def Retrieve( self ):
# thing to retrieve:
# image (complex)
# support (real)
# partially coherent intensity (real)
# Gaussian partial coherence function (real)
# 3D Gaussian partial coherence parameters
self.finalImage = fftshift( self._cImage.eval( session=self.__sess__ ) )
self.finalSupport = fftshift( np.absolute( self._support.eval( session=self.__sess__ ) ) )
self.finalImage, self.finalSupport = post.centerObject( self.finalImage, self.finalSupport )
# partial coherence function in Fourier space
self.finalPCC = fftshift( self._blurKernel.eval( session=self.__sess__ ) )
self.finalPCSignal = fftshift( self._imgBlurred.eval( session=self.__sess__ ) )
self.finalGaussPCCParams = self.__sess__.run( self._var_list )
self.__sess__.close()
return
def Retrieve(self):
self.finalImage = self._cImage
self.finalSupport = self._support
self.finalImage, self.finalSupport = post.centerObject(
self.finalImage, self.finalSupport)
return
def Retrieve(self):
self.finalImage, self.finalSupport = post.centerObject(
self._cImage.numpy(), np.absolute(self._support.numpy()))
return
def Retrieve(self):
self.finalImage, self.finalSupport = post.centerObject(
self._cImage.numpy(), np.absolute(self._support.numpy()))
if hasattr(self, '_pccSolver'):
self.pccParameters = self._pccSolver.trainable_variables[0].numpy()
return
