Opencv之分离高斯滤波

1.介绍

    高斯滤波的原理可以参考我的一篇博客：图像的高斯平滑之c++实现（qt + 不调包），更快的高斯滤波可以参考我的另外一篇博客：Opencv之快速高斯模糊（IIR递归高斯模糊），建议读者把这两篇博客也阅读一下。

    高斯函数具有可分离性，用下面的公式很形象的表达出来：

2.代码

    花大量时间介绍可分离高斯滤波，我觉得还不如来一点代码，清晰明朗。

#include<opencv2\opencv.hpp>
#include<iostream>
#include<vector>
#include<math.h>

using namespace cv;
using namespace std;


void getGaussianArray(vector<double>& gauss_arry, const double& sigma) {
	int i;
	int arr_size = gauss_arry.size();
	int center_i = arr_size / 2;
	double sum = 0.0f;
	for (i = 0; i < arr_size; ++i) {
		gauss_arry[i] = exp(-(1.0f)* (((i - center_i)*(i - center_i)) /
			(2.0f*sigma*sigma)));
		sum += gauss_arry[i];
	}
	for (i = 0; i < arr_size; ++i)
		gauss_arry[i] /= sum;
}

void gaussian(Mat& src, vector<double>& arrayX, vector<double>& arrayY) {
	int center = arrayX.size() / 2;
	Mat temp = src.clone();

	// 先x轴方向计算
	for (int i = 0; i < src.rows; ++i) {
		for (int j = 0; j < src.cols; ++j) {
			if (i > center - 1 && j > center - 1 && i < src.rows - center && j < src.cols - center) {
				double sum = 0.0;
				for (int l = 0; l < arrayX.size(); ++l)
					sum += src.ptr<uchar>(i)[j - l + center] * arrayX[l];
				temp.ptr<uchar>(i)[j] = MAX(MIN(sum, 255), 0);
			}
		}
	}

	// 接着y轴方向计算
	for (int i = 0; i < src.rows; ++i) {
		for (int j = 0; j < src.cols; ++j) {
			if (i > center - 1 && j > center - 1 && i < src.rows - center && j < src.cols - center) {
				double sum = 0.0;
				for (int k = 0; k < arrayX.size(); ++k)
					sum += temp.ptr<uchar>(i - k + center)[j] * arrayY[k];
				src.ptr<uchar>(i)[j] = MAX(MIN(sum, 255), 0);
			}
		}
	}
}

void splitGaussianFilterFast(Mat& src, Mat& dst, int n, double sigmaX, double sigmaY)
{
	vector<double>arrayX(n);
	vector<double>arrayY(n);
	vector<cv::Mat> channels;
	dst = src.clone();
	cv::split(src, channels);
	getGaussianArray(arrayX, sigmaX);
	getGaussianArray(arrayY, sigmaY);
	for (int i = 0; i < 3; i++) {
		gaussian(channels[i], arrayX, arrayY);
	}
	cv::merge(channels, dst);
}

int main(void)
{
	cv::Mat src = cv::imread("test.png");
	cv::Mat dst;
	cv::Mat dst2;
	double time0 = static_cast<double>(getTickCount());
	splitGaussianFilterFast(src, dst, 53, 2.0f, 2.0f);
	time0 = ((double)getTickCount() - time0) / getTickFrequency();
	cout << "分离卷积高斯方法运行时间为：" << time0 << "秒" << endl;

	time0 = static_cast<double>(getTickCount());
	GaussianBlur(src, dst2, cv::Size(53, 53), 2.0f, 2.0f);
	time0 = ((double)getTickCount() - time0) / getTickFrequency();
	cout << "Opencv自带方法运行时间为：" << time0 << "秒" << endl;

	cv::imshow("src", src);
	cv::imshow("dst", dst);
	cv::imshow("dst2", dst2);
	cv::waitKey(0);
	cv::destroyAllWindows();
	return 0;
}