mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-06 05:16:40 +08:00
Josh Warner (Mac)
130 lines
4.5 KiB
Python
130 lines
4.5 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, **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
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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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'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.
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's&p' Replaces random pixels with 0 or 1.
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'speckle' Multiplicative noise using out = image + n*image, where
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n is uniform noise with specified mean & variance.
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seed : int
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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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mean : float
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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
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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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amount : float
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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
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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].
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"""
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mode = mode.lower()
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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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'poisson': '',
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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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allowedkwargs = {
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'gaussian_values': ['mean', 'var'],
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'sp_values': ['amount'],
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's&p_values': ['amount', 'salt_vs_pepper']}
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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 = np.clip(image + noise, 0., 1.)
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elif mode == 'poisson':
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# Generating noise for each unique value in image.
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out = np.zeros_like(image)
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for val in np.unique(image):
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# Generate mask for a unique value, replace w/values drawn from
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# Poisson distribution about the unique value
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mask = image == val
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out[mask] = np.poisson(val, mask.sum())
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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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# This mode makes no effort to avoid repeat sampling. Thus, the
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# exact number of replaced pixels is only approximate.
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out = image.copy()
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# Salt mode
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num_salt = np.ceil(
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kwargs['amount'] * image.size * kwargs['salt_vs_pepper'])
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coords = [np.random.randint(0, i - 1, int(num_salt))
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for i in image.shape]
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out[coords] = 1
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# Pepper mode
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num_pepper = np.ceil(
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kwargs['amount'] * image.size * (1. - kwargs['salt_vs_pepper']))
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coords = [np.random.randint(0, i - 1, int(num_pepper))
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for i in image.shape]
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out[coords] = 0
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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 = np.clip(image + image * noise, 0., 1.)
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return out
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