def draw_single_sequential_images():
image = misc.imresize(ndimage.imread("quokka.jpg")[0:643, 0:643], (128, 128))
st = lambda aug: iaa.Sometimes(0.5, aug)
seq = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
st(iaa.Crop(percent=(0, 0.1))),
st(iaa.GaussianBlur((0, 3.0))),
st(iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.2), per_channel=0.5)),
st(iaa.Dropout((0.0, 0.1), per_channel=0.5)),
st(iaa.Add((-10, 10), per_channel=0.5)),
st(iaa.Multiply((0.5, 1.5), per_channel=0.5)),
st(iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5)),
st(iaa.Affine(
scale={"x": (0.8, 1.2), "y": (0.8, 1.2)},
translate_px={"x": (-16, 16), "y": (-16, 16)},
rotate=(-45, 45),
shear=(-16, 16),
order=ia.ALL,
cval=(0, 1.0),
mode=ia.ALL
)),
st(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25))
],
random_order=True
)
grid = seq.draw_grid(image, cols=8, rows=8)
misc.imsave("examples_grid.jpg", grid)
def main():
images = [
misc.imresize(
ndimage.imread("../quokka.jpg")[0:643, 0:643], (128, 128)),
misc.imresize(data.astronaut(), (128, 128))
]
augmenters = [
iaa.Noop(name="Noop"),
iaa.Crop(px=(0, 8), name="Crop-px"),
iaa.Crop(percent=(0, 0.1), name="Crop-percent"),
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
iaa.Grayscale(0.5, name="Grayscale0.5"),
iaa.Grayscale(1.0, name="Grayscale1.0"),
iaa.GaussianBlur((0, 3.0), name="GaussianBlur"),
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.1),
name="AdditiveGaussianNoise"),
iaa.Dropout((0.0, 0.1), name="Dropout"),
iaa.Multiply((0.5, 1.5), name="Multiply"),
iaa.ContrastNormalization(alpha=(0.5, 2.0)),
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
translate_px={
"x": (-16, 16),
"y": (-16, 16)
},
rotate=(-45, 45),
shear=(-16, 16),
order=ia.ALL,
cval=(0, 1.0),
mode=ia.ALL,
name="Affine"),
iaa.ElasticTransformation(alpha=(0.5, 8.0), sigma=1.0)
]
#for i, aug in enumerate(augmenters):
#print(i)
#aug.deepcopy()
#import copy
#copy.deepcopy(aug)
seq = iaa.Sequential([aug.copy() for aug in augmenters], name="Sequential")
st = iaa.Sometimes(0.5, seq.copy(), name="Sometimes")
augmenters.append(seq)
augmenters.append(st)
for augmenter in augmenters:
print("Augmenter: %s" % (augmenter.name, ))
grid = augmenter.draw_grid(images, rows=1, cols=16)
misc.imshow(grid)
def augment_data():
aug_data = np.zeros((4000, 227, 227, 3))
st = lambda aug: iaa.Sometimes(0.5, aug)
seq = iaa.Sequential(
[
iaa.Fliplr(0.5), # horizontally flip 50% of all images
iaa.Flipud(0.5), # vertically flip 50% of all images
st(iaa.Crop(
percent=(0,
0.1))), # crop images by 0-10% of their height/width
st(iaa.GaussianBlur(
(0, 3.0))), # blur images with a sigma between 0 and 3.0
st(
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.2),
per_channel=0.5)), # add gaussian noise to images
st(iaa.Dropout(
(0.0, 0.1),
per_channel=0.5)), # randomly remove up to 10% of the pixels
st(iaa.Add(
(-10, 10), per_channel=0.5
)), # change brightness of images (by -10 to 10 of original value)
st(iaa.Multiply((0.5, 1.5), per_channel=0.5)
), # change brightness of images (50-150% of original value)
st(iaa.ContrastNormalization(
(0.5, 2.0),
per_channel=0.5)), # improve or worsen the contrast
st(
iaa.Affine(
scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
}, # scale images to 80-120% of their size, individually per axis
translate_px={
"x": (-16, 16),
"y": (-16, 16)
}, # translate by -16 to +16 pixels (per axis)
rotate=(-45, 45), # rotate by -45 to +45 degrees
shear=(-16, 16), # shear by -16 to +16 degrees
#order=ia.ALL, # use any of scikit-image's interpolation methods
cval=(
0, 1.0
), # if mode is constant, use a cval between 0 and 1.0
#mode=ia.ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)
)),
st(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)
) # apply elastic transformations with random strengths
],
random_order=True # do all of the above in random order
)
for i in range(4000):
img = plt.imread(trainfileName(i + 1))
image_aug = seq.augment_image(img)
#gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#gray = rgb2gray(img)
aug_data[i, ...] = image_aug.astype(float) / 255
np.save("aug_data_1.npy", aug_data)
#augment_data()
def example_heavy_augmentations():
print("Example: Heavy Augmentations")
import imgaug as ia
#from imgaug import augmenters as iaa
import augmenters as iaa
# random example images
images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
# Sometimes(0.5, ...) applies the given augmenter in 50% of all cases,
# e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image.
st = lambda aug: iaa.Sometimes(0.5, aug)
# Define our sequence of augmentation steps that will be applied to every image
# All augmenters with per_channel=0.5 will sample one value _per image_
# in 50% of all cases. In all other cases they will sample new values
# _per channel_.
seq = iaa.Sequential(
[
iaa.Fliplr(0.5), # horizontally flip 50% of all images
iaa.Flipud(0.5), # vertically flip 50% of all images
st(iaa.Crop(
percent=(0,
0.1))), # crop images by 0-10% of their height/width
st(iaa.GaussianBlur(
(0, 3.0))), # blur images with a sigma between 0 and 3.0
st(
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255),
per_channel=0.5)), # add gaussian noise to images
st(iaa.Dropout(
(0.0, 0.1),
per_channel=0.5)), # randomly remove up to 10% of the pixels
st(iaa.Add(
(-10, 10), per_channel=0.5
)), # change brightness of images (by -10 to 10 of original value)
st(iaa.Multiply((0.5, 1.5), per_channel=0.5)
), # change brightness of images (50-150% of original value)
st(iaa.ContrastNormalization(
(0.5, 2.0),
per_channel=0.5)), # improve or worsen the contrast
st(iaa.Grayscale((0.0, 1.0))), # blend with grayscale image
st(
iaa.Affine(
scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
}, # scale images to 80-120% of their size, individually per axis
translate_px={
"x": (-16, 16),
"y": (-16, 16)
}, # translate by -16 to +16 pixels (per axis)
rotate=(-45, 45), # rotate by -45 to +45 degrees
shear=(-16, 16), # shear by -16 to +16 degrees
order=[
0,
1
], # use scikit-image's interpolation orders 0 (nearest neighbour) and 1 (bilinear)
cval=(
0, 1.0
), # if mode is constant, use a cval between 0 and 1.0
mode=ia.
ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)
)),
st(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)
) # apply elastic transformations with random strengths
],
random_order=True # do all of the above in random order
)
images_aug = seq.augment_images(images)
# -----
# Make sure that the example really does something
assert not np.array_equal(images, images_aug)