# C/C++图像处理算法(一)

``````//添加椒盐噪声
void salt(Mat& src,int number)
{
for (int i = 0; i < number; i++)
{
int r = static_cast<int>(rng.uniform(0, src.rows));
int c = static_cast<int>(rng.uniform(0, src.cols));
int k = (static_cast<int>(rng.uniform(0, 1000))&1);
if(k==1)
src.at<uchar>(r, c) = 255;
else
src.at<uchar>(r, c) = 0;
}
return;
}
``````
``````/*
* @ drt ：高斯方差
* @ Medium ：高斯均值
*/
int Get_Gauss(int Medium, int drt)
{
//产生高斯样本，以U为均值，D为均方差
double sum = 0;
for (int i = 0; i<12; i++) sum += rand() / 32767.00;
//计算机中rand()函数为－32767～＋32767（2^15-1）
//故sum＋为0～1之间的均匀随机变量
return int(Medium + drt*(sum - 6));
//产生均值为U，标准差为D的高斯分布的样本，并返回
}
/*
* variance ：高斯噪声的方差
*/

//添加高斯噪声
void ImgAddGaussNoise1(const uchar *srcimgbuff, uchar * dstImgbuff, int srcwith, int srcheigh, int chanels)
{
assert(srcimgbuff != NULL && srcwith > 0 && srcheigh > 0);
int bytecount = srcwith * srcheigh * chanels;

for (size_t i = 0; i < bytecount; i++)
{
dstImgbuff[i] += Get_Gauss(20, 0.02);
}
}
``````
``````//中值求取
void Media(Mat* src, int indexrows, int indexcols, int* meanv, int ker)
{
int lo = (ker - 1) / 2;
vector<int>moreo;
for (int i = indexrows - lo; i <= indexrows + lo; i++)
{
for (int j = indexcols - lo; j <= indexcols + lo; j++)
{
moreo.push_back(src->at<uchar>(i, j));
}
}
sort(moreo.begin(), moreo.end());
*meanv = moreo.at(ker * ker / 2);
return;
}
``````
``````//均值求取
void Meanvalue(Mat* src, int indexrows, int indexcols, float* meanv, int ker)
{
int lo = (ker - 1) / 2;
float total = 0;
for (int i = indexrows - lo; i <= indexrows + lo; i++)
{
for (int j = indexcols - lo; j <= indexcols + lo; j++)
{
total += src->at<uchar>(i, j);
}
}
*meanv = total / (ker * ker);
return;
}
``````
``````//像素方差
void Variance(Mat& src, vector<test>& hierachy, int ker)
{
int row = src.rows;
int col = src.cols;
int lo = (ker - 1) / 2;
for (int ir = lo; ir < row - lo; ir++)
{
for (int jc = lo; jc < col - lo; jc++)
{
float means;
int var;
//计算均值
Meanvalue(&src, ir, jc, &means, ker);
Vvalue(&src, ir, jc, &var, ker, means);
test temp;
temp.menval = var;
temp.x = ir;
temp.y = jc;
hierachy.push_back(temp);
}
}
return;
}
``````
``````//局部方差求取
void Vvalue(Mat* src, int indexrows, int indexcols, int* vall, int ker, float mean)
{
int lo = (ker - 1) / 2;
float total = 0;
for (int i = indexrows - lo; i <= indexrows + lo; i++)
{
for (int j = indexcols - lo; j <= indexcols + lo; j++)
{
total += pow((src->at<uchar>(i, j) - mean), 2);
}
}
*vall = static_cast<int>(total);
return;
}
``````
``````//STL排序方式
bool SortByM1(const test &v1, const test &v2)//注意：本函数的参数的类型一定要与vector中元素的类型一致
{
return v1.menval < v2.menval;//升序排列
}
``````
``````//SSIM 结构相似比
Scalar getMSSIM(const Mat& i1, const Mat& i2)
{
const double C1 = 6.5025, C2 = 58.5225;
/***************************** INITS **********************************/
int d = CV_32F;

Mat I1, I2;
i1.convertTo(I1, d);           // cannot calculate on one byte large values
i2.convertTo(I2, d);

int num = I1.channels();
//cv::imshow("123", I1);
//cv::waitKey();

Mat I2_2 = I2.mul(I2);        // I2^2
Mat I1_2 = I1.mul(I1);        // I1^2
Mat I1_I2 = I1.mul(I2);        // I1 * I2

/*************************** END INITS **********************************/

Mat mu1, mu2;   // PRELIMINARY COMPUTING
GaussianBlur(I1, mu1, Size(11, 11), 1.5);
GaussianBlur(I2, mu2, Size(11, 11), 1.5);

Mat mu1_2 = mu1.mul(mu1);
Mat mu2_2 = mu2.mul(mu2);
Mat mu1_mu2 = mu1.mul(mu2);

Mat sigma1_2, sigma2_2, sigma12;

GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);
sigma1_2 -= mu1_2;

GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);
sigma2_2 -= mu2_2;

GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);
sigma12 -= mu1_mu2;

///////////////////////////////// FORMULA ////////////////////////////////
Mat t1, t2, t3;

t1 = 2 * mu1_mu2 + C1;
t2 = 2 * sigma12 + C2;
t3 = t1.mul(t2);              // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))

t1 = mu1_2 + mu2_2 + C1;
t2 = sigma1_2 + sigma2_2 + C2;
t1 = t1.mul(t2);               // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))

Mat ssim_map;
divide(t3, t1, ssim_map);      // ssim_map =  t3./t1;

Scalar mssim = mean(ssim_map); // mssim = average of ssim map
return mssim;
}
``````