图像处理常用算法(C++/OPENCV) - 好文

//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( 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; }

//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; } //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; }
//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; } //功能：局部均值求取局部方差求取 /* zc 2018/07/08
parameters: Mat* src; //待处理的图像 float* meanv; //保存局部均值 float* dev; //保存局部方差 int
indexrows; //要求局部所在行 int indexcols; //要求局部所在列 int ker; //窗口大小系数 */ 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; }

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