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-rwxr-xr-xWrappers/Python/ccpi/framework/TestData.py684
-rwxr-xr-xWrappers/Python/ccpi/optimisation/algorithms/Algorithm.py11
-rwxr-xr-xWrappers/Python/test/test_algorithms.py2
3 files changed, 352 insertions, 345 deletions
diff --git a/Wrappers/Python/ccpi/framework/TestData.py b/Wrappers/Python/ccpi/framework/TestData.py
index 74d37be..2bb18ce 100755
--- a/Wrappers/Python/ccpi/framework/TestData.py
+++ b/Wrappers/Python/ccpi/framework/TestData.py
@@ -1,342 +1,342 @@
-# -*- coding: utf-8 -*-
-# CCP in Tomographic Imaging (CCPi) Core Imaging Library (CIL).
-
-# Copyright 2017 UKRI-STFC
-# Copyright 2017 University of Manchester
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-# http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
-from ccpi.framework import ImageData, ImageGeometry, DataContainer
-import numpy
-import numpy as np
-from PIL import Image
-import os
-import os.path
-import sys
-
-data_dir = os.path.abspath(os.path.join(
- os.path.dirname(__file__),
- '../data/')
-)
-
-# this is the default location after a conda install
-data_dir = os.path.abspath(
- os.path.join(sys.prefix, 'share','ccpi')
-)
-
-class TestData(object):
- '''Class to return test data
-
- provides 6 dataset:
- BOAT = 'boat.tiff'
- CAMERA = 'camera.png'
- PEPPERS = 'peppers.tiff'
- RESOLUTION_CHART = 'resolution_chart.tiff'
- SIMPLE_PHANTOM_2D = 'hotdog'
- SHAPES = 'shapes.png'
-
- '''
- BOAT = 'boat.tiff'
- CAMERA = 'camera.png'
- PEPPERS = 'peppers.tiff'
- RESOLUTION_CHART = 'resolution_chart.tiff'
- SIMPLE_PHANTOM_2D = 'hotdog'
- SHAPES = 'shapes.png'
-
- def __init__(self, **kwargs):
- self.data_dir = kwargs.get('data_dir', data_dir)
-
- def load(self, which, size=(512,512), scale=(0,1), **kwargs):
- if which not in [TestData.BOAT, TestData.CAMERA,
- TestData.PEPPERS, TestData.RESOLUTION_CHART,
- TestData.SIMPLE_PHANTOM_2D, TestData.SHAPES]:
- raise ValueError('Unknown TestData {}.'.format(which))
- if which == TestData.SIMPLE_PHANTOM_2D:
- N = size[0]
- M = size[1]
- sdata = numpy.zeros((N, M))
- sdata[int(round(N/4)):int(round(3*N/4)), int(round(N/4)):int(round(3*N/4))] = 0.5
- sdata[int(round(M/8)):int(round(7*M/8)), int(round(3*M/8)):int(round(5*M/8))] = 1
- ig = ImageGeometry(voxel_num_x = N, voxel_num_y = M, dimension_labels=[ImageGeometry.HORIZONTAL_X, ImageGeometry.HORIZONTAL_Y])
- data = ig.allocate()
- data.fill(sdata)
-
- elif which == TestData.SHAPES:
-
- tmp = numpy.array(Image.open(os.path.join(self.data_dir, which)).convert('L'))
- N = 200
- M = 300
- ig = ImageGeometry(voxel_num_x = N, voxel_num_y = M, dimension_labels=[ImageGeometry.HORIZONTAL_X, ImageGeometry.HORIZONTAL_Y])
- data = ig.allocate()
- data.fill(tmp/numpy.max(tmp))
-
- else:
- tmp = Image.open(os.path.join(self.data_dir, which))
- print (tmp)
- bands = tmp.getbands()
- if len(bands) > 1:
- ig = ImageGeometry(voxel_num_x=size[0], voxel_num_y=size[1], channels=len(bands),
- dimension_labels=[ImageGeometry.HORIZONTAL_X, ImageGeometry.HORIZONTAL_Y, ImageGeometry.CHANNEL])
- data = ig.allocate()
- else:
- ig = ImageGeometry(voxel_num_x = size[0], voxel_num_y = size[1], dimension_labels=[ImageGeometry.HORIZONTAL_X, ImageGeometry.HORIZONTAL_Y])
- data = ig.allocate()
- data.fill(numpy.array(tmp.resize((size[1],size[0]))))
- if scale is not None:
- dmax = data.as_array().max()
- dmin = data.as_array().min()
- # scale 0,1
- data = (data -dmin) / (dmax - dmin)
- if scale != (0,1):
- #data = (data-dmin)/(dmax-dmin) * (scale[1]-scale[0]) +scale[0])
- data *= (scale[1]-scale[0])
- data += scale[0]
- print ("data.geometry", data.geometry)
- return data
-
- @staticmethod
- def random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs):
- '''Function to add noise to input image
-
- :param image: input dataset, DataContainer of numpy.ndarray
- :param mode: type of noise
- :param seed: seed for random number generator
- :param clip: should clip the data.
- See https://github.com/scikit-image/scikit-image/blob/master/skimage/util/noise.py
-
- '''
- if issubclass(type(image), DataContainer):
- arr = TestData.scikit_random_noise(image.as_array(), mode=mode, seed=seed, clip=clip,
- **kwargs)
- out = image.copy()
- out.fill(arr)
- return out
- elif issubclass(type(image), numpy.ndarray):
- return TestData.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.
-
- 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.
-
- """
- 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
+# -*- coding: utf-8 -*-
+# CCP in Tomographic Imaging (CCPi) Core Imaging Library (CIL).
+
+# Copyright 2017 UKRI-STFC
+# Copyright 2017 University of Manchester
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+# http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+from ccpi.framework import ImageData, ImageGeometry, DataContainer
+import numpy
+import numpy as np
+from PIL import Image
+import os
+import os.path
+import sys
+
+data_dir = os.path.abspath(os.path.join(
+ os.path.dirname(__file__),
+ '../data/')
+)
+
+# this is the default location after a conda install
+data_dir = os.path.abspath(
+ os.path.join(sys.prefix, 'share','ccpi')
+)
+
+class TestData(object):
+ '''Class to return test data
+
+ provides 6 dataset:
+ BOAT = 'boat.tiff'
+ CAMERA = 'camera.png'
+ PEPPERS = 'peppers.tiff'
+ RESOLUTION_CHART = 'resolution_chart.tiff'
+ SIMPLE_PHANTOM_2D = 'hotdog'
+ SHAPES = 'shapes.png'
+
+ '''
+ BOAT = 'boat.tiff'
+ CAMERA = 'camera.png'
+ PEPPERS = 'peppers.tiff'
+ RESOLUTION_CHART = 'resolution_chart.tiff'
+ SIMPLE_PHANTOM_2D = 'hotdog'
+ SHAPES = 'shapes.png'
+
+ def __init__(self, **kwargs):
+ self.data_dir = kwargs.get('data_dir', data_dir)
+
+ def load(self, which, size=(512,512), scale=(0,1), **kwargs):
+ if which not in [TestData.BOAT, TestData.CAMERA,
+ TestData.PEPPERS, TestData.RESOLUTION_CHART,
+ TestData.SIMPLE_PHANTOM_2D, TestData.SHAPES]:
+ raise ValueError('Unknown TestData {}.'.format(which))
+ if which == TestData.SIMPLE_PHANTOM_2D:
+ N = size[0]
+ M = size[1]
+ sdata = numpy.zeros((N, M))
+ sdata[int(round(N/4)):int(round(3*N/4)), int(round(N/4)):int(round(3*N/4))] = 0.5
+ sdata[int(round(M/8)):int(round(7*M/8)), int(round(3*M/8)):int(round(5*M/8))] = 1
+ ig = ImageGeometry(voxel_num_x = N, voxel_num_y = M, dimension_labels=[ImageGeometry.HORIZONTAL_X, ImageGeometry.HORIZONTAL_Y])
+ data = ig.allocate()
+ data.fill(sdata)
+
+ elif which == TestData.SHAPES:
+
+ tmp = numpy.array(Image.open(os.path.join(self.data_dir, which)).convert('L'))
+ N = 200
+ M = 300
+ ig = ImageGeometry(voxel_num_x = N, voxel_num_y = M, dimension_labels=[ImageGeometry.HORIZONTAL_X, ImageGeometry.HORIZONTAL_Y])
+ data = ig.allocate()
+ data.fill(tmp/numpy.max(tmp))
+
+ else:
+ tmp = Image.open(os.path.join(self.data_dir, which))
+ print (tmp)
+ bands = tmp.getbands()
+ if len(bands) > 1:
+ ig = ImageGeometry(voxel_num_x=size[0], voxel_num_y=size[1], channels=len(bands),
+ dimension_labels=[ImageGeometry.HORIZONTAL_X, ImageGeometry.HORIZONTAL_Y, ImageGeometry.CHANNEL])
+ data = ig.allocate()
+ else:
+ ig = ImageGeometry(voxel_num_x = size[0], voxel_num_y = size[1], dimension_labels=[ImageGeometry.HORIZONTAL_X, ImageGeometry.HORIZONTAL_Y])
+ data = ig.allocate()
+ data.fill(numpy.array(tmp.resize((size[1],size[0]))))
+ if scale is not None:
+ dmax = data.as_array().max()
+ dmin = data.as_array().min()
+ # scale 0,1
+ data = (data -dmin) / (dmax - dmin)
+ if scale != (0,1):
+ #data = (data-dmin)/(dmax-dmin) * (scale[1]-scale[0]) +scale[0])
+ data *= (scale[1]-scale[0])
+ data += scale[0]
+ # print ("data.geometry", data.geometry)
+ return data
+
+ @staticmethod
+ def random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs):
+ '''Function to add noise to input image
+
+ :param image: input dataset, DataContainer of numpy.ndarray
+ :param mode: type of noise
+ :param seed: seed for random number generator
+ :param clip: should clip the data.
+ See https://github.com/scikit-image/scikit-image/blob/master/skimage/util/noise.py
+
+ '''
+ if issubclass(type(image), DataContainer):
+ arr = TestData.scikit_random_noise(image.as_array(), mode=mode, seed=seed, clip=clip,
+ **kwargs)
+ out = image.copy()
+ out.fill(arr)
+ return out
+ elif issubclass(type(image), numpy.ndarray):
+ return TestData.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.
+
+ 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.
+
+ """
+ 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
diff --git a/Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py b/Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py
index fec37c5..f08688d 100755
--- a/Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py
+++ b/Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py
@@ -155,8 +155,15 @@ class Algorithm(object):
raise ValueError('Update objective interval must be an integer >= 1')
else:
raise ValueError('Update objective interval must be an integer >= 1')
- def run(self, iterations, verbose=True, callback=None):
- '''run n iterations and update the user with the callback if specified'''
+ def run(self, iterations=None, verbose=True, callback=None):
+ '''run n iterations and update the user with the callback if specified
+
+ :param iterations: number of iterations to run. If not set the algorithm will
+ run until max_iteration or until stop criterion is reached
+ :param verbose: toggles verbose output to screen
+ :param callback: is a function that receives: current iteration number,
+ last objective function value and the current solution
+ '''
if self.should_stop():
print ("Stop cryterion has been reached.")
i = 0
diff --git a/Wrappers/Python/test/test_algorithms.py b/Wrappers/Python/test/test_algorithms.py
index d129382..2b38e3f 100755
--- a/Wrappers/Python/test/test_algorithms.py
+++ b/Wrappers/Python/test/test_algorithms.py
@@ -368,7 +368,7 @@ class TestAlgorithms(unittest.TestCase):
fista = FISTA(x_init=x_init , f=reg, g=fid)
fista.max_iteration = 3000
fista.update_objective_interval = 500
- fista.run(3000, verbose=True)
+ fista.run(verbose=True)
rmse = (fista.get_output() - data).norm() / data.as_array().size
print ("RMSE", rmse)
self.assertLess(rmse, 4.2e-4)
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-09 02:15:15 +0000