Source code for sigima.proc.image.noise
# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
Noise addition computation module.
----------------------------------
"""
# pylint: disable=invalid-name # Allows short reference names like x, y...
# Note:
# ----
# - All `guidata.dataset.DataSet` parameter classes must also be imported in the
# `sigima.params` module.
# - All functions decorated by `computation_function` must be imported in the upper
# level `sigima.proc.image` module.
from __future__ import annotations
import guidata.dataset as gds
from sigima.objects.base import (
NormalDistributionParam,
PoissonDistributionParam,
UniformDistributionParam,
)
from sigima.objects.image import (
ImageObj,
NormalDistribution2DParam,
PoissonDistribution2DParam,
UniformDistribution2DParam,
create_image_from_param,
)
from sigima.proc.decorator import computation_function
from sigima.proc.image.arithmetic import addition
from sigima.proc.image.base import dst_1_to_1
[docs]
@computation_function()
def add_gaussian_noise(src: ImageObj, p: NormalDistributionParam) -> ImageObj:
"""Add Gaussian (normal) noise to the input image.
Args:
src: Source image.
p: Parameters.
Returns:
Result image object.
"""
param = NormalDistribution2DParam() # Do not confuse with NormalDistributionParam
gds.update_dataset(param, p)
assert src.data is not None
shape = src.data.shape
param.height = shape[0]
param.width = shape[1]
param.dtype = src.data.dtype
noise = create_image_from_param(param)
dst = dst_1_to_1(src, "add_gaussian_noise", f"mu={p.mu},sigma={p.sigma}")
dst.data = addition([dst, noise]).data
return dst
[docs]
@computation_function()
def add_poisson_noise(src: ImageObj, p: PoissonDistributionParam) -> ImageObj:
"""Add Poisson noise to the input image.
Args:
src: Source image.
p: Parameters.
Returns:
Result image object.
"""
param = PoissonDistribution2DParam() # Do not confuse with PoissonDistributionParam
gds.update_dataset(param, p)
assert src.data is not None
shape = src.data.shape
param.height = shape[0]
param.width = shape[1]
param.dtype = src.data.dtype
noise = create_image_from_param(param)
dst = dst_1_to_1(src, "add_poisson_noise", f"lam={p.lam}")
dst.data = addition([dst, noise]).data
return dst
