class Flipud(Augmentor):
def __init__(self, p=0, name=None, deterministic=False, random_state=None):
super(Flipud, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
if eu.is_single_number(p):
self.p = Binomial(p)
elif isinstance(p, StochasticParameter):
self.p = p
else:
raise Exception("Expected p type StochasticParameter")
def _augment_images(self, images, random_state, parents, hooks):
nb_images = len(images)
samples = self.p.draw_samples((nb_images, ), random_state=random_state)
for i in range(nb_images):
if samples[i] == 1:
images[i] = np.flipud(images[i])
return images
def _augment_keypoints(self, keypoints_on_images, random_state, parents,
hooks):
nb_images = len(keypoints_on_images)
samples = self.p.draw_samples((nb_images, ), random_state=random_state)
for i, kps_oi in enumerate(keypoints_on_images):
if samples[i] == 1:
height = kps_oi.shape[0]
for kp in kps_oi.keypoints:
kp.y = (height - 1) - kp.y
return keypoints_on_images
def get_parameters(self):
return [self.p]
def __init__(self, p=0, name=None, deterministic=False, random_state=None):
super(Flipud, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
if eu.is_single_number(p):
self.p = Binomial(p)
elif isinstance(p, StochasticParameter):
self.p = p
else:
raise Exception("Expected p type StochasticParameter")
def main():
params = [("Binomial(0.1)", Binomial(0.1)), ("Choice", Choice([0, 1, 2])),
("Choice with p", Choice([0, 1, 2], p=[0.1, 0.2, 0.7])),
("DiscreteUniform(0, 10)", DiscreteUniform(0, 10)),
("Normal(0, 1)", Normal(0, 1)), ("Normal(1, 1)", Normal(1, 1)),
("Normal(1, 2)", Normal(0, 2)),
("Normal(Choice([-1, 1]), 2)", Normal(Choice([-1, 1]), 2)),
("Discretize(Normal(0, 1.0))", Discretize(Normal(0, 1.0))),
("Uniform(0, 10)", Uniform(0, 10)),
("Deterministic(1)", Deterministic(1)),
("Clip(Normal(0, 1), 0, None)",
Clip(Normal(0, 1), minval=0, maxval=None)),
("Multiply(Uniform(0, 10), 2)", Multiply(Uniform(0, 10), 2)),
("Add(Uniform(0, 10), 5)", Add(Uniform(0, 10), 5)),
("Absolute(Normal(0, 1))", Absolute(Normal(0, 1)))]
params_arithmetic = [
("Normal(0, 1.0)", Normal(0.0, 1.0)),
("Normal(0, 1.0) + 5", Normal(0.0, 1.0) + 5),
("5 + Normal(0, 1.0)", 5 + Normal(0.0, 1.0)),
("5 + Normal(0, 1.0)", Add(5, Normal(0.0, 1.0), elementwise=True)),
("Normal(0, 1.0) * 10", Normal(0.0, 1.0) * 10),
("10 * Normal(0, 1.0)", 10 * Normal(0.0, 1.0)),
("10 * Normal(0, 1.0)", Multiply(10,
Normal(0.0, 1.0),
elementwise=True)),
("Normal(0, 1.0) / 10", Normal(0.0, 1.0) / 10),
("10 / Normal(0, 1.0)", 10 / Normal(0.0, 1.0)),
("10 / Normal(0, 1.0)", Divide(10, Normal(0.0, 1.0),
elementwise=True)),
("Normal(0, 1.0) ** 2", Normal(0.0, 1.0)**2),
("2 ** Normal(0, 1.0)", 2**Normal(0.0, 1.0)),
("2 ** Normal(0, 1.0)", Power(2, Normal(0.0, 1.0), elementwise=True))
]
params_noise = [
# ("SimplexNoise", SimplexNoise()),
# ("Sigmoid(SimplexNoise)", Sigmoid(SimplexNoise())),
# ("SimplexNoise(linear)", SimplexNoise(upscale_method="linear")),
# ("SimplexNoise(nearest)", SimplexNoise(upscale_method="nearest")),
# ("FrequencyNoise((-4, 4))", FrequencyNoise(exponent=(-4, 4))),
# ("FrequencyNoise(-2)", FrequencyNoise(exponent=-2)),
# ("FrequencyNoise(2)", FrequencyNoise(exponent=2))
]
images_params = [
param.draw_distribution_graph() for (title, param) in params
]
images_arithmetic = [
param.draw_distribution_graph() for (title, param) in params_arithmetic
]
show_multi_array(images_params)
show_multi_array(images_arithmetic)
def __init__(self,
value=0,
per_channel=False,
name=None,
deterministic=False,
random_state=None):
super(Add, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
if eu.is_single_integer(value):
eu.do_assert(
-255 <= value <= 255,
"Expected value to have range [-255, 255], got value %d." %
(value, ))
self.value = Deterministic(value)
elif eu.is_iterable(value):
eu.do_assert(
len(value) == 2,
"Expected tuple/list with 2 entries, got %d entries." %
(len(value), ))
self.value = DiscreteUniform(value[0], value[1])
elif isinstance(value, StochasticParameter):
self.value = value
else:
raise Exception(
"Expected float or int, tuple/list with 2 entries or StochasticParameter. Got %s."
% (type(value), ))
if per_channel in [True, False, 0, 1, 0.0, 1.0]:
self.per_channel = Deterministic(int(per_channel))
elif eu.is_single_number(per_channel):
eu.do_assert(
0 <= per_channel <= 1.0,
"Expected bool, or number in range [0, 1.0] for per_channel, got %s."
% (type(per_channel), ))
self.per_channel = Binomial(per_channel)
else:
raise Exception(
"Expected per_channel to be boolean or number or StochasticParameter"
)
class Add(Augmentor):
"""
Add a value to all pixels in an image.
Parameters
----------
value : int or iterable of two ints or StochasticParameter, optional(default=0)
Value to add to all
pixels.
* If an int, then that value will be used for all images.
* If a tuple (a, b), then a value from the discrete range [a .. b]
will be used.
* If a StochasticParameter, then a value will be sampled per image
from that parameter.
per_channel : bool or float, optional(default=False)
Whether to use the same value for all channels (False)
or to sample a new value for each channel (True).
If this value is a float p, then for p percent of all images
`per_channel` will be treated as True, otherwise as False.
name : string, optional(default=None)
See `Augmenter.__init__()`
deterministic : bool, optional(default=False)
See `Augmenter.__init__()`
random_state : int or np.random.RandomState or None, optional(default=None)
See `Augmenter.__init__()`
Examples
--------
>>> aug = iaa.Add(10)
always adds a value of 10 to all pixels in the image.
>>> aug = iaa.Add((-10, 10))
adds a value from the discrete range [-10 .. 10] to all pixels of
the input images. The exact value is sampled per image.
>>> aug = iaa.Add((-10, 10), per_channel=True)
adds a value from the discrete range [-10 .. 10] to all pixels of
the input images. The exact value is sampled per image AND channel,
i.e. to a red-channel it might add 5 while subtracting 7 from the
blue channel of the same image.
>>> aug = iaa.Add((-10, 10), per_channel=0.5)
same as previous example, but the `per_channel` feature is only active
for 50 percent of all images.
"""
def __init__(self,
value=0,
per_channel=False,
name=None,
deterministic=False,
random_state=None):
super(Add, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
if eu.is_single_integer(value):
eu.do_assert(
-255 <= value <= 255,
"Expected value to have range [-255, 255], got value %d." %
(value, ))
self.value = Deterministic(value)
elif eu.is_iterable(value):
eu.do_assert(
len(value) == 2,
"Expected tuple/list with 2 entries, got %d entries." %
(len(value), ))
self.value = DiscreteUniform(value[0], value[1])
elif isinstance(value, StochasticParameter):
self.value = value
else:
raise Exception(
"Expected float or int, tuple/list with 2 entries or StochasticParameter. Got %s."
% (type(value), ))
if per_channel in [True, False, 0, 1, 0.0, 1.0]:
self.per_channel = Deterministic(int(per_channel))
elif eu.is_single_number(per_channel):
eu.do_assert(
0 <= per_channel <= 1.0,
"Expected bool, or number in range [0, 1.0] for per_channel, got %s."
% (type(per_channel), ))
self.per_channel = Binomial(per_channel)
else:
raise Exception(
"Expected per_channel to be boolean or number or StochasticParameter"
)
def _augment_images(self, images, random_state, parents, hooks):
input_dtypes = eu.copy_dtypes_for_restore(images)
result = images
nb_images = len(images)
seeds = random_state.randint(0, 10**6, (nb_images, ))
for i in range(nb_images):
image = images[i].astype(np.int32)
rs_image = eu.new_random_state(seeds[i])
per_channel = self.per_channel.draw_sample(random_state=rs_image)
if per_channel == 1:
nb_channels = image.shape[2]
samples = self.value.draw_samples((nb_channels, ),
random_state=rs_image)
for c, sample in enumerate(samples):
# TODO make value range more flexible
eu.do_assert(-255 <= sample <= 255)
image[..., c] += sample
else:
sample = self.value.draw_sample(random_state=rs_image)
# TODO make value range more flexible
eu.do_assert(-255 <= sample <= 255)
image += sample
result[i] = image
# TODO make value range more flexible
eu.clip_augmented_images_(result, 0, 255)
eu.restore_augmented_images_dtypes_(result, input_dtypes)
return result
def _augment_keypoints(self, keypoints_on_images, random_state, parents,
hooks):
return keypoints_on_images
def get_parameters(self):
return [self.value]