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