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assessment.cpp
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assessment.cpp
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/*************************************************************************
* @文件名:
* assessment.cpp
* @说明:
* 该文件包括模糊图像鉴别、图像质量评估相关的实现函数
*************************************************************************/
#include "basicOperation.h"
#include "assessment.h"
/*************************************************************************
* @函数名称:
* blurIdentify()
* @输入:
* IplImage* input - 输入灰度图像
* @返回值:
* uchar - 返回图像标志,清晰返回1,模糊返回0
* @说明:
* 该函数通过计算梯度图像,并进行直方图分析,计算相关鉴别指标GMG和NGN,
* 实现模糊鉴别功能
*************************************************************************/
uchar blurIdentify(const IplImage* input)
{
int rows=input->height;
int cols=input->width;
int i=0,j=0;
uchar flag=0;
CvSize size1,size2;
size1.width=cols;
size1.height=rows;
size2.width=2*cols;
size2.height=rows;
IplImage* tempr=cvCreateImage(size1,IPL_DEPTH_8U,1);
IplImage* tempc=cvCreateImage(size1,IPL_DEPTH_8U,1);
cvZero(tempr);
cvZero(tempc);
/*计算水平梯度图像*/
for (i=0;i<rows;i++)
{
uchar* input_data=(uchar*)(input->imageData+i*input->widthStep);
uchar* tempr_data=(uchar*)(tempr->imageData+i*tempr->widthStep);
for (j=0;j<cols-1;j++)
{
tempr_data[j]=abs(input_data[j+1]-input_data[j]);
}
}
/*计算垂直梯度图像*/
for (i = 0; i<rows - 1; i++)
{
uchar* input_data1 = (uchar*)(input->imageData + i*input->widthStep);
uchar* input_data2 = (uchar*)(input->imageData + (i + 1)*input->widthStep);
uchar* tempc_data = (uchar*)(tempc->imageData + i*tempc->widthStep);
for (j = 0; j<cols; j++)
{
tempc_data[j]=abs(input_data2[j]-input_data1[j]);
}
}
/*将两个梯度图像合并为一个,以便统计计算*/
IplImage* gradient=cvCreateImage(size2,IPL_DEPTH_8U,1);
cvZero(gradient);
cvSetImageROI(gradient,cvRect(0,0,cols,rows));
cvCopy(tempr,gradient); //将水平梯度图存入
cvResetImageROI(gradient);
cvSetImageROI(gradient,cvRect(cols,0,cols,rows));
cvCopy(tempc,gradient); //将垂直梯度图存入
cvResetImageROI(gradient);
int nHistSize=256;
float fRange[]={0,255}; //灰度级范围
float* pfRanges[]={fRange};
//CvHistogram* hist=cvCreateHist(1,&nHistSize,CV_HIST_ARRAY,pfRanges); //CV_HIST_ARRAY多维密集数组
//cvCalcHist(&gradient,hist);
CvHistogram* hist1=cvCreateHist(1,&nHistSize,CV_HIST_ARRAY,pfRanges); //CV_HIST_ARRAY多维密集数组
CvHistogram* hist2=cvCreateHist(1,&nHistSize,CV_HIST_ARRAY,pfRanges); //CV_HIST_ARRAY多维密集数组
cvCalcHist(&tempr,hist1);
cvCalcHist(&tempc, hist2);
//int NGN=cvCountNonZero(hist->bins);
int NX=cvCountNonZero(hist1->bins);
int NY = cvCountNonZero(hist2->bins);
double GMG=calGMG(input);
double s = (NX + NY) / (2 * 256.0);
double BIM=s*GMG;
if(BIM>700)
{
flag=1;
}
//测试代码,显示梯度图像
/*cvNamedWindow("gx",1);
cvShowImage("gx",tempr);
cvNamedWindow("gy",1);
cvShowImage("gy",tempc);
cvNamedWindow("g",1);
cvShowImage("g",gradient);*/
printf("GMG=%f,NX=%d,NY=%d,s=%f,BIM=%f\n", GMG, NX, NY,s, BIM);
//for(i=0;i<256;i++)
//{
// printf("%.f ",((CvMatND*)hist->bins)->data.fl[i]);
//}
cvReleaseImage(&tempr);
cvReleaseImage(&tempc);
cvReleaseImage(&gradient);
cvReleaseHist(&hist1);
cvReleaseHist(&hist2);
return 1;
}
/*************************************************************************
* @函数名称:
* calGMG()
* @输入:
* IplImage* input - 输入灰度图像
* @返回值:
* double - 灰度平均梯度值GMG
* @说明:
* 该函数计算图像灰度平均梯度值,其值越大表示图像越清晰
*************************************************************************/
double calGMG(const IplImage* input)
{
int rows=input->height;
int cols=input->width;
int i=0,j=0;
int num=(rows-1)*(cols-1);
double sum=0;
double GMG=0;
for(i=0;i<rows-1;i++)
{
uchar* input_data1=(uchar*)(input->imageData+i*input->widthStep);
uchar* input_data2 = (uchar*)(input->imageData + (i + 1)*input->widthStep);
for(j=0;j<cols-1;j++)
{
sum+=sqrt(((input_data1[j+1]-input_data1[j])*(input_data1[j+1]-input_data1[j])
+((input_data2[j]-input_data1[j])*(input_data2[j]-input_data1[j])))/2);
}
}
GMG=sum/num;
return GMG;
}
/*************************************************************************
* @函数名称:
* calLuminanceSim()
* @输入:
* IplImage* input1 - 输入图像1
* IplImage* input2 - 输入图像2
* @返回值:
* double - 返回图像亮度相似性
* @说明:
* 计算图像的亮度相似度,符合亮度掩盖模型
* 计算公式为:l(x,y)=(2*u_x*u_y+c1)/(u_x*u_x+u_y*u_y+c1)
*************************************************************************/
double calLuminanceSim(const IplImage* input1, const IplImage* input2)
{
double lum=0,c1=0;
double k1=0.01;
CvScalar mean1,mean2;
mean1=cvAvg(input1);
mean2=cvAvg(input2);
c1=(k1*255)*(k1*255);
lum=(2*mean1.val[0]*mean2.val[0]+c1)/(mean1.val[0]*mean1.val[0]+mean2.val[0]*mean2.val[0]+c1);
return lum;
}
/*************************************************************************
* @函数名称:
* calContrastSim()
* @输入:
* IplImage* input1 - 输入图像1
* IplImage* input2 - 输入图像2
* @返回值:
* double - 返回图像对比度相似性
* @说明:
* 计算图像对比度相似性,考虑了图像的对比度掩盖效应
* 计算公式为:l(x,y)=(2*std_x*std_y+c2)/(std_x*std_x+std_y*std_y+c2)
*************************************************************************/
double calContrastSim(const IplImage* input1, const IplImage* input2)
{
double con=0, c2=0;
double k2=0.03;
CvScalar stdev1, stdev2;
cvAvgSdv(input1,NULL,&stdev1);
cvAvgSdv(input2,NULL,&stdev2);
c2=(k2*255 )*(k2*255);
con=(2*stdev1.val[0]*stdev2.val[0]+c2)/(stdev1.val[0]*stdev1.val[0]+stdev2.val[0]*stdev2.val[0]+c2);
return con;
}
/*************************************************************************
* @函数名称:
* calStructSim()
* @输入:
* const IplImage* input1 - 输入图像1
* const IplImage* input2 - 输入图像2
* @返回值:
* double - 结构度相似性
* @说明:
* 计算图像结构度相似性,用图像像素间的相关来刻画结构关系
* 计算公式为:l(x,y)=(cov_xy+c2)/(std_x+std_y+c2)
*************************************************************************/
double calStructSim(const IplImage* input1, const IplImage* input2)
{
double stc=0, c3=0;
double k2=0.03;
CvScalar stdev1, stdev2;
CvScalar mean1,mean2;
cvAvgSdv(input1,&mean1,&stdev1);
cvAvgSdv(input2,&mean2,&stdev2);
double cov = 0;
double sum = 0;
int i, j;
int rows=input1->height;
int cols=input1->width;
for (i = 0; i < rows; i++)
{
uchar* input1_data=(uchar*)(input1->imageData+i*input1->widthStep);
uchar* input2_data=(uchar*)(input2->imageData+i*input2->widthStep);
for (j = 0; j < cols; j++)
{
sum += ((input1_data[j] - mean1.val[0]) *(input2_data[j] - mean2.val[0]));
}
}
cov = sum / (rows*cols);
c3 = (k2 * 255) * (k2 * 255) / 2;
stc=(cov+c3)/(stdev1.val[0]*stdev2.val[0]+c3);
return stc;
}
/*************************************************************************
* @函数名称:
* estSNR()
* @输入:
* IplImage* input1 - 输入图像
* @返回值:
* double - 估计的信噪比
* @说明:
* 通过计算局部方差估计信噪比
* 局部方差的最大值为信号方差,最小值为噪声方差,再用经验公式修正
*************************************************************************/
//double estSNR(IplImage* input)
//{
// double snr=0, max, min, k;
// CvSize size = cvGetSize(input);
//
// Mat f = Mat_<float>(input), av, v;
// Mat g = f;// Mat(f, Rect(2, 2, size.width - 4, size.height - 4));
// Mat mask = Mat::ones(5, 5, CV_32F);
// mask = mask / 25;
//
// cvFilter2D(g, av, -1, mask);
// pow((g - av), 2, v);
// filter2D(v, v, -1, mask);
// minMaxLoc(v, &min, &max);
// snr = 10 * log(max / min);
// /*snr = 1.04*snr - 7;//k为维纳滤波参数
// k=pow(10, (-snr / 10));
// k = 5 * k;
// return k;*/
//
// return snr;
//}
/*************************************************************************
* @函数名称:
* calMISSIM()
* @输入:
* const IplImage* image1 - 输入图像1
* const IplImage* image2 - 输入图像2
* int n - 每个方块的大小
* @返回值:
* double MISSIM - 返回图像的平均改进结构相似度
* @说明:
* 计算图像的平均改进结构相似度
**************************************************************************/
double calMISSIM(const IplImage* image1, const IplImage* image2, int n)
{
double MISSIM = 0;
int i, j, k;
int row1 = image1->height;
int col1 = image1->width;
int row2 = image2->height;
int col2 = image2->width;
if (row1 != row2 || col1 != col2)
{
printf("Size can't match in calMISSIM()!!");
}
int nr = cvFloor(row1 / n);
int nc = cvFloor(col1 / n);
int N = nr*nc;
double ISSIM=0;
double sum = 0;
CvMat tmp1;
CvMat tmp2;
IplImage* temp1 = cvCreateImage(cvSize(n, n), image1->depth, image1->nChannels);
IplImage* temp2 = cvCreateImage(cvSize(n, n), image1->depth, image1->nChannels);
for (i = 0, k = 0; i < nr; i++)
{
for (j = 0; j < nc; j++, k++)
{
cvGetSubRect(image1, &tmp1, cvRect(j*n, i*n, n, n));
cvGetSubRect(image2, &tmp2, cvRect(j*n, i*n, n, n));
cvScale(&tmp1, temp1, 1, 0);
cvScale(&tmp2, temp2, 1, 0);
ISSIM = calISSIM(temp1, temp2);
sum += ISSIM;
}
}
MISSIM = sum / N;
cvReleaseImage(&temp1);
cvReleaseImage(&temp2);
return MISSIM;
}
/*************************************************************************
* @函数名称:
* calISSIM()
* @输入:
* const IplImage* image1 - 输入图像1
* const IplImage* image2 - 输入图像2
* @返回值:
* double ISSIM - 返回图像的改进的结构相似度
* @说明:
* 计算图像的改进的结构相似度
**************************************************************************/
double calISSIM(const IplImage* image1, const IplImage* image2)
{
double ISSIM = 0;
double l = 0, c = 0, g = 0, s = 0;
l = calLuminanceSim(image1, image2);
c = calContrastSim(image1, image2);
g = calGradSim(image1, image2);
s = calStructSim(image1, image2);
//printf("l=%f\n", l);
//printf("c=%f\n", c);
//printf("g=%f\n", g);
//printf("s=%f\n", s);
ISSIM = pow(l, 1)*pow(c, 1)*pow(g,1)*pow(s, 1);
return ISSIM;
}
/*************************************************************************
* @函数名称:
* calGradSim()
* @输入:
* const IplImage* image1 - 输入图像1
* const IplImage* image2 - 输入图像2
* @返回值:
* double g - 梯度相似性
* @说明:
* 计算图像梯度相似性
*************************************************************************/
double calGradSim(const IplImage* image1, const IplImage* image2)
{
double c4 = 0;
double g = 0;
IplImage* g1;
IplImage* g2;
IplImage* tmp;
g1=gradientImage(image1);
g2=gradientImage(image2);
tmp = cvCloneImage(g1);
cvMul(g1, g2, tmp);
cvMul(g1, g1, g1);
cvMul(g2, g2, g2);
CvScalar s1 = cvSum(tmp);
CvScalar s2 = cvSum(g1);
CvScalar s3 = cvSum(g2);
c4 = (0.03 * 255) * (0.03 * 255);
g = (2 * s1.val[0] + c4) / (s2.val[0] +s3.val[0] + c4);
cvReleaseImage(&g1);
cvReleaseImage(&g2);
cvReleaseImage(&tmp);
return g;
}
/*************************************************************************
* @函数名称:
* gradientImage()
* @输入:
* const IplImage* input - 输入8U图像
* @输出:
* IplImage* gradient - 输出8U梯度图像
* @说明:
* 计算图像的梯度幅值矩阵
**************************************************************************/
IplImage* gradientImage(const IplImage* input)
{
int i, j;
int row = input->height;
int col = input->width;
IplImage* gradient=cvCreateImage(cvSize(col, row), input->depth,input->nChannels);
IplImage* gx = cvCreateImage(cvSize(col, row), input->depth, input->nChannels);
IplImage* gy = cvCreateImage(cvSize(col, row), input->depth, input->nChannels);
cvZero(gradient);
cvZero(gx);
cvZero(gy);
/*计算水平梯度图像*/
for (i = 0; i < row; i++)
{
uchar* current = (uchar*)(input->imageData+i*input->widthStep);
uchar* gxcurrent = (uchar*)(gx->imageData + i*gx->widthStep);
for (j = 0; j < col - 1; j++)
{
gxcurrent[j] = abs(current[j + 1] - current[j]);
}
}
/*计算垂直梯度图像*/
for (i = 0; i < row - 1; i++)
{
uchar* current = (uchar*)(input->imageData + i*input->widthStep);
uchar* next = (uchar*)(input->imageData + (i+1)*input->widthStep);
uchar* gycurrent = (uchar*)(gx->imageData + i*gx->widthStep);
for (j = 0; j < col; j++)
{
gycurrent[j] = abs(next[j] - current[j]);
}
}
cvAdd(gx,gy,gradient);
cvReleaseImage(&gx);
cvReleaseImage(&gy);
return gradient;
}
/*************************************************************************
* @函数名称:
* calINRSS()
* @输入:
* const IplImage* input - 输入8U图像
* @输出:
* double INRSS - 输出INRSS值
* @说明:
* 计算图像无参考结构相似度
**************************************************************************/
double calINRSS(const IplImage* input)
{
double INRSS = 0;
double missim = 0;
IplImage* lp_image = cvCloneImage(input);
cvSmooth(input, lp_image, CV_GAUSSIAN, 7, 7, 6);
missim = calMISSIM(input, lp_image, 8);
INRSS = 1 - missim;
cvReleaseImage(&lp_image);
return INRSS;
}
/*************************************************************************
* @函数名称:
* calRingMetric()
* @输入:
* const IplImage* input - 输入8U图像
* int d - 使用的模糊核边长的一半
* @输出:
* double INRSS - 输出INRSS值
* @说明:
* 计算图像的平行边缘值用以评价振铃效应
**************************************************************************/
double calRingMetric(const IplImage* input, int d)
{
int i = 0, j = 0, p = 0, q = 0;
int id = 0, jd = 0, is = 0, js = 0;
double rm = 0;
double cos45 = cos(45 / 180 * PI);
double sin45 = sin(45 / 180 * PI);
double cos135 = cos(135 / 180 * PI);
double sin135 = sin(135 / 180 * PI);
IplImage* edge = cvCloneImage(input);
CvMat* rm1 = cvCreateMat(input->height, input->width, CV_8UC1);
CvMat* rm2 = cvCreateMat(input->height, input->width, CV_8UC1);
CvMat* rm3 = cvCreateMat(input->height, input->width, CV_8UC1);
CvMat* rm4 = cvCreateMat(input->height, input->width, CV_8UC1);
cvZero(rm1);
cvZero(rm2);
cvZero(rm3);
cvZero(rm4);
//边缘检测
cvCanny(input, edge, 0.04*255, 0.1*255, 3);
cvScale(edge, edge, 1.0 / 255, 0);
//cvNamedWindow("psf",1);
//cvShowImage("psf", edge);
int lambda = 3;
for (i = d + lambda; i < input->height - (d + lambda); i++)
{
uchar* pe = (uchar*)(edge->imageData + i*edge->widthStep);
uchar* prm1 = (uchar*)(rm1->data.ptr + i*rm1->step);
uchar* prm2 = (uchar*)(rm2->data.ptr + i*rm2->step);
uchar* prm3 = (uchar*)(rm3->data.ptr + i*rm3->step);
uchar* prm4 = (uchar*)(rm4->data.ptr + i*rm4->step);
for (j = d + lambda; j < input->width - (d + lambda); j++)
{
if (pe[j] == 1)
{
//0度检测
for (p = d - lambda; p < d + lambda; p++)
{
id = i + d * 1;
jd = j;
}
}
}
}
return rm;
}