1、添加椒盐噪声
Mat addSaltNoise(const Mat srcImage, int n)
{
Mat dstImage = srcImage.clone();
for (int k = 0; k < n; k++)
{
//随机取值行列
int i = rand() % dstImage.rows;
int j = rand() % dstImage.cols;
//图像通道判定
if (dstImage.channels() == 1)
{
dstImage.at<uchar>(i, j) = 255; //盐噪声
}
else
{
dstImage.at<Vec3b>(i, j)[0] = 255;
dstImage.at<Vec3b>(i, j)[1] = 255;
dstImage.at<Vec3b>(i, j)[2] = 255;
}
}
for (int k = 0; k < n; k++)
{
//随机取值行列
int i = rand() % dstImage.rows;
int j = rand() % dstImage.cols;
//图像通道判定
if (dstImage.channels() == 1)
{
dstImage.at<uchar>(i, j) = 0; //椒噪声
}
else
{
dstImage.at<Vec3b>(i, j)[0] = 0;
dstImage.at<Vec3b>(i, j)[1] = 0;
dstImage.at<Vec3b>(i, j)[2] = 0;
}
}
return dstImage;
}
int n = 1;
while (n<=500)
{
ImgName = "000";
ImgName1 = "000";
string str;
string str1;
stringstream ss;
stringstream ss1;
ss << n;
ss >> str;
ImgName = ImgName + str;
ImgName = "image\\" + ImgName + ".bmp";
Mat srcImage = imread(ImgName, 0);
srcImage = addSaltNoise(srcImage, 3000);
imshow("1", srcImage);
string first = "s";
imwrite(ImgName+ "s" +".bmp", srcImage);
waitKey(1);
n++;
}
2、添加高斯噪声
//生成高斯噪声
double generateGaussianNoise(double mu, double sigma)
{
//定义小值
//const double epsilon = numeric_limits<double>::min();
const double epsilon = 0.001;
static double z0, z1;
static bool flag = false;
flag = !flag;
//flag为假构造高斯随机变量X
if (!flag)
return z1 * sigma + mu;
double u1, u2;
//构造随机变量
do
{
u1 = rand() * (1.0 / RAND_MAX);
u2 = rand() * (1.0 / RAND_MAX);
} while (u1 <= epsilon);
//flag为真构造高斯随机变量
z0 = sqrt(-2.0*log(u1))*cos(2 * CV_PI*u2);
z1 = sqrt(-2.0*log(u1))*sin(2 * CV_PI*u2);
return z0*sigma + mu;
}
//为图像添加高斯噪声
Mat addGaussianNoise(Mat &srcImag)
{
Mat dstImage = srcImag.clone();
int channels = dstImage.channels();
int rowsNumber = dstImage.rows;
int colsNumber = dstImage.cols*channels;
//判断图像的连续性
if (dstImage.isContinuous())
{
colsNumber *= rowsNumber;
rowsNumber = 1;
}
for (int i = 0; i < rowsNumber; i++)
{
for (int j = 0; j < colsNumber; j++)
{
//添加高斯噪声
int val = dstImage.ptr<uchar>(i)[j] +
generateGaussianNoise(0, 2.235) * 32;
if (val < 0)
val = 0;
if (val>255)
val = 255;
dstImage.ptr<uchar>(i)[j] = (uchar)val;
}
}
return dstImage;
}
int n = 1;
ofstream OutFile;//利用构造函数创建txt文本，并且打开该文本
ofstream outFile1;
BOOL isProcessed = FALSE;
while (n<=500)
{
ImgName = "000";
ImgName1 = "000";
string str;
string str1;
stringstream ss;
stringstream ss1;
ss << n;
ss >> str;
ss1 << n + 1;
ss1 >> str1;
ImgName = ImgName + str;
ImgName1 = ImgName1 + str1;
ImgNamesave = ImgName;
ImgName = "image\\" + ImgName + ".bmp";
Mat srcImage = imread(ImgName, 0);
srcImage = addGaussianNoise(srcImage);
imshow("1", srcImage);
string first = "s";
imwrite(ImgName+ "s" +".bmp", srcImage);
waitKey(1);
n++;
3、图像滤波
int n = 1;
while (n<=500)
{
ImgName = "000";
ImgName1 = "000";
string str;
string str1;
stringstream ss;
stringstream ss1;
ss << n;
ss >> str;
ss1 << n + 1;
ss1 >> str1;
ImgName = ImgName + str;
ImgName1 = ImgName1 + str1;
ImgNamesave = ImgName;
ImgName = "image\\" + ImgName + ".bmp";
Mat srcImage = imread(ImgName, 0);
medianBlur(srcImage, srcImage, 5); //执行时间5ms
imshow("1", srcImage);
string first = "s";
imwrite(ImgName+ "s" +".bmp", srcImage);
waitKey(1);
n++;
4、图像亮度调整
int n = 1;
while (n<=500)
{
ImgName = "000";
ImgName1 = "000";
string str;
string str1;
stringstream ss;
stringstream ss1;
ss << n;
ss >> str;
ss1 << n + 1;
ss1 >> str1;
ImgName = ImgName + str;
ImgName1 = ImgName1 + str1;
ImgNamesave = ImgName;
ImgName = "image\\" + ImgName + ".bmp";
Mat srcImage = imread(ImgName, 0);
for (int i = 0; i < srcImage.rows; i++)
{
for (int j = 0; j < srcImage.cols; j++)
{
srcImage.at<uchar>(i, j)= srcImage.at<uchar>(i, j)/1.07;
if(srcImage.at<uchar>(i, j)>253)
{
srcImage.at<uchar>(i, j) = 253;
}
}
}
//medianBlur(srcImage, srcImage, 5); //执行时间5ms
//srcImage = addGaussianNoise(srcImage);
imshow("1", srcImage);
string first = "s";
imwrite(ImgName+ "s" +".bmp", srcImage);
waitKey(1);
n++;
}
5、图像旋转
int n = 1;
ofstream OutFile;//利用构造函数创建txt文本，并且打开该文本
ofstream outFile1;
BOOL isProcessed = FALSE;
while (n<=500)
{
ImgName = "000";
ImgName1 = "000";
string str;
string str1;
stringstream ss;
stringstream ss1;
ss << n;
ss >> str;
ss1 << n + 1;
ss1 >> str1;
ImgName = ImgName + str;
ImgName1 = ImgName1 + str1;
ImgNamesave = ImgName;
ImgName = "image\\" + ImgName + ".bmp";
Mat srcImage = imread(ImgName, 0);
cv::flip(srcImage,srcImage,1);//左右翻转
cv::flip(srcImage,srcImage,0);//上下翻转(没有意义)
cv::flip(srcImage,srcImage,-1);//水平垂直翻转(没有意义)
imshow("1", srcImage);
string first = "s";
imwrite(ImgName+ "s" +".bmp", srcImage);
waitKey(1);
n++;
}
6、图像仿射变换
int n = 1;
while (n<=500)
{
ImgName = "000";
ImgName1 = "000";
string str;
string str1;
stringstream ss;
stringstream ss1;
ss << n;
ss >> str;
ss1 << n + 1;
ss1 >> str1;
ImgName = ImgName + str;
ImgName1 = ImgName1 + str1;
ImgNamesave = ImgName;
ImgName = "image\\" + ImgName + ".bmp";
Mat srcImage = imread(ImgName, 0);
Mat srcImage_warp;
Point2f srcPoints[3];//原图中的三点 ,一个包含三维点（x，y）的数组，其中x、y是浮点型数
Point2f dstPoints[3];//目标图中的三点
//第一种仿射变换的调用方式：三点法
//三个点对的值,上面也说了，只要知道你想要变换后图的三个点的坐标，就可以实现仿射变换
srcPoints[0] = Point2f(0, 0);
srcPoints[1] = Point2f(0, srcImage.rows);
srcPoints[2] = Point2f(srcImage.cols, 0);
//映射后的三个坐标值
dstPoints[0] = Point2f(0, srcImage.rows*0.08);
dstPoints[1] = Point2f(srcImage.cols*0.05, srcImage.rows*0.95);
dstPoints[2] = Point2f(srcImage.cols*0.95, srcImage.rows*0.05);
Mat M1 = getAffineTransform(srcPoints, dstPoints);//由三个点对计算变换矩阵
warpAffine(srcImage, srcImage, M1, srcImage.size());//仿射变换
imshow("1", srcImage);
string first = "s";
imwrite(ImgName+ "s" +".bmp", srcImage);
//isProcessed = ProcessSymmetricVImage1(srcImage);
waitKey(1);
n++;
}