影象處理常用演算法(C++/OPENCV)
阿新 • • 發佈:2019-02-17
//新增椒鹽噪聲 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) { //產生高斯樣本,以U為均值,D為均方差 double sum = 0; for (int i = 0; i<12; i++) sum += rand() / 32767.00; //計算機中rand()函式為-32767~+32767(2^15-1) //故sum+為0~1之間的均勻隨機變數 return int(Medium + drt*(sum - 6)); //產生均值為U,標準差為D的高斯分佈的樣本,並返回 } /* * variance :高斯噪聲的方差 */ //新增高斯噪聲 void ImgAddGaussNoise1( uchar * dstImgbuff, int srcwith, int srcheigh, int chanels) { assert( srcwith > 0 && srcheigh > 0); int bytecount = srcwith * srcheigh * chanels; for (size_t i = 0; i < bytecount; i++) { int iTemp = dstImgbuff[i] + Get_Gauss(0, 20); iTemp = iTemp > 255 ? 255 : iTemp; iTemp = iTemp < 0 ? 0 : iTemp; dstImgbuff[i] = iTemp; } }
//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; }
//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;
}
//區域性方差求取
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;
}
//畫素方差
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;
//計算均值
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;
}
//STL排序方式
bool SortByM1(const test &v1, const test &v2)//注意:本函式的引數的型別一定要與vector中元素的型別一致
{
return v1.menval < v2.menval;//升序排列
}
//SSIM 結構相似比
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;
}
//功能:區域性均值求取 區域性方差求取
/* zc 2018/07/08
parameters:
Mat* src; //待處理的影象
float* meanv; //儲存區域性均值
float* dev; //儲存區域性方差
int indexrows; //要求區域性所在行
int indexcols; //要求區域性所在列
int ker; //視窗大小系數
*/
void Meanvalue(Mat* src, float* meanv, float* dev, int indexrows, int indexcols, int ker)
{
int lo = (ker - 1) / 2;
float total = 0;
float total2 = 0;
for (int i = indexrows - lo; i <= indexrows + lo; i++)
{
for (int j = indexcols - lo; j <= indexcols + lo; j++)
{
float temp = static_cast<float>(src->at<uchar>(i, j));
total += temp;
total2 += temp*temp;
}
}
int size = ker * ker;
*meanv = total / size; //均值
*dev = (total2 - (total*total) / size) / size; //方差
return;
}