From 993022bdddef2c81067db70be6c65a6f943c24bb Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Thu, 25 Jan 2018 11:47:16 +0000
Subject: removed src dir
---
src/CMakeLists.txt | 14 -
src/Python/CMakeLists.txt | 183 ----
src/Python/FindAnacondaEnvironment.cmake | 154 ---
src/Python/Matlab2Python_utils.cpp | 276 ------
src/Python/Regularizer.py | 322 ------
src/Python/ccpi/__init__.py | 0
src/Python/ccpi/imaging/Regularizer.py | 334 -------
src/Python/ccpi/imaging/__init__.py | 0
src/Python/ccpi/reconstruction/AstraDevice.py | 95 --
src/Python/ccpi/reconstruction/DeviceModel.py | 63 --
.../ccpi/reconstruction/FISTAReconstructor.py | 882 -----------------
src/Python/ccpi/reconstruction/Reconstructor.py | 598 -----------
src/Python/ccpi/reconstruction/__init__.py | 0
src/Python/compile-fista.bat.in | 7 -
src/Python/compile-fista.sh.in | 9 -
src/Python/compile.bat.in | 7 -
src/Python/compile.sh.in | 9 -
src/Python/conda-recipe/bld.bat | 14 -
src/Python/conda-recipe/build.sh | 14 -
src/Python/conda-recipe/meta.yaml | 30 -
src/Python/demo/demo_dendrites.py | 168 ----
src/Python/fista-recipe/build.sh | 10 -
src/Python/fista-recipe/meta.yaml | 29 -
src/Python/fista_module.cpp | 1047 --------------------
src/Python/setup-fista.py.in | 27 -
src/Python/setup.py | 64 --
src/Python/setup.py.in | 69 --
src/Python/setup_test.py | 58 --
src/Python/test.py | 42 -
src/Python/test/astra_test.py | 85 --
src/Python/test/create_phantom_projections.py | 49 -
src/Python/test/readhd5.py | 42 -
src/Python/test/simple_astra_test.py | 25 -
src/Python/test/test_reconstructor-os.py | 403 --------
src/Python/test/test_reconstructor-os_phantom.py | 480 ---------
src/Python/test/test_reconstructor.py | 359 -------
src/Python/test/test_regularizers.py | 412 --------
src/Python/test/test_regularizers_3d.py | 425 --------
src/Python/test_reconstructor.py | 301 ------
src/Python/test_regularizers.py | 412 --------
40 files changed, 7518 deletions(-)
delete mode 100644 src/CMakeLists.txt
delete mode 100644 src/Python/CMakeLists.txt
delete mode 100644 src/Python/FindAnacondaEnvironment.cmake
delete mode 100644 src/Python/Matlab2Python_utils.cpp
delete mode 100644 src/Python/Regularizer.py
delete mode 100644 src/Python/ccpi/__init__.py
delete mode 100644 src/Python/ccpi/imaging/Regularizer.py
delete mode 100644 src/Python/ccpi/imaging/__init__.py
delete mode 100644 src/Python/ccpi/reconstruction/AstraDevice.py
delete mode 100644 src/Python/ccpi/reconstruction/DeviceModel.py
delete mode 100644 src/Python/ccpi/reconstruction/FISTAReconstructor.py
delete mode 100644 src/Python/ccpi/reconstruction/Reconstructor.py
delete mode 100644 src/Python/ccpi/reconstruction/__init__.py
delete mode 100644 src/Python/compile-fista.bat.in
delete mode 100644 src/Python/compile-fista.sh.in
delete mode 100644 src/Python/compile.bat.in
delete mode 100644 src/Python/compile.sh.in
delete mode 100644 src/Python/conda-recipe/bld.bat
delete mode 100644 src/Python/conda-recipe/build.sh
delete mode 100644 src/Python/conda-recipe/meta.yaml
delete mode 100644 src/Python/demo/demo_dendrites.py
delete mode 100644 src/Python/fista-recipe/build.sh
delete mode 100644 src/Python/fista-recipe/meta.yaml
delete mode 100644 src/Python/fista_module.cpp
delete mode 100644 src/Python/setup-fista.py.in
delete mode 100644 src/Python/setup.py
delete mode 100644 src/Python/setup.py.in
delete mode 100644 src/Python/setup_test.py
delete mode 100644 src/Python/test.py
delete mode 100644 src/Python/test/astra_test.py
delete mode 100644 src/Python/test/create_phantom_projections.py
delete mode 100644 src/Python/test/readhd5.py
delete mode 100644 src/Python/test/simple_astra_test.py
delete mode 100644 src/Python/test/test_reconstructor-os.py
delete mode 100644 src/Python/test/test_reconstructor-os_phantom.py
delete mode 100644 src/Python/test/test_reconstructor.py
delete mode 100644 src/Python/test/test_regularizers.py
delete mode 100644 src/Python/test/test_regularizers_3d.py
delete mode 100644 src/Python/test_reconstructor.py
delete mode 100644 src/Python/test_regularizers.py
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
deleted file mode 100644
index cbe2fec..0000000
--- a/src/CMakeLists.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright 2017 Edoardo Pasca
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-add_subdirectory(Python)
\ No newline at end of file
diff --git a/src/Python/CMakeLists.txt b/src/Python/CMakeLists.txt
deleted file mode 100644
index 506159a..0000000
--- a/src/Python/CMakeLists.txt
+++ /dev/null
@@ -1,183 +0,0 @@
-# Copyright 2017 Edoardo Pasca
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# variables that must be set for conda compilation
-
-#PREFIX=C:\Apps\Miniconda2\envs\cil\Library
-#LIBRARY_INC=C:\\Apps\\Miniconda2\\envs\\cil\\Library\\include
-set (NUMPY_VERSION 1.12)
-
-## Tries to parse the output of conda env list to determine the current
-## active conda environment
-message ("Trying to determine your active conda environment...")
-execute_process(COMMAND "conda" "env" "list"
- OUTPUT_VARIABLE _CONDA_ENVS
- RESULT_VARIABLE _CONDA_RESULT
- ERROR_VARIABLE _CONDA_ERR)
- if(NOT _CONDA_RESULT)
- string(REPLACE "\n" ";" ENV_LIST ${_CONDA_ENVS})
- foreach(line ${ENV_LIST})
- string(REGEX MATCHALL "(.+)[*](.+)" match ${line})
- if (NOT ${match} EQUAL "")
- #message("MATCHED " ${CMAKE_MATCH_0})
- #message("MATCHED " ${CMAKE_MATCH_1})
- #message("MATCHED " ${CMAKE_MATCH_2})
- string(STRIP ${CMAKE_MATCH_1} CONDA_ENVIRONMENT)
- string(STRIP ${CMAKE_MATCH_2} CONDA_ENVIRONMENT_PATH)
- endif()
- endforeach()
- else()
- message(FATAL_ERROR "ERROR with conda command " ${_CONDA_ERR})
- endif()
-
-if (${CONDA_ENVIRONMENT} AND ${CONDA_ENVIRONMENT_PATH})
- message (FATAL_ERROR "CONDA NOT FOUND")
-else()
- message("**********************************************************")
- message("Using current conda environmnet " ${CONDA_ENVIRONMENT})
- message("Using current conda environmnet path " ${CONDA_ENVIRONMENT_PATH})
-endif()
-
-message("CIL VERSION " ${CIL_VERSION})
-
-# set the Python variables for the Conda environment
-include(FindAnacondaEnvironment.cmake)
-findPythonForAnacondaEnvironment(${CONDA_ENVIRONMENT_PATH})
-
-message("Python found " ${PYTHON_VERSION_STRING})
-message("Python found Major " ${PYTHON_VERSION_MAJOR})
-message("Python found Minor " ${PYTHON_VERSION_MINOR})
-
-findPythonPackagesPath()
-message("PYTHON_PACKAGES_FOUND " ${PYTHON_PACKAGES_PATH})
-
-## CACHE SOME VARIABLES ##
-set (CONDA_ENVIRONMENT ${CONDA_ENVIRONMENT} CACHE INTERNAL "active conda environment" FORCE)
-set (CONDA_ENVIRONMENT_PATH ${CONDA_ENVIRONMENT_PATH} CACHE INTERNAL "active conda environment" FORCE)
-
-set (PYTHON_VERSION_STRING ${PYTHON_VERSION_STRING} CACHE INTERNAL "conda environment Python version string" FORCE)
-set (PYTHON_VERSION_MAJOR ${PYTHON_VERSION_MAJOR} CACHE INTERNAL "conda environment Python version major" FORCE)
-set (PYTHON_VERSION_MINOR ${PYTHON_VERSION_MINOR} CACHE INTERNAL "conda environment Python version minor" FORCE)
-set (PYTHON_VERSION_PATCH ${PYTHON_VERSION_PATCH} CACHE INTERNAL "conda environment Python version patch" FORCE)
-set (PYTHON_PACKAGES_PATH ${PYTHON_PACKAGES_PATH} CACHE INTERNAL "conda environment Python packages path" FORCE)
-
-if (WIN32)
- #set (CONDA_ENVIRONMENT_PATH "C:\\Apps\\Miniconda2\\envs\\${CONDA_ENVIRONMENT}" CACHE PATH "Main environment directory")
- set (CONDA_ENVIRONMENT_PREFIX "${CONDA_ENVIRONMENT_PATH}\\Library" CACHE PATH "env dir")
- set (CONDA_ENVIRONMENT_LIBRARY_INC "${CONDA_ENVIRONMENT_PREFIX}\\include" CACHE PATH "env dir")
-elseif (UNIX)
- #set (CONDA_ENVIRONMENT_PATH "/apps/anaconda/2.4/envs/${CONDA_ENVIRONMENT}" CACHE PATH "Main environment directory")
- set (CONDA_ENVIRONMENT_PREFIX "${CONDA_ENVIRONMENT_PATH}/lib/python${PYTHON_VERSION_MAJOR}.${PYTHON_VERSION_MINOR}" CACHE PATH "env dir")
- set (CONDA_ENVIRONMENT_LIBRARY_INC "${CONDA_ENVIRONMENT_PREFIX}/include" CACHE PATH "env dir")
-endif()
-
-######### CONFIGURE REGULARIZER PACKAGE #############
-
-# copy the Pyhon files of the package regularizer
-file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/ccpi/imaging/)
-file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/__init__.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi)
-# regularizers
-file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/imaging/__init__.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/imaging)
-file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/imaging/Regularizer.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/imaging)
-
-# Copy and configure the relative conda build and recipes
-configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup.py.in ${CMAKE_CURRENT_BINARY_DIR}/setup.py)
-file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe)
-file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/conda-recipe/meta.yaml DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe)
-
-if (WIN32)
-
- file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/conda-recipe/bld.bat DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe/)
- configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile.bat.in ${CMAKE_CURRENT_BINARY_DIR}/compile.bat)
-
-elseif(UNIX)
-
- message ("We are on UNIX")
- file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/conda-recipe/build.sh DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe/)
- # assumes we will use bash
- configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile.sh.in ${CMAKE_CURRENT_BINARY_DIR}/compile.sh)
-
-endif()
-
-########## CONFIGURE FISTA RECONSTRUCTOR PACKAGE
-# fista reconstructor
-file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/reconstruction/FISTAReconstructor.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/reconstruction)
-file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/reconstruction/__init__.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/reconstruction)
-file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/reconstruction/DeviceModel.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/reconstruction)
-file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/reconstruction/AstraDevice.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/reconstruction)
-
-configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup-fista.py.in ${CMAKE_CURRENT_BINARY_DIR}/setup-fista.py)
-file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/fista-recipe)
-file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/fista-recipe/meta.yaml DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/fista-recipe)
-
-if (WIN32)
-
- file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/fista-recipe/bld.bat DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/fista-recipe/)
- configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile-fista.bat.in ${CMAKE_CURRENT_BINARY_DIR}/compile-fista.bat)
-
-elseif(UNIX)
- message ("We are on UNIX")
- file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/fista-recipe/build.sh DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/fista-recipe/)
- # assumes we will use bash
- configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile-fista.sh.in ${CMAKE_CURRENT_BINARY_DIR}/compile-fista.sh)
-endif()
-
-############################# TARGETS
-
-########################## REGULARIZER PACKAGE ###############################
-
-# runs cmake on the build tree to update the code from source
-add_custom_target(update_code
- COMMAND ${CMAKE_COMMAND}
- ARGS ${CMAKE_SOURCE_DIR}
- WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
- )
-
-
-add_custom_target(fista
- COMMAND bash
- compile-fista.sh
- WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
- DEPENDS ${update_code}
- )
-
-add_custom_target(regularizers
- COMMAND bash
- compile.sh
- WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
- DEPENDS update_code
- )
-
-add_custom_target(install-fista
- COMMAND ${CONDA_EXECUTABLE}
- install --force --use-local ccpi-fista=${CIL_VERSION} -c ccpi -c conda-forge
- WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
- )
-
-add_custom_target(install-regularizers
- COMMAND ${CONDA_EXECUTABLE}
- install --force --use-local ccpi-regularizers=${CIL_VERSION} -c ccpi -c conda-forge
- WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
- )
-### add tests
-
-#add_executable(RegularizersTest )
-#find_package(tiff)
-#if (TIFF_FOUND)
-# message("LibTIFF Found")
-# message("TIFF_INCLUDE_DIR " ${TIFF_INCLUDE_DIR})
-# message("TIFF_LIBRARIES" ${TIFF_LIBRARIES})
-#else()
-# message("LibTIFF not found")
-#endif()
diff --git a/src/Python/FindAnacondaEnvironment.cmake b/src/Python/FindAnacondaEnvironment.cmake
deleted file mode 100644
index 6475128..0000000
--- a/src/Python/FindAnacondaEnvironment.cmake
+++ /dev/null
@@ -1,154 +0,0 @@
-# Copyright 2017 Edoardo Pasca
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# #.rst:
-# FindAnacondaEnvironment
-# --------------
-#
-# Find Python executable and library for a specific Anaconda environment
-#
-# This module finds the Python interpreter for a specific Anaconda enviroment,
-# if installed and determines where the include files and libraries are.
-# This code sets the following variables:
-#
-# ::
-# PYTHONINTERP_FOUND - if the Python interpret has been found
-# PYTHON_EXECUTABLE - the Python interpret found
-# PYTHON_LIBRARY - path to the python library
-# PYTHON_INCLUDE_PATH - path to where Python.h is found (deprecated)
-# PYTHON_INCLUDE_DIRS - path to where Python.h is found
-# PYTHONLIBS_VERSION_STRING - version of the Python libs found (since CMake 2.8.8)
-# PYTHON_VERSION_MAJOR - major Python version
-# PYTHON_VERSION_MINOR - minor Python version
-# PYTHON_VERSION_PATCH - patch Python version
-
-
-
-function (findPythonForAnacondaEnvironment env)
- if (WIN32)
- file(TO_CMAKE_PATH ${env}/python.exe PYTHON_EXECUTABLE)
- elseif (UNIX)
- file(TO_CMAKE_PATH ${env}/bin/python PYTHON_EXECUTABLE)
- endif()
-
-
- message("findPythonForAnacondaEnvironment Found Python Executable" ${PYTHON_EXECUTABLE})
- ####### FROM FindPythonInterpr ########
- # determine python version string
- if(PYTHON_EXECUTABLE)
- execute_process(COMMAND "${PYTHON_EXECUTABLE}" -c
- "import sys; sys.stdout.write(';'.join([str(x) for x in sys.version_info[:3]]))"
- OUTPUT_VARIABLE _VERSION
- RESULT_VARIABLE _PYTHON_VERSION_RESULT
- ERROR_QUIET)
- if(NOT _PYTHON_VERSION_RESULT)
- string(REPLACE ";" "." _PYTHON_VERSION_STRING "${_VERSION}")
- list(GET _VERSION 0 _PYTHON_VERSION_MAJOR)
- list(GET _VERSION 1 _PYTHON_VERSION_MINOR)
- list(GET _VERSION 2 _PYTHON_VERSION_PATCH)
- if(PYTHON_VERSION_PATCH EQUAL 0)
- # it's called "Python 2.7", not "2.7.0"
- string(REGEX REPLACE "\\.0$" "" _PYTHON_VERSION_STRING "${PYTHON_VERSION_STRING}")
- endif()
- else()
- # sys.version predates sys.version_info, so use that
- execute_process(COMMAND "${PYTHON_EXECUTABLE}" -c "import sys; sys.stdout.write(sys.version)"
- OUTPUT_VARIABLE _VERSION
- RESULT_VARIABLE _PYTHON_VERSION_RESULT
- ERROR_QUIET)
- if(NOT _PYTHON_VERSION_RESULT)
- string(REGEX REPLACE " .*" "" _PYTHON_VERSION_STRING "${_VERSION}")
- string(REGEX REPLACE "^([0-9]+)\\.[0-9]+.*" "\\1" _PYTHON_VERSION_MAJOR "${PYTHON_VERSION_STRING}")
- string(REGEX REPLACE "^[0-9]+\\.([0-9])+.*" "\\1" _PYTHON_VERSION_MINOR "${PYTHON_VERSION_STRING}")
- if(PYTHON_VERSION_STRING MATCHES "^[0-9]+\\.[0-9]+\\.([0-9]+)")
- set(PYTHON_VERSION_PATCH "${CMAKE_MATCH_1}")
- else()
- set(PYTHON_VERSION_PATCH "0")
- endif()
- else()
- # sys.version was first documented for Python 1.5, so assume
- # this is older.
- set(PYTHON_VERSION_STRING "1.4" PARENT_SCOPE)
- set(PYTHON_VERSION_MAJOR "1" PARENT_SCOPE)
- set(PYTHON_VERSION_MINOR "4" PARENT_SCOPE)
- set(PYTHON_VERSION_PATCH "0" PARENT_SCOPE)
- endif()
- endif()
- unset(_PYTHON_VERSION_RESULT)
- unset(_VERSION)
- endif()
- ###############################################
-
- set (PYTHON_EXECUTABLE ${PYTHON_EXECUTABLE} PARENT_SCOPE)
- set (PYTHONINTERP_FOUND "ON" PARENT_SCOPE)
- set (PYTHON_VERSION_STRING ${_PYTHON_VERSION_STRING} PARENT_SCOPE)
- set (PYTHON_VERSION_MAJOR ${_PYTHON_VERSION_MAJOR} PARENT_SCOPE)
- set (PYTHON_VERSION_MINOR ${_PYTHON_VERSION_MINOR} PARENT_SCOPE)
- set (PYTHON_VERSION_PATCH ${_PYTHON_VERSION_PATCH} PARENT_SCOPE)
- message("My version found " ${PYTHON_VERSION_STRING})
- ## find conda executable
- if (WIN32)
- set (CONDA_EXECUTABLE ${env}/Script/conda PARENT_SCOPE)
- elseif(UNIX)
- set (CONDA_EXECUTABLE ${env}/bin/conda PARENT_SCOPE)
- endif()
-endfunction()
-
-
-
-set(Python_ADDITIONAL_VERSIONS 3.5)
-
-find_package(PythonInterp)
-if (PYTHONINTERP_FOUND)
-
- message("Found interpret " ${PYTHON_EXECUTABLE})
- message("Python Library " ${PYTHON_LIBRARY})
- message("Python Include Dir " ${PYTHON_INCLUDE_DIR})
- message("Python Include Path " ${PYTHON_INCLUDE_PATH})
-
- foreach(pv ${PYTHON_VERSION_STRING})
- message("Found interpret " ${pv})
- endforeach()
-endif()
-
-
-
-find_package(PythonLibs)
-if (PYTHONLIB_FOUND)
- message("Found PythonLibs PYTHON_LIBRARIES " ${PYTHON_LIBRARIES})
- message("Found PythonLibs PYTHON_INCLUDE_PATH " ${PYTHON_INCLUDE_PATH})
- message("Found PythonLibs PYTHON_INCLUDE_DIRS " ${PYTHON_INCLUDE_DIRS})
- message("Found PythonLibs PYTHONLIBS_VERSION_STRING " ${PYTHONLIBS_VERSION_STRING} )
-else()
- message("No PythonLibs Found")
-endif()
-
-
-
-
-function(findPythonPackagesPath)
- execute_process(COMMAND ${PYTHON_EXECUTABLE} -c "from distutils.sysconfig import *; print (get_python_lib())"
- RESULT_VARIABLE PYTHON_CVPY_PROCESS
- OUTPUT_VARIABLE PYTHON_STD_PACKAGES_PATH
- OUTPUT_STRIP_TRAILING_WHITESPACE)
- #message("STD_PACKAGES " ${PYTHON_STD_PACKAGES_PATH})
- if("${PYTHON_STD_PACKAGES_PATH}" MATCHES "site-packages")
- set(_PYTHON_PACKAGES_PATH "python${PYTHON_VERSION_MAJOR_MINOR}/site-packages")
- endif()
-
- SET(PYTHON_PACKAGES_PATH "${PYTHON_STD_PACKAGES_PATH}" PARENT_SCOPE)
-
-endfunction()
-
-
diff --git a/src/Python/Matlab2Python_utils.cpp b/src/Python/Matlab2Python_utils.cpp
deleted file mode 100644
index ee76bc7..0000000
--- a/src/Python/Matlab2Python_utils.cpp
+++ /dev/null
@@ -1,276 +0,0 @@
-/*
-This work is part of the Core Imaging Library developed by
-Visual Analytics and Imaging System Group of the Science Technology
-Facilities Council, STFC
-
-Copyright 2017 Daniil Kazanteev
-Copyright 2017 Srikanth Nagella, Edoardo Pasca
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-*/
-
-#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-
-#include <iostream>
-#include <cmath>
-
-#include <boost/python.hpp>
-#include <boost/python/numpy.hpp>
-#include "boost/tuple/tuple.hpp"
-
-#if defined(_WIN32) || defined(_WIN32) || defined(__WIN32__) || defined(_WIN64)
-#include <windows.h>
-// this trick only if compiler is MSVC
-__if_not_exists(uint8_t) { typedef __int8 uint8_t; }
-__if_not_exists(uint16_t) { typedef __int8 uint16_t; }
-#endif
-
-namespace bp = boost::python;
-namespace np = boost::python::numpy;
-
-/*! in the Matlab implementation this is called as
-void mexFunction(
-int nlhs, mxArray *plhs[],
-int nrhs, const mxArray *prhs[])
-where:
-prhs Array of pointers to the INPUT mxArrays
-nrhs int number of INPUT mxArrays
-
-nlhs Array of pointers to the OUTPUT mxArrays
-plhs int number of OUTPUT mxArrays
-
-***********************************************************
-
-***********************************************************
-double mxGetScalar(const mxArray *pm);
-args: pm Pointer to an mxArray; cannot be a cell mxArray, a structure mxArray, or an empty mxArray.
-Returns: Pointer to the value of the first real (nonimaginary) element of the mxArray. In C, mxGetScalar returns a double.
-***********************************************************
-char *mxArrayToString(const mxArray *array_ptr);
-args: array_ptr Pointer to mxCHAR array.
-Returns: C-style string. Returns NULL on failure. Possible reasons for failure include out of memory and specifying an array that is not an mxCHAR array.
-Description: Call mxArrayToString to copy the character data of an mxCHAR array into a C-style string.
-***********************************************************
-mxClassID mxGetClassID(const mxArray *pm);
-args: pm Pointer to an mxArray
-Returns: Numeric identifier of the class (category) of the mxArray that pm points to.For user-defined types,
-mxGetClassId returns a unique value identifying the class of the array contents.
-Use mxIsClass to determine whether an array is of a specific user-defined type.
-
-mxClassID Value MATLAB Type MEX Type C Primitive Type
-mxINT8_CLASS int8 int8_T char, byte
-mxUINT8_CLASS uint8 uint8_T unsigned char, byte
-mxINT16_CLASS int16 int16_T short
-mxUINT16_CLASS uint16 uint16_T unsigned short
-mxINT32_CLASS int32 int32_T int
-mxUINT32_CLASS uint32 uint32_T unsigned int
-mxINT64_CLASS int64 int64_T long long
-mxUINT64_CLASS uint64 uint64_T unsigned long long
-mxSINGLE_CLASS single float float
-mxDOUBLE_CLASS double double double
-
-****************************************************************
-double *mxGetPr(const mxArray *pm);
-args: pm Pointer to an mxArray of type double
-Returns: Pointer to the first element of the real data. Returns NULL in C (0 in Fortran) if there is no real data.
-****************************************************************
-mxArray *mxCreateNumericArray(mwSize ndim, const mwSize *dims,
-mxClassID classid, mxComplexity ComplexFlag);
-args: ndimNumber of dimensions. If you specify a value for ndim that is less than 2, mxCreateNumericArray automatically sets the number of dimensions to 2.
-dims Dimensions array. Each element in the dimensions array contains the size of the array in that dimension.
-For example, in C, setting dims[0] to 5 and dims[1] to 7 establishes a 5-by-7 mxArray. Usually there are ndim elements in the dims array.
-classid Identifier for the class of the array, which determines the way the numerical data is represented in memory.
-For example, specifying mxINT16_CLASS in C causes each piece of numerical data in the mxArray to be represented as a 16-bit signed integer.
-ComplexFlag If the mxArray you are creating is to contain imaginary data, set ComplexFlag to mxCOMPLEX in C (1 in Fortran). Otherwise, set ComplexFlag to mxREAL in C (0 in Fortran).
-Returns: Pointer to the created mxArray, if successful. If unsuccessful in a standalone (non-MEX file) application, returns NULL in C (0 in Fortran).
-If unsuccessful in a MEX file, the MEX file terminates and returns control to the MATLAB prompt. The function is unsuccessful when there is not
-enough free heap space to create the mxArray.
-*/
-
-void mexErrMessageText(char* text) {
- std::cerr << text << std::endl;
-}
-
-/*
-double mxGetScalar(const mxArray *pm);
-args: pm Pointer to an mxArray; cannot be a cell mxArray, a structure mxArray, or an empty mxArray.
-Returns: Pointer to the value of the first real (nonimaginary) element of the mxArray. In C, mxGetScalar returns a double.
-*/
-
-template<typename T>
-double mxGetScalar(const np::ndarray plh) {
- return (double)bp::extract<T>(plh[0]);
-}
-
-
-
-template<typename T>
-T * mxGetData(const np::ndarray pm) {
- //args: pm Pointer to an mxArray; cannot be a cell mxArray, a structure mxArray, or an empty mxArray.
- //Returns: Pointer to the value of the first real(nonimaginary) element of the mxArray.In C, mxGetScalar returns a double.
- /*Access the numpy array pointer:
- char * get_data() const;
- Returns: Array�s raw data pointer as a char
- Note: This returns char so stride math works properly on it.User will have to reinterpret_cast it.
- probably this would work.
- A = reinterpret_cast<float *>(prhs[0]);
- */
- //return reinterpret_cast<T *>(prhs[0]);
-}
-
-template<typename T>
-np::ndarray zeros(int dims , int * dim_array, T el) {
- bp::tuple shape;
- if (dims == 3)
- shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
- else if (dims == 2)
- shape = bp::make_tuple(dim_array[0], dim_array[1]);
- np::dtype dtype = np::dtype::get_builtin<T>();
- np::ndarray zz = np::zeros(shape, dtype);
- return zz;
-}
-
-
-bp::list mexFunction( np::ndarray input ) {
- int number_of_dims = input.get_nd();
- int dim_array[3];
-
- dim_array[0] = input.shape(0);
- dim_array[1] = input.shape(1);
- if (number_of_dims == 2) {
- dim_array[2] = -1;
- }
- else {
- dim_array[2] = input.shape(2);
- }
-
- /**************************************************************************/
- np::ndarray zz = zeros(3, dim_array, (int)0);
- np::ndarray fzz = zeros(3, dim_array, (float)0);
- /**************************************************************************/
-
- int * A = reinterpret_cast<int *>( input.get_data() );
- int * B = reinterpret_cast<int *>( zz.get_data() );
- float * C = reinterpret_cast<float *>(fzz.get_data());
-
- //Copy data and cast
- for (int i = 0; i < dim_array[0]; i++) {
- for (int j = 0; j < dim_array[1]; j++) {
- for (int k = 0; k < dim_array[2]; k++) {
- int index = k + dim_array[2] * j + dim_array[2] * dim_array[1] * i;
- int val = (*(A + index));
- float fval = sqrt((float)val);
- std::memcpy(B + index , &val, sizeof(int));
- std::memcpy(C + index , &fval, sizeof(float));
- }
- }
- }
-
-
- bp::list result;
-
- result.append<int>(number_of_dims);
- result.append<int>(dim_array[0]);
- result.append<int>(dim_array[1]);
- result.append<int>(dim_array[2]);
- result.append<np::ndarray>(zz);
- result.append<np::ndarray>(fzz);
-
- //result.append<bp::tuple>(tup);
- return result;
-
-}
-bp::list doSomething(np::ndarray input, PyObject *pyobj , PyObject *pyobj2) {
-
- boost::python::object output(boost::python::handle<>(boost::python::borrowed(pyobj)));
- int isOutput = !(output == boost::python::api::object());
-
- boost::python::object calculate(boost::python::handle<>(boost::python::borrowed(pyobj2)));
- int isCalculate = !(calculate == boost::python::api::object());
-
- int number_of_dims = input.get_nd();
- int dim_array[3];
-
- dim_array[0] = input.shape(0);
- dim_array[1] = input.shape(1);
- if (number_of_dims == 2) {
- dim_array[2] = -1;
- }
- else {
- dim_array[2] = input.shape(2);
- }
-
- /**************************************************************************/
- np::ndarray zz = zeros(3, dim_array, (int)0);
- np::ndarray fzz = zeros(3, dim_array, (float)0);
- /**************************************************************************/
-
- int * A = reinterpret_cast<int *>(input.get_data());
- int * B = reinterpret_cast<int *>(zz.get_data());
- float * C = reinterpret_cast<float *>(fzz.get_data());
-
- //Copy data and cast
- for (int i = 0; i < dim_array[0]; i++) {
- for (int j = 0; j < dim_array[1]; j++) {
- for (int k = 0; k < dim_array[2]; k++) {
- int index = k + dim_array[2] * j + dim_array[2] * dim_array[1] * i;
- int val = (*(A + index));
- float fval = sqrt((float)val);
- std::memcpy(B + index, &val, sizeof(int));
- std::memcpy(C + index, &fval, sizeof(float));
- // if the PyObj is not None evaluate the function
- if (isOutput)
- output(fval);
- if (isCalculate) {
- float nfval = (float)bp::extract<float>(calculate(val));
- if (isOutput)
- output(nfval);
- std::memcpy(C + index, &nfval, sizeof(float));
- }
- }
- }
- }
-
-
- bp::list result;
-
- result.append<int>(number_of_dims);
- result.append<int>(dim_array[0]);
- result.append<int>(dim_array[1]);
- result.append<int>(dim_array[2]);
- result.append<np::ndarray>(zz);
- result.append<np::ndarray>(fzz);
-
- //result.append<bp::tuple>(tup);
- return result;
-
-}
-
-
-BOOST_PYTHON_MODULE(prova)
-{
- np::initialize();
-
- //To specify that this module is a package
- bp::object package = bp::scope();
- package.attr("__path__") = "prova";
-
- np::dtype dt1 = np::dtype::get_builtin<uint8_t>();
- np::dtype dt2 = np::dtype::get_builtin<uint16_t>();
-
- //import_array();
- //numpy_boost_python_register_type<float, 1>();
- //numpy_boost_python_register_type<float, 2>();
- //numpy_boost_python_register_type<float, 3>();
- //numpy_boost_python_register_type<double, 3>();
- def("mexFunction", mexFunction);
- def("doSomething", doSomething);
-}
diff --git a/src/Python/Regularizer.py b/src/Python/Regularizer.py
deleted file mode 100644
index 15dbbb4..0000000
--- a/src/Python/Regularizer.py
+++ /dev/null
@@ -1,322 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Aug 8 14:26:00 2017
-
-@author: ofn77899
-"""
-
-import regularizers
-import numpy as np
-from enum import Enum
-import timeit
-
-class Regularizer():
- '''Class to handle regularizer algorithms to be used during reconstruction
-
- Currently 5 CPU (OMP) regularization algorithms are available:
-
- 1) SplitBregman_TV
- 2) FGP_TV
- 3) LLT_model
- 4) PatchBased_Regul
- 5) TGV_PD
-
- Usage:
- the regularizer can be invoked as object or as static method
- Depending on the actual regularizer the input parameter may vary, and
- a different default setting is defined.
- reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-
- out = reg(input=u0, regularization_parameter=10., number_of_iterations=30,
- tolerance_constant=1e-4,
- TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
- out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10.,
- number_of_iterations=30, tolerance_constant=1e-4,
- TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
- A number of optional parameters can be passed or skipped
- out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
-
- '''
- class Algorithm(Enum):
- SplitBregman_TV = regularizers.SplitBregman_TV
- FGP_TV = regularizers.FGP_TV
- LLT_model = regularizers.LLT_model
- PatchBased_Regul = regularizers.PatchBased_Regul
- TGV_PD = regularizers.TGV_PD
- # Algorithm
-
- class TotalVariationPenalty(Enum):
- isotropic = 0
- l1 = 1
- # TotalVariationPenalty
-
- def __init__(self , algorithm, debug = True):
- self.setAlgorithm ( algorithm )
- self.debug = debug
- # __init__
-
- def setAlgorithm(self, algorithm):
- self.algorithm = algorithm
- self.pars = self.getDefaultParsForAlgorithm(algorithm)
- # setAlgorithm
-
- def getDefaultParsForAlgorithm(self, algorithm):
- pars = dict()
-
- if algorithm == Regularizer.Algorithm.SplitBregman_TV :
- pars['algorithm'] = algorithm
- pars['input'] = None
- pars['regularization_parameter'] = None
- pars['number_of_iterations'] = 35
- pars['tolerance_constant'] = 0.0001
- pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
-
- elif algorithm == Regularizer.Algorithm.FGP_TV :
- pars['algorithm'] = algorithm
- pars['input'] = None
- pars['regularization_parameter'] = None
- pars['number_of_iterations'] = 50
- pars['tolerance_constant'] = 0.001
- pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
-
- elif algorithm == Regularizer.Algorithm.LLT_model:
- pars['algorithm'] = algorithm
- pars['input'] = None
- pars['regularization_parameter'] = None
- pars['time_step'] = None
- pars['number_of_iterations'] = None
- pars['tolerance_constant'] = None
- pars['restrictive_Z_smoothing'] = 0
-
- elif algorithm == Regularizer.Algorithm.PatchBased_Regul:
- pars['algorithm'] = algorithm
- pars['input'] = None
- pars['searching_window_ratio'] = None
- pars['similarity_window_ratio'] = None
- pars['PB_filtering_parameter'] = None
- pars['regularization_parameter'] = None
-
- elif algorithm == Regularizer.Algorithm.TGV_PD:
- pars['algorithm'] = algorithm
- pars['input'] = None
- pars['first_order_term'] = None
- pars['second_order_term'] = None
- pars['number_of_iterations'] = None
- pars['regularization_parameter'] = None
-
- else:
- raise Exception('Unknown regularizer algorithm')
-
- return pars
- # parsForAlgorithm
-
- def setParameter(self, **kwargs):
- '''set named parameter for the regularization engine
-
- raises Exception if the named parameter is not recognized
- Typical usage is:
-
- reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
- reg.setParameter(input=u0)
- reg.setParameter(regularization_parameter=10.)
-
- it can be also used as
- reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
- reg.setParameter(input=u0 , regularization_parameter=10.)
- '''
-
- for key , value in kwargs.items():
- if key in self.pars.keys():
- self.pars[key] = value
- else:
- raise Exception('Wrong parameter {0} for regularizer algorithm'.format(key))
- # setParameter
-
- def getParameter(self, **kwargs):
- ret = {}
- for key , value in kwargs.items():
- if key in self.pars.keys():
- ret[key] = self.pars[key]
- else:
- raise Exception('Wrong parameter {0} for regularizer algorithm'.format(key))
- # setParameter
-
-
- def __call__(self, input = None, regularization_parameter = None, **kwargs):
- '''Actual call for the regularizer.
-
- One can either set the regularization parameters first and then call the
- algorithm or set the regularization parameter during the call (as
- is done in the static methods).
- '''
-
- if kwargs is not None:
- for key, value in kwargs.items():
- #print("{0} = {1}".format(key, value))
- self.pars[key] = value
-
- if input is not None:
- self.pars['input'] = input
- if regularization_parameter is not None:
- self.pars['regularization_parameter'] = regularization_parameter
-
- if self.debug:
- print ("--------------------------------------------------")
- for key, value in self.pars.items():
- if key== 'algorithm' :
- print("{0} = {1}".format(key, value.__name__))
- elif key == 'input':
- print("{0} = {1}".format(key, np.shape(value)))
- else:
- print("{0} = {1}".format(key, value))
-
-
- if None in self.pars:
- raise Exception("Not all parameters have been provided")
-
- input = self.pars['input']
- regularization_parameter = self.pars['regularization_parameter']
- if self.algorithm == Regularizer.Algorithm.SplitBregman_TV :
- return self.algorithm(input, regularization_parameter,
- self.pars['number_of_iterations'],
- self.pars['tolerance_constant'],
- self.pars['TV_penalty'].value )
- elif self.algorithm == Regularizer.Algorithm.FGP_TV :
- return self.algorithm(input, regularization_parameter,
- self.pars['number_of_iterations'],
- self.pars['tolerance_constant'],
- self.pars['TV_penalty'].value )
- elif self.algorithm == Regularizer.Algorithm.LLT_model :
- #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
- # no default
- return self.algorithm(input,
- regularization_parameter,
- self.pars['time_step'] ,
- self.pars['number_of_iterations'],
- self.pars['tolerance_constant'],
- self.pars['restrictive_Z_smoothing'] )
- elif self.algorithm == Regularizer.Algorithm.PatchBased_Regul :
- #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
- # no default
- return self.algorithm(input, regularization_parameter,
- self.pars['searching_window_ratio'] ,
- self.pars['similarity_window_ratio'] ,
- self.pars['PB_filtering_parameter'])
- elif self.algorithm == Regularizer.Algorithm.TGV_PD :
- #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
- # no default
- if len(np.shape(input)) == 2:
- return self.algorithm(input, regularization_parameter,
- self.pars['first_order_term'] ,
- self.pars['second_order_term'] ,
- self.pars['number_of_iterations'])
- elif len(np.shape(input)) == 3:
- #assuming it's 3D
- # run independent calls on each slice
- out3d = input.copy()
- for i in range(np.shape(input)[2]):
- out = self.algorithm(input, regularization_parameter,
- self.pars['first_order_term'] ,
- self.pars['second_order_term'] ,
- self.pars['number_of_iterations'])
- # copy the result in the 3D image
- out3d.T[i] = out[0].copy()
- # append the rest of the info that the algorithm returns
- output = [out3d]
- for i in range(1,len(out)):
- output.append(out[i])
- return output
-
-
-
-
-
- # __call__
-
- @staticmethod
- def SplitBregman_TV(input, regularization_parameter , **kwargs):
- start_time = timeit.default_timer()
- reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
- out = list( reg(input, regularization_parameter, **kwargs) )
- out.append(reg.pars)
- txt = reg.printParametersToString()
- txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
- out.append(txt)
- return out
-
- @staticmethod
- def FGP_TV(input, regularization_parameter , **kwargs):
- start_time = timeit.default_timer()
- reg = Regularizer(Regularizer.Algorithm.FGP_TV)
- out = list( reg(input, regularization_parameter, **kwargs) )
- out.append(reg.pars)
- txt = reg.printParametersToString()
- txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
- out.append(txt)
- return out
-
- @staticmethod
- def LLT_model(input, regularization_parameter , time_step, number_of_iterations,
- tolerance_constant, restrictive_Z_smoothing=0):
- start_time = timeit.default_timer()
- reg = Regularizer(Regularizer.Algorithm.LLT_model)
- out = list( reg(input, regularization_parameter, time_step=time_step,
- number_of_iterations=number_of_iterations,
- tolerance_constant=tolerance_constant,
- restrictive_Z_smoothing=restrictive_Z_smoothing) )
- out.append(reg.pars)
- txt = reg.printParametersToString()
- txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
- out.append(txt)
- return out
-
- @staticmethod
- def PatchBased_Regul(input, regularization_parameter,
- searching_window_ratio,
- similarity_window_ratio,
- PB_filtering_parameter):
- start_time = timeit.default_timer()
- reg = Regularizer(Regularizer.Algorithm.PatchBased_Regul)
- out = list( reg(input,
- regularization_parameter,
- searching_window_ratio=searching_window_ratio,
- similarity_window_ratio=similarity_window_ratio,
- PB_filtering_parameter=PB_filtering_parameter )
- )
- out.append(reg.pars)
- txt = reg.printParametersToString()
- txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
- out.append(txt)
- return out
-
- @staticmethod
- def TGV_PD(input, regularization_parameter , first_order_term,
- second_order_term, number_of_iterations):
- start_time = timeit.default_timer()
-
- reg = Regularizer(Regularizer.Algorithm.TGV_PD)
- out = list( reg(input, regularization_parameter,
- first_order_term=first_order_term,
- second_order_term=second_order_term,
- number_of_iterations=number_of_iterations) )
- out.append(reg.pars)
- txt = reg.printParametersToString()
- txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
- out.append(txt)
-
- return out
-
- def printParametersToString(self):
- txt = r''
- for key, value in self.pars.items():
- if key== 'algorithm' :
- txt += "{0} = {1}".format(key, value.__name__)
- elif key == 'input':
- txt += "{0} = {1}".format(key, np.shape(value))
- else:
- txt += "{0} = {1}".format(key, value)
- txt += '\n'
- return txt
-
diff --git a/src/Python/ccpi/__init__.py b/src/Python/ccpi/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/Python/ccpi/imaging/Regularizer.py b/src/Python/ccpi/imaging/Regularizer.py
deleted file mode 100644
index 23799d6..0000000
--- a/src/Python/ccpi/imaging/Regularizer.py
+++ /dev/null
@@ -1,334 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Aug 8 14:26:00 2017
-
-@author: ofn77899
-"""
-
-from ccpi.imaging import cpu_regularizers
-import numpy as np
-from enum import Enum
-import timeit
-
-class Regularizer():
- '''Class to handle regularizer algorithms to be used during reconstruction
-
- Currently 5 CPU (OMP) regularization algorithms are available:
-
- 1) SplitBregman_TV
- 2) FGP_TV
- 3) LLT_model
- 4) PatchBased_Regul
- 5) TGV_PD
-
- Usage:
- the regularizer can be invoked as object or as static method
- Depending on the actual regularizer the input parameter may vary, and
- a different default setting is defined.
- reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-
- out = reg(input=u0, regularization_parameter=10., number_of_iterations=30,
- tolerance_constant=1e-4,
- TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
- out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10.,
- number_of_iterations=30, tolerance_constant=1e-4,
- TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
- A number of optional parameters can be passed or skipped
- out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
-
- '''
- class Algorithm(Enum):
- SplitBregman_TV = cpu_regularizers.SplitBregman_TV
- FGP_TV = cpu_regularizers.FGP_TV
- LLT_model = cpu_regularizers.LLT_model
- PatchBased_Regul = cpu_regularizers.PatchBased_Regul
- TGV_PD = cpu_regularizers.TGV_PD
- # Algorithm
-
- class TotalVariationPenalty(Enum):
- isotropic = 0
- l1 = 1
- # TotalVariationPenalty
-
- def __init__(self , algorithm, debug = True):
- self.setAlgorithm ( algorithm )
- self.debug = debug
- # __init__
-
- def setAlgorithm(self, algorithm):
- self.algorithm = algorithm
- self.pars = self.getDefaultParsForAlgorithm(algorithm)
- # setAlgorithm
-
- def getDefaultParsForAlgorithm(self, algorithm):
- pars = dict()
-
- if algorithm == Regularizer.Algorithm.SplitBregman_TV :
- pars['algorithm'] = algorithm
- pars['input'] = None
- pars['regularization_parameter'] = None
- pars['number_of_iterations'] = 35
- pars['tolerance_constant'] = 0.0001
- pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
-
- elif algorithm == Regularizer.Algorithm.FGP_TV :
- pars['algorithm'] = algorithm
- pars['input'] = None
- pars['regularization_parameter'] = None
- pars['number_of_iterations'] = 50
- pars['tolerance_constant'] = 0.001
- pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
-
- elif algorithm == Regularizer.Algorithm.LLT_model:
- pars['algorithm'] = algorithm
- pars['input'] = None
- pars['regularization_parameter'] = None
- pars['time_step'] = None
- pars['number_of_iterations'] = None
- pars['tolerance_constant'] = None
- pars['restrictive_Z_smoothing'] = 0
-
- elif algorithm == Regularizer.Algorithm.PatchBased_Regul:
- pars['algorithm'] = algorithm
- pars['input'] = None
- pars['searching_window_ratio'] = None
- pars['similarity_window_ratio'] = None
- pars['PB_filtering_parameter'] = None
- pars['regularization_parameter'] = None
-
- elif algorithm == Regularizer.Algorithm.TGV_PD:
- pars['algorithm'] = algorithm
- pars['input'] = None
- pars['first_order_term'] = None
- pars['second_order_term'] = None
- pars['number_of_iterations'] = None
- pars['regularization_parameter'] = None
-
- else:
- raise Exception('Unknown regularizer algorithm')
-
- self.acceptedInputKeywords = pars.keys()
-
- return pars
- # parsForAlgorithm
-
- def setParameter(self, **kwargs):
- '''set named parameter for the regularization engine
-
- raises Exception if the named parameter is not recognized
- Typical usage is:
-
- reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
- reg.setParameter(input=u0)
- reg.setParameter(regularization_parameter=10.)
-
- it can be also used as
- reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
- reg.setParameter(input=u0 , regularization_parameter=10.)
- '''
-
- for key , value in kwargs.items():
- if key in self.pars.keys():
- self.pars[key] = value
- else:
- raise Exception('Wrong parameter {0} for regularizer algorithm'.format(key))
- # setParameter
-
- def getParameter(self, key):
- if type(key) is str:
- if key in self.acceptedInputKeywords:
- return self.pars[key]
- else:
- raise Exception('Unrecongnised parameter: {0} '.format(key) )
- elif type(key) is list:
- outpars = []
- for k in key:
- outpars.append(self.getParameter(k))
- return outpars
- else:
- raise Exception('Unhandled input {0}' .format(str(type(key))))
- # getParameter
-
-
- def __call__(self, input = None, regularization_parameter = None,
- output_all = False, **kwargs):
- '''Actual call for the regularizer.
-
- One can either set the regularization parameters first and then call the
- algorithm or set the regularization parameter during the call (as
- is done in the static methods).
- '''
-
- if kwargs is not None:
- for key, value in kwargs.items():
- #print("{0} = {1}".format(key, value))
- self.pars[key] = value
-
- if input is not None:
- self.pars['input'] = input
- if regularization_parameter is not None:
- self.pars['regularization_parameter'] = regularization_parameter
-
- if self.debug:
- print ("--------------------------------------------------")
- for key, value in self.pars.items():
- if key== 'algorithm' :
- print("{0} = {1}".format(key, value.__name__))
- elif key == 'input':
- print("{0} = {1}".format(key, np.shape(value)))
- else:
- print("{0} = {1}".format(key, value))
-
-
- if None in self.pars:
- raise Exception("Not all parameters have been provided")
-
- input = self.pars['input']
- regularization_parameter = self.pars['regularization_parameter']
- if self.algorithm == Regularizer.Algorithm.SplitBregman_TV :
- ret = self.algorithm(input, regularization_parameter,
- self.pars['number_of_iterations'],
- self.pars['tolerance_constant'],
- self.pars['TV_penalty'].value )
- elif self.algorithm == Regularizer.Algorithm.FGP_TV :
- ret = self.algorithm(input, regularization_parameter,
- self.pars['number_of_iterations'],
- self.pars['tolerance_constant'],
- self.pars['TV_penalty'].value )
- elif self.algorithm == Regularizer.Algorithm.LLT_model :
- #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
- # no default
- ret = self.algorithm(input,
- regularization_parameter,
- self.pars['time_step'] ,
- self.pars['number_of_iterations'],
- self.pars['tolerance_constant'],
- self.pars['restrictive_Z_smoothing'] )
- elif self.algorithm == Regularizer.Algorithm.PatchBased_Regul :
- #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
- # no default
- ret = self.algorithm(input, regularization_parameter,
- self.pars['searching_window_ratio'] ,
- self.pars['similarity_window_ratio'] ,
- self.pars['PB_filtering_parameter'])
- elif self.algorithm == Regularizer.Algorithm.TGV_PD :
- #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
- # no default
- if len(np.shape(input)) == 2:
- ret = self.algorithm(input, regularization_parameter,
- self.pars['first_order_term'] ,
- self.pars['second_order_term'] ,
- self.pars['number_of_iterations'])
- elif len(np.shape(input)) == 3:
- #assuming it's 3D
- # run independent calls on each slice
- out3d = input.copy()
- for i in range(np.shape(input)[0]):
- out = self.algorithm(input[i], regularization_parameter,
- self.pars['first_order_term'] ,
- self.pars['second_order_term'] ,
- self.pars['number_of_iterations'])
- # copy the result in the 3D image
- out3d[i] = out[0].copy()
- # append the rest of the info that the algorithm returns
- output = [out3d]
- for i in range(1,len(out)):
- output.append(out[i])
- ret = output
-
-
-
- if output_all:
- return ret
- else:
- return ret[0]
-
- # __call__
-
- @staticmethod
- def SplitBregman_TV(input, regularization_parameter , **kwargs):
- start_time = timeit.default_timer()
- reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
- out = list( reg(input, regularization_parameter, **kwargs) )
- out.append(reg.pars)
- txt = reg.printParametersToString()
- txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
- out.append(txt)
- return out
-
- @staticmethod
- def FGP_TV(input, regularization_parameter , **kwargs):
- start_time = timeit.default_timer()
- reg = Regularizer(Regularizer.Algorithm.FGP_TV)
- out = list( reg(input, regularization_parameter, **kwargs) )
- out.append(reg.pars)
- txt = reg.printParametersToString()
- txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
- out.append(txt)
- return out
-
- @staticmethod
- def LLT_model(input, regularization_parameter , time_step, number_of_iterations,
- tolerance_constant, restrictive_Z_smoothing=0):
- start_time = timeit.default_timer()
- reg = Regularizer(Regularizer.Algorithm.LLT_model)
- out = list( reg(input, regularization_parameter, time_step=time_step,
- number_of_iterations=number_of_iterations,
- tolerance_constant=tolerance_constant,
- restrictive_Z_smoothing=restrictive_Z_smoothing) )
- out.append(reg.pars)
- txt = reg.printParametersToString()
- txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
- out.append(txt)
- return out
-
- @staticmethod
- def PatchBased_Regul(input, regularization_parameter,
- searching_window_ratio,
- similarity_window_ratio,
- PB_filtering_parameter):
- start_time = timeit.default_timer()
- reg = Regularizer(Regularizer.Algorithm.PatchBased_Regul)
- out = list( reg(input,
- regularization_parameter,
- searching_window_ratio=searching_window_ratio,
- similarity_window_ratio=similarity_window_ratio,
- PB_filtering_parameter=PB_filtering_parameter )
- )
- out.append(reg.pars)
- txt = reg.printParametersToString()
- txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
- out.append(txt)
- return out
-
- @staticmethod
- def TGV_PD(input, regularization_parameter , first_order_term,
- second_order_term, number_of_iterations):
- start_time = timeit.default_timer()
-
- reg = Regularizer(Regularizer.Algorithm.TGV_PD)
- out = list( reg(input, regularization_parameter,
- first_order_term=first_order_term,
- second_order_term=second_order_term,
- number_of_iterations=number_of_iterations) )
- out.append(reg.pars)
- txt = reg.printParametersToString()
- txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
- out.append(txt)
-
- return out
-
- def printParametersToString(self):
- txt = r''
- for key, value in self.pars.items():
- if key== 'algorithm' :
- txt += "{0} = {1}".format(key, value.__name__)
- elif key == 'input':
- txt += "{0} = {1}".format(key, np.shape(value))
- else:
- txt += "{0} = {1}".format(key, value)
- txt += '\n'
- return txt
-
diff --git a/src/Python/ccpi/imaging/__init__.py b/src/Python/ccpi/imaging/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/Python/ccpi/reconstruction/AstraDevice.py b/src/Python/ccpi/reconstruction/AstraDevice.py
deleted file mode 100644
index 57435f8..0000000
--- a/src/Python/ccpi/reconstruction/AstraDevice.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import astra
-from ccpi.reconstruction.DeviceModel import DeviceModel
-import numpy
-
-class AstraDevice(DeviceModel):
- '''Concrete class for Astra Device'''
-
- def __init__(self,
- device_type,
- data_aquisition_geometry,
- reconstructed_volume_geometry):
-
- super(AstraDevice, self).__init__(device_type,
- data_aquisition_geometry,
- reconstructed_volume_geometry)
-
- self.type = device_type
- self.proj_geom = astra.creators.create_proj_geom(
- device_type,
- self.acquisition_data_geometry['detectorSpacingX'],
- self.acquisition_data_geometry['detectorSpacingY'],
- self.acquisition_data_geometry['cameraX'],
- self.acquisition_data_geometry['cameraY'],
- self.acquisition_data_geometry['angles'],
- )
-
- self.vol_geom = astra.creators.create_vol_geom(
- self.reconstructed_volume_geometry['X'],
- self.reconstructed_volume_geometry['Y'],
- self.reconstructed_volume_geometry['Z']
- )
-
- def doForwardProject(self, volume):
- '''Forward projects the volume according to the device geometry
-
-Uses Astra-toolbox
-'''
-
- try:
- sino_id, y = astra.creators.create_sino3d_gpu(
- volume, self.proj_geom, self.vol_geom)
- astra.matlab.data3d('delete', sino_id)
- return y
- except Exception as e:
- print(e)
- print("Value Error: ", self.proj_geom, self.vol_geom)
-
- def doBackwardProject(self, projections):
- '''Backward projects the projections according to the device geometry
-
-Uses Astra-toolbox
-'''
- idx, volume = \
- astra.creators.create_backprojection3d_gpu(
- projections,
- self.proj_geom,
- self.vol_geom)
-
- astra.matlab.data3d('delete', idx)
- return volume
-
- def createReducedDevice(self, proj_par={'cameraY' : 1} , vol_par={'Z':1}):
- '''Create a new device based on the current device by changing some parameter
-
-VERY RISKY'''
- acquisition_data_geometry = self.acquisition_data_geometry.copy()
- for k,v in proj_par.items():
- if k in acquisition_data_geometry.keys():
- acquisition_data_geometry[k] = v
- proj_geom = [
- acquisition_data_geometry['cameraX'],
- acquisition_data_geometry['cameraY'],
- acquisition_data_geometry['detectorSpacingX'],
- acquisition_data_geometry['detectorSpacingY'],
- acquisition_data_geometry['angles']
- ]
-
- reconstructed_volume_geometry = self.reconstructed_volume_geometry.copy()
- for k,v in vol_par.items():
- if k in reconstructed_volume_geometry.keys():
- reconstructed_volume_geometry[k] = v
-
- vol_geom = [
- reconstructed_volume_geometry['X'],
- reconstructed_volume_geometry['Y'],
- reconstructed_volume_geometry['Z']
- ]
- return AstraDevice(self.type, proj_geom, vol_geom)
-
-
-
-if __name__=="main":
- a = AstraDevice()
-
-
diff --git a/src/Python/ccpi/reconstruction/DeviceModel.py b/src/Python/ccpi/reconstruction/DeviceModel.py
deleted file mode 100644
index eeb9a34..0000000
--- a/src/Python/ccpi/reconstruction/DeviceModel.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from abc import ABCMeta, abstractmethod
-from enum import Enum
-
-class DeviceModel(metaclass=ABCMeta):
- '''Abstract class that defines the device for projection and backprojection
-
-This class defines the methods that must be implemented by concrete classes.
-
- '''
-
- class DeviceType(Enum):
- '''Type of device
-PARALLEL BEAM
-PARALLEL BEAM 3D
-CONE BEAM
-HELICAL'''
-
- PARALLEL = 'parallel'
- PARALLEL3D = 'parallel3d'
- CONE_BEAM = 'cone-beam'
- HELICAL = 'helical'
-
- def __init__(self,
- device_type,
- data_aquisition_geometry,
- reconstructed_volume_geometry):
- '''Initializes the class
-
-Mandatory parameters are:
-device_type from DeviceType Enum
-data_acquisition_geometry: tuple (camera_X, camera_Y, detectorSpacingX,
- detectorSpacingY, angles)
-reconstructed_volume_geometry: tuple (dimX,dimY,dimZ)
-'''
- self.device_geometry = device_type
- self.acquisition_data_geometry = {
- 'cameraX': data_aquisition_geometry[0],
- 'cameraY': data_aquisition_geometry[1],
- 'detectorSpacingX' : data_aquisition_geometry[2],
- 'detectorSpacingY' : data_aquisition_geometry[3],
- 'angles' : data_aquisition_geometry[4],}
- self.reconstructed_volume_geometry = {
- 'X': reconstructed_volume_geometry[0] ,
- 'Y': reconstructed_volume_geometry[1] ,
- 'Z': reconstructed_volume_geometry[2] }
-
- @abstractmethod
- def doForwardProject(self, volume):
- '''Forward projects the volume according to the device geometry'''
- return NotImplemented
-
-
- @abstractmethod
- def doBackwardProject(self, projections):
- '''Backward projects the projections according to the device geometry'''
- return NotImplemented
-
- @abstractmethod
- def createReducedDevice(self):
- '''Create a Device to do forward/backward projections on 2D slices'''
- return NotImplemented
-
-
diff --git a/src/Python/ccpi/reconstruction/FISTAReconstructor.py b/src/Python/ccpi/reconstruction/FISTAReconstructor.py
deleted file mode 100644
index e40ad24..0000000
--- a/src/Python/ccpi/reconstruction/FISTAReconstructor.py
+++ /dev/null
@@ -1,882 +0,0 @@
-# -*- coding: utf-8 -*-
-###############################################################################
-#This work is part of the Core Imaging Library developed by
-#Visual Analytics and Imaging System Group of the Science Technology
-#Facilities Council, STFC
-#
-#Copyright 2017 Edoardo Pasca, Srikanth Nagella
-#Copyright 2017 Daniil Kazantsev
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#http://www.apache.org/licenses/LICENSE-2.0
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-###############################################################################
-
-
-
-import numpy
-#from ccpi.reconstruction.parallelbeam import alg
-
-#from ccpi.imaging.Regularizer import Regularizer
-from enum import Enum
-
-import astra
-from ccpi.reconstruction.AstraDevice import AstraDevice
-
-
-
-class FISTAReconstructor():
- '''FISTA-based reconstruction algorithm using ASTRA-toolbox
-
- '''
- # <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>>
- # ___Input___:
- # params.[] file:
- # - .proj_geom (geometry of the projector) [required]
- # - .vol_geom (geometry of the reconstructed object) [required]
- # - .sino (vectorized in 2D or 3D sinogram) [required]
- # - .iterFISTA (iterations for the main loop, default 40)
- # - .L_const (Lipschitz constant, default Power method) )
- # - .X_ideal (ideal image, if given)
- # - .weights (statisitcal weights, size of the sinogram)
- # - .ROI (Region-of-interest, only if X_ideal is given)
- # - .initialize (a 'warm start' using SIRT method from ASTRA)
- #----------------Regularization choices------------------------
- # - .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
- # - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
- # - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter)
- # - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
- # - .Regul_Iterations (iterations for the selected penalty, default 25)
- # - .Regul_tauLLT (time step parameter for LLT term)
- # - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
- # - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
- #----------------Visualization parameters------------------------
- # - .show (visualize reconstruction 1/0, (0 default))
- # - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
- # - .slice (for 3D volumes - slice number to imshow)
- # ___Output___:
- # 1. X - reconstructed image/volume
- # 2. output - a structure with
- # - .Resid_error - residual error (if X_ideal is given)
- # - .objective: value of the objective function
- # - .L_const: Lipshitz constant to avoid recalculations
-
- # References:
- # 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
- # Problems" by A. Beck and M Teboulle
- # 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
- # 3. "A novel tomographic reconstruction method based on the robust
- # Student's t function for suppressing data outliers" D. Kazantsev et.al.
- # D. Kazantsev, 2016-17
- def __init__(self, projector_geometry,
- output_geometry,
- input_sinogram,
- device,
- **kwargs):
- # handle parmeters:
- # obligatory parameters
- self.pars = dict()
- self.pars['projector_geometry'] = projector_geometry # proj_geom
- self.pars['output_geometry'] = output_geometry # vol_geom
- self.pars['input_sinogram'] = input_sinogram # sino
- sliceZ, nangles, detectors = numpy.shape(input_sinogram)
- self.pars['detectors'] = detectors
- self.pars['number_of_angles'] = nangles
- self.pars['SlicesZ'] = sliceZ
- self.pars['output_volume'] = None
- self.pars['device_model'] = device
-
- self.use_device = True
-
- print (self.pars)
- # handle optional input parameters (at instantiation)
-
- # Accepted input keywords
- kw = (
- # mandatory fields
- 'projector_geometry',
- 'output_geometry',
- 'input_sinogram',
- 'detectors',
- 'number_of_angles',
- 'SlicesZ',
- # optional fields
- 'number_of_iterations',
- 'Lipschitz_constant' ,
- 'ideal_image' ,
- 'weights' ,
- 'region_of_interest' ,
- 'initialize' ,
- 'regularizer' ,
- 'ring_lambda_R_L1',
- 'ring_alpha',
- 'subsets',
- 'output_volume',
- 'os_subsets',
- 'os_indices',
- 'os_bins',
- 'device_model',
- 'reduced_device_model')
- self.acceptedInputKeywords = list(kw)
-
- # handle keyworded parameters
- if kwargs is not None:
- for key, value in kwargs.items():
- if key in kw:
- #print("{0} = {1}".format(key, value))
- self.pars[key] = value
-
- # set the default values for the parameters if not set
- if 'number_of_iterations' in kwargs.keys():
- self.pars['number_of_iterations'] = kwargs['number_of_iterations']
- else:
- self.pars['number_of_iterations'] = 40
- if 'weights' in kwargs.keys():
- self.pars['weights'] = kwargs['weights']
- else:
- self.pars['weights'] = \
- numpy.ones(numpy.shape(
- self.pars['input_sinogram']))
- if 'Lipschitz_constant' in kwargs.keys():
- self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
- else:
- self.pars['Lipschitz_constant'] = None
-
- if not 'ideal_image' in kwargs.keys():
- self.pars['ideal_image'] = None
-
- if not 'region_of_interest'in kwargs.keys() :
- if self.pars['ideal_image'] == None:
- self.pars['region_of_interest'] = None
- else:
- ## nonzero if the image is larger than m
- fsm = numpy.frompyfunc(lambda x,m: 1 if x>m else 0, 2,1)
-
- self.pars['region_of_interest'] = fsm(self.pars['ideal_image'], 0)
-
- # the regularizer must be a correctly instantiated object
- if not 'regularizer' in kwargs.keys() :
- self.pars['regularizer'] = None
-
- #RING REMOVAL
- if not 'ring_lambda_R_L1' in kwargs.keys():
- self.pars['ring_lambda_R_L1'] = 0
- if not 'ring_alpha' in kwargs.keys():
- self.pars['ring_alpha'] = 1
-
- # ORDERED SUBSET
- if not 'subsets' in kwargs.keys():
- self.pars['subsets'] = 0
- else:
- self.createOrderedSubsets()
-
- if not 'initialize' in kwargs.keys():
- self.pars['initialize'] = False
-
- reduced_device = device.createReducedDevice()
- self.setParameter(reduced_device_model=reduced_device)
-
-
-
- def setParameter(self, **kwargs):
- '''set named parameter for the reconstructor engine
-
- raises Exception if the named parameter is not recognized
-
- '''
- for key , value in kwargs.items():
- if key in self.acceptedInputKeywords:
- self.pars[key] = value
- else:
- raise Exception('Wrong parameter {0} for '.format(key) +
- 'reconstructor')
- # setParameter
-
- def getParameter(self, key):
- if type(key) is str:
- if key in self.acceptedInputKeywords:
- return self.pars[key]
- else:
- raise Exception('Unrecongnised parameter: {0} '.format(key) )
- elif type(key) is list:
- outpars = []
- for k in key:
- outpars.append(self.getParameter(k))
- return outpars
- else:
- raise Exception('Unhandled input {0}' .format(str(type(key))))
-
-
- def calculateLipschitzConstantWithPowerMethod(self):
- ''' using Power method (PM) to establish L constant'''
-
- N = self.pars['output_geometry']['GridColCount']
- proj_geom = self.pars['projector_geometry']
- vol_geom = self.pars['output_geometry']
- weights = self.pars['weights']
- SlicesZ = self.pars['SlicesZ']
-
-
-
- if (proj_geom['type'] == 'parallel') or \
- (proj_geom['type'] == 'parallel3d'):
- #% for parallel geometry we can do just one slice
- #print('Calculating Lipshitz constant for parallel beam geometry...')
- niter = 5;# % number of iteration for the PM
- #N = params.vol_geom.GridColCount;
- #x1 = rand(N,N,1);
- x1 = numpy.random.rand(1,N,N)
- #sqweight = sqrt(weights(:,:,1));
- sqweight = numpy.sqrt(weights[0:1,:,:])
- proj_geomT = proj_geom.copy();
- proj_geomT['DetectorRowCount'] = 1;
- vol_geomT = vol_geom.copy();
- vol_geomT['GridSliceCount'] = 1;
-
- #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-
-
- for i in range(niter):
- # [id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geomT, vol_geomT);
- # s = norm(x1(:));
- # x1 = x1/s;
- # [sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
- # y = sqweight.*y;
- # astra_mex_data3d('delete', sino_id);
- # astra_mex_data3d('delete', id);
- #print ("iteration {0}".format(i))
-
- sino_id, y = astra.creators.create_sino3d_gpu(x1,
- proj_geomT,
- vol_geomT)
-
- y = (sqweight * y).copy() # element wise multiplication
-
- #b=fig.add_subplot(2,1,2)
- #imgplot = plt.imshow(x1[0])
- #plt.show()
-
- #astra_mex_data3d('delete', sino_id);
- astra.matlab.data3d('delete', sino_id)
- del x1
-
- idx,x1 = astra.creators.create_backprojection3d_gpu((sqweight*y).copy(),
- proj_geomT,
- vol_geomT)
- del y
-
-
- s = numpy.linalg.norm(x1)
- ### this line?
- x1 = (x1/s).copy();
-
- # ### this line?
- # sino_id, y = astra.creators.create_sino3d_gpu(x1,
- # proj_geomT,
- # vol_geomT);
- # y = sqweight * y;
- astra.matlab.data3d('delete', sino_id);
- astra.matlab.data3d('delete', idx)
- print ("iteration {0} s= {1}".format(i,s))
-
- #end
- del proj_geomT
- del vol_geomT
- #plt.show()
- else:
- #% divergen beam geometry
- print('Calculating Lipshitz constant for divergen beam geometry...')
- niter = 8; #% number of iteration for PM
- x1 = numpy.random.rand(SlicesZ , N , N);
- #sqweight = sqrt(weights);
- sqweight = numpy.sqrt(weights[0])
-
- sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
- y = sqweight*y;
- #astra_mex_data3d('delete', sino_id);
- astra.matlab.data3d('delete', sino_id);
-
- for i in range(niter):
- #[id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
- idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y,
- proj_geom,
- vol_geom)
- s = numpy.linalg.norm(x1)
- ### this line?
- x1 = x1/s;
- ### this line?
- #[sino_id, y] = astra_create_sino3d_gpu(x1, proj_geom, vol_geom);
- sino_id, y = astra.creators.create_sino3d_gpu(x1,
- proj_geom,
- vol_geom);
-
- y = sqweight*y;
- #astra_mex_data3d('delete', sino_id);
- #astra_mex_data3d('delete', id);
- astra.matlab.data3d('delete', sino_id);
- astra.matlab.data3d('delete', idx);
- #end
- #clear x1
- del x1
-
-
- return s
-
-
- def setRegularizer(self, regularizer):
- if regularizer is not None:
- self.pars['regularizer'] = regularizer
-
-
- def initialize(self):
- # convenience variable storage
- proj_geom = self.pars['projector_geometry']
- vol_geom = self.pars['output_geometry']
- sino = self.pars['input_sinogram']
-
- # a 'warm start' with SIRT method
- # Create a data object for the reconstruction
- rec_id = astra.matlab.data3d('create', '-vol',
- vol_geom);
-
- #sinogram_id = astra_mex_data3d('create', '-proj3d', proj_geom, sino);
- sinogram_id = astra.matlab.data3d('create', '-proj3d',
- proj_geom,
- sino)
-
- sirt_config = astra.astra_dict('SIRT3D_CUDA')
- sirt_config['ReconstructionDataId' ] = rec_id
- sirt_config['ProjectionDataId'] = sinogram_id
-
- sirt = astra.algorithm.create(sirt_config)
- astra.algorithm.run(sirt, iterations=35)
- X = astra.matlab.data3d('get', rec_id)
-
- # clean up memory
- astra.matlab.data3d('delete', rec_id)
- astra.matlab.data3d('delete', sinogram_id)
- astra.algorithm.delete(sirt)
-
-
-
- return X
-
- def createOrderedSubsets(self, subsets=None):
- if subsets is None:
- try:
- subsets = self.getParameter('subsets')
- except Exception():
- subsets = 0
- #return subsets
- else:
- self.setParameter(subsets=subsets)
-
-
- angles = self.getParameter('projector_geometry')['ProjectionAngles']
-
- #binEdges = numpy.linspace(angles.min(),
- # angles.max(),
- # subsets + 1)
- binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
- # get rearranged subset indices
- IndicesReorg = numpy.zeros((numpy.shape(angles)), dtype=numpy.int32)
- counterM = 0
- for ii in range(binsDiscr.max()):
- counter = 0
- for jj in range(subsets):
- curr_index = ii + jj + counter
- #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
- if binsDiscr[jj] > ii:
- if (counterM < numpy.size(IndicesReorg)):
- IndicesReorg[counterM] = curr_index
- counterM = counterM + 1
-
- counter = counter + binsDiscr[jj] - 1
-
- # store the OS in parameters
- self.setParameter(os_subsets=subsets,
- os_bins=binsDiscr,
- os_indices=IndicesReorg)
-
-
- def prepareForIteration(self):
- print ("FISTA Reconstructor: prepare for iteration")
-
- self.residual_error = numpy.zeros((self.pars['number_of_iterations']))
- self.objective = numpy.zeros((self.pars['number_of_iterations']))
-
- #2D array (for 3D data) of sparse "ring"
- detectors, nangles, sliceZ = numpy.shape(self.pars['input_sinogram'])
- self.r = numpy.zeros((detectors, sliceZ), dtype=numpy.float)
- # another ring variable
- self.r_x = self.r.copy()
-
- self.residual = numpy.zeros(numpy.shape(self.pars['input_sinogram']))
-
- if self.getParameter('Lipschitz_constant') is None:
- self.pars['Lipschitz_constant'] = \
- self.calculateLipschitzConstantWithPowerMethod()
- # errors vector (if the ground truth is given)
- self.Resid_error = numpy.zeros((self.getParameter('number_of_iterations')));
- # objective function values vector
- self.objective = numpy.zeros((self.getParameter('number_of_iterations')));
-
-
- # prepareForIteration
-
- def iterate (self, Xin=None):
- if self.getParameter('subsets') == 0:
- return self.iterateStandard(Xin)
- else:
- return self.iterateOrderedSubsets(Xin)
-
- def iterateStandard(self, Xin=None):
- print ("FISTA Reconstructor: iterate")
-
- if Xin is None:
- if self.getParameter('initialize'):
- X = self.initialize()
- else:
- N = vol_geom['GridColCount']
- X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
- else:
- # copy by reference
- X = Xin
- # store the output volume in the parameters
- self.setParameter(output_volume=X)
- X_t = X.copy()
- # convenience variable storage
- proj_geom , vol_geom, sino , \
- SlicesZ , ring_lambda_R_L1 , weights = \
- self.getParameter([ 'projector_geometry' ,
- 'output_geometry',
- 'input_sinogram',
- 'SlicesZ' ,
- 'ring_lambda_R_L1',
- 'weights'])
-
- t = 1
-
- device = self.getParameter('device_model')
- reduced_device = self.getParameter('reduced_device_model')
-
- for i in range(self.getParameter('number_of_iterations')):
- print("iteration", i)
- X_old = X.copy()
- t_old = t
- r_old = self.r.copy()
- pg = self.getParameter('projector_geometry')['type']
- if pg == 'parallel' or \
- pg == 'fanflat' or \
- pg == 'fanflat_vec':
- # if the geometry is parallel use slice-by-slice
- # projection-backprojection routine
- #sino_updt = zeros(size(sino),'single');
-
- if self.use_device :
- self.sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
-
- for kkk in range(SlicesZ):
- self.sino_updt[kkk] = \
- reduced_device.doForwardProject( X_t[kkk:kkk+1] )
- else:
- proj_geomT = proj_geom.copy()
- proj_geomT['DetectorRowCount'] = 1
- vol_geomT = vol_geom.copy()
- vol_geomT['GridSliceCount'] = 1;
- self.sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
- for kkk in range(SlicesZ):
- sino_id, self.sino_updt[kkk] = \
- astra.creators.create_sino3d_gpu(
- X_t[kkk:kkk+1], proj_geomT, vol_geomT)
- astra.matlab.data3d('delete', sino_id)
- else:
- # for divergent 3D geometry (watch the GPU memory overflow in
- # ASTRA versions < 1.8)
- #[sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
-
- if self.use_device:
- self.sino_updt = device.doForwardProject(X_t)
- else:
- sino_id, self.sino_updt = astra.creators.create_sino3d_gpu(
- X_t, proj_geom, vol_geom)
- astra.matlab.data3d('delete', sino_id)
-
-
- ## RING REMOVAL
- if ring_lambda_R_L1 != 0:
- self.ringRemoval(i)
- else:
- self.residual = weights * (self.sino_updt - sino)
- self.objective[i] = 0.5 * numpy.linalg.norm(self.residual)
- #objective(i) = 0.5*norm(residual(:)); % for the objective function output
- ## Projection/Backprojection Routine
- X, X_t = self.projectionBackprojection(X, X_t)
-
- ## REGULARIZATION
- Y = self.regularize(X)
- X = Y.copy()
- ## Update Loop
- X , X_t, t = self.updateLoop(i, X, X_old, r_old, t, t_old)
-
- print ("t" , t)
- print ("X min {0} max {1}".format(X_t.min(),X_t.max()))
- self.setParameter(output_volume=X)
- return X
- ## iterate
-
- def ringRemoval(self, i):
- print ("FISTA Reconstructor: ring removal")
- residual = self.residual
- lambdaR_L1 , alpha_ring , weights , L_const , sino= \
- self.getParameter(['ring_lambda_R_L1',
- 'ring_alpha' , 'weights',
- 'Lipschitz_constant',
- 'input_sinogram'])
- r_x = self.r_x
- sino_updt = self.sino_updt
-
- SlicesZ, anglesNumb, Detectors = \
- numpy.shape(self.getParameter('input_sinogram'))
- if lambdaR_L1 > 0 :
- for kkk in range(anglesNumb):
-
- residual[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
- ((sino_updt[:,kkk,:]).squeeze() - \
- (sino[:,kkk,:]).squeeze() -\
- (alpha_ring * r_x)
- )
- vec = residual.sum(axis = 1)
- #if SlicesZ > 1:
- # vec = vec[:,1,:].squeeze()
- self.r = (r_x - (1./L_const) * vec).copy()
- self.objective[i] = (0.5 * (residual ** 2).sum())
-
- def projectionBackprojection(self, X, X_t):
- print ("FISTA Reconstructor: projection-backprojection routine")
-
- # a few useful variables
- SlicesZ, anglesNumb, Detectors = \
- numpy.shape(self.getParameter('input_sinogram'))
- residual = self.residual
- proj_geom , vol_geom , L_const = \
- self.getParameter(['projector_geometry' ,
- 'output_geometry',
- 'Lipschitz_constant'])
-
- device, reduced_device = self.getParameter(['device_model',
- 'reduced_device_model'])
-
- if self.getParameter('projector_geometry')['type'] == 'parallel' or \
- self.getParameter('projector_geometry')['type'] == 'fanflat' or \
- self.getParameter('projector_geometry')['type'] == 'fanflat_vec':
- # if the geometry is parallel use slice-by-slice
- # projection-backprojection routine
- #sino_updt = zeros(size(sino),'single');
- x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
-
- if self.use_device:
- proj_geomT = proj_geom.copy()
- proj_geomT['DetectorRowCount'] = 1
- vol_geomT = vol_geom.copy()
- vol_geomT['GridSliceCount'] = 1;
-
- for kkk in range(SlicesZ):
-
- x_id, x_temp[kkk] = \
- astra.creators.create_backprojection3d_gpu(
- residual[kkk:kkk+1],
- proj_geomT, vol_geomT)
- astra.matlab.data3d('delete', x_id)
- else:
- for kkk in range(SliceZ):
- x_temp[kkk] = \
- reduced_device.doBackwardProject(residual[kkk:kkk+1])
- else:
- if self.use_device:
- x_id, x_temp = \
- astra.creators.create_backprojection3d_gpu(
- residual, proj_geom, vol_geom)
- astra.matlab.data3d('delete', x_id)
- else:
- x_temp = \
- device.doBackwardProject(residual)
-
-
- X = X_t - (1/L_const) * x_temp
- #astra.matlab.data3d('delete', sino_id)
- return (X , X_t)
-
-
- def regularize(self, X , output_all=False):
- #print ("FISTA Reconstructor: regularize")
-
- regularizer = self.getParameter('regularizer')
- if regularizer is not None:
- return regularizer(input=X,
- output_all=output_all)
- else:
- return X
-
- def updateLoop(self, i, X, X_old, r_old, t, t_old):
- print ("FISTA Reconstructor: update loop")
- lambdaR_L1 = self.getParameter('ring_lambda_R_L1')
-
- t = (1 + numpy.sqrt(1 + 4 * t**2))/2
- X_t = X + (((t_old -1)/t) * (X - X_old))
-
- if lambdaR_L1 > 0:
- self.r = numpy.max(
- numpy.abs(self.r) - lambdaR_L1 , 0) * \
- numpy.sign(self.r)
- self.r_x = self.r + \
- (((t_old-1)/t) * (self.r - r_old))
-
- if self.getParameter('region_of_interest') is None:
- string = 'Iteration Number {0} | Objective {1} \n'
- print (string.format( i, self.objective[i]))
- else:
- ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
- 'ideal_image')
-
- Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
- string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
- print (string.format(i,Resid_error[i], self.objective[i]))
- return (X , X_t, t)
-
- def iterateOrderedSubsets(self, Xin=None):
- print ("FISTA Reconstructor: Ordered Subsets iterate")
-
- if Xin is None:
- if self.getParameter('initialize'):
- X = self.initialize()
- else:
- N = vol_geom['GridColCount']
- X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
- else:
- # copy by reference
- X = Xin
- # store the output volume in the parameters
- self.setParameter(output_volume=X)
- X_t = X.copy()
-
- # some useful constants
- proj_geom , vol_geom, sino , \
- SlicesZ, weights , alpha_ring ,\
- lambdaR_L1 , L_const , iterFISTA = self.getParameter(
- ['projector_geometry' , 'output_geometry', 'input_sinogram',
- 'SlicesZ' , 'weights', 'ring_alpha' ,
- 'ring_lambda_R_L1', 'Lipschitz_constant',
- 'number_of_iterations'])
-
-
- # errors vector (if the ground truth is given)
- Resid_error = numpy.zeros((iterFISTA));
- # objective function values vector
- #objective = numpy.zeros((iterFISTA));
- objective = self.objective
-
-
- t = 1
-
- ## additional for
- proj_geomSUB = proj_geom.copy()
- self.residual2 = numpy.zeros(numpy.shape(sino))
- residual2 = self.residual2
- sino_updt_FULL = self.residual.copy()
- r_x = self.r.copy()
-
- print ("starting iterations")
- ## % Outer FISTA iterations loop
- for i in range(self.getParameter('number_of_iterations')):
- # With OS approach it becomes trickier to correlate independent
- # subsets, hence additional work is required one solution is to
- # work with a full sinogram at times
-
- r_old = self.r.copy()
- t_old = t
- SlicesZ, anglesNumb, Detectors = \
- numpy.shape(self.getParameter('input_sinogram')) ## https://github.com/vais-ral/CCPi-FISTA_Reconstruction/issues/4
- if (i > 1 and lambdaR_L1 > 0) :
- for kkk in range(anglesNumb):
-
- residual2[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
- ((sino_updt_FULL[:,kkk,:]).squeeze() - \
- (sino[:,kkk,:]).squeeze() -\
- (alpha_ring * r_x)
- )
-
- vec = self.residual.sum(axis = 1)
- #if SlicesZ > 1:
- # vec = vec[:,1,:] # 1 or 0?
- r_x = self.r_x
- # update ring variable
- self.r = (r_x - (1./L_const) * vec).copy()
-
- # subset loop
- counterInd = 1
- geometry_type = self.getParameter('projector_geometry')['type']
- angles = self.getParameter('projector_geometry')['ProjectionAngles']
-
- for ss in range(self.getParameter('subsets')):
- #print ("Subset {0}".format(ss))
- X_old = X.copy()
- t_old = t
-
- # the number of projections per subset
- numProjSub = self.getParameter('os_bins')[ss]
- CurrSubIndices = self.getParameter('os_indices')\
- [counterInd:counterInd+numProjSub]
- #print ("Len CurrSubIndices {0}".format(numProjSub))
- mask = numpy.zeros(numpy.shape(angles), dtype=bool)
- #cc = 0
- for j in range(len(CurrSubIndices)):
- mask[int(CurrSubIndices[j])] = True
- proj_geomSUB['ProjectionAngles'] = angles[mask]
-
- if self.use_device:
- device = self.getParameter('device_model')\
- .createReducedDevice(
- proj_par={'angles':angles[mask]},
- vol_par={})
-
- shape = list(numpy.shape(self.getParameter('input_sinogram')))
- shape[1] = numProjSub
- sino_updt_Sub = numpy.zeros(shape)
- if geometry_type == 'parallel' or \
- geometry_type == 'fanflat' or \
- geometry_type == 'fanflat_vec' :
-
- for kkk in range(SlicesZ):
- if self.use_device:
- sinoT = device.doForwardProject(X_t[kkk:kkk+1])
- else:
- sino_id, sinoT = astra.creators.create_sino3d_gpu (
- X_t[kkk:kkk+1] , proj_geomSUB, vol_geom)
- astra.matlab.data3d('delete', sino_id)
- sino_updt_Sub[kkk] = sinoT.T.copy()
-
- else:
- # for 3D geometry (watch the GPU memory overflow in
- # ASTRA < 1.8)
- if self.use_device:
- sino_updt_Sub = device.doForwardProject(X_t)
-
- else:
- sino_id, sino_updt_Sub = \
- astra.creators.create_sino3d_gpu (X_t, proj_geomSUB, vol_geom)
-
- astra.matlab.data3d('delete', sino_id)
-
- #print ("shape(sino_updt_Sub)",numpy.shape(sino_updt_Sub))
- if lambdaR_L1 > 0 :
- ## RING REMOVAL
- #print ("ring removal")
- residualSub , sino_updt_Sub, sino_updt_FULL = \
- self.ringRemovalOrderedSubsets(ss,
- counterInd,
- sino_updt_Sub,
- sino_updt_FULL)
- else:
- #PWLS model
- #print ("PWLS model")
- residualSub = weights[:,CurrSubIndices,:] * \
- ( sino_updt_Sub - \
- sino[:,CurrSubIndices,:].squeeze() )
- objective[i] = 0.5 * numpy.linalg.norm(residualSub)
-
- # projection/backprojection routine
- if geometry_type == 'parallel' or \
- geometry_type == 'fanflat' or \
- geometry_type == 'fanflat_vec' :
- # if geometry is 2D use slice-by-slice projection-backprojection
- # routine
- x_temp = numpy.zeros(numpy.shape(X), dtype=numpy.float32)
- for kkk in range(SlicesZ):
- if self.use_device:
- x_temp[kkk] = device.doBackwardProject(
- residualSub[kkk:kkk+1])
- else:
- x_id, x_temp[kkk] = \
- astra.creators.create_backprojection3d_gpu(
- residualSub[kkk:kkk+1],
- proj_geomSUB, vol_geom)
- astra.matlab.data3d('delete', x_id)
-
- else:
- if self.use_device:
- x_temp = device.doBackwardProject(
- residualSub)
- else:
- x_id, x_temp = \
- astra.creators.create_backprojection3d_gpu(
- residualSub, proj_geomSUB, vol_geom)
-
- astra.matlab.data3d('delete', x_id)
-
- X = X_t - (1/L_const) * x_temp
-
- ## REGULARIZATION
- X = self.regularize(X)
-
- ## Update subset Loop
- t = (1 + numpy.sqrt(1 + 4 * t**2))/2
- X_t = X + (((t_old -1)/t) * (X - X_old))
- # FINAL
- ## update iteration loop
- if lambdaR_L1 > 0:
- self.r = numpy.max(
- numpy.abs(self.r) - lambdaR_L1 , 0) * \
- numpy.sign(self.r)
- self.r_x = self.r + \
- (((t_old-1)/t) * (self.r - r_old))
-
- if self.getParameter('region_of_interest') is None:
- string = 'Iteration Number {0} | Objective {1} \n'
- print (string.format( i, self.objective[i]))
- else:
- ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
- 'ideal_image')
-
- Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
- string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
- print (string.format(i,Resid_error[i], self.objective[i]))
- print("X min {0} max {1}".format(X.min(),X.max()))
- self.setParameter(output_volume=X)
- counterInd = counterInd + numProjSub
-
- return X
-
- def ringRemovalOrderedSubsets(self, ss,counterInd,
- sino_updt_Sub, sino_updt_FULL):
- residual = self.residual
- r_x = self.r_x
- weights , alpha_ring , sino = \
- self.getParameter( ['weights', 'ring_alpha', 'input_sinogram'])
- numProjSub = self.getParameter('os_bins')[ss]
- CurrSubIndices = self.getParameter('os_indices')\
- [counterInd:counterInd+numProjSub]
-
- shape = list(numpy.shape(self.getParameter('input_sinogram')))
- shape[1] = numProjSub
-
- residualSub = numpy.zeros(shape)
-
- for kkk in range(numProjSub):
- #print ("ring removal indC ... {0}".format(kkk))
- indC = int(CurrSubIndices[kkk])
- residualSub[:,kkk,:] = weights[:,indC,:].squeeze() * \
- (sino_updt_Sub[:,kkk,:].squeeze() - \
- sino[:,indC,:].squeeze() - alpha_ring * r_x)
- # filling the full sinogram
- sino_updt_FULL[:,indC,:] = sino_updt_Sub[:,kkk,:].squeeze()
-
- return (residualSub , sino_updt_Sub, sino_updt_FULL)
-
-
diff --git a/src/Python/ccpi/reconstruction/Reconstructor.py b/src/Python/ccpi/reconstruction/Reconstructor.py
deleted file mode 100644
index ba67327..0000000
--- a/src/Python/ccpi/reconstruction/Reconstructor.py
+++ /dev/null
@@ -1,598 +0,0 @@
-# -*- coding: utf-8 -*-
-###############################################################################
-#This work is part of the Core Imaging Library developed by
-#Visual Analytics and Imaging System Group of the Science Technology
-#Facilities Council, STFC
-#
-#Copyright 2017 Edoardo Pasca, Srikanth Nagella
-#Copyright 2017 Daniil Kazantsev
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#http://www.apache.org/licenses/LICENSE-2.0
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-###############################################################################
-
-
-
-import numpy
-import h5py
-from ccpi.reconstruction.parallelbeam import alg
-
-from Regularizer import Regularizer
-from enum import Enum
-
-import astra
-
-
-class Reconstructor:
-
- class Algorithm(Enum):
- CGLS = alg.cgls
- CGLS_CONV = alg.cgls_conv
- SIRT = alg.sirt
- MLEM = alg.mlem
- CGLS_TICHONOV = alg.cgls_tikhonov
- CGLS_TVREG = alg.cgls_TVreg
- FISTA = 'fista'
-
- def __init__(self, algorithm = None, projection_data = None,
- angles = None, center_of_rotation = None ,
- flat_field = None, dark_field = None,
- iterations = None, resolution = None, isLogScale = False, threads = None,
- normalized_projection = None):
-
- self.pars = dict()
- self.pars['algorithm'] = algorithm
- self.pars['projection_data'] = projection_data
- self.pars['normalized_projection'] = normalized_projection
- self.pars['angles'] = angles
- self.pars['center_of_rotation'] = numpy.double(center_of_rotation)
- self.pars['flat_field'] = flat_field
- self.pars['iterations'] = iterations
- self.pars['dark_field'] = dark_field
- self.pars['resolution'] = resolution
- self.pars['isLogScale'] = isLogScale
- self.pars['threads'] = threads
- if (iterations != None):
- self.pars['iterationValues'] = numpy.zeros((iterations))
-
- if projection_data != None and dark_field != None and flat_field != None:
- norm = self.normalize(projection_data, dark_field, flat_field, 0.1)
- self.pars['normalized_projection'] = norm
-
-
- def setPars(self, parameters):
- keys = ['algorithm','projection_data' ,'normalized_projection', \
- 'angles' , 'center_of_rotation' , 'flat_field', \
- 'iterations','dark_field' , 'resolution', 'isLogScale' , \
- 'threads' , 'iterationValues', 'regularize']
-
- for k in keys:
- if k not in parameters.keys():
- self.pars[k] = None
- else:
- self.pars[k] = parameters[k]
-
-
- def sanityCheck(self):
- projection_data = self.pars['projection_data']
- dark_field = self.pars['dark_field']
- flat_field = self.pars['flat_field']
- angles = self.pars['angles']
-
- if projection_data != None and dark_field != None and \
- angles != None and flat_field != None:
- data_shape = numpy.shape(projection_data)
- angle_shape = numpy.shape(angles)
-
- if angle_shape[0] != data_shape[0]:
- #raise Exception('Projections and angles dimensions do not match: %d vs %d' % \
- # (angle_shape[0] , data_shape[0]) )
- return (False , 'Projections and angles dimensions do not match: %d vs %d' % \
- (angle_shape[0] , data_shape[0]) )
-
- if data_shape[1:] != numpy.shape(flat_field):
- #raise Exception('Projection and flat field dimensions do not match')
- return (False , 'Projection and flat field dimensions do not match')
- if data_shape[1:] != numpy.shape(dark_field):
- #raise Exception('Projection and dark field dimensions do not match')
- return (False , 'Projection and dark field dimensions do not match')
-
- return (True , '' )
- elif self.pars['normalized_projection'] != None:
- data_shape = numpy.shape(self.pars['normalized_projection'])
- angle_shape = numpy.shape(angles)
-
- if angle_shape[0] != data_shape[0]:
- #raise Exception('Projections and angles dimensions do not match: %d vs %d' % \
- # (angle_shape[0] , data_shape[0]) )
- return (False , 'Projections and angles dimensions do not match: %d vs %d' % \
- (angle_shape[0] , data_shape[0]) )
- else:
- return (True , '' )
- else:
- return (False , 'Not enough data')
-
- def reconstruct(self, parameters = None):
- if parameters != None:
- self.setPars(parameters)
-
- go , reason = self.sanityCheck()
- if go:
- return self._reconstruct()
- else:
- raise Exception(reason)
-
-
- def _reconstruct(self, parameters=None):
- if parameters!=None:
- self.setPars(parameters)
- parameters = self.pars
-
- if parameters['algorithm'] != None and \
- parameters['normalized_projection'] != None and \
- parameters['angles'] != None and \
- parameters['center_of_rotation'] != None and \
- parameters['iterations'] != None and \
- parameters['resolution'] != None and\
- parameters['threads'] != None and\
- parameters['isLogScale'] != None:
-
-
- if parameters['algorithm'] in (Reconstructor.Algorithm.CGLS,
- Reconstructor.Algorithm.MLEM, Reconstructor.Algorithm.SIRT):
- #store parameters
- self.pars = parameters
- result = parameters['algorithm'](
- parameters['normalized_projection'] ,
- parameters['angles'],
- parameters['center_of_rotation'],
- parameters['resolution'],
- parameters['iterations'],
- parameters['threads'] ,
- parameters['isLogScale']
- )
- return result
- elif parameters['algorithm'] in (Reconstructor.Algorithm.CGLS_CONV,
- Reconstructor.Algorithm.CGLS_TICHONOV,
- Reconstructor.Algorithm.CGLS_TVREG) :
- self.pars = parameters
- result = parameters['algorithm'](
- parameters['normalized_projection'] ,
- parameters['angles'],
- parameters['center_of_rotation'],
- parameters['resolution'],
- parameters['iterations'],
- parameters['threads'] ,
- parameters['regularize'],
- numpy.zeros((parameters['iterations'])),
- parameters['isLogScale']
- )
-
- elif parameters['algorithm'] == Reconstructor.Algorithm.FISTA:
- pass
-
- else:
- if parameters['projection_data'] != None and \
- parameters['dark_field'] != None and \
- parameters['flat_field'] != None:
- norm = self.normalize(parameters['projection_data'],
- parameters['dark_field'],
- parameters['flat_field'], 0.1)
- self.pars['normalized_projection'] = norm
- return self._reconstruct(parameters)
-
-
-
- def _normalize(self, projection, dark, flat, def_val=0):
- a = (projection - dark)
- b = (flat-dark)
- with numpy.errstate(divide='ignore', invalid='ignore'):
- c = numpy.true_divide( a, b )
- c[ ~ numpy.isfinite( c )] = def_val # set to not zero if 0/0
- return c
-
- def normalize(self, projections, dark, flat, def_val=0):
- norm = [self._normalize(projection, dark, flat, def_val) for projection in projections]
- return numpy.asarray (norm, dtype=numpy.float32)
-
-
-
-class FISTA():
- '''FISTA-based reconstruction algorithm using ASTRA-toolbox
-
- '''
- # <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>>
- # ___Input___:
- # params.[] file:
- # - .proj_geom (geometry of the projector) [required]
- # - .vol_geom (geometry of the reconstructed object) [required]
- # - .sino (vectorized in 2D or 3D sinogram) [required]
- # - .iterFISTA (iterations for the main loop, default 40)
- # - .L_const (Lipschitz constant, default Power method) )
- # - .X_ideal (ideal image, if given)
- # - .weights (statisitcal weights, size of the sinogram)
- # - .ROI (Region-of-interest, only if X_ideal is given)
- # - .initialize (a 'warm start' using SIRT method from ASTRA)
- #----------------Regularization choices------------------------
- # - .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
- # - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
- # - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter)
- # - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
- # - .Regul_Iterations (iterations for the selected penalty, default 25)
- # - .Regul_tauLLT (time step parameter for LLT term)
- # - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
- # - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
- #----------------Visualization parameters------------------------
- # - .show (visualize reconstruction 1/0, (0 default))
- # - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
- # - .slice (for 3D volumes - slice number to imshow)
- # ___Output___:
- # 1. X - reconstructed image/volume
- # 2. output - a structure with
- # - .Resid_error - residual error (if X_ideal is given)
- # - .objective: value of the objective function
- # - .L_const: Lipshitz constant to avoid recalculations
-
- # References:
- # 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
- # Problems" by A. Beck and M Teboulle
- # 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
- # 3. "A novel tomographic reconstruction method based on the robust
- # Student's t function for suppressing data outliers" D. Kazantsev et.al.
- # D. Kazantsev, 2016-17
- def __init__(self, projector_geometry, output_geometry, input_sinogram, **kwargs):
- self.params = dict()
- self.params['projector_geometry'] = projector_geometry
- self.params['output_geometry'] = output_geometry
- self.params['input_sinogram'] = input_sinogram
- detectors, nangles, sliceZ = numpy.shape(input_sinogram)
- self.params['detectors'] = detectors
- self.params['number_og_angles'] = nangles
- self.params['SlicesZ'] = sliceZ
-
- # Accepted input keywords
- kw = ('number_of_iterations', 'Lipschitz_constant' , 'ideal_image' ,
- 'weights' , 'region_of_interest' , 'initialize' ,
- 'regularizer' ,
- 'ring_lambda_R_L1',
- 'ring_alpha')
-
- # handle keyworded parameters
- if kwargs is not None:
- for key, value in kwargs.items():
- if key in kw:
- #print("{0} = {1}".format(key, value))
- self.pars[key] = value
-
- # set the default values for the parameters if not set
- if 'number_of_iterations' in kwargs.keys():
- self.pars['number_of_iterations'] = kwargs['number_of_iterations']
- else:
- self.pars['number_of_iterations'] = 40
- if 'weights' in kwargs.keys():
- self.pars['weights'] = kwargs['weights']
- else:
- self.pars['weights'] = numpy.ones(numpy.shape(self.params['input_sinogram']))
- if 'Lipschitz_constant' in kwargs.keys():
- self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
- else:
- self.pars['Lipschitz_constant'] = self.calculateLipschitzConstantWithPowerMethod()
-
- if not self.pars['ideal_image'] in kwargs.keys():
- self.pars['ideal_image'] = None
-
- if not self.pars['region_of_interest'] :
- if self.pars['ideal_image'] == None:
- pass
- else:
- self.pars['region_of_interest'] = numpy.nonzero(self.pars['ideal_image']>0.0)
-
- if not self.pars['regularizer'] :
- self.pars['regularizer'] = None
- else:
- # the regularizer must be a correctly instantiated object
- if not self.pars['ring_lambda_R_L1']:
- self.pars['ring_lambda_R_L1'] = 0
- if not self.pars['ring_alpha']:
- self.pars['ring_alpha'] = 1
-
-
-
-
- def calculateLipschitzConstantWithPowerMethod(self):
- ''' using Power method (PM) to establish L constant'''
-
- #N = params.vol_geom.GridColCount
- N = self.pars['output_geometry'].GridColCount
- proj_geom = self.params['projector_geometry']
- vol_geom = self.params['output_geometry']
- weights = self.pars['weights']
- SlicesZ = self.pars['SlicesZ']
-
- if (proj_geom['type'] == 'parallel') or (proj_geom['type'] == 'parallel3d'):
- #% for parallel geometry we can do just one slice
- #fprintf('%s \n', 'Calculating Lipshitz constant for parallel beam geometry...');
- niter = 15;# % number of iteration for the PM
- #N = params.vol_geom.GridColCount;
- #x1 = rand(N,N,1);
- x1 = numpy.random.rand(1,N,N)
- #sqweight = sqrt(weights(:,:,1));
- sqweight = numpy.sqrt(weights.T[0])
- proj_geomT = proj_geom.copy();
- proj_geomT.DetectorRowCount = 1;
- vol_geomT = vol_geom.copy();
- vol_geomT['GridSliceCount'] = 1;
-
-
- for i in range(niter):
- if i == 0:
- #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
- sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
- y = sqweight * y # element wise multiplication
- #astra_mex_data3d('delete', sino_id);
- astra.matlab.data3d('delete', sino_id)
-
- idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, proj_geomT, vol_geomT);
- s = numpy.linalg.norm(x1)
- ### this line?
- x1 = x1/s;
- ### this line?
- sino_id, y = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
- y = sqweight*y;
- astra.matlab.data3d('delete', sino_id);
- astra.matlab.data3d('delete', idx);
- #end
- del proj_geomT
- del vol_geomT
- else
- #% divergen beam geometry
- #fprintf('%s \n', 'Calculating Lipshitz constant for divergen beam geometry...');
- niter = 8; #% number of iteration for PM
- x1 = numpy.random.rand(SlicesZ , N , N);
- #sqweight = sqrt(weights);
- sqweight = numpy.sqrt(weights.T[0])
-
- sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
- y = sqweight*y;
- #astra_mex_data3d('delete', sino_id);
- astra.matlab.data3d('delete', sino_id);
-
- for i in range(niter):
- #[id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
- idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y,
- proj_geom,
- vol_geom)
- s = numpy.linalg.norm(x1)
- ### this line?
- x1 = x1/s;
- ### this line?
- #[sino_id, y] = astra_create_sino3d_gpu(x1, proj_geom, vol_geom);
- sino_id, y = astra.creators.create_sino3d_gpu(x1,
- proj_geom,
- vol_geom);
-
- y = sqweight*y;
- #astra_mex_data3d('delete', sino_id);
- #astra_mex_data3d('delete', id);
- astra.matlab.data3d('delete', sino_id);
- astra.matlab.data3d('delete', idx);
- #end
- #clear x1
- del x1
-
- return s
-
-
- def setRegularizer(self, regularizer):
- if regularizer
- self.pars['regularizer'] = regularizer
-
-
-
-
-
-def getEntry(location):
- for item in nx[location].keys():
- print (item)
-
-
-print ("Loading Data")
-
-##fname = "D:\\Documents\\Dataset\\IMAT\\20170419_crabtomo\\crabtomo\\Sample\\IMAT00005153_crabstomo_Sample_000.tif"
-####ind = [i * 1049 for i in range(360)]
-#### use only 360 images
-##images = 200
-##ind = [int(i * 1049 / images) for i in range(images)]
-##stack_image = dxchange.reader.read_tiff_stack(fname, ind, digit=None, slc=None)
-
-#fname = "D:\\Documents\\Dataset\\CGLS\\24737_fd.nxs"
-fname = "C:\\Users\\ofn77899\\Documents\\CCPi\\CGLS\\24737_fd_2.nxs"
-nx = h5py.File(fname, "r")
-
-# the data are stored in a particular location in the hdf5
-for item in nx['entry1/tomo_entry/data'].keys():
- print (item)
-
-data = nx.get('entry1/tomo_entry/data/rotation_angle')
-angles = numpy.zeros(data.shape)
-data.read_direct(angles)
-print (angles)
-# angles should be in degrees
-
-data = nx.get('entry1/tomo_entry/data/data')
-stack = numpy.zeros(data.shape)
-data.read_direct(stack)
-print (data.shape)
-
-print ("Data Loaded")
-
-
-# Normalize
-data = nx.get('entry1/tomo_entry/instrument/detector/image_key')
-itype = numpy.zeros(data.shape)
-data.read_direct(itype)
-# 2 is dark field
-darks = [stack[i] for i in range(len(itype)) if itype[i] == 2 ]
-dark = darks[0]
-for i in range(1, len(darks)):
- dark += darks[i]
-dark = dark / len(darks)
-#dark[0][0] = dark[0][1]
-
-# 1 is flat field
-flats = [stack[i] for i in range(len(itype)) if itype[i] == 1 ]
-flat = flats[0]
-for i in range(1, len(flats)):
- flat += flats[i]
-flat = flat / len(flats)
-#flat[0][0] = dark[0][1]
-
-
-# 0 is projection data
-proj = [stack[i] for i in range(len(itype)) if itype[i] == 0 ]
-angle_proj = [angles[i] for i in range(len(itype)) if itype[i] == 0 ]
-angle_proj = numpy.asarray (angle_proj)
-angle_proj = angle_proj.astype(numpy.float32)
-
-# normalized data are
-# norm = (projection - dark)/(flat-dark)
-
-def normalize(projection, dark, flat, def_val=0.1):
- a = (projection - dark)
- b = (flat-dark)
- with numpy.errstate(divide='ignore', invalid='ignore'):
- c = numpy.true_divide( a, b )
- c[ ~ numpy.isfinite( c )] = def_val # set to not zero if 0/0
- return c
-
-
-norm = [normalize(projection, dark, flat) for projection in proj]
-norm = numpy.asarray (norm)
-norm = norm.astype(numpy.float32)
-
-#recon = Reconstructor(algorithm = Algorithm.CGLS, normalized_projection = norm,
-# angles = angle_proj, center_of_rotation = 86.2 ,
-# flat_field = flat, dark_field = dark,
-# iterations = 15, resolution = 1, isLogScale = False, threads = 3)
-
-#recon = Reconstructor(algorithm = Reconstructor.Algorithm.CGLS, projection_data = proj,
-# angles = angle_proj, center_of_rotation = 86.2 ,
-# flat_field = flat, dark_field = dark,
-# iterations = 15, resolution = 1, isLogScale = False, threads = 3)
-#img_cgls = recon.reconstruct()
-#
-#pars = dict()
-#pars['algorithm'] = Reconstructor.Algorithm.SIRT
-#pars['projection_data'] = proj
-#pars['angles'] = angle_proj
-#pars['center_of_rotation'] = numpy.double(86.2)
-#pars['flat_field'] = flat
-#pars['iterations'] = 15
-#pars['dark_field'] = dark
-#pars['resolution'] = 1
-#pars['isLogScale'] = False
-#pars['threads'] = 3
-#
-#img_sirt = recon.reconstruct(pars)
-#
-#recon.pars['algorithm'] = Reconstructor.Algorithm.MLEM
-#img_mlem = recon.reconstruct()
-
-############################################################
-############################################################
-#recon.pars['algorithm'] = Reconstructor.Algorithm.CGLS_CONV
-#recon.pars['regularize'] = numpy.double(0.1)
-#img_cgls_conv = recon.reconstruct()
-
-niterations = 15
-threads = 3
-
-img_cgls = alg.cgls(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-img_mlem = alg.mlem(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-img_sirt = alg.sirt(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-
-iteration_values = numpy.zeros((niterations,))
-img_cgls_conv = alg.cgls_conv(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
- iteration_values, False)
-print ("iteration values %s" % str(iteration_values))
-
-iteration_values = numpy.zeros((niterations,))
-img_cgls_tikhonov = alg.cgls_tikhonov(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
- numpy.double(1e-5), iteration_values , False)
-print ("iteration values %s" % str(iteration_values))
-iteration_values = numpy.zeros((niterations,))
-img_cgls_TVreg = alg.cgls_TVreg(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
- numpy.double(1e-5), iteration_values , False)
-print ("iteration values %s" % str(iteration_values))
-
-
-##numpy.save("cgls_recon.npy", img_data)
-import matplotlib.pyplot as plt
-fig, ax = plt.subplots(1,6,sharey=True)
-ax[0].imshow(img_cgls[80])
-ax[0].axis('off') # clear x- and y-axes
-ax[1].imshow(img_sirt[80])
-ax[1].axis('off') # clear x- and y-axes
-ax[2].imshow(img_mlem[80])
-ax[2].axis('off') # clear x- and y-axesplt.show()
-ax[3].imshow(img_cgls_conv[80])
-ax[3].axis('off') # clear x- and y-axesplt.show()
-ax[4].imshow(img_cgls_tikhonov[80])
-ax[4].axis('off') # clear x- and y-axesplt.show()
-ax[5].imshow(img_cgls_TVreg[80])
-ax[5].axis('off') # clear x- and y-axesplt.show()
-
-
-plt.show()
-
-#viewer = edo.CILViewer()
-#viewer.setInputAsNumpy(img_cgls2)
-#viewer.displaySliceActor(0)
-#viewer.startRenderLoop()
-
-import vtk
-
-def NumpyToVTKImageData(numpyarray):
- if (len(numpy.shape(numpyarray)) == 3):
- doubleImg = vtk.vtkImageData()
- shape = numpy.shape(numpyarray)
- doubleImg.SetDimensions(shape[0], shape[1], shape[2])
- doubleImg.SetOrigin(0,0,0)
- doubleImg.SetSpacing(1,1,1)
- doubleImg.SetExtent(0, shape[0]-1, 0, shape[1]-1, 0, shape[2]-1)
- #self.img3D.SetScalarType(vtk.VTK_UNSIGNED_SHORT, vtk.vtkInformation())
- doubleImg.AllocateScalars(vtk.VTK_DOUBLE,1)
-
- for i in range(shape[0]):
- for j in range(shape[1]):
- for k in range(shape[2]):
- doubleImg.SetScalarComponentFromDouble(
- i,j,k,0, numpyarray[i][j][k])
- #self.setInput3DData( numpy_support.numpy_to_vtk(numpyarray) )
- # rescale to appropriate VTK_UNSIGNED_SHORT
- stats = vtk.vtkImageAccumulate()
- stats.SetInputData(doubleImg)
- stats.Update()
- iMin = stats.GetMin()[0]
- iMax = stats.GetMax()[0]
- scale = vtk.VTK_UNSIGNED_SHORT_MAX / (iMax - iMin)
-
- shiftScaler = vtk.vtkImageShiftScale ()
- shiftScaler.SetInputData(doubleImg)
- shiftScaler.SetScale(scale)
- shiftScaler.SetShift(iMin)
- shiftScaler.SetOutputScalarType(vtk.VTK_UNSIGNED_SHORT)
- shiftScaler.Update()
- return shiftScaler.GetOutput()
-
-#writer = vtk.vtkMetaImageWriter()
-#writer.SetFileName(alg + "_recon.mha")
-#writer.SetInputData(NumpyToVTKImageData(img_cgls2))
-#writer.Write()
diff --git a/src/Python/ccpi/reconstruction/__init__.py b/src/Python/ccpi/reconstruction/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/Python/compile-fista.bat.in b/src/Python/compile-fista.bat.in
deleted file mode 100644
index b1db686..0000000
--- a/src/Python/compile-fista.bat.in
+++ /dev/null
@@ -1,7 +0,0 @@
-set CIL_VERSION=@CIL_VERSION@
-
-set PREFIX=@CONDA_ENVIRONMENT_PREFIX@
-set LIBRARY_INC=@CONDA_ENVIRONMENT_LIBRARY_INC@
-
-REM activate @CONDA_ENVIRONMENT@
-conda build fista-recipe --python=@PYTHON_VERSION_MAJOR@.@PYTHON_VERSION_MINOR@ --numpy=@NUMPY_VERSION@ -c ccpi -c conda-forge
diff --git a/src/Python/compile-fista.sh.in b/src/Python/compile-fista.sh.in
deleted file mode 100644
index 267f014..0000000
--- a/src/Python/compile-fista.sh.in
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/sh
-# compile within the right conda environment
-#module load python/anaconda
-#source activate @CONDA_ENVIRONMENT@
-
-export CIL_VERSION=@CIL_VERSION@
-export LIBRARY_INC=@CONDA_ENVIRONMENT_LIBRARY_INC@
-
-conda build fista-recipe --python=@PYTHON_VERSION_MAJOR@.@PYTHON_VERSION_MINOR@ --numpy=@NUMPY_VERSION@ -c ccpi
diff --git a/src/Python/compile.bat.in b/src/Python/compile.bat.in
deleted file mode 100644
index e5342ed..0000000
--- a/src/Python/compile.bat.in
+++ /dev/null
@@ -1,7 +0,0 @@
-set CIL_VERSION=@CIL_VERSION@
-
-set PREFIX=@CONDA_ENVIRONMENT_PREFIX@
-set LIBRARY_INC=@CONDA_ENVIRONMENT_LIBRARY_INC@
-
-REM activate @CONDA_ENVIRONMENT@
-conda build conda-recipe --python=@PYTHON_VERSION_MAJOR@.@PYTHON_VERSION_MINOR@ --numpy=@NUMPY_VERSION@ -c ccpi -c conda-forge
\ No newline at end of file
diff --git a/src/Python/compile.sh.in b/src/Python/compile.sh.in
deleted file mode 100644
index 93fdba2..0000000
--- a/src/Python/compile.sh.in
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/sh
-# compile within the right conda environment
-#module load python/anaconda
-#source activate @CONDA_ENVIRONMENT@
-
-export CIL_VERSION=@CIL_VERSION@
-export LIBRARY_INC=@CONDA_ENVIRONMENT_LIBRARY_INC@
-
-conda build conda-recipe --python=@PYTHON_VERSION_MAJOR@.@PYTHON_VERSION_MINOR@ --numpy=@NUMPY_VERSION@ -c ccpi
diff --git a/src/Python/conda-recipe/bld.bat b/src/Python/conda-recipe/bld.bat
deleted file mode 100644
index 69491de..0000000
--- a/src/Python/conda-recipe/bld.bat
+++ /dev/null
@@ -1,14 +0,0 @@
-IF NOT DEFINED CIL_VERSION (
-ECHO CIL_VERSION Not Defined.
-exit 1
-)
-
-mkdir "%SRC_DIR%\ccpi"
-xcopy /e "%RECIPE_DIR%\..\.." "%SRC_DIR%\ccpi"
-
-cd %SRC_DIR%\ccpi\Python
-
-%PYTHON% setup.py build_ext
-if errorlevel 1 exit 1
-%PYTHON% setup.py install
-if errorlevel 1 exit 1
diff --git a/src/Python/conda-recipe/build.sh b/src/Python/conda-recipe/build.sh
deleted file mode 100644
index 855047f..0000000
--- a/src/Python/conda-recipe/build.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-
-if [ -z "$CIL_VERSION" ]; then
- echo "Need to set CIL_VERSION"
- exit 1
-fi
-mkdir "$SRC_DIR/ccpi"
-cp -r "$RECIPE_DIR/../.." "$SRC_DIR/ccpi"
-
-cd $SRC_DIR/ccpi/Python
-
-$PYTHON setup.py build_ext
-$PYTHON setup.py install
-
-
diff --git a/src/Python/conda-recipe/meta.yaml b/src/Python/conda-recipe/meta.yaml
deleted file mode 100644
index 9ef9156..0000000
--- a/src/Python/conda-recipe/meta.yaml
+++ /dev/null
@@ -1,30 +0,0 @@
-package:
- name: ccpi-regularizers
- version: {{ environ['CIL_VERSION'] }}
-
-
-build:
- preserve_egg_dir: False
- script_env:
- - CIL_VERSION
-# number: 0
-
-requirements:
- build:
- - python
- - numpy
- - setuptools
- - boost ==1.65
- - boost-cpp ==1.65
- - cython
-
- run:
- - python
- - numpy
- - boost ==1.64
-
-
-about:
- home: http://www.ccpi.ac.uk
- license: BSD license
- summary: 'CCPi Core Imaging Library Quantification Toolbox'
diff --git a/src/Python/demo/demo_dendrites.py b/src/Python/demo/demo_dendrites.py
deleted file mode 100644
index f5dc845..0000000
--- a/src/Python/demo/demo_dendrites.py
+++ /dev/null
@@ -1,168 +0,0 @@
-
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Aug 23 16:34:49 2017
-
-@author: ofn77899
-Based on DemoRD2.m
-"""
-
-import h5py
-import numpy
-
-from ccpi.reconstruction.FISTAReconstructor import FISTAReconstructor
-import astra
-import matplotlib.pyplot as plt
-from ccpi.imaging.Regularizer import Regularizer
-from ccpi.reconstruction.AstraDevice import AstraDevice
-from ccpi.reconstruction.DeviceModel import DeviceModel
-
-def RMSE(signal1, signal2):
- '''RMSE Root Mean Squared Error'''
- if numpy.shape(signal1) == numpy.shape(signal2):
- err = (signal1 - signal2)
- err = numpy.sum( err * err )/numpy.size(signal1); # MSE
- err = sqrt(err); # RMSE
- return err
- else:
- raise Exception('Input signals must have the same shape')
-
-filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/demos/DendrData.h5'
-nx = h5py.File(filename, "r")
-#getEntry(nx, '/')
-# I have exported the entries as children of /
-entries = [entry for entry in nx['/'].keys()]
-print (entries)
-
-Sino3D = numpy.asarray(nx.get('/Sino3D'), dtype="float32")
-Weights3D = numpy.asarray(nx.get('/Weights3D'), dtype="float32")
-angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
-angles_rad = numpy.asarray(nx.get('/angles_rad'), dtype="float32")
-recon_size = numpy.asarray(nx.get('/recon_size'), dtype=int)[0]
-size_det = numpy.asarray(nx.get('/size_det'), dtype=int)[0]
-slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
-
-Z_slices = 20
-det_row_count = Z_slices
-# next definition is just for consistency of naming
-det_col_count = size_det
-
-detectorSpacingX = 1.0
-detectorSpacingY = detectorSpacingX
-
-
-proj_geom = astra.creators.create_proj_geom('parallel3d',
- detectorSpacingX,
- detectorSpacingY,
- det_row_count,
- det_col_count,
- angles_rad)
-
-#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
-image_size_x = recon_size
-image_size_y = recon_size
-image_size_z = Z_slices
-vol_geom = astra.creators.create_vol_geom( image_size_x,
- image_size_y,
- image_size_z)
-
-
-## Create a Acquisition Device Model
-## Must specify some parameters of the acquisition:
-
-astradevice = AstraDevice(
- DeviceModel.DeviceType.PARALLEL3D.value,
- [det_row_count , det_col_count ,
- detectorSpacingX, detectorSpacingY ,
- angles_rad
- ],
- [ image_size_x, image_size_y, image_size_z ] )
-
-fistaRecon = FISTAReconstructor(proj_geom,
- vol_geom,
- Sino3D ,
- weights=Weights3D,
- device=astradevice,
- Lipschitz_constant = 767893952.0,
- subsets = 8)
-
-print("Reconstruction using FISTA-OS-PWLS without regularization...")
-fistaRecon.setParameter(number_of_iterations = 5)
-
-### adjust the regularization parameter
-##lc = fistaRecon.getParameter('Lipschitz_constant')
-##fistaRecon.getParameter('regularizer')\
-## .setParameter(regularization_parameter=5e6/lc)
-fistaRecon.use_device = True
-if True:
- fistaRecon.prepareForIteration()
- X = fistaRecon.iterate(numpy.load("../test/X.npy"))
- numpy.save("FISTA-OS-PWLS.npy",X)
-
-## setup a regularizer algorithm
-regul = Regularizer(Regularizer.Algorithm.FGP_TV)
-regul.setParameter(regularization_parameter=5e6,
- number_of_iterations=50,
- tolerance_constant=1e-4,
- TV_penalty=Regularizer.TotalVariationPenalty.isotropic)
-if False:
- # adjust the regularization parameter
- lc = fistaRecon.getParameter('Lipschitz_constant')
- regul.setParameter(regularization_parameter=5e6/lc)
- fistaRecon.setParameter(regularizer=regul)
- fistaRecon.prepareForIteration()
- X = fistaRecon.iterate(numpy.load("../test/X.npy"))
- numpy.save("FISTA-OS-PWLS-TV.npy",X)
-
-if False:
- # adjust the regularization parameter
- lc = fistaRecon.getParameter('Lipschitz_constant')
- regul.setParameter(regularization_parameter=5e6/lc)
- fistaRecon.setParameter(regularizer=regul)
- fistaRecon.setParameter(ring_lambda_R_L1=0.002, ring_alpha=21)
- fistaRecon.prepareForIteration()
- X = fistaRecon.iterate(numpy.load("../test/X.npy"))
- numpy.save("FISTA-OS-GH-TV.npy",X)
-
-if False:
- # adjust the regularization parameter
- lc = fistaRecon.getParameter('Lipschitz_constant')
- regul.setParameter(
- algorithm=Regularizer.Algorithm.TGV_PD,
- regularization_parameter=0.5/lc,
- number_of_iterations=5)
- fistaRecon.setParameter(regularizer=regul)
- fistaRecon.setParameter(ring_lambda_R_L1=0.002, ring_alpha=21)
- fistaRecon.prepareForIteration()
- X = fistaRecon.iterate(numpy.load("../test/X.npy"))
- numpy.save("FISTA-OS-GH-TGV.npy",X)
-
-if False:
- # adjust the regularization parameter
- lc = fistaRecon.getParameter('Lipschitz_constant')
- regul.setParameter(
- algorithm=Regularizer.Algorithm.PatchBased_Regul,
- regularization_parameter=3/lc,
- searching_window_ratio=3,
- similarity_window_ratio=1,
- PB_filtering_parameter=0.04
-
- )
- fistaRecon.setParameter(regularizer=regul)
- fistaRecon.setParameter(ring_lambda_R_L1=0.002, ring_alpha=21)
- fistaRecon.prepareForIteration()
- X = fistaRecon.iterate(numpy.load("../test/X.npy"))
- numpy.save("FISTA-OS-CPU_PB.npy",X)
-
-if False:
- fistaRecon = FISTAReconstructor(proj_geom,
- vol_geom,
- Sino3D ,
- weights=Weights3D,
- device=astradevice,
- Lipschitz_constant = 7.6792e8,
- number_of_iterations=50)
-
- fistaRecon.prepareForIteration()
- X = fistaRecon.iterate(numpy.load("../test/X.npy"))
- numpy.save("FISTA.npy",X)
diff --git a/src/Python/fista-recipe/build.sh b/src/Python/fista-recipe/build.sh
deleted file mode 100644
index e3f3552..0000000
--- a/src/Python/fista-recipe/build.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-if [ -z "$CIL_VERSION" ]; then
- echo "Need to set CIL_VERSION"
- exit 1
-fi
-mkdir "$SRC_DIR/ccpifista"
-cp -r "$RECIPE_DIR/.." "$SRC_DIR/ccpifista"
-
-cd $SRC_DIR/ccpifista
-
-$PYTHON setup-fista.py install
diff --git a/src/Python/fista-recipe/meta.yaml b/src/Python/fista-recipe/meta.yaml
deleted file mode 100644
index 265541f..0000000
--- a/src/Python/fista-recipe/meta.yaml
+++ /dev/null
@@ -1,29 +0,0 @@
-package:
- name: ccpi-fista
- version: {{ environ['CIL_VERSION'] }}
-
-
-build:
- preserve_egg_dir: False
- script_env:
- - CIL_VERSION
-# number: 0
-
-requirements:
- build:
- - python
- - numpy
- - setuptools
-
- run:
- - python
- - numpy
- #- astra-toolbox
- - ccpi-regularizers
-
-
-
-about:
- home: http://www.ccpi.ac.uk
- license: Apache v.2.0 license
- summary: 'CCPi Core Imaging Library (Viewer)'
diff --git a/src/Python/fista_module.cpp b/src/Python/fista_module.cpp
deleted file mode 100644
index f3add76..0000000
--- a/src/Python/fista_module.cpp
+++ /dev/null
@@ -1,1047 +0,0 @@
-/*
-This work is part of the Core Imaging Library developed by
-Visual Analytics and Imaging System Group of the Science Technology
-Facilities Council, STFC
-
-Copyright 2017 Daniil Kazantsev
-Copyright 2017 Srikanth Nagella, Edoardo Pasca
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-*/
-
-#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-
-#include <iostream>
-#include <cmath>
-
-#include <boost/python.hpp>
-#include <boost/python/numpy.hpp>
-#include "boost/tuple/tuple.hpp"
-
-#include "SplitBregman_TV_core.h"
-#include "FGP_TV_core.h"
-#include "LLT_model_core.h"
-#include "PatchBased_Regul_core.h"
-#include "TGV_PD_core.h"
-#include "utils.h"
-
-
-
-#if defined(_WIN32) || defined(_WIN32) || defined(__WIN32__) || defined(_WIN64)
-#include <windows.h>
-// this trick only if compiler is MSVC
-__if_not_exists(uint8_t) { typedef __int8 uint8_t; }
-__if_not_exists(uint16_t) { typedef __int8 uint16_t; }
-#endif
-
-namespace bp = boost::python;
-namespace np = boost::python::numpy;
-
-/*! in the Matlab implementation this is called as
-void mexFunction(
-int nlhs, mxArray *plhs[],
-int nrhs, const mxArray *prhs[])
-where:
-prhs Array of pointers to the INPUT mxArrays
-nrhs int number of INPUT mxArrays
-
-nlhs Array of pointers to the OUTPUT mxArrays
-plhs int number of OUTPUT mxArrays
-
-***********************************************************
-
-***********************************************************
-double mxGetScalar(const mxArray *pm);
-args: pm Pointer to an mxArray; cannot be a cell mxArray, a structure mxArray, or an empty mxArray.
-Returns: Pointer to the value of the first real (nonimaginary) element of the mxArray. In C, mxGetScalar returns a double.
-***********************************************************
-char *mxArrayToString(const mxArray *array_ptr);
-args: array_ptr Pointer to mxCHAR array.
-Returns: C-style string. Returns NULL on failure. Possible reasons for failure include out of memory and specifying an array that is not an mxCHAR array.
-Description: Call mxArrayToString to copy the character data of an mxCHAR array into a C-style string.
-***********************************************************
-mxClassID mxGetClassID(const mxArray *pm);
-args: pm Pointer to an mxArray
-Returns: Numeric identifier of the class (category) of the mxArray that pm points to.For user-defined types,
-mxGetClassId returns a unique value identifying the class of the array contents.
-Use mxIsClass to determine whether an array is of a specific user-defined type.
-
-mxClassID Value MATLAB Type MEX Type C Primitive Type
-mxINT8_CLASS int8 int8_T char, byte
-mxUINT8_CLASS uint8 uint8_T unsigned char, byte
-mxINT16_CLASS int16 int16_T short
-mxUINT16_CLASS uint16 uint16_T unsigned short
-mxINT32_CLASS int32 int32_T int
-mxUINT32_CLASS uint32 uint32_T unsigned int
-mxINT64_CLASS int64 int64_T long long
-mxUINT64_CLASS uint64 uint64_T unsigned long long
-mxSINGLE_CLASS single float float
-mxDOUBLE_CLASS double double double
-
-****************************************************************
-double *mxGetPr(const mxArray *pm);
-args: pm Pointer to an mxArray of type double
-Returns: Pointer to the first element of the real data. Returns NULL in C (0 in Fortran) if there is no real data.
-****************************************************************
-mxArray *mxCreateNumericArray(mwSize ndim, const mwSize *dims,
-mxClassID classid, mxComplexity ComplexFlag);
-args: ndimNumber of dimensions. If you specify a value for ndim that is less than 2, mxCreateNumericArray automatically sets the number of dimensions to 2.
-dims Dimensions array. Each element in the dimensions array contains the size of the array in that dimension.
-For example, in C, setting dims[0] to 5 and dims[1] to 7 establishes a 5-by-7 mxArray. Usually there are ndim elements in the dims array.
-classid Identifier for the class of the array, which determines the way the numerical data is represented in memory.
-For example, specifying mxINT16_CLASS in C causes each piece of numerical data in the mxArray to be represented as a 16-bit signed integer.
-ComplexFlag If the mxArray you are creating is to contain imaginary data, set ComplexFlag to mxCOMPLEX in C (1 in Fortran). Otherwise, set ComplexFlag to mxREAL in C (0 in Fortran).
-Returns: Pointer to the created mxArray, if successful. If unsuccessful in a standalone (non-MEX file) application, returns NULL in C (0 in Fortran).
-If unsuccessful in a MEX file, the MEX file terminates and returns control to the MATLAB prompt. The function is unsuccessful when there is not
-enough free heap space to create the mxArray.
-*/
-
-
-
-bp::list SplitBregman_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int methTV) {
-
- // the result is in the following list
- bp::list result;
-
- int number_of_dims, dimX, dimY, dimZ, ll, j, count;
- //const int *dim_array;
- float *A, *U = NULL, *U_old = NULL, *Dx = NULL, *Dy = NULL, *Dz = NULL, *Bx = NULL, *By = NULL, *Bz = NULL, lambda, mu, epsil, re, re1, re_old;
-
- //number_of_dims = mxGetNumberOfDimensions(prhs[0]);
- //dim_array = mxGetDimensions(prhs[0]);
-
- number_of_dims = input.get_nd();
- int dim_array[3];
-
- dim_array[0] = input.shape(0);
- dim_array[1] = input.shape(1);
- if (number_of_dims == 2) {
- dim_array[2] = -1;
- }
- else {
- dim_array[2] = input.shape(2);
- }
-
- // Parameter handling is be done in Python
- ///*Handling Matlab input data*/
- //if ((nrhs < 2) || (nrhs > 5)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1')");
-
- ///*Handling Matlab input data*/
- //A = (float *)mxGetData(prhs[0]); /*noisy image (2D/3D) */
- A = reinterpret_cast<float *>(input.get_data());
-
- //mu = (float)mxGetScalar(prhs[1]); /* regularization parameter */
- mu = (float)d_mu;
-
- //iter = 35; /* default iterations number */
-
- //epsil = 0.0001; /* default tolerance constant */
- epsil = (float)d_epsil;
- //methTV = 0; /* default isotropic TV penalty */
- //if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5)) iter = (int)mxGetScalar(prhs[2]); /* iterations number */
- //if ((nrhs == 4) || (nrhs == 5)) epsil = (float)mxGetScalar(prhs[3]); /* tolerance constant */
- //if (nrhs == 5) {
- // char *penalty_type;
- // penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
- // if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
- // if (strcmp(penalty_type, "l1") == 0) methTV = 1; /* enable 'l1' penalty */
- // mxFree(penalty_type);
- //}
- //if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); }
-
- lambda = 2.0f*mu;
- count = 1;
- re_old = 0.0f;
- /*Handling Matlab output data*/
- dimY = dim_array[0]; dimX = dim_array[1]; dimZ = dim_array[2];
-
- if (number_of_dims == 2) {
- dimZ = 1; /*2D case*/
- //U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- //U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- //Dx = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- //Dy = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- //Bx = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- //By = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]);
- np::dtype dtype = np::dtype::get_builtin<float>();
-
- np::ndarray npU = np::zeros(shape, dtype);
- np::ndarray npU_old = np::zeros(shape, dtype);
- np::ndarray npDx = np::zeros(shape, dtype);
- np::ndarray npDy = np::zeros(shape, dtype);
- np::ndarray npBx = np::zeros(shape, dtype);
- np::ndarray npBy = np::zeros(shape, dtype);
-
- U = reinterpret_cast<float *>(npU.get_data());
- U_old = reinterpret_cast<float *>(npU_old.get_data());
- Dx = reinterpret_cast<float *>(npDx.get_data());
- Dy = reinterpret_cast<float *>(npDy.get_data());
- Bx = reinterpret_cast<float *>(npBx.get_data());
- By = reinterpret_cast<float *>(npBy.get_data());
-
-
-
- copyIm(A, U, dimX, dimY, dimZ); /*initialize */
-
- /* begin outer SB iterations */
- for (ll = 0; ll < iter; ll++) {
-
- /*storing old values*/
- copyIm(U, U_old, dimX, dimY, dimZ);
-
- /*GS iteration */
- gauss_seidel2D(U, A, Dx, Dy, Bx, By, dimX, dimY, lambda, mu);
-
- if (methTV == 1) updDxDy_shrinkAniso2D(U, Dx, Dy, Bx, By, dimX, dimY, lambda);
- else updDxDy_shrinkIso2D(U, Dx, Dy, Bx, By, dimX, dimY, lambda);
-
- updBxBy2D(U, Dx, Dy, Bx, By, dimX, dimY);
-
- /* calculate norm to terminate earlier */
- re = 0.0f; re1 = 0.0f;
- for (j = 0; j < dimX*dimY*dimZ; j++)
- {
- re += pow(U_old[j] - U[j], 2);
- re1 += pow(U_old[j], 2);
- }
- re = sqrt(re) / sqrt(re1);
- if (re < epsil) count++;
- if (count > 4) break;
-
- /* check that the residual norm is decreasing */
- if (ll > 2) {
- if (re > re_old) break;
- }
- re_old = re;
- /*printf("%f %i %i \n", re, ll, count); */
-
- /*copyIm(U_old, U, dimX, dimY, dimZ); */
-
- }
- //printf("SB iterations stopped at iteration: %i\n", ll);
- result.append<np::ndarray>(npU);
- result.append<int>(ll);
- }
- if (number_of_dims == 3) {
- /*U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- U_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- Dx = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- Dy = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- Dz = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- Bx = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- By = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- Bz = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));*/
- bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
- np::dtype dtype = np::dtype::get_builtin<float>();
-
- np::ndarray npU = np::zeros(shape, dtype);
- np::ndarray npU_old = np::zeros(shape, dtype);
- np::ndarray npDx = np::zeros(shape, dtype);
- np::ndarray npDy = np::zeros(shape, dtype);
- np::ndarray npDz = np::zeros(shape, dtype);
- np::ndarray npBx = np::zeros(shape, dtype);
- np::ndarray npBy = np::zeros(shape, dtype);
- np::ndarray npBz = np::zeros(shape, dtype);
-
- U = reinterpret_cast<float *>(npU.get_data());
- U_old = reinterpret_cast<float *>(npU_old.get_data());
- Dx = reinterpret_cast<float *>(npDx.get_data());
- Dy = reinterpret_cast<float *>(npDy.get_data());
- Dz = reinterpret_cast<float *>(npDz.get_data());
- Bx = reinterpret_cast<float *>(npBx.get_data());
- By = reinterpret_cast<float *>(npBy.get_data());
- Bz = reinterpret_cast<float *>(npBz.get_data());
-
- copyIm(A, U, dimX, dimY, dimZ); /*initialize */
-
- /* begin outer SB iterations */
- for (ll = 0; ll<iter; ll++) {
-
- /*storing old values*/
- copyIm(U, U_old, dimX, dimY, dimZ);
-
- /*GS iteration */
- gauss_seidel3D(U, A, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda, mu);
-
- if (methTV == 1) updDxDyDz_shrinkAniso3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda);
- else updDxDyDz_shrinkIso3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda);
-
- updBxByBz3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ);
-
- /* calculate norm to terminate earlier */
- re = 0.0f; re1 = 0.0f;
- for (j = 0; j<dimX*dimY*dimZ; j++)
- {
- re += pow(U[j] - U_old[j], 2);
- re1 += pow(U[j], 2);
- }
- re = sqrt(re) / sqrt(re1);
- if (re < epsil) count++;
- if (count > 4) break;
-
- /* check that the residual norm is decreasing */
- if (ll > 2) {
- if (re > re_old) break;
- }
- /*printf("%f %i %i \n", re, ll, count); */
- re_old = re;
- }
- //printf("SB iterations stopped at iteration: %i\n", ll);
- result.append<np::ndarray>(npU);
- result.append<int>(ll);
- }
- return result;
-
- }
-
-
-
-bp::list FGP_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int methTV) {
-
- // the result is in the following list
- bp::list result;
-
- int number_of_dims, dimX, dimY, dimZ, ll, j, count;
- float *A, *D = NULL, *D_old = NULL, *P1 = NULL, *P2 = NULL, *P3 = NULL, *P1_old = NULL, *P2_old = NULL, *P3_old = NULL, *R1 = NULL, *R2 = NULL, *R3 = NULL;
- float lambda, tk, tkp1, re, re1, re_old, epsil, funcval;
-
- //number_of_dims = mxGetNumberOfDimensions(prhs[0]);
- //dim_array = mxGetDimensions(prhs[0]);
-
- number_of_dims = input.get_nd();
- int dim_array[3];
-
- dim_array[0] = input.shape(0);
- dim_array[1] = input.shape(1);
- if (number_of_dims == 2) {
- dim_array[2] = -1;
- }
- else {
- dim_array[2] = input.shape(2);
- }
- // Parameter handling is be done in Python
- ///*Handling Matlab input data*/
- //if ((nrhs < 2) || (nrhs > 5)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1')");
-
- ///*Handling Matlab input data*/
- //A = (float *)mxGetData(prhs[0]); /*noisy image (2D/3D) */
- A = reinterpret_cast<float *>(input.get_data());
-
- //mu = (float)mxGetScalar(prhs[1]); /* regularization parameter */
- lambda = (float)d_mu;
-
- //iter = 35; /* default iterations number */
-
- //epsil = 0.0001; /* default tolerance constant */
- epsil = (float)d_epsil;
- //methTV = 0; /* default isotropic TV penalty */
- //if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5)) iter = (int)mxGetScalar(prhs[2]); /* iterations number */
- //if ((nrhs == 4) || (nrhs == 5)) epsil = (float)mxGetScalar(prhs[3]); /* tolerance constant */
- //if (nrhs == 5) {
- // char *penalty_type;
- // penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
- // if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
- // if (strcmp(penalty_type, "l1") == 0) methTV = 1; /* enable 'l1' penalty */
- // mxFree(penalty_type);
- //}
- //if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); }
-
- //plhs[1] = mxCreateNumericMatrix(1, 1, mxSINGLE_CLASS, mxREAL);
- bp::tuple shape1 = bp::make_tuple(dim_array[0], dim_array[1]);
- np::dtype dtype = np::dtype::get_builtin<float>();
- np::ndarray out1 = np::zeros(shape1, dtype);
-
- //float *funcvalA = (float *)mxGetData(plhs[1]);
- float * funcvalA = reinterpret_cast<float *>(out1.get_data());
- //if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); }
-
- /*Handling Matlab output data*/
- dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
-
- tk = 1.0f;
- tkp1 = 1.0f;
- count = 1;
- re_old = 0.0f;
-
- if (number_of_dims == 2) {
- dimZ = 1; /*2D case*/
- /*D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- D_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- P1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- P2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- P1_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- P2_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- R1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- R2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));*/
-
- bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]);
- np::dtype dtype = np::dtype::get_builtin<float>();
-
-
- np::ndarray npD = np::zeros(shape, dtype);
- np::ndarray npD_old = np::zeros(shape, dtype);
- np::ndarray npP1 = np::zeros(shape, dtype);
- np::ndarray npP2 = np::zeros(shape, dtype);
- np::ndarray npP1_old = np::zeros(shape, dtype);
- np::ndarray npP2_old = np::zeros(shape, dtype);
- np::ndarray npR1 = np::zeros(shape, dtype);
- np::ndarray npR2 = np::zeros(shape, dtype);
-
- D = reinterpret_cast<float *>(npD.get_data());
- D_old = reinterpret_cast<float *>(npD_old.get_data());
- P1 = reinterpret_cast<float *>(npP1.get_data());
- P2 = reinterpret_cast<float *>(npP2.get_data());
- P1_old = reinterpret_cast<float *>(npP1_old.get_data());
- P2_old = reinterpret_cast<float *>(npP2_old.get_data());
- R1 = reinterpret_cast<float *>(npR1.get_data());
- R2 = reinterpret_cast<float *>(npR2.get_data());
-
- /* begin iterations */
- for (ll = 0; ll<iter; ll++) {
- /* computing the gradient of the objective function */
- Obj_func2D(A, D, R1, R2, lambda, dimX, dimY);
-
- /*Taking a step towards minus of the gradient*/
- Grad_func2D(P1, P2, D, R1, R2, lambda, dimX, dimY);
-
-
-
-
- /*updating R and t*/
- tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
- Rupd_func2D(P1, P1_old, P2, P2_old, R1, R2, tkp1, tk, dimX, dimY);
-
- /* calculate norm */
- re = 0.0f; re1 = 0.0f;
- for (j = 0; j<dimX*dimY*dimZ; j++)
- {
- re += pow(D[j] - D_old[j], 2);
- re1 += pow(D[j], 2);
- }
- re = sqrt(re) / sqrt(re1);
- if (re < epsil) count++;
- if (count > 3) {
- Obj_func2D(A, D, P1, P2, lambda, dimX, dimY);
- funcval = 0.0f;
- for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
- //funcvalA[0] = sqrt(funcval);
- float fv = sqrt(funcval);
- std::memcpy(funcvalA, &fv, sizeof(float));
- break;
- }
-
- /* check that the residual norm is decreasing */
- if (ll > 2) {
- if (re > re_old) {
- Obj_func2D(A, D, P1, P2, lambda, dimX, dimY);
- funcval = 0.0f;
- for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
- //funcvalA[0] = sqrt(funcval);
- float fv = sqrt(funcval);
- std::memcpy(funcvalA, &fv, sizeof(float));
- break;
- }
- }
- re_old = re;
- /*printf("%f %i %i \n", re, ll, count); */
-
- /*storing old values*/
- copyIm(D, D_old, dimX, dimY, dimZ);
- copyIm(P1, P1_old, dimX, dimY, dimZ);
- copyIm(P2, P2_old, dimX, dimY, dimZ);
- tk = tkp1;
-
- /* calculating the objective function value */
- if (ll == (iter - 1)) {
- Obj_func2D(A, D, P1, P2, lambda, dimX, dimY);
- funcval = 0.0f;
- for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
- //funcvalA[0] = sqrt(funcval);
- float fv = sqrt(funcval);
- std::memcpy(funcvalA, &fv, sizeof(float));
- }
- }
- //printf("FGP-TV iterations stopped at iteration %i with the function value %f \n", ll, funcvalA[0]);
- result.append<np::ndarray>(npD);
- result.append<np::ndarray>(out1);
- result.append<int>(ll);
- }
- if (number_of_dims == 3) {
- /*D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- D_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- P1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- P2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- P3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- P1_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- P2_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- P3_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- R1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- R2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- R3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));*/
- bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
- np::dtype dtype = np::dtype::get_builtin<float>();
-
- np::ndarray npD = np::zeros(shape, dtype);
- np::ndarray npD_old = np::zeros(shape, dtype);
- np::ndarray npP1 = np::zeros(shape, dtype);
- np::ndarray npP2 = np::zeros(shape, dtype);
- np::ndarray npP3 = np::zeros(shape, dtype);
- np::ndarray npP1_old = np::zeros(shape, dtype);
- np::ndarray npP2_old = np::zeros(shape, dtype);
- np::ndarray npP3_old = np::zeros(shape, dtype);
- np::ndarray npR1 = np::zeros(shape, dtype);
- np::ndarray npR2 = np::zeros(shape, dtype);
- np::ndarray npR3 = np::zeros(shape, dtype);
-
- D = reinterpret_cast<float *>(npD.get_data());
- D_old = reinterpret_cast<float *>(npD_old.get_data());
- P1 = reinterpret_cast<float *>(npP1.get_data());
- P2 = reinterpret_cast<float *>(npP2.get_data());
- P3 = reinterpret_cast<float *>(npP3.get_data());
- P1_old = reinterpret_cast<float *>(npP1_old.get_data());
- P2_old = reinterpret_cast<float *>(npP2_old.get_data());
- P3_old = reinterpret_cast<float *>(npP3_old.get_data());
- R1 = reinterpret_cast<float *>(npR1.get_data());
- R2 = reinterpret_cast<float *>(npR2.get_data());
- R3 = reinterpret_cast<float *>(npR3.get_data());
- /* begin iterations */
- for (ll = 0; ll<iter; ll++) {
- /* computing the gradient of the objective function */
- Obj_func3D(A, D, R1, R2, R3, lambda, dimX, dimY, dimZ);
- /*Taking a step towards minus of the gradient*/
- Grad_func3D(P1, P2, P3, D, R1, R2, R3, lambda, dimX, dimY, dimZ);
-
- /* projection step */
- Proj_func3D(P1, P2, P3, dimX, dimY, dimZ);
-
- /*updating R and t*/
- tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
- Rupd_func3D(P1, P1_old, P2, P2_old, P3, P3_old, R1, R2, R3, tkp1, tk, dimX, dimY, dimZ);
-
- /* calculate norm - stopping rules*/
- re = 0.0f; re1 = 0.0f;
- for (j = 0; j<dimX*dimY*dimZ; j++)
- {
- re += pow(D[j] - D_old[j], 2);
- re1 += pow(D[j], 2);
- }
- re = sqrt(re) / sqrt(re1);
- /* stop if the norm residual is less than the tolerance EPS */
- if (re < epsil) count++;
- if (count > 3) {
- Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ);
- funcval = 0.0f;
- for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
- //funcvalA[0] = sqrt(funcval);
- float fv = sqrt(funcval);
- std::memcpy(funcvalA, &fv, sizeof(float));
- break;
- }
-
- /* check that the residual norm is decreasing */
- if (ll > 2) {
- if (re > re_old) {
- Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ);
- funcval = 0.0f;
- for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
- //funcvalA[0] = sqrt(funcval);
- float fv = sqrt(funcval);
- std::memcpy(funcvalA, &fv, sizeof(float));
- break;
- }
- }
-
- re_old = re;
- /*printf("%f %i %i \n", re, ll, count); */
-
- /*storing old values*/
- copyIm(D, D_old, dimX, dimY, dimZ);
- copyIm(P1, P1_old, dimX, dimY, dimZ);
- copyIm(P2, P2_old, dimX, dimY, dimZ);
- copyIm(P3, P3_old, dimX, dimY, dimZ);
- tk = tkp1;
-
- if (ll == (iter - 1)) {
- Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ);
- funcval = 0.0f;
- for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
- //funcvalA[0] = sqrt(funcval);
- float fv = sqrt(funcval);
- std::memcpy(funcvalA, &fv, sizeof(float));
- }
-
- }
- //printf("FGP-TV iterations stopped at iteration %i with the function value %f \n", ll, funcvalA[0]);
- result.append<np::ndarray>(npD);
- result.append<np::ndarray>(out1);
- result.append<int>(ll);
- }
-
- return result;
-}
-
-bp::list LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher) {
- // the result is in the following list
- bp::list result;
-
- int number_of_dims, dimX, dimY, dimZ, ll, j, count;
- //const int *dim_array;
- float *U0, *U = NULL, *U_old = NULL, *D1 = NULL, *D2 = NULL, *D3 = NULL, lambda, tau, re, re1, epsil, re_old;
- unsigned short *Map = NULL;
-
- number_of_dims = input.get_nd();
- int dim_array[3];
-
- dim_array[0] = input.shape(0);
- dim_array[1] = input.shape(1);
- if (number_of_dims == 2) {
- dim_array[2] = -1;
- }
- else {
- dim_array[2] = input.shape(2);
- }
-
- ///*Handling Matlab input data*/
- //U0 = (float *)mxGetData(prhs[0]); /*origanal noise image/volume*/
- //if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input in single precision is required"); }
- //lambda = (float)mxGetScalar(prhs[1]); /*regularization parameter*/
- //tau = (float)mxGetScalar(prhs[2]); /* time-step */
- //iter = (int)mxGetScalar(prhs[3]); /*iterations number*/
- //epsil = (float)mxGetScalar(prhs[4]); /* tolerance constant */
- //switcher = (int)mxGetScalar(prhs[5]); /*switch on (1) restrictive smoothing in Z dimension*/
-
- U0 = reinterpret_cast<float *>(input.get_data());
- lambda = (float)d_lambda;
- tau = (float)d_tau;
- // iter is passed as parameter
- epsil = (float)d_epsil;
- // switcher is passed as parameter
- /*Handling Matlab output data*/
- dimX = dim_array[0]; dimY = dim_array[1]; dimZ = 1;
-
- if (number_of_dims == 2) {
- /*2D case*/
- /*U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- D1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- D2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));*/
-
- bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]);
- np::dtype dtype = np::dtype::get_builtin<float>();
-
-
- np::ndarray npU = np::zeros(shape, dtype);
- np::ndarray npU_old = np::zeros(shape, dtype);
- np::ndarray npD1 = np::zeros(shape, dtype);
- np::ndarray npD2 = np::zeros(shape, dtype);
-
-
- U = reinterpret_cast<float *>(npU.get_data());
- U_old = reinterpret_cast<float *>(npU_old.get_data());
- D1 = reinterpret_cast<float *>(npD1.get_data());
- D2 = reinterpret_cast<float *>(npD2.get_data());
-
- /*Copy U0 to U*/
- copyIm(U0, U, dimX, dimY, dimZ);
-
- count = 1;
- re_old = 0.0f;
-
- for (ll = 0; ll < iter; ll++) {
-
- copyIm(U, U_old, dimX, dimY, dimZ);
-
- /*estimate inner derrivatives */
- der2D(U, D1, D2, dimX, dimY, dimZ);
- /* calculate div^2 and update */
- div_upd2D(U0, U, D1, D2, dimX, dimY, dimZ, lambda, tau);
-
- /* calculate norm to terminate earlier */
- re = 0.0f; re1 = 0.0f;
- for (j = 0; j<dimX*dimY*dimZ; j++)
- {
- re += pow(U_old[j] - U[j], 2);
- re1 += pow(U_old[j], 2);
- }
- re = sqrt(re) / sqrt(re1);
- if (re < epsil) count++;
- if (count > 4) break;
-
- /* check that the residual norm is decreasing */
- if (ll > 2) {
- if (re > re_old) break;
- }
- re_old = re;
-
- } /*end of iterations*/
- //printf("HO iterations stopped at iteration: %i\n", ll);
-
- result.append<np::ndarray>(npU);
- }
- else if (number_of_dims == 3) {
- /*3D case*/
- dimZ = dim_array[2];
- /*U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- U_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- D1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- D2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- D3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- if (switcher != 0) {
- Map = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(3, dim_array, mxUINT16_CLASS, mxREAL));
- }*/
- bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
- np::dtype dtype = np::dtype::get_builtin<float>();
-
-
- np::ndarray npU = np::zeros(shape, dtype);
- np::ndarray npU_old = np::zeros(shape, dtype);
- np::ndarray npD1 = np::zeros(shape, dtype);
- np::ndarray npD2 = np::zeros(shape, dtype);
- np::ndarray npD3 = np::zeros(shape, dtype);
- np::ndarray npMap = np::zeros(shape, np::dtype::get_builtin<unsigned short>());
- Map = reinterpret_cast<unsigned short *>(npMap.get_data());
- if (switcher != 0) {
- //Map = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(3, dim_array, mxUINT16_CLASS, mxREAL));
-
- Map = reinterpret_cast<unsigned short *>(npMap.get_data());
- }
-
- U = reinterpret_cast<float *>(npU.get_data());
- U_old = reinterpret_cast<float *>(npU_old.get_data());
- D1 = reinterpret_cast<float *>(npD1.get_data());
- D2 = reinterpret_cast<float *>(npD2.get_data());
- D3 = reinterpret_cast<float *>(npD2.get_data());
-
- /*Copy U0 to U*/
- copyIm(U0, U, dimX, dimY, dimZ);
-
- count = 1;
- re_old = 0.0f;
-
-
- if (switcher == 1) {
- /* apply restrictive smoothing */
- calcMap(U, Map, dimX, dimY, dimZ);
- /*clear outliers */
- cleanMap(Map, dimX, dimY, dimZ);
- }
- for (ll = 0; ll < iter; ll++) {
-
- copyIm(U, U_old, dimX, dimY, dimZ);
-
- /*estimate inner derrivatives */
- der3D(U, D1, D2, D3, dimX, dimY, dimZ);
- /* calculate div^2 and update */
- div_upd3D(U0, U, D1, D2, D3, Map, switcher, dimX, dimY, dimZ, lambda, tau);
-
- /* calculate norm to terminate earlier */
- re = 0.0f; re1 = 0.0f;
- for (j = 0; j<dimX*dimY*dimZ; j++)
- {
- re += pow(U_old[j] - U[j], 2);
- re1 += pow(U_old[j], 2);
- }
- re = sqrt(re) / sqrt(re1);
- if (re < epsil) count++;
- if (count > 4) break;
-
- /* check that the residual norm is decreasing */
- if (ll > 2) {
- if (re > re_old) break;
- }
- re_old = re;
-
- } /*end of iterations*/
- //printf("HO iterations stopped at iteration: %i\n", ll);
- result.append<np::ndarray>(npU);
- if (switcher != 0) result.append<np::ndarray>(npMap);
-
- }
- return result;
-}
-
-
-bp::list PatchBased_Regul(np::ndarray input, double d_lambda, int SearchW_real, int SimilW, double d_h) {
- // the result is in the following list
- bp::list result;
-
- int N, M, Z, numdims, SearchW, /*SimilW, SearchW_real,*/ padXY, newsizeX, newsizeY, newsizeZ, switchpad_crop;
- //const int *dims;
- float *A, *B = NULL, *Ap = NULL, *Bp = NULL, h, lambda;
-
- numdims = input.get_nd();
- int dims[3];
-
- dims[0] = input.shape(0);
- dims[1] = input.shape(1);
- if (numdims == 2) {
- dims[2] = -1;
- }
- else {
- dims[2] = input.shape(2);
- }
- /*numdims = mxGetNumberOfDimensions(prhs[0]);
- dims = mxGetDimensions(prhs[0]);*/
-
- N = dims[0];
- M = dims[1];
- Z = dims[2];
-
- //if ((numdims < 2) || (numdims > 3)) { mexErrMsgTxt("The input should be 2D image or 3D volume"); }
- //if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input in single precision is required"); }
-
- //if (nrhs != 5) mexErrMsgTxt("Five inputs reqired: Image(2D,3D), SearchW, SimilW, Threshold, Regularization parameter");
-
- ///*Handling inputs*/
- //A = (float *)mxGetData(prhs[0]); /* the image to regularize/filter */
- A = reinterpret_cast<float *>(input.get_data());
- //SearchW_real = (int)mxGetScalar(prhs[1]); /* the searching window ratio */
- //SimilW = (int)mxGetScalar(prhs[2]); /* the similarity window ratio */
- //h = (float)mxGetScalar(prhs[3]); /* parameter for the PB filtering function */
- //lambda = (float)mxGetScalar(prhs[4]); /* regularization parameter */
-
- //if (h <= 0) mexErrMsgTxt("Parmeter for the PB penalty function should be > 0");
- //if (lambda <= 0) mexErrMsgTxt(" Regularization parmeter should be > 0");
-
- lambda = (float)d_lambda;
- h = (float)d_h;
- SearchW = SearchW_real + 2 * SimilW;
-
- /* SearchW_full = 2*SearchW + 1; */ /* the full searching window size */
- /* SimilW_full = 2*SimilW + 1; */ /* the full similarity window size */
-
-
- padXY = SearchW + 2 * SimilW; /* padding sizes */
- newsizeX = N + 2 * (padXY); /* the X size of the padded array */
- newsizeY = M + 2 * (padXY); /* the Y size of the padded array */
- newsizeZ = Z + 2 * (padXY); /* the Z size of the padded array */
- int N_dims[] = { newsizeX, newsizeY, newsizeZ };
- /******************************2D case ****************************/
- if (numdims == 2) {
- ///*Handling output*/
- //B = (float*)mxGetData(plhs[0] = mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
- ///*allocating memory for the padded arrays */
- //Ap = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
- //Bp = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
- ///**************************************************************************/
-
- bp::tuple shape = bp::make_tuple(N, M);
- np::dtype dtype = np::dtype::get_builtin<float>();
-
- np::ndarray npB = np::zeros(shape, dtype);
-
- shape = bp::make_tuple(newsizeX, newsizeY);
- np::ndarray npAp = np::zeros(shape, dtype);
- np::ndarray npBp = np::zeros(shape, dtype);
- B = reinterpret_cast<float *>(npB.get_data());
- Ap = reinterpret_cast<float *>(npAp.get_data());
- Bp = reinterpret_cast<float *>(npBp.get_data());
-
- /*Perform padding of image A to the size of [newsizeX * newsizeY] */
- switchpad_crop = 0; /*padding*/
- pad_crop(A, Ap, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
-
- /* Do PB regularization with the padded array */
- PB_FUNC2D(Ap, Bp, newsizeY, newsizeX, padXY, SearchW, SimilW, (float)h, (float)lambda);
-
- switchpad_crop = 1; /*cropping*/
- pad_crop(Bp, B, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
-
- result.append<np::ndarray>(npB);
- }
- else
- {
- /******************************3D case ****************************/
- ///*Handling output*/
- //B = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dims, mxSINGLE_CLASS, mxREAL));
- ///*allocating memory for the padded arrays */
- //Ap = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
- //Bp = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
- /**************************************************************************/
- bp::tuple shape = bp::make_tuple(dims[0], dims[1], dims[2]);
- bp::tuple shape_AB = bp::make_tuple(N_dims[0], N_dims[1], N_dims[2]);
- np::dtype dtype = np::dtype::get_builtin<float>();
-
- np::ndarray npB = np::zeros(shape, dtype);
- np::ndarray npAp = np::zeros(shape_AB, dtype);
- np::ndarray npBp = np::zeros(shape_AB, dtype);
- B = reinterpret_cast<float *>(npB.get_data());
- Ap = reinterpret_cast<float *>(npAp.get_data());
- Bp = reinterpret_cast<float *>(npBp.get_data());
- /*Perform padding of image A to the size of [newsizeX * newsizeY * newsizeZ] */
- switchpad_crop = 0; /*padding*/
- pad_crop(A, Ap, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
-
- /* Do PB regularization with the padded array */
- PB_FUNC3D(Ap, Bp, newsizeY, newsizeX, newsizeZ, padXY, SearchW, SimilW, (float)h, (float)lambda);
-
- switchpad_crop = 1; /*cropping*/
- pad_crop(Bp, B, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
-
- result.append<np::ndarray>(npB);
- } /*end else ndims*/
-
- return result;
-}
-
-bp::list TGV_PD(np::ndarray input, double d_lambda, double d_alpha1, double d_alpha0, int iter) {
- // the result is in the following list
- bp::list result;
- int number_of_dims, /*iter,*/ dimX, dimY, dimZ, ll;
- //const int *dim_array;
- float *A, *U, *U_old, *P1, *P2, *Q1, *Q2, *Q3, *V1, *V1_old, *V2, *V2_old, lambda, L2, tau, sigma, alpha1, alpha0;
-
- //number_of_dims = mxGetNumberOfDimensions(prhs[0]);
- //dim_array = mxGetDimensions(prhs[0]);
- number_of_dims = input.get_nd();
- int dim_array[3];
-
- dim_array[0] = input.shape(0);
- dim_array[1] = input.shape(1);
- if (number_of_dims == 2) {
- dim_array[2] = -1;
- }
- else {
- dim_array[2] = input.shape(2);
- }
- /*Handling Matlab input data*/
- //A = (float *)mxGetData(prhs[0]); /*origanal noise image/volume*/
- //if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input in single precision is required"); }
-
- A = reinterpret_cast<float *>(input.get_data());
-
- //lambda = (float)mxGetScalar(prhs[1]); /*regularization parameter*/
- //alpha1 = (float)mxGetScalar(prhs[2]); /*first-order term*/
- //alpha0 = (float)mxGetScalar(prhs[3]); /*second-order term*/
- //iter = (int)mxGetScalar(prhs[4]); /*iterations number*/
- //if (nrhs != 5) mexErrMsgTxt("Five input parameters is reqired: Image(2D/3D), Regularization parameter, alpha1, alpha0, Iterations");
- lambda = (float)d_lambda;
- alpha1 = (float)d_alpha1;
- alpha0 = (float)d_alpha0;
-
- /*Handling Matlab output data*/
- dimX = dim_array[0]; dimY = dim_array[1];
-
- if (number_of_dims == 2) {
- /*2D case*/
- dimZ = 1;
- bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]);
- np::dtype dtype = np::dtype::get_builtin<float>();
-
- np::ndarray npU = np::zeros(shape, dtype);
- np::ndarray npP1 = np::zeros(shape, dtype);
- np::ndarray npP2 = np::zeros(shape, dtype);
- np::ndarray npQ1 = np::zeros(shape, dtype);
- np::ndarray npQ2 = np::zeros(shape, dtype);
- np::ndarray npQ3 = np::zeros(shape, dtype);
- np::ndarray npV1 = np::zeros(shape, dtype);
- np::ndarray npV1_old = np::zeros(shape, dtype);
- np::ndarray npV2 = np::zeros(shape, dtype);
- np::ndarray npV2_old = np::zeros(shape, dtype);
- np::ndarray npU_old = np::zeros(shape, dtype);
-
- U = reinterpret_cast<float *>(npU.get_data());
- U_old = reinterpret_cast<float *>(npU_old.get_data());
- P1 = reinterpret_cast<float *>(npP1.get_data());
- P2 = reinterpret_cast<float *>(npP2.get_data());
- Q1 = reinterpret_cast<float *>(npQ1.get_data());
- Q2 = reinterpret_cast<float *>(npQ2.get_data());
- Q3 = reinterpret_cast<float *>(npQ3.get_data());
- V1 = reinterpret_cast<float *>(npV1.get_data());
- V1_old = reinterpret_cast<float *>(npV1_old.get_data());
- V2 = reinterpret_cast<float *>(npV2.get_data());
- V2_old = reinterpret_cast<float *>(npV2_old.get_data());
- //U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-
- /*dual variables*/
- /*P1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- P2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-
- Q1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- Q2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- Q3 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-
- U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-
- V1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- V1_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- V2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- V2_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));*/
- /*printf("%i \n", i);*/
- L2 = 12.0; /*Lipshitz constant*/
- tau = 1.0 / pow(L2, 0.5);
- sigma = 1.0 / pow(L2, 0.5);
-
- /*Copy A to U*/
- copyIm(A, U, dimX, dimY, dimZ);
- /* Here primal-dual iterations begin for 2D */
- for (ll = 0; ll < iter; ll++) {
-
- /* Calculate Dual Variable P */
- DualP_2D(U, V1, V2, P1, P2, dimX, dimY, dimZ, sigma);
-
- /*Projection onto convex set for P*/
- ProjP_2D(P1, P2, dimX, dimY, dimZ, alpha1);
-
- /* Calculate Dual Variable Q */
- DualQ_2D(V1, V2, Q1, Q2, Q3, dimX, dimY, dimZ, sigma);
-
- /*Projection onto convex set for Q*/
- ProjQ_2D(Q1, Q2, Q3, dimX, dimY, dimZ, alpha0);
-
- /*saving U into U_old*/
- copyIm(U, U_old, dimX, dimY, dimZ);
-
- /*adjoint operation -> divergence and projection of P*/
- DivProjP_2D(U, A, P1, P2, dimX, dimY, dimZ, lambda, tau);
-
- /*get updated solution U*/
- newU(U, U_old, dimX, dimY, dimZ);
-
- /*saving V into V_old*/
- copyIm(V1, V1_old, dimX, dimY, dimZ);
- copyIm(V2, V2_old, dimX, dimY, dimZ);
-
- /* upd V*/
- UpdV_2D(V1, V2, P1, P2, Q1, Q2, Q3, dimX, dimY, dimZ, tau);
-
- /*get new V*/
- newU(V1, V1_old, dimX, dimY, dimZ);
- newU(V2, V2_old, dimX, dimY, dimZ);
- } /*end of iterations*/
-
- result.append<np::ndarray>(npU);
- }
-
-
-
-
- return result;
-}
-
-BOOST_PYTHON_MODULE(cpu_regularizers)
-{
- np::initialize();
-
- //To specify that this module is a package
- bp::object package = bp::scope();
- package.attr("__path__") = "cpu_regularizers";
-
- np::dtype dt1 = np::dtype::get_builtin<uint8_t>();
- np::dtype dt2 = np::dtype::get_builtin<uint16_t>();
-
- def("SplitBregman_TV", SplitBregman_TV);
- def("FGP_TV", FGP_TV);
- def("LLT_model", LLT_model);
- def("PatchBased_Regul", PatchBased_Regul);
- def("TGV_PD", TGV_PD);
-}
diff --git a/src/Python/setup-fista.py.in b/src/Python/setup-fista.py.in
deleted file mode 100644
index c5c9f4d..0000000
--- a/src/Python/setup-fista.py.in
+++ /dev/null
@@ -1,27 +0,0 @@
-from distutils.core import setup
-#from setuptools import setup, find_packages
-import os
-
-cil_version=os.environ['CIL_VERSION']
-if cil_version == '':
- print("Please set the environmental variable CIL_VERSION")
- sys.exit(1)
-
-setup(
- name="ccpi-fista",
- version=cil_version,
- packages=['ccpi','ccpi.reconstruction'],
- install_requires=['numpy'],
-
- zip_safe = False,
-
- # metadata for upload to PyPI
- author="Edoardo Pasca",
- author_email="edo.paskino@gmail.com",
- description='CCPi Core Imaging Library - FISTA Reconstructor module',
- license="Apache v2.0",
- keywords="tomography interative reconstruction",
- url="http://www.ccpi.ac.uk", # project home page, if any
-
- # could also include long_description, download_url, classifiers, etc.
-)
diff --git a/src/Python/setup.py b/src/Python/setup.py
deleted file mode 100644
index 154f979..0000000
--- a/src/Python/setup.py
+++ /dev/null
@@ -1,64 +0,0 @@
-#!/usr/bin/env python
-
-import setuptools
-from distutils.core import setup
-from distutils.extension import Extension
-from Cython.Distutils import build_ext
-
-import os
-import sys
-import numpy
-import platform
-
-cil_version=os.environ['CIL_VERSION']
-if cil_version == '':
- print("Please set the environmental variable CIL_VERSION")
- sys.exit(1)
-
-library_include_path = ""
-library_lib_path = ""
-try:
- library_include_path = os.environ['LIBRARY_INC']
- library_lib_path = os.environ['LIBRARY_LIB']
-except:
- library_include_path = os.environ['PREFIX']+'/include'
- pass
-
-extra_include_dirs = [numpy.get_include(), library_include_path]
-extra_library_dirs = [library_include_path+"/../lib", "C:\\Apps\\Miniconda2\\envs\\cil27\\Library\\lib"]
-extra_compile_args = ['-fopenmp','-O2', '-funsigned-char', '-Wall', '-std=c++0x']
-extra_libraries = []
-if platform.system() == 'Windows':
- extra_compile_args[0:] = ['/DWIN32','/EHsc','/DBOOST_ALL_NO_LIB' , '/openmp' ]
- extra_include_dirs += ["..\\..\\main_func\\regularizers_CPU\\","."]
- if sys.version_info.major == 3 :
- extra_libraries += ['boost_python3-vc140-mt-1_64', 'boost_numpy3-vc140-mt-1_64']
- else:
- extra_libraries += ['boost_python-vc90-mt-1_64', 'boost_numpy-vc90-mt-1_64']
-else:
- extra_include_dirs += ["../../main_func/regularizers_CPU","."]
- if sys.version_info.major == 3:
- extra_libraries += ['boost_python3', 'boost_numpy3','gomp']
- else:
- extra_libraries += ['boost_python', 'boost_numpy','gomp']
-
-setup(
- name='ccpi',
- description='CCPi Core Imaging Library - FISTA Reconstruction Module',
- version=cil_version,
- cmdclass = {'build_ext': build_ext},
- ext_modules = [Extension("ccpi.imaging.cpu_regularizers",
- sources=["fista_module.cpp",
- "../../main_func/regularizers_CPU/FGP_TV_core.c",
- "../../main_func/regularizers_CPU/SplitBregman_TV_core.c",
- "../../main_func/regularizers_CPU/LLT_model_core.c",
- "../../main_func/regularizers_CPU/PatchBased_Regul_core.c",
- "../../main_func/regularizers_CPU/TGV_PD_core.c",
- "../../main_func/regularizers_CPU/utils.c"
- ],
- include_dirs=extra_include_dirs, library_dirs=extra_library_dirs, extra_compile_args=extra_compile_args, libraries=extra_libraries ),
-
- ],
- zip_safe = False,
- packages = {'ccpi','ccpi.fistareconstruction'},
-)
diff --git a/src/Python/setup.py.in b/src/Python/setup.py.in
deleted file mode 100644
index 12e8af1..0000000
--- a/src/Python/setup.py.in
+++ /dev/null
@@ -1,69 +0,0 @@
-#!/usr/bin/env python
-
-import setuptools
-from distutils.core import setup
-from distutils.extension import Extension
-from Cython.Distutils import build_ext
-
-import os
-import sys
-import numpy
-import platform
-
-cil_version=@CIL_VERSION@
-
-library_include_path = ""
-library_lib_path = ""
-try:
- library_include_path = os.environ['LIBRARY_INC']
- library_lib_path = os.environ['LIBRARY_LIB']
-except:
- library_include_path = os.environ['PREFIX']+'/include'
- pass
-
-extra_include_dirs = [numpy.get_include(), library_include_path]
-extra_library_dirs = [os.path.join(library_include_path, "..", "lib")]
-extra_compile_args = ['-fopenmp','-O2', '-funsigned-char', '-Wall', '-std=c++0x']
-extra_libraries = []
-extra_include_dirs += [os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" , "regularizers_CPU"),
- os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" , "regularizers_GPU") ,
- "@CMAKE_CURRENT_SOURCE_DIR@"]
-
-if platform.system() == 'Windows':
- extra_compile_args[0:] = ['/DWIN32','/EHsc','/DBOOST_ALL_NO_LIB' , '/openmp' ]
-
- if sys.version_info.major == 3 :
- extra_libraries += ['boost_python3-vc140-mt-1_64', 'boost_numpy3-vc140-mt-1_64']
- else:
- extra_libraries += ['boost_python-vc90-mt-1_64', 'boost_numpy-vc90-mt-1_64']
-else:
- if sys.version_info.major == 3:
- extra_libraries += ['boost_python3', 'boost_numpy3','gomp']
- else:
- extra_libraries += ['boost_python', 'boost_numpy','gomp']
-
-setup(
- name='ccpi',
- description='CCPi Core Imaging Library - Image Regularizers',
- version=cil_version,
- cmdclass = {'build_ext': build_ext},
- ext_modules = [Extension("ccpi.imaging.cpu_regularizers",
- sources=[os.path.join("@CMAKE_CURRENT_SOURCE_DIR@" , "fista_module.cpp" ),
- os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" , "regularizers_CPU", "FGP_TV_core.c"),
- os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" , "regularizers_CPU", "SplitBregman_TV_core.c"),
- os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" , "regularizers_CPU", "LLT_model_core.c"),
- os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" , "regularizers_CPU", "PatchBased_Regul_core.c"),
- os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" , "regularizers_CPU", "TGV_PD_core.c"),
- os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" , "regularizers_CPU", "utils.c")
- ],
- include_dirs=extra_include_dirs,
- library_dirs=extra_library_dirs,
- extra_compile_args=extra_compile_args,
- libraries=extra_libraries ),
-
- ],
- zip_safe = False,
- packages = {'ccpi','ccpi.imaging'},
-)
-
-
diff --git a/src/Python/setup_test.py b/src/Python/setup_test.py
deleted file mode 100644
index 7c86175..0000000
--- a/src/Python/setup_test.py
+++ /dev/null
@@ -1,58 +0,0 @@
-#!/usr/bin/env python
-
-import setuptools
-from distutils.core import setup
-from distutils.extension import Extension
-from Cython.Distutils import build_ext
-
-import os
-import sys
-import numpy
-import platform
-
-cil_version=os.environ['CIL_VERSION']
-if cil_version == '':
- print("Please set the environmental variable CIL_VERSION")
- sys.exit(1)
-
-library_include_path = ""
-library_lib_path = ""
-try:
- library_include_path = os.environ['LIBRARY_INC']
- library_lib_path = os.environ['LIBRARY_LIB']
-except:
- library_include_path = os.environ['PREFIX']+'/include'
- pass
-
-extra_include_dirs = [numpy.get_include(), library_include_path]
-extra_library_dirs = [library_include_path+"/../lib", "C:\\Apps\\Miniconda2\\envs\\cil27\\Library\\lib"]
-extra_compile_args = ['-fopenmp','-O2', '-funsigned-char', '-Wall', '-std=c++0x']
-extra_libraries = []
-if platform.system() == 'Windows':
- extra_compile_args[0:] = ['/DWIN32','/EHsc','/DBOOST_ALL_NO_LIB']
- #extra_include_dirs += ["..\\ContourTree\\", "..\\win32\\" , "..\\Core\\","."]
- if sys.version_info.major == 3 :
- extra_libraries += ['boost_python3-vc140-mt-1_64', 'boost_numpy3-vc140-mt-1_64']
- else:
- extra_libraries += ['boost_python-vc90-mt-1_64', 'boost_numpy-vc90-mt-1_64']
-else:
- #extra_include_dirs += ["../ContourTree/", "../Core/","."]
- if sys.version_info.major == 3:
- extra_libraries += ['boost_python3', 'boost_numpy3','gomp']
- else:
- extra_libraries += ['boost_python', 'boost_numpy','gomp']
-
-setup(
- name='ccpi',
- description='CCPi Core Imaging Library - FISTA Reconstruction Module',
- version=cil_version,
- cmdclass = {'build_ext': build_ext},
- ext_modules = [Extension("prova",
- sources=[ "Matlab2Python_utils.cpp",
- ],
- include_dirs=extra_include_dirs, library_dirs=extra_library_dirs, extra_compile_args=extra_compile_args, libraries=extra_libraries ),
-
- ],
- zip_safe = False,
- packages = {'ccpi','ccpi.reconstruction'},
-)
diff --git a/src/Python/test.py b/src/Python/test.py
deleted file mode 100644
index db47380..0000000
--- a/src/Python/test.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Aug 3 14:08:09 2017
-
-@author: ofn77899
-"""
-
-import prova
-import numpy as np
-
-a = np.asarray([i for i in range(1*2*3)])
-a = a.reshape([1,2,3])
-print (a)
-b = prova.mexFunction(a)
-#print (b)
-print (b[4].shape)
-print (b[4])
-print (b[5])
-
-def print_element(input):
- print ("f: {0}".format(input))
-
-prova.doSomething(a, print_element, None)
-
-c = []
-def append_to_list(input, shouldPrint=False):
- c.append(input)
- if shouldPrint:
- print ("{0} appended to list {1}".format(input, c))
-
-def element_wise_algebra(input, shouldPrint=True):
- ret = input - 7
- if shouldPrint:
- print ("element_wise {0}".format(ret))
- return ret
-
-prova.doSomething(a, append_to_list, None)
-#print ("this is c: {0}".format(c))
-
-b = prova.doSomething(a, None, element_wise_algebra)
-#print (a)
-print (b[5])
diff --git a/src/Python/test/astra_test.py b/src/Python/test/astra_test.py
deleted file mode 100644
index 42c375a..0000000
--- a/src/Python/test/astra_test.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import astra
-import numpy
-import filefun
-
-
-# read in the same data as the DemoRD2
-angles = filefun.dlmread("DemoRD2/angles.csv")
-darks_ar = filefun.dlmread("DemoRD2/darks_ar.csv", separator=",")
-flats_ar = filefun.dlmread("DemoRD2/flats_ar.csv", separator=",")
-
-if True:
- Sino3D = numpy.load("DemoRD2/Sino3D.npy")
-else:
- sino = filefun.dlmread("DemoRD2/sino_01.csv", separator=",")
- a = map (lambda x:x, numpy.shape(sino))
- a.append(20)
-
- Sino3D = numpy.zeros(tuple(a), dtype="float")
-
- for i in range(1,numpy.shape(Sino3D)[2]+1):
- print("Read file DemoRD2/sino_%02d.csv" % i)
- sino = filefun.dlmread("DemoRD2/sino_%02d.csv" % i, separator=",")
- Sino3D.T[i-1] = sino.T
-
-Weights3D = numpy.asarray(Sino3D, dtype="float")
-
-##angles_rad = angles*(pi/180); % conversion to radians
-##size_det = size(data_raw3D,1); % detectors dim
-##angSize = size(data_raw3D, 2); % angles dim
-##slices_tot = size(data_raw3D, 3); % no of slices
-##recon_size = 950; % reconstruction size
-
-
-angles_rad = angles * numpy.pi /180.
-size_det, angSize, slices_tot = numpy.shape(Sino3D)
-size_det, angSize, slices_tot = [int(i) for i in numpy.shape(Sino3D)]
-recon_size = 950
-Z_slices = 3;
-det_row_count = Z_slices;
-
-#proj_geom = astra_create_proj_geom('parallel3d', 1, 1,
-# det_row_count, size_det, angles_rad);
-
-detectorSpacingX = 1.0
-detectorSpacingY = detectorSpacingX
-proj_geom = astra.create_proj_geom('parallel3d',
- detectorSpacingX,
- detectorSpacingY,
- det_row_count,
- size_det,
- angles_rad)
-
-#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
-vol_geom = astra.create_vol_geom(recon_size,recon_size,Z_slices);
-
-sino = numpy.zeros((size_det, angSize, slices_tot), dtype="float")
-
-#weights = ones(size(sino));
-weights = numpy.ones(numpy.shape(sino))
-
-#####################################################################
-## PowerMethod for Lipschitz constant
-
-N = vol_geom['GridColCount']
-x1 = numpy.random.rand(1,N,N)
-#sqweight = sqrt(weights(:,:,1));
-sqweight = numpy.sqrt(weights.T[0]).T
-##proj_geomT = proj_geom;
-proj_geomT = proj_geom.copy()
-##proj_geomT.DetectorRowCount = 1;
-proj_geomT['DetectorRowCount'] = 1
-##vol_geomT = vol_geom;
-vol_geomT = vol_geom.copy()
-##vol_geomT.GridSliceCount = 1;
-vol_geomT['GridSliceCount'] = 1
-
-##[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-
-#sino_id, y = astra.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
-sino_id, y = astra.create_sino(x1, proj_geomT, vol_geomT);
-
-##y = sqweight.*y;
-##astra_mex_data3d('delete', sino_id);
-
-
diff --git a/src/Python/test/create_phantom_projections.py b/src/Python/test/create_phantom_projections.py
deleted file mode 100644
index 20a9278..0000000
--- a/src/Python/test/create_phantom_projections.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from ccpi.reconstruction.AstraDevice import AstraDevice
-from ccpi.reconstruction.DeviceModel import DeviceModel
-import h5py
-import numpy
-import matplotlib.pyplot as plt
-
-nx = h5py.File('phant3D_256.h5', "r")
-phantom = numpy.asarray(nx.get('/dataset1'))
-pX,pY,pZ = numpy.shape(phantom)
-
-filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/demos/DendrData.h5'
-nxa = h5py.File(filename, "r")
-#getEntry(nx, '/')
-# I have exported the entries as children of /
-entries = [entry for entry in nxa['/'].keys()]
-print (entries)
-
-angles_rad = numpy.asarray(nxa.get('/angles_rad'), dtype="float32")
-
-
-device = AstraDevice(
- DeviceModel.DeviceType.PARALLEL3D.value,
- [ pX , pY , 1., 1., angles_rad],
- [ pX, pY, pZ ] )
-
-
-proj = device.doForwardProject(phantom)
-stack = [proj[:,i,:] for i in range(len(angles_rad))]
-stack = numpy.asarray(stack)
-
-
-fig = plt.figure()
-a=fig.add_subplot(1,2,1)
-a.set_title('proj')
-imgplot = plt.imshow(proj[:,100,:])
-a=fig.add_subplot(1,2,2)
-a.set_title('stack')
-imgplot = plt.imshow(stack[100])
-plt.show()
-
-pf = h5py.File("phantom3D256_projections.h5" , "w")
-pf.create_dataset("/projections", data=stack)
-pf.create_dataset("/sinogram", data=proj)
-pf.create_dataset("/angles", data=angles_rad)
-pf.create_dataset("/reconstruction_volume" , data=numpy.asarray([pX, pY, pZ]))
-pf.create_dataset("/camera/size" , data=numpy.asarray([pX , pY ]))
-pf.create_dataset("/camera/spacing" , data=numpy.asarray([1.,1.]))
-pf.flush()
-pf.close()
diff --git a/src/Python/test/readhd5.py b/src/Python/test/readhd5.py
deleted file mode 100644
index eff6c43..0000000
--- a/src/Python/test/readhd5.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Aug 23 16:34:49 2017
-
-@author: ofn77899
-"""
-
-import h5py
-import numpy
-
-def getEntry(nx, location):
- for item in nx[location].keys():
- print (item)
-
-filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/demos/DendrData.h5'
-nx = h5py.File(filename, "r")
-#getEntry(nx, '/')
-# I have exported the entries as children of /
-entries = [entry for entry in nx['/'].keys()]
-print (entries)
-
-Sino3D = numpy.asarray(nx.get('/Sino3D'))
-Weights3D = numpy.asarray(nx.get('/Weights3D'))
-angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
-angles_rad = numpy.asarray(nx.get('/angles_rad'))
-recon_size = numpy.asarray(nx.get('/recon_size'), dtype=int)[0]
-size_det = numpy.asarray(nx.get('/size_det'), dtype=int)[0]
-
-slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
-
-#from ccpi.viewer.CILViewer2D import CILViewer2D
-#v = CILViewer2D()
-#v.setInputAsNumpy(Weights3D)
-#v.startRenderLoop()
-
-import matplotlib.pyplot as plt
-fig = plt.figure()
-
-a=fig.add_subplot(1,1,1)
-a.set_title('noise')
-imgplot = plt.imshow(Weights3D[0].T)
-plt.show()
diff --git a/src/Python/test/simple_astra_test.py b/src/Python/test/simple_astra_test.py
deleted file mode 100644
index 905eeea..0000000
--- a/src/Python/test/simple_astra_test.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import astra
-import numpy
-
-detectorSpacingX = 1.0
-detectorSpacingY = 1.0
-det_row_count = 128
-det_col_count = 128
-
-angles_rad = numpy.asarray([i for i in range(360)], dtype=float) / 180. * numpy.pi
-
-proj_geom = astra.creators.create_proj_geom('parallel3d',
- detectorSpacingX,
- detectorSpacingY,
- det_row_count,
- det_col_count,
- angles_rad)
-
-image_size_x = 64
-image_size_y = 64
-image_size_z = 32
-
-vol_geom = astra.creators.create_vol_geom(image_size_x,image_size_y,image_size_z)
-
-x1 = numpy.random.rand(image_size_z,image_size_y,image_size_x)
-sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom)
diff --git a/src/Python/test/test_reconstructor-os.py b/src/Python/test/test_reconstructor-os.py
deleted file mode 100644
index 21b7ecd..0000000
--- a/src/Python/test/test_reconstructor-os.py
+++ /dev/null
@@ -1,403 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Aug 23 16:34:49 2017
-
-@author: ofn77899
-Based on DemoRD2.m
-"""
-
-import h5py
-import numpy
-
-from ccpi.reconstruction.FISTAReconstructor import FISTAReconstructor
-import astra
-import matplotlib.pyplot as plt
-from ccpi.imaging.Regularizer import Regularizer
-from ccpi.reconstruction.AstraDevice import AstraDevice
-from ccpi.reconstruction.DeviceModel import DeviceModel
-
-def RMSE(signal1, signal2):
- '''RMSE Root Mean Squared Error'''
- if numpy.shape(signal1) == numpy.shape(signal2):
- err = (signal1 - signal2)
- err = numpy.sum( err * err )/numpy.size(signal1); # MSE
- err = sqrt(err); # RMSE
- return err
- else:
- raise Exception('Input signals must have the same shape')
-
-filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/demos/DendrData.h5'
-nx = h5py.File(filename, "r")
-#getEntry(nx, '/')
-# I have exported the entries as children of /
-entries = [entry for entry in nx['/'].keys()]
-print (entries)
-
-Sino3D = numpy.asarray(nx.get('/Sino3D'), dtype="float32")
-Weights3D = numpy.asarray(nx.get('/Weights3D'), dtype="float32")
-angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
-angles_rad = numpy.asarray(nx.get('/angles_rad'), dtype="float32")
-recon_size = numpy.asarray(nx.get('/recon_size'), dtype=int)[0]
-size_det = numpy.asarray(nx.get('/size_det'), dtype=int)[0]
-slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
-
-Z_slices = 20
-det_row_count = Z_slices
-# next definition is just for consistency of naming
-det_col_count = size_det
-
-detectorSpacingX = 1.0
-detectorSpacingY = detectorSpacingX
-
-
-proj_geom = astra.creators.create_proj_geom('parallel3d',
- detectorSpacingX,
- detectorSpacingY,
- det_row_count,
- det_col_count,
- angles_rad)
-
-#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
-image_size_x = recon_size
-image_size_y = recon_size
-image_size_z = Z_slices
-vol_geom = astra.creators.create_vol_geom( image_size_x,
- image_size_y,
- image_size_z)
-
-## First pass the arguments to the FISTAReconstructor and test the
-## Lipschitz constant
-astradevice = AstraDevice(DeviceModel.DeviceType.PARALLEL3D.value,
- [proj_geom['DetectorRowCount'] ,
- proj_geom['DetectorColCount'] ,
- proj_geom['DetectorSpacingX'] ,
- proj_geom['DetectorSpacingY'] ,
- proj_geom['ProjectionAngles']
- ],
- [
- vol_geom['GridColCount'],
- vol_geom['GridRowCount'],
- vol_geom['GridSliceCount'] ] )
-fistaRecon = FISTAReconstructor(proj_geom,
- vol_geom,
- Sino3D ,
- weights=Weights3D,
- device=astradevice)
-
-print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
-fistaRecon.setParameter(number_of_iterations = 2)
-fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
-fistaRecon.setParameter(ring_alpha = 21)
-fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
-fistaRecon.setParameter(ring_lambda_R_L1 = 0)
-subsets = 8
-fistaRecon.setParameter(subsets=subsets)
-
-
-#reg = Regularizer(Regularizer.Algorithm.FGP_TV)
-#reg.setParameter(regularization_parameter=0.005,
-# number_of_iterations=50)
-reg = Regularizer(Regularizer.Algorithm.FGP_TV)
-reg.setParameter(regularization_parameter=5e6,
- tolerance_constant=0.0001,
- number_of_iterations=50)
-
-fistaRecon.setParameter(regularizer=reg)
-lc = fistaRecon.getParameter('Lipschitz_constant')
-reg.setParameter(regularization_parameter=5e6/lc)
-
-## Ordered subset
-if True:
- subsets = 8
- fistaRecon.setParameter(subsets=subsets)
- fistaRecon.createOrderedSubsets()
-else:
- angles = fistaRecon.getParameter('projector_geometry')['ProjectionAngles']
- #binEdges = numpy.linspace(angles.min(),
- # angles.max(),
- # subsets + 1)
- binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
- # get rearranged subset indices
- IndicesReorg = numpy.zeros((numpy.shape(angles)))
- counterM = 0
- for ii in range(binsDiscr.max()):
- counter = 0
- for jj in range(subsets):
- curr_index = ii + jj + counter
- #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
- if binsDiscr[jj] > ii:
- if (counterM < numpy.size(IndicesReorg)):
- IndicesReorg[counterM] = curr_index
- counterM = counterM + 1
-
- counter = counter + binsDiscr[jj] - 1
-
-
-if False:
- print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
- print ("prepare for iteration")
- fistaRecon.prepareForIteration()
-
-
-
- print("initializing ...")
- if False:
- # if X doesn't exist
- #N = params.vol_geom.GridColCount
- N = vol_geom['GridColCount']
- print ("N " + str(N))
- X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
- else:
- #X = fistaRecon.initialize()
- X = numpy.load("X.npy")
-
- print (numpy.shape(X))
- X_t = X.copy()
- print ("initialized")
- proj_geom , vol_geom, sino , \
- SlicesZ, weights , alpha_ring = fistaRecon.getParameter(
- ['projector_geometry' , 'output_geometry',
- 'input_sinogram', 'SlicesZ' , 'weights', 'ring_alpha'])
- lambdaR_L1 , alpha_ring , weights , L_const= \
- fistaRecon.getParameter(['ring_lambda_R_L1',
- 'ring_alpha' , 'weights',
- 'Lipschitz_constant'])
-
- #fistaRecon.setParameter(number_of_iterations = 3)
- iterFISTA = fistaRecon.getParameter('number_of_iterations')
- # errors vector (if the ground truth is given)
- Resid_error = numpy.zeros((iterFISTA));
- # objective function values vector
- objective = numpy.zeros((iterFISTA));
-
-
- t = 1
-
-
- ## additional for
- proj_geomSUB = proj_geom.copy()
- fistaRecon.residual2 = numpy.zeros(numpy.shape(fistaRecon.pars['input_sinogram']))
- residual2 = fistaRecon.residual2
- sino_updt_FULL = fistaRecon.residual.copy()
- r_x = fistaRecon.r.copy()
-
- print ("starting iterations")
-## % Outer FISTA iterations loop
- for i in range(fistaRecon.getParameter('number_of_iterations')):
-## % With OS approach it becomes trickier to correlate independent subsets, hence additional work is required
-## % one solution is to work with a full sinogram at times
-## if ((i >= 3) && (lambdaR_L1 > 0))
-## [sino_id2, sino_updt2] = astra_create_sino3d_cuda(X, proj_geom, vol_geom);
-## astra_mex_data3d('delete', sino_id2);
-## end
- # With OS approach it becomes trickier to correlate independent subsets,
- # hence additional work is required one solution is to work with a full
- # sinogram at times
-
- r_old = fistaRecon.r.copy()
- t_old = t
- SlicesZ, anglesNumb, Detectors = \
- numpy.shape(fistaRecon.getParameter('input_sinogram')) ## https://github.com/vais-ral/CCPi-FISTA_Reconstruction/issues/4
- if (i > 1 and lambdaR_L1 > 0) :
- for kkk in range(anglesNumb):
-
- residual2[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
- ((sino_updt_FULL[:,kkk,:]).squeeze() - \
- (sino[:,kkk,:]).squeeze() -\
- (alpha_ring * r_x)
- )
-
- vec = fistaRecon.residual.sum(axis = 1)
- #if SlicesZ > 1:
- # vec = vec[:,1,:] # 1 or 0?
- r_x = fistaRecon.r_x
- # update ring variable
- fistaRecon.r = (r_x - (1./L_const) * vec).copy()
-
- # subset loop
- counterInd = 1
- geometry_type = fistaRecon.getParameter('projector_geometry')['type']
- angles = fistaRecon.getParameter('projector_geometry')['ProjectionAngles']
-
-## if geometry_type == 'parallel' or \
-## geometry_type == 'fanflat' or \
-## geometry_type == 'fanflat_vec' :
-##
-## for kkk in range(SlicesZ):
-## sino_id, sinoT[kkk] = \
-## astra.creators.create_sino3d_gpu(
-## X_t[kkk:kkk+1], proj_geomSUB, vol_geom)
-## sino_updt_Sub[kkk] = sinoT.T.copy()
-##
-## else:
-## sino_id, sino_updt_Sub = \
-## astra.creators.create_sino3d_gpu(X_t, proj_geomSUB, vol_geom)
-##
-## astra.matlab.data3d('delete', sino_id)
-
- for ss in range(fistaRecon.getParameter('subsets')):
- print ("Subset {0}".format(ss))
- X_old = X.copy()
- t_old = t
-
- # the number of projections per subset
- numProjSub = fistaRecon.getParameter('os_bins')[ss]
- CurrSubIndices = fistaRecon.getParameter('os_indices')\
- [counterInd:counterInd+numProjSub]
- #print ("Len CurrSubIndices {0}".format(numProjSub))
- mask = numpy.zeros(numpy.shape(angles), dtype=bool)
- cc = 0
- for j in range(len(CurrSubIndices)):
- mask[int(CurrSubIndices[j])] = True
- proj_geomSUB['ProjectionAngles'] = angles[mask]
-
- shape = list(numpy.shape(fistaRecon.getParameter('input_sinogram')))
- shape[1] = numProjSub
- sino_updt_Sub = numpy.zeros(shape)
-
- if geometry_type == 'parallel' or \
- geometry_type == 'fanflat' or \
- geometry_type == 'fanflat_vec' :
-
- for kkk in range(SlicesZ):
- sino_id, sinoT = astra.creators.create_sino3d_gpu (
- X_t[kkk:kkk+1] , proj_geomSUB, vol_geom)
- sino_updt_Sub[kkk] = sinoT.T.copy()
-
- else:
- # for 3D geometry (watch the GPU memory overflow in ASTRA < 1.8)
- sino_id, sino_updt_Sub = \
- astra.creators.create_sino3d_gpu (X_t, proj_geomSUB, vol_geom)
-
- astra.matlab.data3d('delete', sino_id)
-
-
-
-
- ## RING REMOVAL
- residual = fistaRecon.residual
-
-
- if lambdaR_L1 > 0 :
- print ("ring removal")
- residualSub = numpy.zeros(shape)
- ## for a chosen subset
- ## for kkk = 1:numProjSub
- ## indC = CurrSubIndeces(kkk);
- ## residualSub(:,kkk,:) = squeeze(weights(:,indC,:)).*(squeeze(sino_updt_Sub(:,kkk,:)) - (squeeze(sino(:,indC,:)) - alpha_ring.*r_x));
- ## sino_updt_FULL(:,indC,:) = squeeze(sino_updt_Sub(:,kkk,:)); % filling the full sinogram
- ## end
- for kkk in range(numProjSub):
- #print ("ring removal indC ... {0}".format(kkk))
- indC = int(CurrSubIndices[kkk])
- residualSub[:,kkk,:] = weights[:,indC,:].squeeze() * \
- (sino_updt_Sub[:,kkk,:].squeeze() - \
- sino[:,indC,:].squeeze() - alpha_ring * r_x)
- # filling the full sinogram
- sino_updt_FULL[:,indC,:] = sino_updt_Sub[:,kkk,:].squeeze()
-
- else:
- #PWLS model
- # I guess we need to use mask here instead
- residualSub = weights[:,CurrSubIndices,:] * \
- ( sino_updt_Sub - sino[:,CurrSubIndices,:].squeeze() )
- objective[i] = 0.5 * numpy.linalg.norm(residualSub)
-
- if geometry_type == 'parallel' or \
- geometry_type == 'fanflat' or \
- geometry_type == 'fanflat_vec' :
- # if geometry is 2D use slice-by-slice projection-backprojection
- # routine
- x_temp = numpy.zeros(numpy.shape(X), dtype=numpy.float32)
- for kkk in range(SlicesZ):
-
- x_id, x_temp[kkk] = \
- astra.creators.create_backprojection3d_gpu(
- residualSub[kkk:kkk+1],
- proj_geomSUB, vol_geom)
-
- else:
- x_id, x_temp = \
- astra.creators.create_backprojection3d_gpu(
- residualSub, proj_geomSUB, vol_geom)
-
- astra.matlab.data3d('delete', x_id)
- X = X_t - (1/L_const) * x_temp
-
-
-
- ## REGULARIZATION
- ## SKIPPING FOR NOW
- ## Should be simpli
- # regularizer = fistaRecon.getParameter('regularizer')
- # for slices:
- # out = regularizer(input=X)
- print ("regularizer")
- reg = fistaRecon.getParameter('regularizer')
-
- X = reg(input=X,
- output_all=False)
-
-
- ## FINAL
- print ("final")
- lambdaR_L1 = fistaRecon.getParameter('ring_lambda_R_L1')
- if lambdaR_L1 > 0:
- fistaRecon.r = numpy.max(
- numpy.abs(fistaRecon.r) - lambdaR_L1 , 0) * \
- numpy.sign(fistaRecon.r)
- # updating r
- r_x = fistaRecon.r + ((t_old-1)/t) * (fistaRecon.r - r_old)
-
-
- if fistaRecon.getParameter('region_of_interest') is None:
- string = 'Iteration Number {0} | Objective {1} \n'
- print (string.format( i, objective[i]))
- else:
- ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
- 'ideal_image')
-
- Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
- string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
- print (string.format(i,Resid_error[i], objective[i]))
-
- numpy.save("X_out_os.npy", X)
-
-else:
- astradevice = AstraDevice(DeviceModel.DeviceType.PARALLEL3D.value,
- [proj_geom['DetectorRowCount'] ,
- proj_geom['DetectorColCount'] ,
- proj_geom['DetectorSpacingX'] ,
- proj_geom['DetectorSpacingY'] ,
- proj_geom['ProjectionAngles']
- ],
- [
- vol_geom['GridColCount'],
- vol_geom['GridRowCount'],
- vol_geom['GridSliceCount'] ] )
- regul = Regularizer(Regularizer.Algorithm.FGP_TV)
- regul.setParameter(regularization_parameter=5e6,
- number_of_iterations=50,
- tolerance_constant=1e-4,
- TV_penalty=Regularizer.TotalVariationPenalty.isotropic)
-
- fistaRecon = FISTAReconstructor(proj_geom,
- vol_geom,
- Sino3D ,
- weights=Weights3D,
- device=astradevice,
- regularizer = regul,
- subsets=8)
-
- print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
- fistaRecon.setParameter(number_of_iterations = 2)
- fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
- fistaRecon.setParameter(ring_alpha = 21)
- fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
- #fistaRecon.setParameter(subsets=8)
-
- lc = fistaRecon.getParameter('Lipschitz_constant')
- fistaRecon.getParameter('regularizer').setParameter(regularization_parameter=5e6/lc)
-
- fistaRecon.prepareForIteration()
- X = fistaRecon.iterate(numpy.load("X.npy"))
diff --git a/src/Python/test/test_reconstructor-os_phantom.py b/src/Python/test/test_reconstructor-os_phantom.py
deleted file mode 100644
index 01f1354..0000000
--- a/src/Python/test/test_reconstructor-os_phantom.py
+++ /dev/null
@@ -1,480 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Aug 23 16:34:49 2017
-
-@author: ofn77899
-Based on DemoRD2.m
-"""
-
-import h5py
-import numpy
-
-from ccpi.reconstruction.FISTAReconstructor import FISTAReconstructor
-import astra
-import matplotlib.pyplot as plt
-from ccpi.imaging.Regularizer import Regularizer
-from ccpi.reconstruction.AstraDevice import AstraDevice
-from ccpi.reconstruction.DeviceModel import DeviceModel
-
-#from ccpi.viewer.CILViewer2D import *
-
-
-def RMSE(signal1, signal2):
- '''RMSE Root Mean Squared Error'''
- if numpy.shape(signal1) == numpy.shape(signal2):
- err = (signal1 - signal2)
- err = numpy.sum( err * err )/numpy.size(signal1); # MSE
- err = sqrt(err); # RMSE
- return err
- else:
- raise Exception('Input signals must have the same shape')
-
-filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/src/Python/test/phantom3D256_projections.h5'
-nx = h5py.File(filename, "r")
-#getEntry(nx, '/')
-# I have exported the entries as children of /
-entries = [entry for entry in nx['/'].keys()]
-print (entries)
-
-projections = numpy.asarray(nx.get('/projections'), dtype="float32")
-#Weights3D = numpy.asarray(nx.get('/Weights3D'), dtype="float32")
-#angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
-angles_rad = numpy.asarray(nx.get('/angles'), dtype="float32")
-angSize = numpy.size(angles_rad)
-image_size_x, image_size_y, image_size_z = \
- numpy.asarray(nx.get('/reconstruction_volume'), dtype=int)
-det_col_count, det_row_count = \
- numpy.asarray(nx.get('/camera/size'))
-#slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
-detectorSpacingX, detectorSpacingY = numpy.asarray(nx.get('/camera/spacing'), dtype=int)
-
-Z_slices = 20
-#det_row_count = image_size_y
-# next definition is just for consistency of naming
-#det_col_count = image_size_x
-
-detectorSpacingX = 1.0
-detectorSpacingY = detectorSpacingX
-
-
-proj_geom = astra.creators.create_proj_geom('parallel3d',
- detectorSpacingX,
- detectorSpacingY,
- det_row_count,
- det_col_count,
- angles_rad)
-
-#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
-##image_size_x = recon_size
-##image_size_y = recon_size
-##image_size_z = Z_slices
-vol_geom = astra.creators.create_vol_geom( image_size_x,
- image_size_y,
- image_size_z)
-
-## First pass the arguments to the FISTAReconstructor and test the
-## Lipschitz constant
-astradevice = AstraDevice(DeviceModel.DeviceType.PARALLEL3D.value,
- [proj_geom['DetectorRowCount'] ,
- proj_geom['DetectorColCount'] ,
- proj_geom['DetectorSpacingX'] ,
- proj_geom['DetectorSpacingY'] ,
- proj_geom['ProjectionAngles']
- ],
- [
- vol_geom['GridColCount'],
- vol_geom['GridRowCount'],
- vol_geom['GridSliceCount'] ] )
-## create the sinogram
-Sino3D = numpy.transpose(projections, axes=[1,0,2])
-
-fistaRecon = FISTAReconstructor(proj_geom,
- vol_geom,
- Sino3D ,
- #weights=Weights3D,
- device=astradevice)
-
-print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
-fistaRecon.setParameter(number_of_iterations = 4)
-#fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
-fistaRecon.setParameter(ring_alpha = 21)
-fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
-#fistaRecon.setParameter(ring_lambda_R_L1 = 0)
-subsets = 8
-fistaRecon.setParameter(subsets=subsets)
-
-
-#reg = Regularizer(Regularizer.Algorithm.FGP_TV)
-#reg.setParameter(regularization_parameter=0.005,
-# number_of_iterations=50)
-reg = Regularizer(Regularizer.Algorithm.FGP_TV)
-reg.setParameter(regularization_parameter=5e6,
- tolerance_constant=0.0001,
- number_of_iterations=50)
-
-#fistaRecon.setParameter(regularizer=reg)
-#lc = fistaRecon.getParameter('Lipschitz_constant')
-#reg.setParameter(regularization_parameter=5e6/lc)
-
-## Ordered subset
-if True:
- #subsets = 8
- fistaRecon.setParameter(subsets=subsets)
- fistaRecon.createOrderedSubsets()
-else:
- angles = fistaRecon.getParameter('projector_geometry')['ProjectionAngles']
- #binEdges = numpy.linspace(angles.min(),
- # angles.max(),
- # subsets + 1)
- binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
- # get rearranged subset indices
- IndicesReorg = numpy.zeros((numpy.shape(angles)))
- counterM = 0
- for ii in range(binsDiscr.max()):
- counter = 0
- for jj in range(subsets):
- curr_index = ii + jj + counter
- #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
- if binsDiscr[jj] > ii:
- if (counterM < numpy.size(IndicesReorg)):
- IndicesReorg[counterM] = curr_index
- counterM = counterM + 1
-
- counter = counter + binsDiscr[jj] - 1
-
-
-if True:
- print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
- print ("prepare for iteration")
- fistaRecon.prepareForIteration()
-
-
-
- print("initializing ...")
- if True:
- # if X doesn't exist
- #N = params.vol_geom.GridColCount
- N = vol_geom['GridColCount']
- print ("N " + str(N))
- X = numpy.asarray(numpy.ones((image_size_x,image_size_y,image_size_z)),
- dtype=numpy.float) * 0.001
- X = numpy.asarray(numpy.zeros((image_size_x,image_size_y,image_size_z)),
- dtype=numpy.float)
- else:
- #X = fistaRecon.initialize()
- X = numpy.load("X.npy")
-
- print (numpy.shape(X))
- X_t = X.copy()
- print ("initialized")
- proj_geom , vol_geom, sino , \
- SlicesZ, weights , alpha_ring = fistaRecon.getParameter(
- ['projector_geometry' , 'output_geometry',
- 'input_sinogram', 'SlicesZ' , 'weights', 'ring_alpha'])
- lambdaR_L1 , alpha_ring , weights , L_const= \
- fistaRecon.getParameter(['ring_lambda_R_L1',
- 'ring_alpha' , 'weights',
- 'Lipschitz_constant'])
-
- #fistaRecon.setParameter(number_of_iterations = 3)
- iterFISTA = fistaRecon.getParameter('number_of_iterations')
- # errors vector (if the ground truth is given)
- Resid_error = numpy.zeros((iterFISTA));
- # objective function values vector
- objective = numpy.zeros((iterFISTA));
-
-
- t = 1
-
-
- ## additional for
- proj_geomSUB = proj_geom.copy()
- fistaRecon.residual2 = numpy.zeros(numpy.shape(fistaRecon.pars['input_sinogram']))
- residual2 = fistaRecon.residual2
- sino_updt_FULL = fistaRecon.residual.copy()
- r_x = fistaRecon.r.copy()
-
- results = []
- print ("starting iterations")
-## % Outer FISTA iterations loop
- for i in range(fistaRecon.getParameter('number_of_iterations')):
-## % With OS approach it becomes trickier to correlate independent subsets, hence additional work is required
-## % one solution is to work with a full sinogram at times
-## if ((i >= 3) && (lambdaR_L1 > 0))
-## [sino_id2, sino_updt2] = astra_create_sino3d_cuda(X, proj_geom, vol_geom);
-## astra_mex_data3d('delete', sino_id2);
-## end
- # With OS approach it becomes trickier to correlate independent subsets,
- # hence additional work is required one solution is to work with a full
- # sinogram at times
-
-
- #t_old = t
- SlicesZ, anglesNumb, Detectors = \
- numpy.shape(fistaRecon.getParameter('input_sinogram'))
- ## https://github.com/vais-ral/CCPi-FISTA_Reconstruction/issues/4
- r_old = fistaRecon.r.copy()
-
- if (i > 1 and lambdaR_L1 > 0) :
- for kkk in range(anglesNumb):
-
- residual2[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
- ((sino_updt_FULL[:,kkk,:]).squeeze() - \
- (sino[:,kkk,:]).squeeze() -\
- (alpha_ring * r_x)
- )
- #r_old = fistaRecon.r.copy()
- vec = fistaRecon.residual.sum(axis = 1)
- #if SlicesZ > 1:
- # vec = vec[:,1,:] # 1 or 0?
- r_x = fistaRecon.r_x
- # update ring variable
- fistaRecon.r = (r_x - (1./L_const) * vec)
-
- # subset loop
- counterInd = 1
- geometry_type = fistaRecon.getParameter('projector_geometry')['type']
- angles = fistaRecon.getParameter('projector_geometry')['ProjectionAngles']
-
-## if geometry_type == 'parallel' or \
-## geometry_type == 'fanflat' or \
-## geometry_type == 'fanflat_vec' :
-##
-## for kkk in range(SlicesZ):
-## sino_id, sinoT[kkk] = \
-## astra.creators.create_sino3d_gpu(
-## X_t[kkk:kkk+1], proj_geomSUB, vol_geom)
-## sino_updt_Sub[kkk] = sinoT.T.copy()
-##
-## else:
-## sino_id, sino_updt_Sub = \
-## astra.creators.create_sino3d_gpu(X_t, proj_geomSUB, vol_geom)
-##
-## astra.matlab.data3d('delete', sino_id)
-
- for ss in range(fistaRecon.getParameter('subsets')):
- print ("Subset {0}".format(ss))
- X_old = X.copy()
- t_old = t
- print ("X[0][0][0] {0} t {1}".format(X[0][0][0], t))
-
- # the number of projections per subset
- numProjSub = fistaRecon.getParameter('os_bins')[ss]
- CurrSubIndices = fistaRecon.getParameter('os_indices')\
- [counterInd:counterInd+numProjSub]
- shape = list(numpy.shape(fistaRecon.getParameter('input_sinogram')))
- shape[1] = numProjSub
- sino_updt_Sub = numpy.zeros(shape)
-
- #print ("Len CurrSubIndices {0}".format(numProjSub))
- mask = numpy.zeros(numpy.shape(angles), dtype=bool)
- cc = 0
- for j in range(len(CurrSubIndices)):
- mask[int(CurrSubIndices[j])] = True
-
- ## this is a reduced device
- rdev = fistaRecon.getParameter('device_model')\
- .createReducedDevice(proj_par={'angles' : angles[mask]},
- vol_par={})
- proj_geomSUB['ProjectionAngles'] = angles[mask]
-
-
-
- if geometry_type == 'parallel' or \
- geometry_type == 'fanflat' or \
- geometry_type == 'fanflat_vec' :
-
- for kkk in range(SlicesZ):
- sino_id, sinoT = astra.creators.create_sino3d_gpu (
- X_t[kkk:kkk+1] , proj_geomSUB, vol_geom)
- sino_updt_Sub[kkk] = sinoT.T.copy()
- astra.matlab.data3d('delete', sino_id)
- else:
- # for 3D geometry (watch the GPU memory overflow in ASTRA < 1.8)
- sino_id, sino_updt_Sub = \
- astra.creators.create_sino3d_gpu (X_t,
- proj_geomSUB,
- vol_geom)
-
- astra.matlab.data3d('delete', sino_id)
-
-
-
-
- ## RING REMOVAL
- residual = fistaRecon.residual
-
-
- if lambdaR_L1 > 0 :
- print ("ring removal")
- residualSub = numpy.zeros(shape)
- ## for a chosen subset
- ## for kkk = 1:numProjSub
- ## indC = CurrSubIndeces(kkk);
- ## residualSub(:,kkk,:) = squeeze(weights(:,indC,:)).*(squeeze(sino_updt_Sub(:,kkk,:)) - (squeeze(sino(:,indC,:)) - alpha_ring.*r_x));
- ## sino_updt_FULL(:,indC,:) = squeeze(sino_updt_Sub(:,kkk,:)); % filling the full sinogram
- ## end
- for kkk in range(numProjSub):
- #print ("ring removal indC ... {0}".format(kkk))
- indC = int(CurrSubIndices[kkk])
- residualSub[:,kkk,:] = weights[:,indC,:].squeeze() * \
- (sino_updt_Sub[:,kkk,:].squeeze() - \
- sino[:,indC,:].squeeze() - alpha_ring * r_x)
- # filling the full sinogram
- sino_updt_FULL[:,indC,:] = sino_updt_Sub[:,kkk,:].squeeze()
-
- else:
- #PWLS model
- # I guess we need to use mask here instead
- residualSub = weights[:,CurrSubIndices,:] * \
- ( sino_updt_Sub - \
- sino[:,CurrSubIndices,:].squeeze() )
- # it seems that in the original code the following like is not
- # calculated in the case of ring removal
- objective[i] = 0.5 * numpy.linalg.norm(residualSub)
-
- #backprojection
- if geometry_type == 'parallel' or \
- geometry_type == 'fanflat' or \
- geometry_type == 'fanflat_vec' :
- # if geometry is 2D use slice-by-slice projection-backprojection
- # routine
- x_temp = numpy.zeros(numpy.shape(X), dtype=numpy.float32)
- for kkk in range(SlicesZ):
-
- x_id, x_temp[kkk] = \
- astra.creators.create_backprojection3d_gpu(
- residualSub[kkk:kkk+1],
- proj_geomSUB, vol_geom)
- astra.matlab.data3d('delete', x_id)
-
- else:
- x_id, x_temp = \
- astra.creators.create_backprojection3d_gpu(
- residualSub, proj_geomSUB, vol_geom)
-
- astra.matlab.data3d('delete', x_id)
-
- X = X_t - (1/L_const) * x_temp
-
-
-
- ## REGULARIZATION
- ## SKIPPING FOR NOW
- ## Should be simpli
- # regularizer = fistaRecon.getParameter('regularizer')
- # for slices:
- # out = regularizer(input=X)
- print ("regularizer")
- reg = fistaRecon.getParameter('regularizer')
-
- if reg is not None:
- X = reg(input=X,
- output_all=False)
-
- t = (1 + numpy.sqrt(1 + 4 * t **2))/2
- X_t = X + (((t_old -1)/t) * (X-X_old))
- counterInd = counterInd + numProjSub - 1
- if i == 1:
- r_old = fistaRecon.r.copy()
-
- ## FINAL
- print ("final")
- lambdaR_L1 = fistaRecon.getParameter('ring_lambda_R_L1')
- if lambdaR_L1 > 0:
- fistaRecon.r = numpy.max(
- numpy.abs(fistaRecon.r) - lambdaR_L1 , 0) * \
- numpy.sign(fistaRecon.r)
- # updating r
- r_x = fistaRecon.r + ((t_old-1)/t) * (fistaRecon.r - r_old)
-
-
- if fistaRecon.getParameter('region_of_interest') is None:
- string = 'Iteration Number {0} | Objective {1} \n'
- print (string.format( i, objective[i]))
- else:
- ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
- 'ideal_image')
-
- Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
- string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
- print (string.format(i,Resid_error[i], objective[i]))
-
- results.append(X[10])
- numpy.save("X_out_os.npy", X)
-
-else:
-
-
-
- astradevice = AstraDevice(DeviceModel.DeviceType.PARALLEL3D.value,
- [proj_geom['DetectorRowCount'] ,
- proj_geom['DetectorColCount'] ,
- proj_geom['DetectorSpacingX'] ,
- proj_geom['DetectorSpacingY'] ,
- proj_geom['ProjectionAngles']
- ],
- [
- vol_geom['GridColCount'],
- vol_geom['GridRowCount'],
- vol_geom['GridSliceCount'] ] )
- regul = Regularizer(Regularizer.Algorithm.FGP_TV)
- regul.setParameter(regularization_parameter=5e6,
- number_of_iterations=50,
- tolerance_constant=1e-4,
- TV_penalty=Regularizer.TotalVariationPenalty.isotropic)
-
- fistaRecon = FISTAReconstructor(proj_geom,
- vol_geom,
- Sino3D ,
- weights=Weights3D,
- device=astradevice,
- #regularizer = regul,
- subsets=8)
-
- print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
- fistaRecon.setParameter(number_of_iterations = 1)
- fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
- fistaRecon.setParameter(ring_alpha = 21)
- fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
- #fistaRecon.setParameter(subsets=8)
-
- #lc = fistaRecon.getParameter('Lipschitz_constant')
- #fistaRecon.getParameter('regularizer').setParameter(regularization_parameter=5e6/lc)
-
- fistaRecon.prepareForIteration()
- X = fistaRecon.iterate(numpy.load("X.npy"))
-
-
-# plot
-fig = plt.figure()
-cols = 3
-
-## add the difference
-rd = []
-for i in range(1,len(results)):
- rd.append(results[i-1])
- rd.append(results[i])
- rd.append(results[i] - results[i-1])
-
-rows = (lambda x: int(numpy.floor(x/cols) + 1) if x%cols != 0 else int(x/cols)) \
- (len (rd))
-for i in range(len (results)):
- a=fig.add_subplot(rows,cols,i+1)
- imgplot = plt.imshow(results[i], vmin=0, vmax=1)
- a.text(0.05, 0.95, "iteration {0}".format(i),
- verticalalignment='top')
-## i = i + 1
-## a=fig.add_subplot(rows,cols,i+1)
-## imgplot = plt.imshow(results[i], vmin=0, vmax=10)
-## a.text(0.05, 0.95, "iteration {0}".format(i),
-## verticalalignment='top')
-
-## a=fig.add_subplot(rows,cols,i+2)
-## imgplot = plt.imshow(results[i]-results[i-1], vmin=0, vmax=10)
-## a.text(0.05, 0.95, "difference {0}-{1}".format(i, i-1),
-## verticalalignment='top')
-
-
-
-plt.show()
diff --git a/src/Python/test/test_reconstructor.py b/src/Python/test/test_reconstructor.py
deleted file mode 100644
index 40065e7..0000000
--- a/src/Python/test/test_reconstructor.py
+++ /dev/null
@@ -1,359 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Aug 23 16:34:49 2017
-
-@author: ofn77899
-Based on DemoRD2.m
-"""
-
-import h5py
-import numpy
-
-from ccpi.reconstruction.FISTAReconstructor import FISTAReconstructor
-import astra
-import matplotlib.pyplot as plt
-from ccpi.imaging.Regularizer import Regularizer
-from ccpi.reconstruction.AstraDevice import AstraDevice
-from ccpi.reconstruction.DeviceModel import DeviceModel
-
-def RMSE(signal1, signal2):
- '''RMSE Root Mean Squared Error'''
- if numpy.shape(signal1) == numpy.shape(signal2):
- err = (signal1 - signal2)
- err = numpy.sum( err * err )/numpy.size(signal1); # MSE
- err = sqrt(err); # RMSE
- return err
- else:
- raise Exception('Input signals must have the same shape')
-
-def createAstraDevice(projector_geometry, output_geometry):
- '''TODO remove'''
-
- device = AstraDevice(DeviceModel.DeviceType.PARALLEL3D.value,
- [projector_geometry['DetectorRowCount'] ,
- projector_geometry['DetectorColCount'] ,
- projector_geometry['DetectorSpacingX'] ,
- projector_geometry['DetectorSpacingY'] ,
- projector_geometry['ProjectionAngles']
- ],
- [
- output_geometry['GridColCount'],
- output_geometry['GridRowCount'],
- output_geometry['GridSliceCount'] ] )
- return device
-
-filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/demos/DendrData.h5'
-nx = h5py.File(filename, "r")
-#getEntry(nx, '/')
-# I have exported the entries as children of /
-entries = [entry for entry in nx['/'].keys()]
-print (entries)
-
-Sino3D = numpy.asarray(nx.get('/Sino3D'), dtype="float32")
-Weights3D = numpy.asarray(nx.get('/Weights3D'), dtype="float32")
-angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
-angles_rad = numpy.asarray(nx.get('/angles_rad'), dtype="float32")
-recon_size = numpy.asarray(nx.get('/recon_size'), dtype=int)[0]
-size_det = numpy.asarray(nx.get('/size_det'), dtype=int)[0]
-slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
-
-Z_slices = 20
-det_row_count = Z_slices
-# next definition is just for consistency of naming
-det_col_count = size_det
-
-detectorSpacingX = 1.0
-detectorSpacingY = detectorSpacingX
-
-
-proj_geom = astra.creators.create_proj_geom('parallel3d',
- detectorSpacingX,
- detectorSpacingY,
- det_row_count,
- det_col_count,
- angles_rad)
-
-#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
-image_size_x = recon_size
-image_size_y = recon_size
-image_size_z = Z_slices
-vol_geom = astra.creators.create_vol_geom( image_size_x,
- image_size_y,
- image_size_z)
-
-## First pass the arguments to the FISTAReconstructor and test the
-## Lipschitz constant
-
-##fistaRecon = FISTAReconstructor(proj_geom,
-## vol_geom,
-## Sino3D ,
-## weights=Weights3D)
-##
-##print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
-##fistaRecon.setParameter(number_of_iterations = 12)
-##fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
-##fistaRecon.setParameter(ring_alpha = 21)
-##fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
-##
-##reg = Regularizer(Regularizer.Algorithm.LLT_model)
-##reg.setParameter(regularization_parameter=25,
-## time_step=0.0003,
-## tolerance_constant=0.0001,
-## number_of_iterations=300)
-##fistaRecon.setParameter(regularizer=reg)
-
-## Ordered subset
-if False:
- subsets = 16
- angles = fistaRecon.getParameter('projector_geometry')['ProjectionAngles']
- #binEdges = numpy.linspace(angles.min(),
- # angles.max(),
- # subsets + 1)
- binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
- # get rearranged subset indices
- IndicesReorg = numpy.zeros((numpy.shape(angles)))
- counterM = 0
- for ii in range(binsDiscr.max()):
- counter = 0
- for jj in range(subsets):
- curr_index = ii + jj + counter
- #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
- if binsDiscr[jj] > ii:
- if (counterM < numpy.size(IndicesReorg)):
- IndicesReorg[counterM] = curr_index
- counterM = counterM + 1
-
- counter = counter + binsDiscr[jj] - 1
-
-
-if False:
- print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
- print ("prepare for iteration")
- fistaRecon.prepareForIteration()
-
-
-
- print("initializing ...")
- if False:
- # if X doesn't exist
- #N = params.vol_geom.GridColCount
- N = vol_geom['GridColCount']
- print ("N " + str(N))
- X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
- else:
- #X = fistaRecon.initialize()
- X = numpy.load("X.npy")
-
- print (numpy.shape(X))
- X_t = X.copy()
- print ("initialized")
- proj_geom , vol_geom, sino , \
- SlicesZ = fistaRecon.getParameter(['projector_geometry' ,
- 'output_geometry',
- 'input_sinogram',
- 'SlicesZ'])
-
- #fistaRecon.setParameter(number_of_iterations = 3)
- iterFISTA = fistaRecon.getParameter('number_of_iterations')
- # errors vector (if the ground truth is given)
- Resid_error = numpy.zeros((iterFISTA));
- # objective function values vector
- objective = numpy.zeros((iterFISTA));
-
-
- t = 1
-
-
- print ("starting iterations")
-## % Outer FISTA iterations loop
- for i in range(fistaRecon.getParameter('number_of_iterations')):
- X_old = X.copy()
- t_old = t
- r_old = fistaRecon.r.copy()
- if fistaRecon.getParameter('projector_geometry')['type'] == 'parallel' or \
- fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat' or \
- fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat_vec' :
- # if the geometry is parallel use slice-by-slice
- # projection-backprojection routine
- #sino_updt = zeros(size(sino),'single');
- proj_geomT = proj_geom.copy()
- proj_geomT['DetectorRowCount'] = 1
- vol_geomT = vol_geom.copy()
- vol_geomT['GridSliceCount'] = 1;
- sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
- for kkk in range(SlicesZ):
- sino_id, sino_updt[kkk] = \
- astra.creators.create_sino3d_gpu(
- X_t[kkk:kkk+1], proj_geom, vol_geom)
- astra.matlab.data3d('delete', sino_id)
- else:
- # for divergent 3D geometry (watch the GPU memory overflow in
- # ASTRA versions < 1.8)
- #[sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
- sino_id, sino_updt = astra.creators.create_sino3d_gpu(
- X_t, proj_geom, vol_geom)
-
- ## RING REMOVAL
- residual = fistaRecon.residual
- lambdaR_L1 , alpha_ring , weights , L_const= \
- fistaRecon.getParameter(['ring_lambda_R_L1',
- 'ring_alpha' , 'weights',
- 'Lipschitz_constant'])
- r_x = fistaRecon.r_x
- SlicesZ, anglesNumb, Detectors = \
- numpy.shape(fistaRecon.getParameter('input_sinogram'))
- if lambdaR_L1 > 0 :
- print ("ring removal")
- for kkk in range(anglesNumb):
-
- residual[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
- ((sino_updt[:,kkk,:]).squeeze() - \
- (sino[:,kkk,:]).squeeze() -\
- (alpha_ring * r_x)
- )
- vec = residual.sum(axis = 1)
- #if SlicesZ > 1:
- # vec = vec[:,1,:].squeeze()
- fistaRecon.r = (r_x - (1./L_const) * vec).copy()
- objective[i] = (0.5 * (residual ** 2).sum())
-## % the ring removal part (Group-Huber fidelity)
-## for kkk = 1:anglesNumb
-## residual(:,kkk,:) = squeeze(weights(:,kkk,:)).*
-## (squeeze(sino_updt(:,kkk,:)) -
-## (squeeze(sino(:,kkk,:)) - alpha_ring.*r_x));
-## end
-## vec = sum(residual,2);
-## if (SlicesZ > 1)
-## vec = squeeze(vec(:,1,:));
-## end
-## r = r_x - (1./L_const).*vec;
-## objective(i) = (0.5*sum(residual(:).^2)); % for the objective function output
-
-
-
- # Projection/Backprojection Routine
- if fistaRecon.getParameter('projector_geometry')['type'] == 'parallel' or \
- fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat' or\
- fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat_vec':
- x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
- print ("Projection/Backprojection Routine")
- for kkk in range(SlicesZ):
-
- x_id, x_temp[kkk] = \
- astra.creators.create_backprojection3d_gpu(
- residual[kkk:kkk+1],
- proj_geomT, vol_geomT)
- astra.matlab.data3d('delete', x_id)
- else:
- x_id, x_temp = \
- astra.creators.create_backprojection3d_gpu(
- residual, proj_geom, vol_geom)
-
- X = X_t - (1/L_const) * x_temp
- astra.matlab.data3d('delete', sino_id)
- astra.matlab.data3d('delete', x_id)
-
-
- ## REGULARIZATION
- ## SKIPPING FOR NOW
- ## Should be simpli
- # regularizer = fistaRecon.getParameter('regularizer')
- # for slices:
- # out = regularizer(input=X)
- print ("skipping regularizer")
-
-
- ## FINAL
- print ("final")
- lambdaR_L1 = fistaRecon.getParameter('ring_lambda_R_L1')
- if lambdaR_L1 > 0:
- fistaRecon.r = numpy.max(
- numpy.abs(fistaRecon.r) - lambdaR_L1 , 0) * \
- numpy.sign(fistaRecon.r)
- t = (1 + numpy.sqrt(1 + 4 * t**2))/2
- X_t = X + (((t_old -1)/t) * (X - X_old))
-
- if lambdaR_L1 > 0:
- fistaRecon.r_x = fistaRecon.r + \
- (((t_old-1)/t) * (fistaRecon.r - r_old))
-
- if fistaRecon.getParameter('region_of_interest') is None:
- string = 'Iteration Number {0} | Objective {1} \n'
- print (string.format( i, objective[i]))
- else:
- ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
- 'ideal_image')
-
- Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
- string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
- print (string.format(i,Resid_error[i], objective[i]))
-
-## if (lambdaR_L1 > 0)
-## r = max(abs(r)-lambdaR_L1, 0).*sign(r); % soft-thresholding operator for ring vector
-## end
-##
-## t = (1 + sqrt(1 + 4*t^2))/2; % updating t
-## X_t = X + ((t_old-1)/t).*(X - X_old); % updating X
-##
-## if (lambdaR_L1 > 0)
-## r_x = r + ((t_old-1)/t).*(r - r_old); % updating r
-## end
-##
-## if (show == 1)
-## figure(10); imshow(X(:,:,slice), [0 maxvalplot]);
-## if (lambdaR_L1 > 0)
-## figure(11); plot(r); title('Rings offset vector')
-## end
-## pause(0.01);
-## end
-## if (strcmp(X_ideal, 'none' ) == 0)
-## Resid_error(i) = RMSE(X(ROI), X_ideal(ROI));
-## fprintf('%s %i %s %s %.4f %s %s %f \n', 'Iteration Number:', i, '|', 'Error RMSE:', Resid_error(i), '|', 'Objective:', objective(i));
-## else
-## fprintf('%s %i %s %s %f \n', 'Iteration Number:', i, '|', 'Objective:', objective(i));
-## end
-else:
-
- # create a device for forward/backprojection
- #astradevice = createAstraDevice(proj_geom, vol_geom)
-
- astradevice = AstraDevice(DeviceModel.DeviceType.PARALLEL3D.value,
- [proj_geom['DetectorRowCount'] ,
- proj_geom['DetectorColCount'] ,
- proj_geom['DetectorSpacingX'] ,
- proj_geom['DetectorSpacingY'] ,
- proj_geom['ProjectionAngles']
- ],
- [
- vol_geom['GridColCount'],
- vol_geom['GridRowCount'],
- vol_geom['GridSliceCount'] ] )
-
- regul = Regularizer(Regularizer.Algorithm.FGP_TV)
- regul.setParameter(regularization_parameter=5e6,
- number_of_iterations=50,
- tolerance_constant=1e-4,
- TV_penalty=Regularizer.TotalVariationPenalty.isotropic)
-
- fistaRecon = FISTAReconstructor(proj_geom,
- vol_geom,
- Sino3D ,
- device = astradevice,
- weights=Weights3D,
- regularizer = regul
- )
-
- print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
- fistaRecon.setParameter(number_of_iterations = 18)
- fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
- fistaRecon.setParameter(ring_alpha = 21)
- fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
-
-
-
- fistaRecon.prepareForIteration()
- X = numpy.load("X.npy")
-
-
- X = fistaRecon.iterate(X)
- #numpy.save("X_out.npy", X)
diff --git a/src/Python/test/test_regularizers.py b/src/Python/test/test_regularizers.py
deleted file mode 100644
index 27e4ed3..0000000
--- a/src/Python/test/test_regularizers.py
+++ /dev/null
@@ -1,412 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Aug 4 11:10:05 2017
-
-@author: ofn77899
-"""
-
-#from ccpi.viewer.CILViewer2D import Converter
-#import vtk
-
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-from enum import Enum
-import timeit
-#from PIL import Image
-#from Regularizer import Regularizer
-from ccpi.imaging.Regularizer import Regularizer
-
-###############################################################################
-#https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956
-#NRMSE a normalization of the root of the mean squared error
-#NRMSE is simply 1 - [RMSE / (maxval - minval)]. Where maxval is the maximum
-# intensity from the two images being compared, and respectively the same for
-# minval. RMSE is given by the square root of MSE:
-# sqrt[(sum(A - B) ** 2) / |A|],
-# where |A| means the number of elements in A. By doing this, the maximum value
-# given by RMSE is maxval.
-
-def nrmse(im1, im2):
- a, b = im1.shape
- rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b))
- max_val = max(np.max(im1), np.max(im2))
- min_val = min(np.min(im1), np.min(im2))
- return 1 - (rmse / (max_val - min_val))
-###############################################################################
-
-###############################################################################
-#
-# 2D Regularizers
-#
-###############################################################################
-#Example:
-# figure;
-# Im = double(imread('lena_gray_256.tif'))/255; % loading image
-# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
-# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-
-
-#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
-filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
-#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
-
-#reader = vtk.vtkTIFFReader()
-#reader.SetFileName(os.path.normpath(filename))
-#reader.Update()
-Im = plt.imread(filename)
-#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255
-#img.show()
-Im = np.asarray(Im, dtype='float32')
-
-
-
-
-#imgplot = plt.imshow(Im)
-perc = 0.05
-u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
-# map the u0 u0->u0>0
-f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-u0 = f(u0).astype('float32')
-
-## plot
-fig = plt.figure()
-#a=fig.add_subplot(3,3,1)
-#a.set_title('Original')
-#imgplot = plt.imshow(Im)
-
-a=fig.add_subplot(2,3,1)
-a.set_title('noise')
-imgplot = plt.imshow(u0,cmap="gray")
-
-reg_output = []
-##############################################################################
-# Call regularizer
-
-####################### SplitBregman_TV #####################################
-# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-
-use_object = True
-if use_object:
- reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
- print (reg.pars)
- reg.setParameter(input=u0)
- reg.setParameter(regularization_parameter=10.)
- # or
- # reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30,
- #tolerance_constant=1e-4,
- #TV_Penalty=Regularizer.TotalVariationPenalty.l1)
- plotme = reg() [0]
- pars = reg.pars
- textstr = reg.printParametersToString()
-
- #out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
- #tolerance_constant=1e-4,
- # TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
-#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
-# tolerance_constant=1e-4,
-# TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
-else:
- out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
- pars = out2[2]
- reg_output.append(out2)
- plotme = reg_output[-1][0]
- textstr = out2[-1]
-
-a=fig.add_subplot(2,3,2)
-
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(plotme,cmap="gray")
-
-###################### FGP_TV #########################################
-# u = FGP_TV(single(u0), 0.05, 100, 1e-04);
-out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.0005,
- number_of_iterations=50)
-pars = out2[-2]
-
-reg_output.append(out2)
-
-a=fig.add_subplot(2,3,3)
-
-textstr = out2[-1]
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0])
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
-
-###################### LLT_model #########################################
-# * u0 = Im + .03*randn(size(Im)); % adding noise
-# [Den] = LLT_model(single(u0), 10, 0.1, 1);
-#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0);
-#input, regularization_parameter , time_step, number_of_iterations,
-# tolerance_constant, restrictive_Z_smoothing=0
-out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
- time_step=0.0003,
- tolerance_constant=0.0001,
- number_of_iterations=300)
-pars = out2[-2]
-
-reg_output.append(out2)
-
-a=fig.add_subplot(2,3,4)
-
-textstr = out2[-1]
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
-
-
-# ###################### PatchBased_Regul #########################################
-# # Quick 2D denoising example in Matlab:
-# # Im = double(imread('lena_gray_256.tif'))/255; % loading image
-# # u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# # ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05);
-
-out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
- searching_window_ratio=3,
- similarity_window_ratio=1,
- PB_filtering_parameter=0.08)
-pars = out2[-2]
-reg_output.append(out2)
-
-a=fig.add_subplot(2,3,5)
-
-
-textstr = out2[-1]
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
-
-
-# ###################### TGV_PD #########################################
-# # Quick 2D denoising example in Matlab:
-# # Im = double(imread('lena_gray_256.tif'))/255; % loading image
-# # u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# # u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
-
-
-out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
- first_order_term=1.3,
- second_order_term=1,
- number_of_iterations=550)
-pars = out2[-2]
-reg_output.append(out2)
-
-a=fig.add_subplot(2,3,6)
-
-
-textstr = out2[-1]
-
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
-
-
-plt.show()
-
-################################################################################
-##
-## 3D Regularizers
-##
-################################################################################
-##Example:
-## figure;
-## Im = double(imread('lena_gray_256.tif'))/255; % loading image
-## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
-## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-#
-##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha"
-#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha"
-#
-#reader = vtk.vtkMetaImageReader()
-#reader.SetFileName(os.path.normpath(filename))
-#reader.Update()
-##vtk returns 3D images, let's take just the one slice there is as 2D
-#Im = Converter.vtk2numpy(reader.GetOutput())
-#Im = Im.astype('float32')
-##imgplot = plt.imshow(Im)
-#perc = 0.05
-#u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
-## map the u0 u0->u0>0
-#f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-#u0 = f(u0).astype('float32')
-#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(),
-# reader.GetOutput().GetOrigin())
-#converter.Update()
-#writer = vtk.vtkMetaImageWriter()
-#writer.SetInputData(converter.GetOutput())
-#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha")
-##writer.Write()
-#
-#
-### plot
-#fig3D = plt.figure()
-##a=fig.add_subplot(3,3,1)
-##a.set_title('Original')
-##imgplot = plt.imshow(Im)
-#sliceNo = 32
-#
-#a=fig3D.add_subplot(2,3,1)
-#a.set_title('noise')
-#imgplot = plt.imshow(u0.T[sliceNo])
-#
-#reg_output3d = []
-#
-###############################################################################
-## Call regularizer
-#
-######################## SplitBregman_TV #####################################
-## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-#
-##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-#
-##out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
-## #tolerance_constant=1e-4,
-## TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-#
-#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
-# tolerance_constant=1e-4,
-# TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-#
-#
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-####################### FGP_TV #########################################
-## u = FGP_TV(single(u0), 0.05, 100, 1e-04);
-#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
-# number_of_iterations=200)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-####################### LLT_model #########################################
-## * u0 = Im + .03*randn(size(Im)); % adding noise
-## [Den] = LLT_model(single(u0), 10, 0.1, 1);
-##Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0);
-##input, regularization_parameter , time_step, number_of_iterations,
-## tolerance_constant, restrictive_Z_smoothing=0
-#out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
-# time_step=0.0003,
-# tolerance_constant=0.0001,
-# number_of_iterations=300)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-####################### PatchBased_Regul #########################################
-## Quick 2D denoising example in Matlab:
-## Im = double(imread('lena_gray_256.tif'))/255; % loading image
-## u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-## ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05);
-#
-#out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
-# searching_window_ratio=3,
-# similarity_window_ratio=1,
-# PB_filtering_parameter=0.08)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-
-###################### TGV_PD #########################################
-# Quick 2D denoising example in Matlab:
-# Im = double(imread('lena_gray_256.tif'))/255; % loading image
-# u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
-
-
-#out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
-# first_order_term=1.3,
-# second_order_term=1,
-# number_of_iterations=550)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
diff --git a/src/Python/test/test_regularizers_3d.py b/src/Python/test/test_regularizers_3d.py
deleted file mode 100644
index 2d11a7e..0000000
--- a/src/Python/test/test_regularizers_3d.py
+++ /dev/null
@@ -1,425 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Aug 4 11:10:05 2017
-
-@author: ofn77899
-"""
-
-#from ccpi.viewer.CILViewer2D import Converter
-#import vtk
-
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-from enum import Enum
-import timeit
-#from PIL import Image
-#from Regularizer import Regularizer
-from ccpi.imaging.Regularizer import Regularizer
-
-###############################################################################
-#https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956
-#NRMSE a normalization of the root of the mean squared error
-#NRMSE is simply 1 - [RMSE / (maxval - minval)]. Where maxval is the maximum
-# intensity from the two images being compared, and respectively the same for
-# minval. RMSE is given by the square root of MSE:
-# sqrt[(sum(A - B) ** 2) / |A|],
-# where |A| means the number of elements in A. By doing this, the maximum value
-# given by RMSE is maxval.
-
-def nrmse(im1, im2):
- a, b = im1.shape
- rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b))
- max_val = max(np.max(im1), np.max(im2))
- min_val = min(np.min(im1), np.min(im2))
- return 1 - (rmse / (max_val - min_val))
-###############################################################################
-
-###############################################################################
-#
-# 2D Regularizers
-#
-###############################################################################
-#Example:
-# figure;
-# Im = double(imread('lena_gray_256.tif'))/255; % loading image
-# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
-# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-
-
-#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
-filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
-#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
-
-#reader = vtk.vtkTIFFReader()
-#reader.SetFileName(os.path.normpath(filename))
-#reader.Update()
-Im = plt.imread(filename)
-#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255
-#img.show()
-Im = np.asarray(Im, dtype='float32')
-
-# create a 3D image by stacking N of this images
-
-
-#imgplot = plt.imshow(Im)
-perc = 0.05
-u_n = Im + (perc* np.random.normal(size=np.shape(Im)))
-y,z = np.shape(u_n)
-u_n = np.reshape(u_n , (1,y,z))
-
-u0 = u_n.copy()
-for i in range (19):
- u_n = Im + (perc* np.random.normal(size=np.shape(Im)))
- u_n = np.reshape(u_n , (1,y,z))
-
- u0 = np.vstack ( (u0, u_n) )
-
-# map the u0 u0->u0>0
-f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-u0 = f(u0).astype('float32')
-
-print ("Passed image shape {0}".format(np.shape(u0)))
-
-## plot
-fig = plt.figure()
-#a=fig.add_subplot(3,3,1)
-#a.set_title('Original')
-#imgplot = plt.imshow(Im)
-sliceno = 10
-
-a=fig.add_subplot(2,3,1)
-a.set_title('noise')
-imgplot = plt.imshow(u0[sliceno],cmap="gray")
-
-reg_output = []
-##############################################################################
-# Call regularizer
-
-####################### SplitBregman_TV #####################################
-# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-
-use_object = True
-if use_object:
- reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
- print (reg.pars)
- reg.setParameter(input=u0)
- reg.setParameter(regularization_parameter=10.)
- # or
- # reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30,
- #tolerance_constant=1e-4,
- #TV_Penalty=Regularizer.TotalVariationPenalty.l1)
- plotme = reg() [0]
- pars = reg.pars
- textstr = reg.printParametersToString()
-
- #out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
- #tolerance_constant=1e-4,
- # TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
-#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
-# tolerance_constant=1e-4,
-# TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
-else:
- out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
- pars = out2[2]
- reg_output.append(out2)
- plotme = reg_output[-1][0]
- textstr = out2[-1]
-
-a=fig.add_subplot(2,3,2)
-
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(plotme[sliceno],cmap="gray")
-
-###################### FGP_TV #########################################
-# u = FGP_TV(single(u0), 0.05, 100, 1e-04);
-out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.0005,
- number_of_iterations=50)
-pars = out2[-2]
-
-reg_output.append(out2)
-
-a=fig.add_subplot(2,3,3)
-
-textstr = out2[-1]
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0][sliceno])
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
-
-###################### LLT_model #########################################
-# * u0 = Im + .03*randn(size(Im)); % adding noise
-# [Den] = LLT_model(single(u0), 10, 0.1, 1);
-#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0);
-#input, regularization_parameter , time_step, number_of_iterations,
-# tolerance_constant, restrictive_Z_smoothing=0
-out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
- time_step=0.0003,
- tolerance_constant=0.0001,
- number_of_iterations=300)
-pars = out2[-2]
-
-reg_output.append(out2)
-
-a=fig.add_subplot(2,3,4)
-
-textstr = out2[-1]
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
-
-
-# ###################### PatchBased_Regul #########################################
-# # Quick 2D denoising example in Matlab:
-# # Im = double(imread('lena_gray_256.tif'))/255; % loading image
-# # u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# # ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05);
-
-out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
- searching_window_ratio=3,
- similarity_window_ratio=1,
- PB_filtering_parameter=0.08)
-pars = out2[-2]
-reg_output.append(out2)
-
-a=fig.add_subplot(2,3,5)
-
-
-textstr = out2[-1]
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
-
-
-# ###################### TGV_PD #########################################
-# # Quick 2D denoising example in Matlab:
-# # Im = double(imread('lena_gray_256.tif'))/255; % loading image
-# # u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# # u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
-
-
-out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
- first_order_term=1.3,
- second_order_term=1,
- number_of_iterations=550)
-pars = out2[-2]
-reg_output.append(out2)
-
-a=fig.add_subplot(2,3,6)
-
-
-textstr = out2[-1]
-
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
-
-
-plt.show()
-
-################################################################################
-##
-## 3D Regularizers
-##
-################################################################################
-##Example:
-## figure;
-## Im = double(imread('lena_gray_256.tif'))/255; % loading image
-## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
-## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-#
-##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha"
-#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha"
-#
-#reader = vtk.vtkMetaImageReader()
-#reader.SetFileName(os.path.normpath(filename))
-#reader.Update()
-##vtk returns 3D images, let's take just the one slice there is as 2D
-#Im = Converter.vtk2numpy(reader.GetOutput())
-#Im = Im.astype('float32')
-##imgplot = plt.imshow(Im)
-#perc = 0.05
-#u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
-## map the u0 u0->u0>0
-#f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-#u0 = f(u0).astype('float32')
-#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(),
-# reader.GetOutput().GetOrigin())
-#converter.Update()
-#writer = vtk.vtkMetaImageWriter()
-#writer.SetInputData(converter.GetOutput())
-#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha")
-##writer.Write()
-#
-#
-### plot
-#fig3D = plt.figure()
-##a=fig.add_subplot(3,3,1)
-##a.set_title('Original')
-##imgplot = plt.imshow(Im)
-#sliceNo = 32
-#
-#a=fig3D.add_subplot(2,3,1)
-#a.set_title('noise')
-#imgplot = plt.imshow(u0.T[sliceNo])
-#
-#reg_output3d = []
-#
-###############################################################################
-## Call regularizer
-#
-######################## SplitBregman_TV #####################################
-## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-#
-##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-#
-##out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
-## #tolerance_constant=1e-4,
-## TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-#
-#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
-# tolerance_constant=1e-4,
-# TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-#
-#
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-####################### FGP_TV #########################################
-## u = FGP_TV(single(u0), 0.05, 100, 1e-04);
-#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
-# number_of_iterations=200)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-####################### LLT_model #########################################
-## * u0 = Im + .03*randn(size(Im)); % adding noise
-## [Den] = LLT_model(single(u0), 10, 0.1, 1);
-##Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0);
-##input, regularization_parameter , time_step, number_of_iterations,
-## tolerance_constant, restrictive_Z_smoothing=0
-#out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
-# time_step=0.0003,
-# tolerance_constant=0.0001,
-# number_of_iterations=300)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-####################### PatchBased_Regul #########################################
-## Quick 2D denoising example in Matlab:
-## Im = double(imread('lena_gray_256.tif'))/255; % loading image
-## u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-## ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05);
-#
-#out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
-# searching_window_ratio=3,
-# similarity_window_ratio=1,
-# PB_filtering_parameter=0.08)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-
-###################### TGV_PD #########################################
-# Quick 2D denoising example in Matlab:
-# Im = double(imread('lena_gray_256.tif'))/255; % loading image
-# u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
-
-
-#out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
-# first_order_term=1.3,
-# second_order_term=1,
-# number_of_iterations=550)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
diff --git a/src/Python/test_reconstructor.py b/src/Python/test_reconstructor.py
deleted file mode 100644
index 07668ba..0000000
--- a/src/Python/test_reconstructor.py
+++ /dev/null
@@ -1,301 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Aug 23 16:34:49 2017
-
-@author: ofn77899
-Based on DemoRD2.m
-"""
-
-import h5py
-import numpy
-
-from ccpi.fista.FISTAReconstructor import FISTAReconstructor
-import astra
-import matplotlib.pyplot as plt
-
-def RMSE(signal1, signal2):
- '''RMSE Root Mean Squared Error'''
- if numpy.shape(signal1) == numpy.shape(signal2):
- err = (signal1 - signal2)
- err = numpy.sum( err * err )/numpy.size(signal1); # MSE
- err = sqrt(err); # RMSE
- return err
- else:
- raise Exception('Input signals must have the same shape')
-
-filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/demos/DendrData.h5'
-nx = h5py.File(filename, "r")
-#getEntry(nx, '/')
-# I have exported the entries as children of /
-entries = [entry for entry in nx['/'].keys()]
-print (entries)
-
-Sino3D = numpy.asarray(nx.get('/Sino3D'), dtype="float32")
-Weights3D = numpy.asarray(nx.get('/Weights3D'), dtype="float32")
-angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
-angles_rad = numpy.asarray(nx.get('/angles_rad'), dtype="float32")
-recon_size = numpy.asarray(nx.get('/recon_size'), dtype=int)[0]
-size_det = numpy.asarray(nx.get('/size_det'), dtype=int)[0]
-slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
-
-Z_slices = 20
-det_row_count = Z_slices
-# next definition is just for consistency of naming
-det_col_count = size_det
-
-detectorSpacingX = 1.0
-detectorSpacingY = detectorSpacingX
-
-
-proj_geom = astra.creators.create_proj_geom('parallel3d',
- detectorSpacingX,
- detectorSpacingY,
- det_row_count,
- det_col_count,
- angles_rad)
-
-#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
-image_size_x = recon_size
-image_size_y = recon_size
-image_size_z = Z_slices
-vol_geom = astra.creators.create_vol_geom( image_size_x,
- image_size_y,
- image_size_z)
-
-## First pass the arguments to the FISTAReconstructor and test the
-## Lipschitz constant
-
-fistaRecon = FISTAReconstructor(proj_geom,
- vol_geom,
- Sino3D ,
- weights=Weights3D)
-
-print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
-fistaRecon.setParameter(number_of_iterations = 12)
-fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
-fistaRecon.setParameter(ring_alpha = 21)
-fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
-
-## Ordered subset
-if False:
- subsets = 16
- angles = fistaRecon.getParameter('projector_geometry')['ProjectionAngles']
- #binEdges = numpy.linspace(angles.min(),
- # angles.max(),
- # subsets + 1)
- binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
- # get rearranged subset indices
- IndicesReorg = numpy.zeros((numpy.shape(angles)))
- counterM = 0
- for ii in range(binsDiscr.max()):
- counter = 0
- for jj in range(subsets):
- curr_index = ii + jj + counter
- #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
- if binsDiscr[jj] > ii:
- if (counterM < numpy.size(IndicesReorg)):
- IndicesReorg[counterM] = curr_index
- counterM = counterM + 1
-
- counter = counter + binsDiscr[jj] - 1
-
-
-if False:
- print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
- print ("prepare for iteration")
- fistaRecon.prepareForIteration()
-
-
-
- print("initializing ...")
- if False:
- # if X doesn't exist
- #N = params.vol_geom.GridColCount
- N = vol_geom['GridColCount']
- print ("N " + str(N))
- X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
- else:
- #X = fistaRecon.initialize()
- X = numpy.load("X.npy")
-
- print (numpy.shape(X))
- X_t = X.copy()
- print ("initialized")
- proj_geom , vol_geom, sino , \
- SlicesZ = fistaRecon.getParameter(['projector_geometry' ,
- 'output_geometry',
- 'input_sinogram',
- 'SlicesZ'])
-
- #fistaRecon.setParameter(number_of_iterations = 3)
- iterFISTA = fistaRecon.getParameter('number_of_iterations')
- # errors vector (if the ground truth is given)
- Resid_error = numpy.zeros((iterFISTA));
- # objective function values vector
- objective = numpy.zeros((iterFISTA));
-
-
- t = 1
-
-
- print ("starting iterations")
-## % Outer FISTA iterations loop
- for i in range(fistaRecon.getParameter('number_of_iterations')):
- X_old = X.copy()
- t_old = t
- r_old = fistaRecon.r.copy()
- if fistaRecon.getParameter('projector_geometry')['type'] == 'parallel' or \
- fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat' or \
- fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat_vec' :
- # if the geometry is parallel use slice-by-slice
- # projection-backprojection routine
- #sino_updt = zeros(size(sino),'single');
- proj_geomT = proj_geom.copy()
- proj_geomT['DetectorRowCount'] = 1
- vol_geomT = vol_geom.copy()
- vol_geomT['GridSliceCount'] = 1;
- sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
- for kkk in range(SlicesZ):
- sino_id, sino_updt[kkk] = \
- astra.creators.create_sino3d_gpu(
- X_t[kkk:kkk+1], proj_geom, vol_geom)
- astra.matlab.data3d('delete', sino_id)
- else:
- # for divergent 3D geometry (watch the GPU memory overflow in
- # ASTRA versions < 1.8)
- #[sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
- sino_id, sino_updt = astra.creators.create_sino3d_gpu(
- X_t, proj_geom, vol_geom)
-
- ## RING REMOVAL
- residual = fistaRecon.residual
- lambdaR_L1 , alpha_ring , weights , L_const= \
- fistaRecon.getParameter(['ring_lambda_R_L1',
- 'ring_alpha' , 'weights',
- 'Lipschitz_constant'])
- r_x = fistaRecon.r_x
- SlicesZ, anglesNumb, Detectors = \
- numpy.shape(fistaRecon.getParameter('input_sinogram'))
- if lambdaR_L1 > 0 :
- print ("ring removal")
- for kkk in range(anglesNumb):
-
- residual[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
- ((sino_updt[:,kkk,:]).squeeze() - \
- (sino[:,kkk,:]).squeeze() -\
- (alpha_ring * r_x)
- )
- vec = residual.sum(axis = 1)
- #if SlicesZ > 1:
- # vec = vec[:,1,:].squeeze()
- fistaRecon.r = (r_x - (1./L_const) * vec).copy()
- objective[i] = (0.5 * (residual ** 2).sum())
-## % the ring removal part (Group-Huber fidelity)
-## for kkk = 1:anglesNumb
-## residual(:,kkk,:) = squeeze(weights(:,kkk,:)).*
-## (squeeze(sino_updt(:,kkk,:)) -
-## (squeeze(sino(:,kkk,:)) - alpha_ring.*r_x));
-## end
-## vec = sum(residual,2);
-## if (SlicesZ > 1)
-## vec = squeeze(vec(:,1,:));
-## end
-## r = r_x - (1./L_const).*vec;
-## objective(i) = (0.5*sum(residual(:).^2)); % for the objective function output
-
-
-
- # Projection/Backprojection Routine
- if fistaRecon.getParameter('projector_geometry')['type'] == 'parallel' or \
- fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat' or\
- fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat_vec':
- x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
- print ("Projection/Backprojection Routine")
- for kkk in range(SlicesZ):
-
- x_id, x_temp[kkk] = \
- astra.creators.create_backprojection3d_gpu(
- residual[kkk:kkk+1],
- proj_geomT, vol_geomT)
- astra.matlab.data3d('delete', x_id)
- else:
- x_id, x_temp = \
- astra.creators.create_backprojection3d_gpu(
- residual, proj_geom, vol_geom)
-
- X = X_t - (1/L_const) * x_temp
- astra.matlab.data3d('delete', sino_id)
- astra.matlab.data3d('delete', x_id)
-
-
- ## REGULARIZATION
- ## SKIPPING FOR NOW
- ## Should be simpli
- # regularizer = fistaRecon.getParameter('regularizer')
- # for slices:
- # out = regularizer(input=X)
- print ("skipping regularizer")
-
-
- ## FINAL
- print ("final")
- lambdaR_L1 = fistaRecon.getParameter('ring_lambda_R_L1')
- if lambdaR_L1 > 0:
- fistaRecon.r = numpy.max(
- numpy.abs(fistaRecon.r) - lambdaR_L1 , 0) * \
- numpy.sign(fistaRecon.r)
- t = (1 + numpy.sqrt(1 + 4 * t**2))/2
- X_t = X + (((t_old -1)/t) * (X - X_old))
-
- if lambdaR_L1 > 0:
- fistaRecon.r_x = fistaRecon.r + \
- (((t_old-1)/t) * (fistaRecon.r - r_old))
-
- if fistaRecon.getParameter('region_of_interest') is None:
- string = 'Iteration Number {0} | Objective {1} \n'
- print (string.format( i, objective[i]))
- else:
- ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
- 'ideal_image')
-
- Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
- string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
- print (string.format(i,Resid_error[i], objective[i]))
-
-## if (lambdaR_L1 > 0)
-## r = max(abs(r)-lambdaR_L1, 0).*sign(r); % soft-thresholding operator for ring vector
-## end
-##
-## t = (1 + sqrt(1 + 4*t^2))/2; % updating t
-## X_t = X + ((t_old-1)/t).*(X - X_old); % updating X
-##
-## if (lambdaR_L1 > 0)
-## r_x = r + ((t_old-1)/t).*(r - r_old); % updating r
-## end
-##
-## if (show == 1)
-## figure(10); imshow(X(:,:,slice), [0 maxvalplot]);
-## if (lambdaR_L1 > 0)
-## figure(11); plot(r); title('Rings offset vector')
-## end
-## pause(0.01);
-## end
-## if (strcmp(X_ideal, 'none' ) == 0)
-## Resid_error(i) = RMSE(X(ROI), X_ideal(ROI));
-## fprintf('%s %i %s %s %.4f %s %s %f \n', 'Iteration Number:', i, '|', 'Error RMSE:', Resid_error(i), '|', 'Objective:', objective(i));
-## else
-## fprintf('%s %i %s %s %f \n', 'Iteration Number:', i, '|', 'Objective:', objective(i));
-## end
-else:
- fistaRecon = FISTAReconstructor(proj_geom,
- vol_geom,
- Sino3D ,
- weights=Weights3D)
-
- print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
- fistaRecon.setParameter(number_of_iterations = 12)
- fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
- fistaRecon.setParameter(ring_alpha = 21)
- fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
- fistaRecon.prepareForIteration()
- X = fistaRecon.iterate(numpy.load("X.npy"))
diff --git a/src/Python/test_regularizers.py b/src/Python/test_regularizers.py
deleted file mode 100644
index e76262c..0000000
--- a/src/Python/test_regularizers.py
+++ /dev/null
@@ -1,412 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Aug 4 11:10:05 2017
-
-@author: ofn77899
-"""
-
-#from ccpi.viewer.CILViewer2D import Converter
-#import vtk
-
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-from enum import Enum
-import timeit
-#from PIL import Image
-#from Regularizer import Regularizer
-from ccpi.imaging.Regularizer import Regularizer
-
-###############################################################################
-#https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956
-#NRMSE a normalization of the root of the mean squared error
-#NRMSE is simply 1 - [RMSE / (maxval - minval)]. Where maxval is the maximum
-# intensity from the two images being compared, and respectively the same for
-# minval. RMSE is given by the square root of MSE:
-# sqrt[(sum(A - B) ** 2) / |A|],
-# where |A| means the number of elements in A. By doing this, the maximum value
-# given by RMSE is maxval.
-
-def nrmse(im1, im2):
- a, b = im1.shape
- rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b))
- max_val = max(np.max(im1), np.max(im2))
- min_val = min(np.min(im1), np.min(im2))
- return 1 - (rmse / (max_val - min_val))
-###############################################################################
-
-###############################################################################
-#
-# 2D Regularizers
-#
-###############################################################################
-#Example:
-# figure;
-# Im = double(imread('lena_gray_256.tif'))/255; % loading image
-# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
-# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-
-
-#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
-filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
-#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
-
-#reader = vtk.vtkTIFFReader()
-#reader.SetFileName(os.path.normpath(filename))
-#reader.Update()
-Im = plt.imread(filename)
-#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255
-#img.show()
-Im = np.asarray(Im, dtype='float32')
-
-
-
-
-#imgplot = plt.imshow(Im)
-perc = 0.05
-u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
-# map the u0 u0->u0>0
-f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-u0 = f(u0).astype('float32')
-
-## plot
-fig = plt.figure()
-#a=fig.add_subplot(3,3,1)
-#a.set_title('Original')
-#imgplot = plt.imshow(Im)
-
-a=fig.add_subplot(2,3,1)
-a.set_title('noise')
-imgplot = plt.imshow(u0,cmap="gray")
-
-reg_output = []
-##############################################################################
-# Call regularizer
-
-####################### SplitBregman_TV #####################################
-# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-
-use_object = True
-if use_object:
- reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
- print (reg.pars)
- reg.setParameter(input=u0)
- reg.setParameter(regularization_parameter=10.)
- # or
- # reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30,
- #tolerance_constant=1e-4,
- #TV_Penalty=Regularizer.TotalVariationPenalty.l1)
- plotme = reg() [0]
- pars = reg.pars
- textstr = reg.printParametersToString()
-
- #out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
- #tolerance_constant=1e-4,
- # TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
-#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
-# tolerance_constant=1e-4,
-# TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
-else:
- out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
- pars = out2[2]
- reg_output.append(out2)
- plotme = reg_output[-1][0]
- textstr = out2[-1]
-
-a=fig.add_subplot(2,3,2)
-
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(plotme,cmap="gray")
-
-###################### FGP_TV #########################################
-# u = FGP_TV(single(u0), 0.05, 100, 1e-04);
-out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
- number_of_iterations=200)
-pars = out2[-2]
-
-reg_output.append(out2)
-
-a=fig.add_subplot(2,3,3)
-
-textstr = out2[-1]
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0])
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
-
-###################### LLT_model #########################################
-# * u0 = Im + .03*randn(size(Im)); % adding noise
-# [Den] = LLT_model(single(u0), 10, 0.1, 1);
-#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0);
-#input, regularization_parameter , time_step, number_of_iterations,
-# tolerance_constant, restrictive_Z_smoothing=0
-out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
- time_step=0.0003,
- tolerance_constant=0.0001,
- number_of_iterations=300)
-pars = out2[-2]
-
-reg_output.append(out2)
-
-a=fig.add_subplot(2,3,4)
-
-textstr = out2[-1]
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
-
-
-# ###################### PatchBased_Regul #########################################
-# # Quick 2D denoising example in Matlab:
-# # Im = double(imread('lena_gray_256.tif'))/255; % loading image
-# # u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# # ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05);
-
-out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
- searching_window_ratio=3,
- similarity_window_ratio=1,
- PB_filtering_parameter=0.08)
-pars = out2[-2]
-reg_output.append(out2)
-
-a=fig.add_subplot(2,3,5)
-
-
-textstr = out2[-1]
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
-
-
-# ###################### TGV_PD #########################################
-# # Quick 2D denoising example in Matlab:
-# # Im = double(imread('lena_gray_256.tif'))/255; % loading image
-# # u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# # u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
-
-
-out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
- first_order_term=1.3,
- second_order_term=1,
- number_of_iterations=550)
-pars = out2[-2]
-reg_output.append(out2)
-
-a=fig.add_subplot(2,3,6)
-
-
-textstr = out2[-1]
-
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
- verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
-
-
-plt.show()
-
-################################################################################
-##
-## 3D Regularizers
-##
-################################################################################
-##Example:
-## figure;
-## Im = double(imread('lena_gray_256.tif'))/255; % loading image
-## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
-## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-#
-##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha"
-#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha"
-#
-#reader = vtk.vtkMetaImageReader()
-#reader.SetFileName(os.path.normpath(filename))
-#reader.Update()
-##vtk returns 3D images, let's take just the one slice there is as 2D
-#Im = Converter.vtk2numpy(reader.GetOutput())
-#Im = Im.astype('float32')
-##imgplot = plt.imshow(Im)
-#perc = 0.05
-#u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
-## map the u0 u0->u0>0
-#f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-#u0 = f(u0).astype('float32')
-#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(),
-# reader.GetOutput().GetOrigin())
-#converter.Update()
-#writer = vtk.vtkMetaImageWriter()
-#writer.SetInputData(converter.GetOutput())
-#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha")
-##writer.Write()
-#
-#
-### plot
-#fig3D = plt.figure()
-##a=fig.add_subplot(3,3,1)
-##a.set_title('Original')
-##imgplot = plt.imshow(Im)
-#sliceNo = 32
-#
-#a=fig3D.add_subplot(2,3,1)
-#a.set_title('noise')
-#imgplot = plt.imshow(u0.T[sliceNo])
-#
-#reg_output3d = []
-#
-###############################################################################
-## Call regularizer
-#
-######################## SplitBregman_TV #####################################
-## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-#
-##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-#
-##out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
-## #tolerance_constant=1e-4,
-## TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-#
-#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
-# tolerance_constant=1e-4,
-# TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-#
-#
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-####################### FGP_TV #########################################
-## u = FGP_TV(single(u0), 0.05, 100, 1e-04);
-#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
-# number_of_iterations=200)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-####################### LLT_model #########################################
-## * u0 = Im + .03*randn(size(Im)); % adding noise
-## [Den] = LLT_model(single(u0), 10, 0.1, 1);
-##Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0);
-##input, regularization_parameter , time_step, number_of_iterations,
-## tolerance_constant, restrictive_Z_smoothing=0
-#out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
-# time_step=0.0003,
-# tolerance_constant=0.0001,
-# number_of_iterations=300)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-####################### PatchBased_Regul #########################################
-## Quick 2D denoising example in Matlab:
-## Im = double(imread('lena_gray_256.tif'))/255; % loading image
-## u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-## ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05);
-#
-#out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
-# searching_window_ratio=3,
-# similarity_window_ratio=1,
-# PB_filtering_parameter=0.08)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-#
-
-###################### TGV_PD #########################################
-# Quick 2D denoising example in Matlab:
-# Im = double(imread('lena_gray_256.tif'))/255; % loading image
-# u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-# u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
-
-
-#out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
-# first_order_term=1.3,
-# second_order_term=1,
-# number_of_iterations=550)
-#pars = out2[-2]
-#reg_output3d.append(out2)
-#
-#a=fig3D.add_subplot(2,3,2)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-# verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
--
cgit v1.2.3