def __init__(self, other_param, minval=None, maxval=None):
StochasticParameter.__init__(self)
assert isinstance(other_param, StochasticParameter)
assert minval is None or ia.is_single_number(minval)
assert maxval is None or ia.is_single_number(maxval)
self.other_param = other_param
self.minval = minval
self.maxval = maxval
def __init__(self, value):
StochasticParameter.__init__(self)
if isinstance(value, StochasticParameter):
self.value = value.draw_sample()
elif ia.is_single_number(value) or ia.is_string(value):
self.value = value
else:
raise Exception(
"Expected StochasticParameter object or number or string, got %s."
% (type(value), ))
def __init__(self, a, b):
StochasticParameter.__init__(self)
assert isinstance(
a, (int, float, StochasticParameter)
), "Expected a to be int, float or StochasticParameter, got %s" % (
type(a), )
assert isinstance(
b, (int, float, StochasticParameter)
), "Expected b to be int, float or StochasticParameter, got %s" % (
type(b), )
if ia.is_single_number(a):
self.a = Deterministic(a)
else:
self.a = a
if ia.is_single_number(b):
self.b = Deterministic(b)
else:
self.b = b
def __init__(self, p):
StochasticParameter.__init__(self)
if isinstance(p, StochasticParameter):
self.p = p
elif ia.is_single_number(p):
assert 0 <= p <= 1.0, "Expected probability p to be in range [0.0, 1.0], got %s." % (
p, )
self.p = Deterministic(float(p))
else:
raise Exception(
"Expected StochasticParameter or float/int value, got %s." %
(type(p), ))
def __init__(self, loc, scale):
StochasticParameter.__init__(self)
if isinstance(loc, StochasticParameter):
self.loc = loc
elif ia.is_single_number(loc):
self.loc = Deterministic(loc)
else:
raise Exception(
"Expected float, int or StochasticParameter as loc, got %s, %s."
% (type(loc), ))
if isinstance(scale, StochasticParameter):
self.scale = scale
elif ia.is_single_number(scale):
assert scale >= 0, "Expected scale to be in range [0, inf) got %s (type %s)." % (
scale, type(scale))
self.scale = Deterministic(scale)
else:
raise Exception(
"Expected float, int or StochasticParameter as scale, got %s, %s."
% (type(scale), ))
def __init__(self,
shear=(-40, 41),
cval=255,
vertical=False,
name=None,
deterministic=False,
random_state=None):
"""Initialize the augmentator
# Arguments
shear [float or tuple of 2 floats]: if it is a single number, then
image will be sheared in that degree. If it is a tuple of 2
numbers, then the shear value will be chosen randomly
cval [int]: fill-in value to new pixels
"""
super(ItalicizeLine, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
if isinstance(shear, StochasticParameter):
self.shear = shear
elif ia.is_single_number(shear):
self.shear = Deterministic(shear)
elif ia.is_iterable(shear):
ia.do_assert(
len(shear) == 2,
"Expected rotate tuple/list with 2 entries, got {} entries.".
format((len(shear))))
ia.do_assert(all([ia.is_single_number(val) for val in shear]),
"Expected floats/ints in shear tuple/list.")
self.shear = Uniform(shear[0], shear[1])
else:
raise Exception(
"Expected float, int, tuple/list with 2 entries or "
"StochasticParameter. Got {}.".format(type(shear)))
self.cval = cval
self.vertical = vertical
def __init__(self,
angle=(-10, 10),
cval=255,
name=None,
deterministic=False,
random_state=None):
"""Initialize the augmentator
# Arguments
angle [float or tuple of 2 floats]: if it is a single number, then
image will be rotated in that degree. If it is a tuple of 2
numbers, then the angle value will be chosen randomly
cval [int]: fill-in value to new pixels
"""
super(RotateLine, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
if isinstance(angle, StochasticParameter):
self.angle = angle
elif ia.is_single_number(angle):
self.angle = Deterministic(angle)
elif ia.is_iterable(angle):
ia.do_assert(
len(angle) == 2,
"Expected rotate tuple/list with 2 entries, got {} entries.".
format((len(angle))))
ia.do_assert(all([ia.is_single_number(val) for val in angle]),
"Expected floats/ints in angle tuple/list.")
self.angle = Uniform(angle[0], angle[1])
else:
raise Exception(
"Expected float, int, tuple/list with 2 entries or "
"StochasticParameter. Got {}.".format(type(angle)))
self.cval = cval
