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