mirror of
https://github.com/opencv/opencv.git
synced 2024-12-05 01:39:13 +08:00
649 lines
22 KiB
C++
649 lines
22 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// Intel License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of Intel Corporation may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
/****************************************************************************************\
|
|
|
|
Calculation of a texture descriptors from GLCM (Grey Level Co-occurrence Matrix'es)
|
|
The code was submitted by Daniel Eaton [danieljameseaton@yahoo.com]
|
|
|
|
\****************************************************************************************/
|
|
|
|
#include "precomp.hpp"
|
|
|
|
#include <math.h>
|
|
#include <assert.h>
|
|
|
|
#define CV_MAX_NUM_GREY_LEVELS_8U 256
|
|
|
|
struct CvGLCM
|
|
{
|
|
int matrixSideLength;
|
|
int numMatrices;
|
|
double*** matrices;
|
|
|
|
int numLookupTableElements;
|
|
int forwardLookupTable[CV_MAX_NUM_GREY_LEVELS_8U];
|
|
int reverseLookupTable[CV_MAX_NUM_GREY_LEVELS_8U];
|
|
|
|
double** descriptors;
|
|
int numDescriptors;
|
|
int descriptorOptimizationType;
|
|
int optimizationType;
|
|
};
|
|
|
|
|
|
static void icvCreateGLCM_LookupTable_8u_C1R( const uchar* srcImageData, int srcImageStep,
|
|
CvSize srcImageSize, CvGLCM* destGLCM,
|
|
int* steps, int numSteps, int* memorySteps );
|
|
|
|
static void
|
|
icvCreateGLCMDescriptors_AllowDoubleNest( CvGLCM* destGLCM, int matrixIndex );
|
|
|
|
|
|
CV_IMPL CvGLCM*
|
|
cvCreateGLCM( const IplImage* srcImage,
|
|
int stepMagnitude,
|
|
const int* srcStepDirections,/* should be static array..
|
|
or if not the user should handle de-allocation */
|
|
int numStepDirections,
|
|
int optimizationType )
|
|
{
|
|
static const int defaultStepDirections[] = { 0,1, -1,1, -1,0, -1,-1 };
|
|
|
|
int* memorySteps = 0;
|
|
CvGLCM* newGLCM = 0;
|
|
int* stepDirections = 0;
|
|
|
|
CV_FUNCNAME( "cvCreateGLCM" );
|
|
|
|
__BEGIN__;
|
|
|
|
uchar* srcImageData = 0;
|
|
CvSize srcImageSize;
|
|
int srcImageStep;
|
|
int stepLoop;
|
|
const int maxNumGreyLevels8u = CV_MAX_NUM_GREY_LEVELS_8U;
|
|
|
|
if( !srcImage )
|
|
CV_ERROR( CV_StsNullPtr, "" );
|
|
|
|
if( srcImage->nChannels != 1 )
|
|
CV_ERROR( CV_BadNumChannels, "Number of channels must be 1");
|
|
|
|
if( srcImage->depth != IPL_DEPTH_8U )
|
|
CV_ERROR( CV_BadDepth, "Depth must be equal IPL_DEPTH_8U");
|
|
|
|
// no Directions provided, use the default ones - 0 deg, 45, 90, 135
|
|
if( !srcStepDirections )
|
|
{
|
|
srcStepDirections = defaultStepDirections;
|
|
}
|
|
|
|
CV_CALL( stepDirections = (int*)cvAlloc( numStepDirections*2*sizeof(stepDirections[0])));
|
|
memcpy( stepDirections, srcStepDirections, numStepDirections*2*sizeof(stepDirections[0]));
|
|
|
|
cvGetImageRawData( srcImage, &srcImageData, &srcImageStep, &srcImageSize );
|
|
|
|
// roll together Directions and magnitudes together with knowledge of image (step)
|
|
CV_CALL( memorySteps = (int*)cvAlloc( numStepDirections*sizeof(memorySteps[0])));
|
|
|
|
for( stepLoop = 0; stepLoop < numStepDirections; stepLoop++ )
|
|
{
|
|
stepDirections[stepLoop*2 + 0] *= stepMagnitude;
|
|
stepDirections[stepLoop*2 + 1] *= stepMagnitude;
|
|
|
|
memorySteps[stepLoop] = stepDirections[stepLoop*2 + 0]*srcImageStep +
|
|
stepDirections[stepLoop*2 + 1];
|
|
}
|
|
|
|
CV_CALL( newGLCM = (CvGLCM*)cvAlloc(sizeof(newGLCM)));
|
|
memset( newGLCM, 0, sizeof(*newGLCM) );
|
|
|
|
newGLCM->matrices = 0;
|
|
newGLCM->numMatrices = numStepDirections;
|
|
newGLCM->optimizationType = optimizationType;
|
|
|
|
if( optimizationType <= CV_GLCM_OPTIMIZATION_LUT )
|
|
{
|
|
int lookupTableLoop, imageColLoop, imageRowLoop, lineOffset = 0;
|
|
|
|
// if optimization type is set to lut, then make one for the image
|
|
if( optimizationType == CV_GLCM_OPTIMIZATION_LUT )
|
|
{
|
|
for( imageRowLoop = 0; imageRowLoop < srcImageSize.height;
|
|
imageRowLoop++, lineOffset += srcImageStep )
|
|
{
|
|
for( imageColLoop = 0; imageColLoop < srcImageSize.width; imageColLoop++ )
|
|
{
|
|
newGLCM->forwardLookupTable[srcImageData[lineOffset+imageColLoop]]=1;
|
|
}
|
|
}
|
|
|
|
newGLCM->numLookupTableElements = 0;
|
|
|
|
for( lookupTableLoop = 0; lookupTableLoop < maxNumGreyLevels8u; lookupTableLoop++ )
|
|
{
|
|
if( newGLCM->forwardLookupTable[ lookupTableLoop ] != 0 )
|
|
{
|
|
newGLCM->forwardLookupTable[ lookupTableLoop ] =
|
|
newGLCM->numLookupTableElements;
|
|
newGLCM->reverseLookupTable[ newGLCM->numLookupTableElements ] =
|
|
lookupTableLoop;
|
|
|
|
newGLCM->numLookupTableElements++;
|
|
}
|
|
}
|
|
}
|
|
// otherwise make a "LUT" which contains all the gray-levels (for code-reuse)
|
|
else if( optimizationType == CV_GLCM_OPTIMIZATION_NONE )
|
|
{
|
|
for( lookupTableLoop = 0; lookupTableLoop <maxNumGreyLevels8u; lookupTableLoop++ )
|
|
{
|
|
newGLCM->forwardLookupTable[ lookupTableLoop ] = lookupTableLoop;
|
|
newGLCM->reverseLookupTable[ lookupTableLoop ] = lookupTableLoop;
|
|
}
|
|
newGLCM->numLookupTableElements = maxNumGreyLevels8u;
|
|
}
|
|
|
|
newGLCM->matrixSideLength = newGLCM->numLookupTableElements;
|
|
icvCreateGLCM_LookupTable_8u_C1R( srcImageData, srcImageStep, srcImageSize,
|
|
newGLCM, stepDirections,
|
|
numStepDirections, memorySteps );
|
|
}
|
|
else if( optimizationType == CV_GLCM_OPTIMIZATION_HISTOGRAM )
|
|
{
|
|
CV_ERROR( CV_StsBadFlag, "Histogram-based method is not implemented" );
|
|
|
|
/* newGLCM->numMatrices *= 2;
|
|
newGLCM->matrixSideLength = maxNumGreyLevels8u*2;
|
|
|
|
icvCreateGLCM_Histogram_8uC1R( srcImageStep, srcImageSize, srcImageData,
|
|
newGLCM, numStepDirections,
|
|
stepDirections, memorySteps );
|
|
*/
|
|
}
|
|
|
|
__END__;
|
|
|
|
cvFree( &memorySteps );
|
|
cvFree( &stepDirections );
|
|
|
|
if( cvGetErrStatus() < 0 )
|
|
{
|
|
cvFree( &newGLCM );
|
|
}
|
|
|
|
return newGLCM;
|
|
}
|
|
|
|
|
|
CV_IMPL void
|
|
cvReleaseGLCM( CvGLCM** GLCM, int flag )
|
|
{
|
|
CV_FUNCNAME( "cvReleaseGLCM" );
|
|
|
|
__BEGIN__;
|
|
|
|
int matrixLoop;
|
|
|
|
if( !GLCM )
|
|
CV_ERROR( CV_StsNullPtr, "" );
|
|
|
|
if( *GLCM )
|
|
EXIT; // repeated deallocation: just skip it.
|
|
|
|
if( (flag == CV_GLCM_GLCM || flag == CV_GLCM_ALL) && (*GLCM)->matrices )
|
|
{
|
|
for( matrixLoop = 0; matrixLoop < (*GLCM)->numMatrices; matrixLoop++ )
|
|
{
|
|
if( (*GLCM)->matrices[ matrixLoop ] )
|
|
{
|
|
cvFree( (*GLCM)->matrices[matrixLoop] );
|
|
cvFree( (*GLCM)->matrices + matrixLoop );
|
|
}
|
|
}
|
|
|
|
cvFree( &((*GLCM)->matrices) );
|
|
}
|
|
|
|
if( (flag == CV_GLCM_DESC || flag == CV_GLCM_ALL) && (*GLCM)->descriptors )
|
|
{
|
|
for( matrixLoop = 0; matrixLoop < (*GLCM)->numMatrices; matrixLoop++ )
|
|
{
|
|
cvFree( (*GLCM)->descriptors + matrixLoop );
|
|
}
|
|
cvFree( &((*GLCM)->descriptors) );
|
|
}
|
|
|
|
if( flag == CV_GLCM_ALL )
|
|
{
|
|
cvFree( GLCM );
|
|
}
|
|
|
|
__END__;
|
|
}
|
|
|
|
|
|
static void
|
|
icvCreateGLCM_LookupTable_8u_C1R( const uchar* srcImageData,
|
|
int srcImageStep,
|
|
CvSize srcImageSize,
|
|
CvGLCM* destGLCM,
|
|
int* steps,
|
|
int numSteps,
|
|
int* memorySteps )
|
|
{
|
|
int* stepIncrementsCounter = 0;
|
|
|
|
CV_FUNCNAME( "icvCreateGLCM_LookupTable_8u_C1R" );
|
|
|
|
__BEGIN__;
|
|
|
|
int matrixSideLength = destGLCM->matrixSideLength;
|
|
int stepLoop, sideLoop1, sideLoop2;
|
|
int colLoop, rowLoop, lineOffset = 0;
|
|
double*** matrices = 0;
|
|
|
|
// allocate memory to the matrices
|
|
CV_CALL( destGLCM->matrices = (double***)cvAlloc( sizeof(matrices[0])*numSteps ));
|
|
matrices = destGLCM->matrices;
|
|
|
|
for( stepLoop=0; stepLoop<numSteps; stepLoop++ )
|
|
{
|
|
CV_CALL( matrices[stepLoop] = (double**)cvAlloc( sizeof(matrices[0])*matrixSideLength ));
|
|
CV_CALL( matrices[stepLoop][0] = (double*)cvAlloc( sizeof(matrices[0][0])*
|
|
matrixSideLength*matrixSideLength ));
|
|
|
|
memset( matrices[stepLoop][0], 0, matrixSideLength*matrixSideLength*
|
|
sizeof(matrices[0][0]) );
|
|
|
|
for( sideLoop1 = 1; sideLoop1 < matrixSideLength; sideLoop1++ )
|
|
{
|
|
matrices[stepLoop][sideLoop1] = matrices[stepLoop][sideLoop1-1] + matrixSideLength;
|
|
}
|
|
}
|
|
|
|
CV_CALL( stepIncrementsCounter = (int*)cvAlloc( numSteps*sizeof(stepIncrementsCounter[0])));
|
|
memset( stepIncrementsCounter, 0, numSteps*sizeof(stepIncrementsCounter[0]) );
|
|
|
|
// generate GLCM for each step
|
|
for( rowLoop=0; rowLoop<srcImageSize.height; rowLoop++, lineOffset+=srcImageStep )
|
|
{
|
|
for( colLoop=0; colLoop<srcImageSize.width; colLoop++ )
|
|
{
|
|
int pixelValue1 = destGLCM->forwardLookupTable[srcImageData[lineOffset + colLoop]];
|
|
|
|
for( stepLoop=0; stepLoop<numSteps; stepLoop++ )
|
|
{
|
|
int col2, row2;
|
|
row2 = rowLoop + steps[stepLoop*2 + 0];
|
|
col2 = colLoop + steps[stepLoop*2 + 1];
|
|
|
|
if( col2>=0 && row2>=0 && col2<srcImageSize.width && row2<srcImageSize.height )
|
|
{
|
|
int memoryStep = memorySteps[ stepLoop ];
|
|
int pixelValue2 = destGLCM->forwardLookupTable[ srcImageData[ lineOffset + colLoop + memoryStep ] ];
|
|
|
|
// maintain symmetry
|
|
matrices[stepLoop][pixelValue1][pixelValue2] ++;
|
|
matrices[stepLoop][pixelValue2][pixelValue1] ++;
|
|
|
|
// incremenet counter of total number of increments
|
|
stepIncrementsCounter[stepLoop] += 2;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// normalize matrices. each element is a probability of gray value i,j adjacency in direction/magnitude k
|
|
for( sideLoop1=0; sideLoop1<matrixSideLength; sideLoop1++ )
|
|
{
|
|
for( sideLoop2=0; sideLoop2<matrixSideLength; sideLoop2++ )
|
|
{
|
|
for( stepLoop=0; stepLoop<numSteps; stepLoop++ )
|
|
{
|
|
matrices[stepLoop][sideLoop1][sideLoop2] /= double(stepIncrementsCounter[stepLoop]);
|
|
}
|
|
}
|
|
}
|
|
|
|
destGLCM->matrices = matrices;
|
|
|
|
__END__;
|
|
|
|
cvFree( &stepIncrementsCounter );
|
|
|
|
if( cvGetErrStatus() < 0 )
|
|
cvReleaseGLCM( &destGLCM, CV_GLCM_GLCM );
|
|
}
|
|
|
|
|
|
CV_IMPL void
|
|
cvCreateGLCMDescriptors( CvGLCM* destGLCM, int descriptorOptimizationType )
|
|
{
|
|
CV_FUNCNAME( "cvCreateGLCMDescriptors" );
|
|
|
|
__BEGIN__;
|
|
|
|
int matrixLoop;
|
|
|
|
if( !destGLCM )
|
|
CV_ERROR( CV_StsNullPtr, "" );
|
|
|
|
if( !(destGLCM->matrices) )
|
|
CV_ERROR( CV_StsNullPtr, "Matrices are not allocated" );
|
|
|
|
CV_CALL( cvReleaseGLCM( &destGLCM, CV_GLCM_DESC ));
|
|
|
|
if( destGLCM->optimizationType != CV_GLCM_OPTIMIZATION_HISTOGRAM )
|
|
{
|
|
destGLCM->descriptorOptimizationType = destGLCM->numDescriptors = descriptorOptimizationType;
|
|
}
|
|
else
|
|
{
|
|
CV_ERROR( CV_StsBadFlag, "Histogram-based method is not implemented" );
|
|
// destGLCM->descriptorOptimizationType = destGLCM->numDescriptors = CV_GLCMDESC_OPTIMIZATION_HISTOGRAM;
|
|
}
|
|
|
|
CV_CALL( destGLCM->descriptors = (double**)
|
|
cvAlloc( destGLCM->numMatrices*sizeof(destGLCM->descriptors[0])));
|
|
|
|
for( matrixLoop = 0; matrixLoop < destGLCM->numMatrices; matrixLoop ++ )
|
|
{
|
|
CV_CALL( destGLCM->descriptors[ matrixLoop ] =
|
|
(double*)cvAlloc( destGLCM->numDescriptors*sizeof(destGLCM->descriptors[0][0])));
|
|
memset( destGLCM->descriptors[matrixLoop], 0, destGLCM->numDescriptors*sizeof(double) );
|
|
|
|
switch( destGLCM->descriptorOptimizationType )
|
|
{
|
|
case CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST:
|
|
icvCreateGLCMDescriptors_AllowDoubleNest( destGLCM, matrixLoop );
|
|
break;
|
|
default:
|
|
CV_ERROR( CV_StsBadFlag,
|
|
"descriptorOptimizationType different from CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST\n"
|
|
"is not supported" );
|
|
/*
|
|
case CV_GLCMDESC_OPTIMIZATION_ALLOWTRIPLENEST:
|
|
icvCreateGLCMDescriptors_AllowTripleNest( destGLCM, matrixLoop );
|
|
break;
|
|
case CV_GLCMDESC_OPTIMIZATION_HISTOGRAM:
|
|
if(matrixLoop < destGLCM->numMatrices>>1)
|
|
icvCreateGLCMDescriptors_Histogram( destGLCM, matrixLoop);
|
|
break;
|
|
*/
|
|
}
|
|
}
|
|
|
|
__END__;
|
|
|
|
if( cvGetErrStatus() < 0 )
|
|
cvReleaseGLCM( &destGLCM, CV_GLCM_DESC );
|
|
}
|
|
|
|
|
|
static void
|
|
icvCreateGLCMDescriptors_AllowDoubleNest( CvGLCM* destGLCM, int matrixIndex )
|
|
{
|
|
int sideLoop1, sideLoop2;
|
|
int matrixSideLength = destGLCM->matrixSideLength;
|
|
|
|
double** matrix = destGLCM->matrices[ matrixIndex ];
|
|
double* descriptors = destGLCM->descriptors[ matrixIndex ];
|
|
|
|
double* marginalProbability =
|
|
(double*)cvAlloc( matrixSideLength * sizeof(marginalProbability[0]));
|
|
memset( marginalProbability, 0, matrixSideLength * sizeof(double) );
|
|
|
|
double maximumProbability = 0;
|
|
double marginalProbabilityEntropy = 0;
|
|
double correlationMean = 0, correlationStdDeviation = 0, correlationProductTerm = 0;
|
|
|
|
for( sideLoop1=0; sideLoop1<matrixSideLength; sideLoop1++ )
|
|
{
|
|
int actualSideLoop1 = destGLCM->reverseLookupTable[ sideLoop1 ];
|
|
|
|
for( sideLoop2=0; sideLoop2<matrixSideLength; sideLoop2++ )
|
|
{
|
|
double entryValue = matrix[ sideLoop1 ][ sideLoop2 ];
|
|
|
|
int actualSideLoop2 = destGLCM->reverseLookupTable[ sideLoop2 ];
|
|
int sideLoopDifference = actualSideLoop1 - actualSideLoop2;
|
|
int sideLoopDifferenceSquared = sideLoopDifference*sideLoopDifference;
|
|
|
|
marginalProbability[ sideLoop1 ] += entryValue;
|
|
correlationMean += actualSideLoop1*entryValue;
|
|
|
|
maximumProbability = MAX( maximumProbability, entryValue );
|
|
|
|
if( actualSideLoop2 > actualSideLoop1 )
|
|
{
|
|
descriptors[ CV_GLCMDESC_CONTRAST ] += sideLoopDifferenceSquared * entryValue;
|
|
}
|
|
|
|
descriptors[ CV_GLCMDESC_HOMOGENITY ] += entryValue / ( 1.0 + sideLoopDifferenceSquared );
|
|
|
|
if( entryValue > 0 )
|
|
{
|
|
descriptors[ CV_GLCMDESC_ENTROPY ] += entryValue * log( entryValue );
|
|
}
|
|
|
|
descriptors[ CV_GLCMDESC_ENERGY ] += entryValue*entryValue;
|
|
}
|
|
|
|
if( marginalProbability[ actualSideLoop1 ] > 0 )
|
|
marginalProbabilityEntropy += marginalProbability[ actualSideLoop1 ]*log(marginalProbability[ actualSideLoop1 ]);
|
|
}
|
|
|
|
marginalProbabilityEntropy = -marginalProbabilityEntropy;
|
|
|
|
descriptors[ CV_GLCMDESC_CONTRAST ] += descriptors[ CV_GLCMDESC_CONTRAST ];
|
|
descriptors[ CV_GLCMDESC_ENTROPY ] = -descriptors[ CV_GLCMDESC_ENTROPY ];
|
|
descriptors[ CV_GLCMDESC_MAXIMUMPROBABILITY ] = maximumProbability;
|
|
|
|
double HXY = 0, HXY1 = 0, HXY2 = 0;
|
|
|
|
HXY = descriptors[ CV_GLCMDESC_ENTROPY ];
|
|
|
|
for( sideLoop1=0; sideLoop1<matrixSideLength; sideLoop1++ )
|
|
{
|
|
double sideEntryValueSum = 0;
|
|
int actualSideLoop1 = destGLCM->reverseLookupTable[ sideLoop1 ];
|
|
|
|
for( sideLoop2=0; sideLoop2<matrixSideLength; sideLoop2++ )
|
|
{
|
|
double entryValue = matrix[ sideLoop1 ][ sideLoop2 ];
|
|
|
|
sideEntryValueSum += entryValue;
|
|
|
|
int actualSideLoop2 = destGLCM->reverseLookupTable[ sideLoop2 ];
|
|
|
|
correlationProductTerm += (actualSideLoop1 - correlationMean) * (actualSideLoop2 - correlationMean) * entryValue;
|
|
|
|
double clusterTerm = actualSideLoop1 + actualSideLoop2 - correlationMean - correlationMean;
|
|
|
|
descriptors[ CV_GLCMDESC_CLUSTERTENDENCY ] += clusterTerm * clusterTerm * entryValue;
|
|
descriptors[ CV_GLCMDESC_CLUSTERSHADE ] += clusterTerm * clusterTerm * clusterTerm * entryValue;
|
|
|
|
double HXYValue = marginalProbability[ actualSideLoop1 ] * marginalProbability[ actualSideLoop2 ];
|
|
if( HXYValue>0 )
|
|
{
|
|
double HXYValueLog = log( HXYValue );
|
|
HXY1 += entryValue * HXYValueLog;
|
|
HXY2 += HXYValue * HXYValueLog;
|
|
}
|
|
}
|
|
|
|
correlationStdDeviation += (actualSideLoop1-correlationMean) * (actualSideLoop1-correlationMean) * sideEntryValueSum;
|
|
}
|
|
|
|
HXY1 = -HXY1;
|
|
HXY2 = -HXY2;
|
|
|
|
descriptors[ CV_GLCMDESC_CORRELATIONINFO1 ] = ( HXY - HXY1 ) / ( correlationMean );
|
|
descriptors[ CV_GLCMDESC_CORRELATIONINFO2 ] = sqrt( 1.0 - exp( -2.0 * (HXY2 - HXY ) ) );
|
|
|
|
correlationStdDeviation = sqrt( correlationStdDeviation );
|
|
|
|
descriptors[ CV_GLCMDESC_CORRELATION ] = correlationProductTerm / (correlationStdDeviation*correlationStdDeviation );
|
|
|
|
delete [] marginalProbability;
|
|
}
|
|
|
|
|
|
CV_IMPL double cvGetGLCMDescriptor( CvGLCM* GLCM, int step, int descriptor )
|
|
{
|
|
double value = DBL_MAX;
|
|
|
|
CV_FUNCNAME( "cvGetGLCMDescriptor" );
|
|
|
|
__BEGIN__;
|
|
|
|
if( !GLCM )
|
|
CV_ERROR( CV_StsNullPtr, "" );
|
|
|
|
if( !(GLCM->descriptors) )
|
|
CV_ERROR( CV_StsNullPtr, "" );
|
|
|
|
if( (unsigned)step >= (unsigned)(GLCM->numMatrices))
|
|
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__;
|
|
|
|
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;
|
|
}
|
|
|