forked from 170010011/fr
193 lines
7.5 KiB
Python
193 lines
7.5 KiB
Python
|
import numpy as np
|
||
|
|
from .dtype import img_as_float
|
||
|
|
|
||
|
|
|
||
|
|
__all__ = ['random_noise']
|
||
|
|
|
||
|
|
|
||
|
|
def random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs):
|
||
|
|
"""
|
||
|
|
Function to add random noise of various types to a floating-point image.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
image : ndarray
|
||
|
|
Input image data. Will be converted to float.
|
||
|
|
mode : str, optional
|
||
|
|
One of the following strings, selecting the type of noise to add:
|
||
|
|
|
||
|
|
- 'gaussian' Gaussian-distributed additive noise.
|
||
|
|
- 'localvar' Gaussian-distributed additive noise, with specified
|
||
|
|
local variance at each point of `image`.
|
||
|
|
- 'poisson' Poisson-distributed noise generated from the data.
|
||
|
|
- 'salt' Replaces random pixels with 1.
|
||
|
|
- 'pepper' Replaces random pixels with 0 (for unsigned images) or
|
||
|
|
-1 (for signed images).
|
||
|
|
- 's&p' Replaces random pixels with either 1 or `low_val`, where
|
||
|
|
`low_val` is 0 for unsigned images or -1 for signed
|
||
|
|
images.
|
||
|
|
- 'speckle' Multiplicative noise using out = image + n*image, where
|
||
|
|
n is Gaussian noise with specified mean & variance.
|
||
|
|
seed : int, optional
|
||
|
|
If provided, this will set the random seed before generating noise,
|
||
|
|
for valid pseudo-random comparisons.
|
||
|
|
clip : bool, optional
|
||
|
|
If True (default), the output will be clipped after noise applied
|
||
|
|
for modes `'speckle'`, `'poisson'`, and `'gaussian'`. This is
|
||
|
|
needed to maintain the proper image data range. If False, clipping
|
||
|
|
is not applied, and the output may extend beyond the range [-1, 1].
|
||
|
|
mean : float, optional
|
||
|
|
Mean of random distribution. Used in 'gaussian' and 'speckle'.
|
||
|
|
Default : 0.
|
||
|
|
var : float, optional
|
||
|
|
Variance of random distribution. Used in 'gaussian' and 'speckle'.
|
||
|
|
Note: variance = (standard deviation) ** 2. Default : 0.01
|
||
|
|
local_vars : ndarray, optional
|
||
|
|
Array of positive floats, same shape as `image`, defining the local
|
||
|
|
variance at every image point. Used in 'localvar'.
|
||
|
|
amount : float, optional
|
||
|
|
Proportion of image pixels to replace with noise on range [0, 1].
|
||
|
|
Used in 'salt', 'pepper', and 'salt & pepper'. Default : 0.05
|
||
|
|
salt_vs_pepper : float, optional
|
||
|
|
Proportion of salt vs. pepper noise for 's&p' on range [0, 1].
|
||
|
|
Higher values represent more salt. Default : 0.5 (equal amounts)
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
out : ndarray
|
||
|
|
Output floating-point image data on range [0, 1] or [-1, 1] if the
|
||
|
|
input `image` was unsigned or signed, respectively.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Speckle, Poisson, Localvar, and Gaussian noise may generate noise outside
|
||
|
|
the valid image range. The default is to clip (not alias) these values,
|
||
|
|
but they may be preserved by setting `clip=False`. Note that in this case
|
||
|
|
the output may contain values outside the ranges [0, 1] or [-1, 1].
|
||
|
|
Use this option with care.
|
||
|
|
|
||
|
|
Because of the prevalence of exclusively positive floating-point images in
|
||
|
|
intermediate calculations, it is not possible to intuit if an input is
|
||
|
|
signed based on dtype alone. Instead, negative values are explicitly
|
||
|
|
searched for. Only if found does this function assume signed input.
|
||
|
|
Unexpected results only occur in rare, poorly exposes cases (e.g. if all
|
||
|
|
values are above 50 percent gray in a signed `image`). In this event,
|
||
|
|
manually scaling the input to the positive domain will solve the problem.
|
||
|
|
|
||
|
|
The Poisson distribution is only defined for positive integers. To apply
|
||
|
|
this noise type, the number of unique values in the image is found and
|
||
|
|
the next round power of two is used to scale up the floating-point result,
|
||
|
|
after which it is scaled back down to the floating-point image range.
|
||
|
|
|
||
|
|
To generate Poisson noise against a signed image, the signed image is
|
||
|
|
temporarily converted to an unsigned image in the floating point domain,
|
||
|
|
Poisson noise is generated, then it is returned to the original range.
|
||
|
|
|
||
|
|
"""
|
||
|
|
mode = mode.lower()
|
||
|
|
|
||
|
|
# Detect if a signed image was input
|
||
|
|
if image.min() < 0:
|
||
|
|
low_clip = -1.
|
||
|
|
else:
|
||
|
|
low_clip = 0.
|
||
|
|
|
||
|
|
image = img_as_float(image)
|
||
|
|
if seed is not None:
|
||
|
|
np.random.seed(seed=seed)
|
||
|
|
|
||
|
|
allowedtypes = {
|
||
|
|
'gaussian': 'gaussian_values',
|
||
|
|
'localvar': 'localvar_values',
|
||
|
|
'poisson': 'poisson_values',
|
||
|
|
'salt': 'sp_values',
|
||
|
|
'pepper': 'sp_values',
|
||
|
|
's&p': 's&p_values',
|
||
|
|
'speckle': 'gaussian_values'}
|
||
|
|
|
||
|
|
kwdefaults = {
|
||
|
|
'mean': 0.,
|
||
|
|
'var': 0.01,
|
||
|
|
'amount': 0.05,
|
||
|
|
'salt_vs_pepper': 0.5,
|
||
|
|
'local_vars': np.zeros_like(image) + 0.01}
|
||
|
|
|
||
|
|
allowedkwargs = {
|
||
|
|
'gaussian_values': ['mean', 'var'],
|
||
|
|
'localvar_values': ['local_vars'],
|
||
|
|
'sp_values': ['amount'],
|
||
|
|
's&p_values': ['amount', 'salt_vs_pepper'],
|
||
|
|
'poisson_values': []}
|
||
|
|
|
||
|
|
for key in kwargs:
|
||
|
|
if key not in allowedkwargs[allowedtypes[mode]]:
|
||
|
|
raise ValueError('%s keyword not in allowed keywords %s' %
|
||
|
|
(key, allowedkwargs[allowedtypes[mode]]))
|
||
|
|
|
||
|
|
# Set kwarg defaults
|
||
|
|
for kw in allowedkwargs[allowedtypes[mode]]:
|
||
|
|
kwargs.setdefault(kw, kwdefaults[kw])
|
||
|
|
|
||
|
|
if mode == 'gaussian':
|
||
|
|
noise = np.random.normal(kwargs['mean'], kwargs['var'] ** 0.5,
|
||
|
|
image.shape)
|
||
|
|
out = image + noise
|
||
|
|
|
||
|
|
elif mode == 'localvar':
|
||
|
|
# Ensure local variance input is correct
|
||
|
|
if (kwargs['local_vars'] <= 0).any():
|
||
|
|
raise ValueError('All values of `local_vars` must be > 0.')
|
||
|
|
|
||
|
|
# Safe shortcut usage broadcasts kwargs['local_vars'] as a ufunc
|
||
|
|
out = image + np.random.normal(0, kwargs['local_vars'] ** 0.5)
|
||
|
|
|
||
|
|
elif mode == 'poisson':
|
||
|
|
# Determine unique values in image & calculate the next power of two
|
||
|
|
vals = len(np.unique(image))
|
||
|
|
vals = 2 ** np.ceil(np.log2(vals))
|
||
|
|
|
||
|
|
# Ensure image is exclusively positive
|
||
|
|
if low_clip == -1.:
|
||
|
|
old_max = image.max()
|
||
|
|
image = (image + 1.) / (old_max + 1.)
|
||
|
|
|
||
|
|
# Generating noise for each unique value in image.
|
||
|
|
out = np.random.poisson(image * vals) / float(vals)
|
||
|
|
|
||
|
|
# Return image to original range if input was signed
|
||
|
|
if low_clip == -1.:
|
||
|
|
out = out * (old_max + 1.) - 1.
|
||
|
|
|
||
|
|
elif mode == 'salt':
|
||
|
|
# Re-call function with mode='s&p' and p=1 (all salt noise)
|
||
|
|
out = random_noise(image, mode='s&p', seed=seed,
|
||
|
|
amount=kwargs['amount'], salt_vs_pepper=1.)
|
||
|
|
|
||
|
|
elif mode == 'pepper':
|
||
|
|
# Re-call function with mode='s&p' and p=1 (all pepper noise)
|
||
|
|
out = random_noise(image, mode='s&p', seed=seed,
|
||
|
|
amount=kwargs['amount'], salt_vs_pepper=0.)
|
||
|
|
|
||
|
|
elif mode == 's&p':
|
||
|
|
out = image.copy()
|
||
|
|
p = kwargs['amount']
|
||
|
|
q = kwargs['salt_vs_pepper']
|
||
|
|
flipped = np.random.choice([True, False], size=image.shape,
|
||
|
|
p=[p, 1 - p])
|
||
|
|
salted = np.random.choice([True, False], size=image.shape,
|
||
|
|
p=[q, 1 - q])
|
||
|
|
peppered = ~salted
|
||
|
|
out[flipped & salted] = 1
|
||
|
|
out[flipped & peppered] = low_clip
|
||
|
|
|
||
|
|
elif mode == 'speckle':
|
||
|
|
noise = np.random.normal(kwargs['mean'], kwargs['var'] ** 0.5,
|
||
|
|
image.shape)
|
||
|
|
out = image + image * noise
|
||
|
|
|
||
|
|
# Clip back to original range, if necessary
|
||
|
|
if clip:
|
||
|
|
out = np.clip(out, low_clip, 1.0)
|
||
|
|
|
||
|
|
return out
|