diff options
Diffstat (limited to 'Wrappers')
18 files changed, 346 insertions, 159 deletions
diff --git a/Wrappers/Python/ccpi/data/__init__.py b/Wrappers/Python/ccpi/data/__init__.py deleted file mode 100644 index 2884108..0000000 --- a/Wrappers/Python/ccpi/data/__init__.py +++ /dev/null @@ -1,66 +0,0 @@ -# -*- 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/framework/TestData.py b/Wrappers/Python/ccpi/framework/TestData.py new file mode 100755 index 0000000..752bc13 --- /dev/null +++ b/Wrappers/Python/ccpi/framework/TestData.py @@ -0,0 +1,98 @@ +# -*- coding: utf-8 -*-
+from ccpi.framework import ImageData, ImageGeometry
+import numpy
+from PIL import Image
+import os
+import os.path
+
+data_dir = os.path.abspath(os.path.join(
+ os.path.dirname(__file__),
+ '../data/')
+)
+
+class TestData(object):
+ BOAT = 'boat.tiff'
+ CAMERA = 'camera.png'
+ PEPPERS = 'peppers.tiff'
+ RESOLUTION_CHART = 'resolution_chart.tiff'
+ SIMPLE_PHANTOM_2D = 'simple_jakobs_phantom'
+
+ def __init__(self, **kwargs):
+ self.data_dir = kwargs.get('data_dir', data_dir)
+
+ def load(self, which, size=(512,512), scale=(0,1), **kwargs):
+ if which not in [TestData.BOAT, TestData.CAMERA,
+ TestData.PEPPERS, TestData.RESOLUTION_CHART,
+ TestData.SIMPLE_PHANTOM_2D]:
+ raise ValueError('Unknown TestData {}.'.format(which))
+ if which == TestData.SIMPLE_PHANTOM_2D:
+ N = size[0]
+ M = size[1]
+ sdata = numpy.zeros((N,M))
+ sdata[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5
+ sdata[round(M/8):round(7*M/8),round(3*M/8):round(5*M/8)] = 1
+ ig = ImageGeometry(voxel_num_x = N, voxel_num_y = M, dimension_labels=[ImageGeometry.HORIZONTAL_X, ImageGeometry.HORIZONTAL_Y])
+ data = ig.allocate()
+ data.fill(sdata)
+ else:
+ tmp = Image.open(os.path.join(self.data_dir, which))
+ print (tmp)
+ bands = tmp.getbands()
+ if len(bands) > 1:
+ ig = ImageGeometry(voxel_num_x=size[0], voxel_num_y=size[1], channels=len(bands),
+ dimension_labels=[ImageGeometry.HORIZONTAL_X, ImageGeometry.HORIZONTAL_Y, ImageGeometry.CHANNEL])
+ data = ig.allocate()
+ else:
+ ig = ImageGeometry(voxel_num_x = size[0], voxel_num_y = size[1], dimension_labels=[ImageGeometry.HORIZONTAL_X, ImageGeometry.HORIZONTAL_Y])
+ data = ig.allocate()
+ data.fill(numpy.array(tmp.resize((size[1],size[0]))))
+ if scale is not None:
+ dmax = data.as_array().max()
+ dmin = data.as_array().min()
+ # scale 0,1
+ data = (data -dmin) / (dmax - dmin)
+ if scale != (0,1):
+ #data = (data-dmin)/(dmax-dmin) * (scale[1]-scale[0]) +scale[0])
+ data *= (scale[1]-scale[0])
+ data += scale[0]
+ print ("data.geometry", data.geometry)
+ return data
+
+ 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/framework/__init__.py b/Wrappers/Python/ccpi/framework/__init__.py index 229edb5..8926897 100755 --- a/Wrappers/Python/ccpi/framework/__init__.py +++ b/Wrappers/Python/ccpi/framework/__init__.py @@ -24,3 +24,5 @@ from .framework import DataProcessor from .framework import AX, PixelByPixelDataProcessor, CastDataContainer
from .BlockDataContainer import BlockDataContainer
from .BlockGeometry import BlockGeometry
+
+from .TestData import TestData
diff --git a/Wrappers/Python/ccpi/framework/framework.py b/Wrappers/Python/ccpi/framework/framework.py index dbe7d0a..3840f2c 100755 --- a/Wrappers/Python/ccpi/framework/framework.py +++ b/Wrappers/Python/ccpi/framework/framework.py @@ -63,7 +63,8 @@ class ImageGeometry(object): center_x=0, center_y=0, center_z=0, - channels=1): + channels=1, + **kwargs): self.voxel_num_x = voxel_num_x self.voxel_num_y = voxel_num_y @@ -80,25 +81,44 @@ class ImageGeometry(object): 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) + 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) + 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) + 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) + shape = (self.voxel_num_y, self.voxel_num_x) dim_labels = [ImageGeometry.HORIZONTAL_Y, ImageGeometry.HORIZONTAL_X] - self.dimension_labels = dim_labels + labels = kwargs.get('dimension_labels', None) + if labels is None: + self.shape = shape + self.dimension_labels = dim_labels + else: + order = self.get_order_by_label(labels, dim_labels) + if order != [0,1,2]: + # resort + self.shape = tuple([shape[i] for i in order]) + self.dimension_labels = labels + + def get_order_by_label(self, dimension_labels, default_dimension_labels): + order = [] + for i, el in enumerate(dimension_labels): + for j, ek in enumerate(default_dimension_labels): + if el == ek: + order.append(j) + break + return order + def get_min_x(self): return self.center_x - 0.5*self.voxel_num_x*self.voxel_size_x @@ -146,7 +166,10 @@ class ImageGeometry(object): 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 dimension_labels is None: + out = ImageData(geometry=self, dimension_labels=self.dimension_labels) + else: + out = ImageData(geometry=self, dimension_labels=dimension_labels) if isinstance(value, Number): if value != 0: out += value @@ -164,9 +187,7 @@ class ImageGeometry(object): 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. @@ -244,6 +265,8 @@ class AcquisitionGeometry(object): self.channels = channels self.angle_unit=kwargs.get(AcquisitionGeometry.ANGLE_UNIT, AcquisitionGeometry.DEGREE) + + # default labels if channels > 1: if pixel_num_v > 1: shape = (channels, num_of_angles , pixel_num_v, pixel_num_h) @@ -262,9 +285,31 @@ class AcquisitionGeometry(object): else: shape = (num_of_angles, pixel_num_h) dim_labels = [AcquisitionGeometry.ANGLE, AcquisitionGeometry.HORIZONTAL] - self.shape = shape + + labels = kwargs.get('dimension_labels', None) + if labels is None: + self.shape = shape + self.dimension_labels = dim_labels + else: + if len(labels) != len(dim_labels): + raise ValueError('Wrong number of labels. Expected {} got {}'.format(len(dim_labels), len(labels))) + order = self.get_order_by_label(labels, dim_labels) + if order != [0,1,2]: + # resort + self.shape = tuple([shape[i] for i in order]) + self.dimension_labels = labels + + def get_order_by_label(self, dimension_labels, default_dimension_labels): + order = [] + for i, el in enumerate(dimension_labels): + for j, ek in enumerate(default_dimension_labels): + if el == ek: + order.append(j) + break + return order + + - self.dimension_labels = dim_labels def clone(self): '''returns a copy of the AcquisitionGeometry''' @@ -292,7 +337,10 @@ class AcquisitionGeometry(object): 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 dimension_labels is None: + out = AcquisitionData(geometry=self, dimension_labels=self.dimension_labels) + else: + out = AcquisitionData(geometry=self, dimension_labels=dimension_labels) if isinstance(value, Number): if value != 0: out += value @@ -310,9 +358,7 @@ class AcquisitionGeometry(object): 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): @@ -658,7 +704,7 @@ class DataContainer(object): # geometry=self.geometry) return out else: - raise ValueError(message(type(self),"Wrong size for data memory: ", out.shape,self.shape)) + raise ValueError(message(type(self),"Wrong size for data memory: out {} x2 {} expected {}".format( out.shape,x2.shape ,self.shape))) elif issubclass(type(out), DataContainer) and isinstance(x2, (int,float,complex)): if self.check_dimensions(out): kwargs['out']=out.as_array() @@ -806,7 +852,7 @@ class ImageData(DataContainer): 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) + shape, dimension_labels = self.get_shape_labels(self.geometry, dimension_labels) array = numpy.zeros( shape , dtype=numpy.float32) super(ImageData, self).__init__(array, deep_copy, diff --git a/Wrappers/Python/ccpi/optimisation/functions/KullbackLeibler.py b/Wrappers/Python/ccpi/optimisation/functions/KullbackLeibler.py index d808e63..4dd77cf 100644 --- a/Wrappers/Python/ccpi/optimisation/functions/KullbackLeibler.py +++ b/Wrappers/Python/ccpi/optimisation/functions/KullbackLeibler.py @@ -107,16 +107,20 @@ class KullbackLeibler(Function): return 0.5*((z + 1) - ((z-1)**2 + 4 * tau * self.b).sqrt()) else: - tmp = x + tau * self.bnoise - self.b.multiply(4*tau, out=out) - out.add((tmp-1)**2, out=out) + #tmp = x + tau * self.bnoise + tmp = tau * self.bnoise + tmp += x + tmp -= 1 + + self.b.multiply(4*tau, out=out) + + out.add((tmp)**2, out=out) out.sqrt(out=out) out *= -1 - out.add(tmp+1, out=out) + tmp += 2 + out += tmp out *= 0.5 - - - + def __rmul__(self, scalar): ''' Multiplication of L2NormSquared with a scalar diff --git a/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py b/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py index 6ffaf70..d98961b 100644 --- a/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py +++ b/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py @@ -14,23 +14,47 @@ from ccpi.optimisation.operators import FiniteDiff, SparseFiniteDiff #%% class Gradient(LinearOperator): - + CORRELATION_SPACE = "Space" + CORRELATION_SPACECHANNEL = "SpaceChannels" + # 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, bnd_cond = 'Neumann', **kwargs): super(Gradient, self).__init__() self.gm_domain = gm_domain # Domain of Grad Operator - self.correlation = kwargs.get('correlation','Space') + self.correlation = kwargs.get('correlation',Gradient.CORRELATION_SPACE) - if self.correlation=='Space': + if self.correlation==Gradient.CORRELATION_SPACE: if self.gm_domain.channels>1: - self.gm_range = BlockGeometry(*[self.gm_domain for _ in range(self.gm_domain.length-1)] ) - self.ind = numpy.arange(1,self.gm_domain.length) - else: + self.gm_range = BlockGeometry(*[self.gm_domain for _ in range(self.gm_domain.length-1)] ) + if self.gm_domain.length == 4: + # 3D + Channel + # expected Grad_order = ['channels', 'direction_z', 'direction_y', 'direction_x'] + expected_order = [ImageGeometry.CHANNEL, ImageGeometry.VERTICAL, ImageGeometry.HORIZONTAL_Y, ImageGeometry.HORIZONTAL_X] + else: + # 2D + Channel + # expected Grad_order = ['channels', 'direction_y', 'direction_x'] + expected_order = [ImageGeometry.CHANNEL, ImageGeometry.HORIZONTAL_Y, ImageGeometry.HORIZONTAL_X] + order = self.gm_domain.get_order_by_label(self.gm_domain.dimension_labels, expected_order) + self.ind = order[1:] + #self.ind = numpy.arange(1,self.gm_domain.length) + else: + # no channel info self.gm_range = BlockGeometry(*[self.gm_domain for _ in range(self.gm_domain.length) ] ) - self.ind = numpy.arange(self.gm_domain.length) - elif self.correlation=='SpaceChannels': + if self.gm_domain.length == 3: + # 3D + # expected Grad_order = ['direction_z', 'direction_y', 'direction_x'] + expected_order = [ImageGeometry.VERTICAL, ImageGeometry.HORIZONTAL_Y, ImageGeometry.HORIZONTAL_X] + else: + # 2D + expected_order = [ImageGeometry.HORIZONTAL_Y, ImageGeometry.HORIZONTAL_X] + self.ind = self.gm_domain.get_order_by_label(self.gm_domain.dimension_labels, expected_order) + # self.ind = numpy.arange(self.gm_domain.length) + elif self.correlation==Gradient.CORRELATION_SPACECHANNEL: if self.gm_domain.channels>1: self.gm_range = BlockGeometry(*[self.gm_domain for _ in range(self.gm_domain.length)]) self.ind = range(self.gm_domain.length) @@ -86,10 +110,11 @@ class Gradient(LinearOperator): def range_geometry(self): return self.gm_range - def norm(self): + def norm(self, **kwargs): x0 = self.gm_domain.allocate('random') - self.s1, sall, svec = LinearOperator.PowerMethod(self, 10, x0) + iterations = kwargs.get('iterations', 10) + self.s1, sall, svec = LinearOperator.PowerMethod(self, iterations, x0) return self.s1 def __rmul__(self, scalar): @@ -136,14 +161,21 @@ if __name__ == '__main__': from ccpi.optimisation.operators import Identity, BlockOperator - M, N = 2, 3 + M, N = 20, 30 ig = ImageGeometry(M, N) arr = ig.allocate('random_int' ) # check direct of Gradient and sparse matrix G = Gradient(ig) + norm1 = G.norm(iterations=300) + print ("should be sqrt(8) {} {}".format(numpy.sqrt(8), norm1)) G_sp = G.matrix() + ig4 = ImageGeometry(M,N, channels=3) + G4 = Gradient(ig4, correlation=Gradient.CORRELATION_SPACECHANNEL) + norm4 = G4.norm(iterations=300) + print ("should be sqrt(12) {} {}".format(numpy.sqrt(12), norm4)) + res1 = G.direct(arr) res1y = numpy.reshape(G_sp[0].toarray().dot(arr.as_array().flatten('F')), ig.shape, 'F') diff --git a/Wrappers/Python/conda-recipe/meta.yaml b/Wrappers/Python/conda-recipe/meta.yaml index 6564014..9d03220 100644 --- a/Wrappers/Python/conda-recipe/meta.yaml +++ b/Wrappers/Python/conda-recipe/meta.yaml @@ -35,6 +35,7 @@ requirements: - scipy - matplotlib - h5py + - pillow about: home: http://www.ccpi.ac.uk diff --git a/Wrappers/Python/ccpi/data/boat.tiff b/Wrappers/Python/data/boat.tiff Binary files differindex fc1205a..fc1205a 100644 --- a/Wrappers/Python/ccpi/data/boat.tiff +++ b/Wrappers/Python/data/boat.tiff diff --git a/Wrappers/Python/ccpi/data/camera.png b/Wrappers/Python/data/camera.png Binary files differindex 49be869..49be869 100644 --- a/Wrappers/Python/ccpi/data/camera.png +++ b/Wrappers/Python/data/camera.png diff --git a/Wrappers/Python/ccpi/data/peppers.tiff b/Wrappers/Python/data/peppers.tiff Binary files differindex 8c956f8..8c956f8 100644 --- a/Wrappers/Python/ccpi/data/peppers.tiff +++ b/Wrappers/Python/data/peppers.tiff diff --git a/Wrappers/Python/data/resolution_chart.tiff b/Wrappers/Python/data/resolution_chart.tiff Binary files differnew file mode 100755 index 0000000..d09cef3 --- /dev/null +++ b/Wrappers/Python/data/resolution_chart.tiff diff --git a/Wrappers/Python/ccpi/data/test_show_data.py b/Wrappers/Python/data/test_show_data.py index 7325c27..7325c27 100644 --- a/Wrappers/Python/ccpi/data/test_show_data.py +++ b/Wrappers/Python/data/test_show_data.py diff --git a/Wrappers/Python/demos/PDHG_examples/PDHG_TV_Denoising_Gaussian.py b/Wrappers/Python/demos/PDHG_examples/PDHG_TV_Denoising_Gaussian.py index afdb6a2..cba5bcb 100644 --- a/Wrappers/Python/demos/PDHG_examples/PDHG_TV_Denoising_Gaussian.py +++ b/Wrappers/Python/demos/PDHG_examples/PDHG_TV_Denoising_Gaussian.py @@ -1,22 +1,20 @@ #======================================================================== -# 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. -# +# CCP in Tomographic Imaging (CCPi) Core Imaging Library (CIL). + +# Copyright 2017 UKRI-STFC +# Copyright 2017 University of Manchester + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. #========================================================================= """ @@ -50,42 +48,60 @@ 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.framework import TestData +import os, sys +loader = TestData(data_dir=os.path.join(sys.prefix, 'share','ccpi')) + # Load Data -N = 100 +N = 256 +M = 300 -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) + +# user can change the size of the input data +# you can choose between +# TestData.PEPPERS 2D + Channel +# TestData.BOAT 2D +# TestData.CAMERA 2D +# TestData.RESOLUTION_CHART 2D +# TestData.SIMPLE_PHANTOM_2D 2D +data = loader.load(TestData.PEPPERS, size=(N,M), scale=(0,1)) + +ig = data.geometry 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) ) +noisy_data = ImageData( data.as_array() + np.random.normal(0, 0.1, size=data.shape) ) + +print ("min {} max {}".format(data.as_array().min(), data.as_array().max())) # Show Ground Truth and Noisy Data -plt.figure(figsize=(15,15)) -plt.subplot(2,1,1) +plt.figure() +plt.subplot(1,3,1) plt.imshow(data.as_array()) plt.title('Ground Truth') plt.colorbar() -plt.subplot(2,1,2) +plt.subplot(1,3,2) plt.imshow(noisy_data.as_array()) plt.title('Noisy Data') plt.colorbar() +plt.subplot(1,3,3) +plt.imshow((data - noisy_data).as_array()) +plt.title('diff') +plt.colorbar() + plt.show() # Regularisation Parameter -alpha = 0.2 +alpha = .1 method = '0' if method == '0': # Create operators - op1 = Gradient(ig) + op1 = Gradient(ig, correlation=Gradient.CORRELATION_SPACE) op2 = Identity(ig, ag) # Create BlockOperator @@ -114,28 +130,33 @@ 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) +pdhg.max_iteration = 10000 +pdhg.update_objective_interval = 100 +pdhg.run(1000, verbose=True) # Show Results -plt.figure(figsize=(15,15)) -plt.subplot(3,1,1) +plt.figure() +plt.subplot(1,3,1) plt.imshow(data.as_array()) plt.title('Ground Truth') plt.colorbar() -plt.subplot(3,1,2) +plt.clim(0,1) +plt.subplot(1,3,2) plt.imshow(noisy_data.as_array()) plt.title('Noisy Data') plt.colorbar() -plt.subplot(3,1,3) +plt.clim(0,1) +plt.subplot(1,3,3) plt.imshow(pdhg.get_output().as_array()) plt.title('TV Reconstruction') +plt.clim(0,1) 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.plot(np.linspace(0,N,M), noisy_data.as_array()[int(N/2),:], label = 'Noisy data') +plt.plot(np.linspace(0,N,M), data.as_array()[int(N/2),:], label = 'GTruth') +plt.plot(np.linspace(0,N,M), pdhg.get_output().as_array()[int(N/2),:], label = 'TV reconstruction') + plt.legend() plt.title('Middle Line Profiles') plt.show() diff --git a/Wrappers/Python/setup.py b/Wrappers/Python/setup.py index bceea46..8bd33a6 100644 --- a/Wrappers/Python/setup.py +++ b/Wrappers/Python/setup.py @@ -31,7 +31,7 @@ if cil_version == '': setup( name="ccpi-framework", version=cil_version, - packages=['ccpi' , 'ccpi.io', 'ccpi.data', + packages=['ccpi' , 'ccpi.io', 'ccpi.framework', 'ccpi.optimisation', 'ccpi.optimisation.operators', 'ccpi.optimisation.algorithms', @@ -39,6 +39,9 @@ setup( 'ccpi.processors', 'ccpi.contrib','ccpi.contrib.optimisation', 'ccpi.contrib.optimisation.algorithms'], + data_files = [('share/ccpi', ['data/boat.tiff', 'data/peppers.tiff', + 'data/camera.png', + 'data/resolution_chart.tiff'])], # Project uses reStructuredText, so ensure that the docutils get # installed or upgraded on the target machine @@ -53,8 +56,9 @@ setup( # zip_safe = False, # metadata for upload to PyPI - author="Edoardo Pasca", - author_email="edoardo.pasca@stfc.ac.uk", + author="CCPi developers", + maintainer="Edoardo Pasca", + maintainer_email="edoardo.pasca@stfc.ac.uk", description='CCPi Core Imaging Library - Python Framework Module', license="Apache v2.0", keywords="Python Framework", diff --git a/Wrappers/Python/test/test_DataContainer.py b/Wrappers/Python/test/test_DataContainer.py index e92d4c6..4c53df8 100755 --- a/Wrappers/Python/test/test_DataContainer.py +++ b/Wrappers/Python/test/test_DataContainer.py @@ -487,6 +487,13 @@ class TestDataContainer(unittest.TestCase): self.assertNumpyArrayEqual(vol1.as_array(), numpy.ones(vol.shape) * 4) self.assertEqual(vol.number_of_dimensions, 3) + + ig2 = ImageGeometry (voxel_num_x=2,voxel_num_y=3,voxel_num_z=4, + dimension_labels=[ImageGeometry.HORIZONTAL_X, ImageGeometry.HORIZONTAL_Y, + ImageGeometry.VERTICAL]) + data = ig2.allocate() + self.assertNumpyArrayEqual(numpy.asarray(data.shape), numpy.asarray(ig2.shape)) + self.assertNumpyArrayEqual(numpy.asarray(data.shape), data.as_array().shape) def test_AcquisitionData(self): sgeometry = AcquisitionGeometry(dimension=2, angles=numpy.linspace(0, 180, num=10), @@ -494,6 +501,29 @@ class TestDataContainer(unittest.TestCase): pixel_num_h=5, channels=2) sino = AcquisitionData(geometry=sgeometry) self.assertEqual(sino.shape, (2, 10, 3, 5)) + + ag = AcquisitionGeometry (pixel_num_h=2,pixel_num_v=3,channels=4, dimension=2, angles=numpy.linspace(0, 180, num=10), + geom_type='parallel', ) + print (ag.shape) + print (ag.dimension_labels) + + data = ag.allocate() + self.assertNumpyArrayEqual(numpy.asarray(data.shape), numpy.asarray(ag.shape)) + self.assertNumpyArrayEqual(numpy.asarray(data.shape), data.as_array().shape) + + print (data.shape, ag.shape, data.as_array().shape) + + ag2 = AcquisitionGeometry (pixel_num_h=2,pixel_num_v=3,channels=4, dimension=2, angles=numpy.linspace(0, 180, num=10), + geom_type='parallel', + dimension_labels=[AcquisitionGeometry.VERTICAL , + AcquisitionGeometry.ANGLE, AcquisitionGeometry.HORIZONTAL, AcquisitionGeometry.CHANNEL]) + + data = ag2.allocate() + print (data.shape, ag2.shape, data.as_array().shape) + self.assertNumpyArrayEqual(numpy.asarray(data.shape), numpy.asarray(ag2.shape)) + self.assertNumpyArrayEqual(numpy.asarray(data.shape), data.as_array().shape) + + def test_ImageGeometry_allocate(self): vgeometry = ImageGeometry(voxel_num_x=4, voxel_num_y=3, channels=2) image = vgeometry.allocate() diff --git a/Wrappers/Python/test/test_Gradient.py b/Wrappers/Python/test/test_Gradient.py index c6b2d2e..6d9d3be 100755 --- a/Wrappers/Python/test/test_Gradient.py +++ b/Wrappers/Python/test/test_Gradient.py @@ -84,3 +84,18 @@ class TestGradient(unittest.TestCase): res = G2D.direct(u4) print(res[0].as_array()) print(res[1].as_array()) + + M, N = 20, 30 + ig = ImageGeometry(M, N) + arr = ig.allocate('random_int' ) + + # check direct of Gradient and sparse matrix + G = Gradient(ig) + norm1 = G.norm(iterations=300) + print ("should be sqrt(8) {} {}".format(numpy.sqrt(8), norm1)) + numpy.testing.assert_almost_equal(norm1, numpy.sqrt(8), decimal=1) + ig4 = ImageGeometry(M,N, channels=3) + G4 = Gradient(ig4, correlation=Gradient.CORRELATION_SPACECHANNEL) + norm4 = G4.norm(iterations=300) + print ("should be sqrt(12) {} {}".format(numpy.sqrt(12), norm4)) + numpy.testing.assert_almost_equal(norm4, numpy.sqrt(12), decimal=1) diff --git a/Wrappers/Python/test/test_NexusReader.py b/Wrappers/Python/test/test_NexusReader.py index 55543ba..a498d71 100755 --- a/Wrappers/Python/test/test_NexusReader.py +++ b/Wrappers/Python/test/test_NexusReader.py @@ -21,67 +21,67 @@ class TestNexusReader(unittest.TestCase): def tearDown(self): os.remove(self.filename) - - def testGetDimensions(self): + def testAll(self): + # def testGetDimensions(self): nr = NexusReader(self.filename) self.assertEqual(nr.get_sinogram_dimensions(), (135, 91, 160), "Sinogram dimensions are not correct") - def testGetProjectionDimensions(self): + # def testGetProjectionDimensions(self): nr = NexusReader(self.filename) self.assertEqual(nr.get_projection_dimensions(), (91,135,160), "Projection dimensions are not correct") - def testLoadProjectionWithoutDimensions(self): + # def testLoadProjectionWithoutDimensions(self): nr = NexusReader(self.filename) projections = nr.load_projection() self.assertEqual(projections.shape, (91,135,160), "Loaded projection data dimensions are not correct") - def testLoadProjectionWithDimensions(self): + # def testLoadProjectionWithDimensions(self): nr = NexusReader(self.filename) projections = nr.load_projection((slice(0,1), slice(0,135), slice(0,160))) self.assertEqual(projections.shape, (1,135,160), "Loaded projection data dimensions are not correct") - def testLoadProjectionCompareSingle(self): + # def testLoadProjectionCompareSingle(self): nr = NexusReader(self.filename) projections_full = nr.load_projection() projections_part = nr.load_projection((slice(0,1), slice(0,135), slice(0,160))) numpy.testing.assert_array_equal(projections_part, projections_full[0:1,:,:]) - def testLoadProjectionCompareMulti(self): + # def testLoadProjectionCompareMulti(self): nr = NexusReader(self.filename) projections_full = nr.load_projection() projections_part = nr.load_projection((slice(0,3), slice(0,135), slice(0,160))) numpy.testing.assert_array_equal(projections_part, projections_full[0:3,:,:]) - def testLoadProjectionCompareRandom(self): + # def testLoadProjectionCompareRandom(self): nr = NexusReader(self.filename) projections_full = nr.load_projection() projections_part = nr.load_projection((slice(1,8), slice(5,10), slice(8,20))) numpy.testing.assert_array_equal(projections_part, projections_full[1:8,5:10,8:20]) - def testLoadProjectionCompareFull(self): + # def testLoadProjectionCompareFull(self): nr = NexusReader(self.filename) projections_full = nr.load_projection() projections_part = nr.load_projection((slice(None,None), slice(None,None), slice(None,None))) numpy.testing.assert_array_equal(projections_part, projections_full[:,:,:]) - def testLoadFlatCompareFull(self): + # def testLoadFlatCompareFull(self): nr = NexusReader(self.filename) flats_full = nr.load_flat() flats_part = nr.load_flat((slice(None,None), slice(None,None), slice(None,None))) numpy.testing.assert_array_equal(flats_part, flats_full[:,:,:]) - def testLoadDarkCompareFull(self): + # def testLoadDarkCompareFull(self): nr = NexusReader(self.filename) darks_full = nr.load_dark() darks_part = nr.load_dark((slice(None,None), slice(None,None), slice(None,None))) numpy.testing.assert_array_equal(darks_part, darks_full[:,:,:]) - def testProjectionAngles(self): + # def testProjectionAngles(self): nr = NexusReader(self.filename) angles = nr.get_projection_angles() self.assertEqual(angles.shape, (91,), "Loaded projection number of angles are not correct") - def test_get_acquisition_data_subset(self): + # def test_get_acquisition_data_subset(self): nr = NexusReader(self.filename) key = nr.get_image_keys() sl = nr.get_acquisition_data_subset(0,10) diff --git a/Wrappers/Python/test/test_functions.py b/Wrappers/Python/test/test_functions.py index af419c7..082548b 100644 --- a/Wrappers/Python/test/test_functions.py +++ b/Wrappers/Python/test/test_functions.py @@ -299,7 +299,7 @@ class TestFunction(unittest.TestCase): A = 0.5 * Identity(ig) old_chisq = Norm2sq(A, b, 1.0) - new_chisq = FunctionOperatorComposition(A, L2NormSquared(b=b)) + new_chisq = FunctionOperatorComposition(L2NormSquared(b=b),A) yold = old_chisq(u) ynew = new_chisq(u) |
