From 79a39d8e85ffa69db7872816435eecda404f43ab Mon Sep 17 00:00:00 2001 From: Edoardo Pasca Date: Thu, 20 Jun 2019 16:30:11 +0100 Subject: add random_noise to TestData --- Wrappers/Python/ccpi/framework/TestData.py | 269 ++++++++++++++++++++++++----- 1 file changed, 221 insertions(+), 48 deletions(-) (limited to 'Wrappers') diff --git a/Wrappers/Python/ccpi/framework/TestData.py b/Wrappers/Python/ccpi/framework/TestData.py index e7dc908..20931d7 100755 --- a/Wrappers/Python/ccpi/framework/TestData.py +++ b/Wrappers/Python/ccpi/framework/TestData.py @@ -69,53 +69,226 @@ class TestData(object): print ("data.geometry", data.geometry) return data - def camera(**kwargs): - - tmp = Image.open(os.path.join(data_dir, 'camera.png')) - - size = kwargs.get('size',(512, 512)) - - data = numpy.array(tmp.resize(size)) - - data = data/data.max() - - return ImageData(data) - - - def boat(**kwargs): - - tmp = Image.open(os.path.join(data_dir, 'boat.tiff')) - - size = kwargs.get('size',(512, 512)) - - data = numpy.array(tmp.resize(size)) - - data = data/data.max() - - return ImageData(data) - - - def peppers(**kwargs): - - tmp = Image.open(os.path.join(data_dir, 'peppers.tiff')) - - size = kwargs.get('size',(512, 512)) - - data = numpy.array(tmp.resize(size)) - - data = data/data.max() - - return ImageData(data) - - def shapes(**kwargs): - - tmp = Image.open(os.path.join(data_dir, 'shapes.png')).convert('LA') - - size = kwargs.get('size',(300, 200)) + @staticmethod + def random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs): + if issubclass(type(image), DataContainer): + arr = scikit_random_noise(image, mode=mode, seed=seed, clip=clip, + **kwargs) + out = image.copy() + out.fill(arr) + return out + elif issubclass(type(image), numpy.ndarray): + return scikit_random_noise(image, mode=mode, seed=seed, clip=clip, + **kwargs) + + @staticmethod + def scikit_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 uniform 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. - data = numpy.array(tmp.resize(size)) - - data = data/data.max() + This function is adapted from scikit-image. + https://github.com/scikit-image/scikit-image/blob/master/skimage/util/noise.py + + Copyright (C) 2019, the scikit-image team + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + 3. Neither the name of skimage nor the names of its contributors may be + used to endorse or promote products derived from this software without + specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. - return ImageData(data) - + """ + mode = mode.lower() + + # Detect if a signed image was input + if image.min() < 0: + low_clip = -1. + else: + low_clip = 0. + + image = numpy.asarray(image, dtype=(np.float64)) + 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 \ No newline at end of file -- cgit v1.2.3