图像二值化（java代码）
帖子上看到了一个将一张彩色图片转换成黑白的灰度图片的解决方法，把解决方法记录一下
import java.awt.Color; import java.awt.image.BufferedImage; import java.io.File; import java.io.IOException; import javax.imageio.ImageIO; public class Binary { public static void main(String[] args) throws IOException { Image imagesoure = new Image(); System.out.println("This is the end"); } } class Image { int width; int height; int area = width * height; // BufferedImage outBinary=img; public Image() throws IOException { File file = new File("F:/77.jpg"); BufferedImage img = ImageIO.read(file); BufferedImage outBinary = img; width = img.getWidth(); height = img.getHeight(); int area = width * height; int gray[][] = new int[width][height]; int u = 0;// 灰度平均值 int graybinary; int graysum = 0; int graymean = 0; int grayfrontmean = 0; int graybackmean = 0; Color color; int pixl[][] = new int[width][height]; int pixelsR; int pixelsG; int pixelsB; int pixelGray; int T = 0; int front = 0; int back = 0; for (int i = 1; i < width; i++) { // 不算边界行和列，为避免越界 for (int j = 1; j < height; j++) { pixl[i][j] = img.getRGB(i, j); color = new Color(pixl[i][j]); pixelsR = color.getRed();// R空间 pixelsB = color.getBlue();// G空间 pixelsG = color.getGreen();// B空间 pixelGray = (int) (0.3 * pixelsR + 0.59 * pixelsG + 0.11 * pixelsB);// 计算每个坐标点的灰度 gray[i][j] = (pixelGray << 16) + (pixelGray << 8) + (pixelGray); graysum += pixelGray; } } graymean = (int) (graysum / area);// 整个图的灰度平均值 u = graymean; System.out.println(u); for (int i = 0; i < width; i++) // 计算整个图的二值化阈值 { for (int j = 0; j < height; j++) { if (((gray[i][j]) & (0x0000ff)) < graymean) { graybackmean += ((gray[i][j]) & (0x0000ff)); back++; } else { grayfrontmean += ((gray[i][j]) & (0x0000ff)); front++; } } } int frontvalue = (int) (grayfrontmean / front);// 前景中心 int backvalue = (int) (graybackmean / back);// 背景中心 float G[] = new float[frontvalue - backvalue + 1];// 方差数组 int s = 0; System.out.println(front); System.out.println(frontvalue); System.out.println(backvalue); for (int i1 = backvalue; i1 < frontvalue + 1; i1++)// 以前景中心和背景中心为区间采用大津法算法 { back = 0; front = 0; grayfrontmean = 0; graybackmean = 0; for (int i = 0; i < width; i++) { for (int j = 0; j < height; j++) { if (((gray[i][j]) & (0x0000ff)) < (i1 + 1)) { graybackmean += ((gray[i][j]) & (0x0000ff)); back++; } else { grayfrontmean += ((gray[i][j]) & (0x0000ff)); front++; } } } grayfrontmean = (int) (grayfrontmean / front); graybackmean = (int) (graybackmean / back); G[s] = (((float) back / area) * (graybackmean - u) * (graybackmean - u) + ((float) front / area) * (grayfrontmean - u) * (grayfrontmean - u)); s++; } float max = G[0]; int index = 0; for (int i = 1; i < frontvalue - backvalue + 1; i++) { if (max < G[i]) { max = G[i]; index = i; } } // System.out.println(G[index]); // System.out.println(index); for (int i = 0; i < width; i++) { for (int j = 0; j < height; j++) { if (((gray[i][j]) & (0x0000ff)) < (index + backvalue)) { outBinary.setRGB(i, j, 0x000000); } else { outBinary.setRGB(i, j, 0xffffff); } } } File f = new File("F:/88.jpg"); ImageIO.write(outBinary, "jpg", f); } }