opencv 双边滤波

原理讲解，因为下面这篇文章都讲解清楚了，主要就是一个公式，通过计算颜色差异的权重与高斯滤波的权重乘积即可。

OpenCV双边滤波详解及实代码实现_青城山小和尚-优快云博客_opencv 双边滤波g​​​​​​​g

opencv学习(二十二)之双边滤波bilateralFilter_烟雨博客-优快云博客_bilateralfilter

 核心就是颜色差异较大即边界部分时r会很小，趋向于0，因此整个w权重趋向于0，只有自己这个点权重为1，因此相当于这个点的颜色不变，就保留了边界；当颜色差异不大时r约等于1，因此整个w就约等于d即高斯滤波，即平坦区域是高斯滤波系数。

再结合下面的图应该就能理解了

 

直接给我我的实现代码，没有做优化，三维也是分离为三个通道单独处理的，重在逻辑清楚，代码能看懂

#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>

using namespace std;
using namespace cv;

//定义全局变量
const int g_ndMaxValue = 100;
const int g_nsigmaColorMaxValue = 200;
const int g_nsigmaSpaceMaxValue = 200;
int g_ndValue = 11;
int g_nsigmaColorValue = 10;
int g_nsigmaSpaceValue = 10;

Mat g_srcImage;
Mat g_dstImage;

void on_bilateralFilterTrackbar(int, void*);
//高斯滤波的核
vector<vector<double>> getGaussianArray(int iKernalSize, double dSigma)
{
	//assert(iKernalSize < 3 || iKernalSize % 2 == 0);

	const double pi = 3.14159265359;
	vector<vector<double>> kernal(iKernalSize, vector<double>(iKernalSize, 0));
	int r = iKernalSize / 2;
	//double sum = 0.0f;
	double dSigmaSquare2 = (-2.0f) * dSigma*dSigma;
	for (int y = -r; y <= r; ++y)
	{
		for (int x = -r; x <= r; ++x)
		{
			kernal[y + r][x + r] = exp((y*y + x*x) / dSigmaSquare2);
			//sum += kernal[y + r][x + r];
		}
	}
// 	for (int y = 0; y < iKernalSize; ++y)
// 	{
// 		for (int x = 0; x < iKernalSize; ++x)
// 		{
// 			kernal[y][x] /= sum;
// 		}
// 	}
	return kernal;
}
vector<double> getSpaceWeightArray(double sigmaSpace)
{
	//由于公式中颜色差绝对值也一定在0-255之间，所以提前算好打表，之后只需要计算颜色差的绝对值，从表里取即可
	double dSigmaSquare2 = (-2.0f) * sigmaSpace*sigmaSpace;
	vector<double> weight(255, 0);
	for (int i = 0; i < 255; ++i)
	{
		weight[i] = exp(i*i / dSigmaSquare2);
	}
	return weight;
}
void MyBilateralFilterTrackbar(const Mat & src, Mat &dst, int d, double sigmaColor, double sigmaSpace)
{
	auto colorKernel = getGaussianArray(d, sigmaColor);
	auto spaceWeight = getSpaceWeightArray(sigmaSpace);
	int h = src.rows;
	int w = src.cols;
	int r = d / 2;
	if (dst.empty())
	{
		dst.create(h, w, src.type());
	}
	Mat paddingSrc;
	//拷贝边缘
	cv::copyMakeBorder(src, paddingSrc, r, r, r, r, cv::BORDER_REPLICATE);
	for (int y = r; y < h + r; ++y)
	{
		for (int x = r; x < w + r; ++x)
		{
			double sumWeights = 0;
			double v = 0;
			for (int _y = -r; _y <= r; ++_y)
			{
				for (int _x = -r; _x <= r; ++_x)
				{
					//套公式
					uchar curColor = paddingSrc.at<uchar>(y+_y, x+_x);
					auto w = colorKernel[_y + r][_x + r] * spaceWeight[abs(paddingSrc.at<uchar>(y, x) - curColor)];
					v += curColor * w;
					sumWeights += w;
				}
			}
			dst.at<uchar>(y - r, x - r) = uchar(v / sumWeights);
		}
	}
}
int main()
{
	g_srcImage = imread("E:/Resources/bilateralFilterTest.jpg");
	if (g_srcImage.empty())
	{
		cout << "图像加载失败!" << endl;
		return -1;
	}
	else
		cout << "图像加载成功!" << endl << endl;

	namedWindow("原图像", WINDOW_AUTOSIZE);
	imshow("原图像", g_srcImage);

	namedWindow("双边滤波图像", WINDOW_AUTOSIZE);
	
	char dName[20];
	sprintf(dName, "邻域直径 %d", g_ndMaxValue);

	char sigmaColorName[20];
	sprintf(sigmaColorName, "sigmaColor %d", g_nsigmaColorMaxValue);

	char sigmaSpaceName[20];
	sprintf(sigmaSpaceName, "sigmaSpace %d", g_nsigmaSpaceMaxValue);

	//创建滚动条
	createTrackbar(dName, "双边滤波图像", &g_ndValue, g_ndMaxValue, on_bilateralFilterTrackbar);
	createTrackbar(sigmaColorName, "双边滤波图像", &g_nsigmaColorValue,g_nsigmaColorMaxValue, on_bilateralFilterTrackbar);
	createTrackbar(sigmaSpaceName, "双边滤波图像", &g_nsigmaSpaceValue,g_nsigmaSpaceMaxValue, on_bilateralFilterTrackbar);
	on_bilateralFilterTrackbar(g_nsigmaSpaceValue, 0);

	waitKey(0);

	return 0;
}

void on_bilateralFilterTrackbar(int, void*)
{
	//cv::bilateralFilter(g_srcImage, g_dstImage, g_ndValue, g_nsigmaColorValue, g_nsigmaSpaceValue);
	if (g_ndValue % 2 == 0)
		g_ndValue++;
	if (g_srcImage.channels() == 3)
	{
		vector<Mat> srcs;
		vector<Mat> dsts(3);
		split(g_srcImage, srcs);
		for (int i = 0; i < 3; ++i)
		{
			MyBilateralFilterTrackbar(srcs[i], dsts[i], g_ndValue, g_nsigmaColorValue, g_nsigmaSpaceValue);
		}
		merge(dsts, g_dstImage);
	}
	else
		MyBilateralFilterTrackbar(g_srcImage, g_dstImage, g_ndValue, g_nsigmaColorValue, g_nsigmaSpaceValue);
	
	imshow("双边滤波图像", g_dstImage);
}