GLCM opencv

头文件：

#pragma once
#include "cvaux.h"
#define CV_MAX_NUM_GREY_LEVELS_8U 256
class cl_Texture
{
public:
	//定义GLCM变量
	struct GLCM
	{
		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;
	};
	cl_Texture(void);
	~cl_Texture(void);
	/* srcStepDirections should be static array..or if not the user should handle de-allocation */

	GLCM* CreateGLCM( const IplImage* srcImage, int stepMagnitude, const int* srcStepDirections, int numStepDirections, int optimizationType );
	void CreateGLCMDescriptors( GLCM* destGLCM, int descriptorOptimizationType);
	double GetGLCMDescriptor( GLCM* GLCM, int step, int descriptor );
	void GetGLCMDescriptorStatistics( GLCM* GLCM, int descriptor, double*_average, double* _standardDeviation );
	IplImage* CreateGLCMImage( GLCM* GLCM, int step );
	void CreateGLCM_LookupTable_8u_C1R( const uchar* srcImageData, int srcImageStep, CvSize srcImageSize, GLCM* destGLCM, int* steps, int numSteps, int* memorySteps );
	void CreateGLCMDescriptors_AllowDoubleNest( GLCM* destGLCM, int matrixIndex );
	void ReleaseGLCM( GLCM** GLCM, int flag );

};
#pragma once

cl_Texture.cpp

#include "stdafx.h"
#include "cl_Texture.h"
#include <cvaux.h>
#include <stdexcept>
cl_Texture::cl_Texture(void)
{
}

cl_Texture::~cl_Texture(void)
{
}
// Calculation of a texture descriptors from GLCM (Grey Level Co-occurrence Matrix'es) The code was submitted by Daniel Eaton [danieljameseaton@...]

cl_Texture::GLCM* cl_Texture::CreateGLCM( const IplImage* srcImage, int stepMagnitude, const int* srcStepDirections, int numStepDirections,int optimizationType )
{
	static const int defaultStepDirections[] = { 0,1,-1,1,-1,0,-1,-1 };
	int* memorySteps = 0;
	cl_Texture::GLCM* newGLCM = 0;
	int* stepDirections = 0;
	uchar* srcImageData = 0;
	CvSize srcImageSize;
	int srcImageStep;
	int stepLoop;
	const int maxNumGreyLevels8u = CV_MAX_NUM_GREY_LEVELS_8U;

	if( !srcImage) return NULL;
	if( srcImage->nChannels != 1 ) return NULL;
	if( srcImage->depth != IPL_DEPTH_8U ) return NULL;
	// Schrittrichtung zur Berechnung der GLCM
	if( !srcStepDirections ){
		srcStepDirections = defaultStepDirections;
	}
	stepDirections = new int [numStepDirections*2];
	memcpy( stepDirections, srcStepDirections, numStepDirections*2*sizeof(stepDirections[0]));

	cvGetImageRawData( srcImage, &srcImageData, &srcImageStep, &srcImageSize );
	
	// roll together Directions and magnitudes together with knowledge of image (step)
	memorySteps = new int [numStepDirections];
	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 = (Cv_GLCM*)cvAlloc(sizeof(newGLCM)));
	newGLCM = new cl_Texture::GLCM ;
	size_t size=sizeof(*newGLCM);
	memset( newGLCM, 0, size );
	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;
		
		CreateGLCM_LookupTable_8u_C1R( srcImageData, srcImageStep, srcImageSize, newGLCM, stepDirections, numStepDirections, memorySteps );
	}
	else if( optimizationType == CV_GLCM_OPTIMIZATION_HISTOGRAM ){
		throw std::exception("Histogram-based method is not implemented" );
		/* newGLCM->numMatrices *= 2;
		newGLCM->matrixSideLength = maxNumGreyLevels8u*2;
		icv_CreateGLCM_Histogram_8uC1R( srcImageStep, srcImageSize,	srcImageData,newGLCM, numStepDirections,stepDirections, memorySteps );*/
	}
	delete[] memorySteps;
	delete[] stepDirections;
	if( cvGetErrStatus() < 0 ){
		delete[] newGLCM;
	}
	return newGLCM;
}

void cl_Texture::ReleaseGLCM( cl_Texture::GLCM** GLCM, int flag )
{
	int matrixLoop;
	if( !GLCM )
		throw std::exception("!GLMC");
	if( *GLCM )
	{
		if( (flag == CV_GLCM_GLCM || flag == CV_GLCM_ALL) && (*GLCM)->matrices )
		{
			for( matrixLoop = 0; matrixLoop < (*GLCM)->numMatrices; matrixLoop++ )
			{
				if( (*GLCM)->matrices[ matrixLoop ] )
				{
					delete[]  (*GLCM)->matrices[matrixLoop];
					delete[] ((*GLCM)->matrices + matrixLoop);
				}	
			}
			delete[] (*GLCM)->matrices;
		}
		if( (flag == CV_GLCM_DESC || flag == CV_GLCM_ALL) && (*GLCM)->descriptors )
		{
			for( matrixLoop = 0; matrixLoop < (*GLCM)->numMatrices; matrixLoop++ )
			{
				delete[] ((*GLCM)->descriptors + matrixLoop);
			}
			delete[] (*GLCM)->descriptors;
		}
		if( flag == CV_GLCM_ALL )
		{
			delete *GLCM;
		}
	}
}

void cl_Texture::CreateGLCM_LookupTable_8u_C1R( const uchar* srcImageData, int srcImageStep, CvSize srcImageSize,cl_Texture::GLCM* destGLCM,  int* steps, int numSteps, int* memorySteps ){
	int* stepIncrementsCounter = 0;
	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 ));
	destGLCM->matrices = new double** [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 ));*/
		matrices[stepLoop] = new double* [matrixSideLength ];
		matrices[stepLoop][0] = new double [matrixSideLength*matrixSideLength ];
		size_t size=sizeof(matrices[stepLoop][0][0])*matrixSideLength*matrixSideLength;
		memset( matrices[stepLoop][0], 0, size );
		for( sideLoop1 = 1; sideLoop1 < matrixSideLength; sideLoop1++ ){
			matrices[stepLoop][sideLoop1] = matrices[stepLoop][sideLoop1-1] + matrixSideLength;
		}
	}
	//CV_CALL( stepIncrementsCounter = (int*)cvAlloc( numSteps*sizeof(stepIncrementsCounter[0])));
	stepIncrementsCounter = new int [numSteps];
	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;
	delete stepIncrementsCounter;
	if( cvGetErrStatus() < 0 )
		ReleaseGLCM( &destGLCM, CV_GLCM_GLCM );
}
void cl_Texture::CreateGLCMDescriptors( cl_Texture::GLCM* destGLCM, int descriptorOptimizationType ){
	int matrixLoop;
	if( !destGLCM )
		throw std::exception("!destGLCM");
	if( !(destGLCM->matrices) )
		throw std::exception("Matrices are not allocated" );
	ReleaseGLCM( &destGLCM, CV_GLCM_DESC );
	if( destGLCM->optimizationType != CV_GLCM_OPTIMIZATION_HISTOGRAM ){
		destGLCM->descriptorOptimizationType = destGLCM->numDescriptors = descriptorOptimizationType;
	}
	else{
		throw std::exception("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])));
	destGLCM->descriptors = new double* [destGLCM->numMatrices];
	memset(destGLCM->descriptors,0,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])));
		destGLCM->descriptors[ matrixLoop ] = new double [destGLCM->numDescriptors];
		memset( destGLCM->descriptors[matrixLoop], 0, destGLCM->numDescriptors*sizeof(destGLCM->descriptors[0][0]) );
		switch( destGLCM->descriptorOptimizationType ){
			case CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST:
				CreateGLCMDescriptors_AllowDoubleNest( destGLCM, matrixLoop);
			break;
			default:
				throw std::exception("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;
			*/
		}
	}
	if( cvGetErrStatus() < 0 )
		ReleaseGLCM( &destGLCM, CV_GLCM_DESC );
}
void::cl_Texture::CreateGLCMDescriptors_AllowDoubleNest( GLCM* 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]));
	double* marginalProbability = new double [matrixSideLength];
	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 + sqrt((double)sideLoopDifferenceSquared) );
			if( entryValue > 0 ){
				descriptors[ CV_GLCMDESC_ENTROPY ] += entryValue * log(entryValue );
			}
			descriptors[ CV_GLCMDESC_ENERGY ] += entryValue*entryValue;
		}
		if( marginalProbability>0 )
			//marginalProbabilityEntropy += marginalProbability[ actualSideLoop1]*log(marginalProbability[ actualSideLoop1 ]);
			marginalProbabilityEntropy += marginalProbability[ sideLoop1]*log(marginalProbability[ sideLoop1 ]);
	}
	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[ sideLoop1 ] * marginalProbability[ sideLoop2 ];
			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;
}

double cl_Texture::GetGLCMDescriptor( cl_Texture::GLCM* GLCM, int step, int descriptor ){
	double value = DBL_MAX;
	if( !GLCM )
		throw std::exception("!GLCM");
	if( !(GLCM->descriptors) )
		throw std::exception("Descriptors are not calculated");
	if ((unsigned)step >= (unsigned)(GLCM->numMatrices))
		throw std::exception("step is not in 0 .. GLCM->numMatrices - 1");
	if( (unsigned)descriptor >= (unsigned)(GLCM->numDescriptors))
		throw std::exception("descriptor is not in 0 ..GLCM->numDescriptors - 1");
	value = GLCM->descriptors[step][descriptor];
	return value;
}
void cl_Texture::GetGLCMDescriptorStatistics( cl_Texture::GLCM* GLCM, int descriptor, double* _average, double* _standardDeviation ){
	if( _average )
		*_average = DBL_MAX;
	if( _standardDeviation )
		*_standardDeviation = DBL_MAX;
	int matrixLoop, numMatrices;
	double average = 0, squareSum = 0;
	if( !GLCM )
		throw std::exception("!GLCM");
	if( !(GLCM->descriptors))
		throw std::exception("Descriptors are not calculated");
	if( (unsigned)descriptor >= (unsigned)(GLCM->numDescriptors) )
				throw std::exception("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));
}
IplImage* cl_Texture::CreateGLCMImage(cl_Texture::GLCM* GLCM, int step ){
	IplImage* dest = 0;
	float* destData;
	int sideLoop1, sideLoop2;
	if( !GLCM )
		throw std::exception("!GLCM");
	if( !(GLCM->matrices) )
		throw std::exception("Matrices are not allocated");
	if( (unsigned)step >= (unsigned)(GLCM->numMatrices) )
		throw std::exception("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;
		}
	}
	if( cvGetErrStatus() < 0 )
		cvReleaseImage( &dest );
	return dest;
}

桑不起啊，内存泄露搞得我测试的好辛苦，原来从网上搞得代码是有问题的，在析构GLCM时有问题，经过调试终于解决了！！国际难题啊，嘎嘎，之后毫无压力！

GLCM mit changed ROI und choosing the image.cpp

#include <iostream>
#include <fstream>
#include "cv.h"
#include "highgui.h"
#include "cl_Texture.h"

using namespace std;

std::string IntToString(int i)
{
	char* buffer = new char[100];
	sprintf(buffer, "%d", i);   
	string s  = string(buffer);
	delete [] buffer;
	return s;
}

int main( int argc, char** argv )
{
    //源图像
	IplImage* pImg; 
	//源图像的灰度图像
	IplImage* gray;
	//源图像路径
	string imagePath;
	
	int count_steps=4;

	//纹理方向
	const int StepDirections[] = { 0,1,-1,1,-1,0,-1,-1 };

	//新建纹理对象
	cl_Texture* texture=new cl_Texture( );
	cl_Texture::GLCM* glcm;

	double d0, d1, d2, d3;
	// store the features in this array
	double * features = new double[4*count_steps]; 

    // to change the ROI
	int x = 0, y =0;

    //打开文件以保存纹理
	ofstream myfile ("glcm.txt",ios::out | ios::app );
	if (!myfile.is_open()) 
		cout << "Unable to open file";
		
		
		//图像路径
		imagePath= "E:\\11.jpg";
		//载入图像
		pImg = cvLoadImage(imagePath.c_str(), 1);
		if(pImg == 0)
		{
			cout<<"Error!!"<<endl;
			return -1;
		}

		// convert it to gray image
		gray=cvCreateImage(cvSize(pImg->width,pImg->height),pImg->depth,1);
		//色彩空间转换，参数CV_BGR2GRAY 是RGB到gray, 
		cvCvtColor(pImg,gray,CV_RGB2GRAY);

		// set the ROI in the image
		cvSetImageROI(gray,cvRect(86+x,75+y,75,62));

        // buid the GLCM
		glcm=texture->CreateGLCM(gray, 1,StepDirections,count_steps,CV_GLCM_OPTIMIZATION_LUT); 
		// get the features from GLCM
		texture->CreateGLCMDescriptors(glcm, CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST); 

		/*************************************************************** 
		***************** Use the features of GLCM  ********************
		****************************************************************
		*/

		for(int i =0; i<count_steps;i++)
		{
			//熵
			d0=texture->GetGLCMDescriptor(glcm,i,CV_GLCMDESC_ENTROPY);
			features[i*count_steps] = d0;
            //能量
			d1=texture->GetGLCMDescriptor(glcm,i,CV_GLCMDESC_ENERGY);
			features[i*count_steps+1] = d1;
            //同质性
			d2=texture->GetGLCMDescriptor(glcm,i,CV_GLCMDESC_HOMOGENITY);
			features[i*count_steps+2] = d2;
            //对比度
			d3=texture->GetGLCMDescriptor(glcm,i,CV_GLCMDESC_CONTRAST);
			features[i*count_steps+3] = d3;

			myfile<<d0<<' '<<d1<<' '<<d2<<' '<<d3<<' ';
		}

		cout<<endl;
		myfile<< "\n";

		cvResetImageROI(gray); // cancel the ROI in the image


	myfile.close();
	cvReleaseImage( &pImg ); 
	cvReleaseImage(&gray);

	delete[] features;

	return 0;
}