def restore_dtypes_(images, dtypes, clip=True, round=True):
if ia.is_np_array(images):
if ia.is_iterable(dtypes):
assert len(dtypes) > 0
if len(dtypes) > 1:
assert all([dtype_i == dtypes[0] for dtype_i in dtypes])
dtypes = dtypes[0]
dtypes = np.dtype(dtypes)
dtype_to = dtypes
if images.dtype.type == dtype_to:
result = images
else:
if round and dtype_to.kind in ["u", "i", "b"]:
images = np.round(images)
if clip:
min_value, _, max_value = get_value_range_of_dtype(dtype_to)
images = np.clip(images, min_value, max_value, out=images)
result = images.astype(dtype_to, copy=False)
elif ia.is_iterable(images):
result = images
dtypes = dtypes if not isinstance(dtypes,
np.dtype) else [dtypes] * len(images)
for i, (image, dtype) in enumerate(zip(images, dtypes)):
assert ia.is_np_array(image)
result[i] = restore_dtypes_(image, dtype, clip=clip)
else:
raise Exception(
"Expected numpy array or iterable of numpy arrays, got type '%s'."
% (type(images), ))
return result
def restore_dtypes_(images, dtypes, clip=True, round=True):
if ia.is_np_array(images):
if ia.is_iterable(dtypes):
assert len(dtypes) > 0
if len(dtypes) > 1:
assert all([dtype_i == dtypes[0] for dtype_i in dtypes])
dtypes = dtypes[0]
dtypes = np.dtype(dtypes)
dtype_to = dtypes
if images.dtype.type == dtype_to:
result = images
else:
if round and dtype_to.kind in ["u", "i", "b"]:
images = np.round(images)
if clip:
min_value, _, max_value = get_value_range_of_dtype(dtype_to)
images = clip_(images, min_value, max_value)
result = images.astype(dtype_to, copy=False)
elif ia.is_iterable(images):
result = images
dtypes = dtypes if not isinstance(dtypes, np.dtype) else [dtypes] * len(images)
for i, (image, dtype) in enumerate(zip(images, dtypes)):
assert ia.is_np_array(image)
result[i] = restore_dtypes_(image, dtype, clip=clip)
else:
raise Exception("Expected numpy array or iterable of numpy arrays, got type '%s'." % (type(images),))
return result
def change_dtypes_(images, dtypes, clip=True, round=True):
if ia.is_np_array(images):
if ia.is_iterable(dtypes):
dtypes = normalize_dtypes(dtypes)
n_distinct_dtypes = len(set([dt.name for dt in dtypes]))
assert len(dtypes) == len(images), (
"If an iterable of dtypes is provided to "
"change_dtypes_(), it must contain as many dtypes as "
"there are images. Got %d dtypes and %d images." %
(len(dtypes), len(images)))
assert n_distinct_dtypes == 1, (
"If an image array is provided to change_dtypes_(), the "
"provided 'dtypes' argument must either be a single dtype "
"or an iterable of N times the *same* dtype for N images. "
"Got %d distinct dtypes." % (n_distinct_dtypes, ))
dtype = dtypes[0]
else:
dtype = normalize_dtype(dtypes)
result = change_dtype_(images, dtype, clip=clip, round=round)
elif ia.is_iterable(images):
dtypes = ([normalize_dtype(dtypes)] * len(images) if
not isinstance(dtypes, list) else normalize_dtypes(dtypes))
assert len(dtypes) == len(images)
result = images
for i, (image, dtype) in enumerate(zip(images, dtypes)):
assert ia.is_np_array(image)
result[i] = change_dtype_(image, dtype, clip=clip, round=round)
else:
raise Exception("Expected numpy array or iterable of numpy arrays, "
"got type '%s'." % (type(images), ))
return result
def __init__(self, k=1, name=None, deterministic=False, random_state=None):
super(AverageBlur, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
# TODO replace this by iap.handle_discrete_kernel_size()
self.mode = "single"
if ia.is_single_number(k):
self.k = iap.Deterministic(int(k))
elif ia.is_iterable(k):
assert len(k) == 2, (
"Expected iterable 'k' to contain exactly 2 entries, "
"got %d." % (len(k), ))
if all([ia.is_single_number(ki) for ki in k]):
self.k = iap.DiscreteUniform(int(k[0]), int(k[1]))
elif all([isinstance(ki, iap.StochasticParameter) for ki in k]):
self.mode = "two"
self.k = (k[0], k[1])
else:
k_tuple = [None, None]
if ia.is_single_number(k[0]):
k_tuple[0] = iap.Deterministic(int(k[0]))
elif ia.is_iterable(k[0]) and all(
[ia.is_single_number(ki) for ki in k[0]]):
k_tuple[0] = iap.DiscreteUniform(int(k[0][0]),
int(k[0][1]))
else:
raise Exception(
"k[0] expected to be int or tuple of two ints, got %s"
% (type(k[0]), ))
if ia.is_single_number(k[1]):
k_tuple[1] = iap.Deterministic(int(k[1]))
elif ia.is_iterable(k[1]) and all(
[ia.is_single_number(ki) for ki in k[1]]):
k_tuple[1] = iap.DiscreteUniform(int(k[1][0]),
int(k[1][1]))
else:
raise Exception(
"k[1] expected to be int or tuple of two ints, got %s"
% (type(k[1]), ))
self.mode = "two"
self.k = k_tuple
elif isinstance(k, iap.StochasticParameter):
self.k = k
else:
raise Exception(
"Expected int, tuple/list with 2 entries or StochasticParameter. Got %s."
% (type(k), ))
def __init__(self, to_colorspace, from_colorspace="RGB", alpha=1.0, name=None, deterministic=False,
random_state=None):
super(ChangeColorspace, self).__init__(name=name, deterministic=deterministic, random_state=random_state)
# TODO somehow merge this with Alpha augmenter?
self.alpha = iap.handle_continuous_param(alpha, "alpha", value_range=(0, 1.0), tuple_to_uniform=True,
list_to_choice=True)
if ia.is_string(to_colorspace):
ia.do_assert(to_colorspace in ChangeColorspace.COLORSPACES)
self.to_colorspace = iap.Deterministic(to_colorspace)
elif ia.is_iterable(to_colorspace):
ia.do_assert(all([ia.is_string(colorspace) for colorspace in to_colorspace]))
ia.do_assert(all([(colorspace in ChangeColorspace.COLORSPACES) for colorspace in to_colorspace]))
self.to_colorspace = iap.Choice(to_colorspace)
elif isinstance(to_colorspace, iap.StochasticParameter):
self.to_colorspace = to_colorspace
else:
raise Exception("Expected to_colorspace to be string, list of strings or StochasticParameter, got %s." % (
type(to_colorspace),))
self.from_colorspace = from_colorspace
ia.do_assert(self.from_colorspace in ChangeColorspace.COLORSPACES)
ia.do_assert(from_colorspace != ChangeColorspace.GRAY)
self.eps = 0.001 # epsilon value to check if alpha is close to 1.0 or 0.0
def assert_is_iterable_of(iterable_var, classes):
"""Assert that `iterable_var` only contains instances of given classes.
Parameters
----------
iterable_var : iterable
See :func:`imgaug.validation.is_iterable_of`.
classes : type or iterable of type
See :func:`imgaug.validation.is_iterable_of`.
"""
valid = is_iterable_of(iterable_var, classes)
if not valid:
expected_types_str = (", ".join([
class_.__name__ for class_ in classes
]) if not isinstance(classes, type) else classes.__name__)
if not ia.is_iterable(iterable_var):
raise AssertionError(
"Expected an iterable of the following types: %s. "
"Got instead a single instance of: %s." %
(expected_types_str, type(iterable_var).__name__))
raise AssertionError(
"Expected an iterable of the following types: %s. "
"Got an iterable of types: %s." %
(expected_types_str,
convert_iterable_to_string_of_types(iterable_var)))
def is_iterable_of(iterable_var, classes):
"""Check whether `iterable_var` contains only instances of given classes.
Parameters
----------
iterable_var : iterable
An iterable of items that will be matched against `classes`.
classes : type or iterable of type
One or more classes that each item in `var` must be an instanceof.
If this is an iterable, a single match per item is enough.
Returns
-------
bool
Whether `var` only contains instances of `classes`.
If `var` was empty, ``True`` will be returned.
"""
if not ia.is_iterable(iterable_var):
return False
for var_i in iterable_var:
if not isinstance(var_i, classes):
return False
return True
def __init__(self,
gamma_log2=0.0,
per_channel=False,
name=None,
deterministic=False,
random_state=None):
super(Gamma, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
if ia.is_single_number(gamma_log2):
self.gamma_log2 = iaa.Deterministic(gamma_log2)
elif ia.is_iterable(gamma_log2):
assert len(
gamma_log2
) == 2, "Expected tuple/list with 2 entries, got %d entries." % (
len(gamma_log2), )
self.gamma_log2 = iaa.Uniform(gamma_log2[0], gamma_log2[1])
elif isinstance(gamma_log2, iaa.StochasticParameter):
self.gamma_log2 = gamma_log2
else:
raise Exception(
"Expected float or int, tuple/list with 2 entries or StochasticParameter. Got %s."
% (type(gamma_log2), ))
if per_channel in [True, False, 0, 1, 0.0, 1.0]:
self.per_channel = iaa.Deterministic(int(per_channel))
elif ia.is_single_number(per_channel):
assert 0 <= per_channel <= 1.0
self.per_channel = iaa.Binomial(per_channel)
else:
raise Exception(
"Expected per_channel to be boolean or number or StochasticParameter"
)
def AdditiveGaussianNoise(loc=0,
scale=0,
per_channel=False,
name='None',
deterministic=False,
random_state=None):
if ia.is_single_number(loc):
loc2 = ia.parameters.Deterministic(loc)
elif ia.is_iterable(loc):
assert len(
loc
) == 2, "Expected tuple/list with 2 entries for argument 'loc', got %d entries." % (
len(scale), )
loc2 = ia.parameters.Uniform(loc[0], loc[1])
elif isinstance(loc, ia.parameters.StochasticParameter):
loc2 = loc
else:
raise Exception(
"Expected float, int, tuple/list with 2 entries or StochasticParameter for argument 'loc'. Got %s."
% (type(loc), ))
if ia.is_single_number(scale):
scale2 = ia.parameters.Deterministic(scale)
elif ia.is_iterable(scale):
assert len(
scale
) == 2, "Expected tuple/list with 2 entries for argument 'scale', got %d entries." % (
len(scale), )
scale2 = ia.parameters.Uniform(scale[0], scale[1])
elif isinstance(scale, ia.parameters.StochasticParameter):
scale2 = scale
else:
raise Exception(
"Expected float, int, tuple/list with 2 entries or StochasticParameter for argument 'scale'. Got %s."
% (type(scale), ))
return AddElementwise(scale,
per_channel=per_channel,
name=name,
deterministic=deterministic,
random_state=random_state)
def __init__(self, k=1, name=None, deterministic=False, random_state=None):
super(MedianBlur, self).__init__(name=name, deterministic=deterministic, random_state=random_state)
# TODO replace this by iap.handle_discrete_kernel_size()
self.k = iap.handle_discrete_param(k, "k", value_range=(1, None), tuple_to_uniform=True, list_to_choice=True,
allow_floats=False)
if ia.is_single_integer(k):
ia.do_assert(k % 2 != 0, "Expected k to be odd, got %d. Add or subtract 1." % (int(k),))
elif ia.is_iterable(k):
ia.do_assert(all([ki % 2 != 0 for ki in k]),
"Expected all values in iterable k to be odd, but at least one was not. "
+ "Add or subtract 1 to/from that value.")
def __init__(self, k=1, name=None, deterministic=False, random_state=None):
super(MedianBlur, self).__init__(name=name, deterministic=deterministic, random_state=random_state)
# TODO replace this by iap.handle_discrete_kernel_size()
self.k = iap.handle_discrete_param(k, "k", value_range=(1, None), tuple_to_uniform=True, list_to_choice=True,
allow_floats=False)
if ia.is_single_integer(k):
ia.do_assert(k % 2 != 0, "Expected k to be odd, got %d. Add or subtract 1." % (int(k),))
elif ia.is_iterable(k):
ia.do_assert(all([ki % 2 != 0 for ki in k]),
"Expected all values in iterable k to be odd, but at least one was not. "
+ "Add or subtract 1 to/from that value.")
def __init__(self, k=1, name=None, deterministic=False, random_state=None):
super(AverageBlur, self).__init__(name=name, deterministic=deterministic, random_state=random_state)
# TODO replace this by iap.handle_discrete_kernel_size()
self.mode = "single"
if ia.is_single_number(k):
self.k = iap.Deterministic(int(k))
elif ia.is_iterable(k):
ia.do_assert(len(k) == 2)
if all([ia.is_single_number(ki) for ki in k]):
self.k = iap.DiscreteUniform(int(k[0]), int(k[1]))
elif all([isinstance(ki, iap.StochasticParameter) for ki in k]):
self.mode = "two"
self.k = (k[0], k[1])
else:
k_tuple = [None, None]
if ia.is_single_number(k[0]):
k_tuple[0] = iap.Deterministic(int(k[0]))
elif ia.is_iterable(k[0]) and all([ia.is_single_number(ki) for ki in k[0]]):
k_tuple[0] = iap.DiscreteUniform(int(k[0][0]), int(k[0][1]))
else:
raise Exception("k[0] expected to be int or tuple of two ints, got %s" % (type(k[0]),))
if ia.is_single_number(k[1]):
k_tuple[1] = iap.Deterministic(int(k[1]))
elif ia.is_iterable(k[1]) and all([ia.is_single_number(ki) for ki in k[1]]):
k_tuple[1] = iap.DiscreteUniform(int(k[1][0]), int(k[1][1]))
else:
raise Exception("k[1] expected to be int or tuple of two ints, got %s" % (type(k[1]),))
self.mode = "two"
self.k = k_tuple
elif isinstance(k, iap.StochasticParameter):
self.k = k
else:
raise Exception("Expected int, tuple/list with 2 entries or StochasticParameter. Got %s." % (type(k),))
def __init__(self, k=1, seed=None, name=None, **old_kwargs):
super(MedianBlur, self).__init__(
seed=seed, name=name, **old_kwargs)
# TODO replace this by iap.handle_discrete_kernel_size()
self.k = iap.handle_discrete_param(
k, "k", value_range=(1, None), tuple_to_uniform=True,
list_to_choice=True, allow_floats=False)
if ia.is_single_integer(k):
assert k % 2 != 0, (
"Expected k to be odd, got %d. Add or subtract 1." % (
int(k),))
elif ia.is_iterable(k):
assert all([ki % 2 != 0 for ki in k]), (
"Expected all values in iterable k to be odd, but at least "
"one was not. Add or subtract 1 to/from that value.")
def CoarseDropout(p=0,
size_px=None,
size_percent=None,
per_channel=False,
min_size=4,
name=None,
deterministic=False,
random_state=None):
"""
The version exactly the same, it would be nice to just route the calling from IMGaug
"""
if ia.is_single_number(p):
p2 = ia.parameters.Binomial(1 - p)
elif ia.is_iterable(p):
do_assert(len(p) == 2)
do_assert(p[0] < p[1])
do_assert(0 <= p[0] <= 1.0)
do_assert(0 <= p[1] <= 1.0)
p2 = ia.parameters.Binomial(ia.parameters.Uniform(1 - p[1], 1 - p[0]))
elif isinstance(p, ia.parameters.StochasticParameter):
p2 = p
else:
raise Exception(
"Expected p to be float or int or StochasticParameter, got %s." %
(type(p), ))
if size_px is not None:
p3 = ia.parameters.FromLowerResolution(other_param=p2,
size_px=size_px,
min_size=min_size)
elif size_percent is not None:
p3 = ia.parameters.FromLowerResolution(other_param=p2,
size_percent=size_percent,
min_size=min_size)
else:
raise Exception("Either size_px or size_percent must be set.")
return MultiplyElementwise(p3,
per_channel=per_channel,
name=name,
deterministic=deterministic,
random_state=random_state)
def __init__(self,
to_colorspace,
from_colorspace="RGB",
alpha=1.0,
name=None,
deterministic=False,
random_state=None):
super(ChangeColorspace, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
# TODO somehow merge this with Alpha augmenter?
self.alpha = iap.handle_continuous_param(alpha,
"alpha",
value_range=(0, 1.0),
tuple_to_uniform=True,
list_to_choice=True)
if ia.is_string(to_colorspace):
ia.do_assert(to_colorspace in ChangeColorspace.COLORSPACES)
self.to_colorspace = iap.Deterministic(to_colorspace)
elif ia.is_iterable(to_colorspace):
ia.do_assert(
all([ia.is_string(colorspace)
for colorspace in to_colorspace]))
ia.do_assert(
all([(colorspace in ChangeColorspace.COLORSPACES)
for colorspace in to_colorspace]))
self.to_colorspace = iap.Choice(to_colorspace)
elif isinstance(to_colorspace, iap.StochasticParameter):
self.to_colorspace = to_colorspace
else:
raise Exception(
"Expected to_colorspace to be string, list of strings or StochasticParameter, got %s."
% (type(to_colorspace), ))
self.from_colorspace = from_colorspace
ia.do_assert(self.from_colorspace in ChangeColorspace.COLORSPACES)
ia.do_assert(from_colorspace != ChangeColorspace.GRAY)
self.eps = 0.001 # epsilon value to check if alpha is close to 1.0 or 0.0
def test_Alpha():
reseed()
base_img = np.zeros((3, 3, 1), dtype=np.uint8)
heatmaps_arr = np.float32([[0.0, 0.0, 1.0], [0.0, 0.0, 1.0],
[0.0, 1.0, 1.0]])
heatmaps_arr_r1 = np.float32([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 1.0]])
heatmaps_arr_l1 = np.float32([[0.0, 1.0, 0.0], [0.0, 1.0, 0.0],
[1.0, 1.0, 0.0]])
heatmaps = ia.HeatmapsOnImage(heatmaps_arr, shape=(3, 3, 3))
aug = iaa.Alpha(1, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 10).astype(np.uint8)
assert np.allclose(observed, expected)
for per_channel in [False, True]:
aug = iaa.Alpha(1,
iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"x": -1}),
per_channel=per_channel)
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == heatmaps.shape
assert 0 - 1e-6 < heatmaps.min_value < 0 + 1e-6
assert 1 - 1e-6 < heatmaps.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_r1)
aug = iaa.Alpha(0, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 20).astype(np.uint8)
assert np.allclose(observed, expected)
for per_channel in [False, True]:
aug = iaa.Alpha(0,
iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"x": -1}),
per_channel=per_channel)
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == heatmaps.shape
assert 0 - 1e-6 < heatmaps.min_value < 0 + 1e-6
assert 1 - 1e-6 < heatmaps.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_l1)
aug = iaa.Alpha(0.75, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 0.75 * 10 + 0.25 * 20).astype(np.uint8)
assert np.allclose(observed, expected)
aug = iaa.Alpha(0.75, None, iaa.Add(20))
observed = aug.augment_image(base_img + 10)
expected = np.round(base_img + 0.75 * 10 + 0.25 * (10 + 20)).astype(
np.uint8)
assert np.allclose(observed, expected)
aug = iaa.Alpha(0.75, iaa.Add(10), None)
observed = aug.augment_image(base_img + 10)
expected = np.round(base_img + 0.75 * (10 + 10) + 0.25 * 10).astype(
np.uint8)
assert np.allclose(observed, expected)
base_img = np.zeros((1, 2, 1), dtype=np.uint8)
nb_iterations = 1000
aug = iaa.Alpha((0.0, 1.0), iaa.Add(10), iaa.Add(110))
values = []
for _ in sm.xrange(nb_iterations):
observed = aug.augment_image(base_img)
observed_val = np.round(np.average(observed)) - 10
values.append(observed_val / 100)
nb_bins = 5
hist, _ = np.histogram(values,
bins=nb_bins,
range=(0.0, 1.0),
density=False)
density_expected = 1.0 / nb_bins
density_tolerance = 0.05
for nb_samples in hist:
density = nb_samples / nb_iterations
assert density_expected - density_tolerance < density < density_expected + density_tolerance
# bad datatype for factor
got_exception = False
try:
_ = iaa.Alpha(False, iaa.Add(10), None)
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
# per_channel
aug = iaa.Alpha(1.0,
iaa.Add((0, 100), per_channel=True),
None,
per_channel=True)
observed = aug.augment_image(np.zeros((1, 1, 1000), dtype=np.uint8))
uq = np.unique(observed)
assert len(uq) > 1
assert np.max(observed) > 80
assert np.min(observed) < 20
aug = iaa.Alpha((0.0, 1.0), iaa.Add(100), None, per_channel=True)
observed = aug.augment_image(np.zeros((1, 1, 1000), dtype=np.uint8))
uq = np.unique(observed)
assert len(uq) > 1
assert np.max(observed) > 80
assert np.min(observed) < 20
aug = iaa.Alpha((0.0, 1.0), iaa.Add(100), iaa.Add(0), per_channel=0.5)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_image(np.zeros((1, 1, 100), dtype=np.uint8))
uq = np.unique(observed)
if len(uq) == 1:
seen[0] += 1
elif len(uq) > 1:
seen[1] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
# bad datatype for per_channel
got_exception = False
try:
_ = iaa.Alpha(0.5, iaa.Add(10), None, per_channel="test")
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
# propagating
aug = iaa.Alpha(0.5, iaa.Add(100), iaa.Add(50), name="AlphaTest")
def propagator(images, augmenter, parents, default):
if "Alpha" in augmenter.name:
return False
else:
return default
hooks = ia.HooksImages(propagator=propagator)
image = np.zeros((10, 10, 3), dtype=np.uint8) + 1
observed = aug.augment_image(image, hooks=hooks)
assert np.array_equal(observed, image)
# -----
# keypoints
# -----
kps = [ia.Keypoint(x=5, y=10), ia.Keypoint(x=6, y=11)]
kpsoi = ia.KeypointsOnImage(kps, shape=(20, 20, 3))
aug = iaa.Alpha(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.499, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
# per_channel
aug = iaa.Alpha(1.0,
iaa.Noop(),
iaa.Affine(translate_px={"x": 1}),
per_channel=True)
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.0,
iaa.Noop(),
iaa.Affine(translate_px={"x": 1}),
per_channel=True)
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(iap.Choice([0.49, 0.51]),
iaa.Noop(),
iaa.Affine(translate_px={"x": 1}),
per_channel=True)
expected_same = kpsoi.deepcopy()
expected_shifted = kpsoi.shift(x=1)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_keypoints([kpsoi])[0]
if keypoints_equal([observed], [expected_same]):
seen[0] += 1
elif keypoints_equal([observed], [expected_shifted]):
seen[1] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
# propagating
aug = iaa.Alpha(0.0,
iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"y": 1}),
name="AlphaTest")
def propagator(kpsoi_to_aug, augmenter, parents, default):
if "Alpha" in augmenter.name:
return False
else:
return default
hooks = ia.HooksKeypoints(propagator=propagator)
observed = aug.augment_keypoints([kpsoi], hooks=hooks)[0]
assert keypoints_equal([observed], [kpsoi])
# -----
# get_parameters()
# -----
first = iaa.Noop()
second = iaa.Sequential([iaa.Add(1)])
aug = iaa.Alpha(0.65, first, second, per_channel=1)
params = aug.get_parameters()
assert isinstance(params[0], iap.Deterministic)
assert isinstance(params[1], iap.Deterministic)
assert 0.65 - 1e-6 < params[0].value < 0.65 + 1e-6
assert params[1].value == 1
# -----
# get_children_lists()
# -----
first = iaa.Noop()
second = iaa.Sequential([iaa.Add(1)])
aug = iaa.Alpha(0.65, first, second, per_channel=1)
children_lsts = aug.get_children_lists()
assert len(children_lsts) == 2
assert ia.is_iterable([lst for lst in children_lsts])
assert first in children_lsts[0]
assert second == children_lsts[1]
def test_Alpha():
reseed()
base_img = np.zeros((3, 3, 1), dtype=np.uint8)
heatmaps_arr = np.float32([[0.0, 0.0, 1.0],
[0.0, 0.0, 1.0],
[0.0, 1.0, 1.0]])
heatmaps_arr_r1 = np.float32([[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 1.0]])
heatmaps_arr_l1 = np.float32([[0.0, 1.0, 0.0],
[0.0, 1.0, 0.0],
[1.0, 1.0, 0.0]])
heatmaps = ia.HeatmapsOnImage(heatmaps_arr, shape=(3, 3, 3))
aug = iaa.Alpha(1, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 10).astype(np.uint8)
assert np.allclose(observed, expected)
for per_channel in [False, True]:
aug = iaa.Alpha(1, iaa.Affine(translate_px={"x": 1}), iaa.Affine(translate_px={"x": -1}),
per_channel=per_channel)
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == heatmaps.shape
assert 0 - 1e-6 < heatmaps.min_value < 0 + 1e-6
assert 1 - 1e-6 < heatmaps.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_r1)
aug = iaa.Alpha(0, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 20).astype(np.uint8)
assert np.allclose(observed, expected)
for per_channel in [False, True]:
aug = iaa.Alpha(0, iaa.Affine(translate_px={"x": 1}), iaa.Affine(translate_px={"x": -1}),
per_channel=per_channel)
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == heatmaps.shape
assert 0 - 1e-6 < heatmaps.min_value < 0 + 1e-6
assert 1 - 1e-6 < heatmaps.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_l1)
aug = iaa.Alpha(0.75, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 0.75 * 10 + 0.25 * 20).astype(np.uint8)
assert np.allclose(observed, expected)
aug = iaa.Alpha(0.75, None, iaa.Add(20))
observed = aug.augment_image(base_img + 10)
expected = np.round(base_img + 0.75 * 10 + 0.25 * (10 + 20)).astype(np.uint8)
assert np.allclose(observed, expected)
aug = iaa.Alpha(0.75, iaa.Add(10), None)
observed = aug.augment_image(base_img + 10)
expected = np.round(base_img + 0.75 * (10 + 10) + 0.25 * 10).astype(np.uint8)
assert np.allclose(observed, expected)
base_img = np.zeros((1, 2, 1), dtype=np.uint8)
nb_iterations = 1000
aug = iaa.Alpha((0.0, 1.0), iaa.Add(10), iaa.Add(110))
values = []
for _ in sm.xrange(nb_iterations):
observed = aug.augment_image(base_img)
observed_val = np.round(np.average(observed)) - 10
values.append(observed_val / 100)
nb_bins = 5
hist, _ = np.histogram(values, bins=nb_bins, range=(0.0, 1.0), density=False)
density_expected = 1.0/nb_bins
density_tolerance = 0.05
for nb_samples in hist:
density = nb_samples / nb_iterations
assert density_expected - density_tolerance < density < density_expected + density_tolerance
# bad datatype for factor
got_exception = False
try:
_ = iaa.Alpha(False, iaa.Add(10), None)
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
# per_channel
aug = iaa.Alpha(1.0, iaa.Add((0, 100), per_channel=True), None, per_channel=True)
observed = aug.augment_image(np.zeros((1, 1, 1000), dtype=np.uint8))
uq = np.unique(observed)
assert len(uq) > 1
assert np.max(observed) > 80
assert np.min(observed) < 20
aug = iaa.Alpha((0.0, 1.0), iaa.Add(100), None, per_channel=True)
observed = aug.augment_image(np.zeros((1, 1, 1000), dtype=np.uint8))
uq = np.unique(observed)
assert len(uq) > 1
assert np.max(observed) > 80
assert np.min(observed) < 20
aug = iaa.Alpha((0.0, 1.0), iaa.Add(100), iaa.Add(0), per_channel=0.5)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_image(np.zeros((1, 1, 100), dtype=np.uint8))
uq = np.unique(observed)
if len(uq) == 1:
seen[0] += 1
elif len(uq) > 1:
seen[1] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
# bad datatype for per_channel
got_exception = False
try:
_ = iaa.Alpha(0.5, iaa.Add(10), None, per_channel="test")
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
# propagating
aug = iaa.Alpha(0.5, iaa.Add(100), iaa.Add(50), name="AlphaTest")
def propagator(images, augmenter, parents, default):
if "Alpha" in augmenter.name:
return False
else:
return default
hooks = ia.HooksImages(propagator=propagator)
image = np.zeros((10, 10, 3), dtype=np.uint8) + 1
observed = aug.augment_image(image, hooks=hooks)
assert np.array_equal(observed, image)
# -----
# keypoints
# -----
kps = [ia.Keypoint(x=5, y=10), ia.Keypoint(x=6, y=11)]
kpsoi = ia.KeypointsOnImage(kps, shape=(20, 20, 3))
aug = iaa.Alpha(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.499, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
# per_channel
aug = iaa.Alpha(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(iap.Choice([0.49, 0.51]), iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
expected_same = kpsoi.deepcopy()
expected_shifted = kpsoi.shift(x=1)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_keypoints([kpsoi])[0]
if keypoints_equal([observed], [expected_same]):
seen[0] += 1
elif keypoints_equal([observed], [expected_shifted]):
seen[1] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
# empty keypoints
aug = iaa.Alpha(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints(ia.KeypointsOnImage([], shape=(1, 2, 3)))
assert len(observed.keypoints) == 0
assert observed.shape == (1, 2, 3)
# propagating
aug = iaa.Alpha(0.0, iaa.Affine(translate_px={"x": 1}), iaa.Affine(translate_px={"y": 1}), name="AlphaTest")
def propagator(kpsoi_to_aug, augmenter, parents, default):
if "Alpha" in augmenter.name:
return False
else:
return default
hooks = ia.HooksKeypoints(propagator=propagator)
observed = aug.augment_keypoints([kpsoi], hooks=hooks)[0]
assert keypoints_equal([observed], [kpsoi])
# -----
# polygons
# -----
ps = [ia.Polygon([(5, 5), (10, 5), (10, 10)])]
psoi = ia.PolygonsOnImage(ps, shape=(20, 20, 3))
aug = iaa.Alpha(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons([psoi])
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(psoi.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.Alpha(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons([psoi])
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(psoi.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.Alpha(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons([psoi])
expected = psoi.shift(left=1)
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(expected.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.Alpha(0.499, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons([psoi])
expected = psoi.shift(left=1)
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(expected.polygons[0])
assert observed[0].polygons[0].is_valid
# per_channel
aug = iaa.Alpha(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
observed = aug.augment_polygons([psoi])
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(psoi.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.Alpha(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
observed = aug.augment_polygons([psoi])
expected = psoi.shift(left=1)
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(expected.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.Alpha(iap.Choice([0.49, 0.51]), iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
expected_same = psoi.deepcopy()
expected_shifted = psoi.shift(left=1)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_polygons([psoi])[0]
if observed.polygons[0].exterior_almost_equals(expected_same.polygons[0]):
seen[0] += 1
elif observed.polygons[0].exterior_almost_equals(expected_shifted.polygons[0]):
seen[1] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
# empty polygons
aug = iaa.Alpha(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons(ia.PolygonsOnImage([], shape=(1, 2, 3)))
assert len(observed.polygons) == 0
assert observed.shape == (1, 2, 3)
# propagating
aug = iaa.Alpha(0.0, iaa.Affine(translate_px={"x": 1}), iaa.Affine(translate_px={"y": 1}), name="AlphaTest")
def propagator(psoi_to_aug, augmenter, parents, default):
if "Alpha" in augmenter.name:
return False
else:
return default
hooks = ia.HooksKeypoints(propagator=propagator) # no hooks for polygons yet, so we use HooksKeypoints
observed = aug.augment_polygons([psoi], hooks=hooks)[0]
assert observed.polygons[0].exterior_almost_equals(psoi.polygons[0])
# -----
# get_parameters()
# -----
first = iaa.Noop()
second = iaa.Sequential([iaa.Add(1)])
aug = iaa.Alpha(0.65, first, second, per_channel=1)
params = aug.get_parameters()
assert isinstance(params[0], iap.Deterministic)
assert isinstance(params[1], iap.Deterministic)
assert 0.65 - 1e-6 < params[0].value < 0.65 + 1e-6
assert params[1].value == 1
# -----
# get_children_lists()
# -----
first = iaa.Noop()
second = iaa.Sequential([iaa.Add(1)])
aug = iaa.Alpha(0.65, first, second, per_channel=1)
children_lsts = aug.get_children_lists()
assert len(children_lsts) == 2
assert ia.is_iterable([lst for lst in children_lsts])
assert first in children_lsts[0]
assert second == children_lsts[1]