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test_readme_examples.py
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test_readme_examples.py
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"""
Script to verify all examples in the readme.
Simply execute
python test_readme_examples.py
The tests in this file are currently not unittests!
They do plot images.
TODO move this to checks/ ?
"""
from __future__ import print_function, division
import numpy as np
def main():
example_standard_situation()
example_heavy_augmentations()
example_show()
#example_grayscale()
example_determinism()
example_keypoints()
example_single_augmenters()
example_withchannels()
example_unusual_distributions()
example_hooks()
example_background_augment_batches()
example_background_pool()
def example_standard_situation():
print("Example: Standard Situation")
# -------
# dummy functions to make the example runnable here
def load_batch(batch_idx):
return np.random.randint(0, 255, (1, 16, 16, 3), dtype=np.uint8)
def train_on_images(images):
pass
# -------
from imgaug import augmenters as iaa
seq = iaa.Sequential([
iaa.Crop(px=(0, 16)), # crop images from each side by 0 to 16px (randomly chosen)
iaa.Fliplr(0.5), # horizontally flip 50% of the images
iaa.GaussianBlur(sigma=(0, 3.0)) # blur images with a sigma of 0 to 3.0
])
for batch_idx in range(1000):
# 'images' should be either a 4D numpy array of shape (N, height, width, channels)
# or a list of 3D numpy arrays, each having shape (height, width, channels).
# Grayscale images must have shape (height, width, 1) each.
# All images must have numpy's dtype uint8. Values are expected to be in
# range 0-255.
images = load_batch(batch_idx)
images_aug = seq.augment_images(images)
train_on_images(images_aug)
# -----
# Make sure that the example really does something
if batch_idx == 0:
assert not np.array_equal(images, images_aug)
def example_heavy_augmentations():
print("Example: Heavy Augmentations")
import imgaug as ia
from imgaug 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, 255), # 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)
def example_show():
print("Example: Show")
from imgaug import augmenters as iaa
images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
seq = iaa.Sequential([iaa.Fliplr(0.5), iaa.GaussianBlur((0, 3.0))])
# show an image with 8*8 augmented versions of image 0
seq.show_grid(images[0], cols=8, rows=8)
# Show an image with 8*8 augmented versions of image 0 and 8*8 augmented
# versions of image 1. The identical augmentations will be applied to
# image 0 and 1.
seq.show_grid([images[0], images[1]], cols=8, rows=8)
# this example is no longer necessary as the library can now handle 2D images
"""
def example_grayscale():
print("Example: Grayscale")
from imgaug import augmenters as iaa
images = np.random.randint(0, 255, (16, 128, 128), dtype=np.uint8)
seq = iaa.Sequential([iaa.Fliplr(0.5), iaa.GaussianBlur((0, 3.0))])
# The library expects a list of images (3D inputs) or a single array (4D inputs).
# So we add an axis to our grayscale array to convert it to shape (16, 128, 128, 1).
images_aug = seq.augment_images(images[:, :, :, np.newaxis])
# -----
# Make sure that the example really does something
assert not np.array_equal(images, images_aug)
"""
def example_determinism():
print("Example: Determinism")
from imgaug import augmenters as iaa
# Standard scenario: You have N RGB-images and additionally 21 heatmaps per image.
# You want to augment each image and its heatmaps identically.
images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
heatmaps = np.random.randint(0, 255, (16, 128, 128, 21), dtype=np.uint8)
seq = iaa.Sequential([iaa.GaussianBlur((0, 3.0)), iaa.Affine(translate_px={"x": (-40, 40)})])
# Convert the stochastic sequence of augmenters to a deterministic one.
# The deterministic sequence will always apply the exactly same effects to the images.
seq_det = seq.to_deterministic() # call this for each batch again, NOT only once at the start
images_aug = seq_det.augment_images(images)
heatmaps_aug = seq_det.augment_images(heatmaps)
# -----
# Make sure that the example really does something
import imgaug as ia
assert not np.array_equal(images, images_aug)
assert not np.array_equal(heatmaps, heatmaps_aug)
images_show = []
for img_idx in range(len(images)):
images_show.extend([images[img_idx], images_aug[img_idx], heatmaps[img_idx][..., 0:3], heatmaps_aug[img_idx][..., 0:3]])
ia.show_grid(images_show, cols=4)
def example_keypoints():
print("Example: Keypoints")
import imgaug as ia
from imgaug import augmenters as iaa
import random
images = np.random.randint(0, 50, (4, 128, 128, 3), dtype=np.uint8)
# Generate random keypoints.
# The augmenters expect a list of imgaug.KeypointsOnImage.
keypoints_on_images = []
for image in images:
height, width = image.shape[0:2]
keypoints = []
for _ in range(4):
x = random.randint(0, width-1)
y = random.randint(0, height-1)
keypoints.append(ia.Keypoint(x=x, y=y))
keypoints_on_images.append(ia.KeypointsOnImage(keypoints, shape=image.shape))
seq = iaa.Sequential([iaa.GaussianBlur((0, 3.0)), iaa.Affine(scale=(0.5, 0.7))])
seq_det = seq.to_deterministic() # call this for each batch again, NOT only once at the start
# augment keypoints and images
images_aug = seq_det.augment_images(images)
keypoints_aug = seq_det.augment_keypoints(keypoints_on_images)
# Example code to show each image and print the new keypoints coordinates
for img_idx, (image_before, image_after, keypoints_before, keypoints_after) in enumerate(zip(images, images_aug, keypoints_on_images, keypoints_aug)):
image_before = keypoints_before.draw_on_image(image_before)
image_after = keypoints_after.draw_on_image(image_after)
ia.imshow(np.concatenate((image_before, image_after), axis=1)) # before and after
for kp_idx, keypoint in enumerate(keypoints_after.keypoints):
keypoint_old = keypoints_on_images[img_idx].keypoints[kp_idx]
x_old, y_old = keypoint_old.x, keypoint_old.y
x_new, y_new = keypoint.x, keypoint.y
print("[Keypoints for image #%d] before aug: x=%d y=%d | after aug: x=%d y=%d" % (img_idx, x_old, y_old, x_new, y_new))
def example_single_augmenters():
print("Example: Single Augmenters")
from imgaug import augmenters as iaa
images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
flipper = iaa.Fliplr(1.0) # always horizontally flip each input image
images[0] = flipper.augment_image(images[0]) # horizontally flip image 0
vflipper = iaa.Flipud(0.9) # vertically flip each input image with 90% probability
images[1] = vflipper.augment_image(images[1]) # probably vertically flip image 1
blurer = iaa.GaussianBlur(3.0)
images[2] = blurer.augment_image(images[2]) # blur image 2 by a sigma of 3.0
images[3] = blurer.augment_image(images[3]) # blur image 3 by a sigma of 3.0 too
translater = iaa.Affine(translate_px={"x": -16}) # move each input image by 16px to the left
images[4] = translater.augment_image(images[4]) # move image 4 to the left
scaler = iaa.Affine(scale={"y": (0.8, 1.2)}) # scale each input image to 80-120% on the y axis
images[5] = scaler.augment_image(images[5]) # scale image 5 by 80-120% on the y axis
def example_withchannels():
print("Example: WithChannels")
from imgaug import augmenters as iaa
import numpy as np
# fake RGB images
images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
# add a random value from the range (-30, 30) to the first two channels of
# input images (e.g. to the R and G channels)
aug = iaa.WithChannels(
channels=[0, 1],
children=iaa.Add((-30, 30))
)
images_aug = aug.augment_images(images)
def example_unusual_distributions():
print("Example: Unusual Distributions")
from imgaug import augmenters as iaa
from imgaug import parameters as iap
images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
# Blur by a value sigma which is sampled from a uniform distribution
# of range 0.1 <= x < 3.0.
# The convenience shortcut for this is: iaa.GaussianBlur((0.1, 3.0))
blurer = iaa.GaussianBlur(iap.Uniform(0.1, 3.0))
images_aug = blurer.augment_images(images)
# Blur by a value sigma which is sampled from a normal distribution N(1.0, 0.1),
# i.e. sample a value that is usually around 1.0.
# Clip the resulting value so that it never gets below 0.1 or above 3.0.
blurer = iaa.GaussianBlur(iap.Clip(iap.Normal(1.0, 0.1), 0.1, 3.0))
images_aug = blurer.augment_images(images)
# Same again, but this time the mean of the normal distribution is not constant,
# but comes itself from a uniform distribution between 0.5 and 1.5.
blurer = iaa.GaussianBlur(iap.Clip(iap.Normal(iap.Uniform(0.5, 1.5), 0.1), 0.1, 3.0))
images_aug = blurer.augment_images(images)
# Use for sigma one of exactly three allowed values: 0.5, 1.0 or 1.5.
blurer = iaa.GaussianBlur(iap.Choice([0.5, 1.0, 1.5]))
images_aug = blurer.augment_images(images)
# Sample sigma from a discrete uniform distribution of range 1 <= sigma <= 5,
# i.e. sigma will have any of the following values: 1, 2, 3, 4, 5.
blurer = iaa.GaussianBlur(iap.DiscreteUniform(1, 5))
images_aug = blurer.augment_images(images)
def example_hooks():
print("Example: Hooks")
import imgaug as ia
from imgaug import augmenters as iaa
import numpy as np
# images and heatmaps, just arrays filled with value 30
images = np.ones((16, 128, 128, 3), dtype=np.uint8) * 30
heatmaps = np.ones((16, 128, 128, 21), dtype=np.uint8) * 30
# add vertical lines to see the effect of flip
images[:, 16:128-16, 120:124, :] = 120
heatmaps[:, 16:128-16, 120:124, :] = 120
seq = iaa.Sequential([
iaa.Fliplr(0.5, name="Flipper"),
iaa.GaussianBlur((0, 3.0), name="GaussianBlur"),
iaa.Dropout(0.02, name="Dropout"),
iaa.AdditiveGaussianNoise(scale=0.01*255, name="MyLittleNoise"),
iaa.AdditiveGaussianNoise(loc=32, scale=0.0001*255, name="SomeOtherNoise"),
iaa.Affine(translate_px={"x": (-40, 40)}, name="Affine")
])
# change the activated augmenters for heatmaps
def activator_heatmaps(images, augmenter, parents, default):
if augmenter.name in ["GaussianBlur", "Dropout", "MyLittleNoise"]:
return False
else:
# default value for all other augmenters
return default
hooks_heatmaps = ia.HooksImages(activator=activator_heatmaps)
seq_det = seq.to_deterministic() # call this for each batch again, NOT only once at the start
images_aug = seq_det.augment_images(images)
heatmaps_aug = seq_det.augment_images(heatmaps, hooks=hooks_heatmaps)
# -----------
ia.show_grid(images_aug)
ia.show_grid(heatmaps_aug[..., 0:3])
def example_background_augment_batches():
print("Example: Background Augmentation via augment_batches()")
import imgaug as ia
from imgaug import augmenters as iaa
import numpy as np
from skimage import data
# Number of batches and batch size for this example
nb_batches = 10
batch_size = 32
# Example augmentation sequence to run in the background
augseq = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.CoarseDropout(p=0.1, size_percent=0.1)
])
# For simplicity, we use the same image here many times
astronaut = data.astronaut()
astronaut = ia.imresize_single_image(astronaut, (64, 64))
# Make batches out of the example image (here: 10 batches, each 32 times
# the example image)
batches = []
for _ in range(nb_batches):
batches.append(
np.array(
[astronaut for _ in range(batch_size)],
dtype=np.uint8
)
)
# Show the augmented images.
# Note that augment_batches() returns a generator.
for images_aug in augseq.augment_batches(batches, background=True):
ia.imshow(ia.draw_grid(images_aug, cols=8))
def example_background_pool():
print("Example: Background Augmentation via pool()")
import numpy as np
import imgaug as ia
from imgaug import augmenters as iaa
# Basic augmentation sequence. PiecewiseAffine is slow and therefore well suited
# for augmentation on multiple CPU cores.
aug = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.PiecewiseAffine((0.0, 0.1))
])
# generator that yields images
def create_image_generator(nb_batches, size):
for _ in range(nb_batches):
# Add e.g. keypoints=... or bounding_boxes=... here to also augment
# keypoints / bounding boxes on these images.
yield ia.Batch(
images=np.random.randint(0, 255, size=size).astype(np.uint8)
)
# 500 batches of images, each containing 10 images of size 128x128x3
my_generator = create_image_generator(500, (10, 128, 128, 3))
# Start a pool to augment on multiple CPU cores.
# * processes=-1 means that all CPU cores except one are used for the
# augmentation, so one is kept free to move data to the GPU
# * maxtasksperchild=20 restarts child workers every 20 tasks -- only use this
# if you encounter problems such as memory leaks. Restarting child workers
# decreases performance.
# * seed=123 makes the result of the whole augmentation process deterministic
# between runs of this script, i.e. reproducible results.
with aug.pool(processes=-1, maxtasksperchild=20, seed=123) as pool:
# Augment on multiple CPU cores.
# * The result of imap_batches() is also a generator.
# * Use map_batches() if your input is a list.
# * chunksize=10 controls how much data to send to each child worker per
# transfer, set it higher for better performance.
batches_aug_generator = pool.imap_batches(my_generator, chunksize=10)
for i, batch_aug in enumerate(batches_aug_generator):
# show first augmented image in first batch
if i == 0:
ia.imshow(batch_aug.images_aug[0])
# do something else with the batch here
if __name__ == "__main__":
main()