def _test_augment_polygons__kernel_size_differs(self, shape, shape_exp):
from imgaug.augmentables.polys import Polygon, PolygonsOnImage
polys = [Polygon([(1.5, 5.5), (5.5, 1.5), (5.5, 5.5)])]
psoi = PolygonsOnImage(polys, shape=shape)
aug = self.augmenter(
(iap.Deterministic(3), iap.Deterministic(2)),
keep_size=False)
psoi_aug = aug.augment_polygons(psoi)
expected = PolygonsOnImage(
[Polygon([
((1.5/shape[1])*shape_exp[1], (5.5/shape[0])*shape_exp[0]),
((5.5/shape[1])*shape_exp[1], (1.5/shape[0])*shape_exp[0]),
((5.5/shape[1])*shape_exp[1], (5.5/shape[0])*shape_exp[0])
])],
shape=shape_exp)
assert_cbaois_equal(psoi_aug, expected)
def _test_augment_keypoints__kernel_size_differs(self, shape,
shape_exp):
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
kps = [Keypoint(x=1.5, y=5.5), Keypoint(x=5.5, y=1.5)]
kpsoi = KeypointsOnImage(kps, shape=shape)
aug = self.augmenter(
(iap.Deterministic(3), iap.Deterministic(2)),
keep_size=False)
kpsoi_aug = aug.augment_keypoints(kpsoi)
expected = KeypointsOnImage.from_xy_array(
np.float32([
[(1.5/shape[1])*shape_exp[1], (5.5/shape[0])*shape_exp[0]],
[(5.5/shape[1])*shape_exp[1], (1.5/shape[0])*shape_exp[0]]
]),
shape=shape_exp)
assert_cbaois_equal(kpsoi_aug, expected)
def test_augment_images__kernel_size_differs(self):
aug = iaa.AveragePooling((iap.Deterministic(3), iap.Deterministic(2)),
keep_size=False)
image = np.uint8([
[50 - 2, 50 - 1, 120 - 4, 120 + 4],
[50 + 1, 50 + 2, 120 + 2, 120 - 1],
[50 - 5, 50 + 5, 120 - 2, 120 + 1],
])
image = np.tile(image[:, :, np.newaxis], (1, 1, 3))
expected = np.uint8([[50, 120]])
expected = np.tile(expected[:, :, np.newaxis], (1, 1, 3))
image_aug = aug.augment_image(image)
diff = np.abs(image_aug.astype(np.int32) - expected)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == (1, 2, 3)
assert np.all(diff <= 1)
def _test_augment_bounding_boxes__kernel_size_differs(
self, shape, shape_exp):
from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage
bbs = [BoundingBox(x1=1.5, y1=2.5, x2=5.5, y2=6.5)]
bbsoi = BoundingBoxesOnImage(bbs, shape=shape)
aug = self.augmenter((iap.Deterministic(3), iap.Deterministic(2)),
keep_size=False)
bbsoi_aug = aug.augment_bounding_boxes(bbsoi)
expected = BoundingBoxesOnImage([
BoundingBox(
x1=(1.5 / shape[1]) * shape_exp[1],
y1=(2.5 / shape[0]) * shape_exp[0],
x2=(5.5 / shape[1]) * shape_exp[1],
y2=(6.5 / shape[0]) * shape_exp[0],
)
],
shape=shape_exp)
assert_cbaois_equal(bbsoi_aug, expected)
def _test_augment_line_strings__kernel_size_differs(
self, shape, shape_exp):
from imgaug.augmentables.lines import LineString, LineStringsOnImage
ls = [LineString([(1.5, 5.5), (5.5, 1.5), (5.5, 5.5)])]
lsoi = LineStringsOnImage(ls, shape=shape)
aug = self.augmenter((iap.Deterministic(3), iap.Deterministic(2)),
keep_size=False)
lsoi_aug = aug.augment_line_strings(lsoi)
expected = LineStringsOnImage([
LineString([((1.5 / shape[1]) * shape_exp[1],
(5.5 / shape[0]) * shape_exp[0]),
((5.5 / shape[1]) * shape_exp[1],
(1.5 / shape[0]) * shape_exp[0]),
((5.5 / shape[1]) * shape_exp[1],
(5.5 / shape[0]) * shape_exp[0])])
],
shape=shape_exp)
assert_cbaois_equal(lsoi_aug, expected)
def test_augment_images__kernel_size_differs__requires_padding(self):
aug = iaa.AveragePooling((iap.Deterministic(3), iap.Deterministic(1)),
keep_size=False)
image = np.uint8([[50 - 2, 50 - 1, 120 - 4, 120 + 4],
[50 + 1, 50 + 2, 120 + 2, 120 - 1]])
image = np.tile(image[:, :, np.newaxis], (1, 1, 3))
expected = np.uint8([[(50 - 2 + 50 + 1 + 50 - 2) / 3,
(50 - 1 + 50 + 2 + 50 - 1) / 3,
(120 - 4 + 120 + 2 + 120 - 4) / 3,
(120 + 4 + 120 - 1 + 120 + 4) / 3]])
expected = np.tile(expected[:, :, np.newaxis], (1, 1, 3))
image_aug = aug.augment_image(image)
diff = np.abs(image_aug.astype(np.int32) - expected)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == (1, 4, 3)
assert np.all(diff <= 1)
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)") % (
str(aug.alpha),
str(aug.hysteresis_thresholds),
str(aug.sobel_kernel_size),
colorizer)
assert observed == expected
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_augmenter(cls, augmenter_name, func_expected,
dependent_on_seed):
# this test verifies:
# - called function seems to be the expected function
# - images produced by augmenter match images produced by function
# - a different seed (and sometimes severity) will lead to a
# different image
# - augmenter can be pickled
severity = 5
aug_cls = getattr(iaa.imgcorruptlike, augmenter_name)
image = np.mod(
np.arange(32*32*3), 256
).reshape((32, 32, 3)).astype(np.uint8)
with iap.no_prefetching():
rng = iarandom.RNG(1)
# Replay sampling of severities.
# Even for deterministic values this is required as currently
# there is an advance() at the end of each draw_samples().
_ = iap.Deterministic(1).draw_samples((1,), rng)
# As for the functions above, we can't just change the seed value
# to get different augmentations as many functions are dependend
# only on the severity. So we change only for some functions only
# the seed and for the others severity+seed.
image_aug1 = aug_cls(severity=severity, seed=1)(image=image)
image_aug2 = aug_cls(severity=severity, seed=1)(image=image)
if dependent_on_seed:
image_aug3 = aug_cls(severity=severity, seed=2)(
image=image)
else:
image_aug3 = aug_cls(severity=severity-1, seed=2)(
image=image)
image_aug_exp = func_expected(
image,
severity=severity,
seed=rng.generate_seed_())
assert aug_cls(severity=severity).func is func_expected
assert np.array_equal(image_aug1, image_aug_exp)
assert np.array_equal(image_aug2, image_aug_exp)
assert not np.array_equal(image_aug3, image_aug2)
# pickling test
aug = aug_cls(severity=(1, 5))
runtest_pickleable_uint8_img(aug, shape=(32, 32, 3))
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))
aug.alpha = remove_prefetching(aug.alpha)
aug.hysteresis_thresholds = remove_prefetching(
aug.hysteresis_thresholds)
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 = 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 test_Superpixels():
reseed()
def _array_equals_tolerant(a, b, tolerance):
diff = np.abs(a.astype(np.int32) - b.astype(np.int32))
return np.all(diff <= tolerance)
base_img = [[255, 255, 255, 0, 0, 0], [255, 235, 255, 0, 20, 0],
[250, 250, 250, 5, 5, 5]]
base_img = np.tile(
np.array(base_img, dtype=np.uint8)[..., np.newaxis], (1, 1, 3))
base_img_superpixels = [[251, 251, 251, 4, 4, 4], [251, 251, 251, 4, 4, 4],
[251, 251, 251, 4, 4, 4]]
base_img_superpixels = np.tile(
np.array(base_img_superpixels, dtype=np.uint8)[..., np.newaxis],
(1, 1, 3))
base_img_superpixels_left = np.copy(base_img_superpixels)
base_img_superpixels_left[:, 3:, :] = base_img[:, 3:, :]
base_img_superpixels_right = np.copy(base_img_superpixels)
base_img_superpixels_right[:, :3, :] = base_img[:, :3, :]
aug = iaa.Superpixels(p_replace=0, n_segments=2)
observed = aug.augment_image(base_img)
expected = base_img
assert np.allclose(observed, expected)
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
observed = aug.augment_image(base_img)
expected = base_img_superpixels
assert _array_equals_tolerant(observed, expected, 2)
aug = iaa.Superpixels(p_replace=1.0, n_segments=iap.Deterministic(2))
observed = aug.augment_image(base_img)
expected = base_img_superpixels
assert _array_equals_tolerant(observed, expected, 2)
aug = iaa.Superpixels(p_replace=iap.Binomial(iap.Choice([0.0, 1.0])),
n_segments=2)
observed = aug.augment_image(base_img)
assert np.allclose(observed, base_img) or _array_equals_tolerant(
observed, base_img_superpixels, 2)
aug = iaa.Superpixels(p_replace=0.5, n_segments=2)
seen = {"none": False, "left": False, "right": False, "both": False}
for _ in sm.xrange(100):
observed = aug.augment_image(base_img)
if _array_equals_tolerant(observed, base_img, 2):
seen["none"] = True
elif _array_equals_tolerant(observed, base_img_superpixels_left, 2):
seen["left"] = True
elif _array_equals_tolerant(observed, base_img_superpixels_right, 2):
seen["right"] = True
elif _array_equals_tolerant(observed, base_img_superpixels, 2):
seen["both"] = True
else:
raise Exception(
"Generated superpixels image does not match any expected image."
)
if all(seen.values()):
break
assert all(seen.values())
# test exceptions for wrong parameter types
got_exception = False
try:
_ = iaa.Superpixels(p_replace="test", n_segments=100)
except Exception:
got_exception = True
assert got_exception
got_exception = False
try:
_ = iaa.Superpixels(p_replace=1, n_segments="test")
except Exception:
got_exception = True
assert got_exception
# test get_parameters()
aug = iaa.Superpixels(p_replace=0.5,
n_segments=2,
max_size=100,
interpolation="nearest")
params = aug.get_parameters()
assert isinstance(params[0], iap.Binomial)
assert isinstance(params[0].p, iap.Deterministic)
assert isinstance(params[1], iap.Deterministic)
assert 0.5 - 1e-4 < params[0].p.value < 0.5 + 1e-4
assert params[1].value == 2
assert params[2] == 100
assert params[3] == "nearest"
def test_p_replace_1_n_segments_stochastic_parameter(self):
aug = iaa.Superpixels(p_replace=1.0, n_segments=iap.Deterministic(2))
observed = aug.augment_image(self.base_img)
expected = self.base_img_superpixels
assert self._array_equals_tolerant(observed, expected, 2)
def test_Superpixels():
reseed()
def _array_equals_tolerant(a, b, tolerance):
diff = np.abs(a.astype(np.int32) - b.astype(np.int32))
return np.all(diff <= tolerance)
base_img = [[255, 255, 255, 0, 0, 0], [255, 235, 255, 0, 20, 0],
[250, 250, 250, 5, 5, 5]]
base_img = np.tile(
np.array(base_img, dtype=np.uint8)[..., np.newaxis], (1, 1, 3))
base_img_superpixels = [[251, 251, 251, 4, 4, 4], [251, 251, 251, 4, 4, 4],
[251, 251, 251, 4, 4, 4]]
base_img_superpixels = np.tile(
np.array(base_img_superpixels, dtype=np.uint8)[..., np.newaxis],
(1, 1, 3))
base_img_superpixels_left = np.copy(base_img_superpixels)
base_img_superpixels_left[:, 3:, :] = base_img[:, 3:, :]
base_img_superpixels_right = np.copy(base_img_superpixels)
base_img_superpixels_right[:, :3, :] = base_img[:, :3, :]
aug = iaa.Superpixels(p_replace=0, n_segments=2)
observed = aug.augment_image(base_img)
expected = base_img
assert np.allclose(observed, expected)
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
observed = aug.augment_image(base_img)
expected = base_img_superpixels
assert _array_equals_tolerant(observed, expected, 2)
aug = iaa.Superpixels(p_replace=1.0, n_segments=iap.Deterministic(2))
observed = aug.augment_image(base_img)
expected = base_img_superpixels
assert _array_equals_tolerant(observed, expected, 2)
aug = iaa.Superpixels(p_replace=iap.Binomial(iap.Choice([0.0, 1.0])),
n_segments=2)
observed = aug.augment_image(base_img)
assert np.allclose(observed, base_img) or _array_equals_tolerant(
observed, base_img_superpixels, 2)
aug = iaa.Superpixels(p_replace=0.5, n_segments=2)
seen = {"none": False, "left": False, "right": False, "both": False}
for _ in sm.xrange(100):
observed = aug.augment_image(base_img)
if _array_equals_tolerant(observed, base_img, 2):
seen["none"] = True
elif _array_equals_tolerant(observed, base_img_superpixels_left, 2):
seen["left"] = True
elif _array_equals_tolerant(observed, base_img_superpixels_right, 2):
seen["right"] = True
elif _array_equals_tolerant(observed, base_img_superpixels, 2):
seen["both"] = True
else:
raise Exception(
"Generated superpixels image does not match any expected image."
)
if all(seen.values()):
break
assert all(seen.values())
# test exceptions for wrong parameter types
got_exception = False
try:
_ = iaa.Superpixels(p_replace="test", n_segments=100)
except Exception:
got_exception = True
assert got_exception
got_exception = False
try:
_ = iaa.Superpixels(p_replace=1, n_segments="test")
except Exception:
got_exception = True
assert got_exception
# test get_parameters()
aug = iaa.Superpixels(p_replace=0.5,
n_segments=2,
max_size=100,
interpolation="nearest")
params = aug.get_parameters()
assert isinstance(params[0], iap.Binomial)
assert isinstance(params[0].p, iap.Deterministic)
assert isinstance(params[1], iap.Deterministic)
assert 0.5 - 1e-4 < params[0].p.value < 0.5 + 1e-4
assert params[1].value == 2
assert params[2] == 100
assert params[3] == "nearest"
###################
# test other dtypes
###################
# bool
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
img = np.array([[False, False, True, True], [False, False, True, True]],
dtype=bool)
img_aug = aug.augment_image(img)
assert img_aug.dtype == img.dtype
assert np.all(img_aug == img)
aug = iaa.Superpixels(p_replace=1.0, n_segments=1)
img = np.array([[True, True, True, True], [False, True, True, True]],
dtype=bool)
img_aug = aug.augment_image(img)
assert img_aug.dtype == img.dtype
assert np.all(img_aug)
for dtype in [np.uint8, np.uint16, np.uint32, np.int8, np.int16, np.int32]:
min_value, center_value, max_value = iadt.get_value_range_of_dtype(
dtype)
if np.dtype(dtype).kind == "i":
values = [
int(center_value),
int(0.1 * max_value),
int(0.2 * max_value),
int(0.5 * max_value), max_value - 100
]
values = [((-1) * value, value) for value in values]
else:
values = [(0, int(center_value)), (10, int(0.1 * max_value)),
(10, int(0.2 * max_value)), (10, int(0.5 * max_value)),
(0, max_value), (int(center_value), max_value)]
for v1, v2 in values:
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
img = np.array([[v1, v1, v2, v2], [v1, v1, v2, v2]], dtype=dtype)
img_aug = aug.augment_image(img)
assert img_aug.dtype == np.dtype(dtype)
assert np.array_equal(img_aug, img)
aug = iaa.Superpixels(p_replace=1.0, n_segments=1)
img = np.array([[v2, v2, v2, v2], [v1, v2, v2, v2]], dtype=dtype)
img_aug = aug.augment_image(img)
assert img_aug.dtype == np.dtype(dtype)
assert np.all(img_aug == int(np.round((7 / 8) * v2 +
(1 / 8) * v1)))
for dtype in []:
def _allclose(a, b):
atol = 1e-4 if dtype == np.float16 else 1e-8
return np.allclose(a, b, atol=atol, rtol=0)
isize = np.dtype(dtype).itemsize
for value in [0, 1.0, 10.0, 1000**(isize - 1)]:
v1 = (-1) * value
v2 = value
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
img = np.array([[v1, v1, v2, v2], [v1, v1, v2, v2]], dtype=dtype)
img_aug = aug.augment_image(img)
assert img_aug.dtype == np.dtype(dtype)
assert _allclose(img_aug, img)
aug = iaa.Superpixels(p_replace=1.0, n_segments=1)
img = np.array([[v2, v2, v2, v2], [v1, v2, v2, v2]], dtype=dtype)
img_aug = aug.augment_image(img)
assert img_aug.dtype == np.dtype(dtype)
assert _allclose(img_aug, (7 / 8) * v2 + (1 / 8) * v1)
def test_SigmoidContrast():
reseed()
img = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
]
img = np.uint8(img)
img3d = np.tile(img[:, :, np.newaxis], (1, 1, 3))
# check basic functionality with per_chanenl on/off (makes no difference due to deterministic
# parameters)
for per_channel in [False, 0, 0.0, True, 1, 1.0]:
for gain, cutoff in itertools.product([5, 10], [0.25, 0.75]):
aug = iaa.SigmoidContrast(gain=iap.Deterministic(gain), cutoff=iap.Deterministic(cutoff), per_channel=per_channel)
img_aug = aug.augment_image(img)
img3d_aug = aug.augment_image(img3d)
assert img_aug.dtype.type == np.uint8
assert img3d_aug.dtype.type == np.uint8
assert np.array_equal(img_aug, skimage.exposure.adjust_sigmoid(img, gain=gain, cutoff=cutoff))
assert np.array_equal(img3d_aug, skimage.exposure.adjust_sigmoid(img3d, gain=gain, cutoff=cutoff))
# check that tuple to uniform works
# note that gain and cutoff are saved in inverted order in _ContrastFuncWrapper to match
# the order of skimage's function
aug = iaa.SigmoidContrast(gain=(1, 2), cutoff=(0.25, 0.75))
assert isinstance(aug.params1d[0], iap.Uniform)
assert isinstance(aug.params1d[0].a, iap.Deterministic)
assert isinstance(aug.params1d[0].b, iap.Deterministic)
assert np.allclose(aug.params1d[0].a.value, 0.25)
assert np.allclose(aug.params1d[0].b.value, 0.75)
assert isinstance(aug.params1d[1], iap.Uniform)
assert isinstance(aug.params1d[1].a, iap.Deterministic)
assert isinstance(aug.params1d[1].b, iap.Deterministic)
assert aug.params1d[1].a.value == 1
assert aug.params1d[1].b.value == 2
# check that list to choice works
# note that gain and cutoff are saved in inverted order in _ContrastFuncWrapper to match
# the order of skimage's function
aug = iaa.SigmoidContrast(gain=[1, 2], cutoff=[0.25, 0.75])
assert isinstance(aug.params1d[0], iap.Choice)
assert all([np.allclose(val, val_choice) for val, val_choice in zip([0.25, 0.75], aug.params1d[0].a)])
assert isinstance(aug.params1d[1], iap.Choice)
assert all([val in aug.params1d[1].a for val in [1, 2]])
# check that per_channel at 50% prob works
aug = iaa.SigmoidContrast(gain=(1, 10), cutoff=(0.25, 0.75), per_channel=0.5)
seen = [False, False]
img1000d = np.zeros((1, 1, 1000), dtype=np.uint8) + 128
for _ in sm.xrange(100):
img_aug = aug.augment_image(img1000d)
assert img_aug.dtype.type == np.uint8
l = len(set(img_aug.flatten().tolist()))
if l == 1:
seen[0] = True
else:
seen[1] = True
if all(seen):
break
assert all(seen)
# check that keypoints are not changed
kpsoi = ia.KeypointsOnImage([ia.Keypoint(1, 1)], shape=(3, 3, 3))
kpsoi_aug = iaa.SigmoidContrast(gain=10, cutoff=0.5).augment_keypoints([kpsoi])
assert keypoints_equal([kpsoi], kpsoi_aug)
# check that heatmaps are not changed
heatmaps = ia.HeatmapsOnImage(np.zeros((3, 3, 1), dtype=np.float32) + 0.5, shape=(3, 3, 3))
heatmaps_aug = iaa.SigmoidContrast(gain=10, cutoff=0.5).augment_heatmaps([heatmaps])[0]
assert np.allclose(heatmaps.arr_0to1, heatmaps_aug.arr_0to1)
def test_LinearContrast():
reseed()
img = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
img = np.uint8(img)
img3d = np.tile(img[:, :, np.newaxis], (1, 1, 3))
# check basic functionality with alpha=1 or 2 (deterministic) and per_chanenl on/off (makes
# no difference due to deterministic alpha)
for per_channel in [False, 0, 0.0, True, 1, 1.0]:
for alpha in [1, 2]:
aug = iaa.LinearContrast(alpha=iap.Deterministic(alpha),
per_channel=per_channel)
img_aug = aug.augment_image(img)
img3d_aug = aug.augment_image(img3d)
assert img_aug.dtype.type == np.uint8
assert img3d_aug.dtype.type == np.uint8
assert np.array_equal(
img_aug, contrast_lib._adjust_linear(img, alpha=alpha))
assert np.array_equal(
img3d_aug, contrast_lib._adjust_linear(img3d, alpha=alpha))
# check that tuple to uniform works
aug = iaa.LinearContrast((1, 2))
assert isinstance(aug.params1d[0], iap.Uniform)
assert isinstance(aug.params1d[0].a, iap.Deterministic)
assert isinstance(aug.params1d[0].b, iap.Deterministic)
assert aug.params1d[0].a.value == 1
assert aug.params1d[0].b.value == 2
# check that list to choice works
aug = iaa.LinearContrast([1, 2])
assert isinstance(aug.params1d[0], iap.Choice)
assert all([val in aug.params1d[0].a for val in [1, 2]])
# check that per_channel at 50% prob works
aug = iaa.LinearContrast((0.5, 2.0), per_channel=0.5)
seen = [False, False]
# must not use just value 128 here, otherwise nothing will change as all values would have
# distance 0 to 128
img1000d = np.zeros((1, 1, 1000), dtype=np.uint8) + 128 + 20
for _ in sm.xrange(100):
img_aug = aug.augment_image(img1000d)
assert img_aug.dtype.type == np.uint8
l = len(set(img_aug.flatten().tolist()))
if l == 1:
seen[0] = True
else:
seen[1] = True
if all(seen):
break
assert all(seen)
# check that keypoints are not changed
kpsoi = ia.KeypointsOnImage([ia.Keypoint(1, 1)], shape=(3, 3, 3))
kpsoi_aug = iaa.LinearContrast(alpha=2).augment_keypoints([kpsoi])
assert keypoints_equal([kpsoi], kpsoi_aug)
# check that heatmaps are not changed
heatmaps = ia.HeatmapsOnImage(np.zeros((3, 3, 1), dtype=np.float32) + 0.5,
shape=(3, 3, 3))
heatmaps_aug = iaa.LinearContrast(alpha=2).augment_heatmaps([heatmaps])[0]
assert np.allclose(heatmaps.arr_0to1, heatmaps_aug.arr_0to1)
def test_GammaContrast():
reseed()
img = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
img = np.uint8(img)
img3d = np.tile(img[:, :, np.newaxis], (1, 1, 3))
# check basic functionality with gamma=1 or 2 (deterministic) and per_chanenl on/off (makes
# no difference due to deterministic gamma)
for per_channel in [False, 0, 0.0, True, 1, 1.0]:
for gamma in [1, 2]:
aug = iaa.GammaContrast(gamma=iap.Deterministic(gamma),
per_channel=per_channel)
img_aug = aug.augment_image(img)
img3d_aug = aug.augment_image(img3d)
assert img_aug.dtype.type == np.uint8
assert img3d_aug.dtype.type == np.uint8
assert np.array_equal(
img_aug, skimage.exposure.adjust_gamma(img, gamma=gamma))
assert np.array_equal(
img3d_aug, skimage.exposure.adjust_gamma(img3d, gamma=gamma))
# check that tuple to uniform works
aug = iaa.GammaContrast((1, 2))
assert isinstance(aug.params1d[0], iap.Uniform)
assert isinstance(aug.params1d[0].a, iap.Deterministic)
assert isinstance(aug.params1d[0].b, iap.Deterministic)
assert aug.params1d[0].a.value == 1
assert aug.params1d[0].b.value == 2
# check that list to choice works
aug = iaa.GammaContrast([1, 2])
assert isinstance(aug.params1d[0], iap.Choice)
assert all([val in aug.params1d[0].a for val in [1, 2]])
# check that per_channel at 50% prob works
aug = iaa.GammaContrast((0.5, 2.0), per_channel=0.5)
seen = [False, False]
img1000d = np.zeros((1, 1, 1000), dtype=np.uint8) + 128
for _ in sm.xrange(100):
img_aug = aug.augment_image(img1000d)
assert img_aug.dtype.type == np.uint8
l = len(set(img_aug.flatten().tolist()))
if l == 1:
seen[0] = True
else:
seen[1] = True
if all(seen):
break
assert all(seen)
# check that keypoints are not changed
kpsoi = ia.KeypointsOnImage([ia.Keypoint(1, 1)], shape=(3, 3, 3))
kpsoi_aug = iaa.GammaContrast(gamma=2).augment_keypoints([kpsoi])
assert keypoints_equal([kpsoi], kpsoi_aug)
# check that heatmaps are not changed
heatmaps = ia.HeatmapsOnImage(np.zeros((3, 3, 1), dtype=np.float32) + 0.5,
shape=(3, 3, 3))
heatmaps_aug = iaa.GammaContrast(gamma=2).augment_heatmaps([heatmaps])[0]
assert np.allclose(heatmaps.arr_0to1, heatmaps_aug.arr_0to1)
###################
# test other dtypes
###################
# uint, int
for dtype in [
np.uint8, np.uint16, np.uint32, np.uint64, np.int8, np.int16,
np.int32, np.int64
]:
min_value, center_value, max_value = meta.get_value_range_of_dtype(
dtype)
exps = [1, 2, 3]
values = [0, 100, int(center_value + 0.1 * max_value)]
tolerances = [
0, 0, 1e-8 * max_value if dtype in [np.uint64, np.int64] else 0
]
for exp in exps:
aug = iaa.GammaContrast(exp)
for value, tolerance in zip(values, tolerances):
image = np.full((3, 3), value, dtype=dtype)
expected = (((image.astype(np.float128) / max_value)**exp) *
max_value).astype(dtype)
image_aug = aug.augment_image(image)
assert image_aug.dtype == np.dtype(dtype)
assert len(np.unique(image_aug)) == 1
value_aug = int(image_aug[0, 0])
value_expected = int(expected[0, 0])
diff = abs(value_aug - value_expected)
assert diff <= tolerance
# float
for dtype in [np.float16, np.float32, np.float64]:
def _allclose(a, b):
atol = 1e-3 if dtype == np.float16 else 1e-8
return np.allclose(a, b, atol=atol, rtol=0)
exps = [1, 2]
isize = np.dtype(dtype).itemsize
values = [0, 1.0, 50.0, 100**(isize - 1)]
for exp in exps:
aug = iaa.GammaContrast(exp)
for value in values:
image = np.full((3, 3), value, dtype=dtype)
expected = (image.astype(np.float128)**exp).astype(dtype)
image_aug = aug.augment_image(image)
assert image_aug.dtype == np.dtype(dtype)
assert _allclose(image_aug, expected)
def test_SigmoidContrast():
reseed()
img = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
img = np.uint8(img)
img3d = np.tile(img[:, :, np.newaxis], (1, 1, 3))
# check basic functionality with per_chanenl on/off (makes no difference due to deterministic
# parameters)
for per_channel in [False, 0, 0.0, True, 1, 1.0]:
for gain, cutoff in itertools.product([5, 10], [0.25, 0.75]):
aug = iaa.SigmoidContrast(gain=iap.Deterministic(gain),
cutoff=iap.Deterministic(cutoff),
per_channel=per_channel)
img_aug = aug.augment_image(img)
img3d_aug = aug.augment_image(img3d)
assert img_aug.dtype.type == np.uint8
assert img3d_aug.dtype.type == np.uint8
assert np.array_equal(
img_aug,
skimage.exposure.adjust_sigmoid(img, gain=gain, cutoff=cutoff))
assert np.array_equal(
img3d_aug,
skimage.exposure.adjust_sigmoid(img3d,
gain=gain,
cutoff=cutoff))
# check that tuple to uniform works
# note that gain and cutoff are saved in inverted order in _ContrastFuncWrapper to match
# the order of skimage's function
aug = iaa.SigmoidContrast(gain=(1, 2), cutoff=(0.25, 0.75))
assert isinstance(aug.params1d[0], iap.Uniform)
assert isinstance(aug.params1d[0].a, iap.Deterministic)
assert isinstance(aug.params1d[0].b, iap.Deterministic)
assert np.allclose(aug.params1d[0].a.value, 0.25)
assert np.allclose(aug.params1d[0].b.value, 0.75)
assert isinstance(aug.params1d[1], iap.Uniform)
assert isinstance(aug.params1d[1].a, iap.Deterministic)
assert isinstance(aug.params1d[1].b, iap.Deterministic)
assert aug.params1d[1].a.value == 1
assert aug.params1d[1].b.value == 2
# check that list to choice works
# note that gain and cutoff are saved in inverted order in _ContrastFuncWrapper to match
# the order of skimage's function
aug = iaa.SigmoidContrast(gain=[1, 2], cutoff=[0.25, 0.75])
assert isinstance(aug.params1d[0], iap.Choice)
assert all([
np.allclose(val, val_choice)
for val, val_choice in zip([0.25, 0.75], aug.params1d[0].a)
])
assert isinstance(aug.params1d[1], iap.Choice)
assert all([val in aug.params1d[1].a for val in [1, 2]])
# check that per_channel at 50% prob works
aug = iaa.SigmoidContrast(gain=(1, 10),
cutoff=(0.25, 0.75),
per_channel=0.5)
seen = [False, False]
img1000d = np.zeros((1, 1, 1000), dtype=np.uint8) + 128
for _ in sm.xrange(100):
img_aug = aug.augment_image(img1000d)
assert img_aug.dtype.type == np.uint8
l = len(set(img_aug.flatten().tolist()))
if l == 1:
seen[0] = True
else:
seen[1] = True
if all(seen):
break
assert all(seen)
# check that keypoints are not changed
kpsoi = ia.KeypointsOnImage([ia.Keypoint(1, 1)], shape=(3, 3, 3))
kpsoi_aug = iaa.SigmoidContrast(gain=10,
cutoff=0.5).augment_keypoints([kpsoi])
assert keypoints_equal([kpsoi], kpsoi_aug)
# check that heatmaps are not changed
heatmaps = ia.HeatmapsOnImage(np.zeros((3, 3, 1), dtype=np.float32) + 0.5,
shape=(3, 3, 3))
heatmaps_aug = iaa.SigmoidContrast(gain=10,
cutoff=0.5).augment_heatmaps([heatmaps
])[0]
assert np.allclose(heatmaps.arr_0to1, heatmaps_aug.arr_0to1)
###################
# test other dtypes
###################
# uint, int
for dtype in [
np.uint8, np.uint16, np.uint32, np.uint64, np.int8, np.int16,
np.int32, np.int64
]:
min_value, center_value, max_value = meta.get_value_range_of_dtype(
dtype)
gains = [5, 20]
cutoffs = [0.25, 0.75]
values = [0, 100, int(center_value + 0.1 * max_value)]
tmax = 1e-8 * max_value if dtype in [np.uint64, np.int64] else 0
tolerances = [tmax, tmax, tmax]
for gain, cutoff in itertools.product(gains, cutoffs):
aug = iaa.SigmoidContrast(gain=gain, cutoff=cutoff)
for value, tolerance in zip(values, tolerances):
image = np.full((3, 3), value, dtype=dtype)
# 1/(1 + exp(gain*(cutoff - I_ij/max)))
expected = (1 / (1 + np.exp(
gain * (cutoff - image.astype(np.float128) / max_value))))
expected = (expected * max_value).astype(dtype)
image_aug = aug.augment_image(image)
assert image_aug.dtype == np.dtype(dtype)
assert len(np.unique(image_aug)) == 1
value_aug = int(image_aug[0, 0])
value_expected = int(expected[0, 0])
diff = abs(value_aug - value_expected)
assert diff <= tolerance
# float
for dtype in [np.float16, np.float32, np.float64]:
def _allclose(a, b):
atol = 1e-3 if dtype == np.float16 else 1e-8
return np.allclose(a, b, atol=atol, rtol=0)
gains = [5, 20]
cutoffs = [0.25, 0.75]
isize = np.dtype(dtype).itemsize
values = [0, 1.0, 50.0, 100**(isize - 1)]
for gain, cutoff in itertools.product(gains, cutoffs):
aug = iaa.SigmoidContrast(gain=gain, cutoff=cutoff)
for value in values:
image = np.full((3, 3), value, dtype=dtype)
expected = (1 /
(1 + np.exp(gain *
(cutoff - image.astype(np.float128))))
).astype(dtype)
image_aug = aug.augment_image(image)
assert image_aug.dtype == np.dtype(dtype)
assert _allclose(image_aug, expected)
