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 test__draw_samples__single_value_hysteresis(self):
seed = 1
nb_images = 1000
aug = iaa.Canny(
alpha=0.2,
hysteresis_thresholds=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
sobel_kernel_size=[3, 5, 7],
random_state=iarandom.RNG(seed))
example_image = np.zeros((5, 5, 3), dtype=np.uint8)
samples = aug._draw_samples([example_image] * nb_images,
random_state=iarandom.RNG(seed))
alpha_samples = samples[0]
hthresh_samples = samples[1]
sobel_samples = samples[2]
rss = iarandom.RNG(seed).duplicate(4)
alpha_expected = iap.Deterministic(0.2).draw_samples((nb_images, ),
rss[0])
hthresh_expected = iap.Choice([0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10]).draw_samples((nb_images, 2),
rss[1])
sobel_expected = iap.Choice([3, 5, 7]).draw_samples((nb_images, ),
rss[2])
invalid = hthresh_expected[:, 0] > hthresh_expected[:, 1]
assert np.any(invalid)
hthresh_expected[invalid, :] = hthresh_expected[invalid, :][:, [1, 0]]
assert hthresh_expected.shape == (nb_images, 2)
assert not np.any(hthresh_expected[:, 0] > hthresh_expected[:, 1])
assert np.allclose(alpha_samples, alpha_expected)
assert np.allclose(hthresh_samples, hthresh_expected)
assert np.allclose(sobel_samples, sobel_expected)
def rgbmore(self, im):
return_im = []
add_return_im = lambda im: return_im.extend(im)
grey = np.array(im.convert(mode='L'))
im = np.array(im)
rgb_grey = np.dstack((im, grey))
edge = iaa.EdgeDetect(alpha=1)(images=rgb_grey)
dir_edge = lambda d: iaa.DirectedEdgeDetect(alpha=1, direction=d)(images=
grey)
dir_edges = np.array(
[dir_edge(d) for d in np.linspace(0, 1, num=3, endpoint=False)])
dir_edges = np.transpose(dir_edges, (1, 2, 0))
canny = iaa.Canny(alpha=1.0,
hysteresis_thresholds=128,
sobel_kernel_size=4,
deterministic=True,
colorizer=iaa.RandomColorsBinaryImageColorizer(
color_true=255, color_false=0))(images=grey)
avg_pool = iaa.AveragePooling(2)(images=grey)
max_pool = iaa.MaxPooling(2)(images=grey)
min_pool = iaa.MinPooling(2)(images=grey)
add_return_im([im, grey])
add_return_im([edge, dir_edges, canny])
add_return_im([avg_pool, max_pool, min_pool])
return np.dstack(return_im)
def test_augment_images__alpha_is_one(self):
colorizer = iaa.RandomColorsBinaryImageColorizer(color_true=254,
color_false=1)
aug = iaa.Canny(alpha=1.0,
hysteresis_thresholds=100,
sobel_kernel_size=3,
colorizer=colorizer,
random_state=1)
image_single_chan = np.uint8([[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 0]])
image = np.tile(image_single_chan[:, :, np.newaxis] * 128, (1, 1, 3))
# canny image, looks a bit unintuitive, but is what OpenCV returns
# can be checked via something like
# print("canny\n", cv2.Canny(image_single_chan*255, threshold1=100,
# threshold2=200,
# apertureSize=3,
# L2gradient=True))
image_canny = np.array([[0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0],
[0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0],
[0, 1, 0, 0, 1, 0, 0]],
dtype=bool)
image_aug_expected = np.copy(image)
image_aug_expected[image_canny] = 254
image_aug_expected[~image_canny] = 1
image_aug = aug.augment_image(image)
assert np.array_equal(image_aug, image_aug_expected)
def test___str___tuple_as_hysteresis(self):
alpha = iap.Deterministic(0.2)
hysteresis_thresholds = (
iap.Deterministic(10),
iap.Deterministic(11)
)
sobel_kernel_size = iap.Deterministic(3)
colorizer = iaa.RandomColorsBinaryImageColorizer(
color_true=10, color_false=20)
aug = iaa.Canny(
alpha=alpha,
hysteresis_thresholds=hysteresis_thresholds,
sobel_kernel_size=sobel_kernel_size,
colorizer=colorizer
)
observed = aug.__str__()
expected = ("Canny(alpha=%s, hysteresis_thresholds=(%s, %s), "
"sobel_kernel_size=%s, colorizer=%s, name=UnnamedCanny, "
"deterministic=False)") % (
str(aug.alpha),
str(aug.hysteresis_thresholds[0]),
str(aug.hysteresis_thresholds[1]),
str(aug.sobel_kernel_size),
colorizer)
assert observed == expected
def test___init___custom_settings(self):
aug = iaa.Canny(
alpha=0.2,
hysteresis_thresholds=([0, 1, 2], iap.DiscreteUniform(1, 10)),
sobel_kernel_size=[3, 5],
colorizer=iaa.RandomColorsBinaryImageColorizer(
color_true=10, color_false=20)
)
assert is_parameter_instance(aug.alpha, iap.Deterministic)
assert isinstance(aug.hysteresis_thresholds, tuple)
assert is_parameter_instance(aug.sobel_kernel_size, iap.Choice)
assert isinstance(aug.colorizer, iaa.RandomColorsBinaryImageColorizer)
assert np.isclose(aug.alpha.value, 0.2)
assert len(aug.hysteresis_thresholds) == 2
assert is_parameter_instance(aug.hysteresis_thresholds[0], iap.Choice)
assert aug.hysteresis_thresholds[0].a == [0, 1, 2]
assert is_parameter_instance(aug.hysteresis_thresholds[1],
iap.DiscreteUniform)
assert np.isclose(aug.hysteresis_thresholds[1].a.value, 1)
assert np.isclose(aug.hysteresis_thresholds[1].b.value, 10)
assert is_parameter_instance(aug.sobel_kernel_size, iap.Choice)
assert aug.sobel_kernel_size.a == [3, 5]
assert is_parameter_instance(aug.colorizer.color_true,
iap.Deterministic)
assert is_parameter_instance(aug.colorizer.color_false,
iap.Deterministic)
assert aug.colorizer.color_true.value == 10
assert aug.colorizer.color_false.value == 20
def test___init___default_settings(self):
aug = iaa.Canny()
assert is_parameter_instance(aug.alpha, iap.Uniform)
assert isinstance(aug.hysteresis_thresholds, tuple)
assert is_parameter_instance(aug.sobel_kernel_size, iap.DiscreteUniform)
assert isinstance(aug.colorizer, iaa.RandomColorsBinaryImageColorizer)
assert np.isclose(aug.alpha.a.value, 0.0)
assert np.isclose(aug.alpha.b.value, 1.0)
assert len(aug.hysteresis_thresholds) == 2
assert is_parameter_instance(aug.hysteresis_thresholds[0],
iap.DiscreteUniform)
assert np.isclose(aug.hysteresis_thresholds[0].a.value, 100-40)
assert np.isclose(aug.hysteresis_thresholds[0].b.value, 100+40)
assert is_parameter_instance(aug.hysteresis_thresholds[1],
iap.DiscreteUniform)
assert np.isclose(aug.hysteresis_thresholds[1].a.value, 200-40)
assert np.isclose(aug.hysteresis_thresholds[1].b.value, 200+40)
assert aug.sobel_kernel_size.a.value == 3
assert aug.sobel_kernel_size.b.value == 7
assert is_parameter_instance(aug.colorizer.color_true,
iap.DiscreteUniform)
assert is_parameter_instance(aug.colorizer.color_false,
iap.DiscreteUniform)
assert aug.colorizer.color_true.a.value == 0
assert aug.colorizer.color_true.b.value == 255
assert aug.colorizer.color_false.a.value == 0
assert aug.colorizer.color_false.b.value == 255
def test__draw_samples__single_value_hysteresis(self):
seed = 1
nb_images = 1000
aug = iaa.Canny(
alpha=0.2,
hysteresis_thresholds=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
sobel_kernel_size=[3, 5, 7],
random_state=np.random.RandomState(seed))
example_image = np.zeros((5, 5, 3), dtype=np.uint8)
samples = aug._draw_samples([example_image] * nb_images,
random_state=np.random.RandomState(seed))
alpha_samples = samples[0]
hthresh_samples = samples[1]
sobel_samples = samples[2]
rss = ia.derive_random_states(np.random.RandomState(seed), 4)
alpha_expected = [0.2] * nb_images
hthresh_expected = rss[1].choice([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
size=(nb_images, 2))
sobel_expected = rss[3].choice([3, 5, 7], size=(nb_images, ))
invalid = hthresh_expected[:, 0] > hthresh_expected[:, 1]
assert np.any(invalid)
hthresh_expected[invalid, :] = hthresh_expected[invalid, :][:, [1, 0]]
assert hthresh_expected.shape == (nb_images, 2)
assert not np.any(hthresh_expected[:, 0] > hthresh_expected[:, 1])
assert np.allclose(alpha_samples, alpha_expected)
assert np.allclose(hthresh_samples, hthresh_expected)
assert np.allclose(sobel_samples, sobel_expected)
def test_augment_images__random_values(self):
colorizer = iaa.RandomColorsBinaryImageColorizer(
color_true=255,
color_false=0
)
image_single_chan = iarandom.RNG(1).integers(
0, 255, size=(100, 100), dtype="uint8")
image = np.tile(image_single_chan[:, :, np.newaxis], (1, 1, 3))
images_canny_uint8 = {}
for thresh1, thresh2, ksize in itertools.product([100],
[200],
[3, 5]):
if thresh1 > thresh2:
continue
image_canny = cv2.Canny(
image,
threshold1=thresh1,
threshold2=thresh2,
apertureSize=ksize,
L2gradient=True)
image_canny_uint8 = np.tile(
image_canny[:, :, np.newaxis], (1, 1, 3))
similar = 0
for key, image_expected in images_canny_uint8.items():
if np.array_equal(image_canny_uint8, image_expected):
similar += 1
assert similar == 0
images_canny_uint8[(thresh1, thresh2, ksize)] = image_canny_uint8
seen = {key: False for key in images_canny_uint8.keys()}
for i in range(500):
aug = iaa.Canny(
alpha=1.0,
hysteresis_thresholds=(iap.Deterministic(100),
iap.Deterministic(200)),
sobel_kernel_size=[3, 5],
colorizer=colorizer,
seed=i)
image_aug = aug.augment_image(image)
match_index = None
for key, image_expected in images_canny_uint8.items():
if np.array_equal(image_aug, image_expected):
match_index = key
break
assert match_index is not None
seen[match_index] = True
assert len(seen.keys()) == len(images_canny_uint8.keys())
if all(seen.values()):
break
assert np.all(seen.values())
def __call__(self, sample):
image, polygon, labels = sample["image"], sample["polygon"], sample[
"labels"]
image = np.array(image)
t = iaa.Canny(alpha=self.alpha)
img = t(image=image)
image = Image.fromarray(img)
sample = {'image': image, 'polygon': polygon, 'labels': labels}
return sample
def test_augment_images__alpha_is_zero(self):
aug = iaa.Canny(alpha=0.0,
hysteresis_thresholds=(0, 10),
sobel_kernel_size=[3, 5, 7],
random_state=1)
image = np.arange(5 * 5 * 3).astype(np.uint8).reshape((5, 5, 3))
image_aug = aug.augment_image(image)
assert np.array_equal(image_aug, image)
def test_augment_images__random_color(self):
class _Color(iap.StochasticParameter):
def __init__(self, values):
super(_Color, self).__init__()
self.values = values
def _draw_samples(self, size, random_state):
v = random_state.choice(self.values)
return np.full(size, v, dtype=np.uint8)
colorizer = iaa.RandomColorsBinaryImageColorizer(
color_true=_Color([253, 254]), color_false=_Color([1, 2]))
image_single_chan = np.uint8([[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 0]])
image = np.tile(image_single_chan[:, :, np.newaxis] * 128, (1, 1, 3))
# canny image, looks a bit unintuitive, but is what OpenCV returns
# can be checked via something like
# print("canny\n", cv2.Canny(image_single_chan*255, threshold1=100,
# threshold2=200,
# apertureSize=3,
# L2gradient=True))
image_canny = np.array([[0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0],
[0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0],
[0, 1, 0, 0, 1, 0, 0]],
dtype=bool)
seen = {
(253, 1): False,
(253, 2): False,
(254, 1): False,
(254, 2): False
}
for i in range(100):
aug = iaa.Canny(alpha=1.0,
hysteresis_thresholds=100,
sobel_kernel_size=3,
colorizer=colorizer,
random_state=i)
image_aug = aug.augment_image(image)
color_true = np.unique(image_aug[image_canny])
color_false = np.unique(image_aug[~image_canny])
assert len(color_true) == 1
assert len(color_false) == 1
color_true = int(color_true[0])
color_false = int(color_false[0])
seen[(int(color_true), int(color_false))] = True
assert len(seen.keys()) == 4
if all(seen.values()):
break
assert np.all(seen.values())
def test_zero_sized_axes(self):
shapes = [(0, 0, 3), (0, 1, 3), (1, 0, 3)]
for shape in shapes:
with self.subTest(shape=shape):
image = np.zeros(shape, dtype=np.uint8)
aug = iaa.Canny(alpha=1)
image_aug = aug(image=image)
assert image_aug.shape == image.shape
def chapter_augmenters_canny():
fn_start = "edges/canny"
aug = iaa.Canny()
run_and_save_augseq(fn_start + ".jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.Canny(alpha=(0.0, 0.5))
run_and_save_augseq(fn_start + "_alpha.jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.Canny(alpha=(0.0, 0.5),
colorizer=iaa.RandomColorsBinaryImageColorizer(
color_true=255, color_false=0))
run_and_save_augseq(fn_start + "_alpha_white_on_black.jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.Canny(alpha=(0.5, 1.0), sobel_kernel_size=[3, 7])
run_and_save_augseq(fn_start + "_sobel_kernel_size.jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.Alpha((0.0, 1.0), iaa.Canny(alpha=1), iaa.MedianBlur(13))
run_and_save_augseq(fn_start + "_alpha_median_blur.jpg",
aug,
[ia.quokka(size=(128, 128)) for _ in range(4 * 2)],
cols=4,
rows=2)
def test__draw_samples__tuple_as_hysteresis(self):
seed = 1
nb_images = 10
aug = iaa.Canny(
alpha=0.2,
hysteresis_thresholds=([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
iap.DiscreteUniform(5, 100)),
sobel_kernel_size=[3, 5, 7],
random_state=iarandom.RNG(seed))
aug.alpha = remove_prefetching(aug.alpha)
aug.hysteresis_thresholds = (
remove_prefetching(aug.hysteresis_thresholds[0]),
remove_prefetching(aug.hysteresis_thresholds[1])
)
aug.sobel_kernel_size = remove_prefetching(aug.sobel_kernel_size)
example_image = np.zeros((5, 5, 3), dtype=np.uint8)
samples = aug._draw_samples([example_image] * nb_images,
random_state=iarandom.RNG(seed))
alpha_samples = samples[0]
hthresh_samples = samples[1]
sobel_samples = samples[2]
rss = iarandom.RNG(seed).duplicate(4)
alpha_expected = iap.Deterministic(0.2).draw_samples((nb_images,),
rss[0])
hthresh_expected = [None, None]
hthresh_expected[0] = iap.Choice(
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).draw_samples((nb_images,),
rss[1])
# TODO simplify this to rss[2].randint(5, 100+1)
# would currenlty be a bit more ugly, because DiscrUniform
# samples two values for a and b first from rss[2]
hthresh_expected[1] = iap.DiscreteUniform(5, 100).draw_samples(
(nb_images,), rss[2])
hthresh_expected = np.stack(hthresh_expected, axis=-1)
sobel_expected = iap.Choice([3, 5, 7]).draw_samples((nb_images,),
rss[3])
invalid = hthresh_expected[:, 0] > hthresh_expected[:, 1]
hthresh_expected[invalid, :] = hthresh_expected[invalid, :][:, [1, 0]]
assert hthresh_expected.shape == (nb_images, 2)
assert not np.any(hthresh_expected[:, 0] > hthresh_expected[:, 1])
assert np.allclose(alpha_samples, alpha_expected)
assert np.allclose(hthresh_samples, hthresh_expected)
assert np.allclose(sobel_samples, sobel_expected)
def test_get_parameters(self):
alpha = iap.Deterministic(0.2)
hysteresis_thresholds = iap.Deterministic(10)
sobel_kernel_size = iap.Deterministic(3)
colorizer = iaa.RandomColorsBinaryImageColorizer(color_true=10,
color_false=20)
aug = iaa.Canny(alpha=alpha,
hysteresis_thresholds=hysteresis_thresholds,
sobel_kernel_size=sobel_kernel_size,
colorizer=colorizer)
params = aug.get_parameters()
assert params[0] is alpha
assert params[1] is hysteresis_thresholds
assert params[2] is sobel_kernel_size
assert params[3] is colorizer
def test___str___single_value_hysteresis(self):
alpha = iap.Deterministic(0.2)
hysteresis_thresholds = iap.Deterministic(10)
sobel_kernel_size = iap.Deterministic(3)
colorizer = iaa.RandomColorsBinaryImageColorizer(color_true=10,
color_false=20)
aug = iaa.Canny(alpha=alpha,
hysteresis_thresholds=hysteresis_thresholds,
sobel_kernel_size=sobel_kernel_size,
colorizer=colorizer)
observed = aug.__str__()
expected = ("Canny(alpha=%s, hysteresis_thresholds=%s, "
"sobel_kernel_size=%s, colorizer=%s, name=UnnamedCanny, "
"deterministic=False)") % (alpha, hysteresis_thresholds,
sobel_kernel_size, colorizer)
assert observed == expected
def test___init___single_value_hysteresis(self):
aug = iaa.Canny(alpha=0.2,
hysteresis_thresholds=[0, 1, 2],
sobel_kernel_size=[3, 5],
colorizer=iaa.RandomColorsBinaryImageColorizer(
color_true=10, color_false=20))
assert isinstance(aug.alpha, iap.Deterministic)
assert isinstance(aug.hysteresis_thresholds, iap.Choice)
assert isinstance(aug.sobel_kernel_size, iap.Choice)
assert isinstance(aug.colorizer, iaa.RandomColorsBinaryImageColorizer)
assert np.isclose(aug.alpha.value, 0.2)
assert aug.hysteresis_thresholds.a == [0, 1, 2]
assert isinstance(aug.sobel_kernel_size, iap.Choice)
assert aug.sobel_kernel_size.a == [3, 5]
assert isinstance(aug.colorizer.color_true, iap.Deterministic)
assert isinstance(aug.colorizer.color_false, iap.Deterministic)
assert aug.colorizer.color_true.value == 10
assert aug.colorizer.color_false.value == 20
def test__draw_samples__tuple_as_hysteresis(self):
seed = 1
nb_images = 10
aug = iaa.Canny(
alpha=0.2,
hysteresis_thresholds=([0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10], iap.DiscreteUniform(5, 100)),
sobel_kernel_size=[3, 5, 7],
random_state=np.random.RandomState(seed))
example_image = np.zeros((5, 5, 3), dtype=np.uint8)
samples = aug._draw_samples([example_image] * nb_images,
random_state=np.random.RandomState(seed))
alpha_samples = samples[0]
hthresh_samples = samples[1]
sobel_samples = samples[2]
rss = ia.derive_random_states(np.random.RandomState(seed), 4)
alpha_expected = [0.2] * nb_images
hthresh_expected = (
rss[1].choice([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
size=(nb_images, )),
# TODO simplify this to rss[2].randint(5, 100+1)
# would currenlty be a bit more ugly, because DiscrUniform
# samples two values for a and b first from rss[2]
iap.DiscreteUniform(5, 100).draw_samples((nb_images, ), rss[2]))
hthresh_expected = np.stack(hthresh_expected, axis=-1)
sobel_expected = rss[3].choice([3, 5, 7], size=(nb_images, ))
invalid = hthresh_expected[:, 0] > hthresh_expected[:, 1]
hthresh_expected[invalid, :] = hthresh_expected[invalid, :][:, [1, 0]]
assert hthresh_expected.shape == (nb_images, 2)
assert not np.any(hthresh_expected[:, 0] > hthresh_expected[:, 1])
assert np.allclose(alpha_samples, alpha_expected)
assert np.allclose(hthresh_samples, hthresh_expected)
assert np.allclose(sobel_samples, sobel_expected)
def test_pickleable(self):
aug = iaa.Canny(random_state=1)
runtest_pickleable_uint8_img(aug, iterations=20)
elif augmentation == 'random_shadow':
transform = RandomShadow(always_apply=True)
transformed_image = transform(image=image)['image']
elif augmentation == 'random_sun_flare':
transform = RandomSunFlare(always_apply=True)
transformed_image = transform(image=image)['image']
elif augmentation == 'spatter':
transform = iaa.imgcorruptlike.Spatter(severity=2)
transformed_image = transform(image=image)
## Edges
elif augmentation == 'canny':
transform = iaa.Canny(alpha=(0.0, 0.9))
transformed_image = transform(image=image)
## Pooling
elif augmentation == 'average_pooling':
transform = iaa.AveragePooling(5)
transformed_image = transform(image=image)
elif augmentation == 'max_pooling':
transform = iaa.MaxPooling(5)
transformed_image = transform(image=image)
elif augmentation == 'min_pooling':
transform = iaa.MinPooling(5)
transformed_image = transform(image=image)
from PIL import Image
img=plt.imread('bird.jpg')
seq = iaa.Sequential([
iaa.Fliplr(p=0),# basically this is original one
iaa.Crop(px=(22, 45),keep_size=False), # crop images from each side by 0 to 16px (randomly chosen)
iaa.Fliplr(1), # horizontally flip 50% of the images
iaa.GaussianBlur(sigma=(5, 7.0)), # blur images with a sigma of 0 to 3.0
iaa.ImpulseNoise(p=(0.6,1)),
iaa.EdgeDetect(alpha=(0.9,1)),
#iaa.AddToBrightness(add=(100,124)),
iaa.Canny(alpha=(0.8,0.9)),
iaa.Grayscale(alpha=1.00),
iaa.ChannelShuffle(p=1),
iaa.geometric.Affine( scale=2,rotate=22, backend='cv2'),
iaa.Cartoon(blur_ksize=(11,13)),
iaa.CenterCropToAspectRatio(1),
iaa.CenterCropToFixedSize(100,100),
iaa.ChangeColorTemperature(kelvin=(2222,3333)),
#iaa.segmentation(),
iaa.CLAHE(clip_limit=(4,8)),
iaa.Rotate(rotate=(-30,90))
])
plt.figure(figsize=(12,12))
for idx,Augmentor in enumerate(seq):
def test_pickleable(self):
aug = iaa.Canny(seed=1)
runtest_pickleable_uint8_img(aug, iterations=20)
import tensorflow as tf
from matplotlib import pyplot as plt
path= 'Image A*/train/*.xml'
import cv2
seq = iaa.Sequential([
iaa.Fliplr(p=0),# basically this is original one
iaa.Sometimes(0.05,(iaa.Crop(px=(22, 45),keep_size=True))), # crop images from each side by 0 to 16px (randomly chosen)
iaa.Sometimes(0.5,(iaa.Fliplr(1))), # horizontally flip 50% of the images
iaa.Sometimes(0.02,iaa.GaussianBlur(sigma=(5, 7.0))), # blur images with a sigma of 0 to 3.0
iaa.Sometimes(0.02 ,iaa.ImpulseNoise(p=(0.6,1))),
iaa.Sometimes(0.02 ,iaa.EdgeDetect(alpha=(0.09,1))),
#iaa.AddToBrightness(add=(100,124)),
iaa.Sometimes(0.02 ,iaa.Canny(alpha=(0.8,0.9))),
iaa.Sometimes(0.5 ,iaa.Grayscale(alpha=1.00)),
iaa.Sometimes(0.5 ,iaa.ChannelShuffle(p=1)),
#iaa.Sometimes(0.02 ,(iaa.geometric.Affine( scale=2,rotate=22,order=1))),
iaa.Sometimes(0.5 ,iaa.Cartoon(blur_ksize=(11,13))),
iaa.Sometimes(0.02 ,iaa.CenterCropToAspectRatio(1)),
iaa.Sometimes(0.02 ,iaa.CenterCropToFixedSize(100,100)),
iaa.Sometimes(0.12 ,iaa.ChangeColorTemperature(kelvin=(2222,3333))),
#iaa.segmentation(),
iaa.Sometimes(0.12 ,iaa.CLAHE(clip_limit=(4,8))),
iaa.Sometimes(0.8 ,iaa.Rotate(rotate=(-90,90),order=1))
])
plt.figure(figsize=(12,12))
aug50 = iaa.UniformColorQuantizationToNBits()
aug51 = iaa.GammaContrast((0.5, 2.0), per_channel=True)
aug52 = iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6), per_channel=True)
aug53 = iaa.LogContrast(gain=(0.6, 1.4), per_channel=True)
aug54 = iaa.LinearContrast((0.4, 1.6), per_channel=True)
# aug55 = iaa.AllChannelsCLAHE(clip_limit=(1, 10), per_channel=True)
aug56 = iaa.Alpha((0.0, 1.0), iaa.AllChannelsHistogramEqualization())
aug57 = iaa.HistogramEqualization(
from_colorspace=iaa.HistogramEqualization.BGR,
to_colorspace=iaa.HistogramEqualization.HSV)
aug58 = iaa.DirectedEdgeDetect(alpha=(0.0, 0.5), direction=(0.0, 0.5))
aug59 = iaa.Canny(
alpha=(0.0, 0.3),
colorizer=iaa.RandomColorsBinaryImageColorizer(
color_true=255,
color_false=0
)
)
def aug_imgaug(aug, image):
image2 = image.copy()
image2 = np.expand_dims(image2, axis=0)
images_aug = aug(images = image2)
return images_aug
class FaceEmbeddings():
"""Class to load model and run inference."""
def create_augmenters(height, width, height_augmentable, width_augmentable,
only_augmenters):
def lambda_func_images(images, random_state, parents, hooks):
return images
def lambda_func_heatmaps(heatmaps, random_state, parents, hooks):
return heatmaps
def lambda_func_keypoints(keypoints, random_state, parents, hooks):
return keypoints
def assertlambda_func_images(images, random_state, parents, hooks):
return True
def assertlambda_func_heatmaps(heatmaps, random_state, parents, hooks):
return True
def assertlambda_func_keypoints(keypoints, random_state, parents, hooks):
return True
augmenters_meta = [
iaa.Sequential([iaa.Noop(), iaa.Noop()],
random_order=False,
name="Sequential_2xNoop"),
iaa.Sequential([iaa.Noop(), iaa.Noop()],
random_order=True,
name="Sequential_2xNoop_random_order"),
iaa.SomeOf((1, 3),
[iaa.Noop(), iaa.Noop(), iaa.Noop()],
random_order=False,
name="SomeOf_3xNoop"),
iaa.SomeOf((1, 3),
[iaa.Noop(), iaa.Noop(), iaa.Noop()],
random_order=True,
name="SomeOf_3xNoop_random_order"),
iaa.OneOf([iaa.Noop(), iaa.Noop(), iaa.Noop()], name="OneOf_3xNoop"),
iaa.Sometimes(0.5, iaa.Noop(), name="Sometimes_Noop"),
iaa.WithChannels([1, 2], iaa.Noop(), name="WithChannels_1_and_2_Noop"),
iaa.Noop(name="Noop"),
iaa.Lambda(func_images=lambda_func_images,
func_heatmaps=lambda_func_heatmaps,
func_keypoints=lambda_func_keypoints,
name="Lambda"),
iaa.AssertLambda(func_images=assertlambda_func_images,
func_heatmaps=assertlambda_func_heatmaps,
func_keypoints=assertlambda_func_keypoints,
name="AssertLambda"),
iaa.AssertShape((None, height_augmentable, width_augmentable, None),
name="AssertShape"),
iaa.ChannelShuffle(0.5, name="ChannelShuffle")
]
augmenters_arithmetic = [
iaa.Add((-10, 10), name="Add"),
iaa.AddElementwise((-10, 10), name="AddElementwise"),
#iaa.AddElementwise((-500, 500), name="AddElementwise"),
iaa.AdditiveGaussianNoise(scale=(5, 10), name="AdditiveGaussianNoise"),
iaa.AdditiveLaplaceNoise(scale=(5, 10), name="AdditiveLaplaceNoise"),
iaa.AdditivePoissonNoise(lam=(1, 5), name="AdditivePoissonNoise"),
iaa.Multiply((0.5, 1.5), name="Multiply"),
iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"),
iaa.Dropout((0.01, 0.05), name="Dropout"),
iaa.CoarseDropout((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseDropout"),
iaa.ReplaceElementwise((0.01, 0.05), (0, 255),
name="ReplaceElementwise"),
#iaa.ReplaceElementwise((0.95, 0.99), (0, 255), name="ReplaceElementwise"),
iaa.SaltAndPepper((0.01, 0.05), name="SaltAndPepper"),
iaa.ImpulseNoise((0.01, 0.05), name="ImpulseNoise"),
iaa.CoarseSaltAndPepper((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseSaltAndPepper"),
iaa.Salt((0.01, 0.05), name="Salt"),
iaa.CoarseSalt((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseSalt"),
iaa.Pepper((0.01, 0.05), name="Pepper"),
iaa.CoarsePepper((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarsePepper"),
iaa.Invert(0.1, name="Invert"),
# ContrastNormalization
iaa.JpegCompression((50, 99), name="JpegCompression")
]
augmenters_blend = [
iaa.Alpha((0.01, 0.99), iaa.Noop(), name="Alpha"),
iaa.AlphaElementwise((0.01, 0.99), iaa.Noop(),
name="AlphaElementwise"),
iaa.SimplexNoiseAlpha(iaa.Noop(), name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha((-2.0, 2.0),
iaa.Noop(),
name="FrequencyNoiseAlpha")
]
augmenters_blur = [
iaa.GaussianBlur(sigma=(1.0, 5.0), name="GaussianBlur"),
iaa.AverageBlur(k=(3, 11), name="AverageBlur"),
iaa.MedianBlur(k=(3, 11), name="MedianBlur"),
iaa.BilateralBlur(d=(3, 11), name="BilateralBlur"),
iaa.MotionBlur(k=(3, 11), name="MotionBlur")
]
augmenters_color = [
# InColorspace (deprecated)
iaa.WithColorspace(to_colorspace="HSV",
children=iaa.Noop(),
name="WithColorspace"),
iaa.WithHueAndSaturation(children=iaa.Noop(),
name="WithHueAndSaturation"),
iaa.MultiplyHueAndSaturation((0.8, 1.2),
name="MultiplyHueAndSaturation"),
iaa.MultiplyHue((-1.0, 1.0), name="MultiplyHue"),
iaa.MultiplySaturation((0.8, 1.2), name="MultiplySaturation"),
iaa.AddToHueAndSaturation((-10, 10), name="AddToHueAndSaturation"),
iaa.AddToHue((-10, 10), name="AddToHue"),
iaa.AddToSaturation((-10, 10), name="AddToSaturation"),
iaa.ChangeColorspace(to_colorspace="HSV", name="ChangeColorspace"),
iaa.Grayscale((0.01, 0.99), name="Grayscale"),
iaa.KMeansColorQuantization((2, 16), name="KMeansColorQuantization"),
iaa.UniformColorQuantization((2, 16), name="UniformColorQuantization")
]
augmenters_contrast = [
iaa.GammaContrast(gamma=(0.5, 2.0), name="GammaContrast"),
iaa.SigmoidContrast(gain=(5, 20),
cutoff=(0.25, 0.75),
name="SigmoidContrast"),
iaa.LogContrast(gain=(0.7, 1.0), name="LogContrast"),
iaa.LinearContrast((0.5, 1.5), name="LinearContrast"),
iaa.AllChannelsCLAHE(clip_limit=(2, 10),
tile_grid_size_px=(3, 11),
name="AllChannelsCLAHE"),
iaa.CLAHE(clip_limit=(2, 10),
tile_grid_size_px=(3, 11),
to_colorspace="HSV",
name="CLAHE"),
iaa.AllChannelsHistogramEqualization(
name="AllChannelsHistogramEqualization"),
iaa.HistogramEqualization(to_colorspace="HSV",
name="HistogramEqualization"),
]
augmenters_convolutional = [
iaa.Convolve(np.float32([[0, 0, 0], [0, 1, 0], [0, 0, 0]]),
name="Convolve_3x3"),
iaa.Sharpen(alpha=(0.01, 0.99), lightness=(0.5, 2), name="Sharpen"),
iaa.Emboss(alpha=(0.01, 0.99), strength=(0, 2), name="Emboss"),
iaa.EdgeDetect(alpha=(0.01, 0.99), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.01, 0.99), name="DirectedEdgeDetect")
]
augmenters_edges = [iaa.Canny(alpha=(0.01, 0.99), name="Canny")]
augmenters_flip = [
iaa.Fliplr(1.0, name="Fliplr"),
iaa.Flipud(1.0, name="Flipud")
]
augmenters_geometric = [
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=0,
mode="constant",
cval=(0, 255),
name="Affine_order_0_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="constant",
cval=(0, 255),
name="Affine_order_1_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=3,
mode="constant",
cval=(0, 255),
name="Affine_order_3_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="edge",
cval=(0, 255),
name="Affine_order_1_edge"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="constant",
cval=(0, 255),
backend="skimage",
name="Affine_order_1_constant_skimage"),
# TODO AffineCv2
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=1,
mode="constant",
name="PiecewiseAffine_4x4_order_1_constant"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=0,
mode="constant",
name="PiecewiseAffine_4x4_order_0_constant"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=1,
mode="edge",
name="PiecewiseAffine_4x4_order_1_edge"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=8,
nb_cols=8,
order=1,
mode="constant",
name="PiecewiseAffine_8x8_order_1_constant"),
iaa.PerspectiveTransform(scale=(0.01, 0.05),
keep_size=False,
name="PerspectiveTransform"),
iaa.PerspectiveTransform(scale=(0.01, 0.05),
keep_size=True,
name="PerspectiveTransform_keep_size"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=0,
mode="constant",
cval=0,
name="ElasticTransformation_order_0_constant"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="constant",
cval=0,
name="ElasticTransformation_order_1_constant"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="nearest",
cval=0,
name="ElasticTransformation_order_1_nearest"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="reflect",
cval=0,
name="ElasticTransformation_order_1_reflect"),
iaa.Rot90((1, 3), keep_size=False, name="Rot90"),
iaa.Rot90((1, 3), keep_size=True, name="Rot90_keep_size")
]
augmenters_pooling = [
iaa.AveragePooling(kernel_size=(1, 16),
keep_size=False,
name="AveragePooling"),
iaa.AveragePooling(kernel_size=(1, 16),
keep_size=True,
name="AveragePooling_keep_size"),
iaa.MaxPooling(kernel_size=(1, 16), keep_size=False,
name="MaxPooling"),
iaa.MaxPooling(kernel_size=(1, 16),
keep_size=True,
name="MaxPooling_keep_size"),
iaa.MinPooling(kernel_size=(1, 16), keep_size=False,
name="MinPooling"),
iaa.MinPooling(kernel_size=(1, 16),
keep_size=True,
name="MinPooling_keep_size"),
iaa.MedianPooling(kernel_size=(1, 16),
keep_size=False,
name="MedianPooling"),
iaa.MedianPooling(kernel_size=(1, 16),
keep_size=True,
name="MedianPooling_keep_size")
]
augmenters_segmentation = [
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=64,
interpolation="cubic",
name="Superpixels_max_size_64_cubic"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=64,
interpolation="linear",
name="Superpixels_max_size_64_linear"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=128,
interpolation="linear",
name="Superpixels_max_size_128_linear"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=224,
interpolation="linear",
name="Superpixels_max_size_224_linear"),
iaa.UniformVoronoi(n_points=(250, 1000), name="UniformVoronoi"),
iaa.RegularGridVoronoi(n_rows=(16, 31),
n_cols=(16, 31),
name="RegularGridVoronoi"),
iaa.RelativeRegularGridVoronoi(n_rows_frac=(0.07, 0.14),
n_cols_frac=(0.07, 0.14),
name="RelativeRegularGridVoronoi"),
]
augmenters_size = [
iaa.Resize((0.8, 1.2), interpolation="nearest", name="Resize_nearest"),
iaa.Resize((0.8, 1.2), interpolation="linear", name="Resize_linear"),
iaa.Resize((0.8, 1.2), interpolation="cubic", name="Resize_cubic"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
keep_size=False,
name="CropAndPad"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="edge",
pad_cval=(0, 255),
keep_size=False,
name="CropAndPad_edge"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
name="CropAndPad_keep_size"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
keep_size=False,
name="Pad"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="edge",
pad_cval=(0, 255),
keep_size=False,
name="Pad_edge"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
name="Pad_keep_size"),
iaa.Crop(percent=(0.05, 0.2), keep_size=False, name="Crop"),
iaa.Crop(percent=(0.05, 0.2), name="Crop_keep_size"),
iaa.PadToFixedSize(width=width + 10,
height=height + 10,
pad_mode="constant",
pad_cval=(0, 255),
name="PadToFixedSize"),
iaa.CropToFixedSize(width=width - 10,
height=height - 10,
name="CropToFixedSize"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="nearest",
name="KeepSizeByResize_CropToFixedSize_nearest"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="linear",
name="KeepSizeByResize_CropToFixedSize_linear"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="cubic",
name="KeepSizeByResize_CropToFixedSize_cubic"),
]
augmenters_weather = [
iaa.FastSnowyLandscape(lightness_threshold=(100, 255),
lightness_multiplier=(1.0, 4.0),
name="FastSnowyLandscape"),
iaa.Clouds(name="Clouds"),
iaa.Fog(name="Fog"),
iaa.CloudLayer(intensity_mean=(196, 255),
intensity_freq_exponent=(-2.5, -2.0),
intensity_coarse_scale=10,
alpha_min=0,
alpha_multiplier=(0.25, 0.75),
alpha_size_px_max=(2, 8),
alpha_freq_exponent=(-2.5, -2.0),
sparsity=(0.8, 1.0),
density_multiplier=(0.5, 1.0),
name="CloudLayer"),
iaa.Snowflakes(name="Snowflakes"),
iaa.SnowflakesLayer(density=(0.005, 0.075),
density_uniformity=(0.3, 0.9),
flake_size=(0.2, 0.7),
flake_size_uniformity=(0.4, 0.8),
angle=(-30, 30),
speed=(0.007, 0.03),
blur_sigma_fraction=(0.0001, 0.001),
name="SnowflakesLayer")
]
augmenters = (augmenters_meta + augmenters_arithmetic + augmenters_blend +
augmenters_blur + augmenters_color + augmenters_contrast +
augmenters_convolutional + augmenters_edges +
augmenters_flip + augmenters_geometric + augmenters_pooling +
augmenters_segmentation + augmenters_size +
augmenters_weather)
if only_augmenters is not None:
augmenters_reduced = []
for augmenter in augmenters:
if any([
re.search(pattern, augmenter.name)
for pattern in only_augmenters
]):
augmenters_reduced.append(augmenter)
augmenters = augmenters_reduced
return augmenters
def augmentation_of_image(self, test_image, output_path):
self.test_image = test_image
self.output_path = output_path
#define the Augmenters
#properties: A range of values signifies that one of these numbers is randmoly chosen for every augmentation for every batch
# Apply affine transformations to each image.
rotate = iaa.Affine(rotate=(-90, 90))
scale = iaa.Affine(scale={
"x": (0.5, 0.9),
"y": (0.5, 0.9)
})
translation = iaa.Affine(translate_percent={
"x": (-0.15, 0.15),
"y": (-0.15, 0.15)
})
shear = iaa.Affine(shear=(-2, 2))
#plagio parallhlogrammo wihthin a range (-8,8)
zoom = iaa.PerspectiveTransform(
scale=(0.01, 0.15),
keep_size=True) # do not change the output size of the image
h_flip = iaa.Fliplr(1.0)
# flip horizontally all images (100%)
v_flip = iaa.Flipud(1.0)
#flip vertically all images
padding = iaa.KeepSizeByResize(
iaa.CropAndPad(percent=(0.05, 0.25))
) #positive values correspond to padding 5%-25% of the image,but keeping the origial output size of the new image
#More augmentations
blur = iaa.GaussianBlur(
sigma=(0, 1.22)
) # blur images with a sigma 0-2,a number ofthis range is randomly chosen everytime.Low values suggested for this application
contrast = iaa.contrast.LinearContrast((0.75, 1.5))
#change the contrast by a factor of 0.75 and 1.5 sampled randomly per image
contrast_channels = iaa.LinearContrast(
(0.75, 1.5), per_channel=True
) #and for 50% of all images also independently per channel:
sharpen = iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5))
#sharpen with an alpha from 0(no sharpening) - 1(full sharpening) and change the lightness form 0.75 to 1.5
gauss_noise = iaa.AdditiveGaussianNoise(
scale=0.111 * 255, per_channel=True
) #some random gaussian noise might occur in cell images,especially when image quality is poor
laplace_noise = iaa.AdditiveLaplaceNoise(
scale=(0, 0.111 * 255)
) #we choose to be in a small range, as it is logical for training the cell images
#Brightness
brightness = iaa.Multiply(
(0.35, 1.65
)) #change brightness between 35% or 165% of the original image
brightness_channels = iaa.Multiply(
(0.5, 1.5), per_channel=0.75
) # change birghtness for 25% of images.For the remaining 75%, change it, but also channel-wise.
#CHANNELS (RGB)=(Red,Green,Blue)
red = iaa.WithChannels(0, iaa.Add(
(10,
100))) #increase each Red-pixels value within the range 10-100
red_rot = iaa.WithChannels(0, iaa.Affine(
rotate=(0, 45))) #rotate each image's red channel by 0-45 degrees
green = iaa.WithChannels(1, iaa.Add(
(10,
100))) #increase each Green-pixels value within the range 10-100
green_rot = iaa.WithChannels(1, iaa.Affine(
rotate=(0,
45))) #rotate each image's green channel by 0-45 degrees
blue = iaa.WithChannels(2, iaa.Add(
(10,
100))) #increase each Blue-pixels value within the range 10-100
blue_rot = iaa.WithChannels(2, iaa.Affine(
rotate=(0, 45))) #rotate each image's blue channel by 0-45 degrees
#colors
channel_shuffle = iaa.ChannelShuffle(1.0)
#shuffle all images of the batch
grayscale = iaa.Grayscale(1.0)
hue_n_saturation = iaa.MultiplyHueAndSaturation(
(0.5, 1.5), per_channel=True
) #change hue and saturation with this range of values for different values
add_hue_saturation = iaa.AddToHueAndSaturation(
(-50, 50),
per_channel=True) #add more hue and saturation to its pixels
#Quantize colors using k-Means clustering
kmeans_color = iaa.KMeansColorQuantization(
n_colors=(4, 16)
) #quantizes to k means 4 to 16 colors (randomly chosen). Quantizes colors up to 16 colors
#Alpha Blending
blend = iaa.AlphaElementwise((0, 1.0), iaa.Grayscale((0, 1.0)))
#blend depending on which value is greater
#Contrast augmentors
clahe = iaa.CLAHE(tile_grid_size_px=((3, 21), [
0, 2, 3, 4, 5, 6, 7
])) #create a clahe contrast augmentor H=(3,21) and W=(0,7)
histogram = iaa.HistogramEqualization(
) #performs histogram equalization
#Augmentation list of metadata augmentors
OneofRed = iaa.OneOf([red])
OneofGreen = iaa.OneOf([green])
OneofBlue = iaa.OneOf([blue])
contrast_n_shit = iaa.OneOf(
[contrast, brightness, brightness_channels])
SomeAug = iaa.SomeOf(
2, [rotate, scale, translation, shear, h_flip, v_flip],
random_order=True)
SomeClahe = iaa.SomeOf(
2, [
clahe,
iaa.CLAHE(clip_limit=(1, 10)),
iaa.CLAHE(tile_grid_size_px=(3, 21)),
iaa.GammaContrast((0.5, 2.0)),
iaa.AllChannelsCLAHE(),
iaa.AllChannelsCLAHE(clip_limit=(1, 10), per_channel=True)
],
random_order=True) #Random selection from clahe augmentors
edgedetection = iaa.OneOf([
iaa.EdgeDetect(alpha=(0, 0.7)),
iaa.DirectedEdgeDetect(alpha=(0, 0.7), direction=(0.0, 1.0))
])
# Search in some images either for all edges or for directed edges.These edges are then marked in a black and white image and overlayed with the original image using an alpha of 0 to 0.7.
canny_filter = iaa.OneOf([
iaa.Canny(),
iaa.Canny(alpha=(0.5, 1.0), sobel_kernel_size=[3, 7])
])
#choose one of the 2 canny filter options
OneofNoise = iaa.OneOf([blur, gauss_noise, laplace_noise])
Color_1 = iaa.OneOf([
channel_shuffle, grayscale, hue_n_saturation, add_hue_saturation,
kmeans_color
])
Color_2 = iaa.OneOf([
channel_shuffle, grayscale, hue_n_saturation, add_hue_saturation,
kmeans_color
])
Flip = iaa.OneOf([histogram, v_flip, h_flip])
#Define the augmentors used in the DA
Augmentors = [
SomeAug, SomeClahe, SomeClahe, edgedetection, sharpen,
canny_filter, OneofRed, OneofGreen, OneofBlue, OneofNoise, Color_1,
Color_2, Flip, contrast_n_shit
]
for i in range(0, 14):
img = cv2.imread(test_image) #read you image
images = np.array(
[img for _ in range(14)], dtype=np.uint8
) # 12 is the size of the array that will hold 8 different images
images_aug = Augmentors[i].augment_images(
images
) #alternate between the different augmentors for a test image
cv2.imwrite(
os.path.join(output_path,
test_image + "new" + str(i) + '.jpg'),
images_aug[i]) #write all changed images
