OpenCV filter2D函数详解

OpenCV filter2D函数简介

OpenCV filter2D将图像与内核进行卷积，将任意线性滤波器应用于图像。支持就地操作。当孔径部分位于图像之外时，该函数根据指定的边界模式插值异常像素值。

该函数实际上计算相关性，而不是卷积：

filter2D函数的原型如下：

void cv::filter2D（InputArray src，

OutputArray dst，

int ddepth，

InputArray kernel，

Point anchor = Point（-1，-1),

double delta = 0，

int borderType = BORDER_DEFAULT

)

参数：

src 输入图像

dst 输出图像，与 src 大小相同、通道数相同

ddepth 目标图像的所需深度

kernel 卷积核（或者更确切地说是相关核），单通道浮点矩阵；如果要将不同的内核应用于不同的通道，请使用 split 将图像分割为单独的颜色平面并单独处理它们。

anchor 内核的锚点，指示内核中过滤点的相对位置；锚应该位于内核内；默认值（-1，-1）表示锚点位于内核中心。

delta 在将过滤像素存储到 dst 之前添加到过滤像素的可选值。

borderType 像素外推方法。可以选以下几种：BORDER_CONSTANT，BORDER_REPLICATE，BORDER_REFLECT，BORDER_REFLECT_101，BORDER_TRANSPARENT，BORDER_REFLECT101，BORDER_DEFAULT，BORDER_ISOLATED。

OpenCV filter2D函数应用

使用OpenCV filter2D函数，通过改变卷积核（kernel）可达成不同的滤波效果。下面就OpenCV filter2D函数的几种常用场景做说明，并以实例做演示。

图像锐化

图像锐化使用的卷积核如下：

Mat kernel = (Mat_<char>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);

下面以实例演示图像锐化操作及锐化效果，示例代码如下：

#include <iostream>
#include <opencv2/opencv.hpp>using namespace cv;
using namespace std;int main(int argc, char** argv)
{Mat src = imread("1.jpg");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}imshow("Input Image", src);Mat kernel = (Mat_<char>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);Mat dst;filter2D(src, dst, src.depth(), kernel);imshow("Output Image", dst);waitKey(0);return 0;
}

试运行，结果如下：

可以看到经过Filter2D滤波后的图像变得更清晰。

均值滤波

OpenCV filter2D函数实现均值滤波的卷积核如下：

Mat kernel = (Mat_<float>(3, 3) << 1, 1, 1, 1, 1, 1, 1, 1, 1) / 9;

下面以实例演示filter2D实现图像均值滤波操作及滤波效果，示例代码如下：

#include <iostream>
#include <opencv2/opencv.hpp>using namespace cv;
using namespace std;int main(int argc, char** argv)
{//sharp test/*Mat src = imread("1.jpg");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}imshow("Input Image", src);Mat kernel = (Mat_<char>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);Mat dst;filter2D(src, dst, src.depth(), kernel);imshow("Output Image", dst);*///Mean filter testMat src = imread("3.png");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}imshow("Input Image", src);Mat kernel = (Mat_<float>(3, 3) << 1, 1, 1, 1, 1, 1, 1, 1, 1) / 9;Mat dst;filter2D(src, dst, src.depth(), kernel);for (size_t i = 0; i < 15; i++){filter2D(dst, dst, src.depth(), kernel);}imshow("Output Image", dst);waitKey(0);return 0;
}

试运行，结果如下：

可以看出，均值滤波可以去除图像椒盐噪声，达到磨皮效果。

高斯滤波

OpenCV filter2D函数实现高斯滤波的卷积核可由高斯核转换得到，方法如下：

Mat kernelGaussian = getGaussianKernel(9, 1.5);
Mat kernel = kernelGaussian * kernelGaussian.t()；

下面以实例演示filter2D实现图像高斯滤波操作及滤波效果，示例代码如下：

#include <iostream>
#include <opencv2/opencv.hpp>using namespace cv;
using namespace std;int main(int argc, char** argv)
{//filter2d sharp test/*Mat src = imread("1.jpg");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}imshow("Input Image", src);Mat kernel = (Mat_<char>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);Mat dst;filter2D(src, dst, src.depth(), kernel);imshow("Output Image", dst);*///filter2d Mean filter test/*Mat src = imread("3.png");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}imshow("Input Image", src);Mat kernel = (Mat_<float>(3, 3) << 1, 1, 1, 1, 1, 1, 1, 1, 1) / 9;Mat dst;filter2D(src, dst, src.depth(), kernel);for (size_t i = 0; i < 15; i++){filter2D(dst, dst, src.depth(), kernel);}imshow("Output Image", dst);*///filter2d Gaussian filter testMat src = imread("3.png");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}imshow("Input Image", src);Mat kernelGaussian = getGaussianKernel(9, 1.5);Mat  kernel = kernelGaussian * kernelGaussian.t();Mat dst;filter2D(src, dst, src.depth(), kernel);for (size_t i = 0; i < 6; i++){filter2D(dst, dst, src.depth(), kernel);}imshow("Output Image", dst);waitKey(0);return 0;
}

试运行，结果如下：

可以看出，同样filter2D均高斯滤波同样可以去除图像椒盐噪声，达成磨皮效果，且所需次数更少。

边缘检测

filter2D还可以使用sobel内核实现边缘检测，soble内核如下：

Mat sobelX = (Mat_<float>(3, 3) << -1, 0, 1,-2, 0, 2,-1, 0, 1);
Mat sobelY = (Mat_<float>(3, 3) << -1, -2, -1,0, 0, 0,1, 2, 1);

下面以实例演示filter2D 用sobel核实现图像边缘检测操作及滤波效果，示例代码如下：

#include <iostream>
#include <opencv2/opencv.hpp>using namespace cv;
using namespace std;int main(int argc, char** argv)
{//filter2d sharp test/*Mat src = imread("1.jpg");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}imshow("Input Image", src);Mat kernel = (Mat_<char>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);Mat dst;filter2D(src, dst, src.depth(), kernel);imshow("Output Image", dst);*///filter2d Mean filter test/*Mat src = imread("3.png");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}imshow("Input Image", src);Mat kernel = (Mat_<float>(3, 3) << 1, 1, 1, 1, 1, 1, 1, 1, 1) / 9;Mat dst;filter2D(src, dst, src.depth(), kernel);for (size_t i = 0; i < 15; i++){filter2D(dst, dst, src.depth(), kernel);}imshow("Output Image", dst);*///filter2d Gaussian filter test/*Mat src = imread("3.png");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}imshow("Input Image", src);Mat kernelGaussian = getGaussianKernel(9, 1.5);Mat  kernel = kernelGaussian * kernelGaussian.t();Mat dst;filter2D(src, dst, src.depth(), kernel);for (size_t i = 0; i < 6; i++){filter2D(dst, dst, src.depth(), kernel);}imshow("Output Image", dst);*///filter2d detect edges testMat src = imread("4.png");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}threshold(src, src, 127, 255, THRESH_BINARY);imshow("Input Image", src);Mat sobelX = (Mat_<float>(3, 3) << -1, 0, 1,-2, 0, 2,-1, 0, 1);Mat sobelY = (Mat_<float>(3, 3) << -1, -2, -1,0, 0, 0,1, 2, 1);Mat edges,edgesX, edgesY;filter2D(src, edgesX, CV_16S, sobelX);filter2D(src, edgesY, CV_16S, sobelX);convertScaleAbs(edgesX, edgesX);convertScaleAbs(edgesY, edgesY);addWeighted(edgesX, 0.5, edgesY, 0.5, 0, edges);imshow("Edges", edges);waitKey(0);return 0;
}

试运行，结果如下：

可以看出确实检测到了边缘，效果并不是很好。

filter2D还可以使用Prewitt核，实现边缘检测。Prewitt核如下：

Mat prewitt_x = (Mat_<int>(3, 3) << -1, 0, 1, -1, 0, 1, -1, 0, 1);
Mat prewitt_y = (Mat_<int>(3, 3) << -1, -1, -1,0, 0, 0, 1, 1, 1);

下面以实例演示filter2D 用Prewitt核实现图像边缘检测操作及滤波效果，示例代码如下：

#include <iostream>
#include <opencv2/opencv.hpp>using namespace cv;
using namespace std;int main(int argc, char** argv)
{//filter2d sharp test/*Mat src = imread("1.jpg");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}imshow("Input Image", src);Mat kernel = (Mat_<char>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);Mat dst;filter2D(src, dst, src.depth(), kernel);imshow("Output Image", dst);*///filter2d Mean filter test/*Mat src = imread("3.png");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}imshow("Input Image", src);Mat kernel = (Mat_<float>(3, 3) << 1, 1, 1, 1, 1, 1, 1, 1, 1) / 9;Mat dst;filter2D(src, dst, src.depth(), kernel);for (size_t i = 0; i < 15; i++){filter2D(dst, dst, src.depth(), kernel);}imshow("Output Image", dst);*///filter2d Gaussian filter test/*Mat src = imread("3.png");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}imshow("Input Image", src);Mat kernelGaussian = getGaussianKernel(9, 1.5);Mat  kernel = kernelGaussian * kernelGaussian.t();Mat dst;filter2D(src, dst, src.depth(), kernel);for (size_t i = 0; i < 6; i++){filter2D(dst, dst, src.depth(), kernel);}imshow("Output Image", dst);*///filter2d detect edges test/*//sobel kernelMat src = imread("4.png");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}threshold(src, src, 127, 255, THRESH_BINARY);imshow("Input Image", src);Mat sobelX = (Mat_<float>(3, 3) << -1, 0, 1,-2, 0, 2,-1, 0, 1);Mat sobelY = (Mat_<float>(3, 3) << -1, -2, -1,0, 0, 0,1, 2, 1);Mat edges,edgesX, edgesY;filter2D(src, edgesX, CV_16S, sobelX);filter2D(src, edgesY, CV_16S, sobelX);convertScaleAbs(edgesX, edgesX);convertScaleAbs(edgesY, edgesY);addWeighted(edgesX, 0.5, edgesY, 0.5, 0, edges);imshow("Edges", edges);*///Prewitt kernelMat src = imread("4.png");if (src.empty()){cout << "Cann't open Image" << endl;return -1;}threshold(src, src, 127, 255, THRESH_BINARY);imshow("Input Image", src);Mat prewitt_x = (Mat_<int>(3, 3) << -1, 0, 1, -1, 0, 1, -1, 0, 1);Mat prewitt_y = (Mat_<int>(3, 3) << -1, -1, -1,0, 0, 0, 1, 1, 1);Mat edges, edgesX, edgesY;filter2D(src, edgesX, src.depth(), prewitt_x);filter2D(src, edgesY, src.depth(), prewitt_y);addWeighted(edgesX, 0.5, edgesY, 0.5, 0, edges);imshow("Edges", edges);waitKey(0);return 0;
}

试运行，结果如下：