目录
- 源码
- 滤波器
- 主函数
- 效果
- 完整源码
平台：Windows 10 20H2
Visual Studio 2015
OpenCV 4.5.3
本文所用源码修改自双边滤波(bilateral filter)以及联合双边滤波（joint bilateral filter）—— flow_specter
源码
滤波器
// 双边滤波
// @ src 待滤波的影像
// @ dst 输出的影像
void BilateralFilter(Mat& src, Mat& dst, int d, double sigmaColor, double sigmaSpace)
{dst = src.clone();int n_rows = dst.rows;int n_cols = dst.cols;int n_channels = dst.channels();int n_cols_with_channels = n_cols * n_channels;int half_kernel_size = d / 2;int index;double pixel_sum;double weight_sum = 0;double temp_bilateral_weight = 0;double color_kernel[256];// 颜色域权重确定// @ color_kernel 颜色域核，1D，长度为256for (int i = 0; i < 256; i++){color_kernel[i] = exp(-1.0 * (i * i) / (2 * sigmaColor * sigmaColor));}// 空间域权重确定// @ distance_kernel 空间域核,1D// **************************************************************************************************************double *distance_kernel;distance_kernel = new double[d * d];int k = d / 2;//二维动态数组申请空间double **distance_kernel_2D = new double*[d];for (int i = 0; i < d; i++)distance_kernel_2D[i] = new double[d];double delta_square = 2 * sigmaSpace * sigmaSpace; //分母for (int i = -k; i <= k; i++){for (int j = -k; j <= k; j++){double distance_numerator = i * i + j * j;distance_kernel_2D[i + k][j + k] = exp(-1.0 * distance_numerator / delta_square);}}// 将2D kernel 转换为 1D kernelfor (int i = 0; i < d; i++){for (int j = 0; j < d; j++){distance_kernel[d * i + j] = distance_kernel_2D[i][j];}}//释放二维动态数组空间for (int i = 0; i < d; i++)delete[] distance_kernel_2D[i];delete[] distance_kernel_2D;// **************************************************************************************************************// 边界不做处理for (int i = half_kernel_size; i < (n_rows - half_kernel_size); i++) {uchar* pt_dst = dst.ptr<uchar>(i);uchar* pt_src = src.ptr<uchar>(i);for (int j = n_channels * half_kernel_size; j < (n_cols_with_channels - n_channels * half_kernel_size); j++) {index = 0;pixel_sum = weight_sum = 0;// 内层kx,ky循环,空间域内滤波for (int kx = i - half_kernel_size; kx <= i + half_kernel_size; kx++) {uchar* pt_k_src = src.ptr<uchar>(kx);for (int ky = j - n_channels * half_kernel_size; ky <= (j + n_channels * half_kernel_size); ky += n_channels) {temp_bilateral_weight = distance_kernel[index++] * color_kernel[(int)abs(pt_src[j] - pt_k_src[ky])];weight_sum += temp_bilateral_weight;pixel_sum += (pt_k_src[ky] * temp_bilateral_weight); // 邻域某像素与中心点的双边权重乘积}}pixel_sum /= weight_sum; // 归一化pt_dst[j] = saturate_cast<uchar>(pixel_sum); //加权赋值}}delete[]distance_kernel;
}
//————————————————
//版权声明：本文为CSDN博主「flow_specter」的原创文章，遵循CC 4.0 BY - SA版权协议，转载请附上原文出处链接及本声明。
//原文链接：https ://blog.csdn.net/flow_specter/article/details/107557303
主函数
图片路径根据实际情况调整，注意反斜杠是转义字符的开头，故“\”应替换为“\”
int main(int argc, char * argv[])
{Mat src = imread("D:\\Work\\OpenCV\\Workplace\\Test_1\\face.jpg");Mat dst;BilateralFilter(src, dst, 23, 35, 10);imshow("原图", src);imshow("输出", dst);waitKey(0);return 0;
}
效果
完整源码
#include <opencv2\opencv.hpp>
#include <iostream>using namespace cv;
using namespace std;// 双边滤波
// @ src 待滤波的影像
// @ dst 输出的影像
void BilateralFilter(Mat& src, Mat& dst, int d, double sigmaColor, double sigmaSpace)
{dst = src.clone();int n_rows = dst.rows;int n_cols = dst.cols;int n_channels = dst.channels();int n_cols_with_channels = n_cols * n_channels;int half_kernel_size = d / 2;int index;double pixel_sum;double weight_sum = 0;double temp_bilateral_weight = 0;double color_kernel[256];// 颜色域权重确定// @ color_kernel 颜色域核，1D，长度为256for (int i = 0; i < 256; i++){color_kernel[i] = exp(-1.0 * (i * i) / (2 * sigmaColor * sigmaColor));}// 空间域权重确定// @ distance_kernel 空间域核,1D// **************************************************************************************************************double *distance_kernel;distance_kernel = new double[d * d];int k = d / 2;//二维动态数组申请空间double **distance_kernel_2D = new double*[d];for (int i = 0; i < d; i++)distance_kernel_2D[i] = new double[d];double delta_square = 2 * sigmaSpace * sigmaSpace; //分母for (int i = -k; i <= k; i++){for (int j = -k; j <= k; j++){double distance_numerator = i * i + j * j;distance_kernel_2D[i + k][j + k] = exp(-1.0 * distance_numerator / delta_square);}}// 将2D kernel 转换为 1D kernelfor (int i = 0; i < d; i++){for (int j = 0; j < d; j++){distance_kernel[d * i + j] = distance_kernel_2D[i][j];}}//释放二维动态数组空间for (int i = 0; i < d; i++)delete[] distance_kernel_2D[i];delete[] distance_kernel_2D;// **************************************************************************************************************// 边界不做处理for (int i = half_kernel_size; i < (n_rows - half_kernel_size); i++) {uchar* pt_dst = dst.ptr<uchar>(i);uchar* pt_src = src.ptr<uchar>(i);for (int j = n_channels * half_kernel_size; j < (n_cols_with_channels - n_channels * half_kernel_size); j++) {index = 0;pixel_sum = weight_sum = 0;// 内层kx,ky循环,空间域内滤波for (int kx = i - half_kernel_size; kx <= i + half_kernel_size; kx++) {uchar* pt_k_src = src.ptr<uchar>(kx);for (int ky = j - n_channels * half_kernel_size; ky <= (j + n_channels * half_kernel_size); ky += n_channels) {temp_bilateral_weight = distance_kernel[index++] * color_kernel[(int)abs(pt_src[j] - pt_k_src[ky])];weight_sum += temp_bilateral_weight;pixel_sum += (pt_k_src[ky] * temp_bilateral_weight); // 邻域某像素与中心点的双边权重乘积}}pixel_sum /= weight_sum; // 归一化pt_dst[j] = saturate_cast<uchar>(pixel_sum); //加权赋值}}delete[]distance_kernel;
}
//————————————————
//版权声明：本文为CSDN博主「flow_specter」的原创文章，遵循CC 4.0 BY - SA版权协议，转载请附上原文出处链接及本声明。
//原文链接：https ://blog.csdn.net/flow_specter/article/details/107557303int main(int argc, char * argv[])
{Mat src = imread("D:\\Work\\OpenCV\\Workplace\\Test_1\\face.jpg");Mat dst;BilateralFilter(src, dst, 23, 35, 10);imshow("原图", src);imshow("输出", dst);waitKey(0);return 0;
}
配置Wifi和SSH服务)
