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augment_data.py
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#!/usr/bin/env python
import os
import numpy as np
from shutil import copyfile, rmtree
import tqdm
import imgaug as ia
import copy
from imgaug import augmenters as iaa
from downsample_data import get_image_files
from scipy import misc as sp
def augment_images (image_paths, count=20):
images = [ sp.imread(i) for i in image_paths ]
seq = augmentations()
new_images = copy.copy(image_paths)
tmp_path = "/tmp/how-much-data-experiments"
if os.path.isdir(tmp_path):
rmtree(tmp_path)
os.makedirs(tmp_path)
for image, path in tqdm.tqdm(zip(images, image_paths)):
filename = os.path.basename(path)
name, ext = os.path.splitext(filename)
repeated = np.concatenate([ np.expand_dims(image, 0) for i in range(0, count) ])
augmented = seq.augment_images(repeated)
for k, aug in enumerate(augmented):
f = "{}/{}_{}{}".format(tmp_path, name, k, ext)
sp.imsave(f, aug)
new_images.append(f)
return np.array(new_images)
def augmentations ():
""" This is the big one from here: https://github.com/aleju/imgaug """
# 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.
sometimes = 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(
[
# apply the following augmenters to most images
iaa.Fliplr(0.5), # horizontally flip 50% of all images
iaa.Flipud(0.2), # vertically flip 20% of all images
# crop images by -5% to 10% of their height/width
sometimes(iaa.CropAndPad(
percent=(-0.05, 0.1),
pad_mode=ia.ALL,
pad_cval=(0, 255)
)),
sometimes(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_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, # translate by -20 to +20 percent (per axis)
rotate=(-45, 45), # rotate by -45 to +45 degrees
shear=(-16, 16), # shear by -16 to +16 degrees
order=[0, 1], # use nearest neighbour or bilinear interpolation (fast)
cval=(0, 255), # if mode is constant, use a cval between 0 and 255
mode=ia.ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)
)),
# 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),
[
sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), # convert images into their superpixel representation
iaa.OneOf([
iaa.GaussianBlur((0, 3.0)), # blur images with a sigma between 0 and 3.0
iaa.AverageBlur(k=(2, 7)), # blur image using local means with kernel sizes between 2 and 7
iaa.MedianBlur(k=(3, 11)), # blur image using local medians with kernel sizes between 2 and 7
]),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)), # sharpen images
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images
# search either for all edges or for directed edges,
# blend the result with the original image using a blobby mask
iaa.SimplexNoiseAlpha(iaa.OneOf([
iaa.EdgeDetect(alpha=(0.5, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.5, 1.0), direction=(0.0, 1.0)),
])),
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5), # add gaussian noise to images
iaa.OneOf([
iaa.Dropout((0.01, 0.1), per_channel=0.5), # randomly remove up to 10% of the pixels
iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2),
]),
iaa.Invert(0.05, per_channel=True), # invert color channels
iaa.Add((-10, 10), per_channel=0.5), # change brightness of images (by -10 to 10 of original value)
iaa.AddToHueAndSaturation((-20, 20)), # change hue and saturation
# either change the brightness of the whole image (sometimes
# per channel) or change the brightness of subareas
iaa.OneOf([
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.FrequencyNoiseAlpha(
exponent=(-4, 0),
first=iaa.Multiply((0.5, 1.5), per_channel=True),
second=iaa.ContrastNormalization((0.5, 2.0))
)
]),
iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast
iaa.Grayscale(alpha=(0.0, 1.0)),
sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)), # move pixels locally around (with random strengths)
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))), # sometimes move parts of the image around
sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1)))
],
random_order=True
)
],
random_order=True
)
return seq
def generate_augmentations (base_directory):
assert base_directory[-1] != "/", "Can't have trailing slash."
augmention_counts = [ 10 ]
dirs = [ d for d in os.listdir(base_directory)
if os.path.isdir(os.path.join(base_directory, d)) ]
images = { d: get_image_files(d, base_directory) for d in dirs }
for category, images in tqdm.tqdm(images.items()):
images = list(images)
for count in augmention_counts:
images = augment_images(images, count=count)
newdir = "{}-augx{}/{}".format(base_directory, count, category)
if os.path.isdir(newdir):
rmtree(newdir)
os.makedirs(newdir)
for image in tqdm.tqdm(images):
copyfile(image, newdir + "/" + os.path.basename(image))
def main():
amounts = [ "1", "3", 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 ]
for a in amounts:
generate_augmentations("experiments/{}".format(a))
if __name__ == "__main__":
main()