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📜  Java DIP-加权平均过滤器

📅  最后修改于: 2020-12-14 05:43:39             🧑  作者: Mango

在加权平均滤波器中，我们赋予了中心值更多的权重，因此中心的贡献大于其余值。由于加权平均滤波，我们可以控制图像的模糊。

我们使用OpenCV函数filter2D将加权平均滤波器应用于图像。可以在Imgproc软件包下找到。其语法如下-

filter2D(src, dst, depth , kernel, anchor, delta, BORDER_DEFAULT );

函数参数描述如下-

Sr.No. Argument & Description
1

src

It is source image.
2

dst

It is destination image.
3

ddepth

It is the depth of dst. A negative value (such as -1) indicates that the depth is the same as the source.
4

kernel

It is the kernel to be scanned through the image.
5

anchor

It is the position of the anchor relative to its kernel. The location Point(-1, -1) indicates the center by default.
6

delta

It is a value to be added to each pixel during the convolution. By default it is 0.
7

BORDER_DEFAULT

We let this value by default.

除了filter2D()方法之外，Imgproc类还提供其他方法。他们简要描述-

Sr.No. Method & Description
1

cvtColor(Mat src, Mat dst, int code, int dstCn)

It converts an image from one color space to another.
2

dilate(Mat src, Mat dst, Mat kernel)

It dilates an image by using a specific structuring element.
3

equalizeHist(Mat src, Mat dst)

It equalizes the histogram of a grayscale image.
4

filter2D(Mat src, Mat dst, int depth, Mat kernel, Point anchor, double delta)

It convolves an image with the kernel.
5

GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX)

It blurs an image using a Gaussian filter.
6

integral(Mat src, Mat sum)

It calculates the integral of an image.

例

下面的示例演示如何使用Imgproc类将加权平均滤镜应用于Graycale图像。

import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;

import org.opencv.highgui.Highgui;
import org.opencv.imgproc.Imgproc;

public class convolution {
   public static void main( String[] args ) {
   
      try {
         int kernelSize = 9;
         System.loadLibrary( Core.NATIVE_LIBRARY_NAME );
         
         Mat source = Highgui.imread("grayscale.jpg",  Highgui.CV_LOAD_IMAGE_GRAYSCALE);
         Mat destination = new Mat(source.rows(),source.cols(),source.type());
         
         Mat kernel = Mat.ones(kernelSize,kernelSize, CvType.CV_32F) {          
         
         for(int i=0; i

输出

当您执行给定的代码时，将看到以下输出-

原始图片

应用加权平均滤波器教程

原始图像与加权平均滤波器进行卷积，如下所示：

加权平均滤波器

1 1 1
1 10 1
1 1 1

卷积图像

应用加权平均滤波器教程