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-rw-r--r--src/Python/fista_module.cpp576
1 files changed, 473 insertions, 103 deletions
diff --git a/src/Python/fista_module.cpp b/src/Python/fista_module.cpp
index 5344083..2492884 100644
--- a/src/Python/fista_module.cpp
+++ b/src/Python/fista_module.cpp
@@ -26,12 +26,10 @@ limitations under the License.
#include <boost/python/numpy.hpp>
#include "boost/tuple/tuple.hpp"
-// include the regularizers
-#include "FGP_TV_core.h"
-#include "LLT_model_core.h"
-#include "PatchBased_Regul_core.h"
#include "SplitBregman_TV_core.h"
-#include "TGV_PD_core.h"
+#include "FGP_TV_core.h"
+
+
#if defined(_WIN32) || defined(_WIN32) || defined(__WIN32__) || defined(_WIN64)
#include <windows.h>
@@ -43,7 +41,6 @@ __if_not_exists(uint16_t) { typedef __int8 uint16_t; }
namespace bp = boost::python;
namespace np = boost::python::numpy;
-
/*! in the Matlab implementation this is called as
void mexFunction(
int nlhs, mxArray *plhs[],
@@ -56,9 +53,7 @@ nlhs Array of pointers to the OUTPUT mxArrays
plhs int number of OUTPUT mxArrays
***********************************************************
-mxGetData
-args: pm Pointer to an mxArray
-Returns: Pointer to the first element of the real data. Returns NULL in C (0 in Fortran) if there is no real data.
+
***********************************************************
double mxGetScalar(const mxArray *pm);
args: pm Pointer to an mxArray; cannot be a cell mxArray, a structure mxArray, or an empty mxArray.
@@ -92,77 +87,143 @@ 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: ndim: Number 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).
-
+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.
+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>
-np::ndarray zeros(int dims, int * dim_array, T el) {
- bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
- np::dtype dtype = np::dtype::get_builtin<T>();
- np::ndarray zz = np::zeros(shape, dtype);
- return zz;
+double mxGetScalar(const np::ndarray plh) {
+ return (double)bp::extract<T>(plh[0]);
}
-bp::list SplitBregman_TV(np::ndarray input, double d_mu, , int niterations, double d_epsil, int TV_type) {
- /* C-OMP implementation of Split Bregman - TV denoising-regularization model (2D/3D)
- *
- * Input Parameters:
- * 1. Noisy image/volume
- * 2. lambda - regularization parameter
- * 3. Number of iterations [OPTIONAL parameter]
- * 4. eplsilon - tolerance constant [OPTIONAL parameter]
- * 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter]
- *
- * Output:
- * Filtered/regularized image
- *
- * All sanity checks and default values are set in Python
+
+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]);
+}
+
+
+
+
+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 = (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 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, iter, dimX, dimY, dimZ, ll, j, count, methTV;
- const int dim_array[3];
+ 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 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] = -11;
+ dim_array[2] = -1;
}
else {
dim_array[2] = input.shape(2);
}
- /*Handling Matlab input data*/
+ // 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*/
+ ///*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 */
- iter = niterations;
+
//epsil = 0.0001; /* default tolerance constant */
epsil = (float)d_epsil;
//methTV = 0; /* default isotropic TV penalty */
- methTV = TV_type;
//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) {
@@ -182,34 +243,31 @@ bp::list SplitBregman_TV(np::ndarray input, double d_mu, , int niterations, doub
if (number_of_dims == 2) {
dimZ = 1; /*2D case*/
- /*
- mxArray *mxCreateNumericArray(mwSize ndim, const mwSize *dims, mxClassID classid, mxComplexity ComplexFlag);
-args: ndim: Number 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).
-
- mxCreateNumericArray initializes all its real data elements to 0.
-*/
-
-/*
- 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));
-*/
//U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
- U = A = reinterpret_cast<float *>input.get_data();
- 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));
+ //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 */
@@ -245,59 +303,370 @@ args: ndim: Number of dimensions. If you specify a value for ndim that is less
/*printf("%f %i %i \n", re, ll, count); */
/*copyIm(U_old, U, dimX, dimY, dimZ); */
+ result.append<np::ndarray>(npU);
+ result.append<int>(ll);
+ }
+ //printf("SB iterations stopped at iteration: %i\n", 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);
}
- printf("SB iterations stopped at iteration: %i\n", 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));
+ return result;
- copyIm(A, U, dimX, dimY, dimZ); /*initialize */
+}
- /* begin outer SB iterations */
+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, iter, dimX, dimY, dimZ, ll, j, count, methTV;
+ 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]);
+
+ 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);
+ }
+
+ // 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"); }
+
+ //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 = zeros(2, dim_array, (float)0);
+
+ 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++) {
- /*storing old values*/
- copyIm(U, U_old, dimX, dimY, dimZ);
+ /* computing the gradient of the objective function */
+ Obj_func2D(A, D, R1, R2, lambda, dimX, dimY);
- /*GS iteration */
- gauss_seidel3D(U, A, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda, mu);
+ /*Taking a step towards minus of the gradient*/
+ Grad_func2D(P1, P2, D, R1, R2, lambda, dimX, dimY);
- 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);
+ /* projection step */
+ Proj_func2D(P1, P2, methTV, dimX, dimY);
- updBxByBz3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ);
+ /*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 to terminate earlier */
+ /* calculate norm */
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 += pow(D[j] - D_old[j], 2);
+ re1 += pow(D[j], 2);
}
re = sqrt(re) / sqrt(re1);
if (re < epsil) count++;
- if (count > 4) break;
+ 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) break;
+ 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());
+ R2 = 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("SB iterations stopped at iteration: %i\n", ll);
+ //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);
}
- bp::list result;
+
return result;
}
-
BOOST_PYTHON_MODULE(fista)
{
@@ -310,6 +679,7 @@ BOOST_PYTHON_MODULE(fista)
np::dtype dt1 = np::dtype::get_builtin<uint8_t>();
np::dtype dt2 = np::dtype::get_builtin<uint16_t>();
-
def("mexFunction", mexFunction);
+ def("SplitBregman_TV", SplitBregman_TV);
+ def("FGP_TV", FGP_TV);
} \ No newline at end of file
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-30 05:22:34 +0000