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+# coding: utf-8
+# /*##########################################################################
+#
+# Copyright (c) 2016 European Synchrotron Radiation Facility
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#
+# ###########################################################################*/
+
+__authors__ = ["N. Vigano", "H. Payno", "P. Paleo"]
+__license__ = "MIT"
+__date__ = "19/09/2017"
+
+import numpy
+
+
+class PhantomGenerator(object):
+ """
+ Class for generating Phantoms
+ """
+
+ class _Ellipsoid:
+ def __init__(self, a, b, c, x0, y0, z0, alpha, mu):
+ self.a = a
+ self.b = b
+ self.c = c
+ self.x0 = x0
+ self.y0 = y0
+ self.z0 = z0
+ self.alpha = alpha * numpy.pi / 180.0
+ self.mu = mu
+ self.cosAlpha = numpy.cos(self.alpha)
+ self.sinAlpha = numpy.sin(self.alpha)
+
+ SHEPP_LOGAN = [
+ # a b c x0 y0 z0 alpha mu
+ _Ellipsoid(0.69, 0.92, 0.90, 0.0, 0.0, 0.0, 0.0, 0.10),
+ _Ellipsoid(0.6624, 0.874, 0.88, 0.0, -0.02, 0.0, 0.0, -0.08),
+ _Ellipsoid(0.11, 0.31, 0.21, 0.22, -0.0, 0.0, -18.0, -0.02),
+ _Ellipsoid(0.16, 0.41, 0.22, -0.22, 0.0, -0.25, 18.0, -0.02),
+ _Ellipsoid(0.21, 0.25, 0.35, 0.0, 0.35, -0.25, 0.0, 0.03),
+ _Ellipsoid(0.046, 0.046, 0.046, 0.0, 0.10, -0.25, 0.0, 0.01),
+ _Ellipsoid(0.046, 0.046, 0.02, 0.0, -0.10, -0.25, 0.0, 0.01),
+ _Ellipsoid(0.046, 0.023, 0.02, -0.08, -0.605, -0.25, 0.0, 0.01),
+ _Ellipsoid(0.023, 0.023, 0.10, 0.0, -0.605, -0.25, 0.0, 0.01),
+ _Ellipsoid(0.023, 0.046, 0.10, 0.06, -0.605, -0.25, 0.0, 0.01)
+ ]
+
+ @staticmethod
+ def get2DPhantomSheppLogan(n, ellipsoidID=None):
+ """
+ generate a classical 2D shepp logan phantom.
+
+ :param n: The width (and height) of the phantom to generate
+ :param ellipsoidID: The Id of the ellipsoid to pick. If None will
+ produce every ellipsoid
+ :return numpy.ndarray: shepp logan phantom
+ """
+ assert(ellipsoidID is None or (ellipsoidID >= 0 and ellipsoidID < len(PhantomGenerator.SHEPP_LOGAN)))
+ if ellipsoidID is None:
+ area = PhantomGenerator._get2DPhantom(n,
+ PhantomGenerator.SHEPP_LOGAN)
+ else:
+ area = PhantomGenerator._get2DPhantom(n,
+ [PhantomGenerator.SHEPP_LOGAN[ellipsoidID]])
+
+ indices = numpy.abs(area) > 0
+ area[indices] = numpy.multiply(area[indices] + 0.1, 5)
+ return area / 100.0
+
+ @staticmethod
+ def _get2DPhantom(n, phantomSpec):
+ area = numpy.ndarray(shape=(n, n))
+ area.fill(0.)
+
+ count = 0
+ for ell in phantomSpec:
+ count = count+1
+ for x in range(n):
+ sumSquareXandY = PhantomGenerator._getSquareXandYsum(n, x, ell)
+ indices = sumSquareXandY <= 1
+ area[indices, x] = ell.mu
+ return area
+
+ @staticmethod
+ def _getSquareXandYsum(n, x, ell):
+ supportX1 = numpy.ndarray(shape=(n, ))
+ supportX2 = numpy.ndarray(shape=(n, ))
+ support_consts = numpy.ndarray(shape=(n, ))
+
+ xScaled = float(2*x-n)/float(n)
+ xCos = xScaled * ell.cosAlpha
+ xSin = -xScaled * ell.sinAlpha
+ supportX1.fill(xCos)
+ supportX2.fill(xSin)
+
+ supportY1 = numpy.arange(n)
+ support_consts.fill(2.)
+ supportY1 = numpy.multiply(support_consts, supportY1)
+ support_consts.fill(n)
+ supportY1 = numpy.subtract(supportY1, support_consts)
+ support_consts.fill(n)
+ supportY1 = numpy.divide(supportY1, support_consts)
+ supportY2 = numpy.array(supportY1)
+
+ support_consts.fill(ell.sinAlpha)
+ supportY1 = numpy.add(supportX1,
+ numpy.multiply(supportY1, support_consts))
+ support_consts.fill(ell.cosAlpha)
+ supportY2 = numpy.add(supportX2,
+ numpy.multiply(supportY2, support_consts))
+
+ support_consts.fill(ell.x0)
+ supportY1 = numpy.subtract(supportY1, support_consts)
+ support_consts.fill(ell.y0)
+ supportY2 = numpy.subtract(supportY2, support_consts)
+
+ support_consts.fill(ell.a)
+ supportY1 = numpy.power((numpy.divide(supportY1, support_consts)),
+ 2)
+ support_consts.fill(ell.b)
+ supportY2 = numpy.power(numpy.divide(supportY2, support_consts),
+ 2)
+
+ return numpy.add(supportY1, supportY2)
+
+ @staticmethod
+ def _getSquareZ(n, ell):
+ supportZ1 = numpy.arange(n)
+ support_consts = numpy.ndarray(shape=(n, ))
+ support_consts.fill(2.)
+ supportZ1 = numpy.multiply(support_consts, supportZ1)
+ support_consts.fill(n)
+ supportZ1 = numpy.subtract(supportZ1, support_consts)
+ support_consts.fill(n)
+ supportZ1 = numpy.divide(supportZ1, support_consts)
+
+ support_consts.fill(ell.z0)
+ supportZ1 = numpy.subtract(supportZ1, ell.z0)
+
+ support_consts.fill(ell.c)
+ return numpy.power(numpy.divide(supportZ1, support_consts),
+ 2)
+