贝叶斯抠图代码 java

贝叶斯抠图是一种基于概率统计理论的抠图算法，可以实现自动抠图，操作简单、效果较好。以下是基于Java实现贝叶斯抠图的代码：

1.导入相关库文件

import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import javax.imageio.ImageIO;

2.主函数

public class Main {

public static void main(String[] args) {
BufferedImage image = null;//定义缓冲图像
try
{
// 读入待抠图的图片文件
image = ImageIO.read(new File("image.jpg"));
}
catch (IOException e)
{
System.out.println(e.getMessage());
System.exit(1);
}
// 选择前景背景种子点
Point foreground = new Point(100, 100);
Point background = new Point(500, 500);
// 调用贝叶斯抠图算法进行抠图并保存结果
BufferedImage result = bayesianSegmentation(image, foreground, background);
File outputfile = new File("result.jpg");
try
{
ImageIO.write(result, "jpg", outputfile);
}
catch (IOException e)
{
System.out.println(e.getMessage());
System.exit(1);
}
}

3.定义Point类表示种子点

class Point{
int x,y;
public Point(int x,int y){
this.x = x;
this.y = y;
}
}

4.贝叶斯抠图核心算法

public static BufferedImage bayesianSegmentation(BufferedImage image, Point fg, Point bg) {
int alphaThreshold = 128; // 透明度阈值
int foregroundClass = 1; // 前景类
int backgroundClass = 0; // 背景类
int[] labels = new int[image.getWidth() * image.getHeight()]; // 聚类结果
BufferedImage result = new BufferedImage(image.getWidth(), image.getHeight(), BufferedImage.TYPE_INT_ARGB);//定义结果图像
// 初始化标签
for (int i = 0; i < labels.length; i++) {
labels[i] = backgroundClass;
}
// 前景种子点标记为前景色
labels[fg.y * image.getWidth() + fg.x] = foregroundClass;
// 背景种子点标记为背景色
labels[bg.y * image.getWidth() + bg.x] = backgroundClass;
// 迭代聚类
for (int i = 0; i < 500; i++)
{
// M步：计算高斯分布参数
double[] fgParams = gaussianParams(image, labels, foregroundClass);
double[] bgParams = gaussianParams(image, labels, backgroundClass);
// E步：根据高斯分布参数，计算每个像素属于前景/背景类的概率
for (int j = 0; j < labels.length; j++)
{
if (labels[j] == foregroundClass || labels[j] == backgroundClass) {
continue;
}
Color color = new Color(image.getRGB(j % image.getWidth(), j / image.getWidth()), true);
// 计算像素属于前景/背景类的概率
double fgProb = gaussianProb(color, fgParams[0], fgParams[1]);
double bgProb = gaussianProb(color, bgParams[0], bgParams[1]);
if (fgProb > bgProb) {
labels[j] = foregroundClass;// 前景类
}
else
{
labels[j] = backgroundClass;// 背景类
}
}
}
// 按照标签划分前景和背景
for (int i = 0; i < labels.length; i++) {
int x = i % image.getWidth();
int y = i / image.getWidth();
if (labels[i] == foregroundClass) {
Color c = new Color(image.getRGB(x, y), true);
if (c.getAlpha() > alphaThreshold) {
result.setRGB(x, y, c.getRGB());
}
}
else
{
result.setRGB(x, y, 0x00000000); // 设置为透明
}
}
return result;
}

5.定义高斯概率密度函数

public static double gaussianProb(Color color, double mean, double variance) {
double sigma = Math.sqrt(variance);
double xDiff = color.getRed() - mean;
double exp = -Math.pow(xDiff, 2.0) / (2 * Math.pow(sigma, 2.0));
double coef = 1 / (sigma * Math.sqrt(2 * Math.PI));
return coef * Math.exp(exp);
}

6.计算高斯分布参数

public static double[] gaussianParams(BufferedImage image, int[] labels, int clazz) {
double mean = 0.0, variance = 0.0;
int count = 0;
for (int i = 0; i < labels.length; i++) {
if (labels[i] == clazz) {
Color color = new Color(image.getRGB(i % image.getWidth(), i / image.getWidth()), true);
mean += color.getRed();
count++;
}
}
mean /= count;
for (int i = 0; i < labels.length; i++) {
if (labels[i] == clazz) {
Color color = new Color(image.getRGB(i % image.getWidth(), i / image.getWidth()), true);
double xDiff = color.getRed() - mean;
variance += Math.pow(xDiff, 2.0);
}
}
variance /= count;
return new double[]{mean, variance};
}

以上就是一个简单的基于Java实现的贝叶斯抠图算法代码。