と仕様が違って、縦横独立に拡大・縮小されるようである。
elif mode == 'logcon':
imgaug_aug = iaa.LogContrast(gain=(5, 15))
elif mode == 'linecon':
imgaug_aug = iaa.LinearContrast((0.5, 2.0)) # 明度変換
elif mode == 'gnoise':
imgaug_aug = iaa.AdditiveGaussianNoise(scale=[0.05*255, 0.2*255]) # Gaussian Noise
elif mode == 'lnoise':
imgaug_aug = iaa.AdditiveLaplaceNoise(scale=[0.05*255, 0.2*255]) # LaplaceNoise
elif mode == 'pnoise':
imgaug_aug = iaa.AdditivePoissonNoise(lam=(16.0, 48.0), per_channel=True) # PoissonNoise
elif mode == 'flatten':
imgaug_aug = iaa.GaussianBlur(sigma=(0.5, 1.0)) # blur: ぼかし (平滑化)
elif mode == 'sharpen':
imgaug_aug = iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)) # sharpen images (鮮鋭化)
elif mode == 'emboss':
imgaug_aug = iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)) # Edge 強調
elif mode == 'invert':
#imgaug_aug = iaa.Invert(1.0) # 色反転 <= これがうまく行かないので自分で作った。
aug_data = []
for b in range(data.shape[0]):
aug_data.append(255-data[b])
return np.array(aug_data), label
elif mode == 'someof': # 上記のうちのどれか1つ
imgaug_aug = iaa.SomeOf(1, [
iaa.Affine(rotate=(-90, 90), order=1, mode="edge"),
iaa.Fliplr(1.0),
iaa.Affine(translate_percent={"x": (-0.125, 0.125)}, order=1, mode="edge"),
iaa.Affine(translate_percent={"y": (-0.125, 0.125)}, order=1, mode="edge"),
iaa.Affine(scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, order=1, mode="edge"),
iaa.LogContrast((0.5, 1.5)),
iaa.LinearContrast((0.5, 2.0)),
iaa.AdditiveGaussianNoise(scale=[0.05*255, 0.25*255]),
iaa.AdditiveLaplaceNoise(scale=[0.05*255, 0.25*255]),
iaa.AdditivePoissonNoise(lam=(16.0, 48.0), per_channel=True),
iaa.GaussianBlur(sigma=(0.5, 1.0)),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)),
iaa.Invert(1.0) # 14
])
#elif mode == 'plural': # 異なる系統の変換を複数(1つの変換あとに画素値がマイナスになるとError..)
# imgaug_aug = self.randomDataAugument(2)
else:
print("現在 imgaug で選択できる DA のモードは以下の通りです。")
print(self.imgaug_mode_list, "\n")
raise ValueError("予期されないモードが選択されています。")
aug_data = imgaug_aug.augment_images(data)
aug_data = np.clip(aug_data, 0, 255)
# 注意: 戻り値の範囲は [0, 255] です。
return aug_data, label
# # aug58 = iaa.DirectedEdgeDetect(alpha=(0.0, 0.5), direction=(0.0, 0.5)) # # aug59 = iaa.Canny( # # alpha=(0.0, 0.1), # # colorizer=iaa.RandomColorsBinaryImageColorizer( # # color_true=255, # # color_false=0 # # ) # # ) scale=(0, 20) aug1 = iaa.Add((-20, 20)) aug2 = iaa.AddElementwise((-20, 20), per_channel=0.5) aug3 = aug = iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0)) aug4 = iaa.AdditiveLaplaceNoise(scale=(0, 0.2*255)) aug5 = iaa.AdditivePoissonNoise(scale) aug6 = iaa.Multiply((0.5, 1.5), per_channel=0.5) aug7 = iaa.Cutout(nb_iterations=2, size=0.05) aug8 = iaa.Cutout(fill_mode="constant", size=0.05, cval=255) aug9 = iaa.Cutout(fill_mode="gaussian", fill_per_channel=True, size=0.05) aug10 = iaa.Dropout(p=(0, 0.05)) aug11 = iaa.CoarseDropout((0.0, 0.05), size_percent=(0.02, 0.2)) aug12 = iaa.Dropout2d(p=0.05, nb_keep_channels=0) aug13 = iaa.ImpulseNoise(0.1) aug14 = iaa.CoarseSaltAndPepper(0.05, size_percent=(0.01, 0.6)) aug15 = iaa.CoarsePepper(0.05, size_percent=(0.01, 0.2)) # aug16 = iaa.Invert(0.25, per_channel=0.4) # aug17 = iaa.Invert(0.1)
