def main():
black_and_white = iaa.RandomColorsBinaryImageColorizer(
color_true=255, color_false=0)
print("alpha=1.0, black and white")
image = ia.quokka_square((128, 128))
aug = iaa.Canny(alpha=1.0, colorizer=black_and_white)
ia.imshow(ia.draw_grid(aug(images=[image] * (5*5))))
print("alpha=1.0, random color")
image = ia.quokka_square((128, 128))
aug = iaa.Canny(alpha=1.0)
ia.imshow(ia.draw_grid(aug(images=[image] * (5*5))))
print("alpha=1.0, sobel ksize=[3, 13], black and white")
image = ia.quokka_square((128, 128))
aug = iaa.Canny(alpha=1.0, sobel_kernel_size=[3, 7],
colorizer=black_and_white)
ia.imshow(ia.draw_grid(aug(images=[image] * (5*5))))
print("alpha=1.0, sobel ksize=3, black and white")
image = ia.quokka_square((128, 128))
aug = iaa.Canny(alpha=1.0, sobel_kernel_size=3,
colorizer=black_and_white)
ia.imshow(ia.draw_grid(aug(images=[image] * (5*5))))
print("fully random")
image = ia.quokka_square((128, 128))
aug = iaa.Canny()
ia.imshow(ia.draw_grid(aug(images=[image] * (5*5))))
def main():
image = ia.quokka_square((200, 200))
kpsoi = ia.quokka_keypoints((200, 200), extract="square")
aug = iaa.Jigsaw(10, 10)
images_aug, kpsois_aug = aug(images=[image] * 16,
keypoints=[kpsoi] * 16)
images_show = [kpsoi_aug.draw_on_image(image_aug)
for image_aug, kpsoi_aug in zip(images_aug, kpsois_aug)]
ia.imshow(ia.draw_grid(images_show))
gen_time = timeit.timeit(
"iaa.generate_jigsaw_destinations(10, 10, 2, rng)",
number=128,
setup=(
"import imgaug.augmenters as iaa; "
"import imgaug.random as iarandom; "
"rng = iarandom.RNG(0)"
)
)
print("Time to generate 128x dest:", gen_time)
destinations = iaa.generate_jigsaw_destinations(10, 10, 1, seed=1)
image_jig = iaa.apply_jigsaw(image, destinations)
ia.imshow(image_jig)
def test_parameters_affect_images(self):
params = [("scale", {
"x": 1.3
}), ("scale", {
"y": 1.5
}), ("translate_px", {
"x": 5
}), ("translate_px", {
"y": 10
}), ("translate_percent", {
"x": 0.3
}), ("translate_percent", {
"y": 0.4
}), ("rotate", 10), ("shear", {
"x": 20
}), ("shear", {
"y": 20
})]
image = ia.quokka_square((64, 64))
images_aug = []
for param_name, param_val in params:
kwargs = {param_name: param_val}
aug = iaa.pillike.Affine(**kwargs)
image_aug = aug(image=image)
images_aug.append(image_aug)
for i, image_aug in enumerate(images_aug):
assert not np.array_equal(image_aug, image)
for j, other_image_aug in enumerate(images_aug):
if i != j:
assert not np.array_equal(image_aug, other_image_aug)
def main():
image = ia.quokka_square()
images_aug = []
for kelvin in np.linspace(1000, 10000, 64):
images_aug.append(iaa.ChangeColorTemperature(kelvin)(image=image))
ia.imshow(ia.draw_grid(images_aug))
def main():
image = ia.quokka_square((128, 128))
images_aug = []
for mul in np.linspace(0.0, 2.0, 10):
aug = iaa.MultiplyHueAndSaturation(mul)
image_aug = aug.augment_image(image)
images_aug.append(image_aug)
for mul_hue in np.linspace(0.0, 5.0, 10):
aug = iaa.MultiplyHueAndSaturation(mul_hue=mul_hue)
image_aug = aug.augment_image(image)
images_aug.append(image_aug)
for mul_saturation in np.linspace(0.0, 5.0, 10):
aug = iaa.MultiplyHueAndSaturation(mul_saturation=mul_saturation)
image_aug = aug.augment_image(image)
images_aug.append(image_aug)
ia.imshow(ia.draw_grid(images_aug, rows=3))
images_aug = []
images_aug.extend(iaa.MultiplyHue().augment_images([image] * 10))
images_aug.extend(iaa.MultiplySaturation().augment_images([image] * 10))
ia.imshow(ia.draw_grid(images_aug, rows=2))
def main():
image = ia.quokka_square((256, 256))
reggrid_sampler = iaa.DropoutPointsSampler(
iaa.RegularGridPointsSampler(n_rows=50, n_cols=50), 0.5)
uniform_sampler = iaa.UniformPointsSampler(50 * 50)
augs = [
iaa.Voronoi(points_sampler=reggrid_sampler,
p_replace=1.0,
max_size=128),
iaa.Voronoi(points_sampler=uniform_sampler,
p_replace=1.0,
max_size=128),
iaa.UniformVoronoi(50 * 50, p_replace=1.0, max_size=128),
iaa.RegularGridVoronoi(50,
50,
p_drop_points=0.4,
p_replace=1.0,
max_size=128),
]
images = [aug(image=image) for aug in augs]
ia.imshow(np.hstack(images))
def chapter_augmenters_savedebugimageeverynbatches():
fn_start = "debug/savedebugimageeverynbatches"
image = ia.quokka_square((128, 128))
bbsoi = ia.quokka_bounding_boxes((128, 128), extract="square")
segmaps = ia.quokka_segmentation_map((128, 128), extract="square")
with tempfile.TemporaryDirectory() as folder_path:
seq = iaa.Sequential([
iaa.Sequential(
[iaa.Fliplr(0.5), iaa.Crop(px=(0, 16))], random_order=True),
iaa.SaveDebugImageEveryNBatches(folder_path, 100)
])
_ = seq(images=[image] * 4,
segmentation_maps=[segmaps] * 4,
bounding_boxes=[bbsoi] * 4)
image_debug_path = os.path.join(folder_path, "batch_latest.png")
image_debug = imageio.imread(image_debug_path)
utils.save("overview_of_augmenters",
fn_start + ".jpg",
image_debug,
quality=95)
def main():
image = data.astronaut()
cv2.namedWindow("aug", cv2.WINDOW_NORMAL)
cv2.imshow("aug", image)
cv2.waitKey(TIME_PER_STEP)
# for value in cycle(np.arange(-255, 255, VAL_PER_STEP)):
for value in np.arange(-255, 255, VAL_PER_STEP):
aug = iaa.AddToHueAndSaturation(value=value)
img_aug = aug.augment_image(image)
img_aug = iaa.pad(img_aug, bottom=40)
img_aug = ia.draw_text(img_aug,
x=0,
y=img_aug.shape[0] - 38,
text="value=%d" % (value, ),
size=30)
cv2.imshow("aug", img_aug)
cv2.waitKey(TIME_PER_STEP)
images_aug = iaa.AddToHueAndSaturation(
value=(-255, 255), per_channel=True).augment_images([image] * 64)
ia.imshow(ia.draw_grid(images_aug))
image = ia.quokka_square((128, 128))
images_aug = []
images_aug.extend(iaa.AddToHue().augment_images([image] * 10))
images_aug.extend(iaa.AddToSaturation().augment_images([image] * 10))
ia.imshow(ia.draw_grid(images_aug, rows=2))
def main():
for size in [64, 128, 256, 512, 1024]:
for nb_bits in [1, 2, 3, 4, 5, 6, 7, 8]:
time_iaa = timeit.timeit(
"iaa.quantize_uniform_to_n_bits(image, %d)" % (nb_bits, ),
number=1000,
setup=("import imgaug as ia; "
"import imgaug.augmenters as iaa; "
"image = ia.quokka_square((%d, %d))" % (size, size)))
time_pil = timeit.timeit(
"np.asarray("
"PIL.ImageOps.posterize(PIL.Image.fromarray(image), %d)"
")" % (nb_bits, ),
number=1000,
setup=("import numpy as np; "
"import PIL.Image; "
"import PIL.ImageOps; "
"import imgaug as ia; "
"image = ia.quokka_square((%d, %d))" % (size, size)))
print("[size=%04d, bits=%d] iaa=%.4f pil=%.4f" %
(size, nb_bits, time_iaa, time_pil))
image = ia.quokka_square((128, 128))
images_q = [
iaa.quantize_uniform_to_n_bits(image, nb_bits)
for nb_bits in [1, 2, 3, 4, 5, 6, 7, 8]
]
ia.imshow(ia.draw_grid(images_q, cols=8, rows=1))
def main():
for size in [64, 128, 256, 512, 1024]:
for threshold in [64, 128, 192]:
time_iaa = timeit.timeit(
"iaa.solarize(image, %d)" % (threshold, ),
number=1000,
setup=("import imgaug as ia; "
"import imgaug.augmenters as iaa; "
"image = ia.quokka_square((%d, %d))" % (size, size)))
time_pil = timeit.timeit(
"np.asarray("
"PIL.ImageOps.solarize(PIL.Image.fromarray(image), %d)"
")" % (threshold, ),
number=1000,
setup=("import numpy as np; "
"import PIL.Image; "
"import PIL.ImageOps; "
"import imgaug as ia; "
"image = ia.quokka_square((%d, %d))" % (size, size)))
print("[size=%04d, thresh=%03d] iaa=%.4f pil=%.4f" %
(size, threshold, time_iaa, time_pil))
image = ia.quokka_square((128, 128))
images_aug = iaa.Solarize(1.0)(images=[image] * (5 * 5))
ia.imshow(ia.draw_grid(images_aug))
def main():
nb_rows = 8
nb_cols = 8
h, w = (128, 128)
sample_size = 128
noise_gens = [
iap.SimplexNoise(),
iap.FrequencyNoise(exponent=-4, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=-2, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=0, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=2, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=4, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size),
upscale_method=["nearest", "linear", "cubic"]),
iap.IterativeNoiseAggregator(
other_param=iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size),
upscale_method=["nearest", "linear", "cubic"]),
iterations=(1, 3),
aggregation_method=["max", "avg"]
),
iap.IterativeNoiseAggregator(
other_param=iap.Sigmoid(
iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size),
upscale_method=["nearest", "linear", "cubic"]),
threshold=(-10, 10),
activated=0.33,
mul=20,
add=-10
),
iterations=(1, 3),
aggregation_method=["max", "avg"]
)
]
samples = [[] for _ in range(len(noise_gens))]
for _ in range(nb_rows * nb_cols):
for i, noise_gen in enumerate(noise_gens):
samples[i].append(noise_gen.draw_samples((h, w)))
rows = [np.hstack(row) for row in samples]
grid = np.vstack(rows)
ia.imshow((grid*255).astype(np.uint8))
images = [ia.quokka_square(size=(128, 128)) for _ in range(16)]
seqs = [
iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0)),
iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=True),
iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0)),
iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=True)
]
images_aug = []
for seq in seqs:
images_aug.append(np.hstack(seq.augment_images(images)))
images_aug = np.vstack(images_aug)
ia.imshow(images_aug)
def main():
nb_rows = 8
nb_cols = 8
h, w = (128, 128)
sample_size = 128
noise_gens = [
iap.SimplexNoise(),
iap.FrequencyNoise(exponent=-4, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=-2, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=0, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=2, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=4, size_px_max=sample_size, upscale_method="cubic"),
iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size), upscale_method=["nearest", "linear", "cubic"]),
iap.IterativeNoiseAggregator(
other_param=iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size), upscale_method=["nearest", "linear", "cubic"]),
iterations=(1, 3),
aggregation_method=["max", "avg"]
),
iap.IterativeNoiseAggregator(
other_param=iap.Sigmoid(
iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size), upscale_method=["nearest", "linear", "cubic"]),
threshold=(-10, 10),
activated=0.33,
mul=20,
add=-10
),
iterations=(1, 3),
aggregation_method=["max", "avg"]
)
]
samples = [[] for _ in range(len(noise_gens))]
for _ in range(nb_rows * nb_cols):
for i, noise_gen in enumerate(noise_gens):
samples[i].append(noise_gen.draw_samples((h, w)))
rows = [np.hstack(row) for row in samples]
grid = np.vstack(rows)
misc.imshow((grid*255).astype(np.uint8))
images = [ia.quokka_square(size=(128, 128)) for _ in range(16)]
seqs = [
iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0)),
iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=True),
iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0)),
iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=True)
]
images_aug = []
for seq in seqs:
images_aug.append(np.hstack(seq.augment_images(images)))
images_aug = np.vstack(images_aug)
misc.imshow(images_aug)
def main():
image = ia.quokka_square(size=(128, 128))
images = []
for i in range(15):
aug = iaa.WithHueAndSaturation(iaa.WithChannels(0, iaa.Add(i * 20)))
images.append(aug.augment_image(image))
for i in range(15):
aug = iaa.WithHueAndSaturation(iaa.WithChannels(1, iaa.Add(i * 20)))
images.append(aug.augment_image(image))
ia.imshow(ia.draw_grid(images, rows=2))
def chapter_augmenters_removecbasbyoutofimagefraction():
fn_start = "meta/removecbasbyoutofimagefraction"
image = ia.quokka_square((100, 100))
bb = ia.BoundingBox(x1=50-25, y1=0, x2=50+25, y2=100)
bbsoi = ia.BoundingBoxesOnImage([bb], shape=image.shape)
# example 1
aug = iaa.Sequential([
iaa.Affine(translate_px={"x": (-100, 100)}),
iaa.RemoveCBAsByOutOfImageFraction(0.5)
])
images_aug, bbsois_aug = aug(images=[image] * (2*4),
bounding_boxes=[bbsoi] * (2*4))
images_drawn = [bbsoi_aug.draw_on_image(image_aug)
for image_aug, bbsoi_aug in zip(images_aug, bbsois_aug)]
utils.save(
"overview_of_augmenters",
fn_start + ".jpg",
utils.grid(images_drawn, cols=6, rows=3),
quality=95
)
# example 2
aug_without = iaa.Affine(translate_px={"x": 51})
aug_with = iaa.Sequential([
iaa.Affine(translate_px={"x": 51}),
iaa.RemoveCBAsByOutOfImageFraction(0.5)
])
image_without, bbsoi_without = aug_without(
image=image, bounding_boxes=bbsoi)
image_with, bbsoi_with = aug_with(
image=image, bounding_boxes=bbsoi)
assert len(bbsoi_without.bounding_boxes) == 1
assert len(bbsoi_with.bounding_boxes) == 0
images_aug = [bbsoi_without.draw_on_image(image_without),
bbsoi_with.draw_on_image(image_with)]
utils.save(
"overview_of_augmenters",
fn_start + "_comparison.jpg",
utils.grid(images_aug, cols=2, rows=1),
quality=95
)
def main():
image = ia.quokka_square((256, 256))
ia.imshow(
ia.draw_grid([
iaa.quantize_colors_uniform(image, 2),
iaa.quantize_colors_uniform(image, 4),
iaa.quantize_colors_uniform(image, 8),
iaa.quantize_colors_uniform(image, 16),
iaa.quantize_colors_uniform(image, 32),
iaa.quantize_colors_uniform(image, 64)
], cols=6)
)
aug = iaa.UniformColorQuantization((2, 16))
ia.imshow(ia.draw_grid(aug(images=[image] * 16)))
def run(clazz):
image = ia.quokka_square((128, 128))
aug = clazz(2)
ia.imshow(ia.draw_grid(aug.augment_images([image] * (5 * 5))))
aug = clazz(2, keep_size=False)
ia.imshow(ia.draw_grid(aug.augment_images([image] * (5 * 5))))
aug_pool = clazz(((0, 10), (0, 10)))
aug_blur = clazz(((0, 10), (0, 10)))
ia.imshow(
np.hstack([
ia.draw_grid(aug_pool.augment_images([image] * (4 * 5)), cols=4),
ia.draw_grid(aug_blur.augment_images([image] * (4 * 5)), cols=4)
]))
def main():
image = ia.quokka_square((200, 200))
aug = iaa.Jigsaw(10, 10)
images_aug = aug(images=[image] * 16)
ia.imshow(ia.draw_grid(images_aug))
gen_time = timeit.timeit(
"iaa.generate_jigsaw_destinations(10, 10, 2, rng)",
number=128,
setup=("import imgaug.augmenters as iaa; "
"import imgaug.random as iarandom; "
"rng = iarandom.RNG(0)"))
print("Time to generate 128x dest:", gen_time)
destinations = iaa.generate_jigsaw_destinations(10, 10, 1, random_state=1)
image_jig = iaa.apply_jigsaw(image, destinations)
ia.imshow(image_jig)
def chapter_parameters_introduction():
ia.seed(1)
from imgaug import augmenters as iaa
from imgaug import parameters as iap
seq = iaa.Sequential([
iaa.GaussianBlur(sigma=iap.Uniform(0.0, 1.0)),
iaa.ContrastNormalization(
iap.Choice([1.0, 1.5, 3.0], p=[0.5, 0.3, 0.2])),
iaa.Affine(rotate=iap.Normal(0.0, 30),
translate_px=iap.RandomSign(iap.Poisson(3))),
iaa.AddElementwise(iap.Discretize(
(iap.Beta(0.5, 0.5) * 2 - 1.0) * 64)),
iaa.Multiply(iap.Positive(iap.Normal(0.0, 0.1)) + 1.0)
])
images = np.array([ia.quokka_square(size=(128, 128)) for i in range(16)])
images_aug = [seq.augment_image(images[i]) for i in range(len(images))]
save("parameters", "introduction.jpg", grid(images_aug, cols=4, rows=4))
def _test_integrationtest(cls, size, validate_grads):
image = ia.quokka_square((size, size))
image_cartoon = iaa.stylize_cartoon(image, blur_ksize=5,
segmentation_size=2.0)
image_avg = np.average(image.astype(np.float32), axis=2)
image_cartoon_avg = np.average(image_cartoon.astype(np.float32), axis=2)
if validate_grads:
gradx_image = image_avg[:, :-1] - image_avg[:, 1:]
grady_image = image_avg[:-1, :] - image_avg[1:, :]
gradx_cartoon = image_cartoon_avg[:, :-1] - image_cartoon_avg[:, 1:]
grady_cartoon = image_cartoon_avg[:-1, :] - image_cartoon_avg[1:, :]
assert (
(
np.average(np.abs(gradx_cartoon))
+ np.average(np.abs(grady_cartoon))
)
<
(
np.average(np.abs(gradx_image))
+ np.average(np.abs(grady_image))
)
)
# average saturation of cartoon image should be increased
image_hsv = colorlib.change_colorspace_(np.copy(image),
to_colorspace=iaa.CSPACE_HSV)
cartoon_hsv = colorlib.change_colorspace_(np.copy(image_cartoon),
to_colorspace=iaa.CSPACE_HSV)
assert (
np.average(cartoon_hsv[:, :, 1])
> np.average(image_hsv[:, :, 1])
)
# as edges are all drawn in completely black, there should be more
# completely black pixels in the cartoon image
image_black = np.sum(image_avg <= 0.01)
cartoon_black = np.sum(image_cartoon_avg <= 0.01)
assert cartoon_black > image_black
def main():
image = ia.quokka_square((256, 256))
image_q2 = iaa.quantize_kmeans(image, 2)
image_q16 = iaa.quantize_kmeans(image, 16)
ia.imshow(np.hstack([image_q2, image_q16]))
from_cs = "RGB"
to_cs = ["RGB", "Lab"]
kwargs = {"from_colorspace": from_cs, "to_colorspace": to_cs}
augs = [
iaa.KMeansColorQuantization(2, **kwargs),
iaa.KMeansColorQuantization(4, **kwargs),
iaa.KMeansColorQuantization(8, **kwargs),
iaa.KMeansColorQuantization((2, 16), **kwargs),
]
images_aug = []
for aug in augs:
images_aug.extend(aug(images=[image] * 8))
ia.imshow(ia.draw_grid(images_aug, cols=8))
def generate_debug():
ia.seed(1)
image = ia.quokka_square((128, 128))
bbsoi = ia.quokka_bounding_boxes((128, 128), extract="square")
segmaps = ia.quokka_segmentation_map((128, 128), extract="square")
with tempfile.TemporaryDirectory() as folder_path:
seq = iaa.Sequential([
iaa.Sequential(
[iaa.Fliplr(0.5), iaa.Crop(px=(0, 16))], random_order=True),
iaa.SaveDebugImageEveryNBatches(folder_path, 100)
])
_ = seq(images=[image] * 4,
segmentation_maps=[segmaps] * 4,
bounding_boxes=[bbsoi] * 4)
image_debug_path = os.path.join(folder_path, "batch_latest.png")
image_debug = imageio.imread(image_debug_path)
_save("savedebugimageeverynbatches.jpg", image_debug)
def chapter_augmenters_clipcbastoimageplanes():
fn_start = "meta/clipcbastoimageplanes"
image = ia.quokka_square((100, 100))
bb = ia.BoundingBox(x1=50-25, y1=0, x2=50+25, y2=100)
bbsoi = ia.BoundingBoxesOnImage([bb], shape=image.shape)
aug = iaa.Sequential([
iaa.Affine(translate_px={"x": (-100, 100)}),
iaa.ClipCBAsToImagePlanes()
])
images_aug, bbsois_aug = aug(images=[image] * (2*4),
bounding_boxes=[bbsoi] * (2*4))
images_drawn = [bbsoi_aug.draw_on_image(image_aug)
for image_aug, bbsoi_aug in zip(images_aug, bbsois_aug)]
utils.save(
"overview_of_augmenters",
fn_start + ".jpg",
utils.grid(images_drawn, cols=4, rows=2),
quality=95
)
def draw_per_augmenter_images():
print("[draw_per_augmenter_images] Loading image...")
#image = misc.imresize(ndimage.imread("quokka.jpg")[0:643, 0:643], (128, 128))
image = ia.quokka_square(size=(128, 128))
keypoints = [ia.Keypoint(x=34, y=15), ia.Keypoint(x=85, y=13), ia.Keypoint(x=63, y=73)] # left ear, right ear, mouth
keypoints = [ia.KeypointsOnImage(keypoints, shape=image.shape)]
print("[draw_per_augmenter_images] Initializing...")
rows_augmenters = [
(0, "Noop", [("", iaa.Noop()) for _ in sm.xrange(5)]),
(0, "Crop\n(top, right,\nbottom, left)", [(str(vals), iaa.Crop(px=vals)) for vals in [(2, 0, 0, 0), (0, 8, 8, 0), (4, 0, 16, 4), (8, 0, 0, 32), (32, 64, 0, 0)]]),
(0, "Pad\n(top, right,\nbottom, left)", [(str(vals), iaa.Pad(px=vals)) for vals in [(2, 0, 0, 0), (0, 8, 8, 0), (4, 0, 16, 4), (8, 0, 0, 32), (32, 64, 0, 0)]]),
(0, "Fliplr", [(str(p), iaa.Fliplr(p)) for p in [0, 0, 1, 1, 1]]),
(0, "Flipud", [(str(p), iaa.Flipud(p)) for p in [0, 0, 1, 1, 1]]),
(0, "Superpixels\np_replace=1", [("n_segments=%d" % (n_segments,), iaa.Superpixels(p_replace=1.0, n_segments=n_segments)) for n_segments in [25, 50, 75, 100, 125]]),
(0, "Superpixels\nn_segments=100", [("p_replace=%.2f" % (p_replace,), iaa.Superpixels(p_replace=p_replace, n_segments=100)) for p_replace in [0, 0.25, 0.5, 0.75, 1.0]]),
(0, "Invert", [("p=%d" % (p,), iaa.Invert(p=p)) for p in [0, 0, 1, 1, 1]]),
(0, "Invert\n(per_channel)", [("p=%.2f" % (p,), iaa.Invert(p=p, per_channel=True)) for p in [0.5, 0.5, 0.5, 0.5, 0.5]]),
(0, "Add", [("value=%d" % (val,), iaa.Add(val)) for val in [-45, -25, 0, 25, 45]]),
(0, "Add\n(per channel)", [("value=(%d, %d)" % (vals[0], vals[1],), iaa.Add(vals, per_channel=True)) for vals in [(-55, -35), (-35, -15), (-10, 10), (15, 35), (35, 55)]]),
(0, "AddToHueAndSaturation", [("value=%d" % (val,), iaa.AddToHueAndSaturation(val)) for val in [-45, -25, 0, 25, 45]]),
(0, "Multiply", [("value=%.2f" % (val,), iaa.Multiply(val)) for val in [0.25, 0.5, 1.0, 1.25, 1.5]]),
(1, "Multiply\n(per channel)", [("value=(%.2f, %.2f)" % (vals[0], vals[1],), iaa.Multiply(vals, per_channel=True)) for vals in [(0.15, 0.35), (0.4, 0.6), (0.9, 1.1), (1.15, 1.35), (1.4, 1.6)]]),
(0, "GaussianBlur", [("sigma=%.2f" % (sigma,), iaa.GaussianBlur(sigma=sigma)) for sigma in [0.25, 0.50, 1.0, 2.0, 4.0]]),
(0, "AverageBlur", [("k=%d" % (k,), iaa.AverageBlur(k=k)) for k in [1, 3, 5, 7, 9]]),
(0, "MedianBlur", [("k=%d" % (k,), iaa.MedianBlur(k=k)) for k in [1, 3, 5, 7, 9]]),
(0, "BilateralBlur\nsigma_color=250,\nsigma_space=250", [("d=%d" % (d,), iaa.BilateralBlur(d=d, sigma_color=250, sigma_space=250)) for d in [1, 3, 5, 7, 9]]),
(0, "Sharpen\n(alpha=1)", [("lightness=%.2f" % (lightness,), iaa.Sharpen(alpha=1, lightness=lightness)) for lightness in [0, 0.5, 1.0, 1.5, 2.0]]),
(0, "Emboss\n(alpha=1)", [("strength=%.2f" % (strength,), iaa.Emboss(alpha=1, strength=strength)) for strength in [0, 0.5, 1.0, 1.5, 2.0]]),
(0, "EdgeDetect", [("alpha=%.2f" % (alpha,), iaa.EdgeDetect(alpha=alpha)) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(0, "DirectedEdgeDetect\n(alpha=1)", [("direction=%.2f" % (direction,), iaa.DirectedEdgeDetect(alpha=1, direction=direction)) for direction in [0.0, 1*(360/5)/360, 2*(360/5)/360, 3*(360/5)/360, 4*(360/5)/360]]),
(0, "AdditiveGaussianNoise", [("scale=%.2f*255" % (scale,), iaa.AdditiveGaussianNoise(scale=scale * 255)) for scale in [0.025, 0.05, 0.1, 0.2, 0.3]]),
(0, "AdditiveGaussianNoise\n(per channel)", [("scale=%.2f*255" % (scale,), iaa.AdditiveGaussianNoise(scale=scale * 255, per_channel=True)) for scale in [0.025, 0.05, 0.1, 0.2, 0.3]]),
(0, "Dropout", [("p=%.2f" % (p,), iaa.Dropout(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "Dropout\n(per channel)", [("p=%.2f" % (p,), iaa.Dropout(p=p, per_channel=True)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(3, "CoarseDropout\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseDropout(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "CoarseDropout\n(p=0.2, per channel)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseDropout(p=0.2, size_percent=size_percent, per_channel=True, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "SaltAndPepper", [("p=%.2f" % (p,), iaa.SaltAndPepper(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "Salt", [("p=%.2f" % (p,), iaa.Salt(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "Pepper", [("p=%.2f" % (p,), iaa.Pepper(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "CoarseSaltAndPepper\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseSaltAndPepper(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "CoarseSalt\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseSalt(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "CoarsePepper\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarsePepper(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "ContrastNormalization", [("alpha=%.1f" % (alpha,), iaa.ContrastNormalization(alpha=alpha)) for alpha in [0.5, 0.75, 1.0, 1.25, 1.50]]),
(0, "ContrastNormalization\n(per channel)", [("alpha=(%.2f, %.2f)" % (alphas[0], alphas[1],), iaa.ContrastNormalization(alpha=alphas, per_channel=True)) for alphas in [(0.4, 0.6), (0.65, 0.85), (0.9, 1.1), (1.15, 1.35), (1.4, 1.6)]]),
(0, "Grayscale", [("alpha=%.1f" % (alpha,), iaa.Grayscale(alpha=alpha)) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(6, "PerspectiveTransform", [("scale=%.3f" % (scale,), iaa.PerspectiveTransform(scale=scale)) for scale in [0.025, 0.05, 0.075, 0.10, 0.125]]),
(0, "PiecewiseAffine", [("scale=%.3f" % (scale,), iaa.PiecewiseAffine(scale=scale)) for scale in [0.015, 0.03, 0.045, 0.06, 0.075]]),
(0, "Affine: Scale", [("%.1fx" % (scale,), iaa.Affine(scale=scale)) for scale in [0.1, 0.5, 1.0, 1.5, 1.9]]),
(0, "Affine: Translate", [("x=%d y=%d" % (x, y), iaa.Affine(translate_px={"x": x, "y": y})) for x, y in [(-32, -16), (-16, -32), (-16, -8), (16, 8), (16, 32)]]),
(0, "Affine: Rotate", [("%d deg" % (rotate,), iaa.Affine(rotate=rotate)) for rotate in [-90, -45, 0, 45, 90]]),
(0, "Affine: Shear", [("%d deg" % (shear,), iaa.Affine(shear=shear)) for shear in [-45, -25, 0, 25, 45]]),
(0, "Affine: Modes", [(mode, iaa.Affine(translate_px=-32, mode=mode)) for mode in ["constant", "edge", "symmetric", "reflect", "wrap"]]),
(0, "Affine: cval", [("%d" % (int(cval*255),), iaa.Affine(translate_px=-32, cval=int(cval*255), mode="constant")) for cval in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(
2, "Affine: all", [
(
"",
iaa.Affine(
scale={"x": (0.5, 1.5), "y": (0.5, 1.5)},
translate_px={"x": (-32, 32), "y": (-32, 32)},
rotate=(-45, 45),
shear=(-32, 32),
mode=ia.ALL,
cval=(0.0, 1.0)
)
)
for _ in sm.xrange(5)
]
),
(1, "ElasticTransformation\n(sigma=0.2)", [("alpha=%.1f" % (alpha,), iaa.ElasticTransformation(alpha=alpha, sigma=0.2)) for alpha in [0.1, 0.5, 1.0, 3.0, 9.0]]),
(0, "Alpha\nwith EdgeDetect(1.0)", [("factor=%.1f" % (factor,), iaa.Alpha(factor=factor, first=iaa.EdgeDetect(1.0))) for factor in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(4, "Alpha\nwith EdgeDetect(1.0)\n(per channel)", [("factor=(%.2f, %.2f)" % (factor[0], factor[1]), iaa.Alpha(factor=factor, first=iaa.EdgeDetect(1.0), per_channel=0.5)) for factor in [(0.0, 0.2), (0.15, 0.35), (0.4, 0.6), (0.65, 0.85), (0.8, 1.0)]]),
(15, "SimplexNoiseAlpha\nwith EdgeDetect(1.0)", [("", iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0))) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(9, "FrequencyNoiseAlpha\nwith EdgeDetect(1.0)", [("exponent=%.1f" % (exponent,), iaa.FrequencyNoiseAlpha(exponent=exponent, first=iaa.EdgeDetect(1.0), size_px_max=16, upscale_method="linear", sigmoid=False)) for exponent in [-4, -2, 0, 2, 4]])
]
print("[draw_per_augmenter_images] Augmenting...")
rows = []
for (row_seed, row_name, augmenters) in rows_augmenters:
ia.seed(row_seed)
#for img_title, augmenter in augmenters:
# #aug.reseed(1000)
# pass
row_images = []
row_keypoints = []
row_titles = []
for img_title, augmenter in augmenters:
aug_det = augmenter.to_deterministic()
row_images.append(aug_det.augment_image(image))
row_keypoints.append(aug_det.augment_keypoints(keypoints)[0])
row_titles.append(img_title)
rows.append((row_name, row_images, row_keypoints, row_titles))
# matplotlib drawin routine
"""
print("[draw_per_augmenter_images] Plotting...")
width = 8
height = int(1.5 * len(rows_augmenters))
fig = plt.figure(figsize=(width, height))
grid_rows = len(rows)
grid_cols = 1 + 5
gs = gridspec.GridSpec(grid_rows, grid_cols, width_ratios=[2, 1, 1, 1, 1, 1])
axes = []
for i in sm.xrange(grid_rows):
axes.append([plt.subplot(gs[i, col_idx]) for col_idx in sm.xrange(grid_cols)])
fig.tight_layout()
#fig.subplots_adjust(bottom=0.2 / grid_rows, hspace=0.22)
#fig.subplots_adjust(wspace=0.005, hspace=0.425, bottom=0.02)
fig.subplots_adjust(wspace=0.005, hspace=0.005, bottom=0.02)
for row_idx, (row_name, row_images, row_keypoints, row_titles) in enumerate(rows):
axes_row = axes[row_idx]
for col_idx in sm.xrange(grid_cols):
ax = axes_row[col_idx]
ax.cla()
ax.axis("off")
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
if col_idx == 0:
ax.text(0, 0.5, row_name, color="black")
else:
cell_image = row_images[col_idx-1]
cell_keypoints = row_keypoints[col_idx-1]
cell_image_kp = cell_keypoints.draw_on_image(cell_image, size=5)
ax.imshow(cell_image_kp)
x = 0
y = 145
#ax.text(x, y, row_titles[col_idx-1], color="black", backgroundcolor="white", fontsize=6)
ax.text(x, y, row_titles[col_idx-1], color="black", fontsize=7)
fig.savefig("examples.jpg", bbox_inches="tight")
#plt.show()
"""
# simpler and faster drawing routine
"""
output_image = ExamplesImage(128, 128, 128+64, 32)
for (row_name, row_images, row_keypoints, row_titles) in rows:
row_images_kps = []
for image, keypoints in zip(row_images, row_keypoints):
row_images_kps.append(keypoints.draw_on_image(image, size=5))
output_image.add_row(row_name, row_images_kps, row_titles)
misc.imsave("examples.jpg", output_image.draw())
"""
# routine to draw many single files
seen = defaultdict(lambda: 0)
markups = []
for (row_name, row_images, row_keypoints, row_titles) in rows:
output_image = ExamplesImage(128, 128, 128+64, 32)
row_images_kps = []
for image, keypoints in zip(row_images, row_keypoints):
row_images_kps.append(keypoints.draw_on_image(image, size=5))
output_image.add_row(row_name, row_images_kps, row_titles)
if "\n" in row_name:
row_name_clean = row_name[0:row_name.find("\n")+1]
else:
row_name_clean = row_name
row_name_clean = re.sub(r"[^a-z0-9]+", "_", row_name_clean.lower())
row_name_clean = row_name_clean.strip("_")
if seen[row_name_clean] > 0:
row_name_clean = "%s_%d" % (row_name_clean, seen[row_name_clean] + 1)
fp = os.path.join(IMAGES_DIR, "examples_%s.jpg" % (row_name_clean,))
#misc.imsave(fp, output_image.draw())
save(fp, output_image.draw())
seen[row_name_clean] += 1
markup_descr = row_name.replace('"', '') \
.replace("\n", " ") \
.replace("(", "") \
.replace(")", "")
markup = '' % (markup_descr, fp, markup_descr)
markups.append(markup)
for markup in markups:
print(markup)
def draw_per_augmenter_images():
print("[draw_per_augmenter_images] Loading image...")
#image = misc.imresize(ndimage.imread("quokka.jpg")[0:643, 0:643], (128, 128))
image = ia.quokka_square(size=(128, 128))
keypoints = [ia.Keypoint(x=34, y=15), ia.Keypoint(x=85, y=13), ia.Keypoint(x=63, y=73)] # left ear, right ear, mouth
keypoints = [ia.KeypointsOnImage(keypoints, shape=image.shape)]
print("[draw_per_augmenter_images] Initializing...")
rows_augmenters = [
(0, "Noop", [("", iaa.Noop()) for _ in sm.xrange(5)]),
(0, "Crop\n(top, right,\nbottom, left)", [(str(vals), iaa.Crop(px=vals)) for vals in [(2, 0, 0, 0), (0, 8, 8, 0), (4, 0, 16, 4), (8, 0, 0, 32), (32, 64, 0, 0)]]),
(0, "Pad\n(top, right,\nbottom, left)", [(str(vals), iaa.Pad(px=vals)) for vals in [(2, 0, 0, 0), (0, 8, 8, 0), (4, 0, 16, 4), (8, 0, 0, 32), (32, 64, 0, 0)]]),
(0, "Fliplr", [(str(p), iaa.Fliplr(p)) for p in [0, 0, 1, 1, 1]]),
(0, "Flipud", [(str(p), iaa.Flipud(p)) for p in [0, 0, 1, 1, 1]]),
(0, "Superpixels\np_replace=1", [("n_segments=%d" % (n_segments,), iaa.Superpixels(p_replace=1.0, n_segments=n_segments)) for n_segments in [25, 50, 75, 100, 125]]),
(0, "Superpixels\nn_segments=100", [("p_replace=%.2f" % (p_replace,), iaa.Superpixels(p_replace=p_replace, n_segments=100)) for p_replace in [0, 0.25, 0.5, 0.75, 1.0]]),
(0, "Invert", [("p=%d" % (p,), iaa.Invert(p=p)) for p in [0, 0, 1, 1, 1]]),
(0, "Invert\n(per_channel)", [("p=%.2f" % (p,), iaa.Invert(p=p, per_channel=True)) for p in [0.5, 0.5, 0.5, 0.5, 0.5]]),
(0, "Add", [("value=%d" % (val,), iaa.Add(val)) for val in [-45, -25, 0, 25, 45]]),
(0, "Add\n(per channel)", [("value=(%d, %d)" % (vals[0], vals[1],), iaa.Add(vals, per_channel=True)) for vals in [(-55, -35), (-35, -15), (-10, 10), (15, 35), (35, 55)]]),
(0, "AddToHueAndSaturation", [("value=%d" % (val,), iaa.AddToHueAndSaturation(val)) for val in [-45, -25, 0, 25, 45]]),
(0, "Multiply", [("value=%.2f" % (val,), iaa.Multiply(val)) for val in [0.25, 0.5, 1.0, 1.25, 1.5]]),
(1, "Multiply\n(per channel)", [("value=(%.2f, %.2f)" % (vals[0], vals[1],), iaa.Multiply(vals, per_channel=True)) for vals in [(0.15, 0.35), (0.4, 0.6), (0.9, 1.1), (1.15, 1.35), (1.4, 1.6)]]),
(0, "GaussianBlur", [("sigma=%.2f" % (sigma,), iaa.GaussianBlur(sigma=sigma)) for sigma in [0.25, 0.50, 1.0, 2.0, 4.0]]),
(0, "AverageBlur", [("k=%d" % (k,), iaa.AverageBlur(k=k)) for k in [1, 3, 5, 7, 9]]),
(0, "MedianBlur", [("k=%d" % (k,), iaa.MedianBlur(k=k)) for k in [1, 3, 5, 7, 9]]),
(0, "BilateralBlur\nsigma_color=250,\nsigma_space=250", [("d=%d" % (d,), iaa.BilateralBlur(d=d, sigma_color=250, sigma_space=250)) for d in [1, 3, 5, 7, 9]]),
(0, "Sharpen\n(alpha=1)", [("lightness=%.2f" % (lightness,), iaa.Sharpen(alpha=1, lightness=lightness)) for lightness in [0, 0.5, 1.0, 1.5, 2.0]]),
(0, "Emboss\n(alpha=1)", [("strength=%.2f" % (strength,), iaa.Emboss(alpha=1, strength=strength)) for strength in [0, 0.5, 1.0, 1.5, 2.0]]),
(0, "EdgeDetect", [("alpha=%.2f" % (alpha,), iaa.EdgeDetect(alpha=alpha)) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(0, "DirectedEdgeDetect\n(alpha=1)", [("direction=%.2f" % (direction,), iaa.DirectedEdgeDetect(alpha=1, direction=direction)) for direction in [0.0, 1*(360/5)/360, 2*(360/5)/360, 3*(360/5)/360, 4*(360/5)/360]]),
(0, "AdditiveGaussianNoise", [("scale=%.2f*255" % (scale,), iaa.AdditiveGaussianNoise(scale=scale * 255)) for scale in [0.025, 0.05, 0.1, 0.2, 0.3]]),
(0, "AdditiveGaussianNoise\n(per channel)", [("scale=%.2f*255" % (scale,), iaa.AdditiveGaussianNoise(scale=scale * 255, per_channel=True)) for scale in [0.025, 0.05, 0.1, 0.2, 0.3]]),
(0, "Dropout", [("p=%.2f" % (p,), iaa.Dropout(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "Dropout\n(per channel)", [("p=%.2f" % (p,), iaa.Dropout(p=p, per_channel=True)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(3, "CoarseDropout\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseDropout(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "CoarseDropout\n(p=0.2, per channel)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseDropout(p=0.2, size_percent=size_percent, per_channel=True, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "SaltAndPepper", [("p=%.2f" % (p,), iaa.SaltAndPepper(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "Salt", [("p=%.2f" % (p,), iaa.Salt(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "Pepper", [("p=%.2f" % (p,), iaa.Pepper(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "CoarseSaltAndPepper\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseSaltAndPepper(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "CoarseSalt\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseSalt(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "CoarsePepper\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarsePepper(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "ContrastNormalization", [("alpha=%.1f" % (alpha,), iaa.ContrastNormalization(alpha=alpha)) for alpha in [0.5, 0.75, 1.0, 1.25, 1.50]]),
(0, "ContrastNormalization\n(per channel)", [("alpha=(%.2f, %.2f)" % (alphas[0], alphas[1],), iaa.ContrastNormalization(alpha=alphas, per_channel=True)) for alphas in [(0.4, 0.6), (0.65, 0.85), (0.9, 1.1), (1.15, 1.35), (1.4, 1.6)]]),
(0, "Grayscale", [("alpha=%.1f" % (alpha,), iaa.Grayscale(alpha=alpha)) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(6, "PerspectiveTransform", [("scale=%.3f" % (scale,), iaa.PerspectiveTransform(scale=scale)) for scale in [0.025, 0.05, 0.075, 0.10, 0.125]]),
(0, "PiecewiseAffine", [("scale=%.3f" % (scale,), iaa.PiecewiseAffine(scale=scale)) for scale in [0.015, 0.03, 0.045, 0.06, 0.075]]),
(0, "Affine: Scale", [("%.1fx" % (scale,), iaa.Affine(scale=scale)) for scale in [0.1, 0.5, 1.0, 1.5, 1.9]]),
(0, "Affine: Translate", [("x=%d y=%d" % (x, y), iaa.Affine(translate_px={"x": x, "y": y})) for x, y in [(-32, -16), (-16, -32), (-16, -8), (16, 8), (16, 32)]]),
(0, "Affine: Rotate", [("%d deg" % (rotate,), iaa.Affine(rotate=rotate)) for rotate in [-90, -45, 0, 45, 90]]),
(0, "Affine: Shear", [("%d deg" % (shear,), iaa.Affine(shear=shear)) for shear in [-45, -25, 0, 25, 45]]),
(0, "Affine: Modes", [(mode, iaa.Affine(translate_px=-32, mode=mode)) for mode in ["constant", "edge", "symmetric", "reflect", "wrap"]]),
(0, "Affine: cval", [("%d" % (int(cval*255),), iaa.Affine(translate_px=-32, cval=int(cval*255), mode="constant")) for cval in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(
2, "Affine: all", [
(
"",
iaa.Affine(
scale={"x": (0.5, 1.5), "y": (0.5, 1.5)},
translate_px={"x": (-32, 32), "y": (-32, 32)},
rotate=(-45, 45),
shear=(-32, 32),
mode=ia.ALL,
cval=(0.0, 1.0)
)
)
for _ in sm.xrange(5)
]
),
(1, "ElasticTransformation\n(sigma=0.2)", [("alpha=%.1f" % (alpha,), iaa.ElasticTransformation(alpha=alpha, sigma=0.2)) for alpha in [0.1, 0.5, 1.0, 3.0, 9.0]]),
(0, "Alpha\nwith EdgeDetect(1.0)", [("factor=%.1f" % (factor,), iaa.Alpha(factor=factor, first=iaa.EdgeDetect(1.0))) for factor in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(4, "Alpha\nwith EdgeDetect(1.0)\n(per channel)", [("factor=(%.2f, %.2f)" % (factor[0], factor[1]), iaa.Alpha(factor=factor, first=iaa.EdgeDetect(1.0), per_channel=0.5)) for factor in [(0.0, 0.2), (0.15, 0.35), (0.4, 0.6), (0.65, 0.85), (0.8, 1.0)]]),
(15, "SimplexNoiseAlpha\nwith EdgeDetect(1.0)", [("", iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0))) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(9, "FrequencyNoiseAlpha\nwith EdgeDetect(1.0)", [("exponent=%.1f" % (exponent,), iaa.FrequencyNoiseAlpha(exponent=exponent, first=iaa.EdgeDetect(1.0), size_px_max=16, upscale_method="linear", sigmoid=False)) for exponent in [-4, -2, 0, 2, 4]])
]
print("[draw_per_augmenter_images] Augmenting...")
rows = []
for (row_seed, row_name, augmenters) in rows_augmenters:
ia.seed(row_seed)
#for img_title, augmenter in augmenters:
# #aug.reseed(1000)
# pass
row_images = []
row_keypoints = []
row_titles = []
for img_title, augmenter in augmenters:
aug_det = augmenter.to_deterministic()
row_images.append(aug_det.augment_image(image))
row_keypoints.append(aug_det.augment_keypoints(keypoints)[0])
row_titles.append(img_title)
rows.append((row_name, row_images, row_keypoints, row_titles))
# matplotlib drawin routine
"""
print("[draw_per_augmenter_images] Plotting...")
width = 8
height = int(1.5 * len(rows_augmenters))
fig = plt.figure(figsize=(width, height))
grid_rows = len(rows)
grid_cols = 1 + 5
gs = gridspec.GridSpec(grid_rows, grid_cols, width_ratios=[2, 1, 1, 1, 1, 1])
axes = []
for i in sm.xrange(grid_rows):
axes.append([plt.subplot(gs[i, col_idx]) for col_idx in sm.xrange(grid_cols)])
fig.tight_layout()
#fig.subplots_adjust(bottom=0.2 / grid_rows, hspace=0.22)
#fig.subplots_adjust(wspace=0.005, hspace=0.425, bottom=0.02)
fig.subplots_adjust(wspace=0.005, hspace=0.005, bottom=0.02)
for row_idx, (row_name, row_images, row_keypoints, row_titles) in enumerate(rows):
axes_row = axes[row_idx]
for col_idx in sm.xrange(grid_cols):
ax = axes_row[col_idx]
ax.cla()
ax.axis("off")
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
if col_idx == 0:
ax.text(0, 0.5, row_name, color="black")
else:
cell_image = row_images[col_idx-1]
cell_keypoints = row_keypoints[col_idx-1]
cell_image_kp = cell_keypoints.draw_on_image(cell_image, size=5)
ax.imshow(cell_image_kp)
x = 0
y = 145
#ax.text(x, y, row_titles[col_idx-1], color="black", backgroundcolor="white", fontsize=6)
ax.text(x, y, row_titles[col_idx-1], color="black", fontsize=7)
fig.savefig("examples.jpg", bbox_inches="tight")
#plt.show()
"""
# simpler and faster drawing routine
"""
output_image = ExamplesImage(128, 128, 128+64, 32)
for (row_name, row_images, row_keypoints, row_titles) in rows:
row_images_kps = []
for image, keypoints in zip(row_images, row_keypoints):
row_images_kps.append(keypoints.draw_on_image(image, size=5))
output_image.add_row(row_name, row_images_kps, row_titles)
misc.imsave("examples.jpg", output_image.draw())
"""
# routine to draw many single files
seen = defaultdict(lambda: 0)
markups = []
for (row_name, row_images, row_keypoints, row_titles) in rows:
output_image = ExamplesImage(128, 128, 128+64, 32)
row_images_kps = []
for image, keypoints in zip(row_images, row_keypoints):
row_images_kps.append(keypoints.draw_on_image(image, size=5))
output_image.add_row(row_name, row_images_kps, row_titles)
if "\n" in row_name:
row_name_clean = row_name[0:row_name.find("\n")+1]
else:
row_name_clean = row_name
row_name_clean = re.sub(r"[^a-z0-9]+", "_", row_name_clean.lower())
row_name_clean = row_name_clean.strip("_")
if seen[row_name_clean] > 0:
row_name_clean = "%s_%d" % (row_name_clean, seen[row_name_clean] + 1)
fp = os.path.join(IMAGES_DIR, "examples_%s.jpg" % (row_name_clean,))
#misc.imsave(fp, output_image.draw())
save(fp, output_image.draw())
seen[row_name_clean] += 1
markup_descr = row_name.replace('"', '') \
.replace("\n", " ") \
.replace("(", "") \
.replace(")", "")
markup = '' % (markup_descr, fp, markup_descr)
markups.append(markup)
for markup in markups:
print(markup)
def main():
parser = argparse.ArgumentParser(description="Check augmenters visually.")
parser.add_argument(
"--only",
default=None,
help=
"If this is set, then only the results of an augmenter with this name will be shown. "
"Optionally, comma-separated list.",
required=False)
args = parser.parse_args()
images = [
ia.quokka_square(size=(128, 128)),
ia.imresize_single_image(data.astronaut(), (128, 128))
]
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=50, y=40),
ia.Keypoint(x=70, y=38),
ia.Keypoint(x=62, y=52)
],
shape=images[0].shape),
ia.KeypointsOnImage([
ia.Keypoint(x=55, y=32),
ia.Keypoint(x=42, y=95),
ia.Keypoint(x=75, y=89)
],
shape=images[1].shape)
]
bounding_boxes = [
ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=10, y1=10, x2=20, y2=20),
ia.BoundingBox(x1=40, y1=50, x2=70, y2=60)
],
shape=images[0].shape),
ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=10, y1=10, x2=20, y2=20),
ia.BoundingBox(x1=40, y1=50, x2=70, y2=60)
],
shape=images[1].shape)
]
augmenters = [
iaa.Sequential([
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="Sequential"),
iaa.SomeOf(2,
children=[
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="SomeOf"),
iaa.OneOf(children=[
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="OneOf"),
iaa.Sometimes(0.5,
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
name="Sometimes"),
iaa.WithColorspace("HSV",
children=[iaa.Add(20)],
name="WithColorspace"),
iaa.WithChannels([0], children=[iaa.Add(20)], name="WithChannels"),
iaa.AddToHueAndSaturation((-20, 20),
per_channel=True,
name="AddToHueAndSaturation"),
iaa.Noop(name="Noop"),
iaa.Resize({
"width": 64,
"height": 64
}, name="Resize"),
iaa.CropAndPad(px=(-8, 8), name="CropAndPad-px"),
iaa.Pad(px=(0, 8), name="Pad-px"),
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.Superpixels(p_replace=0.75, n_segments=50, name="Superpixels"),
iaa.Grayscale(0.5, name="Grayscale0.5"),
iaa.Grayscale(1.0, name="Grayscale1.0"),
iaa.GaussianBlur((0, 3.0), name="GaussianBlur"),
iaa.AverageBlur(k=(3, 11), name="AverageBlur"),
iaa.MedianBlur(k=(3, 11), name="MedianBlur"),
iaa.BilateralBlur(d=10, name="BilateralBlur"),
iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0, 2.0), name="Sharpen"),
iaa.Emboss(alpha=(0.1, 1.0), strength=(0, 2.0), name="Emboss"),
iaa.EdgeDetect(alpha=(0.1, 1.0), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.1, 1.0),
direction=(0, 1.0),
name="DirectedEdgeDetect"),
iaa.Add((-50, 50), name="Add"),
iaa.Add((-50, 50), per_channel=True, name="AddPerChannel"),
iaa.AddElementwise((-50, 50), name="AddElementwise"),
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.1 * 255),
name="AdditiveGaussianNoise"),
iaa.Multiply((0.5, 1.5), name="Multiply"),
iaa.Multiply((0.5, 1.5), per_channel=True, name="MultiplyPerChannel"),
iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"),
iaa.Dropout((0.0, 0.1), name="Dropout"),
iaa.CoarseDropout(p=0.05,
size_percent=(0.05, 0.5),
name="CoarseDropout"),
iaa.Invert(p=0.5, name="Invert"),
iaa.Invert(p=0.5, per_channel=True, name="InvertPerChannel"),
iaa.ContrastNormalization(alpha=(0.5, 2.0),
name="ContrastNormalization"),
iaa.SaltAndPepper(p=0.05, name="SaltAndPepper"),
iaa.Salt(p=0.05, name="Salt"),
iaa.Pepper(p=0.05, name="Pepper"),
iaa.CoarseSaltAndPepper(p=0.05,
size_percent=(0.01, 0.1),
name="CoarseSaltAndPepper"),
iaa.CoarseSalt(p=0.05, size_percent=(0.01, 0.1), name="CoarseSalt"),
iaa.CoarsePepper(p=0.05, size_percent=(0.01, 0.1),
name="CoarsePepper"),
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, 255),
mode=ia.ALL,
name="Affine"),
iaa.PiecewiseAffine(scale=0.03,
nb_rows=(2, 6),
nb_cols=(2, 6),
name="PiecewiseAffine"),
iaa.PerspectiveTransform(scale=0.1, name="PerspectiveTransform"),
iaa.ElasticTransformation(alpha=(0.5, 8.0),
sigma=1.0,
name="ElasticTransformation"),
iaa.Alpha(factor=(0.0, 1.0),
first=iaa.Add(100),
second=iaa.Dropout(0.5),
per_channel=False,
name="Alpha"),
iaa.Alpha(factor=(0.0, 1.0),
first=iaa.Add(100),
second=iaa.Dropout(0.5),
per_channel=True,
name="AlphaPerChannel"),
iaa.Alpha(factor=(0.0, 1.0),
first=iaa.Affine(rotate=(-45, 45)),
per_channel=True,
name="AlphaAffine"),
iaa.AlphaElementwise(factor=(0.0, 1.0),
first=iaa.Add(50),
second=iaa.ContrastNormalization(2.0),
per_channel=False,
name="AlphaElementwise"),
iaa.AlphaElementwise(factor=(0.0, 1.0),
first=iaa.Add(50),
second=iaa.ContrastNormalization(2.0),
per_channel=True,
name="AlphaElementwisePerChannel"),
iaa.AlphaElementwise(factor=(0.0, 1.0),
first=iaa.Affine(rotate=(-45, 45)),
per_channel=True,
name="AlphaElementwiseAffine"),
iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0),
per_channel=False,
name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0),
per_channel=False,
name="FrequencyNoiseAlpha")
]
augmenters.append(
iaa.Sequential([iaa.Sometimes(0.2, aug.copy()) for aug in augmenters],
name="Sequential"))
augmenters.append(
iaa.Sometimes(0.5, [aug.copy() for aug in augmenters],
name="Sometimes"))
for augmenter in augmenters:
if args.only is None or augmenter.name in [
v.strip() for v in args.only.split(",")
]:
print("Augmenter: %s" % (augmenter.name, ))
grid = []
for image, kps, bbs in zip(images, keypoints, bounding_boxes):
aug_det = augmenter.to_deterministic()
imgs_aug = aug_det.augment_images(
np.tile(image[np.newaxis, ...], (16, 1, 1, 1)))
kps_aug = aug_det.augment_keypoints([kps] * 16)
bbs_aug = aug_det.augment_bounding_boxes([bbs] * 16)
imgs_aug_drawn = [
kps_aug_one.draw_on_image(img_aug)
for img_aug, kps_aug_one in zip(imgs_aug, kps_aug)
]
imgs_aug_drawn = [
bbs_aug_one.draw_on_image(img_aug)
for img_aug, bbs_aug_one in zip(imgs_aug_drawn, bbs_aug)
]
grid.append(np.hstack(imgs_aug_drawn))
ia.imshow(np.vstack(grid))
def main():
parser = argparse.ArgumentParser(description="Check augmenters visually.")
parser.add_argument('--only', default=None, help="If this is set, then only the results of an augmenter with this name will be shown. Optionally, comma-separated list.", required=False)
args = parser.parse_args()
images = [
ia.quokka_square(size=(128, 128)),
misc.imresize(data.astronaut(), (128, 128))
]
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=50, y=40),
ia.Keypoint(x=70, y=38),
ia.Keypoint(x=62, y=52)
],
shape=images[0].shape
),
ia.KeypointsOnImage([
ia.Keypoint(x=55, y=32),
ia.Keypoint(x=42, y=95),
ia.Keypoint(x=75, y=89)
],
shape=images[1].shape
)
]
bounding_boxes = [
ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=10, y1=10, x2=20, y2=20),
ia.BoundingBox(x1=40, y1=50, x2=70, y2=60)
],
shape=images[0].shape
),
ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=10, y1=10, x2=20, y2=20),
ia.BoundingBox(x1=40, y1=50, x2=70, y2=60)
],
shape=images[1].shape
)
]
# missing: InColorspace, Lambda, AssertLambda, AssertShape, Convolve
augmenters = [
iaa.Sequential([
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1*255),
iaa.Crop(percent=0.1)
], name="Sequential"),
iaa.SomeOf(2, children=[
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1*255),
iaa.Crop(percent=0.1)
], name="SomeOf"),
iaa.OneOf(children=[
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1*255),
iaa.Crop(percent=0.1)
], name="OneOf"),
iaa.Sometimes(0.5, iaa.AdditiveGaussianNoise(scale=0.1*255), name="Sometimes"),
iaa.WithColorspace("HSV", children=[iaa.Add(20)], name="WithColorspace"),
iaa.WithChannels([0], children=[iaa.Add(20)], name="WithChannels"),
iaa.AddToHueAndSaturation((-20, 20), per_channel=True, name="AddToHueAndSaturation"),
iaa.Noop(name="Noop"),
iaa.Scale({"width": 64, "height": 64}, name="Scale"),
iaa.CropAndPad(px=(-8, 8), name="CropAndPad-px"),
iaa.Pad(px=(0, 8), name="Pad-px"),
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.Superpixels(p_replace=0.75, n_segments=50, name="Superpixels"),
iaa.Grayscale(0.5, name="Grayscale0.5"),
iaa.Grayscale(1.0, name="Grayscale1.0"),
iaa.GaussianBlur((0, 3.0), name="GaussianBlur"),
iaa.AverageBlur(k=(3, 11), name="AverageBlur"),
iaa.MedianBlur(k=(3, 11), name="MedianBlur"),
iaa.BilateralBlur(d=10, name="BilateralBlur"),
iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0, 2.0), name="Sharpen"),
iaa.Emboss(alpha=(0.1, 1.0), strength=(0, 2.0), name="Emboss"),
iaa.EdgeDetect(alpha=(0.1, 1.0), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.1, 1.0), direction=(0, 1.0), name="DirectedEdgeDetect"),
iaa.Add((-50, 50), name="Add"),
iaa.Add((-50, 50), per_channel=True, name="AddPerChannel"),
iaa.AddElementwise((-50, 50), name="AddElementwise"),
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.1*255), name="AdditiveGaussianNoise"),
iaa.Multiply((0.5, 1.5), name="Multiply"),
iaa.Multiply((0.5, 1.5), per_channel=True, name="MultiplyPerChannel"),
iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"),
iaa.Dropout((0.0, 0.1), name="Dropout"),
iaa.CoarseDropout(p=0.05, size_percent=(0.05, 0.5), name="CoarseDropout"),
iaa.Invert(p=0.5, name="Invert"),
iaa.Invert(p=0.5, per_channel=True, name="InvertPerChannel"),
iaa.ContrastNormalization(alpha=(0.5, 2.0), name="ContrastNormalization"),
iaa.SaltAndPepper(p=0.05, name="SaltAndPepper"),
iaa.Salt(p=0.05, name="Salt"),
iaa.Pepper(p=0.05, name="Pepper"),
iaa.CoarseSaltAndPepper(p=0.05, size_percent=(0.01, 0.1), name="CoarseSaltAndPepper"),
iaa.CoarseSalt(p=0.05, size_percent=(0.01, 0.1), name="CoarseSalt"),
iaa.CoarsePepper(p=0.05, size_percent=(0.01, 0.1), name="CoarsePepper"),
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, 255),
mode=ia.ALL,
name="Affine"
),
iaa.PiecewiseAffine(scale=0.03, nb_rows=(2, 6), nb_cols=(2, 6), name="PiecewiseAffine"),
iaa.PerspectiveTransform(scale=0.1, name="PerspectiveTransform"),
iaa.ElasticTransformation(alpha=(0.5, 8.0), sigma=1.0, name="ElasticTransformation"),
iaa.Alpha(
factor=(0.0, 1.0),
first=iaa.Add(100),
second=iaa.Dropout(0.5),
per_channel=False,
name="Alpha"
),
iaa.Alpha(
factor=(0.0, 1.0),
first=iaa.Add(100),
second=iaa.Dropout(0.5),
per_channel=True,
name="AlphaPerChannel"
),
iaa.Alpha(
factor=(0.0, 1.0),
first=iaa.Affine(rotate=(-45, 45)),
per_channel=True,
name="AlphaAffine"
),
iaa.AlphaElementwise(
factor=(0.0, 1.0),
first=iaa.Add(50),
second=iaa.ContrastNormalization(2.0),
per_channel=False,
name="AlphaElementwise"
),
iaa.AlphaElementwise(
factor=(0.0, 1.0),
first=iaa.Add(50),
second=iaa.ContrastNormalization(2.0),
per_channel=True,
name="AlphaElementwisePerChannel"
),
iaa.AlphaElementwise(
factor=(0.0, 1.0),
first=iaa.Affine(rotate=(-45, 45)),
per_channel=True,
name="AlphaElementwiseAffine"
),
iaa.SimplexNoiseAlpha(
#first=iaa.GaussianBlur((1.0, 3.0)),
#first=iaa.MedianBlur((3, 7)),
first=iaa.EdgeDetect(1.0),
#first=iaa.Affine(rotate=-45), #(-45, 45)),
per_channel=False,
name="SimplexNoiseAlpha"
),
iaa.FrequencyNoiseAlpha(
#first=iaa.GaussianBlur((1.0, 3.0)),
#first=iaa.MedianBlur((3, 7)),
first=iaa.EdgeDetect(1.0),
#first=iaa.Affine(rotate=-45), #(-45, 45)),
per_channel=False,
name="FrequencyNoiseAlpha"
)
]
augmenters.append(iaa.Sequential([iaa.Sometimes(0.2, aug.copy()) for aug in augmenters], name="Sequential"))
augmenters.append(iaa.Sometimes(0.5, [aug.copy() for aug in augmenters], name="Sometimes"))
for augmenter in augmenters:
if args.only is None or augmenter.name in [v.strip() for v in args.only.split(",")]:
print("Augmenter: %s" % (augmenter.name,))
grid = []
for image, kps, bbs in zip(images, keypoints, bounding_boxes):
aug_det = augmenter.to_deterministic()
imgs_aug = aug_det.augment_images(np.tile(image[np.newaxis, ...], (16, 1, 1, 1)))
kps_aug = aug_det.augment_keypoints([kps] * 16)
bbs_aug = aug_det.augment_bounding_boxes([bbs] * 16)
imgs_aug_drawn = [kps_aug_one.draw_on_image(img_aug) for img_aug, kps_aug_one in zip(imgs_aug, kps_aug)]
imgs_aug_drawn = [bbs_aug_one.draw_on_image(img_aug) for img_aug, bbs_aug_one in zip(imgs_aug_drawn, bbs_aug)]
grid.append(np.hstack(imgs_aug_drawn))
misc.imshow(np.vstack(grid))
def main():
image = ia.quokka_square((128, 128))
aug = iaa.MeanShiftBlur()
images_aug = aug(images=[image] * 16)
ia.imshow(ia.draw_grid(images_aug))
def draw_single_sequential_images():
ia.seed(44)
#image = misc.imresize(ndimage.imread("quokka.jpg")[0:643, 0:643], (128, 128))
image = ia.quokka_square(size=(128, 128))
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
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
)
grid = seq.draw_grid(image, cols=8, rows=8)
misc.imsave("examples_grid.jpg", grid)
def main():
parser = argparse.ArgumentParser(description="Check augmenters visually.")
parser.add_argument(
'--only',
default=None,
help=
"If this is set, then only the results of an augmenter with this name will be shown.",
required=False)
args = parser.parse_args()
images = [
ia.quokka_square(size=(128, 128)),
misc.imresize(data.astronaut(), (128, 128))
]
augmenters = [
iaa.Noop(name="Noop"),
iaa.OneOf(children=[
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="OneOf"),
iaa.AddToHueAndSaturation((-20, 20),
per_channel=True,
name="AddHueAndSaturation"),
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.Superpixels(p_replace=0.75, n_segments=50, name="Superpixels"),
iaa.Grayscale(0.5, name="Grayscale0.5"),
iaa.Grayscale(1.0, name="Grayscale1.0"),
iaa.AverageBlur(k=(3, 11), name="AverageBlur"),
iaa.GaussianBlur((0, 3.0), name="GaussianBlur"),
iaa.MedianBlur(k=(3, 11), name="MedianBlur"),
iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0, 2.0), name="Sharpen"),
iaa.Emboss(alpha=(0.1, 1.0), strength=(0, 2.0), name="Emboss"),
iaa.EdgeDetect(alpha=(0.1, 1.0), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.1, 1.0),
direction=(0, 1.0),
name="DirectedEdgeDetect"),
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.1 * 255),
name="AdditiveGaussianNoise"),
iaa.Dropout((0.0, 0.1), name="Dropout"),
iaa.CoarseDropout(p=0.05,
size_percent=(0.05, 0.5),
name="CoarseDropout"),
iaa.Invert(p=0.5, name="Invert"),
iaa.Invert(p=0.5, per_channel=True, name="InvertPerChannel"),
iaa.Add((-50, 50), name="Add"),
iaa.Add((-50, 50), per_channel=True, name="AddPerChannel"),
iaa.AddElementwise((-50, 50), name="AddElementwise"),
iaa.Multiply((0.5, 1.5), name="Multiply"),
iaa.Multiply((0.5, 1.5), per_channel=True, name="MultiplyPerChannel"),
iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"),
iaa.ContrastNormalization(alpha=(0.5, 2.0),
name="ContrastNormalization"),
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, 255),
mode=ia.ALL,
name="Affine"),
iaa.PiecewiseAffine(scale=0.03,
nb_rows=(2, 6),
nb_cols=(2, 6),
name="PiecewiseAffine"),
iaa.ElasticTransformation(alpha=(0.5, 8.0),
sigma=1.0,
name="ElasticTransformation")
]
augmenters.append(
iaa.Sequential([iaa.Sometimes(0.2, aug.copy()) for aug in augmenters],
name="Sequential"))
augmenters.append(
iaa.Sometimes(0.5, [aug.copy() for aug in augmenters],
name="Sometimes"))
for augmenter in augmenters:
if args.only is None or augmenter.name == args.only:
print("Augmenter: %s" % (augmenter.name, ))
grid = augmenter.draw_grid(images, rows=1, cols=16)
misc.imshow(grid)
def draw_single_sequential_images():
ia.seed(44)
#image = misc.imresize(ndimage.imread("quokka.jpg")[0:643, 0:643], (128, 128))
image = ia.quokka_square(size=(128, 128))
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
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
)
grid = seq.draw_grid(image, cols=8, rows=8)
misc.imsave("examples_grid.jpg", grid)
