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/*
* This work is part of the Core Imaging Library developed by
* Visual Analytics and Imaging System Group of the Science Technology
* Facilities Council, STFC and Diamond Light Source Ltd.
*
* Copyright 2017 Daniil Kazantsev
* Copyright 2017 Srikanth Nagella, Edoardo Pasca
* Copyright 2018 Diamond Light Source Ltd.
*
* 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.
*/
#include "matrix.h"
#include "mex.h"
#include "PatchSelect_core.h"
/* C-OMP implementation of non-local weight pre-calculation for non-local priors
* Weights and associated indices are stored into pre-allocated arrays and passed
* to the regulariser
*
*
* Input Parameters:
* 1. 2D/3D grayscale image/volume
* 2. Searching window (half-size of the main bigger searching window, e.g. 11)
* 3. Similarity window (half-size of the patch window, e.g. 2)
* 4. The number of neighbours to take (the most prominent after sorting neighbours will be taken)
* 5. noise-related parameter to calculate non-local weights
*
* Output [2D]:
* 1. AR_i - indeces of i neighbours
* 2. AR_j - indeces of j neighbours
* 3. Weights_ij - associated weights
*
* Output [3D]:
* 1. AR_i - indeces of i neighbours
* 2. AR_j - indeces of j neighbours
* 3. AR_k - indeces of j neighbours
* 4. Weights_ijk - associated weights
*/
/**************************************************/
void mexFunction(
int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[])
{
int number_of_dims, SearchWindow, SimilarWin, NumNeighb;
mwSize dimX, dimY, dimZ;
const mwSize *dim_array;
unsigned short *H_i=NULL, *H_j=NULL, *H_k=NULL;
float *A, *Weights = NULL, h;
mwSize dim_array2[3]; /* for 2D data */
mwSize dim_array3[4]; /* for 3D data */
dim_array = mxGetDimensions(prhs[0]);
number_of_dims = mxGetNumberOfDimensions(prhs[0]);
/*Handling Matlab input data*/
A = (float *) mxGetData(prhs[0]); /* a 2D or 3D image/volume */
SearchWindow = (int) mxGetScalar(prhs[1]); /* Large Searching window */
SimilarWin = (int) mxGetScalar(prhs[2]); /* Similarity window (patch-search)*/
NumNeighb = (int) mxGetScalar(prhs[3]); /* the total number of neighbours to take */
h = (float) mxGetScalar(prhs[4]); /* NLM parameter */
dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
dim_array2[0] = dimX; dim_array2[1] = dimY; dim_array2[2] = NumNeighb; /* 2D case */
dim_array3[0] = dimX; dim_array3[1] = dimY; dim_array3[2] = dimZ; dim_array3[3] = NumNeighb; /* 3D case */
/****************2D INPUT ***************/
if (number_of_dims == 2) {
dimZ = 0;
H_i = (unsigned short*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array2, mxUINT16_CLASS, mxREAL));
H_j = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(3, dim_array2, mxUINT16_CLASS, mxREAL));
Weights = (float*)mxGetPr(plhs[2] = mxCreateNumericArray(3, dim_array2, mxSINGLE_CLASS, mxREAL));
}
/****************3D INPUT ***************/
if (number_of_dims == 3) {
H_i = (unsigned short*)mxGetPr(plhs[0] = mxCreateNumericArray(4, dim_array3, mxUINT16_CLASS, mxREAL));
H_j = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(4, dim_array3, mxUINT16_CLASS, mxREAL));
H_k = (unsigned short*)mxGetPr(plhs[2] = mxCreateNumericArray(4, dim_array3, mxUINT16_CLASS, mxREAL));
Weights = (float*)mxGetPr(plhs[3] = mxCreateNumericArray(4, dim_array3, mxSINGLE_CLASS, mxREAL));
}
PatchSelect_CPU_main(A, H_i, H_j, H_k, Weights, (long)(dimX), (long)(dimY), (long)(dimZ), SearchWindow, SimilarWin, NumNeighb, h);
}
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