opencv-基于c++实现的opencv图像处理算法之图像滤波算法高斯滤波.zip

#include <iostream> #include <opencv2/core.hpp> #include <opencv2/highgui.hpp> #include <opencv2/imgproc.hpp> /////////////////////////////// //x，y方向联合实现获取高斯模板 ////////////////////////////// void generateGaussMask(cv::Mat& Mask,cv::Size wsize, double sigma){ Mask.create(wsize,CV_64F); int h = wsize.height; int w = wsize.width; int center_h = (h - 1) / 2; int center_w = (w - 1) / 2; double sum = 0.0; double x, y; for (int i = 0; i < h; ++i){ y = pow(i - center_h, 2); for (int j = 0; j < w; ++j){ x = pow(j - center_w, 2); //因为最后都要归一化的，常数部分可以不计算，也减少了运算量 double g = exp(-(x + y) / (2 * sigma*sigma)); Mask.at<double>(i, j) = g; sum += g; } } Mask = Mask / sum; } //////////////////////////// //按二维高斯函数实现高斯滤波 /////////////////////////// void GaussianFilter(cv::Mat& src, cv::Mat& dst, cv::Mat window){ int hh = (window.rows - 1) / 2; int hw = (window.cols - 1) / 2; dst = cv::Mat::zeros(src.size(),src.type()); //边界填充 cv::Mat Newsrc; cv::copyMakeBorder(src, Newsrc, hh, hh, hw, hw, cv::BORDER_REPLICATE);//边界复制 //高斯滤波 for (int i = hh; i < src.rows + hh;++i){ for (int j = hw; j < src.cols + hw; ++j){ double sum[3] = { 0 }; for (int r = -hh; r <= hh; ++r){ for (int c = -hw; c <= hw; ++c){ if (src.channels() == 1){ sum[0] = sum[0] + Newsrc.at<uchar>(i + r, j + c) * window.at<double>(r + hh, c + hw); } else if (src.channels() == 3){ cv::Vec3b rgb = Newsrc.at<cv::Vec3b>(i+r,j + c); sum[0] = sum[0] + rgb[0] * window.at<double>(r + hh, c + hw);//B sum[1] = sum[1] + rgb[1] * window.at<double>(r + hh, c + hw);//G sum[2] = sum[2] + rgb[2] * window.at<double>(r + hh, c + hw);//R } } } for (int k = 0; k < src.channels(); ++k){ if (sum[k] < 0) sum[k] = 0; else if (sum[k]>255) sum[k] = 255; } if (src.channels() == 1) { dst.at<uchar>(i - hh, j - hw) = static_cast<uchar>(sum[0]); } else if (src.channels() == 3) { cv::Vec3b rgb = { static_cast<uchar>(sum[0]), static_cast<uchar>(sum[1]), static_cast<uchar>(sum[2]) }; dst.at<cv::Vec3b>(i-hh, j-hw) = rgb; } } } } //////////////////////////////////// //分离计算实现高斯滤波，更加高效 /////////////////////////////////// void separateGaussianFilter(cv::Mat& src, cv::Mat& dst, int wsize, double sigma){ //获取一维高斯滤波模板 cv::Mat window; window.create(1 , wsize, CV_64F); int center = (wsize - 1) / 2; double sum = 0.0; for (int i = 0; i < wsize; ++i){ double g = exp(-(pow(i - center, 2)) / (2 * sigma*sigma)); window.at<double>(0, i) = g; sum += g; } window = window / sum; //std::cout << window << std::endl; //边界填充 int boder = (wsize - 1) / 2; dst = cv::Mat::zeros(src.size(), src.type()); cv::Mat Newsrc; cv::copyMakeBorder(src, Newsrc, boder, boder, boder, boder, cv::BORDER_REPLICATE);//边界复制 //高斯滤波--水平方向 for (int i = boder; i < src.rows + boder; ++i){ for (int j = boder; j < src.cols + boder; ++j){ double sum[3] = { 0 }; for (int r = -boder; r <= boder; ++r){ if (src.channels() == 1){ sum[0] = sum[0] + Newsrc.at<uchar>(i, j + r) * window.at<double>(0, r + boder); //行不变列变 } else if (src.channels() == 3){ cv::Vec3b rgb = Newsrc.at<cv::Vec3b>(i, j +r); sum[0] = sum[0] + rgb[0] * window.at<double>(0, r + boder);//B sum[1] = sum[1] + rgb[1] * window.at<double>(0, r + boder);//G sum[2] = sum[2] + rgb[2] * window.at<double>(0, r + boder);//R } } for (int k = 0; k < src.channels(); ++k){ if (sum[k] < 0) sum[k] = 0; else if (sum[k]>255) sum[k] = 255; } if (src.channels() == 1){ dst.at<uchar>(i - boder, j - boder) = static_cast<uchar>(sum[0]); } else if (src.channels() == 3){ cv::Vec3b rgb = { static_cast<uchar>(sum[0]), static_cast<uchar>(sum[1]), static_cast<uchar>(sum[2]) }; dst.at<cv::Vec3b>(i - boder, j - boder) = rgb; } } } //高斯滤波--垂直方向 //对水平方向处理后的dst边界填充 cv::copyMakeBorder(dst, Newsrc, boder, boder, boder, boder, cv::BORDER_REPLICATE);//边界复制 for (int i = boder; i < src.rows + boder; ++i){ for (int j = boder; j < src.cols + boder; ++j){ double sum[3] = { 0 }; for (int r = -boder; r <= boder; ++r){ if (src.channels() == 1){ sum[0] = sum[0] + Newsrc.at<uchar>(i + r, j ) * window.at<double>(0, r + boder); //列不变行变 } else if (src.channels() == 3){ cv::Vec3b rgb = Newsrc.at<cv::Vec3b>(i + r, j); sum[0] = sum[0] + rgb[0] * window.at<double>(0, r + boder);//B sum[1] = sum[1] + rgb[1] * window.at<double>(0, r + boder);//G sum[2] = sum[2] + rgb[2] * window.at<double>(0, r + boder);//R } } for (int k = 0; k < src.channels(); ++k){ if (sum[k] < 0) sum[k] = 0; else if (sum[k]>255) sum[k] = 255; } if (src.channels() == 1){ dst.at<uchar>(i - boder, j - boder) = static_cast<uchar>(sum[0]); } else if (src.channels() == 3){ cv::Vec3b rgb = { static_cast<uchar>(sum[0]), static_cast<uchar>(sum[1]), static_cast<uchar>(sum[2]) }; dst.at<cv::Vec3b>(i - boder, j - boder) = rgb; } } } } int main(){ cv::Mat src = cv::imread("I:\\Learning-and-Practice\\2019Change\\Image process algorithm\\Img\\lena.jpg"); if (src.empty()){ return -1; } cv::Mat Mask; cv::Mat dst1; cv::Mat dst2; //暴力实现 generateGaussMask(Mask, cv::Size(5, 5), 10);//获取二维高斯滤波模板 //std::cout << Mask << std::endl; GaussianFilter(src, dst1, Mask); //分离实现 separateGaussianFilter( src, dst2, 5, 10); cv::namedWindow("src"); cv::imshow("src", src); cv::namedWindow("暴力实现"); cv::imshow("暴力实现", dst1); cv::namedWindow("分离实现"); cv::imshow("分离实现", dst2); // cv::imwrite("I:\\Learning-and-Practice\\2019Change\\Image process algorithm\\Image Filtering\\GaussianFilter\\woman.jpg", dst); cv::waitKey(0); return 0; }