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Diffstat (limited to 'silx/image/phantomgenerator.py')
| -rw-r--r-- | silx/image/phantomgenerator.py | 160 |
1 files changed, 0 insertions, 160 deletions
diff --git a/silx/image/phantomgenerator.py b/silx/image/phantomgenerator.py deleted file mode 100644 index 10b249b..0000000 --- a/silx/image/phantomgenerator.py +++ /dev/null @@ -1,160 +0,0 @@ -# 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) - |