diff options
90 files changed, 6594 insertions, 265 deletions
diff --git a/Wrappers/Python/build/lib/ccpi/__init__.py b/Wrappers/Python/build/lib/ccpi/__init__.py new file mode 100644 index 0000000..cf2d93d --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/__init__.py @@ -0,0 +1,18 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018 Edoardo Pasca + +# 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.
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/contrib/__init__.py b/Wrappers/Python/build/lib/ccpi/contrib/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/contrib/__init__.py diff --git a/Wrappers/Python/build/lib/ccpi/data/__init__.py b/Wrappers/Python/build/lib/ccpi/data/__init__.py new file mode 100644 index 0000000..af10536 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/data/__init__.py @@ -0,0 +1,67 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018 Edoardo Pasca + +# 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 ccpi.framework import ImageData +import numpy +from PIL import Image +import os +import os.path + +data_dir = os.path.abspath(os.path.dirname(__file__)) + + +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) + diff --git a/Wrappers/Python/build/lib/ccpi/framework/__init__.py b/Wrappers/Python/build/lib/ccpi/framework/__init__.py new file mode 100644 index 0000000..229edb5 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/framework/__init__.py @@ -0,0 +1,26 @@ +# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 5 16:00:18 2019
+
+@author: ofn77899
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy
+import sys
+from datetime import timedelta, datetime
+import warnings
+from functools import reduce
+
+
+from .framework import DataContainer
+from .framework import ImageData, AcquisitionData
+from .framework import ImageGeometry, AcquisitionGeometry
+from .framework import find_key, message
+from .framework import DataProcessor
+from .framework import AX, PixelByPixelDataProcessor, CastDataContainer
+from .BlockDataContainer import BlockDataContainer
+from .BlockGeometry import BlockGeometry
diff --git a/Wrappers/Python/build/lib/ccpi/framework/framework.py b/Wrappers/Python/build/lib/ccpi/framework/framework.py new file mode 100644 index 0000000..dbe7d0a --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/framework/framework.py @@ -0,0 +1,1437 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 Edoardo Pasca + +# 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 + +import numpy +import sys +from datetime import timedelta, datetime +import warnings +from functools import reduce +from numbers import Number + + +def find_key(dic, val): + """return the key of dictionary dic given the value""" + return [k for k, v in dic.items() if v == val][0] + +def message(cls, msg, *args): + msg = "{0}: " + msg + for i in range(len(args)): + msg += " {%d}" %(i+1) + args = list(args) + args.insert(0, cls.__name__ ) + + return msg.format(*args ) + + +class ImageGeometry(object): + RANDOM = 'random' + RANDOM_INT = 'random_int' + CHANNEL = 'channel' + ANGLE = 'angle' + VERTICAL = 'vertical' + HORIZONTAL_X = 'horizontal_x' + HORIZONTAL_Y = 'horizontal_y' + + def __init__(self, + voxel_num_x=0, + voxel_num_y=0, + voxel_num_z=0, + voxel_size_x=1, + voxel_size_y=1, + voxel_size_z=1, + center_x=0, + center_y=0, + center_z=0, + channels=1): + + self.voxel_num_x = voxel_num_x + self.voxel_num_y = voxel_num_y + self.voxel_num_z = voxel_num_z + self.voxel_size_x = voxel_size_x + self.voxel_size_y = voxel_size_y + self.voxel_size_z = voxel_size_z + self.center_x = center_x + self.center_y = center_y + self.center_z = center_z + self.channels = channels + + # this is some code repetition + if self.channels > 1: + if self.voxel_num_z>1: + self.length = 4 + self.shape = (self.channels, self.voxel_num_z, self.voxel_num_y, self.voxel_num_x) + dim_labels = [ImageGeometry.CHANNEL, ImageGeometry.VERTICAL, + ImageGeometry.HORIZONTAL_Y, ImageGeometry.HORIZONTAL_X] + else: + self.length = 3 + self.shape = (self.channels, self.voxel_num_y, self.voxel_num_x) + dim_labels = [ImageGeometry.CHANNEL, ImageGeometry.HORIZONTAL_Y, ImageGeometry.HORIZONTAL_X] + else: + if self.voxel_num_z>1: + self.length = 3 + self.shape = (self.voxel_num_z, self.voxel_num_y, self.voxel_num_x) + dim_labels = [ImageGeometry.VERTICAL, ImageGeometry.HORIZONTAL_Y, + ImageGeometry.HORIZONTAL_X] + else: + self.length = 2 + self.shape = (self.voxel_num_y, self.voxel_num_x) + dim_labels = [ImageGeometry.HORIZONTAL_Y, ImageGeometry.HORIZONTAL_X] + + self.dimension_labels = dim_labels + + def get_min_x(self): + return self.center_x - 0.5*self.voxel_num_x*self.voxel_size_x + + def get_max_x(self): + return self.center_x + 0.5*self.voxel_num_x*self.voxel_size_x + + def get_min_y(self): + return self.center_y - 0.5*self.voxel_num_y*self.voxel_size_y + + def get_max_y(self): + return self.center_y + 0.5*self.voxel_num_y*self.voxel_size_y + + def get_min_z(self): + if not self.voxel_num_z == 0: + return self.center_z - 0.5*self.voxel_num_z*self.voxel_size_z + else: + return 0 + + def get_max_z(self): + if not self.voxel_num_z == 0: + return self.center_z + 0.5*self.voxel_num_z*self.voxel_size_z + else: + return 0 + + def clone(self): + '''returns a copy of ImageGeometry''' + return ImageGeometry( + self.voxel_num_x, + self.voxel_num_y, + self.voxel_num_z, + self.voxel_size_x, + self.voxel_size_y, + self.voxel_size_z, + self.center_x, + self.center_y, + self.center_z, + self.channels) + def __str__ (self): + repres = "" + repres += "Number of channels: {0}\n".format(self.channels) + repres += "voxel_num : x{0},y{1},z{2}\n".format(self.voxel_num_x, self.voxel_num_y, self.voxel_num_z) + repres += "voxel_size : x{0},y{1},z{2}\n".format(self.voxel_size_x, self.voxel_size_y, self.voxel_size_z) + repres += "center : x{0},y{1},z{2}\n".format(self.center_x, self.center_y, self.center_z) + return repres + def allocate(self, value=0, dimension_labels=None, **kwargs): + '''allocates an ImageData according to the size expressed in the instance''' + out = ImageData(geometry=self) + if isinstance(value, Number): + if value != 0: + out += value + else: + if value == ImageGeometry.RANDOM: + seed = kwargs.get('seed', None) + if seed is not None: + numpy.random.seed(seed) + out.fill(numpy.random.random_sample(self.shape)) + elif value == ImageGeometry.RANDOM_INT: + seed = kwargs.get('seed', None) + if seed is not None: + numpy.random.seed(seed) + max_value = kwargs.get('max_value', 100) + out.fill(numpy.random.randint(max_value,size=self.shape)) + else: + raise ValueError('Value {} unknown'.format(value)) + if dimension_labels is not None: + if dimension_labels != self.dimension_labels: + return out.subset(dimensions=dimension_labels) + return out + # The following methods return 2 members of the class, therefore I + # don't think we need to implement them. + # Additionally using __len__ is confusing as one would think this is + # an iterable. + #def __len__(self): + # '''returns the length of the geometry''' + # return self.length + #def shape(self): + # '''Returns the shape of the array of the ImageData it describes''' + # return self.shape + +class AcquisitionGeometry(object): + RANDOM = 'random' + RANDOM_INT = 'random_int' + ANGLE_UNIT = 'angle_unit' + DEGREE = 'degree' + RADIAN = 'radian' + CHANNEL = 'channel' + ANGLE = 'angle' + VERTICAL = 'vertical' + HORIZONTAL = 'horizontal' + def __init__(self, + geom_type, + dimension, + angles, + pixel_num_h=0, + pixel_size_h=1, + pixel_num_v=0, + pixel_size_v=1, + dist_source_center=None, + dist_center_detector=None, + channels=1, + **kwargs + ): + """ + General inputs for standard type projection geometries + detectorDomain or detectorpixelSize: + If 2D + If scalar: Width of detector or single detector pixel + If 2-vec: Error + If 3D + If scalar: Width in both dimensions + If 2-vec: Vertical then horizontal size + grid + If 2D + If scalar: number of detectors + If 2-vec: error + If 3D + If scalar: Square grid that size + If 2-vec vertical then horizontal size + cone or parallel + 2D or 3D + parallel_parameters: ? + cone_parameters: + source_to_center_dist (if parallel: NaN) + center_to_detector_dist (if parallel: NaN) + standard or nonstandard (vec) geometry + angles + angles_format radians or degrees + """ + self.geom_type = geom_type # 'parallel' or 'cone' + self.dimension = dimension # 2D or 3D + self.angles = angles + num_of_angles = len (angles) + + self.dist_source_center = dist_source_center + self.dist_center_detector = dist_center_detector + + self.pixel_num_h = pixel_num_h + self.pixel_size_h = pixel_size_h + self.pixel_num_v = pixel_num_v + self.pixel_size_v = pixel_size_v + + self.channels = channels + self.angle_unit=kwargs.get(AcquisitionGeometry.ANGLE_UNIT, + AcquisitionGeometry.DEGREE) + if channels > 1: + if pixel_num_v > 1: + shape = (channels, num_of_angles , pixel_num_v, pixel_num_h) + dim_labels = [AcquisitionGeometry.CHANNEL , + AcquisitionGeometry.ANGLE , AcquisitionGeometry.VERTICAL , + AcquisitionGeometry.HORIZONTAL] + else: + shape = (channels , num_of_angles, pixel_num_h) + dim_labels = [AcquisitionGeometry.CHANNEL , + AcquisitionGeometry.ANGLE, AcquisitionGeometry.HORIZONTAL] + else: + if pixel_num_v > 1: + shape = (num_of_angles, pixel_num_v, pixel_num_h) + dim_labels = [AcquisitionGeometry.ANGLE , AcquisitionGeometry.VERTICAL , + AcquisitionGeometry.HORIZONTAL] + else: + shape = (num_of_angles, pixel_num_h) + dim_labels = [AcquisitionGeometry.ANGLE, AcquisitionGeometry.HORIZONTAL] + self.shape = shape + + self.dimension_labels = dim_labels + + def clone(self): + '''returns a copy of the AcquisitionGeometry''' + return AcquisitionGeometry(self.geom_type, + self.dimension, + self.angles, + self.pixel_num_h, + self.pixel_size_h, + self.pixel_num_v, + self.pixel_size_v, + self.dist_source_center, + self.dist_center_detector, + self.channels) + + def __str__ (self): + repres = "" + repres += "Number of dimensions: {0}\n".format(self.dimension) + repres += "angles: {0}\n".format(self.angles) + repres += "voxel_num : h{0},v{1}\n".format(self.pixel_num_h, self.pixel_num_v) + repres += "voxel size: h{0},v{1}\n".format(self.pixel_size_h, self.pixel_size_v) + repres += "geometry type: {0}\n".format(self.geom_type) + repres += "distance source-detector: {0}\n".format(self.dist_source_center) + repres += "distance center-detector: {0}\n".format(self.dist_source_center) + repres += "number of channels: {0}\n".format(self.channels) + return repres + def allocate(self, value=0, dimension_labels=None): + '''allocates an AcquisitionData according to the size expressed in the instance''' + out = AcquisitionData(geometry=self) + if isinstance(value, Number): + if value != 0: + out += value + else: + if value == AcquisitionData.RANDOM: + seed = kwargs.get('seed', None) + if seed is not None: + numpy.random.seed(seed) + out.fill(numpy.random.random_sample(self.shape)) + elif value == AcquisitionData.RANDOM_INT: + seed = kwargs.get('seed', None) + if seed is not None: + numpy.random.seed(seed) + max_value = kwargs.get('max_value', 100) + out.fill(numpy.random.randint(max_value,size=self.shape)) + else: + raise ValueError('Value {} unknown'.format(value)) + if dimension_labels is not None: + if dimension_labels != self.dimension_labels: + return out.subset(dimensions=dimension_labels) + return out + +class DataContainer(object): + '''Generic class to hold data + + Data is currently held in a numpy arrays''' + + __container_priority__ = 1 + def __init__ (self, array, deep_copy=True, dimension_labels=None, + **kwargs): + '''Holds the data''' + + self.shape = numpy.shape(array) + self.number_of_dimensions = len (self.shape) + self.dimension_labels = {} + self.geometry = None # Only relevant for AcquisitionData and ImageData + + if dimension_labels is not None and \ + len (dimension_labels) == self.number_of_dimensions: + for i in range(self.number_of_dimensions): + self.dimension_labels[i] = dimension_labels[i] + else: + for i in range(self.number_of_dimensions): + self.dimension_labels[i] = 'dimension_{0:02}'.format(i) + + if type(array) == numpy.ndarray: + if deep_copy: + self.array = array.copy() + else: + self.array = array + else: + raise TypeError('Array must be NumpyArray, passed {0}'\ + .format(type(array))) + + # finally copy the geometry + if 'geometry' in kwargs.keys(): + self.geometry = kwargs['geometry'] + else: + # assume it is parallel beam + pass + + def get_dimension_size(self, dimension_label): + if dimension_label in self.dimension_labels.values(): + acq_size = -1 + for k,v in self.dimension_labels.items(): + if v == dimension_label: + acq_size = self.shape[k] + return acq_size + else: + raise ValueError('Unknown dimension {0}. Should be one of'.format(dimension_label, + self.dimension_labels)) + def get_dimension_axis(self, dimension_label): + if dimension_label in self.dimension_labels.values(): + for k,v in self.dimension_labels.items(): + if v == dimension_label: + return k + else: + raise ValueError('Unknown dimension {0}. Should be one of'.format(dimension_label, + self.dimension_labels.values())) + + + def as_array(self, dimensions=None): + '''Returns the DataContainer as Numpy Array + + Returns the pointer to the array if dimensions is not set. + If dimensions is set, it first creates a new DataContainer with the subset + and then it returns the pointer to the array''' + if dimensions is not None: + return self.subset(dimensions).as_array() + return self.array + + + def subset(self, dimensions=None, **kw): + '''Creates a DataContainer containing a subset of self according to the + labels in dimensions''' + if dimensions is None: + if kw == {}: + return self.array.copy() + else: + reduced_dims = [v for k,v in self.dimension_labels.items()] + for dim_l, dim_v in kw.items(): + for k,v in self.dimension_labels.items(): + if v == dim_l: + reduced_dims.pop(k) + return self.subset(dimensions=reduced_dims, **kw) + else: + # check that all the requested dimensions are in the array + # this is done by checking the dimension_labels + proceed = True + unknown_key = '' + # axis_order contains the order of the axis that the user wants + # in the output DataContainer + axis_order = [] + if type(dimensions) == list: + for dl in dimensions: + if dl not in self.dimension_labels.values(): + proceed = False + unknown_key = dl + break + else: + axis_order.append(find_key(self.dimension_labels, dl)) + if not proceed: + raise KeyError('Subset error: Unknown key specified {0}'.format(dl)) + + # slice away the unwanted data from the array + unwanted_dimensions = self.dimension_labels.copy() + left_dimensions = [] + for ax in sorted(axis_order): + this_dimension = unwanted_dimensions.pop(ax) + left_dimensions.append(this_dimension) + #print ("unwanted_dimensions {0}".format(unwanted_dimensions)) + #print ("left_dimensions {0}".format(left_dimensions)) + #new_shape = [self.shape[ax] for ax in axis_order] + #print ("new_shape {0}".format(new_shape)) + command = "self.array[" + for i in range(self.number_of_dimensions): + if self.dimension_labels[i] in unwanted_dimensions.values(): + value = 0 + for k,v in kw.items(): + if k == self.dimension_labels[i]: + value = v + + command = command + str(value) + else: + command = command + ":" + if i < self.number_of_dimensions -1: + command = command + ',' + command = command + ']' + + cleaned = eval(command) + # cleaned has collapsed dimensions in the same order of + # self.array, but we want it in the order stated in the + # "dimensions". + # create axes order for numpy.transpose + axes = [] + for key in dimensions: + #print ("key {0}".format( key)) + for i in range(len( left_dimensions )): + ld = left_dimensions[i] + #print ("ld {0}".format( ld)) + if ld == key: + axes.append(i) + #print ("axes {0}".format(axes)) + + cleaned = numpy.transpose(cleaned, axes).copy() + + return type(self)(cleaned , True, dimensions) + + def fill(self, array, **dimension): + '''fills the internal numpy array with the one provided''' + if dimension == {}: + if issubclass(type(array), DataContainer) or\ + issubclass(type(array), numpy.ndarray): + if array.shape != self.shape: + raise ValueError('Cannot fill with the provided array.' + \ + 'Expecting {0} got {1}'.format( + self.shape,array.shape)) + if issubclass(type(array), DataContainer): + numpy.copyto(self.array, array.array) + else: + #self.array[:] = array + numpy.copyto(self.array, array) + else: + + command = 'self.array[' + i = 0 + for k,v in self.dimension_labels.items(): + for dim_label, dim_value in dimension.items(): + if dim_label == v: + command = command + str(dim_value) + else: + command = command + ":" + if i < self.number_of_dimensions -1: + command = command + ',' + i += 1 + command = command + "] = array[:]" + exec(command) + + + def check_dimensions(self, other): + return self.shape == other.shape + + ## algebra + + def __add__(self, other): + return self.add(other) + def __mul__(self, other): + return self.multiply(other) + def __sub__(self, other): + return self.subtract(other) + def __div__(self, other): + return self.divide(other) + def __truediv__(self, other): + return self.divide(other) + def __pow__(self, other): + return self.power(other) + + + + # reverse operand + def __radd__(self, other): + return self + other + # __radd__ + + def __rsub__(self, other): + return (-1 * self) + other + # __rsub__ + + def __rmul__(self, other): + return self * other + # __rmul__ + + def __rdiv__(self, other): + print ("call __rdiv__") + return pow(self / other, -1) + # __rdiv__ + def __rtruediv__(self, other): + return self.__rdiv__(other) + + def __rpow__(self, other): + if isinstance(other, (int, float)) : + fother = numpy.ones(numpy.shape(self.array)) * other + return type(self)(fother ** self.array , + dimension_labels=self.dimension_labels, + geometry=self.geometry) + elif issubclass(type(other), DataContainer): + if self.check_dimensions(other): + return type(self)(other.as_array() ** self.array , + dimension_labels=self.dimension_labels, + geometry=self.geometry) + else: + raise ValueError('Dimensions do not match') + # __rpow__ + + # in-place arithmetic operators: + # (+=, -=, *=, /= , //=, + # must return self + + def __iadd__(self, other): + kw = {'out':self} + return self.add(other, **kw) + + def __imul__(self, other): + kw = {'out':self} + return self.multiply(other, **kw) + + def __isub__(self, other): + kw = {'out':self} + return self.subtract(other, **kw) + + def __idiv__(self, other): + kw = {'out':self} + return self.divide(other, **kw) + + def __itruediv__(self, other): + kw = {'out':self} + return self.divide(other, **kw) + + + + def __str__ (self, representation=False): + repres = "" + repres += "Number of dimensions: {0}\n".format(self.number_of_dimensions) + repres += "Shape: {0}\n".format(self.shape) + repres += "Axis labels: {0}\n".format(self.dimension_labels) + if representation: + repres += "Representation: \n{0}\n".format(self.array) + return repres + + def clone(self): + '''returns a copy of itself''' + + return type(self)(self.array, + dimension_labels=self.dimension_labels, + deep_copy=True, + geometry=self.geometry ) + + def get_data_axes_order(self,new_order=None): + '''returns the axes label of self as a list + + if new_order is None returns the labels of the axes as a sorted-by-key list + if new_order is a list of length number_of_dimensions, returns a list + with the indices of the axes in new_order with respect to those in + self.dimension_labels: i.e. + self.dimension_labels = {0:'horizontal',1:'vertical'} + new_order = ['vertical','horizontal'] + returns [1,0] + ''' + if new_order is None: + + axes_order = [i for i in range(len(self.shape))] + for k,v in self.dimension_labels.items(): + axes_order[k] = v + return axes_order + else: + if len(new_order) == self.number_of_dimensions: + axes_order = [i for i in range(self.number_of_dimensions)] + + for i in range(len(self.shape)): + found = False + for k,v in self.dimension_labels.items(): + if new_order[i] == v: + axes_order[i] = k + found = True + if not found: + raise ValueError('Axis label {0} not found.'.format(new_order[i])) + return axes_order + else: + raise ValueError('Expecting {0} axes, got {2}'\ + .format(len(self.shape),len(new_order))) + + + def copy(self): + '''alias of clone''' + return self.clone() + + ## binary operations + + def pixel_wise_binary(self, pwop, x2, *args, **kwargs): + out = kwargs.get('out', None) + if out is None: + if isinstance(x2, (int, float, complex)): + out = pwop(self.as_array() , x2 , *args, **kwargs ) + elif isinstance(x2, (numpy.int, numpy.int8, numpy.int16, numpy.int32, numpy.int64,\ + numpy.float, numpy.float16, numpy.float32, numpy.float64, \ + numpy.complex)): + out = pwop(self.as_array() , x2 , *args, **kwargs ) + elif issubclass(type(x2) , DataContainer): + out = pwop(self.as_array() , x2.as_array() , *args, **kwargs ) + return type(self)(out, + deep_copy=False, + dimension_labels=self.dimension_labels, + geometry=self.geometry) + + + elif issubclass(type(out), DataContainer) and issubclass(type(x2), DataContainer): + if self.check_dimensions(out) and self.check_dimensions(x2): + kwargs['out'] = out.as_array() + pwop(self.as_array(), x2.as_array(), *args, **kwargs ) + #return type(self)(out.as_array(), + # deep_copy=False, + # dimension_labels=self.dimension_labels, + # geometry=self.geometry) + return out + else: + raise ValueError(message(type(self),"Wrong size for data memory: ", out.shape,self.shape)) + elif issubclass(type(out), DataContainer) and isinstance(x2, (int,float,complex)): + if self.check_dimensions(out): + kwargs['out']=out.as_array() + pwop(self.as_array(), x2, *args, **kwargs ) + return out + else: + raise ValueError(message(type(self),"Wrong size for data memory: ", out.shape,self.shape)) + elif issubclass(type(out), numpy.ndarray): + if self.array.shape == out.shape and self.array.dtype == out.dtype: + kwargs['out'] = out + pwop(self.as_array(), x2, *args, **kwargs) + #return type(self)(out, + # deep_copy=False, + # dimension_labels=self.dimension_labels, + # geometry=self.geometry) + else: + raise ValueError (message(type(self), "incompatible class:" , pwop.__name__, type(out))) + + def add(self, other, *args, **kwargs): + if hasattr(other, '__container_priority__') and \ + self.__class__.__container_priority__ < other.__class__.__container_priority__: + return other.add(self, *args, **kwargs) + return self.pixel_wise_binary(numpy.add, other, *args, **kwargs) + + def subtract(self, other, *args, **kwargs): + if hasattr(other, '__container_priority__') and \ + self.__class__.__container_priority__ < other.__class__.__container_priority__: + return other.subtract(self, *args, **kwargs) + return self.pixel_wise_binary(numpy.subtract, other, *args, **kwargs) + + def multiply(self, other, *args, **kwargs): + if hasattr(other, '__container_priority__') and \ + self.__class__.__container_priority__ < other.__class__.__container_priority__: + return other.multiply(self, *args, **kwargs) + return self.pixel_wise_binary(numpy.multiply, other, *args, **kwargs) + + def divide(self, other, *args, **kwargs): + if hasattr(other, '__container_priority__') and \ + self.__class__.__container_priority__ < other.__class__.__container_priority__: + return other.divide(self, *args, **kwargs) + return self.pixel_wise_binary(numpy.divide, other, *args, **kwargs) + + def power(self, other, *args, **kwargs): + return self.pixel_wise_binary(numpy.power, other, *args, **kwargs) + + def maximum(self, x2, *args, **kwargs): + return self.pixel_wise_binary(numpy.maximum, x2, *args, **kwargs) + + def minimum(self,x2, out=None, *args, **kwargs): + return self.pixel_wise_binary(numpy.minimum, x2=x2, out=out, *args, **kwargs) + + + ## unary operations + def pixel_wise_unary(self, pwop, *args, **kwargs): + out = kwargs.get('out', None) + if out is None: + out = pwop(self.as_array() , *args, **kwargs ) + return type(self)(out, + deep_copy=False, + dimension_labels=self.dimension_labels, + geometry=self.geometry) + elif issubclass(type(out), DataContainer): + if self.check_dimensions(out): + kwargs['out'] = out.as_array() + pwop(self.as_array(), *args, **kwargs ) + else: + raise ValueError(message(type(self),"Wrong size for data memory: ", out.shape,self.shape)) + elif issubclass(type(out), numpy.ndarray): + if self.array.shape == out.shape and self.array.dtype == out.dtype: + kwargs['out'] = out + pwop(self.as_array(), *args, **kwargs) + else: + raise ValueError (message(type(self), "incompatible class:" , pwop.__name__, type(out))) + + def abs(self, *args, **kwargs): + return self.pixel_wise_unary(numpy.abs, *args, **kwargs) + + def sign(self, *args, **kwargs): + return self.pixel_wise_unary(numpy.sign, *args, **kwargs) + + def sqrt(self, *args, **kwargs): + return self.pixel_wise_unary(numpy.sqrt, *args, **kwargs) + + def conjugate(self, *args, **kwargs): + return self.pixel_wise_unary(numpy.conjugate, *args, **kwargs) + #def __abs__(self): + # operation = FM.OPERATION.ABS + # return self.callFieldMath(operation, None, self.mask, self.maskOnValue) + # __abs__ + + ## reductions + def sum(self, *args, **kwargs): + return self.as_array().sum(*args, **kwargs) + def squared_norm(self): + '''return the squared euclidean norm of the DataContainer viewed as a vector''' + #shape = self.shape + #size = reduce(lambda x,y:x*y, shape, 1) + #y = numpy.reshape(self.as_array(), (size, )) + return self.dot(self.conjugate()) + #return self.dot(self) + def norm(self): + '''return the euclidean norm of the DataContainer viewed as a vector''' + return numpy.sqrt(self.squared_norm()) + + + def dot(self, other, *args, **kwargs): + '''return the inner product of 2 DataContainers viewed as vectors''' + method = kwargs.get('method', 'reduce') + + if method not in ['numpy','reduce']: + raise ValueError('dot: specified method not valid. Expecting numpy or reduce got {} '.format( + method)) + + if self.shape == other.shape: + # return (self*other).sum() + if method == 'numpy': + return numpy.dot(self.as_array().ravel(), other.as_array()) + elif method == 'reduce': + # see https://github.com/vais-ral/CCPi-Framework/pull/273 + # notice that Python seems to be smart enough to use + # the appropriate type to hold the result of the reduction + sf = reduce(lambda x,y: x + y[0]*y[1], + zip(self.as_array().ravel(), + other.as_array().ravel()), + 0) + return sf + else: + raise ValueError('Shapes are not aligned: {} != {}'.format(self.shape, other.shape)) + + + + + +class ImageData(DataContainer): + '''DataContainer for holding 2D or 3D DataContainer''' + __container_priority__ = 1 + + + def __init__(self, + array = None, + deep_copy=False, + dimension_labels=None, + **kwargs): + + self.geometry = kwargs.get('geometry', None) + if array is None: + if self.geometry is not None: + shape, dimension_labels = self.get_shape_labels(self.geometry) + + array = numpy.zeros( shape , dtype=numpy.float32) + super(ImageData, self).__init__(array, deep_copy, + dimension_labels, **kwargs) + + else: + raise ValueError('Please pass either a DataContainer, ' +\ + 'a numpy array or a geometry') + else: + if self.geometry is not None: + shape, labels = self.get_shape_labels(self.geometry, dimension_labels) + if array.shape != shape: + raise ValueError('Shape mismatch {} {}'.format(shape, array.shape)) + + if issubclass(type(array) , DataContainer): + # if the array is a DataContainer get the info from there + if not ( array.number_of_dimensions == 2 or \ + array.number_of_dimensions == 3 or \ + array.number_of_dimensions == 4): + raise ValueError('Number of dimensions are not 2 or 3 or 4: {0}'\ + .format(array.number_of_dimensions)) + + #DataContainer.__init__(self, array.as_array(), deep_copy, + # array.dimension_labels, **kwargs) + super(ImageData, self).__init__(array.as_array(), deep_copy, + array.dimension_labels, **kwargs) + elif issubclass(type(array) , numpy.ndarray): + if not ( array.ndim == 2 or array.ndim == 3 or array.ndim == 4 ): + raise ValueError( + 'Number of dimensions are not 2 or 3 or 4 : {0}'\ + .format(array.ndim)) + + if dimension_labels is None: + if array.ndim == 4: + dimension_labels = [ImageGeometry.CHANNEL, + ImageGeometry.VERTICAL, + ImageGeometry.HORIZONTAL_Y, + ImageGeometry.HORIZONTAL_X] + elif array.ndim == 3: + dimension_labels = [ImageGeometry.VERTICAL, + ImageGeometry.HORIZONTAL_Y, + ImageGeometry.HORIZONTAL_X] + else: + dimension_labels = [ ImageGeometry.HORIZONTAL_Y, + ImageGeometry.HORIZONTAL_X] + + #DataContainer.__init__(self, array, deep_copy, dimension_labels, **kwargs) + super(ImageData, self).__init__(array, deep_copy, + dimension_labels, **kwargs) + + # load metadata from kwargs if present + for key, value in kwargs.items(): + if (type(value) == list or type(value) == tuple) and \ + ( len (value) == 3 and len (value) == 2) : + if key == 'origin' : + self.origin = value + if key == 'spacing' : + self.spacing = value + + def subset(self, dimensions=None, **kw): + # FIXME: this is clearly not rigth + # it should be something like + # out = DataContainer.subset(self, dimensions, **kw) + # followed by regeneration of the proper geometry. + out = super(ImageData, self).subset(dimensions, **kw) + #out.geometry = self.recalculate_geometry(dimensions , **kw) + out.geometry = self.geometry + return out + + def get_shape_labels(self, geometry, dimension_labels=None): + channels = geometry.channels + horiz_x = geometry.voxel_num_x + horiz_y = geometry.voxel_num_y + vert = 1 if geometry.voxel_num_z is None\ + else geometry.voxel_num_z # this should be 1 for 2D + if dimension_labels is None: + if channels > 1: + if vert > 1: + shape = (channels, vert, horiz_y, horiz_x) + dim_labels = [ImageGeometry.CHANNEL, + ImageGeometry.VERTICAL, + ImageGeometry.HORIZONTAL_Y, + ImageGeometry.HORIZONTAL_X] + else: + shape = (channels , horiz_y, horiz_x) + dim_labels = [ImageGeometry.CHANNEL, + ImageGeometry.HORIZONTAL_Y, + ImageGeometry.HORIZONTAL_X] + else: + if vert > 1: + shape = (vert, horiz_y, horiz_x) + dim_labels = [ImageGeometry.VERTICAL, + ImageGeometry.HORIZONTAL_Y, + ImageGeometry.HORIZONTAL_X] + else: + shape = (horiz_y, horiz_x) + dim_labels = [ImageGeometry.HORIZONTAL_Y, + ImageGeometry.HORIZONTAL_X] + dimension_labels = dim_labels + else: + shape = [] + for i in range(len(dimension_labels)): + dim = dimension_labels[i] + if dim == ImageGeometry.CHANNEL: + shape.append(channels) + elif dim == ImageGeometry.HORIZONTAL_Y: + shape.append(horiz_y) + elif dim == ImageGeometry.VERTICAL: + shape.append(vert) + elif dim == ImageGeometry.HORIZONTAL_X: + shape.append(horiz_x) + if len(shape) != len(dimension_labels): + raise ValueError('Missing {0} axes {1} shape {2}'.format( + len(dimension_labels) - len(shape), dimension_labels, shape)) + shape = tuple(shape) + + return (shape, dimension_labels) + + +class AcquisitionData(DataContainer): + '''DataContainer for holding 2D or 3D sinogram''' + __container_priority__ = 1 + + + def __init__(self, + array = None, + deep_copy=True, + dimension_labels=None, + **kwargs): + self.geometry = kwargs.get('geometry', None) + if array is None: + if 'geometry' in kwargs.keys(): + geometry = kwargs['geometry'] + self.geometry = geometry + + shape, dimension_labels = self.get_shape_labels(geometry, dimension_labels) + + + array = numpy.zeros( shape , dtype=numpy.float32) + super(AcquisitionData, self).__init__(array, deep_copy, + dimension_labels, **kwargs) + else: + if self.geometry is not None: + shape, labels = self.get_shape_labels(self.geometry, dimension_labels) + if array.shape != shape: + raise ValueError('Shape mismatch {} {}'.format(shape, array.shape)) + + if issubclass(type(array) ,DataContainer): + # if the array is a DataContainer get the info from there + if not ( array.number_of_dimensions == 2 or \ + array.number_of_dimensions == 3 or \ + array.number_of_dimensions == 4): + raise ValueError('Number of dimensions are not 2 or 3 or 4: {0}'\ + .format(array.number_of_dimensions)) + + #DataContainer.__init__(self, array.as_array(), deep_copy, + # array.dimension_labels, **kwargs) + super(AcquisitionData, self).__init__(array.as_array(), deep_copy, + array.dimension_labels, **kwargs) + elif issubclass(type(array) ,numpy.ndarray): + if not ( array.ndim == 2 or array.ndim == 3 or array.ndim == 4 ): + raise ValueError( + 'Number of dimensions are not 2 or 3 or 4 : {0}'\ + .format(array.ndim)) + + if dimension_labels is None: + if array.ndim == 4: + dimension_labels = [AcquisitionGeometry.CHANNEL, + AcquisitionGeometry.ANGLE, + AcquisitionGeometry.VERTICAL, + AcquisitionGeometry.HORIZONTAL] + elif array.ndim == 3: + dimension_labels = [AcquisitionGeometry.ANGLE, + AcquisitionGeometry.VERTICAL, + AcquisitionGeometry.HORIZONTAL] + else: + dimension_labels = [AcquisitionGeometry.ANGLE, + AcquisitionGeometry.HORIZONTAL] + + super(AcquisitionData, self).__init__(array, deep_copy, + dimension_labels, **kwargs) + + def get_shape_labels(self, geometry, dimension_labels=None): + channels = geometry.channels + horiz = geometry.pixel_num_h + vert = geometry.pixel_num_v + angles = geometry.angles + num_of_angles = numpy.shape(angles)[0] + + if dimension_labels is None: + if channels > 1: + if vert > 1: + shape = (channels, num_of_angles , vert, horiz) + dim_labels = [AcquisitionGeometry.CHANNEL, + AcquisitionGeometry.ANGLE, + AcquisitionGeometry.VERTICAL, + AcquisitionGeometry.HORIZONTAL] + else: + shape = (channels , num_of_angles, horiz) + dim_labels = [AcquisitionGeometry.CHANNEL, + AcquisitionGeometry.ANGLE, + AcquisitionGeometry.HORIZONTAL] + else: + if vert > 1: + shape = (num_of_angles, vert, horiz) + dim_labels = [AcquisitionGeometry.ANGLE, + AcquisitionGeometry.VERTICAL, + AcquisitionGeometry.HORIZONTAL + ] + else: + shape = (num_of_angles, horiz) + dim_labels = [AcquisitionGeometry.ANGLE, + AcquisitionGeometry.HORIZONTAL + ] + + dimension_labels = dim_labels + else: + shape = [] + for i in range(len(dimension_labels)): + dim = dimension_labels[i] + + if dim == AcquisitionGeometry.CHANNEL: + shape.append(channels) + elif dim == AcquisitionGeometry.ANGLE: + shape.append(num_of_angles) + elif dim == AcquisitionGeometry.VERTICAL: + shape.append(vert) + elif dim == AcquisitionGeometry.HORIZONTAL: + shape.append(horiz) + if len(shape) != len(dimension_labels): + raise ValueError('Missing {0} axes.\nExpected{1} got {2}'\ + .format( + len(dimension_labels) - len(shape), + dimension_labels, shape) + ) + shape = tuple(shape) + return (shape, dimension_labels) + + + +class DataProcessor(object): + + '''Defines a generic DataContainer processor + + accepts DataContainer as inputs and + outputs DataContainer + additional attributes can be defined with __setattr__ + ''' + + def __init__(self, **attributes): + if not 'store_output' in attributes.keys(): + attributes['store_output'] = True + attributes['output'] = False + attributes['runTime'] = -1 + attributes['mTime'] = datetime.now() + attributes['input'] = None + for key, value in attributes.items(): + self.__dict__[key] = value + + + def __setattr__(self, name, value): + if name == 'input': + self.set_input(value) + elif name in self.__dict__.keys(): + self.__dict__[name] = value + self.__dict__['mTime'] = datetime.now() + else: + raise KeyError('Attribute {0} not found'.format(name)) + #pass + + def set_input(self, dataset): + if issubclass(type(dataset), DataContainer): + if self.check_input(dataset): + self.__dict__['input'] = dataset + else: + raise TypeError("Input type mismatch: got {0} expecting {1}"\ + .format(type(dataset), DataContainer)) + + def check_input(self, dataset): + '''Checks parameters of the input DataContainer + + Should raise an Error if the DataContainer does not match expectation, e.g. + if the expected input DataContainer is 3D and the Processor expects 2D. + ''' + raise NotImplementedError('Implement basic checks for input DataContainer') + + def get_output(self, out=None): + + for k,v in self.__dict__.items(): + if v is None and k != 'output': + raise ValueError('Key {0} is None'.format(k)) + shouldRun = False + if self.runTime == -1: + shouldRun = True + elif self.mTime > self.runTime: + shouldRun = True + + # CHECK this + if self.store_output and shouldRun: + self.runTime = datetime.now() + try: + self.output = self.process(out=out) + return self.output + except TypeError as te: + self.output = self.process() + return self.output + self.runTime = datetime.now() + try: + return self.process(out=out) + except TypeError as te: + return self.process() + + + def set_input_processor(self, processor): + if issubclass(type(processor), DataProcessor): + self.__dict__['input'] = processor + else: + raise TypeError("Input type mismatch: got {0} expecting {1}"\ + .format(type(processor), DataProcessor)) + + def get_input(self): + '''returns the input DataContainer + + It is useful in the case the user has provided a DataProcessor as + input + ''' + if issubclass(type(self.input), DataProcessor): + dsi = self.input.get_output() + else: + dsi = self.input + return dsi + + def process(self, out=None): + raise NotImplementedError('process must be implemented') + + + + +class DataProcessor23D(DataProcessor): + '''Regularizers DataProcessor + ''' + + def check_input(self, dataset): + '''Checks number of dimensions input DataContainer + + Expected input is 2D or 3D + ''' + if dataset.number_of_dimensions == 2 or \ + dataset.number_of_dimensions == 3: + return True + else: + raise ValueError("Expected input dimensions is 2 or 3, got {0}"\ + .format(dataset.number_of_dimensions)) + +###### Example of DataProcessors + +class AX(DataProcessor): + '''Example DataProcessor + The AXPY routines perform a vector multiplication operation defined as + + y := a*x + where: + + a is a scalar + + x a DataContainer. + ''' + + def __init__(self): + kwargs = {'scalar':None, + 'input':None, + } + + #DataProcessor.__init__(self, **kwargs) + super(AX, self).__init__(**kwargs) + + def check_input(self, dataset): + return True + + def process(self, out=None): + + dsi = self.get_input() + a = self.scalar + if out is None: + y = DataContainer( a * dsi.as_array() , True, + dimension_labels=dsi.dimension_labels ) + #self.setParameter(output_dataset=y) + return y + else: + out.fill(a * dsi.as_array()) + + +###### Example of DataProcessors + +class CastDataContainer(DataProcessor): + '''Example DataProcessor + Cast a DataContainer array to a different type. + + y := a*x + where: + + a is a scalar + + x a DataContainer. + ''' + + def __init__(self, dtype=None): + kwargs = {'dtype':dtype, + 'input':None, + } + + #DataProcessor.__init__(self, **kwargs) + super(CastDataContainer, self).__init__(**kwargs) + + def check_input(self, dataset): + return True + + def process(self, out=None): + + dsi = self.get_input() + dtype = self.dtype + if out is None: + y = numpy.asarray(dsi.as_array(), dtype=dtype) + + return type(dsi)(numpy.asarray(dsi.as_array(), dtype=dtype), + dimension_labels=dsi.dimension_labels ) + else: + out.fill(numpy.asarray(dsi.as_array(), dtype=dtype)) + + + + + +class PixelByPixelDataProcessor(DataProcessor): + '''Example DataProcessor + + This processor applies a python function to each pixel of the DataContainer + + f is a python function + + x a DataSet. + ''' + + def __init__(self): + kwargs = {'pyfunc':None, + 'input':None, + } + #DataProcessor.__init__(self, **kwargs) + super(PixelByPixelDataProcessor, self).__init__(**kwargs) + + def check_input(self, dataset): + return True + + def process(self, out=None): + + pyfunc = self.pyfunc + dsi = self.get_input() + + eval_func = numpy.frompyfunc(pyfunc,1,1) + + + y = DataContainer( eval_func( dsi.as_array() ) , True, + dimension_labels=dsi.dimension_labels ) + return y + + + + +if __name__ == '__main__': + shape = (2,3,4,5) + size = shape[0] + for i in range(1, len(shape)): + size = size * shape[i] + #print("a refcount " , sys.getrefcount(a)) + a = numpy.asarray([i for i in range( size )]) + print("a refcount " , sys.getrefcount(a)) + a = numpy.reshape(a, shape) + print("a refcount " , sys.getrefcount(a)) + ds = DataContainer(a, False, ['X', 'Y','Z' ,'W']) + print("a refcount " , sys.getrefcount(a)) + print ("ds label {0}".format(ds.dimension_labels)) + subset = ['W' ,'X'] + b = ds.subset( subset ) + print("a refcount " , sys.getrefcount(a)) + print ("b label {0} shape {1}".format(b.dimension_labels, + numpy.shape(b.as_array()))) + c = ds.subset(['Z','W','X']) + print("a refcount " , sys.getrefcount(a)) + + # Create a ImageData sharing the array with c + volume0 = ImageData(c.as_array(), False, dimensions = c.dimension_labels) + volume1 = ImageData(c, False) + + print ("volume0 {0} volume1 {1}".format(id(volume0.array), + id(volume1.array))) + + # Create a ImageData copying the array from c + volume2 = ImageData(c.as_array(), dimensions = c.dimension_labels) + volume3 = ImageData(c) + + print ("volume2 {0} volume3 {1}".format(id(volume2.array), + id(volume3.array))) + + # single number DataSet + sn = DataContainer(numpy.asarray([1])) + + ax = AX() + ax.scalar = 2 + ax.set_input(c) + #ax.apply() + print ("ax in {0} out {1}".format(c.as_array().flatten(), + ax.get_output().as_array().flatten())) + + cast = CastDataContainer(dtype=numpy.float32) + cast.set_input(c) + out = cast.get_output() + out *= 0 + axm = AX() + axm.scalar = 0.5 + axm.set_input_processor(cast) + axm.get_output(out) + #axm.apply() + print ("axm in {0} out {1}".format(c.as_array(), axm.get_output().as_array())) + + # check out in DataSetProcessor + #a = numpy.asarray([i for i in range( size )]) + + + # create a PixelByPixelDataProcessor + + #define a python function which will take only one input (the pixel value) + pyfunc = lambda x: -x if x > 20 else x + clip = PixelByPixelDataProcessor() + clip.pyfunc = pyfunc + clip.set_input(c) + #clip.apply() + + print ("clip in {0} out {1}".format(c.as_array(), clip.get_output().as_array())) + + #dsp = DataProcessor() + #dsp.set_input(ds) + #dsp.input = a + # pipeline + + chain = AX() + chain.scalar = 0.5 + chain.set_input_processor(ax) + print ("chain in {0} out {1}".format(ax.get_output().as_array(), chain.get_output().as_array())) + + # testing arithmetic operations + + print (b) + print ((b+1)) + print ((1+b)) + + print (b) + print ((b*2)) + + print (b) + print ((2*b)) + + print (b) + print ((b/2)) + + print (b) + print ((2/b)) + + print (b) + print ((b**2)) + + print (b) + print ((2**b)) + + print (type(volume3 + 2)) + + s = [i for i in range(3 * 4 * 4)] + s = numpy.reshape(numpy.asarray(s), (3,4,4)) + sino = AcquisitionData( s ) + + shape = (4,3,2) + a = [i for i in range(2*3*4)] + a = numpy.asarray(a) + a = numpy.reshape(a, shape) + print (numpy.shape(a)) + ds = DataContainer(a, True, ['X', 'Y','Z']) + # this means that I expect the X to be of length 2 , + # y of length 3 and z of length 4 + subset = ['Y' ,'Z'] + b0 = ds.subset( subset ) + print ("shape b 3,2? {0}".format(numpy.shape(b0.as_array()))) + # expectation on b is that it is + # 3x2 cut at z = 0 + + subset = ['X' ,'Y'] + b1 = ds.subset( subset , Z=1) + print ("shape b 2,3? {0}".format(numpy.shape(b1.as_array()))) + + + + # create VolumeData from geometry + vgeometry = ImageGeometry(voxel_num_x=2, voxel_num_y=3, channels=2) + vol = ImageData(geometry=vgeometry) + + sgeometry = AcquisitionGeometry(dimension=2, angles=numpy.linspace(0, 180, num=20), + geom_type='parallel', pixel_num_v=3, + pixel_num_h=5 , channels=2) + sino = AcquisitionData(geometry=sgeometry) + sino2 = sino.clone() + + a0 = numpy.asarray([i for i in range(2*3*4)]) + a1 = numpy.asarray([2*i for i in range(2*3*4)]) + + + ds0 = DataContainer(numpy.reshape(a0,(2,3,4))) + ds1 = DataContainer(numpy.reshape(a1,(2,3,4))) + + numpy.testing.assert_equal(ds0.dot(ds1), a0.dot(a1)) + + a2 = numpy.asarray([2*i for i in range(2*3*5)]) + ds2 = DataContainer(numpy.reshape(a2,(2,3,5))) + +# # it should fail if the shape is wrong +# try: +# ds2.dot(ds0) +# self.assertTrue(False) +# except ValueError as ve: +# self.assertTrue(True) + diff --git a/Wrappers/Python/build/lib/ccpi/io/__init__.py b/Wrappers/Python/build/lib/ccpi/io/__init__.py new file mode 100644 index 0000000..9233d7a --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/io/__init__.py @@ -0,0 +1,18 @@ +# -*- coding: utf-8 -*-
+# This work is part of the Core Imaging Library developed by
+# Visual Analytics and Imaging System Group of the Science Technology
+# Facilities Council, STFC
+
+# Copyright 2018 Edoardo Pasca
+
+# 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.
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/io/reader.py b/Wrappers/Python/build/lib/ccpi/io/reader.py new file mode 100644 index 0000000..07e3bf9 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/io/reader.py @@ -0,0 +1,511 @@ +# -*- coding: utf-8 -*-
+# This work is part of the Core Imaging Library developed by
+# Visual Analytics and Imaging System Group of the Science Technology
+# Facilities Council, STFC
+
+# Copyright 2018 Jakob Jorgensen, Daniil Kazantsev, Edoardo Pasca and Srikanth Nagella
+
+# 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.
+
+'''
+This is a reader module with classes for loading 3D datasets.
+
+@author: Mr. Srikanth Nagella
+'''
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+from ccpi.framework import AcquisitionGeometry
+from ccpi.framework import AcquisitionData
+import numpy as np
+import os
+
+h5pyAvailable = True
+try:
+ from h5py import File as NexusFile
+except:
+ h5pyAvailable = False
+
+pilAvailable = True
+try:
+ from PIL import Image
+except:
+ pilAvailable = False
+
+class NexusReader(object):
+ '''
+ Reader class for loading Nexus files.
+ '''
+
+ def __init__(self, nexus_filename=None):
+ '''
+ This takes in input as filename and loads the data dataset.
+ '''
+ self.flat = None
+ self.dark = None
+ self.angles = None
+ self.geometry = None
+ self.filename = nexus_filename
+ self.key_path = 'entry1/tomo_entry/instrument/detector/image_key'
+ self.data_path = 'entry1/tomo_entry/data/data'
+ self.angle_path = 'entry1/tomo_entry/data/rotation_angle'
+
+ def get_image_keys(self):
+ try:
+ with NexusFile(self.filename,'r') as file:
+ return np.array(file[self.key_path])
+ except KeyError as ke:
+ raise KeyError("get_image_keys: " , ke.args[0] , self.key_path)
+
+
+ def load(self, dimensions=None, image_key_id=0):
+ '''
+ This is generic loading function of flat field, dark field and projection data.
+ '''
+ if not h5pyAvailable:
+ raise Exception("Error: h5py is not installed")
+ if self.filename is None:
+ return
+ try:
+ with NexusFile(self.filename,'r') as file:
+ image_keys = np.array(file[self.key_path])
+ projections = None
+ if dimensions == None:
+ projections = np.array(file[self.data_path])
+ result = projections[image_keys==image_key_id]
+ return result
+ else:
+ #When dimensions are specified they need to be mapped to image_keys
+ index_array = np.where(image_keys==image_key_id)
+ projection_indexes = index_array[0][dimensions[0]]
+ new_dimensions = list(dimensions)
+ new_dimensions[0]= projection_indexes
+ new_dimensions = tuple(new_dimensions)
+ result = np.array(file[self.data_path][new_dimensions])
+ return result
+ except:
+ print("Error reading nexus file")
+ raise
+
+ def load_projection(self, dimensions=None):
+ '''
+ Loads the projection data from the nexus file.
+ returns: numpy array with projection data
+ '''
+ try:
+ if 0 not in self.get_image_keys():
+ raise ValueError("Projections are not in the data. Data Path " ,
+ self.data_path)
+ except KeyError as ke:
+ raise KeyError(ke.args[0] , self.data_path)
+ return self.load(dimensions, 0)
+
+ def load_flat(self, dimensions=None):
+ '''
+ Loads the flat field data from the nexus file.
+ returns: numpy array with flat field data
+ '''
+ try:
+ if 1 not in self.get_image_keys():
+ raise ValueError("Flats are not in the data. Data Path " ,
+ self.data_path)
+ except KeyError as ke:
+ raise KeyError(ke.args[0] , self.data_path)
+ return self.load(dimensions, 1)
+
+ def load_dark(self, dimensions=None):
+ '''
+ Loads the Dark field data from the nexus file.
+ returns: numpy array with dark field data
+ '''
+ try:
+ if 2 not in self.get_image_keys():
+ raise ValueError("Darks are not in the data. Data Path " ,
+ self.data_path)
+ except KeyError as ke:
+ raise KeyError(ke.args[0] , self.data_path)
+ return self.load(dimensions, 2)
+
+ def get_projection_angles(self):
+ '''
+ This function returns the projection angles
+ '''
+ if not h5pyAvailable:
+ raise Exception("Error: h5py is not installed")
+ if self.filename is None:
+ return
+ try:
+ with NexusFile(self.filename,'r') as file:
+ angles = np.array(file[self.angle_path],np.float32)
+ image_keys = np.array(file[self.key_path])
+ return angles[image_keys==0]
+ except:
+ print("get_projection_angles Error reading nexus file")
+ raise
+
+
+ def get_sinogram_dimensions(self):
+ '''
+ Return the dimensions of the dataset
+ '''
+ if not h5pyAvailable:
+ raise Exception("Error: h5py is not installed")
+ if self.filename is None:
+ return
+ try:
+ with NexusFile(self.filename,'r') as file:
+ projections = file[self.data_path]
+ image_keys = np.array(file[self.key_path])
+ dims = list(projections.shape)
+ dims[0] = dims[1]
+ dims[1] = np.sum(image_keys==0)
+ return tuple(dims)
+ except:
+ print("Error reading nexus file")
+ raise
+
+ def get_projection_dimensions(self):
+ '''
+ Return the dimensions of the dataset
+ '''
+ if not h5pyAvailable:
+ raise Exception("Error: h5py is not installed")
+ if self.filename is None:
+ return
+ try:
+ with NexusFile(self.filename,'r') as file:
+ try:
+ projections = file[self.data_path]
+ except KeyError as ke:
+ raise KeyError('Error: data path {0} not found\n{1}'\
+ .format(self.data_path,
+ ke.args[0]))
+ #image_keys = np.array(file[self.key_path])
+ image_keys = self.get_image_keys()
+ dims = list(projections.shape)
+ dims[0] = np.sum(image_keys==0)
+ return tuple(dims)
+ except:
+ print("Warning: Error reading image_keys trying accessing data on " , self.data_path)
+ with NexusFile(self.filename,'r') as file:
+ dims = file[self.data_path].shape
+ return tuple(dims)
+
+
+
+ def get_acquisition_data(self, dimensions=None):
+ '''
+ This method load the acquisition data and given dimension and returns an AcquisitionData Object
+ '''
+ data = self.load_projection(dimensions)
+ dims = self.get_projection_dimensions()
+ geometry = AcquisitionGeometry('parallel', '3D',
+ self.get_projection_angles(),
+ pixel_num_h = dims[2],
+ pixel_size_h = 1 ,
+ pixel_num_v = dims[1],
+ pixel_size_v = 1,
+ dist_source_center = None,
+ dist_center_detector = None,
+ channels = 1)
+ return AcquisitionData(data, geometry=geometry,
+ dimension_labels=['angle','vertical','horizontal'])
+
+ def get_acquisition_data_subset(self, ymin=None, ymax=None):
+ '''
+ This method load the acquisition data and given dimension and returns an AcquisitionData Object
+ '''
+ if not h5pyAvailable:
+ raise Exception("Error: h5py is not installed")
+ if self.filename is None:
+ return
+ try:
+
+
+ with NexusFile(self.filename,'r') as file:
+ try:
+ dims = self.get_projection_dimensions()
+ except KeyError:
+ pass
+ dims = file[self.data_path].shape
+ if ymin is None and ymax is None:
+
+ try:
+ image_keys = self.get_image_keys()
+ print ("image_keys", image_keys)
+ projections = np.array(file[self.data_path])
+ data = projections[image_keys==0]
+ except KeyError as ke:
+ print (ke)
+ data = np.array(file[self.data_path])
+
+ else:
+ image_keys = self.get_image_keys()
+ print ("image_keys", image_keys)
+ projections = np.array(file[self.data_path])[image_keys==0]
+ if ymin is None:
+ ymin = 0
+ if ymax > dims[1]:
+ raise ValueError('ymax out of range')
+ data = projections[:,:ymax,:]
+ elif ymax is None:
+ ymax = dims[1]
+ if ymin < 0:
+ raise ValueError('ymin out of range')
+ data = projections[:,ymin:,:]
+ else:
+ if ymax > dims[1]:
+ raise ValueError('ymax out of range')
+ if ymin < 0:
+ raise ValueError('ymin out of range')
+
+ data = projections[: , ymin:ymax , :]
+
+ except:
+ print("Error reading nexus file")
+ raise
+
+
+ try:
+ angles = self.get_projection_angles()
+ except KeyError as ke:
+ n = data.shape[0]
+ angles = np.linspace(0, n, n+1, dtype=np.float32)
+
+ if ymax-ymin > 1:
+
+ geometry = AcquisitionGeometry('parallel', '3D',
+ angles,
+ pixel_num_h = dims[2],
+ pixel_size_h = 1 ,
+ pixel_num_v = ymax-ymin,
+ pixel_size_v = 1,
+ dist_source_center = None,
+ dist_center_detector = None,
+ channels = 1)
+ return AcquisitionData(data, False, geometry=geometry,
+ dimension_labels=['angle','vertical','horizontal'])
+ elif ymax-ymin == 1:
+ geometry = AcquisitionGeometry('parallel', '2D',
+ angles,
+ pixel_num_h = dims[2],
+ pixel_size_h = 1 ,
+ dist_source_center = None,
+ dist_center_detector = None,
+ channels = 1)
+ return AcquisitionData(data.squeeze(), False, geometry=geometry,
+ dimension_labels=['angle','horizontal'])
+ def get_acquisition_data_slice(self, y_slice=0):
+ return self.get_acquisition_data_subset(ymin=y_slice , ymax=y_slice+1)
+ def get_acquisition_data_whole(self):
+ with NexusFile(self.filename,'r') as file:
+ try:
+ dims = self.get_projection_dimensions()
+ except KeyError:
+ print ("Warning: ")
+ dims = file[self.data_path].shape
+
+ ymin = 0
+ ymax = dims[1] - 1
+
+ return self.get_acquisition_data_subset(ymin=ymin, ymax=ymax)
+
+
+
+ def list_file_content(self):
+ try:
+ with NexusFile(self.filename,'r') as file:
+ file.visit(print)
+ except:
+ print("Error reading nexus file")
+ raise
+ def get_acquisition_data_batch(self, bmin=None, bmax=None):
+ if not h5pyAvailable:
+ raise Exception("Error: h5py is not installed")
+ if self.filename is None:
+ return
+ try:
+
+
+ with NexusFile(self.filename,'r') as file:
+ try:
+ dims = self.get_projection_dimensions()
+ except KeyError:
+ dims = file[self.data_path].shape
+ if bmin is None or bmax is None:
+ raise ValueError('get_acquisition_data_batch: please specify fastest index batch limits')
+
+ if bmin >= 0 and bmin < bmax and bmax <= dims[0]:
+ data = np.array(file[self.data_path][bmin:bmax])
+ else:
+ raise ValueError('get_acquisition_data_batch: bmin {0}>0 bmax {1}<{2}'.format(bmin, bmax, dims[0]))
+
+ except:
+ print("Error reading nexus file")
+ raise
+
+
+ try:
+ angles = self.get_projection_angles()[bmin:bmax]
+ except KeyError as ke:
+ n = data.shape[0]
+ angles = np.linspace(0, n, n+1, dtype=np.float32)[bmin:bmax]
+
+ if bmax-bmin > 1:
+
+ geometry = AcquisitionGeometry('parallel', '3D',
+ angles,
+ pixel_num_h = dims[2],
+ pixel_size_h = 1 ,
+ pixel_num_v = bmax-bmin,
+ pixel_size_v = 1,
+ dist_source_center = None,
+ dist_center_detector = None,
+ channels = 1)
+ return AcquisitionData(data, False, geometry=geometry,
+ dimension_labels=['angle','vertical','horizontal'])
+ elif bmax-bmin == 1:
+ geometry = AcquisitionGeometry('parallel', '2D',
+ angles,
+ pixel_num_h = dims[2],
+ pixel_size_h = 1 ,
+ dist_source_center = None,
+ dist_center_detector = None,
+ channels = 1)
+ return AcquisitionData(data.squeeze(), False, geometry=geometry,
+ dimension_labels=['angle','horizontal'])
+
+
+
+class XTEKReader(object):
+ '''
+ Reader class for loading XTEK files
+ '''
+
+ def __init__(self, xtek_config_filename=None):
+ '''
+ This takes in the xtek config filename and loads the dataset and the
+ required geometry parameters
+ '''
+ self.projections = None
+ self.geometry = {}
+ self.filename = xtek_config_filename
+ self.load()
+
+ def load(self):
+ pixel_num_h = 0
+ pixel_num_v = 0
+ xpixel_size = 0
+ ypixel_size = 0
+ source_x = 0
+ detector_x = 0
+ with open(self.filename) as f:
+ content = f.readlines()
+ content = [x.strip() for x in content]
+ for line in content:
+ if line.startswith("SrcToObject"):
+ source_x = float(line.split('=')[1])
+ elif line.startswith("SrcToDetector"):
+ detector_x = float(line.split('=')[1])
+ elif line.startswith("DetectorPixelsY"):
+ pixel_num_v = int(line.split('=')[1])
+ #self.num_of_vertical_pixels = self.calc_v_alighment(self.num_of_vertical_pixels, self.pixels_per_voxel)
+ elif line.startswith("DetectorPixelsX"):
+ pixel_num_h = int(line.split('=')[1])
+ elif line.startswith("DetectorPixelSizeX"):
+ xpixel_size = float(line.split('=')[1])
+ elif line.startswith("DetectorPixelSizeY"):
+ ypixel_size = float(line.split('=')[1])
+ elif line.startswith("Projections"):
+ self.num_projections = int(line.split('=')[1])
+ elif line.startswith("InitialAngle"):
+ self.initial_angle = float(line.split('=')[1])
+ elif line.startswith("Name"):
+ self.experiment_name = line.split('=')[1]
+ elif line.startswith("Scattering"):
+ self.scattering = float(line.split('=')[1])
+ elif line.startswith("WhiteLevel"):
+ self.white_level = float(line.split('=')[1])
+ elif line.startswith("MaskRadius"):
+ self.mask_radius = float(line.split('=')[1])
+
+ #Read Angles
+ angles = self.read_angles()
+ self.geometry = AcquisitionGeometry('cone', '3D', angles, pixel_num_h, xpixel_size, pixel_num_v, ypixel_size, -1 * source_x,
+ detector_x - source_x,
+ )
+
+ def read_angles(self):
+ """
+ Read the angles file .ang or _ctdata.txt file and returns the angles
+ as an numpy array.
+ """
+ input_path = os.path.dirname(self.filename)
+ angles_ctdata_file = os.path.join(input_path, '_ctdata.txt')
+ angles_named_file = os.path.join(input_path, self.experiment_name+'.ang')
+ angles = np.zeros(self.num_projections,dtype='f')
+ #look for _ctdata.txt
+ if os.path.exists(angles_ctdata_file):
+ #read txt file with angles
+ with open(angles_ctdata_file) as f:
+ content = f.readlines()
+ #skip firt three lines
+ #read the middle value of 3 values in each line as angles in degrees
+ index = 0
+ for line in content[3:]:
+ self.angles[index]=float(line.split(' ')[1])
+ index+=1
+ angles = np.deg2rad(self.angles+self.initial_angle);
+ elif os.path.exists(angles_named_file):
+ #read the angles file which is text with first line as header
+ with open(angles_named_file) as f:
+ content = f.readlines()
+ #skip first line
+ index = 0
+ for line in content[1:]:
+ angles[index] = float(line.split(':')[1])
+ index+=1
+ angles = np.flipud(angles+self.initial_angle) #angles are in the reverse order
+ else:
+ raise RuntimeError("Can't find angles file")
+ return angles
+
+ def load_projection(self, dimensions=None):
+ '''
+ This method reads the projection images from the directory and returns a numpy array
+ '''
+ if not pilAvailable:
+ raise('Image library pillow is not installed')
+ if dimensions != None:
+ raise('Extracting subset of data is not implemented')
+ input_path = os.path.dirname(self.filename)
+ pixels = np.zeros((self.num_projections, self.geometry.pixel_num_h, self.geometry.pixel_num_v), dtype='float32')
+ for i in range(1, self.num_projections+1):
+ im = Image.open(os.path.join(input_path,self.experiment_name+"_%04d"%i+".tif"))
+ pixels[i-1,:,:] = np.fliplr(np.transpose(np.array(im))) ##Not sure this is the correct way to populate the image
+
+ #normalising the data
+ #TODO: Move this to a processor
+ pixels = pixels - (self.white_level*self.scattering)/100.0
+ pixels[pixels < 0.0] = 0.000001 # all negative values to approximately 0 as the std log of zero and non negative number is not defined
+ return pixels
+
+ def get_acquisition_data(self, dimensions=None):
+ '''
+ This method load the acquisition data and given dimension and returns an AcquisitionData Object
+ '''
+ data = self.load_projection(dimensions)
+ return AcquisitionData(data, geometry=self.geometry)
+
diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/__init__.py b/Wrappers/Python/build/lib/ccpi/optimisation/__init__.py new file mode 100644 index 0000000..cf2d93d --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/__init__.py @@ -0,0 +1,18 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018 Edoardo Pasca + +# 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.
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/Algorithm.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/Algorithm.py index 12cbabc..a14378c 100644 --- a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/Algorithm.py +++ b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/Algorithm.py @@ -34,7 +34,7 @@ class Algorithm(object): method will stop when the stopping cryterion is met. ''' - def __init__(self): + def __init__(self, **kwargs): '''Constructor Set the minimal number of parameters: @@ -48,11 +48,11 @@ class Algorithm(object): when evaluating the objective is computationally expensive. ''' self.iteration = 0 - self.__max_iteration = 0 + self.__max_iteration = kwargs.get('max_iteration', 0) self.__loss = [] self.memopt = False self.timing = [] - self.update_objective_interval = 1 + self.update_objective_interval = kwargs.get('update_objective_interval', 1) def set_up(self, *args, **kwargs): '''Set up the algorithm''' raise NotImplementedError() @@ -91,9 +91,11 @@ class Algorithm(object): if self.iteration % self.update_objective_interval == 0: self.update_objective() self.iteration += 1 + def get_output(self): '''Returns the solution found''' return self.x + def get_last_loss(self): '''Returns the last stored value of the loss function @@ -146,39 +148,13 @@ class Algorithm(object): print ("Stop cryterion has been reached.") i = 0 -# print("Iteration {:<5} Primal {:<5} Dual {:<5} PDgap".format('','','')) for _ in self: - - - if self.iteration % self.update_objective_interval == 0: - + if (self.iteration -1) % self.update_objective_interval == 0: + if verbose: + print ("Iteration {}/{}, = {}".format(self.iteration-1, + self.max_iteration, self.get_last_objective()) ) if callback is not None: - callback(self.iteration, self.get_last_objective(), self.x) - - else: - - if verbose: - -# if verbose and self.iteration % self.update_objective_interval == 0: - #pass - # \t for tab -# print( "{:04}/{:04} {:<5} {:.4f} {:<5} {:.4f} {:<5} {:.4f}".\ -# format(self.iteration, self.max_iteration,'', \ -# self.get_last_objective()[0],'',\ -# self.get_last_objective()[1],'',\ -# self.get_last_objective()[2])) - - - print ("Iteration {}/{}, {}".format(self.iteration, - self.max_iteration, self.get_last_objective()) ) - - #print ("Iteration {}/{}, Primal, Dual, PDgap = {}".format(self.iteration, - # self.max_iteration, self.get_last_objective()) ) - - -# else: -# if callback is not None: -# callback(self.iteration, self.get_last_objective(), self.x) + callback(self.iteration -1, self.get_last_objective(), self.x) i += 1 if i == iterations: break diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/CGLS.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/CGLS.py new file mode 100644 index 0000000..4d4843c --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/CGLS.py @@ -0,0 +1,87 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018 Edoardo Pasca + +# 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. +""" +Created on Thu Feb 21 11:11:23 2019 + +@author: ofn77899 +""" + +from ccpi.optimisation.algorithms import Algorithm + +class CGLS(Algorithm): + + '''Conjugate Gradient Least Squares algorithm + + Parameters: + x_init: initial guess + operator: operator for forward/backward projections + data: data to operate on + ''' + def __init__(self, **kwargs): + super(CGLS, self).__init__() + self.x = kwargs.get('x_init', None) + self.operator = kwargs.get('operator', None) + self.data = kwargs.get('data', None) + if self.x is not None and self.operator is not None and \ + self.data is not None: + print ("Calling from creator") + self.set_up(x_init =kwargs['x_init'], + operator=kwargs['operator'], + data =kwargs['data']) + + def set_up(self, x_init, operator , data ): + + self.r = data.copy() + self.x = x_init.copy() + + self.operator = operator + self.d = operator.adjoint(self.r) + + + self.normr2 = self.d.squared_norm() + #if isinstance(self.normr2, Iterable): + # self.normr2 = sum(self.normr2) + #self.normr2 = numpy.sqrt(self.normr2) + #print ("set_up" , self.normr2) + + def update(self): + + Ad = self.operator.direct(self.d) + #norm = (Ad*Ad).sum() + #if isinstance(norm, Iterable): + # norm = sum(norm) + norm = Ad.squared_norm() + + alpha = self.normr2/norm + self.x += (self.d * alpha) + self.r -= (Ad * alpha) + s = self.operator.adjoint(self.r) + + normr2_new = s.squared_norm() + #if isinstance(normr2_new, Iterable): + # normr2_new = sum(normr2_new) + #normr2_new = numpy.sqrt(normr2_new) + #print (normr2_new) + + beta = normr2_new/self.normr2 + self.normr2 = normr2_new + self.d = s + beta*self.d + + def update_objective(self): + self.loss.append(self.r.squared_norm())
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/FBPD.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/FBPD.py new file mode 100644 index 0000000..aa07359 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/FBPD.py @@ -0,0 +1,86 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2019 Edoardo Pasca + +# 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. +""" +Created on Thu Feb 21 11:09:03 2019 + +@author: ofn77899 +""" + +from ccpi.optimisation.algorithms import Algorithm +from ccpi.optimisation.functions import ZeroFunction + +class FBPD(Algorithm): + '''FBPD Algorithm + + Parameters: + x_init: initial guess + f: constraint + g: data fidelity + h: regularizer + opt: additional algorithm + ''' + constraint = None + data_fidelity = None + regulariser = None + def __init__(self, **kwargs): + pass + def set_up(self, x_init, operator=None, constraint=None, data_fidelity=None,\ + regulariser=None, opt=None): + + # default inputs + if constraint is None: + self.constraint = ZeroFun() + else: + self.constraint = constraint + if data_fidelity is None: + data_fidelity = ZeroFun() + else: + self.data_fidelity = data_fidelity + if regulariser is None: + self.regulariser = ZeroFun() + else: + self.regulariser = regulariser + + # algorithmic parameters + + + # step-sizes + self.tau = 2 / (self.data_fidelity.L + 2) + self.sigma = (1/self.tau - self.data_fidelity.L/2) / self.regulariser.L + + self.inv_sigma = 1/self.sigma + + # initialization + self.x = x_init + self.y = operator.direct(self.x) + + + def update(self): + + # primal forward-backward step + x_old = self.x + self.x = self.x - self.tau * ( self.data_fidelity.grad(self.x) + self.operator.adjoint(self.y) ) + self.x = self.constraint.prox(self.x, self.tau); + + # dual forward-backward step + self.y = self.y + self.sigma * self.operator.direct(2*self.x - x_old); + self.y = self.y - self.sigma * self.regulariser.prox(self.inv_sigma*self.y, self.inv_sigma); + + # time and criterion + self.loss = self.constraint(self.x) + self.data_fidelity(self.x) + self.regulariser(self.operator.direct(self.x)) diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/GradientDescent.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/GradientDescent.py new file mode 100644 index 0000000..14763c5 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/GradientDescent.py @@ -0,0 +1,76 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2019 Edoardo Pasca + +# 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. +""" +Created on Thu Feb 21 11:05:09 2019 + +@author: ofn77899 +""" +from ccpi.optimisation.algorithms import Algorithm + +class GradientDescent(Algorithm): + '''Implementation of Gradient Descent algorithm + ''' + + def __init__(self, **kwargs): + '''initialisation can be done at creation time if all + proper variables are passed or later with set_up''' + super(GradientDescent, self).__init__() + self.x = None + self.rate = 0 + self.objective_function = None + self.regulariser = None + args = ['x_init', 'objective_function', 'rate'] + for k,v in kwargs.items(): + if k in args: + args.pop(args.index(k)) + if len(args) == 0: + return self.set_up(x_init=kwargs['x_init'], + objective_function=kwargs['objective_function'], + rate=kwargs['rate']) + + def should_stop(self): + '''stopping cryterion, currently only based on number of iterations''' + return self.iteration >= self.max_iteration + + def set_up(self, x_init, objective_function, rate): + '''initialisation of the algorithm''' + self.x = x_init.copy() + self.objective_function = objective_function + self.rate = rate + self.loss.append(objective_function(x_init)) + self.iteration = 0 + try: + self.memopt = self.objective_function.memopt + except AttributeError as ae: + self.memopt = False + if self.memopt: + self.x_update = x_init.copy() + + def update(self): + '''Single iteration''' + if self.memopt: + self.objective_function.gradient(self.x, out=self.x_update) + self.x_update *= -self.rate + self.x += self.x_update + else: + self.x += -self.rate * self.objective_function.gradient(self.x) + + def update_objective(self): + self.loss.append(self.objective_function(self.x)) + diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/PDHG.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/PDHG.py index 0f5e8ef..39b092b 100644 --- a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/PDHG.py +++ b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/PDHG.py @@ -13,117 +13,79 @@ import time from ccpi.optimisation.operators import BlockOperator from ccpi.framework import BlockDataContainer from ccpi.optimisation.functions import FunctionOperatorComposition -import matplotlib.pyplot as plt class PDHG(Algorithm): '''Primal Dual Hybrid Gradient''' def __init__(self, **kwargs): - super(PDHG, self).__init__() + super(PDHG, self).__init__(max_iteration=kwargs.get('max_iteration',0)) self.f = kwargs.get('f', None) self.operator = kwargs.get('operator', None) self.g = kwargs.get('g', None) self.tau = kwargs.get('tau', None) self.sigma = kwargs.get('sigma', None) - self.memopt = kwargs.get('memopt', False) - + if self.f is not None and self.operator is not None and \ self.g is not None: print ("Calling from creator") self.set_up(self.f, + self.g, self.operator, - self.g, self.tau, self.sigma) def set_up(self, f, g, operator, tau = None, sigma = None, opt = None, **kwargs): # algorithmic parameters - + self.operator = operator + self.f = f + self.g = g + self.tau = tau + self.sigma = sigma + self.opt = opt if sigma is None and tau is None: raise ValueError('Need sigma*tau||K||^2<1') - - + self.x_old = self.operator.domain_geometry().allocate() - self.y_old = self.operator.range_geometry().allocate() - - self.xbar = self.x_old.copy() self.x_tmp = self.x_old.copy() self.x = self.x_old.copy() - - self.y_tmp = self.y_old.copy() - self.y = self.y_tmp.copy() - - - - #self.y = self.y_old.copy() - - - #if self.memopt: - # self.y_tmp = self.y_old.copy() - # self.x_tmp = self.x_old.copy() + + self.y_old = self.operator.range_geometry().allocate() + self.y_tmp = self.y_old.copy() + self.y = self.y_old.copy() + + self.xbar = self.x_old.copy() - # relaxation parameter self.theta = 1 def update(self): - if self.memopt: - # Gradient descent, Dual problem solution - # self.y_old += self.sigma * self.operator.direct(self.xbar) - self.operator.direct(self.xbar, out=self.y_tmp) - self.y_tmp *= self.sigma - self.y_tmp += self.y_old - - #self.y = self.f.proximal_conjugate(self.y_old, self.sigma) - self.f.proximal_conjugate(self.y_tmp, self.sigma, out=self.y) - - # Gradient ascent, Primal problem solution - self.operator.adjoint(self.y, out=self.x_tmp) - self.x_tmp *= -1*self.tau - self.x_tmp += self.x_old - - self.g.proximal(self.x_tmp, self.tau, out=self.x) - - #Update - self.x.subtract(self.x_old, out=self.xbar) - self.xbar *= self.theta - self.xbar += self.x - - self.x_old.fill(self.x) - self.y_old.fill(self.y) - - - else: - # Gradient descent, Dual problem solution - self.y_old += self.sigma * self.operator.direct(self.xbar) - self.y = self.f.proximal_conjugate(self.y_old, self.sigma) - - # Gradient ascent, Primal problem solution - self.x_old -= self.tau * self.operator.adjoint(self.y) - self.x = self.g.proximal(self.x_old, self.tau) + # Gradient descent, Dual problem solution + self.operator.direct(self.xbar, out=self.y_tmp) + self.y_tmp *= self.sigma + self.y_tmp += self.y_old - #Update - self.x.subtract(self.x_old, out=self.xbar) - self.xbar *= self.theta - self.xbar += self.x + #self.y = self.f.proximal_conjugate(self.y_old, self.sigma) + self.f.proximal_conjugate(self.y_tmp, self.sigma, out=self.y) + + # Gradient ascent, Primal problem solution + self.operator.adjoint(self.y, out=self.x_tmp) + self.x_tmp *= -1*self.tau + self.x_tmp += self.x_old - self.x_old.fill(self.x) - self.y_old.fill(self.y) - - #xbar = x + theta * (x - x_old) -# self.xbar.fill(self.x) -# self.xbar -= self.x_old -# self.xbar *= self.theta -# self.xbar += self.x - -# self.x_old.fill(self.x) -# self.y_old.fill(self.y) - + self.g.proximal(self.x_tmp, self.tau, out=self.x) + + #Update + self.x.subtract(self.x_old, out=self.xbar) + self.xbar *= self.theta + self.xbar += self.x + + self.x_old.fill(self.x) + self.y_old.fill(self.y) def update_objective(self): - + p1 = self.f(self.operator.direct(self.x)) + self.g(self.x) d1 = -(self.f.convex_conjugate(self.y) + self.g.convex_conjugate(-1*self.operator.adjoint(self.y))) @@ -169,64 +131,44 @@ def PDHG_old(f, g, operator, tau = None, sigma = None, opt = None, **kwargs): for i in range(niter): + - if not memopt: - - y_tmp = y_old + sigma * operator.direct(xbar) - y = f.proximal_conjugate(y_tmp, sigma) - - x_tmp = x_old - tau*operator.adjoint(y) - x = g.proximal(x_tmp, tau) - - x.subtract(x_old, out=xbar) - xbar *= theta - xbar += x - - if i%50==0: - - p1 = f(operator.direct(x)) + g(x) - d1 = - ( f.convex_conjugate(y) + g.convex_conjugate(-1*operator.adjoint(y)) ) - primal.append(p1) - dual.append(d1) - pdgap.append(p1-d1) - print(p1, d1, p1-d1) - - x_old.fill(x) - y_old.fill(y) - - - else: - + if memopt: operator.direct(xbar, out = y_tmp) y_tmp *= sigma - y_tmp += y_old - f.proximal_conjugate(y_tmp, sigma, out=y) - + y_tmp += y_old + else: + y_tmp = y_old + sigma * operator.direct(xbar) + + f.proximal_conjugate(y_tmp, sigma, out=y) + + if memopt: operator.adjoint(y, out = x_tmp) x_tmp *= -1*tau x_tmp += x_old - - g.proximal(x_tmp, tau, out = x) - - x.subtract(x_old, out=xbar) - xbar *= theta - xbar += x + else: + x_tmp = x_old - tau*operator.adjoint(y) - if i%50==0: - - p1 = f(operator.direct(x)) + g(x) - d1 = - ( f.convex_conjugate(y) + g.convex_conjugate(-1*operator.adjoint(y)) ) - primal.append(p1) - dual.append(d1) - pdgap.append(p1-d1) - print(p1, d1, p1-d1) - - x_old.fill(x) - y_old.fill(y) + g.proximal(x_tmp, tau, out=x) + + x.subtract(x_old, out=xbar) + xbar *= theta + xbar += x + + x_old.fill(x) + y_old.fill(y) - + if i%10==0: + + p1 = f(operator.direct(x)) + g(x) + d1 = - ( f.convex_conjugate(y) + g.convex_conjugate(-1*operator.adjoint(y)) ) + primal.append(p1) + dual.append(d1) + pdgap.append(p1-d1) + print(p1, d1, p1-d1) + t_end = time.time() diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/SIRT.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/SIRT.py new file mode 100644 index 0000000..30584d4 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/SIRT.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018 Edoardo Pasca + +# 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 ccpi.optimisation.algorithms import Algorithm + +class SIRT(Algorithm): + + '''Simultaneous Iterative Reconstruction Technique + + Parameters: + x_init: initial guess + operator: operator for forward/backward projections + data: data to operate on + constraint: Function with prox-method, for example IndicatorBox to + enforce box constraints, default is None). + ''' + def __init__(self, **kwargs): + super(SIRT, self).__init__() + self.x = kwargs.get('x_init', None) + self.operator = kwargs.get('operator', None) + self.data = kwargs.get('data', None) + self.constraint = kwargs.get('constraint', None) + if self.x is not None and self.operator is not None and \ + self.data is not None: + print ("Calling from creator") + self.set_up(x_init=kwargs['x_init'], + operator=kwargs['operator'], + data=kwargs['data'], + constraint=kwargs['constraint']) + + def set_up(self, x_init, operator , data, constraint=None ): + + self.x = x_init.copy() + self.operator = operator + self.data = data + self.constraint = constraint + + self.r = data.copy() + + self.relax_par = 1.0 + + # Set up scaling matrices D and M. + self.M = 1/self.operator.direct(self.operator.domain_geometry().allocate(value=1.0)) + self.D = 1/self.operator.adjoint(self.operator.range_geometry().allocate(value=1.0)) + + + def update(self): + + self.r = self.data - self.operator.direct(self.x) + + self.x += self.relax_par * (self.D*self.operator.adjoint(self.M*self.r)) + + if self.constraint != None: + self.x = self.constraint.prox(self.x,None) + + def update_objective(self): + self.loss.append(self.r.squared_norm())
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/__init__.py b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/__init__.py new file mode 100644 index 0000000..2dbacfc --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/algorithms/__init__.py @@ -0,0 +1,33 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2019 Edoardo Pasca + +# 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. +""" +Created on Thu Feb 21 11:03:13 2019 + +@author: ofn77899 +""" + +from .Algorithm import Algorithm +from .CGLS import CGLS +from .SIRT import SIRT +from .GradientDescent import GradientDescent +from .FISTA import FISTA +from .FBPD import FBPD +from .PDHG import PDHG +from .PDHG import PDHG_old + diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/algs.py b/Wrappers/Python/build/lib/ccpi/optimisation/algs.py new file mode 100644 index 0000000..f5ba85e --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/algs.py @@ -0,0 +1,307 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018 Jakob Jorgensen, Daniil Kazantsev and Edoardo Pasca + +# 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. + +import numpy +import time + + + + +def FISTA(x_init, f=None, g=None, opt=None): + '''Fast Iterative Shrinkage-Thresholding Algorithm + + Beck, A. and Teboulle, M., 2009. A fast iterative shrinkage-thresholding + algorithm for linear inverse problems. + SIAM journal on imaging sciences,2(1), pp.183-202. + + Parameters: + x_init: initial guess + f: data fidelity + g: regularizer + h: + opt: additional algorithm + ''' + # default inputs + if f is None: f = ZeroFun() + if g is None: g = ZeroFun() + + # algorithmic parameters + if opt is None: + opt = {'tol': 1e-4, 'iter': 1000, 'memopt':False} + + max_iter = opt['iter'] if 'iter' in opt.keys() else 1000 + tol = opt['tol'] if 'tol' in opt.keys() else 1e-4 + memopt = opt['memopt'] if 'memopt' in opt.keys() else False + + + # initialization + if memopt: + y = x_init.clone() + x_old = x_init.clone() + x = x_init.clone() + u = x_init.clone() + else: + x_old = x_init + y = x_init; + + timing = numpy.zeros(max_iter) + criter = numpy.zeros(max_iter) + + invL = 1/f.L + + t_old = 1 + +# c = f(x_init) + g(x_init) + + # algorithm loop + for it in range(0, max_iter): + + time0 = time.time() + if memopt: + # u = y - invL*f.grad(y) + # store the result in x_old + f.gradient(y, out=u) + u.__imul__( -invL ) + u.__iadd__( y ) + # x = g.prox(u,invL) + g.proximal(u, invL, out=x) + + t = 0.5*(1 + numpy.sqrt(1 + 4*(t_old**2))) + + # y = x + (t_old-1)/t*(x-x_old) + x.subtract(x_old, out=y) + y.__imul__ ((t_old-1)/t) + y.__iadd__( x ) + + x_old.fill(x) + t_old = t + + + else: + u = y - invL*f.gradient(y) + + x = g.proximal(u,invL) + + t = 0.5*(1 + numpy.sqrt(1 + 4*(t_old**2))) + + y = x + (t_old-1)/t*(x-x_old) + + x_old = x.copy() + t_old = t + + # time and criterion +# timing[it] = time.time() - time0 +# criter[it] = f(x) + g(x); + + # stopping rule + #if np.linalg.norm(x - x_old) < tol * np.linalg.norm(x_old) and it > 10: + # break + + #print(it, 'out of', 10, 'iterations', end='\r'); + + #criter = criter[0:it+1]; +# timing = numpy.cumsum(timing[0:it+1]); + + return x #, it, timing, criter + +def FBPD(x_init, operator=None, constraint=None, data_fidelity=None,\ + regulariser=None, opt=None): + '''FBPD Algorithm + + Parameters: + x_init: initial guess + f: constraint + g: data fidelity + h: regularizer + opt: additional algorithm + ''' + # default inputs + if constraint is None: constraint = ZeroFun() + if data_fidelity is None: data_fidelity = ZeroFun() + if regulariser is None: regulariser = ZeroFun() + + # algorithmic parameters + if opt is None: + opt = {'tol': 1e-4, 'iter': 1000} + else: + try: + max_iter = opt['iter'] + except KeyError as ke: + opt[ke] = 1000 + try: + opt['tol'] = 1000 + except KeyError as ke: + opt[ke] = 1e-4 + tol = opt['tol'] + max_iter = opt['iter'] + memopt = opt['memopts'] if 'memopts' in opt.keys() else False + + # step-sizes + tau = 2 / (data_fidelity.L + 2) + sigma = (1/tau - data_fidelity.L/2) / regulariser.L + inv_sigma = 1/sigma + + # initialization + x = x_init + y = operator.direct(x); + + timing = numpy.zeros(max_iter) + criter = numpy.zeros(max_iter) + + + + + # algorithm loop + for it in range(0, max_iter): + + t = time.time() + + # primal forward-backward step + x_old = x; + x = x - tau * ( data_fidelity.grad(x) + operator.adjoint(y) ); + x = constraint.prox(x, tau); + + # dual forward-backward step + y = y + sigma * operator.direct(2*x - x_old); + y = y - sigma * regulariser.prox(inv_sigma*y, inv_sigma); + + # time and criterion + timing[it] = time.time() - t + criter[it] = constraint(x) + data_fidelity(x) + regulariser(operator.direct(x)) + + # stopping rule + #if np.linalg.norm(x - x_old) < tol * np.linalg.norm(x_old) and it > 10: + # break + + criter = criter[0:it+1] + timing = numpy.cumsum(timing[0:it+1]) + + return x, it, timing, criter + +def CGLS(x_init, operator , data , opt=None): + '''Conjugate Gradient Least Squares algorithm + + Parameters: + x_init: initial guess + operator: operator for forward/backward projections + data: data to operate on + opt: additional algorithm + ''' + + if opt is None: + opt = {'tol': 1e-4, 'iter': 1000} + else: + try: + max_iter = opt['iter'] + except KeyError as ke: + opt[ke] = 1000 + try: + opt['tol'] = 1000 + except KeyError as ke: + opt[ke] = 1e-4 + tol = opt['tol'] + max_iter = opt['iter'] + + r = data.copy() + x = x_init.copy() + + d = operator.adjoint(r) + + normr2 = (d**2).sum() + + timing = numpy.zeros(max_iter) + criter = numpy.zeros(max_iter) + + # algorithm loop + for it in range(0, max_iter): + + t = time.time() + + Ad = operator.direct(d) + alpha = normr2/( (Ad**2).sum() ) + x = x + alpha*d + r = r - alpha*Ad + s = operator.adjoint(r) + + normr2_new = (s**2).sum() + beta = normr2_new/normr2 + normr2 = normr2_new + d = s + beta*d + + # time and criterion + timing[it] = time.time() - t + criter[it] = (r**2).sum() + + return x, it, timing, criter + +def SIRT(x_init, operator , data , opt=None, constraint=None): + '''Simultaneous Iterative Reconstruction Technique + + Parameters: + x_init: initial guess + operator: operator for forward/backward projections + data: data to operate on + opt: additional algorithm + constraint: func of Indicator type specifying convex constraint. + ''' + + if opt is None: + opt = {'tol': 1e-4, 'iter': 1000} + else: + try: + max_iter = opt['iter'] + except KeyError as ke: + opt[ke] = 1000 + try: + opt['tol'] = 1000 + except KeyError as ke: + opt[ke] = 1e-4 + tol = opt['tol'] + max_iter = opt['iter'] + + x = x_init.clone() + + timing = numpy.zeros(max_iter) + criter = numpy.zeros(max_iter) + + # Relaxation parameter must be strictly between 0 and 2. For now fix at 1.0 + relax_par = 1.0 + + # Set up scaling matrices D and M. + M = 1/operator.direct(operator.domain_geometry().allocate(value=1.0)) + D = 1/operator.adjoint(operator.range_geometry().allocate(value=1.0)) + + # algorithm loop + for it in range(0, max_iter): + t = time.time() + r = data - operator.direct(x) + + x = x + relax_par * (D*operator.adjoint(M*r)) + + if constraint != None: + x = constraint.prox(x,None) + + timing[it] = time.time() - t + if it > 0: + criter[it-1] = (r**2).sum() + + r = data - operator.direct(x) + criter[it] = (r**2).sum() + return x, it, timing, criter + diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/Function.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/Function.py new file mode 100644 index 0000000..ba33666 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/functions/Function.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 Jakob Jorgensen, Daniil Kazantsev and Edoardo Pasca + +# 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. + +import warnings +from ccpi.optimisation.functions.ScaledFunction import ScaledFunction + +class Function(object): + '''Abstract class representing a function + + Members: + L is the Lipschitz constant of the gradient of the Function + ''' + def __init__(self): + self.L = None + + def __call__(self,x, out=None): + '''Evaluates the function at x ''' + raise NotImplementedError + + def gradient(self, x, out=None): + '''Returns the gradient of the function at x, if the function is differentiable''' + raise NotImplementedError + + def proximal(self, x, tau, out=None): + '''This returns the proximal operator for the function at x, tau''' + raise NotImplementedError + + def convex_conjugate(self, x, out=None): + '''This evaluates the convex conjugate of the function at x''' + raise NotImplementedError + + def proximal_conjugate(self, x, tau, out = None): + '''This returns the proximal operator for the convex conjugate of the function at x, tau''' + raise NotImplementedError + + def grad(self, x): + '''Alias of gradient(x,None)''' + warnings.warn('''This method will disappear in following + versions of the CIL. Use gradient instead''', DeprecationWarning) + return self.gradient(x, out=None) + + def prox(self, x, tau): + '''Alias of proximal(x, tau, None)''' + warnings.warn('''This method will disappear in following + versions of the CIL. Use proximal instead''', DeprecationWarning) + return self.proximal(x, tau, out=None) + + def __rmul__(self, scalar): + '''Defines the multiplication by a scalar on the left + + returns a ScaledFunction''' + return ScaledFunction(self, scalar) + diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/IndicatorBox.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/IndicatorBox.py new file mode 100644 index 0000000..df8dc89 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/functions/IndicatorBox.py @@ -0,0 +1,65 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 Jakob Jorgensen, Daniil Kazantsev and Edoardo Pasca + +# 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 ccpi.optimisation.functions import Function +import numpy + +class IndicatorBox(Function): + '''Box constraints indicator function. + + Calling returns 0 if argument is within the box. The prox operator is projection onto the box. + Only implements one scalar lower and one upper as constraint on all elements. Should generalise + to vectors to allow different constraints one elements. +''' + + def __init__(self,lower=-numpy.inf,upper=numpy.inf): + # Do nothing + super(IndicatorBox, self).__init__() + self.lower = lower + self.upper = upper + + + def __call__(self,x): + + if (numpy.all(x.array>=self.lower) and + numpy.all(x.array <= self.upper) ): + val = 0 + else: + val = numpy.inf + return val + + def prox(self,x,tau=None): + return (x.maximum(self.lower)).minimum(self.upper) + + def proximal(self, x, tau, out=None): + if out is None: + return self.prox(x, tau) + else: + if not x.shape == out.shape: + raise ValueError('Norm1 Incompatible size:', + x.shape, out.shape) + #(x.abs() - tau*self.gamma).maximum(0) * x.sign() + x.abs(out = out) + out.__isub__(tau*self.gamma) + out.maximum(0, out=out) + if self.sign_x is None or not x.shape == self.sign_x.shape: + self.sign_x = x.sign() + else: + x.sign(out=self.sign_x) + + out.__imul__( self.sign_x ) diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/KullbackLeibler.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/KullbackLeibler.py index b53f669..cf1a244 100644 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/KullbackLeibler.py +++ b/Wrappers/Python/build/lib/ccpi/optimisation/functions/KullbackLeibler.py @@ -62,6 +62,7 @@ class KullbackLeibler(Function): if out is None: return 1 - self.b/(x + self.bnoise) else: + x.add(self.bnoise, out=out) self.b.divide(out, out=out) out.subtract(1, out=out) @@ -105,15 +106,12 @@ class KullbackLeibler(Function): z = x + tau * self.bnoise return 0.5*((z + 1) - ((z-1)**2 + 4 * tau * self.b).sqrt()) else: -# z = x + tau * self.bnoise -# out.fill( 0.5*((z + 1) - ((z-1)**2 + 4 * tau * self.b).sqrt()) ) - - tmp1 = x + tau * self.bnoise - 1 - tmp2 = tmp1 + 2 - - self.b.multiply(4*tau, out=out) - tmp1.multiply(tmp1, out=tmp1) - out += tmp1 + + z_m = x + tau * self.bnoise -1 + self.b.multiply(4*tau, out=out) + z_m.multiply(z_m, out=z_m) + out += z_m + out.sqrt(out=out) out *= -1 @@ -133,43 +131,6 @@ class KullbackLeibler(Function): return ScaledFunction(self, scalar) - - -if __name__ == '__main__': - - - from ccpi.framework import ImageGeometry - import numpy - - N, M = 2,3 - ig = ImageGeometry(N, M) - data = ImageData(numpy.random.randint(-10, 10, size=(M, N))) - x = ImageData(numpy.random.randint(-10, 10, size=(M, N))) - - bnoise = ImageData(numpy.random.randint(-10, 10, size=(M, N))) - - f = KullbackLeibler(data) - - print(f(x)) - -# numpy.random.seed(10) -# -# -# x = numpy.random.randint(-10, 10, size = (2,3)) -# b = numpy.random.randint(1, 10, size = (2,3)) -# -# ind1 = x>0 -# -# res = x[ind1] - b * numpy.log(x[ind1]) -# -## ind = x>0 -# -## y = x[ind] -# -# -# -# -# diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/L1Norm.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/L1Norm.py new file mode 100644 index 0000000..4e53f2c --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/functions/L1Norm.py @@ -0,0 +1,234 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca + +# 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 ccpi.optimisation.functions import Function +from ccpi.optimisation.functions.ScaledFunction import ScaledFunction +from ccpi.optimisation.operators import ShrinkageOperator + + +class L1Norm(Function): + + ''' + + Class: L1Norm + + Cases: a) f(x) = ||x||_{1} + + b) f(x) = ||x - b||_{1} + + ''' + + def __init__(self, **kwargs): + + super(L1Norm, self).__init__() + self.b = kwargs.get('b',None) + + def __call__(self, x): + + ''' Evaluate L1Norm at x: f(x) ''' + + y = x + if self.b is not None: + y = x - self.b + return y.abs().sum() + + def gradient(self,x): + #TODO implement subgradient??? + return ValueError('Not Differentiable') + + def convex_conjugate(self,x): + #TODO implement Indicator infty??? + + y = 0 + if self.b is not None: + y = 0 + (self.b * x).sum() + return y + + def proximal(self, x, tau, out=None): + + # TODO implement shrinkage operator, we will need it later e.g SplitBregman + + if out is None: + if self.b is not None: + return self.b + ShrinkageOperator.__call__(self, x - self.b, tau) + else: + return ShrinkageOperator.__call__(self, x, tau) + else: + if self.b is not None: + out.fill(self.b + ShrinkageOperator.__call__(self, x - self.b, tau)) + else: + out.fill(ShrinkageOperator.__call__(self, x, tau)) + + def proximal_conjugate(self, x, tau, out=None): + + if out is None: + if self.b is not None: + return (x - tau*self.b).divide((x - tau*self.b).abs().maximum(1.0)) + else: + return x.divide(x.abs().maximum(1.0)) + else: + if self.b is not None: + out.fill((x - tau*self.b).divide((x - tau*self.b).abs().maximum(1.0))) + else: + out.fill(x.divide(x.abs().maximum(1.0)) ) + + def __rmul__(self, scalar): + return ScaledFunction(self, scalar) + + +#import numpy as np +##from ccpi.optimisation.funcs import Function +#from ccpi.optimisation.functions import Function +#from ccpi.framework import DataContainer, ImageData +# +# +############################# L1NORM FUNCTIONS ############################# +#class SimpleL1Norm(Function): +# +# def __init__(self, alpha=1): +# +# super(SimpleL1Norm, self).__init__() +# self.alpha = alpha +# +# def __call__(self, x): +# return self.alpha * x.abs().sum() +# +# def gradient(self,x): +# return ValueError('Not Differentiable') +# +# def convex_conjugate(self,x): +# return 0 +# +# def proximal(self, x, tau): +# ''' Soft Threshold''' +# return x.sign() * (x.abs() - tau * self.alpha).maximum(0) +# +# def proximal_conjugate(self, x, tau): +# return x.divide((x.abs()/self.alpha).maximum(1.0)) + +#class L1Norm(SimpleL1Norm): +# +# def __init__(self, alpha=1, **kwargs): +# +# super(L1Norm, self).__init__() +# self.alpha = alpha +# self.b = kwargs.get('b',None) +# +# def __call__(self, x): +# +# if self.b is None: +# return SimpleL1Norm.__call__(self, x) +# else: +# return SimpleL1Norm.__call__(self, x - self.b) +# +# def gradient(self, x): +# return ValueError('Not Differentiable') +# +# def convex_conjugate(self,x): +# if self.b is None: +# return SimpleL1Norm.convex_conjugate(self, x) +# else: +# return SimpleL1Norm.convex_conjugate(self, x) + (self.b * x).sum() +# +# def proximal(self, x, tau): +# +# if self.b is None: +# return SimpleL1Norm.proximal(self, x, tau) +# else: +# return self.b + SimpleL1Norm.proximal(self, x - self.b , tau) +# +# def proximal_conjugate(self, x, tau): +# +# if self.b is None: +# return SimpleL1Norm.proximal_conjugate(self, x, tau) +# else: +# return SimpleL1Norm.proximal_conjugate(self, x - tau*self.b, tau) +# + +############################################################################### + + + + +if __name__ == '__main__': + + from ccpi.framework import ImageGeometry + import numpy + N, M = 40,40 + ig = ImageGeometry(N, M) + scalar = 10 + b = ig.allocate('random_int') + u = ig.allocate('random_int') + + f = L1Norm() + f_scaled = scalar * L1Norm() + + f_b = L1Norm(b=b) + f_scaled_b = scalar * L1Norm(b=b) + + # call + + a1 = f(u) + a2 = f_scaled(u) + numpy.testing.assert_equal(scalar * a1, a2) + + a3 = f_b(u) + a4 = f_scaled_b(u) + numpy.testing.assert_equal(scalar * a3, a4) + + # proximal + tau = 0.4 + b1 = f.proximal(u, tau*scalar) + b2 = f_scaled.proximal(u, tau) + + numpy.testing.assert_array_almost_equal(b1.as_array(), b2.as_array(), decimal=4) + + b3 = f_b.proximal(u, tau*scalar) + b4 = f_scaled_b.proximal(u, tau) + + z1 = b + (u-b).sign() * ((u-b).abs() - tau * scalar).maximum(0) + + numpy.testing.assert_array_almost_equal(b3.as_array(), b4.as_array(), decimal=4) +# +# #proximal conjugate +# + c1 = f_scaled.proximal_conjugate(u, tau) + c2 = u.divide((u.abs()/scalar).maximum(1.0)) + + numpy.testing.assert_array_almost_equal(c1.as_array(), c2.as_array(), decimal=4) + + c3 = f_scaled_b.proximal_conjugate(u, tau) + c4 = (u - tau*b).divide( ((u-tau*b).abs()/scalar).maximum(1.0) ) + + numpy.testing.assert_array_almost_equal(c3.as_array(), c4.as_array(), decimal=4) + + + + + + + + + + + + + +
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/L2NormSquared.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/L2NormSquared.py index e73da93..b77d472 100644 --- a/Wrappers/Python/build/lib/ccpi/optimisation/functions/L2NormSquared.py +++ b/Wrappers/Python/build/lib/ccpi/optimisation/functions/L2NormSquared.py @@ -94,21 +94,18 @@ class L2NormSquared(Function): if self.b is None: return x/(1+2*tau) else: - tmp = x.subtract(self.b) tmp /= (1+2*tau) tmp += self.b return tmp else: - out.fill(x) - if self.b is not None: - out -= self.b - out /= (1+2*tau) if self.b is not None: - out += self.b - - + x.subtract(self.b, out=out) + out /= (1+2*tau) + out += self.b + else: + x.divide((1+2*tau), out=out) def proximal_conjugate(self, x, tau, out=None): @@ -287,17 +284,3 @@ if __name__ == '__main__': numpy.testing.assert_array_almost_equal(res1.as_array(), \ res2.as_array(), decimal=4) - - - - - - - - - - - - - - diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/Norm2Sq.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/Norm2Sq.py new file mode 100644 index 0000000..b553d7c --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/functions/Norm2Sq.py @@ -0,0 +1,98 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 Jakob Jorgensen, Daniil Kazantsev and Edoardo Pasca + +# 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 ccpi.optimisation.functions import Function +import numpy +import warnings + +# Define a class for squared 2-norm +class Norm2sq(Function): + ''' + f(x) = c*||A*x-b||_2^2 + + which has + + grad[f](x) = 2*c*A^T*(A*x-b) + + and Lipschitz constant + + L = 2*c*||A||_2^2 = 2*s1(A)^2 + + where s1(A) is the largest singular value of A. + + ''' + + def __init__(self,A,b,c=1.0,memopt=False): + super(Norm2sq, self).__init__() + + self.A = A # Should be an operator, default identity + self.b = b # Default zero DataSet? + self.c = c # Default 1. + if memopt: + try: + self.range_tmp = A.range_geometry().allocate() + self.domain_tmp = A.domain_geometry().allocate() + self.memopt = True + except NameError as ne: + warnings.warn(str(ne)) + self.memopt = False + except NotImplementedError as nie: + print (nie) + warnings.warn(str(nie)) + self.memopt = False + else: + self.memopt = False + + # Compute the Lipschitz parameter from the operator if possible + # Leave it initialised to None otherwise + try: + self.L = 2.0*self.c*(self.A.norm()**2) + except AttributeError as ae: + pass + except NotImplementedError as noe: + pass + + #def grad(self,x): + # return self.gradient(x, out=None) + + def __call__(self,x): + #return self.c* np.sum(np.square((self.A.direct(x) - self.b).ravel())) + #if out is None: + # return self.c*( ( (self.A.direct(x)-self.b)**2).sum() ) + #else: + y = self.A.direct(x) + y.__isub__(self.b) + #y.__imul__(y) + #return y.sum() * self.c + try: + return y.squared_norm() * self.c + except AttributeError as ae: + # added for compatibility with SIRF + return (y.norm()**2) * self.c + + def gradient(self, x, out = None): + if self.memopt: + #return 2.0*self.c*self.A.adjoint( self.A.direct(x) - self.b ) + + self.A.direct(x, out=self.range_tmp) + self.range_tmp -= self.b + self.A.adjoint(self.range_tmp, out=out) + #self.direct_placehold.multiply(2.0*self.c, out=out) + out *= (self.c * 2.0) + else: + return (2.0*self.c)*self.A.adjoint( self.A.direct(x) - self.b ) diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/functions/__init__.py b/Wrappers/Python/build/lib/ccpi/optimisation/functions/__init__.py new file mode 100644 index 0000000..a82ee3e --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/functions/__init__.py @@ -0,0 +1,13 @@ +# -*- coding: utf-8 -*- + +from .Function import Function +from .ZeroFunction import ZeroFunction +from .L1Norm import L1Norm +from .L2NormSquared import L2NormSquared +from .ScaledFunction import ScaledFunction +from .BlockFunction import BlockFunction +from .FunctionOperatorComposition import FunctionOperatorComposition +from .MixedL21Norm import MixedL21Norm +from .IndicatorBox import IndicatorBox +from .KullbackLeibler import KullbackLeibler +from .Norm2Sq import Norm2sq diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/BlockScaledOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/BlockScaledOperator.py new file mode 100644 index 0000000..aeb6c53 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/operators/BlockScaledOperator.py @@ -0,0 +1,67 @@ +from numbers import Number +import numpy +from ccpi.optimisation.operators import ScaledOperator +import functools + +class BlockScaledOperator(ScaledOperator): + '''ScaledOperator + + A class to represent the scalar multiplication of an Operator with a scalar. + It holds an operator and a scalar. Basically it returns the multiplication + of the result of direct and adjoint of the operator with the scalar. + For the rest it behaves like the operator it holds. + + Args: + operator (Operator): a Operator or LinearOperator + scalar (Number): a scalar multiplier + Example: + The scaled operator behaves like the following: + sop = ScaledOperator(operator, scalar) + sop.direct(x) = scalar * operator.direct(x) + sop.adjoint(x) = scalar * operator.adjoint(x) + sop.norm() = operator.norm() + sop.range_geometry() = operator.range_geometry() + sop.domain_geometry() = operator.domain_geometry() + ''' + def __init__(self, operator, scalar, shape=None): + if shape is None: + shape = operator.shape + + if isinstance(scalar, (list, tuple, numpy.ndarray)): + size = functools.reduce(lambda x,y:x*y, shape, 1) + if len(scalar) != size: + raise ValueError('Scalar and operators size do not match: {}!={}' + .format(len(scalar), len(operator))) + self.scalar = scalar[:] + print ("BlockScaledOperator ", self.scalar) + elif isinstance (scalar, Number): + self.scalar = scalar + else: + raise TypeError('expected scalar to be a number of an iterable: got {}'.format(type(scalar))) + self.operator = operator + self.shape = shape + def direct(self, x, out=None): + print ("BlockScaledOperator self.scalar", self.scalar) + #print ("self.scalar", self.scalar[0]* x.get_item(0).as_array()) + return self.scalar * (self.operator.direct(x, out=out)) + def adjoint(self, x, out=None): + if self.operator.is_linear(): + return self.scalar * self.operator.adjoint(x, out=out) + else: + raise TypeError('Operator is not linear') + def norm(self): + return numpy.abs(self.scalar) * self.operator.norm() + def range_geometry(self): + return self.operator.range_geometry() + def domain_geometry(self): + return self.operator.domain_geometry() + @property + def T(self): + '''Return the transposed of self''' + #print ("transpose before" , self.shape) + #shape = (self.shape[1], self.shape[0]) + ##self.shape = shape + ##self.operator.shape = shape + #print ("transpose" , shape) + #return self + return type(self)(self.operator.T, self.scalar)
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/FiniteDifferenceOperator_old.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/FiniteDifferenceOperator_old.py new file mode 100644 index 0000000..387fb4b --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/operators/FiniteDifferenceOperator_old.py @@ -0,0 +1,374 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Mar 1 22:51:17 2019 + +@author: evangelos +""" + +from ccpi.optimisation.operators import LinearOperator +from ccpi.optimisation.ops import PowerMethodNonsquare +from ccpi.framework import ImageData, BlockDataContainer +import numpy as np + +class FiniteDiff(LinearOperator): + + # Works for Neum/Symmetric & periodic boundary conditions + # TODO add central differences??? + # TODO not very well optimised, too many conditions + # TODO add discretisation step, should get that from imageGeometry + + # Grad_order = ['channels', 'direction_z', 'direction_y', 'direction_x'] + # Grad_order = ['channels', 'direction_y', 'direction_x'] + # Grad_order = ['direction_z', 'direction_y', 'direction_x'] + # Grad_order = ['channels', 'direction_z', 'direction_y', 'direction_x'] + + def __init__(self, gm_domain, gm_range=None, direction=0, bnd_cond = 'Neumann'): + '''''' + super(FiniteDiff, self).__init__() + '''FIXME: domain and range should be geometries''' + self.gm_domain = gm_domain + self.gm_range = gm_range + + self.direction = direction + self.bnd_cond = bnd_cond + + # Domain Geometry = Range Geometry if not stated + if self.gm_range is None: + self.gm_range = self.gm_domain + # check direction and "length" of geometry + if self.direction + 1 > len(self.gm_domain.shape): + raise ValueError('Gradient directions more than geometry domain') + + #self.voxel_size = kwargs.get('voxel_size',1) + # this wrongly assumes a homogeneous voxel size + self.voxel_size = self.gm_domain.voxel_size_x + + + def direct(self, x, out=None): + + x_asarr = x.as_array() + x_sz = len(x.shape) + + if out is None: + out = np.zeros_like(x_asarr) + fd_arr = out + else: + fd_arr = out.as_array() +# fd_arr[:]=0 + +# if out is None: +# out = self.gm_domain.allocate().as_array() +# +# fd_arr = out.as_array() +# fd_arr = self.gm_domain.allocate().as_array() + + ######################## Direct for 2D ############################### + if x_sz == 2: + + if self.direction == 1: + + np.subtract( x_asarr[:,1:], x_asarr[:,0:-1], out = fd_arr[:,0:-1] ) + + if self.bnd_cond == 'Neumann': + pass + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[:,0], x_asarr[:,-1], out = fd_arr[:,-1] ) + else: + raise ValueError('No valid boundary conditions') + + if self.direction == 0: + + np.subtract( x_asarr[1:], x_asarr[0:-1], out = fd_arr[0:-1,:] ) + + if self.bnd_cond == 'Neumann': + pass + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[0,:], x_asarr[-1,:], out = fd_arr[-1,:] ) + else: + raise ValueError('No valid boundary conditions') + + ######################## Direct for 3D ############################### + elif x_sz == 3: + + if self.direction == 0: + + np.subtract( x_asarr[1:,:,:], x_asarr[0:-1,:,:], out = fd_arr[0:-1,:,:] ) + + if self.bnd_cond == 'Neumann': + pass + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[0,:,:], x_asarr[-1,:,:], out = fd_arr[-1,:,:] ) + else: + raise ValueError('No valid boundary conditions') + + if self.direction == 1: + + np.subtract( x_asarr[:,1:,:], x_asarr[:,0:-1,:], out = fd_arr[:,0:-1,:] ) + + if self.bnd_cond == 'Neumann': + pass + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[:,0,:], x_asarr[:,-1,:], out = fd_arr[:,-1,:] ) + else: + raise ValueError('No valid boundary conditions') + + + if self.direction == 2: + + np.subtract( x_asarr[:,:,1:], x_asarr[:,:,0:-1], out = fd_arr[:,:,0:-1] ) + + if self.bnd_cond == 'Neumann': + pass + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[:,:,0], x_asarr[:,:,-1], out = fd_arr[:,:,-1] ) + else: + raise ValueError('No valid boundary conditions') + + ######################## Direct for 4D ############################### + elif x_sz == 4: + + if self.direction == 0: + np.subtract( x_asarr[1:,:,:,:], x_asarr[0:-1,:,:,:], out = fd_arr[0:-1,:,:,:] ) + + if self.bnd_cond == 'Neumann': + pass + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[0,:,:,:], x_asarr[-1,:,:,:], out = fd_arr[-1,:,:,:] ) + else: + raise ValueError('No valid boundary conditions') + + if self.direction == 1: + np.subtract( x_asarr[:,1:,:,:], x_asarr[:,0:-1,:,:], out = fd_arr[:,0:-1,:,:] ) + + if self.bnd_cond == 'Neumann': + pass + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[:,0,:,:], x_asarr[:,-1,:,:], out = fd_arr[:,-1,:,:] ) + else: + raise ValueError('No valid boundary conditions') + + if self.direction == 2: + np.subtract( x_asarr[:,:,1:,:], x_asarr[:,:,0:-1,:], out = fd_arr[:,:,0:-1,:] ) + + if self.bnd_cond == 'Neumann': + pass + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[:,:,0,:], x_asarr[:,:,-1,:], out = fd_arr[:,:,-1,:] ) + else: + raise ValueError('No valid boundary conditions') + + if self.direction == 3: + np.subtract( x_asarr[:,:,:,1:], x_asarr[:,:,:,0:-1], out = fd_arr[:,:,:,0:-1] ) + + if self.bnd_cond == 'Neumann': + pass + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[:,:,:,0], x_asarr[:,:,:,-1], out = fd_arr[:,:,:,-1] ) + else: + raise ValueError('No valid boundary conditions') + + else: + raise NotImplementedError + +# res = out #/self.voxel_size + return type(x)(out) + + + def adjoint(self, x, out=None): + + x_asarr = x.as_array() + #x_asarr = x + x_sz = len(x.shape) + + if out is None: + out = np.zeros_like(x_asarr) + fd_arr = out + else: + fd_arr = out.as_array() + +# if out is None: +# out = self.gm_domain.allocate().as_array() +# fd_arr = out +# else: +# fd_arr = out.as_array() +## fd_arr = self.gm_domain.allocate().as_array() + + ######################## Adjoint for 2D ############################### + if x_sz == 2: + + if self.direction == 1: + + np.subtract( x_asarr[:,1:], x_asarr[:,0:-1], out = fd_arr[:,1:] ) + + if self.bnd_cond == 'Neumann': + np.subtract( x_asarr[:,0], 0, out = fd_arr[:,0] ) + np.subtract( -x_asarr[:,-2], 0, out = fd_arr[:,-1] ) + + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[:,0], x_asarr[:,-1], out = fd_arr[:,0] ) + + else: + raise ValueError('No valid boundary conditions') + + if self.direction == 0: + + np.subtract( x_asarr[1:,:], x_asarr[0:-1,:], out = fd_arr[1:,:] ) + + if self.bnd_cond == 'Neumann': + np.subtract( x_asarr[0,:], 0, out = fd_arr[0,:] ) + np.subtract( -x_asarr[-2,:], 0, out = fd_arr[-1,:] ) + + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[0,:], x_asarr[-1,:], out = fd_arr[0,:] ) + + else: + raise ValueError('No valid boundary conditions') + + ######################## Adjoint for 3D ############################### + elif x_sz == 3: + + if self.direction == 0: + + np.subtract( x_asarr[1:,:,:], x_asarr[0:-1,:,:], out = fd_arr[1:,:,:] ) + + if self.bnd_cond == 'Neumann': + np.subtract( x_asarr[0,:,:], 0, out = fd_arr[0,:,:] ) + np.subtract( -x_asarr[-2,:,:], 0, out = fd_arr[-1,:,:] ) + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[0,:,:], x_asarr[-1,:,:], out = fd_arr[0,:,:] ) + else: + raise ValueError('No valid boundary conditions') + + if self.direction == 1: + np.subtract( x_asarr[:,1:,:], x_asarr[:,0:-1,:], out = fd_arr[:,1:,:] ) + + if self.bnd_cond == 'Neumann': + np.subtract( x_asarr[:,0,:], 0, out = fd_arr[:,0,:] ) + np.subtract( -x_asarr[:,-2,:], 0, out = fd_arr[:,-1,:] ) + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[:,0,:], x_asarr[:,-1,:], out = fd_arr[:,0,:] ) + else: + raise ValueError('No valid boundary conditions') + + if self.direction == 2: + np.subtract( x_asarr[:,:,1:], x_asarr[:,:,0:-1], out = fd_arr[:,:,1:] ) + + if self.bnd_cond == 'Neumann': + np.subtract( x_asarr[:,:,0], 0, out = fd_arr[:,:,0] ) + np.subtract( -x_asarr[:,:,-2], 0, out = fd_arr[:,:,-1] ) + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[:,:,0], x_asarr[:,:,-1], out = fd_arr[:,:,0] ) + else: + raise ValueError('No valid boundary conditions') + + ######################## Adjoint for 4D ############################### + elif x_sz == 4: + + if self.direction == 0: + np.subtract( x_asarr[1:,:,:,:], x_asarr[0:-1,:,:,:], out = fd_arr[1:,:,:,:] ) + + if self.bnd_cond == 'Neumann': + np.subtract( x_asarr[0,:,:,:], 0, out = fd_arr[0,:,:,:] ) + np.subtract( -x_asarr[-2,:,:,:], 0, out = fd_arr[-1,:,:,:] ) + + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[0,:,:,:], x_asarr[-1,:,:,:], out = fd_arr[0,:,:,:] ) + else: + raise ValueError('No valid boundary conditions') + + if self.direction == 1: + np.subtract( x_asarr[:,1:,:,:], x_asarr[:,0:-1,:,:], out = fd_arr[:,1:,:,:] ) + + if self.bnd_cond == 'Neumann': + np.subtract( x_asarr[:,0,:,:], 0, out = fd_arr[:,0,:,:] ) + np.subtract( -x_asarr[:,-2,:,:], 0, out = fd_arr[:,-1,:,:] ) + + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[:,0,:,:], x_asarr[:,-1,:,:], out = fd_arr[:,0,:,:] ) + else: + raise ValueError('No valid boundary conditions') + + + if self.direction == 2: + np.subtract( x_asarr[:,:,1:,:], x_asarr[:,:,0:-1,:], out = fd_arr[:,:,1:,:] ) + + if self.bnd_cond == 'Neumann': + np.subtract( x_asarr[:,:,0,:], 0, out = fd_arr[:,:,0,:] ) + np.subtract( -x_asarr[:,:,-2,:], 0, out = fd_arr[:,:,-1,:] ) + + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[:,:,0,:], x_asarr[:,:,-1,:], out = fd_arr[:,:,0,:] ) + else: + raise ValueError('No valid boundary conditions') + + if self.direction == 3: + np.subtract( x_asarr[:,:,:,1:], x_asarr[:,:,:,0:-1], out = fd_arr[:,:,:,1:] ) + + if self.bnd_cond == 'Neumann': + np.subtract( x_asarr[:,:,:,0], 0, out = fd_arr[:,:,:,0] ) + np.subtract( -x_asarr[:,:,:,-2], 0, out = fd_arr[:,:,:,-1] ) + + elif self.bnd_cond == 'Periodic': + np.subtract( x_asarr[:,:,:,0], x_asarr[:,:,:,-1], out = fd_arr[:,:,:,0] ) + else: + raise ValueError('No valid boundary conditions') + + else: + raise NotImplementedError + + out *= -1 #/self.voxel_size + return type(x)(out) + + def range_geometry(self): + '''Returns the range geometry''' + return self.gm_range + + def domain_geometry(self): + '''Returns the domain geometry''' + return self.gm_domain + + def norm(self): + x0 = self.gm_domain.allocate() + x0.fill( np.random.random_sample(x0.shape) ) + self.s1, sall, svec = PowerMethodNonsquare(self, 25, x0) + return self.s1 + + +if __name__ == '__main__': + + from ccpi.framework import ImageGeometry + import numpy + + N, M = 2, 3 + + ig = ImageGeometry(N, M) + + + FD = FiniteDiff(ig, direction = 0, bnd_cond = 'Neumann') + u = FD.domain_geometry().allocate('random_int') + + + res = FD.domain_geometry().allocate() + FD.direct(u, out=res) + + z = FD.direct(u) + print(z.as_array(), res.as_array()) + + for i in range(10): + + z1 = FD.direct(u) + FD.direct(u, out=res) + numpy.testing.assert_array_almost_equal(z1.as_array(), \ + res.as_array(), decimal=4) + + + + + + +# w = G.range_geometry().allocate('random_int') + + + +
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/GradientOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/GradientOperator.py index e0b8a32..6ffaf70 100644 --- a/Wrappers/Python/build/lib/ccpi/optimisation/operators/GradientOperator.py +++ b/Wrappers/Python/build/lib/ccpi/optimisation/operators/GradientOperator.py @@ -111,7 +111,7 @@ class Gradient(LinearOperator): return BlockDataContainer(*mat) - def sum_abs_row(self): + def sum_abs_col(self): tmp = self.gm_range.allocate() res = self.gm_domain.allocate() @@ -120,7 +120,7 @@ class Gradient(LinearOperator): res += spMat.sum_abs_row() return res - def sum_abs_col(self): + def sum_abs_row(self): tmp = self.gm_range.allocate() res = [] diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/IdentityOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/IdentityOperator.py new file mode 100644 index 0000000..a853b8d --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/operators/IdentityOperator.py @@ -0,0 +1,79 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Mar 6 19:30:51 2019 + +@author: evangelos +""" + +from ccpi.optimisation.operators import LinearOperator +import scipy.sparse as sp +import numpy as np +from ccpi.framework import ImageData + + +class Identity(LinearOperator): + + def __init__(self, gm_domain, gm_range=None): + + self.gm_domain = gm_domain + self.gm_range = gm_range + if self.gm_range is None: + self.gm_range = self.gm_domain + + super(Identity, self).__init__() + + def direct(self,x,out=None): + if out is None: + return x.copy() + else: + out.fill(x) + + def adjoint(self,x, out=None): + if out is None: + return x.copy() + else: + out.fill(x) + + def norm(self): + return 1.0 + + def domain_geometry(self): + return self.gm_domain + + def range_geometry(self): + return self.gm_range + + def matrix(self): + + return sp.eye(np.prod(self.gm_domain.shape)) + + def sum_abs_row(self): + + return self.gm_range.allocate(1)#ImageData(np.array(np.reshape(abs(self.matrix()).sum(axis=0), self.gm_domain.shape, 'F'))) + + def sum_abs_col(self): + + return self.gm_domain.allocate(1)#ImageData(np.array(np.reshape(abs(self.matrix()).sum(axis=1), self.gm_domain.shape, 'F'))) + + +if __name__ == '__main__': + + from ccpi.framework import ImageGeometry + + M, N = 2, 3 + ig = ImageGeometry(M, N) + arr = ig.allocate('random_int') + + Id = Identity(ig) + d = Id.matrix() + print(d.toarray()) + + d1 = Id.sum_abs_col() + print(d1.as_array()) + + + + + +
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/Operator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/Operator.py new file mode 100644 index 0000000..2d2089b --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/operators/Operator.py @@ -0,0 +1,30 @@ +# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 5 15:55:56 2019
+
+@author: ofn77899
+"""
+from ccpi.optimisation.operators.ScaledOperator import ScaledOperator
+
+class Operator(object):
+ '''Operator that maps from a space X -> Y'''
+ def is_linear(self):
+ '''Returns if the operator is linear'''
+ return False
+ def direct(self,x, out=None):
+ '''Returns the application of the Operator on x'''
+ raise NotImplementedError
+ def norm(self):
+ '''Returns the norm of the Operator'''
+ raise NotImplementedError
+ def range_geometry(self):
+ '''Returns the range of the Operator: Y space'''
+ raise NotImplementedError
+ def domain_geometry(self):
+ '''Returns the domain of the Operator: X space'''
+ raise NotImplementedError
+ def __rmul__(self, scalar):
+ '''Defines the multiplication by a scalar on the left
+
+ returns a ScaledOperator'''
+ return ScaledOperator(self, scalar)
diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/ScaledOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/ScaledOperator.py new file mode 100644 index 0000000..ba0049e --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/operators/ScaledOperator.py @@ -0,0 +1,51 @@ +from numbers import Number +import numpy + +class ScaledOperator(object): + '''ScaledOperator + A class to represent the scalar multiplication of an Operator with a scalar. + It holds an operator and a scalar. Basically it returns the multiplication + of the result of direct and adjoint of the operator with the scalar. + For the rest it behaves like the operator it holds. + Args: + operator (Operator): a Operator or LinearOperator + scalar (Number): a scalar multiplier + Example: + The scaled operator behaves like the following: + sop = ScaledOperator(operator, scalar) + sop.direct(x) = scalar * operator.direct(x) + sop.adjoint(x) = scalar * operator.adjoint(x) + sop.norm() = operator.norm() + sop.range_geometry() = operator.range_geometry() + sop.domain_geometry() = operator.domain_geometry() + ''' + def __init__(self, operator, scalar): + super(ScaledOperator, self).__init__() + if not isinstance (scalar, Number): + raise TypeError('expected scalar: got {}'.format(type(scalar))) + self.scalar = scalar + self.operator = operator + def direct(self, x, out=None): + if out is None: + return self.scalar * self.operator.direct(x, out=out) + else: + self.operator.direct(x, out=out) + out *= self.scalar + def adjoint(self, x, out=None): + if self.operator.is_linear(): + if out is None: + return self.scalar * self.operator.adjoint(x, out=out) + else: + self.operator.adjoint(x, out=out) + out *= self.scalar + else: + raise TypeError('Operator is not linear') + def norm(self): + return numpy.abs(self.scalar) * self.operator.norm() + def range_geometry(self): + return self.operator.range_geometry() + def domain_geometry(self): + return self.operator.domain_geometry() + def is_linear(self): + return self.operator.is_linear() + diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/ShrinkageOperator.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/ShrinkageOperator.py new file mode 100644 index 0000000..f47c655 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/operators/ShrinkageOperator.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Mar 6 19:30:51 2019 + +@author: evangelos +""" + +from ccpi.framework import DataContainer + +class ShrinkageOperator(): + + def __init__(self): + pass + + def __call__(self, x, tau, out=None): + + return x.sign() * (x.abs() - tau).maximum(0) +
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/optimisation/operators/SparseFiniteDiff.py b/Wrappers/Python/build/lib/ccpi/optimisation/operators/SparseFiniteDiff.py new file mode 100644 index 0000000..c5c2ec9 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/optimisation/operators/SparseFiniteDiff.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Apr 2 14:06:15 2019 + +@author: vaggelis +""" + +import scipy.sparse as sp +import numpy as np +from ccpi.framework import ImageData + +class SparseFiniteDiff(): + + def __init__(self, gm_domain, gm_range=None, direction=0, bnd_cond = 'Neumann'): + + super(SparseFiniteDiff, self).__init__() + self.gm_domain = gm_domain + self.gm_range = gm_range + self.direction = direction + self.bnd_cond = bnd_cond + + if self.gm_range is None: + self.gm_range = self.gm_domain + + self.get_dims = [i for i in gm_domain.shape] + + if self.direction + 1 > len(self.gm_domain.shape): + raise ValueError('Gradient directions more than geometry domain') + + def matrix(self): + + i = self.direction + + mat = sp.spdiags(np.vstack([-np.ones((1,self.get_dims[i])),np.ones((1,self.get_dims[i]))]), [0,1], self.get_dims[i], self.get_dims[i], format = 'lil') + + if self.bnd_cond == 'Neumann': + mat[-1,:] = 0 + elif self.bnd_cond == 'Periodic': + mat[-1,0] = 1 + + tmpGrad = mat if i == 0 else sp.eye(self.get_dims[0]) + + for j in range(1, self.gm_domain.length): + + tmpGrad = sp.kron(mat, tmpGrad ) if j == i else sp.kron(sp.eye(self.get_dims[j]), tmpGrad ) + + return tmpGrad + + def T(self): + return self.matrix().T + + def direct(self, x): + + x_asarr = x.as_array() + res = np.reshape( self.matrix() * x_asarr.flatten('F'), self.gm_domain.shape, 'F') + return type(x)(res) + + def adjoint(self, x): + + x_asarr = x.as_array() + res = np.reshape( self.matrix().T * x_asarr.flatten('F'), self.gm_domain.shape, 'F') + return type(x)(res) + + def sum_abs_row(self): + + res = np.array(np.reshape(abs(self.matrix()).sum(axis=0), self.gm_domain.shape, 'F')) + #res[res==0]=0 + return ImageData(res) + + def sum_abs_col(self): + + res = np.array(np.reshape(abs(self.matrix()).sum(axis=1), self.gm_domain.shape, 'F') ) + #res[res==0]=0 + return ImageData(res) + +if __name__ == '__main__': + + from ccpi.framework import ImageGeometry + from ccpi.optimisation.operators import FiniteDiff + + # 2D + M, N= 2, 3 + ig = ImageGeometry(M, N) + arr = ig.allocate('random_int') + + for i in [0,1]: + + # Neumann + sFD_neum = SparseFiniteDiff(ig, direction=i, bnd_cond='Neumann') + G_neum = FiniteDiff(ig, direction=i, bnd_cond='Neumann') + + # Periodic + sFD_per = SparseFiniteDiff(ig, direction=i, bnd_cond='Periodic') + G_per = FiniteDiff(ig, direction=i, bnd_cond='Periodic') + + u_neum_direct = G_neum.direct(arr) + u_neum_sp_direct = sFD_neum.direct(arr) + np.testing.assert_array_almost_equal(u_neum_direct.as_array(), u_neum_sp_direct.as_array(), decimal=4) + + u_neum_adjoint = G_neum.adjoint(arr) + u_neum_sp_adjoint = sFD_neum.adjoint(arr) + np.testing.assert_array_almost_equal(u_neum_adjoint.as_array(), u_neum_sp_adjoint.as_array(), decimal=4) + + u_per_direct = G_neum.direct(arr) + u_per_sp_direct = sFD_neum.direct(arr) + np.testing.assert_array_almost_equal(u_per_direct.as_array(), u_per_sp_direct.as_array(), decimal=4) + + u_per_adjoint = G_per.adjoint(arr) + u_per_sp_adjoint = sFD_per.adjoint(arr) + np.testing.assert_array_almost_equal(u_per_adjoint.as_array(), u_per_sp_adjoint.as_array(), decimal=4) + + # 3D + M, N, K = 2, 3, 4 + ig3D = ImageGeometry(M, N, K) + arr3D = ig3D.allocate('random_int') + + for i in [0,1,2]: + + # Neumann + sFD_neum3D = SparseFiniteDiff(ig3D, direction=i, bnd_cond='Neumann') + G_neum3D = FiniteDiff(ig3D, direction=i, bnd_cond='Neumann') + + # Periodic + sFD_per3D = SparseFiniteDiff(ig3D, direction=i, bnd_cond='Periodic') + G_per3D = FiniteDiff(ig3D, direction=i, bnd_cond='Periodic') + + u_neum_direct3D = G_neum3D.direct(arr3D) + u_neum_sp_direct3D = sFD_neum3D.direct(arr3D) + np.testing.assert_array_almost_equal(u_neum_direct3D.as_array(), u_neum_sp_direct3D.as_array(), decimal=4) + + u_neum_adjoint3D = G_neum3D.adjoint(arr3D) + u_neum_sp_adjoint3D = sFD_neum3D.adjoint(arr3D) + np.testing.assert_array_almost_equal(u_neum_adjoint3D.as_array(), u_neum_sp_adjoint3D.as_array(), decimal=4) + + u_per_direct3D = G_neum3D.direct(arr3D) + u_per_sp_direct3D = sFD_neum3D.direct(arr3D) + np.testing.assert_array_almost_equal(u_per_direct3D.as_array(), u_per_sp_direct3D.as_array(), decimal=4) + + u_per_adjoint3D = G_per3D.adjoint(arr3D) + u_per_sp_adjoint3D = sFD_per3D.adjoint(arr3D) + np.testing.assert_array_almost_equal(u_per_adjoint3D.as_array(), u_per_sp_adjoint3D.as_array(), decimal=4) + +
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/processors/CenterOfRotationFinder.py b/Wrappers/Python/build/lib/ccpi/processors/CenterOfRotationFinder.py new file mode 100644 index 0000000..936dc05 --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/processors/CenterOfRotationFinder.py @@ -0,0 +1,408 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018 Edoardo Pasca + +# 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 ccpi.framework import DataProcessor, DataContainer, AcquisitionData,\ + AcquisitionGeometry, ImageGeometry, ImageData +import numpy +from scipy import ndimage + +class CenterOfRotationFinder(DataProcessor): + '''Processor to find the center of rotation in a parallel beam experiment + + This processor read in a AcquisitionDataSet and finds the center of rotation + based on Nghia Vo's method. https://doi.org/10.1364/OE.22.019078 + + Input: AcquisitionDataSet + + Output: float. center of rotation in pixel coordinate + ''' + + def __init__(self): + kwargs = { + + } + + #DataProcessor.__init__(self, **kwargs) + super(CenterOfRotationFinder, self).__init__(**kwargs) + + def check_input(self, dataset): + if dataset.number_of_dimensions == 3: + if dataset.geometry.geom_type == 'parallel': + return True + else: + raise ValueError('{0} is suitable only for parallel beam geometry'\ + .format(self.__class__.__name__)) + else: + raise ValueError("Expected input dimensions is 3, got {0}"\ + .format(dataset.number_of_dimensions)) + + + # ######################################################################### + # Copyright (c) 2015, UChicago Argonne, LLC. All rights reserved. # + # # + # Copyright 2015. UChicago Argonne, LLC. This software was produced # + # under U.S. Government contract DE-AC02-06CH11357 for Argonne National # + # Laboratory (ANL), which is operated by UChicago Argonne, LLC for the # + # U.S. Department of Energy. The U.S. Government has rights to use, # + # reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR # + # UChicago Argonne, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR # + # ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is # + # modified to produce derivative works, such modified software should # + # be clearly marked, so as not to confuse it with the version available # + # from ANL. # + # # + # Additionally, redistribution and use in source and binary forms, with # + # or without modification, are permitted provided that the following # + # conditions are met: # + # # + # * Redistributions of source code must retain the above copyright # + # notice, this list of conditions and the following disclaimer. # + # # + # * 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. # + # # + # * Neither the name of UChicago Argonne, LLC, Argonne National # + # Laboratory, ANL, the U.S. Government, 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 UChicago Argonne, LLC AND CONTRIBUTORS # + # "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 UChicago # + # Argonne, LLC OR CONTRIBUTORS 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. # + # ######################################################################### + + @staticmethod + def as_ndarray(arr, dtype=None, copy=False): + if not isinstance(arr, numpy.ndarray): + arr = numpy.array(arr, dtype=dtype, copy=copy) + return arr + + @staticmethod + def as_dtype(arr, dtype, copy=False): + if not arr.dtype == dtype: + arr = numpy.array(arr, dtype=dtype, copy=copy) + return arr + + @staticmethod + def as_float32(arr): + arr = CenterOfRotationFinder.as_ndarray(arr, numpy.float32) + return CenterOfRotationFinder.as_dtype(arr, numpy.float32) + + + + + @staticmethod + def find_center_vo(tomo, ind=None, smin=-40, smax=40, srad=10, step=0.5, + ratio=2., drop=20): + """ + Find rotation axis location using Nghia Vo's method. :cite:`Vo:14`. + + Parameters + ---------- + tomo : ndarray + 3D tomographic data. + ind : int, optional + Index of the slice to be used for reconstruction. + smin, smax : int, optional + Reference to the horizontal center of the sinogram. + srad : float, optional + Fine search radius. + step : float, optional + Step of fine searching. + ratio : float, optional + The ratio between the FOV of the camera and the size of object. + It's used to generate the mask. + drop : int, optional + Drop lines around vertical center of the mask. + + Returns + ------- + float + Rotation axis location. + + Notes + ----- + The function may not yield a correct estimate, if: + + - the sample size is bigger than the field of view of the camera. + In this case the ``ratio`` argument need to be set larger + than the default of 2.0. + + - there is distortion in the imaging hardware. If there's + no correction applied, the center of the projection image may + yield a better estimate. + + - the sample contrast is weak. Paganin's filter need to be applied + to overcome this. + + - the sample was changed during the scan. + """ + tomo = CenterOfRotationFinder.as_float32(tomo) + + if ind is None: + ind = tomo.shape[1] // 2 + _tomo = tomo[:, ind, :] + + + + # Reduce noise by smooth filters. Use different filters for coarse and fine search + _tomo_cs = ndimage.filters.gaussian_filter(_tomo, (3, 1)) + _tomo_fs = ndimage.filters.median_filter(_tomo, (2, 2)) + + # Coarse and fine searches for finding the rotation center. + if _tomo.shape[0] * _tomo.shape[1] > 4e6: # If data is large (>2kx2k) + #_tomo_coarse = downsample(numpy.expand_dims(_tomo_cs,1), level=2)[:, 0, :] + #init_cen = _search_coarse(_tomo_coarse, smin, smax, ratio, drop) + #fine_cen = _search_fine(_tomo_fs, srad, step, init_cen*4, ratio, drop) + init_cen = CenterOfRotationFinder._search_coarse(_tomo_cs, smin, + smax, ratio, drop) + fine_cen = CenterOfRotationFinder._search_fine(_tomo_fs, srad, + step, init_cen, + ratio, drop) + else: + init_cen = CenterOfRotationFinder._search_coarse(_tomo_cs, + smin, smax, + ratio, drop) + fine_cen = CenterOfRotationFinder._search_fine(_tomo_fs, srad, + step, init_cen, + ratio, drop) + + #logger.debug('Rotation center search finished: %i', fine_cen) + return fine_cen + + + @staticmethod + def _search_coarse(sino, smin, smax, ratio, drop): + """ + Coarse search for finding the rotation center. + """ + (Nrow, Ncol) = sino.shape + centerfliplr = (Ncol - 1.0) / 2.0 + + # Copy the sinogram and flip left right, the purpose is to + # make a full [0;2Pi] sinogram + _copy_sino = numpy.fliplr(sino[1:]) + + # This image is used for compensating the shift of sinogram 2 + temp_img = numpy.zeros((Nrow - 1, Ncol), dtype='float32') + temp_img[:] = sino[-1] + + # Start coarse search in which the shift step is 1 + listshift = numpy.arange(smin, smax + 1) + listmetric = numpy.zeros(len(listshift), dtype='float32') + mask = CenterOfRotationFinder._create_mask(2 * Nrow - 1, Ncol, + 0.5 * ratio * Ncol, drop) + for i in listshift: + _sino = numpy.roll(_copy_sino, i, axis=1) + if i >= 0: + _sino[:, 0:i] = temp_img[:, 0:i] + else: + _sino[:, i:] = temp_img[:, i:] + listmetric[i - smin] = numpy.sum(numpy.abs(numpy.fft.fftshift( + #pyfftw.interfaces.numpy_fft.fft2( + # numpy.vstack((sino, _sino))) + numpy.fft.fft2(numpy.vstack((sino, _sino))) + )) * mask) + minpos = numpy.argmin(listmetric) + return centerfliplr + listshift[minpos] / 2.0 + + @staticmethod + def _search_fine(sino, srad, step, init_cen, ratio, drop): + """ + Fine search for finding the rotation center. + """ + Nrow, Ncol = sino.shape + centerfliplr = (Ncol + 1.0) / 2.0 - 1.0 + # Use to shift the sinogram 2 to the raw CoR. + shiftsino = numpy.int16(2 * (init_cen - centerfliplr)) + _copy_sino = numpy.roll(numpy.fliplr(sino[1:]), shiftsino, axis=1) + if init_cen <= centerfliplr: + lefttake = numpy.int16(numpy.ceil(srad + 1)) + righttake = numpy.int16(numpy.floor(2 * init_cen - srad - 1)) + else: + lefttake = numpy.int16(numpy.ceil( + init_cen - (Ncol - 1 - init_cen) + srad + 1)) + righttake = numpy.int16(numpy.floor(Ncol - 1 - srad - 1)) + Ncol1 = righttake - lefttake + 1 + mask = CenterOfRotationFinder._create_mask(2 * Nrow - 1, Ncol1, + 0.5 * ratio * Ncol, drop) + numshift = numpy.int16((2 * srad) / step) + 1 + listshift = numpy.linspace(-srad, srad, num=numshift) + listmetric = numpy.zeros(len(listshift), dtype='float32') + factor1 = numpy.mean(sino[-1, lefttake:righttake]) + num1 = 0 + for i in listshift: + _sino = ndimage.interpolation.shift( + _copy_sino, (0, i), prefilter=False) + factor2 = numpy.mean(_sino[0,lefttake:righttake]) + _sino = _sino * factor1 / factor2 + sinojoin = numpy.vstack((sino, _sino)) + listmetric[num1] = numpy.sum(numpy.abs(numpy.fft.fftshift( + #pyfftw.interfaces.numpy_fft.fft2( + # sinojoin[:, lefttake:righttake + 1]) + numpy.fft.fft2(sinojoin[:, lefttake:righttake + 1]) + )) * mask) + num1 = num1 + 1 + minpos = numpy.argmin(listmetric) + return init_cen + listshift[minpos] / 2.0 + + @staticmethod + def _create_mask(nrow, ncol, radius, drop): + du = 1.0 / ncol + dv = (nrow - 1.0) / (nrow * 2.0 * numpy.pi) + centerrow = numpy.ceil(nrow / 2) - 1 + centercol = numpy.ceil(ncol / 2) - 1 + # added by Edoardo Pasca + centerrow = int(centerrow) + centercol = int(centercol) + mask = numpy.zeros((nrow, ncol), dtype='float32') + for i in range(nrow): + num1 = numpy.round(((i - centerrow) * dv / radius) / du) + (p1, p2) = numpy.int16(numpy.clip(numpy.sort( + (-num1 + centercol, num1 + centercol)), 0, ncol - 1)) + mask[i, p1:p2 + 1] = numpy.ones(p2 - p1 + 1, dtype='float32') + if drop < centerrow: + mask[centerrow - drop:centerrow + drop + 1, + :] = numpy.zeros((2 * drop + 1, ncol), dtype='float32') + mask[:,centercol-1:centercol+2] = numpy.zeros((nrow, 3), dtype='float32') + return mask + + def process(self, out=None): + + projections = self.get_input() + + cor = CenterOfRotationFinder.find_center_vo(projections.as_array()) + + return cor + + +class AcquisitionDataPadder(DataProcessor): + '''Normalization based on flat and dark + + This processor read in a AcquisitionData and normalises it based on + the instrument reading with and without incident photons or neutrons. + + Input: AcquisitionData + Parameter: 2D projection with flat field (or stack) + 2D projection with dark field (or stack) + Output: AcquisitionDataSetn + ''' + + def __init__(self, + center_of_rotation = None, + acquisition_geometry = None, + pad_value = 1e-5): + kwargs = { + 'acquisition_geometry' : acquisition_geometry, + 'center_of_rotation' : center_of_rotation, + 'pad_value' : pad_value + } + + super(AcquisitionDataPadder, self).__init__(**kwargs) + + def check_input(self, dataset): + if self.acquisition_geometry is None: + self.acquisition_geometry = dataset.geometry + if dataset.number_of_dimensions == 3: + return True + else: + raise ValueError("Expected input dimensions is 2 or 3, got {0}"\ + .format(dataset.number_of_dimensions)) + + def process(self, out=None): + projections = self.get_input() + w = projections.get_dimension_size('horizontal') + delta = w - 2 * self.center_of_rotation + + padded_width = int ( + numpy.ceil(abs(delta)) + w + ) + delta_pix = padded_width - w + + voxel_per_pixel = 1 + geom = pbalg.pb_setup_geometry_from_acquisition(projections.as_array(), + self.acquisition_geometry.angles, + self.center_of_rotation, + voxel_per_pixel ) + + padded_geometry = self.acquisition_geometry.clone() + + padded_geometry.pixel_num_h = geom['n_h'] + padded_geometry.pixel_num_v = geom['n_v'] + + delta_pix_h = padded_geometry.pixel_num_h - self.acquisition_geometry.pixel_num_h + delta_pix_v = padded_geometry.pixel_num_v - self.acquisition_geometry.pixel_num_v + + if delta_pix_h == 0: + delta_pix_h = delta_pix + padded_geometry.pixel_num_h = padded_width + #initialize a new AcquisitionData with values close to 0 + out = AcquisitionData(geometry=padded_geometry) + out = out + self.pad_value + + + #pad in the horizontal-vertical plane -> slice on angles + if delta > 0: + #pad left of middle + command = "out.array[" + for i in range(out.number_of_dimensions): + if out.dimension_labels[i] == 'horizontal': + value = '{0}:{1}'.format(delta_pix_h, delta_pix_h+w) + command = command + str(value) + else: + if out.dimension_labels[i] == 'vertical' : + value = '{0}:'.format(delta_pix_v) + command = command + str(value) + else: + command = command + ":" + if i < out.number_of_dimensions -1: + command = command + ',' + command = command + '] = projections.array' + #print (command) + else: + #pad right of middle + command = "out.array[" + for i in range(out.number_of_dimensions): + if out.dimension_labels[i] == 'horizontal': + value = '{0}:{1}'.format(0, w) + command = command + str(value) + else: + if out.dimension_labels[i] == 'vertical' : + value = '{0}:'.format(delta_pix_v) + command = command + str(value) + else: + command = command + ":" + if i < out.number_of_dimensions -1: + command = command + ',' + command = command + '] = projections.array' + #print (command) + #cleaned = eval(command) + exec(command) + return out
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/processors/Normalizer.py b/Wrappers/Python/build/lib/ccpi/processors/Normalizer.py new file mode 100644 index 0000000..da65e5c --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/processors/Normalizer.py @@ -0,0 +1,124 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018 Edoardo Pasca + +# 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 ccpi.framework import DataProcessor, DataContainer, AcquisitionData,\ + AcquisitionGeometry, ImageGeometry, ImageData +import numpy + +class Normalizer(DataProcessor): + '''Normalization based on flat and dark + + This processor read in a AcquisitionData and normalises it based on + the instrument reading with and without incident photons or neutrons. + + Input: AcquisitionData + Parameter: 2D projection with flat field (or stack) + 2D projection with dark field (or stack) + Output: AcquisitionDataSetn + ''' + + def __init__(self, flat_field = None, dark_field = None, tolerance = 1e-5): + kwargs = { + 'flat_field' : flat_field, + 'dark_field' : dark_field, + # very small number. Used when there is a division by zero + 'tolerance' : tolerance + } + + #DataProcessor.__init__(self, **kwargs) + super(Normalizer, self).__init__(**kwargs) + if not flat_field is None: + self.set_flat_field(flat_field) + if not dark_field is None: + self.set_dark_field(dark_field) + + def check_input(self, dataset): + if dataset.number_of_dimensions == 3 or\ + dataset.number_of_dimensions == 2: + return True + else: + raise ValueError("Expected input dimensions is 2 or 3, got {0}"\ + .format(dataset.number_of_dimensions)) + + def set_dark_field(self, df): + if type(df) is numpy.ndarray: + if len(numpy.shape(df)) == 3: + raise ValueError('Dark Field should be 2D') + elif len(numpy.shape(df)) == 2: + self.dark_field = df + elif issubclass(type(df), DataContainer): + self.dark_field = self.set_dark_field(df.as_array()) + + def set_flat_field(self, df): + if type(df) is numpy.ndarray: + if len(numpy.shape(df)) == 3: + raise ValueError('Flat Field should be 2D') + elif len(numpy.shape(df)) == 2: + self.flat_field = df + elif issubclass(type(df), DataContainer): + self.flat_field = self.set_flat_field(df.as_array()) + + @staticmethod + def normalize_projection(projection, flat, dark, tolerance): + a = (projection - dark) + b = (flat-dark) + with numpy.errstate(divide='ignore', invalid='ignore'): + c = numpy.true_divide( a, b ) + c[ ~ numpy.isfinite( c )] = tolerance # set to not zero if 0/0 + return c + + @staticmethod + def estimate_normalised_error(projection, flat, dark, delta_flat, delta_dark): + '''returns the estimated relative error of the normalised projection + + n = (projection - dark) / (flat - dark) + Dn/n = (flat-dark + projection-dark)/((flat-dark)*(projection-dark))*(Df/f + Dd/d) + ''' + a = (projection - dark) + b = (flat-dark) + df = delta_flat / flat + dd = delta_dark / dark + rel_norm_error = (b + a) / (b * a) * (df + dd) + return rel_norm_error + + def process(self, out=None): + + projections = self.get_input() + dark = self.dark_field + flat = self.flat_field + + if projections.number_of_dimensions == 3: + if not (projections.shape[1:] == dark.shape and \ + projections.shape[1:] == flat.shape): + raise ValueError('Flats/Dark and projections size do not match.') + + + a = numpy.asarray( + [ Normalizer.normalize_projection( + projection, flat, dark, self.tolerance) \ + for projection in projections.as_array() ] + ) + elif projections.number_of_dimensions == 2: + a = Normalizer.normalize_projection(projections.as_array(), + flat, dark, self.tolerance) + y = type(projections)( a , True, + dimension_labels=projections.dimension_labels, + geometry=projections.geometry) + return y +
\ No newline at end of file diff --git a/Wrappers/Python/build/lib/ccpi/processors/__init__.py b/Wrappers/Python/build/lib/ccpi/processors/__init__.py new file mode 100644 index 0000000..f8d050e --- /dev/null +++ b/Wrappers/Python/build/lib/ccpi/processors/__init__.py @@ -0,0 +1,9 @@ +# -*- coding: utf-8 -*-
+"""
+Created on Tue Apr 30 13:51:09 2019
+
+@author: ofn77899
+"""
+
+from .CenterOfRotationFinder import CenterOfRotationFinder
+from .Normalizer import Normalizer
diff --git a/Wrappers/Python/ccpi/data/__init__.py b/Wrappers/Python/ccpi/data/__init__.py new file mode 100644 index 0000000..2884108 --- /dev/null +++ b/Wrappers/Python/ccpi/data/__init__.py @@ -0,0 +1,66 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018 Edoardo Pasca + +# 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 ccpi.framework import ImageData +import numpy +from PIL import Image +import os +import os.path + +data_dir = os.path.abspath(os.path.dirname(__file__)) + +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) + diff --git a/Wrappers/Python/ccpi/data/boat.tiff b/Wrappers/Python/ccpi/data/boat.tiff Binary files differnew file mode 100644 index 0000000..fc1205a --- /dev/null +++ b/Wrappers/Python/ccpi/data/boat.tiff diff --git a/Wrappers/Python/ccpi/data/camera.png b/Wrappers/Python/ccpi/data/camera.png Binary files differnew file mode 100644 index 0000000..49be869 --- /dev/null +++ b/Wrappers/Python/ccpi/data/camera.png diff --git a/Wrappers/Python/ccpi/data/peppers.tiff b/Wrappers/Python/ccpi/data/peppers.tiff Binary files differnew file mode 100644 index 0000000..8c956f8 --- /dev/null +++ b/Wrappers/Python/ccpi/data/peppers.tiff diff --git a/Wrappers/Python/ccpi/data/test_show_data.py b/Wrappers/Python/ccpi/data/test_show_data.py new file mode 100644 index 0000000..7325c27 --- /dev/null +++ b/Wrappers/Python/ccpi/data/test_show_data.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue May 7 20:43:48 2019 + +@author: evangelos +""" + +from ccpi.data import camera, boat, peppers +import matplotlib.pyplot as plt + + +d = camera(size=(256,256)) + +plt.imshow(d.as_array()) +plt.colorbar() +plt.show() + +d1 = boat(size=(256,256)) + +plt.imshow(d1.as_array()) +plt.colorbar() +plt.show() + + +d2 = peppers(size=(256,256)) + +plt.imshow(d2.as_array()) +plt.colorbar() +plt.show()
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b/Wrappers/Python/demos/PDHG_TGV_Denoising_SaltPepper.py @@ -0,0 +1,194 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Feb 22 14:53:03 2019 + +@author: evangelos +""" + +from ccpi.framework import ImageData, ImageGeometry + +import numpy as np +import numpy +import matplotlib.pyplot as plt + +from ccpi.optimisation.algorithms import PDHG + +from ccpi.optimisation.operators import BlockOperator, Identity, \ + Gradient, SymmetrizedGradient, ZeroOperator +from ccpi.optimisation.functions import ZeroFunction, L1Norm, \ + MixedL21Norm, BlockFunction + +from skimage.util import random_noise + +# Create phantom for TGV SaltPepper denoising + +N = 100 + +data = np.zeros((N,N)) + +x1 = np.linspace(0, int(N/2), N) +x2 = np.linspace(int(N/2), 0., N) +xv, yv = np.meshgrid(x1, x2) + +xv[int(N/4):int(3*N/4)-1, int(N/4):int(3*N/4)-1] = yv[int(N/4):int(3*N/4)-1, int(N/4):int(3*N/4)-1].T + +data = xv +data = ImageData(data/data.max()) + +ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) +ag = ig + +# Create noisy data. Add Gaussian noise +n1 = random_noise(data.as_array(), mode = 's&p', salt_vs_pepper = 0.9, amount=0.2) +noisy_data = ImageData(n1) + +# Regularisation Parameters +alpha = 0.8 +beta = numpy.sqrt(2)* alpha + +method = '1' + +if method == '0': + + # Create operators + op11 = Gradient(ig) + op12 = Identity(op11.range_geometry()) + + op22 = SymmetrizedGradient(op11.domain_geometry()) + op21 = ZeroOperator(ig, op22.range_geometry()) + + op31 = Identity(ig, ag) + op32 = ZeroOperator(op22.domain_geometry(), ag) + + operator = BlockOperator(op11, -1*op12, op21, op22, op31, op32, shape=(3,2) ) + + f1 = alpha * MixedL21Norm() + f2 = beta * MixedL21Norm() + f3 = L1Norm(b=noisy_data) + f = BlockFunction(f1, f2, f3) + g = ZeroFunction() + +else: + + # Create operators + op11 = Gradient(ig) + op12 = Identity(op11.range_geometry()) + op22 = SymmetrizedGradient(op11.domain_geometry()) + op21 = ZeroOperator(ig, op22.range_geometry()) + + operator = BlockOperator(op11, -1*op12, op21, op22, shape=(2,2) ) + + f1 = alpha * MixedL21Norm() + f2 = beta * MixedL21Norm() + + f = BlockFunction(f1, f2) + g = BlockFunction(L1Norm(b=noisy_data), ZeroFunction()) + +## Compute operator Norm +normK = operator.norm() +# +# Primal & dual stepsizes +sigma = 1 +tau = 1/(sigma*normK**2) + + +# Setup and run the PDHG algorithm +pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True) +pdhg.max_iteration = 2000 +pdhg.update_objective_interval = 50 +pdhg.run(2000) + +#%% +plt.figure(figsize=(15,15)) +plt.subplot(3,1,1) +plt.imshow(data.as_array()) +plt.title('Ground Truth') +plt.colorbar() +plt.subplot(3,1,2) +plt.imshow(noisy_data.as_array()) +plt.title('Noisy Data') +plt.colorbar() +plt.subplot(3,1,3) +plt.imshow(pdhg.get_output()[0].as_array()) +plt.title('TGV Reconstruction') +plt.colorbar() +plt.show() +## +plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth') +plt.plot(np.linspace(0,N,N), pdhg.get_output()[0].as_array()[int(N/2),:], label = 'TV reconstruction') +plt.legend() +plt.title('Middle Line Profiles') +plt.show() + + +#%% Check with CVX solution + +from ccpi.optimisation.operators import SparseFiniteDiff + +try: + from cvxpy import * + cvx_not_installable = True +except ImportError: + cvx_not_installable = False + +if cvx_not_installable: + + u = Variable(ig.shape) + w1 = Variable((N, N)) + w2 = Variable((N, N)) + + # create TGV regulariser + DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') + DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') + + regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u) - vec(w1), \ + DY.matrix() * vec(u) - vec(w2)]), 2, axis = 0)) + \ + beta * sum(norm(vstack([ DX.matrix().transpose() * vec(w1), DY.matrix().transpose() * vec(w2), \ + 0.5 * ( DX.matrix().transpose() * vec(w2) + DY.matrix().transpose() * vec(w1) ), \ + 0.5 * ( DX.matrix().transpose() * vec(w2) + DY.matrix().transpose() * vec(w1) ) ]), 2, axis = 0 ) ) + + constraints = [] + fidelity = pnorm(u - noisy_data.as_array(),1) + solver = MOSEK + + # choose solver + if 'MOSEK' in installed_solvers(): + solver = MOSEK + else: + solver = SCS + + obj = Minimize( regulariser + fidelity) + prob = Problem(obj) + result = prob.solve(verbose = True, solver = solver) + + diff_cvx = numpy.abs( pdhg.get_output()[0].as_array() - u.value ) + + plt.figure(figsize=(15,15)) + plt.subplot(3,1,1) + plt.imshow(pdhg.get_output()[0].as_array()) + plt.title('PDHG solution') + plt.colorbar() + plt.subplot(3,1,2) + plt.imshow(u.value) + plt.title('CVX solution') + plt.colorbar() + plt.subplot(3,1,3) + plt.imshow(diff_cvx) + plt.title('Difference') + plt.colorbar() + plt.show() + + plt.plot(np.linspace(0,N,N), pdhg.get_output()[0].as_array()[int(N/2),:], label = 'PDHG') + plt.plot(np.linspace(0,N,N), u.value[int(N/2),:], label = 'CVX') + plt.legend() + plt.title('Middle Line Profiles') + plt.show() + + print('Primal Objective (CVX) {} '.format(obj.value)) + print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0])) + + + + + diff --git a/Wrappers/Python/demos/PDHG_TGV_Tomo2D.py b/Wrappers/Python/demos/PDHG_TGV_Tomo2D.py new file mode 100644 index 0000000..49d4db6 --- /dev/null +++ b/Wrappers/Python/demos/PDHG_TGV_Tomo2D.py @@ -0,0 +1,124 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca + +# 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 ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, AcquisitionData + +import numpy as np +import numpy +import matplotlib.pyplot as plt + +from ccpi.optimisation.algorithms import PDHG + +from ccpi.optimisation.operators import BlockOperator, Gradient, Identity, \ + SymmetrizedGradient, ZeroOperator +from ccpi.optimisation.functions import ZeroFunction, KullbackLeibler, \ + MixedL21Norm, BlockFunction + +from ccpi.astra.ops import AstraProjectorSimple + +# Create phantom for TV 2D tomography +N = 75 + +data = np.zeros((N,N)) + +x1 = np.linspace(0, int(N/2), N) +x2 = np.linspace(int(N/2), 0., N) +xv, yv = np.meshgrid(x1, x2) + +xv[int(N/4):int(3*N/4)-1, int(N/4):int(3*N/4)-1] = yv[int(N/4):int(3*N/4)-1, int(N/4):int(3*N/4)-1].T +data = xv +data = ImageData(data/data.max()) + +ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) + +detectors = N +angles = np.linspace(0, np.pi, N, dtype=np.float32) + +ag = AcquisitionGeometry('parallel','2D',angles, detectors) +Aop = AstraProjectorSimple(ig, ag, 'gpu') +sin = Aop.direct(data) + +# Create noisy data. Apply Poisson noise +scale = 0.1 +np.random.seed(5) +n1 = scale * np.random.poisson(sin.as_array()/scale) +noisy_data = AcquisitionData(n1, ag) + + +plt.imshow(noisy_data.as_array()) +plt.show() +#%% +# Regularisation Parameters +alpha = 0.7 +beta = 2 + +# Create Operators +op11 = Gradient(ig) +op12 = Identity(op11.range_geometry()) + +op22 = SymmetrizedGradient(op11.domain_geometry()) +op21 = ZeroOperator(ig, op22.range_geometry()) + +op31 = Aop +op32 = ZeroOperator(op22.domain_geometry(), ag) + +operator = BlockOperator(op11, -1*op12, op21, op22, op31, op32, shape=(3,2) ) + +f1 = alpha * MixedL21Norm() +f2 = beta * MixedL21Norm() +f3 = KullbackLeibler(noisy_data) +f = BlockFunction(f1, f2, f3) +g = ZeroFunction() + +# Compute operator Norm +normK = operator.norm() + +# Primal & dual stepsizes +sigma = 1 +tau = 1/(sigma*normK**2) + + +# Setup and run the PDHG algorithm +pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True) +pdhg.max_iteration = 2000 +pdhg.update_objective_interval = 50 +pdhg.run(2000) + +plt.figure(figsize=(15,15)) +plt.subplot(3,1,1) +plt.imshow(data.as_array()) +plt.title('Ground Truth') +plt.colorbar() +plt.subplot(3,1,2) +plt.imshow(noisy_data.as_array()) +plt.title('Noisy Data') +plt.colorbar() +plt.subplot(3,1,3) +plt.imshow(pdhg.get_output()[0].as_array()) +plt.title('TGV Reconstruction') +plt.colorbar() +plt.show() +## +plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth') +plt.plot(np.linspace(0,N,N), pdhg.get_output()[0].as_array()[int(N/2),:], label = 'TGV reconstruction') +plt.legend() +plt.title('Middle Line Profiles') +plt.show() + + diff --git a/Wrappers/Python/demos/PDHG_TV_Denoising_Gaussian.py b/Wrappers/Python/demos/PDHG_TV_Denoising_Gaussian.py new file mode 100644 index 0000000..c830025 --- /dev/null +++ b/Wrappers/Python/demos/PDHG_TV_Denoising_Gaussian.py @@ -0,0 +1,211 @@ +#======================================================================== +# Copyright 2019 Science Technology Facilities Council +# Copyright 2019 University of Manchester +# +# This work is part of the Core Imaging Library developed by Science Technology +# Facilities Council and 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.txt +# +# 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. +# +#========================================================================= + + +""" + +Total Variation Denoising using PDHG algorithm: + + min_{x} max_{y} < K x, y > + g(x) - f^{*}(y) + + +Problem: min_{x} \alpha * ||\nabla x||_{2,1} + \frac{1}{2} * || x - g ||_{2}^{2} + + \alpha: Regularization parameter + + \nabla: Gradient operator + + g: Noisy Data with Gaussian Noise + + Method = 0: K = [ \nabla, + Identity] + + Method = 1: K = \nabla + + +""" + +from ccpi.framework import ImageData, ImageGeometry + +import numpy as np +import numpy +import matplotlib.pyplot as plt + +from ccpi.optimisation.algorithms import PDHG + +from ccpi.optimisation.operators import BlockOperator, Identity, Gradient +from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \ + MixedL21Norm, BlockFunction + + +from ccpi.data import camera + + +# Load Data +data = camera(size=(256,256)) + +N, M = data.shape + +# Image and Acquitition Geometries +ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) +ag = ig + +# Create Noisy data. Add Gaussian noise +np.random.seed(10) +noisy_data = ImageData( data.as_array() + np.random.normal(0, 0.1, size=ig.shape) ) + +# Show Ground Truth and Noisy Data +plt.figure(figsize=(15,15)) +plt.subplot(2,1,1) +plt.imshow(data.as_array()) +plt.title('Ground Truth') +plt.colorbar() +plt.subplot(2,1,2) +plt.imshow(noisy_data.as_array()) +plt.title('Noisy Data') +plt.colorbar() +plt.show() + +# Regularisation Parameter +alpha = 0.2 + +method = '0' + +if method == '0': + + # Create operators + op1 = Gradient(ig) + op2 = Identity(ig, ag) + + # Create BlockOperator + operator = BlockOperator(op1, op2, shape=(2,1) ) + + # Create functions + f1 = alpha * MixedL21Norm() + f2 = 0.5 * L2NormSquared(b = noisy_data) + f = BlockFunction(f1, f2) + + g = ZeroFunction() + +else: + + # Without the "Block Framework" + operator = Gradient(ig) + f = alpha * MixedL21Norm() + g = 0.5 * L2NormSquared(b = noisy_data) + +# Compute Operator Norm +normK = operator.norm() + +# Primal & Dual stepsizes +sigma = 1 +tau = 1/(sigma*normK**2) + +# Setup and Run the PDHG algorithm +pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma) +pdhg.max_iteration = 3000 +pdhg.update_objective_interval = 200 +pdhg.run(3000, verbose=False) + +# Show Results +plt.figure(figsize=(15,15)) +plt.subplot(3,1,1) +plt.imshow(data.as_array()) +plt.title('Ground Truth') +plt.colorbar() +plt.subplot(3,1,2) +plt.imshow(noisy_data.as_array()) +plt.title('Noisy Data') +plt.colorbar() +plt.subplot(3,1,3) +plt.imshow(pdhg.get_output().as_array()) +plt.title('TV Reconstruction') +plt.colorbar() +plt.show() + +plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth') +plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'TV reconstruction') +plt.legend() +plt.title('Middle Line Profiles') +plt.show() + + +#%% Check with CVX solution + +from ccpi.optimisation.operators import SparseFiniteDiff + +try: + from cvxpy import * + cvx_not_installable = True +except ImportError: + cvx_not_installable = False + + +if cvx_not_installable: + + ##Construct problem + u = Variable(ig.shape) + + DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') + DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') + + # Define Total Variation as a regulariser + regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u), DY.matrix() * vec(u)]), 2, axis = 0)) + fidelity = 0.5 * sum_squares(u - noisy_data.as_array()) + + # choose solver + if 'MOSEK' in installed_solvers(): + solver = MOSEK + else: + solver = SCS + + obj = Minimize( regulariser + fidelity) + prob = Problem(obj) + result = prob.solve(verbose = True, solver = MOSEK) + + diff_cvx = numpy.abs( pdhg.get_output().as_array() - u.value ) + + plt.figure(figsize=(15,15)) + plt.subplot(3,1,1) + plt.imshow(pdhg.get_output().as_array()) + plt.title('PDHG solution') + plt.colorbar() + plt.subplot(3,1,2) + plt.imshow(u.value) + plt.title('CVX solution') + plt.colorbar() + plt.subplot(3,1,3) + plt.imshow(diff_cvx) + plt.title('Difference') + plt.colorbar() + plt.show() + + plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'PDHG') + plt.plot(np.linspace(0,N,N), u.value[int(N/2),:], label = 'CVX') + plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'Truth') + + plt.legend() + plt.title('Middle Line Profiles') + plt.show() + + print('Primal Objective (CVX) {} '.format(obj.value)) + print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0])) + diff --git a/Wrappers/Python/demos/PDHG_TV_Denoising_Gaussian_3D.py b/Wrappers/Python/demos/PDHG_TV_Denoising_Gaussian_3D.py new file mode 100644 index 0000000..c86ddc9 --- /dev/null +++ b/Wrappers/Python/demos/PDHG_TV_Denoising_Gaussian_3D.py @@ -0,0 +1,155 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca + +# 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 ccpi.framework import ImageData, ImageGeometry + +import numpy as np +import numpy +import matplotlib.pyplot as plt + +from ccpi.optimisation.algorithms import PDHG + +from ccpi.optimisation.operators import BlockOperator, Identity, Gradient +from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \ + MixedL21Norm, BlockFunction + +from skimage.util import random_noise + +# Create phantom for TV Gaussian denoising +import timeit +import os +from tomophantom import TomoP3D +import tomophantom + +print ("Building 3D phantom using TomoPhantom software") +tic=timeit.default_timer() +model = 13 # select a model number from the library +N = 64 # Define phantom dimensions using a scalar value (cubic phantom) +path = os.path.dirname(tomophantom.__file__) +path_library3D = os.path.join(path, "Phantom3DLibrary.dat") + +#This will generate a N x N x N phantom (3D) +phantom_tm = TomoP3D.Model(model, N, path_library3D) + +# Create noisy data. Add Gaussian noise +ig = ImageGeometry(voxel_num_x=N, voxel_num_y=N, voxel_num_z=N) +ag = ig +n1 = random_noise(phantom_tm, mode = 'gaussian', mean=0, var = 0.001, seed=10) +noisy_data = ImageData(n1) + +sliceSel = int(0.5*N) +plt.figure(figsize=(15,15)) +plt.subplot(3,1,1) +plt.imshow(noisy_data.as_array()[sliceSel,:,:],vmin=0, vmax=1) +plt.title('Axial View') +plt.colorbar() +plt.subplot(3,1,2) +plt.imshow(noisy_data.as_array()[:,sliceSel,:],vmin=0, vmax=1) +plt.title('Coronal View') +plt.colorbar() +plt.subplot(3,1,3) +plt.imshow(noisy_data.as_array()[:,:,sliceSel],vmin=0, vmax=1) +plt.title('Sagittal View') +plt.colorbar() +plt.show() + + +# Regularisation Parameter +alpha = 0.05 + +method = '0' + +if method == '0': + + # Create operators + op1 = Gradient(ig) + op2 = Identity(ig, ag) + + # Create BlockOperator + operator = BlockOperator(op1, op2, shape=(2,1) ) + + # Create functions + + f1 = alpha * MixedL21Norm() + f2 = 0.5 * L2NormSquared(b = noisy_data) + f = BlockFunction(f1, f2) + + g = ZeroFunction() + +else: + + # Without the "Block Framework" + operator = Gradient(ig) + f = alpha * MixedL21Norm() + g = 0.5 * L2NormSquared(b = noisy_data) + + +# Compute operator Norm +normK = operator.norm() + +# Primal & dual stepsizes +sigma = 1 +tau = 1/(sigma*normK**2) + + +# Setup and run the PDHG algorithm +pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True) +pdhg.max_iteration = 2000 +pdhg.update_objective_interval = 200 +pdhg.run(2000) + +fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(10, 8)) + +plt.subplot(2,3,1) +plt.imshow(noisy_data.as_array()[sliceSel,:,:],vmin=0, vmax=1) +plt.axis('off') +plt.title('Axial View') + +plt.subplot(2,3,2) +plt.imshow(noisy_data.as_array()[:,sliceSel,:],vmin=0, vmax=1) +plt.axis('off') +plt.title('Coronal View') + +plt.subplot(2,3,3) +plt.imshow(noisy_data.as_array()[:,:,sliceSel],vmin=0, vmax=1) +plt.axis('off') +plt.title('Sagittal View') + + +plt.subplot(2,3,4) +plt.imshow(pdhg.get_output().as_array()[sliceSel,:,:],vmin=0, vmax=1) +plt.axis('off') +plt.subplot(2,3,5) +plt.imshow(pdhg.get_output().as_array()[:,sliceSel,:],vmin=0, vmax=1) +plt.axis('off') +plt.subplot(2,3,6) +plt.imshow(pdhg.get_output().as_array()[:,:,sliceSel],vmin=0, vmax=1) +plt.axis('off') +im = plt.imshow(pdhg.get_output().as_array()[:,:,sliceSel],vmin=0, vmax=1) + + +fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8, + wspace=0.02, hspace=0.02) + +cb_ax = fig.add_axes([0.83, 0.1, 0.02, 0.8]) +cbar = fig.colorbar(im, cax=cb_ax) + + +plt.show() + diff --git a/Wrappers/Python/demos/PDHG_TV_Denoising_Poisson.py b/Wrappers/Python/demos/PDHG_TV_Denoising_Poisson.py new file mode 100644 index 0000000..70f6b9b --- /dev/null +++ b/Wrappers/Python/demos/PDHG_TV_Denoising_Poisson.py @@ -0,0 +1,207 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 STFC, 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. + +""" + +Total Variation Denoising using PDHG algorithm: + + min_{x} max_{y} < K x, y > + g(x) - f^{*}(y) + + +Problem: min_x, x>0 \alpha * ||\nabla x||_{1} + \int x - g * log(x) + + \nabla: Gradient operator + g: Noisy Data with Poisson Noise + \alpha: Regularization parameter + + Method = 0: K = [ \nabla, + Identity] + + Method = 1: K = \nabla + + +""" + +from ccpi.framework import ImageData, ImageGeometry + +import numpy as np +import numpy +import matplotlib.pyplot as plt + +from ccpi.optimisation.algorithms import PDHG + +from ccpi.optimisation.operators import BlockOperator, Identity, Gradient +from ccpi.optimisation.functions import ZeroFunction, KullbackLeibler, \ + MixedL21Norm, BlockFunction + +from skimage.util import random_noise + +# Create phantom for TV Poisson denoising +N = 100 + +data = np.zeros((N,N)) +data[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 +data[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 +data = ImageData(data) +ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) +ag = ig + +# Create noisy data. Apply Poisson noise +n1 = random_noise(data.as_array(), mode = 'poisson', seed = 10) +noisy_data = ImageData(n1) + +# Regularisation Parameter +alpha = 2 + +method = '1' + +if method == '0': + + # Create operators + op1 = Gradient(ig) + op2 = Identity(ig, ag) + + # Create BlockOperator + operator = BlockOperator(op1, op2, shape=(2,1) ) + + # Create functions + + f1 = alpha * MixedL21Norm() + f2 = KullbackLeibler(noisy_data) + f = BlockFunction(f1, f2) + + g = ZeroFunction() + +else: + + # Without the "Block Framework" + operator = Gradient(ig) + f = alpha * MixedL21Norm() + g = KullbackLeibler(noisy_data) + + +# Compute operator Norm +normK = operator.norm() + +# Primal & dual stepsizes +sigma = 1 +tau = 1/(sigma*normK**2) +opt = {'niter':2000, 'memopt': True} + +# Setup and run the PDHG algorithm +pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True) +pdhg.max_iteration = 2000 +pdhg.update_objective_interval = 50 + +def pdgap_objectives(niter, objective, solution): + + + print( "{:04}/{:04} {:<5} {:.4f} {:<5} {:.4f} {:<5} {:.4f}".\ + format(niter, pdhg.max_iteration,'', \ + objective[0],'',\ + objective[1],'',\ + objective[2])) + +pdhg.run(2000, callback = pdgap_objectives) + + +plt.figure(figsize=(15,15)) +plt.subplot(3,1,1) +plt.imshow(data.as_array()) +plt.title('Ground Truth') +plt.colorbar() +plt.subplot(3,1,2) +plt.imshow(noisy_data.as_array()) +plt.title('Noisy Data') +plt.colorbar() +plt.subplot(3,1,3) +plt.imshow(pdhg.get_output().as_array()) +plt.title('TV Reconstruction') +plt.colorbar() +plt.show() +## +plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth') +plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'TV reconstruction') +plt.legend() +plt.title('Middle Line Profiles') +plt.show() + + +#%% Check with CVX solution + +from ccpi.optimisation.operators import SparseFiniteDiff + +try: + from cvxpy import * + cvx_not_installable = True +except ImportError: + cvx_not_installable = False + + +if cvx_not_installable: + + ##Construct problem + u1 = Variable(ig.shape) + q = Variable() + + DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') + DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') + + # Define Total Variation as a regulariser + regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u1), DY.matrix() * vec(u1)]), 2, axis = 0)) + + fidelity = sum( u1 - multiply(noisy_data.as_array(), log(u1)) ) + constraints = [q>= fidelity, u1>=0] + + solver = ECOS + obj = Minimize( regulariser + q) + prob = Problem(obj, constraints) + result = prob.solve(verbose = True, solver = solver) + + + diff_cvx = numpy.abs( pdhg.get_output().as_array() - u1.value ) + + plt.figure(figsize=(15,15)) + plt.subplot(3,1,1) + plt.imshow(pdhg.get_output().as_array()) + plt.title('PDHG solution') + plt.colorbar() + plt.subplot(3,1,2) + plt.imshow(u1.value) + plt.title('CVX solution') + plt.colorbar() + plt.subplot(3,1,3) + plt.imshow(diff_cvx) + plt.title('Difference') + plt.colorbar() + plt.show() + + plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'PDHG') + plt.plot(np.linspace(0,N,N), u1.value[int(N/2),:], label = 'CVX') + plt.legend() + plt.title('Middle Line Profiles') + plt.show() + + print('Primal Objective (CVX) {} '.format(obj.value)) + print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0])) + + + + + diff --git a/Wrappers/Python/demos/PDHG_TV_Denoising_SaltPepper.py b/Wrappers/Python/demos/PDHG_TV_Denoising_SaltPepper.py new file mode 100644 index 0000000..f5d4ce4 --- /dev/null +++ b/Wrappers/Python/demos/PDHG_TV_Denoising_SaltPepper.py @@ -0,0 +1,198 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca + +# 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. + +""" + +Total Variation Denoising using PDHG algorithm: + + min_{x} max_{y} < K x, y > + g(x) - f^{*}(y) + + +Problem: min_x, x>0 \alpha * ||\nabla x||_{1} + ||x-g||_{1} + + \nabla: Gradient operator + g: Noisy Data with Salt & Pepper Noise + \alpha: Regularization parameter + + Method = 0: K = [ \nabla, + Identity] + + Method = 1: K = \nabla + + +""" + +from ccpi.framework import ImageData, ImageGeometry + +import numpy as np +import numpy +import matplotlib.pyplot as plt + +from ccpi.optimisation.algorithms import PDHG + +from ccpi.optimisation.operators import BlockOperator, Identity, Gradient +from ccpi.optimisation.functions import ZeroFunction, L1Norm, \ + MixedL21Norm, BlockFunction + +from skimage.util import random_noise + +# Create phantom for TV Salt & Pepper denoising +N = 100 + +data = np.zeros((N,N)) +data[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 +data[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 +data = ImageData(data) +ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) +ag = ig + +# Create noisy data. Apply Salt & Pepper noise +n1 = random_noise(data.as_array(), mode = 's&p', salt_vs_pepper = 0.9, amount=0.2) +noisy_data = ImageData(n1) + +# Regularisation Parameter +alpha = 2 + +method = '0' + +if method == '0': + + # Create operators + op1 = Gradient(ig) + op2 = Identity(ig, ag) + + # Create BlockOperator + operator = BlockOperator(op1, op2, shape=(2,1) ) + + # Create functions + + f1 = alpha * MixedL21Norm() + f2 = L1Norm(b = noisy_data) + f = BlockFunction(f1, f2) + + g = ZeroFunction() + +else: + + # Without the "Block Framework" + operator = Gradient(ig) + f = alpha * MixedL21Norm() + g = L1Norm(b = noisy_data) + + +# Compute operator Norm +normK = operator.norm() + +# Primal & dual stepsizes +sigma = 1 +tau = 1/(sigma*normK**2) +opt = {'niter':2000, 'memopt': True} + +# Setup and run the PDHG algorithm +pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True) +pdhg.max_iteration = 2000 +pdhg.update_objective_interval = 50 +pdhg.run(2000) + + +plt.figure(figsize=(15,15)) +plt.subplot(3,1,1) +plt.imshow(data.as_array()) +plt.title('Ground Truth') +plt.colorbar() +plt.subplot(3,1,2) +plt.imshow(noisy_data.as_array()) +plt.title('Noisy Data') +plt.colorbar() +plt.subplot(3,1,3) +plt.imshow(pdhg.get_output().as_array()) +plt.title('TV Reconstruction') +plt.colorbar() +plt.show() +## +plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth') +plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'TV reconstruction') +plt.legend() +plt.title('Middle Line Profiles') +plt.show() + + +##%% Check with CVX solution + +from ccpi.optimisation.operators import SparseFiniteDiff + +try: + from cvxpy import * + cvx_not_installable = True +except ImportError: + cvx_not_installable = False + + +if cvx_not_installable: + + ##Construct problem + u = Variable(ig.shape) + + DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') + DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') + + # Define Total Variation as a regulariser + regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u), DY.matrix() * vec(u)]), 2, axis = 0)) + fidelity = pnorm( u - noisy_data.as_array(),1) + + # choose solver + if 'MOSEK' in installed_solvers(): + solver = MOSEK + else: + solver = SCS + + obj = Minimize( regulariser + fidelity) + prob = Problem(obj) + result = prob.solve(verbose = True, solver = solver) + + diff_cvx = numpy.abs( pdhg.get_output().as_array() - u.value ) + + plt.figure(figsize=(15,15)) + plt.subplot(3,1,1) + plt.imshow(pdhg.get_output().as_array()) + plt.title('PDHG solution') + plt.colorbar() + plt.subplot(3,1,2) + plt.imshow(u.value) + plt.title('CVX solution') + plt.colorbar() + plt.subplot(3,1,3) + plt.imshow(diff_cvx) + plt.title('Difference') + plt.colorbar() + plt.show() + + plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'PDHG') + plt.plot(np.linspace(0,N,N), u.value[int(N/2),:], label = 'CVX') + plt.legend() + plt.title('Middle Line Profiles') + plt.show() + + print('Primal Objective (CVX) {} '.format(obj.value)) + print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0])) + + + + + diff --git a/Wrappers/Python/demos/PDHG_TV_Tomo2D.py b/Wrappers/Python/demos/PDHG_TV_Tomo2D.py new file mode 100644 index 0000000..87d5328 --- /dev/null +++ b/Wrappers/Python/demos/PDHG_TV_Tomo2D.py @@ -0,0 +1,245 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca + +# 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 ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, AcquisitionData + +import numpy as np +import numpy +import matplotlib.pyplot as plt + +from ccpi.optimisation.algorithms import PDHG + +from ccpi.optimisation.operators import BlockOperator, Identity, Gradient +from ccpi.optimisation.functions import ZeroFunction, KullbackLeibler, \ + MixedL21Norm, BlockFunction + +from ccpi.astra.ops import AstraProjectorSimple + +""" + +Total Variation Denoising using PDHG algorithm: + + min_{x} max_{y} < K x, y > + g(x) - f^{*}(y) + + +Problem: min_x, x>0 \alpha * ||\nabla x||_{1} + int A x -g log(Ax + \eta) + + \nabla: Gradient operator + + A: Projection Matrix + g: Noisy sinogram corrupted with Poisson Noise + + \eta: Background Noise + \alpha: Regularization parameter + +""" + +# Create phantom for TV 2D tomography +N = 75 +x = np.zeros((N,N)) +x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 +x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 + +data = ImageData(x) +ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) + +detectors = N +angles = np.linspace(0, np.pi, N, dtype=np.float32) + +ag = AcquisitionGeometry('parallel','2D',angles, detectors) +Aop = AstraProjectorSimple(ig, ag, 'cpu') +sin = Aop.direct(data) + +# Create noisy data. Apply Poisson noise +scale = 2 +n1 = scale * np.random.poisson(sin.as_array()/scale) +noisy_data = AcquisitionData(n1, ag) + +# Regularisation Parameter +alpha = 5 + +# Create operators +op1 = Gradient(ig) +op2 = Aop + +# Create BlockOperator +operator = BlockOperator(op1, op2, shape=(2,1) ) + +# Create functions + +f1 = alpha * MixedL21Norm() +f2 = KullbackLeibler(noisy_data) +f = BlockFunction(f1, f2) + +g = ZeroFunction() + +diag_precon = True + +if diag_precon: + + def tau_sigma_precond(operator): + + tau = 1/operator.sum_abs_row() + sigma = 1/ operator.sum_abs_col() + + return tau, sigma + + tau, sigma = tau_sigma_precond(operator) + +else: + # Compute operator Norm + normK = operator.norm() + # Primal & dual stepsizes + sigma = 10 + tau = 1/(sigma*normK**2) + + +# Setup and run the PDHG algorithm +pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True) +pdhg.max_iteration = 2000 +pdhg.update_objective_interval = 50 +pdhg.run(2000) + + +#%% +plt.figure(figsize=(15,15)) +plt.subplot(3,1,1) +plt.imshow(data.as_array()) +plt.title('Ground Truth') +plt.colorbar() +plt.subplot(3,1,2) +plt.imshow(noisy_data.as_array()) +plt.title('Noisy Data') +plt.colorbar() +plt.subplot(3,1,3) +plt.imshow(pdhg.get_output().as_array()) +plt.title('TV Reconstruction') +plt.colorbar() +plt.show() +## +plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth') +plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'TV reconstruction') +plt.legend() +plt.title('Middle Line Profiles') +plt.show() + + +#%% Check with CVX solution + +from ccpi.optimisation.operators import SparseFiniteDiff +import astra +import numpy + +try: + from cvxpy import * + cvx_not_installable = True +except ImportError: + cvx_not_installable = False + + +if cvx_not_installable: + + + ##Construct problem + u = Variable(N*N) + #q = Variable() + + DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') + DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') + + regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u), DY.matrix() * vec(u)]), 2, axis = 0)) + + # create matrix representation for Astra operator + + vol_geom = astra.create_vol_geom(N, N) + proj_geom = astra.create_proj_geom('parallel', 1.0, detectors, angles) + + proj_id = astra.create_projector('strip', proj_geom, vol_geom) + + matrix_id = astra.projector.matrix(proj_id) + + ProjMat = astra.matrix.get(matrix_id) + + fidelity = sum( ProjMat * u - noisy_data.as_array().ravel() * log(ProjMat * u)) + #constraints = [q>= fidelity, u>=0] + constraints = [u>=0] + + solver = SCS + obj = Minimize( regulariser + fidelity) + prob = Problem(obj, constraints) + result = prob.solve(verbose = True, solver = solver) + + +##%% Check with CVX solution + +from ccpi.optimisation.operators import SparseFiniteDiff + +try: + from cvxpy import * + cvx_not_installable = True +except ImportError: + cvx_not_installable = False + + +if cvx_not_installable: + + ##Construct problem + u = Variable(ig.shape) + + DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') + DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') + + # Define Total Variation as a regulariser + regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u), DY.matrix() * vec(u)]), 2, axis = 0)) + fidelity = pnorm( u - noisy_data.as_array(),1) + + # choose solver + if 'MOSEK' in installed_solvers(): + solver = MOSEK + else: + solver = SCS + + obj = Minimize( regulariser + fidelity) + prob = Problem(obj) + result = prob.solve(verbose = True, solver = solver) + + + plt.figure(figsize=(15,15)) + plt.subplot(3,1,1) + plt.imshow(pdhg.get_output().as_array()) + plt.title('PDHG solution') + plt.colorbar() + plt.subplot(3,1,2) + plt.imshow(np.reshape(u.value, (N, N))) + plt.title('CVX solution') + plt.colorbar() + plt.subplot(3,1,3) + plt.imshow(diff_cvx) + plt.title('Difference') + plt.colorbar() + plt.show() + + plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'PDHG') + plt.plot(np.linspace(0,N,N), u.value[int(N/2),:], label = 'CVX') + plt.legend() + plt.title('Middle Line Profiles') + plt.show() + + print('Primal Objective (CVX) {} '.format(obj.value)) + print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0]))
\ No newline at end of file diff --git a/Wrappers/Python/demos/PDHG_TV_Tomo2D_time.py b/Wrappers/Python/demos/PDHG_TV_Tomo2D_time.py new file mode 100644 index 0000000..045458a --- /dev/null +++ b/Wrappers/Python/demos/PDHG_TV_Tomo2D_time.py @@ -0,0 +1,169 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca + +# 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 ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, AcquisitionData + +import numpy as np +import numpy +import matplotlib.pyplot as plt + +from ccpi.optimisation.algorithms import PDHG + +from ccpi.optimisation.operators import BlockOperator, Gradient +from ccpi.optimisation.functions import ZeroFunction, KullbackLeibler, \ + MixedL21Norm, BlockFunction + +from ccpi.astra.ops import AstraProjectorMC + +import os +import tomophantom +from tomophantom import TomoP2D + +# Create phantom for TV 2D dynamic tomography + +model = 102 # note that the selected model is temporal (2D + time) +N = 50 # set dimension of the phantom +# one can specify an exact path to the parameters file +# path_library2D = '../../../PhantomLibrary/models/Phantom2DLibrary.dat' +path = os.path.dirname(tomophantom.__file__) +path_library2D = os.path.join(path, "Phantom2DLibrary.dat") +#This will generate a N_size x N_size x Time frames phantom (2D + time) +phantom_2Dt = TomoP2D.ModelTemporal(model, N, path_library2D) + +plt.close('all') +plt.figure(1) +plt.rcParams.update({'font.size': 21}) +plt.title('{}''{}'.format('2D+t phantom using model no.',model)) +for sl in range(0,np.shape(phantom_2Dt)[0]): + im = phantom_2Dt[sl,:,:] + plt.imshow(im, vmin=0, vmax=1) + plt.pause(.1) + plt.draw + + +ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N, channels = np.shape(phantom_2Dt)[0]) +data = ImageData(phantom_2Dt, geometry=ig) + +detectors = N +angles = np.linspace(0,np.pi,N) + +ag = AcquisitionGeometry('parallel','2D', angles, detectors, channels = np.shape(phantom_2Dt)[0]) +Aop = AstraProjectorMC(ig, ag, 'gpu') +sin = Aop.direct(data) + +scale = 2 +n1 = scale * np.random.poisson(sin.as_array()/scale) +noisy_data = AcquisitionData(n1, ag) + +tindex = [3, 6, 10] + +fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 10)) +plt.subplot(1,3,1) +plt.imshow(noisy_data.as_array()[tindex[0],:,:]) +plt.axis('off') +plt.title('Time {}'.format(tindex[0])) +plt.subplot(1,3,2) +plt.imshow(noisy_data.as_array()[tindex[1],:,:]) +plt.axis('off') +plt.title('Time {}'.format(tindex[1])) +plt.subplot(1,3,3) +plt.imshow(noisy_data.as_array()[tindex[2],:,:]) +plt.axis('off') +plt.title('Time {}'.format(tindex[2])) + +fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8, + wspace=0.02, hspace=0.02) + +plt.show() + +#%% +# Regularisation Parameter +alpha = 5 + +# Create operators +#op1 = Gradient(ig) +op1 = Gradient(ig, correlation='SpaceChannels') +op2 = Aop + +# Create BlockOperator +operator = BlockOperator(op1, op2, shape=(2,1) ) + +# Create functions + +f1 = alpha * MixedL21Norm() +f2 = KullbackLeibler(noisy_data) +f = BlockFunction(f1, f2) + +g = ZeroFunction() + +# Compute operator Norm +normK = operator.norm() + +# Primal & dual stepsizes +sigma = 1 +tau = 1/(sigma*normK**2) + + +# Setup and run the PDHG algorithm +pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True) +pdhg.max_iteration = 2000 +pdhg.update_objective_interval = 200 +pdhg.run(2000) + + +#%% +fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(10, 8)) + +plt.subplot(2,3,1) +plt.imshow(phantom_2Dt[tindex[0],:,:],vmin=0, vmax=1) +plt.axis('off') +plt.title('Time {}'.format(tindex[0])) + +plt.subplot(2,3,2) +plt.imshow(phantom_2Dt[tindex[1],:,:],vmin=0, vmax=1) +plt.axis('off') +plt.title('Time {}'.format(tindex[1])) + +plt.subplot(2,3,3) +plt.imshow(phantom_2Dt[tindex[2],:,:],vmin=0, vmax=1) +plt.axis('off') +plt.title('Time {}'.format(tindex[2])) + + +plt.subplot(2,3,4) +plt.imshow(pdhg.get_output().as_array()[tindex[0],:,:]) +plt.axis('off') +plt.subplot(2,3,5) +plt.imshow(pdhg.get_output().as_array()[tindex[1],:,:]) +plt.axis('off') +plt.subplot(2,3,6) +plt.imshow(pdhg.get_output().as_array()[tindex[2],:,:]) +plt.axis('off') +im = plt.imshow(pdhg.get_output().as_array()[tindex[0],:,:]) + + +fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8, + wspace=0.02, hspace=0.02) + +cb_ax = fig.add_axes([0.83, 0.1, 0.02, 0.8]) +cbar = fig.colorbar(im, cax=cb_ax) + + +plt.show() + diff --git a/Wrappers/Python/demos/PDHG_Tikhonov_Denoising.py b/Wrappers/Python/demos/PDHG_Tikhonov_Denoising.py new file mode 100644 index 0000000..041d4ee --- /dev/null +++ b/Wrappers/Python/demos/PDHG_Tikhonov_Denoising.py @@ -0,0 +1,176 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca + +# 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 ccpi.framework import ImageData, ImageGeometry + +import numpy as np +import numpy +import matplotlib.pyplot as plt + +from ccpi.optimisation.algorithms import PDHG + +from ccpi.optimisation.operators import BlockOperator, Identity, Gradient +from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, BlockFunction + +from skimage.util import random_noise + +# Create phantom for TV Salt & Pepper denoising +N = 100 + +data = np.zeros((N,N)) +data[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 +data[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 +data = ImageData(data) +ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) +ag = ig + +# Create noisy data. Apply Salt & Pepper noise +n1 = random_noise(data.as_array(), mode = 'gaussian', mean=0, var = 0.05, seed=10) +noisy_data = ImageData(n1) + +# Regularisation Parameter +alpha = 4 + +method = '0' + +if method == '0': + + # Create operators + op1 = Gradient(ig) + op2 = Identity(ig, ag) + + # Create BlockOperator + operator = BlockOperator(op1, op2, shape=(2,1) ) + + # Create functions + + f1 = alpha * L2NormSquared() + f2 = 0.5 * L2NormSquared(b = noisy_data) + f = BlockFunction(f1, f2) + g = ZeroFunction() + +else: + + # Without the "Block Framework" + operator = Gradient(ig) + f = alpha * L2NormSquared() + g = 0.5 * L2NormSquared(b = noisy_data) + + +# Compute operator Norm +normK = operator.norm() + +# Primal & dual stepsizes +sigma = 1 +tau = 1/(sigma*normK**2) +opt = {'niter':2000, 'memopt': True} + +# Setup and run the PDHG algorithm +pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True) +pdhg.max_iteration = 2000 +pdhg.update_objective_interval = 50 +pdhg.run(2000) + + +plt.figure(figsize=(15,15)) +plt.subplot(3,1,1) +plt.imshow(data.as_array()) +plt.title('Ground Truth') +plt.colorbar() +plt.subplot(3,1,2) +plt.imshow(noisy_data.as_array()) +plt.title('Noisy Data') +plt.colorbar() +plt.subplot(3,1,3) +plt.imshow(pdhg.get_output().as_array()) +plt.title('Tikhonov Reconstruction') +plt.colorbar() +plt.show() +## +plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth') +plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'Tikhonov reconstruction') +plt.legend() +plt.title('Middle Line Profiles') +plt.show() + + +##%% Check with CVX solution + +from ccpi.optimisation.operators import SparseFiniteDiff + +try: + from cvxpy import * + cvx_not_installable = True +except ImportError: + cvx_not_installable = False + + +if cvx_not_installable: + + ##Construct problem + u = Variable(ig.shape) + + DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') + DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') + + # Define Total Variation as a regulariser + + regulariser = alpha * sum_squares(norm(vstack([DX.matrix() * vec(u), DY.matrix() * vec(u)]), 2, axis = 0)) + fidelity = 0.5 * sum_squares(u - noisy_data.as_array()) + + # choose solver + if 'MOSEK' in installed_solvers(): + solver = MOSEK + else: + solver = SCS + + obj = Minimize( regulariser + fidelity) + prob = Problem(obj) + result = prob.solve(verbose = True, solver = solver) + + diff_cvx = numpy.abs( pdhg.get_output().as_array() - u.value ) + + plt.figure(figsize=(15,15)) + plt.subplot(3,1,1) + plt.imshow(pdhg.get_output().as_array()) + plt.title('PDHG solution') + plt.colorbar() + plt.subplot(3,1,2) + plt.imshow(u.value) + plt.title('CVX solution') + plt.colorbar() + plt.subplot(3,1,3) + plt.imshow(diff_cvx) + plt.title('Difference') + plt.colorbar() + plt.show() + + plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'PDHG') + plt.plot(np.linspace(0,N,N), u.value[int(N/2),:], label = 'CVX') + plt.legend() + plt.title('Middle Line Profiles') + plt.show() + + print('Primal Objective (CVX) {} '.format(obj.value)) + print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0])) + + + + + diff --git a/Wrappers/Python/demos/PDHG_Tikhonov_Tomo2D.py b/Wrappers/Python/demos/PDHG_Tikhonov_Tomo2D.py new file mode 100644 index 0000000..f17c4fe --- /dev/null +++ b/Wrappers/Python/demos/PDHG_Tikhonov_Tomo2D.py @@ -0,0 +1,108 @@ +# -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC + +# Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca + +# 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 ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, AcquisitionData + +import numpy as np +import numpy +import matplotlib.pyplot as plt + +from ccpi.optimisation.algorithms import PDHG + +from ccpi.optimisation.operators import BlockOperator, Gradient +from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, BlockFunction +from skimage.util import random_noise +from ccpi.astra.ops import AstraProjectorSimple + +# Create phantom for TV 2D tomography +N = 75 +x = np.zeros((N,N)) +x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 +x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 + +data = ImageData(x) +ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) + +detectors = N +angles = np.linspace(0, np.pi, N, dtype=np.float32) + +ag = AcquisitionGeometry('parallel','2D',angles, detectors) +Aop = AstraProjectorSimple(ig, ag, 'gpu') +sin = Aop.direct(data) + +# Create noisy data. Apply Gaussian noise + +np.random.seed(10) +noisy_data = sin + AcquisitionData(np.random.normal(0, 3, sin.shape)) + +# Regularisation Parameter +alpha = 500 + +# Create operators +op1 = Gradient(ig) +op2 = Aop + +# Create BlockOperator +operator = BlockOperator(op1, op2, shape=(2,1) ) + +# Create functions + +f1 = alpha * L2NormSquared() +f2 = 0.5 * L2NormSquared(b=noisy_data) +f = BlockFunction(f1, f2) + +g = ZeroFunction() + +# Compute operator Norm +normK = operator.norm() + +# Primal & dual stepsizes +sigma = 1 +tau = 1/(sigma*normK**2) + + +# Setup and run the PDHG algorithm +pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True) +pdhg.max_iteration = 5000 +pdhg.update_objective_interval = 50 +pdhg.run(2000) + +#%% +plt.figure(figsize=(15,15)) +plt.subplot(3,1,1) +plt.imshow(data.as_array()) +plt.title('Ground Truth') +plt.colorbar() +plt.subplot(3,1,2) +plt.imshow(noisy_data.as_array()) +plt.title('Noisy Data') +plt.colorbar() +plt.subplot(3,1,3) +plt.imshow(pdhg.get_output().as_array()) +plt.title('Tikhonov Reconstruction') +plt.colorbar() +plt.show() +## +plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth') +plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'Tikhonov reconstruction') +plt.legend() +plt.title('Middle Line Profiles') +plt.show() + + diff --git a/Wrappers/Python/environment.yml b/Wrappers/Python/environment.yml new file mode 100644 index 0000000..5cdd6fe --- /dev/null +++ b/Wrappers/Python/environment.yml @@ -0,0 +1,11 @@ +name: test_new +dependencies: + - python=3.6.7=h8dc6b48_1004 + - numpy=1.11.3=py36hdf140aa_1207 + - spyder=3.3.4=py36_0 + - scikit-image=0.15.0=py36h6de7cb9_0 + - scipy=1.2.1=py36hbd7caa9_1 + - astra-toolbox=1.8.3=py36h804c3c0_0 + + + diff --git a/Wrappers/Python/wip/.DS_Store b/Wrappers/Python/wip/.DS_Store Binary files differnew file mode 100644 index 0000000..a9a83e2 --- /dev/null +++ b/Wrappers/Python/wip/.DS_Store diff --git a/Wrappers/Python/wip/Compare_Algs/LeastSq_CGLS_FISTA_PDHG.py b/Wrappers/Python/wip/Compare_Algs/LeastSq_CGLS_FISTA_PDHG.py index c877018..39f0907 100644 --- a/Wrappers/Python/wip/Compare_Algs/LeastSq_CGLS_FISTA_PDHG.py +++ b/Wrappers/Python/wip/Compare_Algs/LeastSq_CGLS_FISTA_PDHG.py @@ -61,7 +61,6 @@ sin = Aop.direct(data) noisy_data = sin # Setup and run Astra CGLS algorithm - vol_geom = astra.create_vol_geom(N, N) proj_geom = astra.create_proj_geom('parallel', 1.0, detectors, angles) diff --git a/Wrappers/Python/wip/Demos/.DS_Store b/Wrappers/Python/wip/Demos/.DS_Store Binary files differnew file mode 100644 index 0000000..5008ddf --- /dev/null +++ b/Wrappers/Python/wip/Demos/.DS_Store diff --git a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian.py b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian.py index 860e76e..5df02b1 100644 --- a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian.py +++ b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian.py @@ -1,21 +1,23 @@ -# -*- coding: utf-8 -*- -# This work is part of the Core Imaging Library developed by -# Visual Analytics and Imaging System Group of the Science Technology -# Facilities Council, STFC - -# Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca - -# 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. +#======================================================================== +# Copyright 2019 Science Technology Facilities Council +# Copyright 2019 University of Manchester +# +# This work is part of the Core Imaging Library developed by Science Technology +# Facilities Council and 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.txt +# +# 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. +# +#========================================================================= """ @@ -25,12 +27,14 @@ Total Variation Denoising using PDHG algorithm: min_{x} max_{y} < K x, y > + g(x) - f^{*}(y) -Problem: min_x \alpha * ||\nabla x||_{1} + || x - g ||_{2}^{2} +Problem: min_{x} \alpha * ||\nabla x||_{2,1} + \frac{1}{2} * || x - g ||_{2}^{2} - \nabla: Gradient operator - g: Noisy Data with Gaussian Noise \alpha: Regularization parameter + \nabla: Gradient operator + + g: Noisy Data with Gaussian Noise + Method = 0: K = [ \nabla, Identity] @@ -50,22 +54,50 @@ from ccpi.optimisation.algorithms import PDHG from ccpi.optimisation.operators import BlockOperator, Identity, Gradient from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \ MixedL21Norm, BlockFunction + + + + +from Data import * + +#%% -# Create phantom for TV Gaussian denoising -N = 200 +data = ImageData(plt.imread('camera.png')) + +# +## Create phantom for TV Gaussian denoising +#N = 200 +# +#data = np.zeros((N,N)) +#data[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 +#data[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 +#data = ImageData(data) +#ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) +#ag = ig +# +# +# +## Replace with http://sipi.usc.edu/database/database.php?volume=misc&image=36#top + -data = np.zeros((N,N)) -data[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -data[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 -data = ImageData(data) -ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) -ag = ig # Create noisy data. Add Gaussian noise np.random.seed(10) noisy_data = ImageData( data.as_array() + np.random.normal(0, 0.05, size=ig.shape) ) +# Show Ground Truth and Noisy Data +plt.figure(figsize=(15,15)) +plt.subplot(2,1,1) +plt.imshow(data.as_array()) +plt.title('Ground Truth') +plt.colorbar() +plt.subplot(2,1,2) +plt.imshow(noisy_data.as_array()) +plt.title('Noisy Data') +plt.colorbar() +plt.show() + # Regularisation Parameter alpha = 2 @@ -80,8 +112,7 @@ if method == '0': # Create BlockOperator operator = BlockOperator(op1, op2, shape=(2,1) ) - # Create functions - + # Create functions f1 = alpha * MixedL21Norm() f2 = 0.5 * L2NormSquared(b = noisy_data) f = BlockFunction(f1, f2) @@ -95,19 +126,20 @@ else: f = alpha * MixedL21Norm() g = 0.5 * L2NormSquared(b = noisy_data) -# Compute operator Norm +# Compute Operator Norm normK = operator.norm() -# Primal & dual stepsizes +# Primal & Dual stepsizes sigma = 1 tau = 1/(sigma*normK**2) -# Setup and run the PDHG algorithm -pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True) +# Setup and Run the PDHG algorithm +pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma) pdhg.max_iteration = 3000 pdhg.update_objective_interval = 200 pdhg.run(3000, verbose=False) +# Show Results plt.figure(figsize=(15,15)) plt.subplot(3,1,1) plt.imshow(data.as_array()) diff --git a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py index 3c295f5..70f6b9b 100644 --- a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py +++ b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py @@ -3,7 +3,7 @@ # Visual Analytics and Imaging System Group of the Science Technology # Facilities Council, STFC -# Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca +# Copyright 2018-2019 STFC, 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. @@ -26,7 +26,7 @@ Total Variation Denoising using PDHG algorithm: Problem: min_x, x>0 \alpha * ||\nabla x||_{1} + \int x - g * log(x) - \nabla: Gradient operator + \nabla: Gradient operator g: Noisy Data with Poisson Noise \alpha: Regularization parameter |
