def scale(image):
# scale image randomly between 0.5 and 1.5
aug1 = iaa.ScaleX((0.5, 1.5))
aug2 = iaa.ScaleY((0.5, 1.5))
image_aug = aug1(image=image)
image_aug = aug2(image=image_aug)
return image_aug
def initialise_augmenter():
# Horizontal and Vertical Flips (set to 1 as the SomeOf function will choose when to apply these itself)
horizontal_flip = iaa.Fliplr(1) #0.5)
vertical_flip = iaa.Flipud(1) #0.5)
# 90, 180 and 270 degree rotations
rotate_90 = iaa.Affine(rotate=90)
rotate_180 = iaa.Affine(rotate=180)
rotate_270 = iaa.Affine(rotate=270)
# Translations of -10% to 10% of the image's pixels
translate_x = iaa.TranslateX(percent=(-0.1, 0.1))
translate_y = iaa.TranslateY(percent=(-0.1, 0.1))
# Scale the image between 0.75 and 1.1 of the original size
scale_x = iaa.ScaleX((0.75, 1.1))
scale_y = iaa.ScaleY((0.75, 1.1))
# Shear the image between -20 and 20 degrees
shear_x = iaa.ShearX((-20, 20))
shear_y = iaa.ShearY((-20, 20))
augmentation = iaa.SomeOf((0, None), [
horizontal_flip, vertical_flip,
iaa.OneOf([rotate_90, rotate_180, rotate_270]), translate_x,
translate_y, scale_x, scale_y, shear_x, shear_y
],
random_order=True)
return augmentation
def chapter_augmenters_scaley():
fn_start = "geometric/scaley"
image = ia.quokka(size=(128, 128))
aug = iaa.ScaleY((0.5, 1.5))
run_and_save_augseq(
fn_start + ".jpg", aug,
[image for _ in range(4*2)], cols=4, rows=2)
# iaa.AdditiveGaussianNoise(
# loc=0, scale=(0.0, 0.02*255), per_channel=0.5
# ),
# iaa.Add((-15, 15), per_channel=0.5),
# iaa.Multiply((0.8, 1.2), per_channel=0.5),
# iaa.imgcorruptlike.Contrast(severity=1),
# iaa.imgcorruptlike.Brightness(severity=2),
iaa.ContrastNormalization((0.1, 1.5), per_channel=0.5),
iaa.WithHueAndSaturation([
iaa.WithChannels(0, iaa.Add((-15, 15))),
iaa.WithChannels(1, iaa.Add((-20, 20))),
]),
iaa.GammaContrast((0.3, 1.5)),
iaa.WithBrightnessChannels(iaa.Add((-30, 70))),
iaa.ScaleX((0.5, 1.5)),
iaa.ScaleY((0.5, 1.5)),
iaa.ShearX((-10, 10)),
iaa.ShearY((-10, 10)),
],
random_order=True)
],
random_order=True)
def augment_pair(fg, label):
# print('Augment start')
label_i, segmaps_aug_i = seq(images=fg, segmentation_maps=label)
# print('Augment ok')
return label_i, segmaps_aug_i
def resize_image(image, target):
image = tf.image.resize(image, INPUT_SHAPE[:2])
return image, target
# augmentation options
AUG = iaa.Sequential([
iaa.SomeOf((0, 1), [
iaa.AddToBrightness((-30, 30)),
iaa.MultiplyBrightness((0.5, 1.5)),
iaa.MultiplySaturation((0.5, 1.5)),
iaa.AddToSaturation((-50, 50))
]),
iaa.OneOf([
iaa.ScaleX((1.0, 1.5)),
iaa.ScaleY((1.0, 1.5)),
iaa.Affine(scale={
"x": (1.0, 1.2),
"y": (1.0, 1.2)
}),
iaa.Affine(rotate=(-20, 20)),
iaa.PiecewiseAffine(scale=(0.01, 0.05)),
iaa.Affine(shear=(-16, 16))
]),
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
iaa.Rot90([1, 2, 3, 4])
])
# cutmix augmentation
beta = 0.5
row_images.append(row[:])
image = row_images[0]
for i in range(1, rows):
image = np.vstack((image, row_images[i]))
# Display
plt.figure(figsize=(cols*3, rows*3))
plt.imshow(image)
ia.seed(GROUP_NUMBER)
#Using augmenter to augment the image
sequential_augmenter = iaa.Sequential([
iaa.ChannelShuffle(0.35),
iaa.ScaleX((0.9, 1.1)),
iaa.ScaleY((0.9, 1.1)),
iaa.Sometimes(0.5, iaa.OneOf([iaa.Fliplr(), iaa.Flipud()])),
iaa.Sometimes(0.5, iaa.Rot90((1,3))),
], random_order = True)
augmented_images = sequential_augmenter.augment_images(images)
# Sample 15 images
cols = 9
rows = 5
row_images = []
for i in range(rows):
indices = np.random.rand(cols)
indices = (indices*NUMBER_IMAGES).astype(np.int64)
iaa.SigmoidContrast(gain=10, cutoff=0.3),
iaa.LogContrast(0.7),
iaa.LogContrast(1.3),
iaa.Sharpen(alpha=0.2, lightness=0.9),
iaa.Sharpen(alpha=0.2, lightness=1.2),
iaa.Fliplr(1),
iaa.Flipud(1),
iaa.Rotate(15),
iaa.Rotate(-15),
iaa.ShearX(-10),
iaa.ShearX(10),
iaa.ShearY(-10),
iaa.ShearY(10),
iaa.ScaleX(0.7),
iaa.ScaleX(1.3),
iaa.ScaleY(0.7),
iaa.ScaleY(1.3),
]
def custom_imshow(imgList, labels):
fig = plt.figure()
rows = 2
cols = 5
for i in range(10):
img = imgList[i]
temp = fig.add_subplot(rows, cols, i+1)
temp.set_title(classes[labels[i]])
temp.imshow(np.transpose(img, (1, 2, 0)))
elif augmentation == 'piecewise_affine':
transform = iaa.PiecewiseAffine(scale=(0.05, 0.09))
transformed_image = transform(image=image)
elif augmentation == 'shift_scale_rotate':
transform = ShiftScaleRotate(always_apply=True, shift_limit=0.1,
scale_limit=0.5)
transformed_image = transform(image=image)['image']
elif augmentation == 'scalex':
transform = iaa.ScaleX((0.5, 1.5))
transformed_image = transform(image=image)
elif augmentation == 'scaley':
transform = iaa.ScaleY((0.5, 1.5))
transformed_image = transform(image=image)
elif augmentation == 'translatex':
transform = iaa.TranslateX(px=(200))
transformed_image = transform(image=image)
elif augmentation == 'translatey':
transform = iaa.TranslateY(px=(-200))
transformed_image = transform(image=image)
## Crop
elif augmentation == 'crop':
transform = Crop(always_apply=True, x_max=400, y_max=400)
transformed_image = transform(image=image)['image']
result_originals = path.joinpath("AugmentedOriginals")
result_masks = path.joinpath("AugmentedMasks")
for i in range(IMAGE_COUNT):
istr = str(i % 900) + ".jpg"
original = imageio.imread(originals.joinpath(istr))
mask = imageio.imread(masks.joinpath(istr))
mask = SegmentationMapsOnImage(mask, shape=mask.shape)
seq = iaa.SomeOf((0, None), random_order=True)
seq.add(iaa.Add((-40, 40), per_channel=0.5))
seq.add(iaa.GaussianBlur(sigma=(0, 2)))
seq.add(iaa.SigmoidContrast(gain=(5, 20), cutoff=(0.3, 0.75), per_channel=True))
seq.add(iaa.HorizontalFlip())
seq.add(iaa.VerticalFlip())
seq.add(iaa.TranslateX(percent=(-0.7, 0.7), cval=33))
seq.add(iaa.TranslateY(percent=(-0.7, 0.7), cval=33))
seq.add(iaa.Rotate(random.randrange(-60, 60), cval=33))
seq.add(iaa.ScaleX((0.5, 1.5), cval=33))
seq.add(iaa.ScaleY((0.5, 1.5), cval=33))
seq.add(iaa.imgcorruptlike.DefocusBlur(severity=1))
aug = iaa.CropAndPad(percent=([-0.3, 0.3], [-0.3, 0.3], [-0.3, 0.3], [-0.3, 0.3]))
results_o, results_m = seq(image=original, segmentation_maps=mask)
istr = str(i) + ".jpg"
imageio.imsave(result_originals.joinpath(istr), results_o)
imageio.imsave(result_masks.joinpath(istr), results_m.arr)