diff options
| -rw-r--r-- | Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py | 40 |
1 files changed, 32 insertions, 8 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py b/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py index 6ffaf70..60978be 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',Gredient.CORRELATION_SPACE) - if self.correlation=='Space': + if self.correlation==Gredient.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.VERTICAL, 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==Gredient.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) |