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647 lines
22 KiB
647 lines
22 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// Intel License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000, Intel Corporation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of Intel Corporation may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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/****************************************************************************************\ |
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Calculation of a texture descriptors from GLCM (Grey Level Co-occurrence Matrix'es) |
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The code was submitted by Daniel Eaton [danieljameseaton@yahoo.com] |
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\****************************************************************************************/ |
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#include "precomp.hpp" |
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#include <math.h> |
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#include <assert.h> |
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#define CV_MAX_NUM_GREY_LEVELS_8U 256 |
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struct CvGLCM |
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{ |
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int matrixSideLength; |
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int numMatrices; |
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double*** matrices; |
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int numLookupTableElements; |
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int forwardLookupTable[CV_MAX_NUM_GREY_LEVELS_8U]; |
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int reverseLookupTable[CV_MAX_NUM_GREY_LEVELS_8U]; |
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double** descriptors; |
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int numDescriptors; |
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int descriptorOptimizationType; |
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int optimizationType; |
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}; |
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static void icvCreateGLCM_LookupTable_8u_C1R( const uchar* srcImageData, int srcImageStep, |
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CvSize srcImageSize, CvGLCM* destGLCM, |
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int* steps, int numSteps, int* memorySteps ); |
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static void |
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icvCreateGLCMDescriptors_AllowDoubleNest( CvGLCM* destGLCM, int matrixIndex ); |
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CV_IMPL CvGLCM* |
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cvCreateGLCM( const IplImage* srcImage, |
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int stepMagnitude, |
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const int* srcStepDirections,/* should be static array.. |
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or if not the user should handle de-allocation */ |
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int numStepDirections, |
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int optimizationType ) |
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{ |
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static const int defaultStepDirections[] = { 0,1, -1,1, -1,0, -1,-1 }; |
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int* memorySteps = 0; |
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CvGLCM* newGLCM = 0; |
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int* stepDirections = 0; |
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CV_FUNCNAME( "cvCreateGLCM" ); |
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__BEGIN__; |
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uchar* srcImageData = 0; |
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CvSize srcImageSize; |
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int srcImageStep; |
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int stepLoop; |
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const int maxNumGreyLevels8u = CV_MAX_NUM_GREY_LEVELS_8U; |
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if( !srcImage ) |
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CV_ERROR( CV_StsNullPtr, "" ); |
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if( srcImage->nChannels != 1 ) |
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CV_ERROR( CV_BadNumChannels, "Number of channels must be 1"); |
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if( srcImage->depth != IPL_DEPTH_8U ) |
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CV_ERROR( CV_BadDepth, "Depth must be equal IPL_DEPTH_8U"); |
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// no Directions provided, use the default ones - 0 deg, 45, 90, 135 |
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if( !srcStepDirections ) |
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{ |
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srcStepDirections = defaultStepDirections; |
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} |
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CV_CALL( stepDirections = (int*)cvAlloc( numStepDirections*2*sizeof(stepDirections[0]))); |
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memcpy( stepDirections, srcStepDirections, numStepDirections*2*sizeof(stepDirections[0])); |
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cvGetImageRawData( srcImage, &srcImageData, &srcImageStep, &srcImageSize ); |
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// roll together Directions and magnitudes together with knowledge of image (step) |
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CV_CALL( memorySteps = (int*)cvAlloc( numStepDirections*sizeof(memorySteps[0]))); |
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for( stepLoop = 0; stepLoop < numStepDirections; stepLoop++ ) |
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{ |
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stepDirections[stepLoop*2 + 0] *= stepMagnitude; |
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stepDirections[stepLoop*2 + 1] *= stepMagnitude; |
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memorySteps[stepLoop] = stepDirections[stepLoop*2 + 0]*srcImageStep + |
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stepDirections[stepLoop*2 + 1]; |
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} |
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CV_CALL( newGLCM = (CvGLCM*)cvAlloc(sizeof(newGLCM))); |
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memset( newGLCM, 0, sizeof(*newGLCM) ); |
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newGLCM->matrices = 0; |
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newGLCM->numMatrices = numStepDirections; |
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newGLCM->optimizationType = optimizationType; |
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if( optimizationType <= CV_GLCM_OPTIMIZATION_LUT ) |
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{ |
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int lookupTableLoop, imageColLoop, imageRowLoop, lineOffset = 0; |
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// if optimization type is set to lut, then make one for the image |
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if( optimizationType == CV_GLCM_OPTIMIZATION_LUT ) |
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{ |
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for( imageRowLoop = 0; imageRowLoop < srcImageSize.height; |
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imageRowLoop++, lineOffset += srcImageStep ) |
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{ |
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for( imageColLoop = 0; imageColLoop < srcImageSize.width; imageColLoop++ ) |
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{ |
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newGLCM->forwardLookupTable[srcImageData[lineOffset+imageColLoop]]=1; |
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} |
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} |
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newGLCM->numLookupTableElements = 0; |
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for( lookupTableLoop = 0; lookupTableLoop < maxNumGreyLevels8u; lookupTableLoop++ ) |
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{ |
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if( newGLCM->forwardLookupTable[ lookupTableLoop ] != 0 ) |
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{ |
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newGLCM->forwardLookupTable[ lookupTableLoop ] = |
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newGLCM->numLookupTableElements; |
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newGLCM->reverseLookupTable[ newGLCM->numLookupTableElements ] = |
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lookupTableLoop; |
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newGLCM->numLookupTableElements++; |
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} |
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} |
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} |
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// otherwise make a "LUT" which contains all the gray-levels (for code-reuse) |
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else if( optimizationType == CV_GLCM_OPTIMIZATION_NONE ) |
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{ |
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for( lookupTableLoop = 0; lookupTableLoop <maxNumGreyLevels8u; lookupTableLoop++ ) |
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{ |
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newGLCM->forwardLookupTable[ lookupTableLoop ] = lookupTableLoop; |
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newGLCM->reverseLookupTable[ lookupTableLoop ] = lookupTableLoop; |
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} |
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newGLCM->numLookupTableElements = maxNumGreyLevels8u; |
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} |
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newGLCM->matrixSideLength = newGLCM->numLookupTableElements; |
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icvCreateGLCM_LookupTable_8u_C1R( srcImageData, srcImageStep, srcImageSize, |
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newGLCM, stepDirections, |
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numStepDirections, memorySteps ); |
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} |
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else if( optimizationType == CV_GLCM_OPTIMIZATION_HISTOGRAM ) |
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{ |
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CV_ERROR( CV_StsBadFlag, "Histogram-based method is not implemented" ); |
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/* newGLCM->numMatrices *= 2; |
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newGLCM->matrixSideLength = maxNumGreyLevels8u*2; |
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icvCreateGLCM_Histogram_8uC1R( srcImageStep, srcImageSize, srcImageData, |
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newGLCM, numStepDirections, |
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stepDirections, memorySteps ); |
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*/ |
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} |
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__END__; |
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cvFree( &memorySteps ); |
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cvFree( &stepDirections ); |
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if( cvGetErrStatus() < 0 ) |
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{ |
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cvFree( &newGLCM ); |
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} |
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return newGLCM; |
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} |
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CV_IMPL void |
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cvReleaseGLCM( CvGLCM** GLCM, int flag ) |
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{ |
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CV_FUNCNAME( "cvReleaseGLCM" ); |
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__BEGIN__; |
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int matrixLoop; |
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if( !GLCM ) |
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CV_ERROR( CV_StsNullPtr, "" ); |
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if( *GLCM ) |
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EXIT; // repeated deallocation: just skip it. |
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if( (flag == CV_GLCM_GLCM || flag == CV_GLCM_ALL) && (*GLCM)->matrices ) |
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{ |
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for( matrixLoop = 0; matrixLoop < (*GLCM)->numMatrices; matrixLoop++ ) |
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{ |
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if( (*GLCM)->matrices[ matrixLoop ] ) |
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{ |
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cvFree( (*GLCM)->matrices[matrixLoop] ); |
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cvFree( (*GLCM)->matrices + matrixLoop ); |
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} |
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} |
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cvFree( &((*GLCM)->matrices) ); |
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} |
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if( (flag == CV_GLCM_DESC || flag == CV_GLCM_ALL) && (*GLCM)->descriptors ) |
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{ |
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for( matrixLoop = 0; matrixLoop < (*GLCM)->numMatrices; matrixLoop++ ) |
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{ |
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cvFree( (*GLCM)->descriptors + matrixLoop ); |
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} |
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cvFree( &((*GLCM)->descriptors) ); |
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} |
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if( flag == CV_GLCM_ALL ) |
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{ |
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cvFree( GLCM ); |
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} |
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__END__; |
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} |
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static void |
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icvCreateGLCM_LookupTable_8u_C1R( const uchar* srcImageData, |
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int srcImageStep, |
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CvSize srcImageSize, |
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CvGLCM* destGLCM, |
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int* steps, |
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int numSteps, |
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int* memorySteps ) |
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{ |
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int* stepIncrementsCounter = 0; |
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CV_FUNCNAME( "icvCreateGLCM_LookupTable_8u_C1R" ); |
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__BEGIN__; |
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int matrixSideLength = destGLCM->matrixSideLength; |
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int stepLoop, sideLoop1, sideLoop2; |
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int colLoop, rowLoop, lineOffset = 0; |
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double*** matrices = 0; |
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// allocate memory to the matrices |
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CV_CALL( destGLCM->matrices = (double***)cvAlloc( sizeof(matrices[0])*numSteps )); |
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matrices = destGLCM->matrices; |
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for( stepLoop=0; stepLoop<numSteps; stepLoop++ ) |
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{ |
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CV_CALL( matrices[stepLoop] = (double**)cvAlloc( sizeof(matrices[0])*matrixSideLength )); |
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CV_CALL( matrices[stepLoop][0] = (double*)cvAlloc( sizeof(matrices[0][0])* |
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matrixSideLength*matrixSideLength )); |
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memset( matrices[stepLoop][0], 0, matrixSideLength*matrixSideLength* |
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sizeof(matrices[0][0]) ); |
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for( sideLoop1 = 1; sideLoop1 < matrixSideLength; sideLoop1++ ) |
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{ |
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matrices[stepLoop][sideLoop1] = matrices[stepLoop][sideLoop1-1] + matrixSideLength; |
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} |
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} |
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CV_CALL( stepIncrementsCounter = (int*)cvAlloc( numSteps*sizeof(stepIncrementsCounter[0]))); |
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memset( stepIncrementsCounter, 0, numSteps*sizeof(stepIncrementsCounter[0]) ); |
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// generate GLCM for each step |
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for( rowLoop=0; rowLoop<srcImageSize.height; rowLoop++, lineOffset+=srcImageStep ) |
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{ |
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for( colLoop=0; colLoop<srcImageSize.width; colLoop++ ) |
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{ |
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int pixelValue1 = destGLCM->forwardLookupTable[srcImageData[lineOffset + colLoop]]; |
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for( stepLoop=0; stepLoop<numSteps; stepLoop++ ) |
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{ |
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int col2, row2; |
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row2 = rowLoop + steps[stepLoop*2 + 0]; |
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col2 = colLoop + steps[stepLoop*2 + 1]; |
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if( col2>=0 && row2>=0 && col2<srcImageSize.width && row2<srcImageSize.height ) |
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{ |
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int memoryStep = memorySteps[ stepLoop ]; |
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int pixelValue2 = destGLCM->forwardLookupTable[ srcImageData[ lineOffset + colLoop + memoryStep ] ]; |
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// maintain symmetry |
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matrices[stepLoop][pixelValue1][pixelValue2] ++; |
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matrices[stepLoop][pixelValue2][pixelValue1] ++; |
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// incremenet counter of total number of increments |
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stepIncrementsCounter[stepLoop] += 2; |
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} |
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} |
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} |
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} |
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// normalize matrices. each element is a probability of gray value i,j adjacency in direction/magnitude k |
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for( sideLoop1=0; sideLoop1<matrixSideLength; sideLoop1++ ) |
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{ |
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for( sideLoop2=0; sideLoop2<matrixSideLength; sideLoop2++ ) |
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{ |
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for( stepLoop=0; stepLoop<numSteps; stepLoop++ ) |
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{ |
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matrices[stepLoop][sideLoop1][sideLoop2] /= double(stepIncrementsCounter[stepLoop]); |
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} |
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} |
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} |
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destGLCM->matrices = matrices; |
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__END__; |
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cvFree( &stepIncrementsCounter ); |
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if( cvGetErrStatus() < 0 ) |
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cvReleaseGLCM( &destGLCM, CV_GLCM_GLCM ); |
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} |
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CV_IMPL void |
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cvCreateGLCMDescriptors( CvGLCM* destGLCM, int descriptorOptimizationType ) |
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{ |
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CV_FUNCNAME( "cvCreateGLCMDescriptors" ); |
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__BEGIN__; |
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int matrixLoop; |
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if( !destGLCM ) |
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CV_ERROR( CV_StsNullPtr, "" ); |
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if( !(destGLCM->matrices) ) |
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CV_ERROR( CV_StsNullPtr, "Matrices are not allocated" ); |
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CV_CALL( cvReleaseGLCM( &destGLCM, CV_GLCM_DESC )); |
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if( destGLCM->optimizationType != CV_GLCM_OPTIMIZATION_HISTOGRAM ) |
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{ |
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destGLCM->descriptorOptimizationType = destGLCM->numDescriptors = descriptorOptimizationType; |
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} |
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else |
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{ |
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CV_ERROR( CV_StsBadFlag, "Histogram-based method is not implemented" ); |
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// destGLCM->descriptorOptimizationType = destGLCM->numDescriptors = CV_GLCMDESC_OPTIMIZATION_HISTOGRAM; |
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} |
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CV_CALL( destGLCM->descriptors = (double**) |
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cvAlloc( destGLCM->numMatrices*sizeof(destGLCM->descriptors[0]))); |
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for( matrixLoop = 0; matrixLoop < destGLCM->numMatrices; matrixLoop ++ ) |
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{ |
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CV_CALL( destGLCM->descriptors[ matrixLoop ] = |
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(double*)cvAlloc( destGLCM->numDescriptors*sizeof(destGLCM->descriptors[0][0]))); |
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memset( destGLCM->descriptors[matrixLoop], 0, destGLCM->numDescriptors*sizeof(double) ); |
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switch( destGLCM->descriptorOptimizationType ) |
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{ |
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case CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST: |
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icvCreateGLCMDescriptors_AllowDoubleNest( destGLCM, matrixLoop ); |
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break; |
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default: |
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CV_ERROR( CV_StsBadFlag, |
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"descriptorOptimizationType different from CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST\n" |
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"is not supported" ); |
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/* |
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case CV_GLCMDESC_OPTIMIZATION_ALLOWTRIPLENEST: |
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icvCreateGLCMDescriptors_AllowTripleNest( destGLCM, matrixLoop ); |
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break; |
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case CV_GLCMDESC_OPTIMIZATION_HISTOGRAM: |
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if(matrixLoop < destGLCM->numMatrices>>1) |
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icvCreateGLCMDescriptors_Histogram( destGLCM, matrixLoop); |
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break; |
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*/ |
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} |
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} |
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__END__; |
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if( cvGetErrStatus() < 0 ) |
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cvReleaseGLCM( &destGLCM, CV_GLCM_DESC ); |
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} |
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static void |
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icvCreateGLCMDescriptors_AllowDoubleNest( CvGLCM* destGLCM, int matrixIndex ) |
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{ |
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int sideLoop1, sideLoop2; |
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int matrixSideLength = destGLCM->matrixSideLength; |
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double** matrix = destGLCM->matrices[ matrixIndex ]; |
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double* descriptors = destGLCM->descriptors[ matrixIndex ]; |
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double* marginalProbability = |
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(double*)cvAlloc( matrixSideLength * sizeof(marginalProbability[0])); |
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memset( marginalProbability, 0, matrixSideLength * sizeof(double) ); |
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double maximumProbability = 0; |
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double marginalProbabilityEntropy = 0; |
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double correlationMean = 0, correlationStdDeviation = 0, correlationProductTerm = 0; |
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for( sideLoop1=0; sideLoop1<matrixSideLength; sideLoop1++ ) |
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{ |
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int actualSideLoop1 = destGLCM->reverseLookupTable[ sideLoop1 ]; |
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for( sideLoop2=0; sideLoop2<matrixSideLength; sideLoop2++ ) |
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{ |
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double entryValue = matrix[ sideLoop1 ][ sideLoop2 ]; |
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int actualSideLoop2 = destGLCM->reverseLookupTable[ sideLoop2 ]; |
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int sideLoopDifference = actualSideLoop1 - actualSideLoop2; |
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int sideLoopDifferenceSquared = sideLoopDifference*sideLoopDifference; |
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marginalProbability[ sideLoop1 ] += entryValue; |
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correlationMean += actualSideLoop1*entryValue; |
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maximumProbability = MAX( maximumProbability, entryValue ); |
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if( actualSideLoop2 > actualSideLoop1 ) |
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{ |
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descriptors[ CV_GLCMDESC_CONTRAST ] += sideLoopDifferenceSquared * entryValue; |
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} |
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descriptors[ CV_GLCMDESC_HOMOGENITY ] += entryValue / ( 1.0 + sideLoopDifferenceSquared ); |
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if( entryValue > 0 ) |
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{ |
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descriptors[ CV_GLCMDESC_ENTROPY ] += entryValue * log( entryValue ); |
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} |
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descriptors[ CV_GLCMDESC_ENERGY ] += entryValue*entryValue; |
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} |
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if( marginalProbability[ actualSideLoop1 ] > 0 ) |
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marginalProbabilityEntropy += marginalProbability[ actualSideLoop1 ]*log(marginalProbability[ actualSideLoop1 ]); |
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} |
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marginalProbabilityEntropy = -marginalProbabilityEntropy; |
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descriptors[ CV_GLCMDESC_CONTRAST ] += descriptors[ CV_GLCMDESC_CONTRAST ]; |
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descriptors[ CV_GLCMDESC_ENTROPY ] = -descriptors[ CV_GLCMDESC_ENTROPY ]; |
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descriptors[ CV_GLCMDESC_MAXIMUMPROBABILITY ] = maximumProbability; |
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double HXY = 0, HXY1 = 0, HXY2 = 0; |
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HXY = descriptors[ CV_GLCMDESC_ENTROPY ]; |
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for( sideLoop1=0; sideLoop1<matrixSideLength; sideLoop1++ ) |
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{ |
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double sideEntryValueSum = 0; |
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int actualSideLoop1 = destGLCM->reverseLookupTable[ sideLoop1 ]; |
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for( sideLoop2=0; sideLoop2<matrixSideLength; sideLoop2++ ) |
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{ |
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double entryValue = matrix[ sideLoop1 ][ sideLoop2 ]; |
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sideEntryValueSum += entryValue; |
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int actualSideLoop2 = destGLCM->reverseLookupTable[ sideLoop2 ]; |
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correlationProductTerm += (actualSideLoop1 - correlationMean) * (actualSideLoop2 - correlationMean) * entryValue; |
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double clusterTerm = actualSideLoop1 + actualSideLoop2 - correlationMean - correlationMean; |
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descriptors[ CV_GLCMDESC_CLUSTERTENDENCY ] += clusterTerm * clusterTerm * entryValue; |
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descriptors[ CV_GLCMDESC_CLUSTERSHADE ] += clusterTerm * clusterTerm * clusterTerm * entryValue; |
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double HXYValue = marginalProbability[ actualSideLoop1 ] * marginalProbability[ actualSideLoop2 ]; |
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if( HXYValue>0 ) |
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{ |
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double HXYValueLog = log( HXYValue ); |
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HXY1 += entryValue * HXYValueLog; |
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HXY2 += HXYValue * HXYValueLog; |
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} |
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} |
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correlationStdDeviation += (actualSideLoop1-correlationMean) * (actualSideLoop1-correlationMean) * sideEntryValueSum; |
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} |
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HXY1 = -HXY1; |
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HXY2 = -HXY2; |
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descriptors[ CV_GLCMDESC_CORRELATIONINFO1 ] = ( HXY - HXY1 ) / ( correlationMean ); |
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descriptors[ CV_GLCMDESC_CORRELATIONINFO2 ] = sqrt( 1.0 - exp( -2.0 * (HXY2 - HXY ) ) ); |
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correlationStdDeviation = sqrt( correlationStdDeviation ); |
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descriptors[ CV_GLCMDESC_CORRELATION ] = correlationProductTerm / (correlationStdDeviation*correlationStdDeviation ); |
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delete [] marginalProbability; |
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} |
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CV_IMPL double cvGetGLCMDescriptor( CvGLCM* GLCM, int step, int descriptor ) |
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{ |
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double value = DBL_MAX; |
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CV_FUNCNAME( "cvGetGLCMDescriptor" ); |
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__BEGIN__; |
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if( !GLCM ) |
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CV_ERROR( CV_StsNullPtr, "" ); |
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if( !(GLCM->descriptors) ) |
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CV_ERROR( CV_StsNullPtr, "" ); |
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if( (unsigned)step >= (unsigned)(GLCM->numMatrices)) |
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CV_ERROR( CV_StsOutOfRange, "step is not in 0 .. GLCM->numMatrices - 1" ); |
|
|
|
if( (unsigned)descriptor >= (unsigned)(GLCM->numDescriptors)) |
|
CV_ERROR( CV_StsOutOfRange, "descriptor is not in 0 .. GLCM->numDescriptors - 1" ); |
|
|
|
value = GLCM->descriptors[step][descriptor]; |
|
|
|
__END__; |
|
|
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return value; |
|
} |
|
|
|
|
|
CV_IMPL void |
|
cvGetGLCMDescriptorStatistics( CvGLCM* GLCM, int descriptor, |
|
double* _average, double* _standardDeviation ) |
|
{ |
|
CV_FUNCNAME( "cvGetGLCMDescriptorStatistics" ); |
|
|
|
if( _average ) |
|
*_average = DBL_MAX; |
|
|
|
if( _standardDeviation ) |
|
*_standardDeviation = DBL_MAX; |
|
|
|
__BEGIN__; |
|
|
|
int matrixLoop, numMatrices; |
|
double average = 0, squareSum = 0; |
|
|
|
if( !GLCM ) |
|
CV_ERROR( CV_StsNullPtr, "" ); |
|
|
|
if( !(GLCM->descriptors)) |
|
CV_ERROR( CV_StsNullPtr, "Descriptors are not calculated" ); |
|
|
|
if( (unsigned)descriptor >= (unsigned)(GLCM->numDescriptors) ) |
|
CV_ERROR( CV_StsOutOfRange, "Descriptor index is out of range" ); |
|
|
|
numMatrices = GLCM->numMatrices; |
|
|
|
for( matrixLoop = 0; matrixLoop < numMatrices; matrixLoop++ ) |
|
{ |
|
double temp = GLCM->descriptors[ matrixLoop ][ descriptor ]; |
|
average += temp; |
|
squareSum += temp*temp; |
|
} |
|
|
|
average /= numMatrices; |
|
|
|
if( _average ) |
|
*_average = average; |
|
|
|
if( _standardDeviation ) |
|
*_standardDeviation = sqrt( (squareSum - average*average*numMatrices)/(numMatrices-1)); |
|
|
|
__END__; |
|
} |
|
|
|
|
|
CV_IMPL IplImage* |
|
cvCreateGLCMImage( CvGLCM* GLCM, int step ) |
|
{ |
|
IplImage* dest = 0; |
|
|
|
CV_FUNCNAME( "cvCreateGLCMImage" ); |
|
|
|
__BEGIN__; |
|
|
|
float* destData; |
|
int sideLoop1, sideLoop2; |
|
|
|
if( !GLCM ) |
|
CV_ERROR( CV_StsNullPtr, "" ); |
|
|
|
if( !(GLCM->matrices) ) |
|
CV_ERROR( CV_StsNullPtr, "Matrices are not allocated" ); |
|
|
|
if( (unsigned)step >= (unsigned)(GLCM->numMatrices) ) |
|
CV_ERROR( CV_StsOutOfRange, "The step index is out of range" ); |
|
|
|
dest = cvCreateImage( cvSize( GLCM->matrixSideLength, GLCM->matrixSideLength ), IPL_DEPTH_32F, 1 ); |
|
destData = (float*)(dest->imageData); |
|
|
|
for( sideLoop1 = 0; sideLoop1 < GLCM->matrixSideLength; |
|
sideLoop1++, (float*&)destData += dest->widthStep ) |
|
{ |
|
for( sideLoop2=0; sideLoop2 < GLCM->matrixSideLength; sideLoop2++ ) |
|
{ |
|
double matrixValue = GLCM->matrices[step][sideLoop1][sideLoop2]; |
|
destData[ sideLoop2 ] = (float)matrixValue; |
|
} |
|
} |
|
|
|
__END__; |
|
|
|
if( cvGetErrStatus() < 0 ) |
|
cvReleaseImage( &dest ); |
|
|
|
return dest; |
|
}
|
|
|