高斯滤波：
1 #include<iostream> 2 #include <opencv2/core/core.hpp> 3 #include <opencv2/highgui/highgui.hpp> 4 #include <opencv2/imgproc/imgproc.hpp> 5 #include "guassfilter.h" 6 7 using namespace std; 8 using namespace cv; 9 10 double **GuassKenel(int size, double sigma) { 11 int i, j; 12 double sum = 0.0; 13 int center = size / 2; 14 const double PI = 4.0*atan(1.0); //圆周率π赋值 15 16 double **arr = new double*[size];//建立一个size*size大小的二维数组 17 for (i = 0; i < size; i++) 18 arr[i] = new double[size]; 19 20 for (i = 0; i < size; i++) 21 for (j = 0; j < size; j++) { 22 arr[i][j] = (1 / (2 * PI*sigma*sigma))*exp(-((i - center)*(i - center) + (j - center)*(j - center)) / (sigma*sigma * 2)); 23 sum += arr[i][j]; 24 } 25 for (i = 0; i < size; i++) { 26 for (j = 0; j < size; j++) 27 arr[i][j] /= sum; 28 } 29 return arr; 30 } 31 32 33 void GaussFilter(const Mat _src, Mat &_dst, int ksize, double ksigma) { 34 35 if (!_src.data) return; 36 double **arra; 37 Mat tmp(_src.size(), _src.type()); 38 for (int i = 0; i < _src.rows; i++) 39 for (int j = 0; j < _src.cols; j++) { 40 //边缘不进行处理 41 if ((i - (ksize / 2)) > 0 && (i + (ksize / 2)) < _src.rows && (j - (ksize / 2)) > 0 && (j + (ksize / 2)) < _src.cols) { 42 arra = GuassKenel(ksize, ksigma); 43 tmp.at<uchar>(i, j) = 0; 44 for (int x = 0; x < ksize; x++) { 45 for (int y = 0; y < ksize; y++) { 46 tmp.at<uchar>(i, j) += arra[x][y] * _src.at<uchar>(i + (ksize / 2) - x, j + (ksize / 2) - y); 47 } 48 } 49 } 50 else { 51 tmp.at<uchar>(i, j) = _src.at<uchar>(i, j); 52 } 53 } 54 tmp.copyTo(_dst); 55 }
拉普拉斯滤波：
#include <iostream> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include "laplace.h" using namespace std; using namespace cv; void LaplaceFilter(const Mat _src, Mat & _dst) { if (!_src.data) return; Mat tmp(_src.size(), _src.type()); for (int i = 0; i < _src.rows; i++) { for (int j = 0; j < _src.cols; j++) { if ((i - 1) > 0 && (i + 1) < _src.rows && (j - 1) > 0 && (j + 1) < _src.cols) { double arra[3][3] = { { 0,-1,0 },{ -1,5,-1 },{ 0,-1,0 } }; tmp.at<uchar>(i, j) = 0; for (int x = 0; x < 3; x++) { for (int y = 0; y < 3; y++) { tmp.at<uchar>(i, j) += arra[x][y] * _src.at<uchar>(i + 1 - x, j + 1 - y); } } } else { tmp.at<uchar>(i, j) = _src.at<uchar>(i, j); } } } tmp.copyTo(_dst); }
均值滤波：
1 #include <iostream> 2 #include <opencv2/core/core.hpp> 3 #include <opencv2/highgui/highgui.hpp> 4 #include <opencv2/imgproc/imgproc.hpp> 5 #include "meanfilter.h" 6 7 using namespace std; 8 using namespace cv; 9 10 double **MeanKenel(int size) { 11 double **arr = new double*[size];//建立一个size*size大小的二维数组 12 for (int i = 0; i < size; i++) 13 arr[i] = new double[size]; 14 15 for (int i = 0; i < size; i++) { 16 for (int j = 0; j < size; j++) { 17 arr[i][j] = 1.0 / 9; 18 } 19 } 20 return arr; 21 } 22 23 void MeanFilter(const Mat _src, Mat & _dst, int ksize) { 24 25 if (!_src.data) return; 26 double **arra; 27 Mat tmp(_src.size(), _src.type()); 28 for (int i = 0; i < _src.rows; i++) { 29 for (int j = 0; j < _src.cols; j++) { 30 if ((i - (ksize / 2)) > 0 && (i + (ksize / 2)) < _src.rows && (j - (ksize / 2)) > 0 && (j + (ksize / 2)) < _src.cols) { 31 arra = MeanKenel(ksize); 32 tmp.at<uchar>(i, j) = 0; 33 for (int x = 0; x < ksize; x++) { 34 for (int y = 0; y < ksize; y++) { 35 tmp.at<uchar>(i, j) += arra[x][y] * _src.at<uchar>(i + (ksize / 2) - x, j + (ksize / 2) - y); 36 } 37 } 38 } 39 else { 40 tmp.at<uchar>(i, j) = _src.at<uchar>(i, j); 41 } 42 } 43 } 44 tmp.copyTo(_dst); 45 }
直方图均衡化：
1 #include <iostream> 2 #include <opencv2/core/core.hpp> 3 #include <opencv2/highgui/highgui.hpp> 4 #include <opencv2/imgproc/imgproc.hpp> 5 #include "equalization.h" 6 7 using namespace std; 8 using namespace cv; 9 10 11 12 void Equalization(Mat& input, Mat &_dst) 13 { 14 int gray[256] = { 0 }; //记录每个灰度级别下的像素个数 15 double gray_prob[256] = { 0 }; //记录灰度分布密度 16 double gray_distribution[256] = { 0 }; //记录累计密度 17 int gray_equal[256] = { 0 }; //均衡化后的灰度值 18 int gray_sum = 0; //像素总数 19 Mat output = input.clone(); 20 gray_sum = input.cols * input.rows; 21 22 //统计每个灰度下的像素个数 23 for (int i = 0; i < input.rows; i++) 24 { 25 uchar* p = input.ptr<uchar>(i); 26 for (int j = 0; j < input.cols; j++) 27 { 28 int vaule = p[j]; 29 gray[vaule]++; 30 } 31 } 32 33 34 //统计灰度频率 35 for (int i = 0; i < 256; i++) 36 { 37 gray_prob[i] = ((double)gray[i] / gray_sum); 38 } 39 40 //计算累计密度 41 gray_distribution[0] = gray_prob[0]; 42 for (int i = 1; i < 256; i++) 43 { 44 gray_distribution[i] = gray_distribution[i - 1] + gray_prob[i]; 45 } 46 47 //重新计算均衡化后的灰度值，四舍五入。参考公式：(N-1)*T+0.5 48 for (int i = 0; i < 256; i++) 49 { 50 gray_equal[i] = (uchar)(255 * gray_distribution[i] + 0.5); 51 } 52 53 54 //直方图均衡化,更新原图每个点的像素值 55 for (int i = 0; i < output.rows; i++) 56 { 57 uchar* p = output.ptr<uchar>(i); 58 for (int j = 0; j < output.cols; j++) 59 { 60 p[j] = gray_equal[p[j]]; 61 } 62 } 63 64 output.copyTo(_dst); 65 }
主函数：
1 #include <iostream> 2 #include <opencv2/core/core.hpp> 3 #include <opencv2/highgui/highgui.hpp> 4 #include <opencv2/imgproc/imgproc.hpp> 5 #include "guassfilter.h" 6 #include "meanfilter.h" 7 #include "laplace.h" 8 #include "equalization.h" 9 10 using namespace std; 11 using namespace cv; 12 13 int main() { 14 15 Mat image = imread("D:\研一\图像处理与计算机视觉\图像处理作业\img.jpg", 0); 16 Mat gauss1, gauss2, gauss3, mean, laplace, equalization; 17 int a, b, c, d; 18 double e, f, g; 19 cout << "请输入第一次高斯滤波器模板的阶数和方差：" << endl; 20 cin >> a >> e; 21 cout << "请输入第二次高斯滤波器模板的阶数和方差：" << endl; 22 cin >> b >> f; 23 cout << "请输入第三次高斯滤波器模板的阶数和方差：" << endl; 24 cin >> c >> g; 25 cout << "请输入均值滤波器模板的阶数：" << endl; 26 cin >> d; 27 cout << "拉普拉斯滤波器卷积核固定选为：" << endl <<" "<< 0 << " " << -1 << " " << 0 << endl << -1 << " " << 5 << " " << -1 << endl <<" "<< 0 << " " << -1 << " " << 0 << endl; 28 GaussFilter(image, gauss1, a, e); 29 GaussFilter(image, gauss2, b, f); 30 GaussFilter(image, gauss3, c, g); 31 MeanFilter(image, mean, d); 32 LaplaceFilter(image, laplace); 33 Equalization(image, equalization); 34 35 namedWindow("原图"); 36 namedWindow("高斯滤波器1"); 37 namedWindow("高斯滤波器2"); 38 namedWindow("高斯滤波器3"); 39 namedWindow("均值滤波器"); 40 namedWindow("拉普拉斯滤波器"); 41 namedWindow("直方图均衡化"); 42 43 imshow("原图", image); 44 imshow("高斯滤波器1", gauss1); 45 imshow("高斯滤波器2", gauss2); 46 imshow("高斯滤波器3", gauss3); 47 imshow("均值滤波器",mean); 48 imshow("拉普拉斯滤波器", laplace); 49 imshow("直方图均衡化", equalization); 50 51 imwrite("原图.jpg", image); 52 imwrite("高斯滤波器1.jpg", gauss1); 53 imwrite("高斯滤波器2.jpg", gauss2); 54 imwrite("高斯滤波器3.jpg", gauss3); 55 imwrite("均值滤波器.jpg", mean); 56 imwrite("拉普拉斯滤波.jpg", laplace); 57 imwrite("直方图均衡化.jpg", equalization); 58 59 waitKey(); 60 return 0; 61 }