added PatchBased_Regul

2 files changed, 123 insertions, 1 deletions

diff --git a/src/Python/fista_module.cpp b/src/Python/fista_module.cpp
index d890b10..c2d9352 100644
--- a/src/Python/fista_module.cpp
+++ b/src/Python/fista_module.cpp
@@ -29,6 +29,7 @@ limitations under the License.
 #include "SplitBregman_TV_core.h"
 #include "FGP_TV_core.h"
 #include "LLT_model_core.h"
+#include "PatchBased_Regul_core.h"
 #include "utils.h"
 
 
@@ -793,7 +794,7 @@ bp::list LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, d
 		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]);
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
 		np::dtype dtype = np::dtype::get_builtin<float>();
 
 
@@ -865,6 +866,126 @@ bp::list LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, d
 }
 
 
+bp::list PatchBased_Regul(np::ndarray input, int SearchW_real, int SimilW,  double d_h, double d_lambda) {
+	// 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 */
+	//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");
+
+	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;
+}
+
 BOOST_PYTHON_MODULE(regularizers)
 {
 	np::initialize();
diff --git a/src/Python/setup.py b/src/Python/setup.py
index a8feb1c..a4eed14 100644
--- a/src/Python/setup.py
+++ b/src/Python/setup.py
@@ -52,6 +52,7 @@ setup(
                                       "..\\..\\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\\utils.c"
                                         ],
                              include_dirs=extra_include_dirs, library_dirs=extra_library_dirs, extra_compile_args=extra_compile_args, libraries=extra_libraries ),