昨天和同学一起去蹭Oracle数据库的课,老师很Nice,学到了两点。第一点B树索引,第二点位图索引。
两种很有用的数据结构,这几天好好研究研究。出乎意料的是:下课老师说我们可以去上机,真好嘿嘿,第二节课英语听力,就没去了。
今天上机(数字图像处理)用java写的图像 加减乘除操作,效果和Matlab的一比很差,不过还是和大家分享下吧!
这是原图:
/* * 图像处理 */ import java.awt.BorderLayout; import java.awt.Color; import java.awt.image.BufferedImage; import java.io.File; import java.io.IOException; import javax.imageio.ImageIO; import javax.swing.ImageIcon; import javax.swing.JFrame; import javax.swing.JLabel; import javax.swing.JPanel; public class Image extends JFrame { public void ShowImage(String img1, String img2, String img3, String img4, String img5) { JPanel panel=new JPanel(new BorderLayout()); JLabel label1=new JLabel(new ImageIcon(img1)); JLabel label2=new JLabel(new ImageIcon(img2)); JLabel label3=new JLabel(new ImageIcon(img3)); JLabel label4=new JLabel(new ImageIcon(img4)); JLabel label5=new JLabel(new ImageIcon(img5)); panel.add(label1,BorderLayout.WEST); panel.add(label2, BorderLayout.EAST); panel.add(label3, BorderLayout.SOUTH); panel.add(label4, BorderLayout.NORTH); panel.add(label5, BorderLayout.CENTER); this.getContentPane().setLayout(new BorderLayout()); this.getContentPane().add(panel,BorderLayout.CENTER); this.setSize(1000, 1000); this.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); this.setTitle("显示图像"); this.setVisible(true); } public static void main(String[] args) throws IOException { BufferedImage bi1 = ImageIO.read(new File("bg.jpg")); BufferedImage bi2 = ImageIO.read(new File("bin.jpg")); int h2 = bi2.getHeight(); int w2 = bi1.getWidth(); int h=bi1.getHeight();//获取图像的高 int w=bi1.getWidth();//获取图像的宽 int rgb=bi1.getRGB(0, 0);//获取指定坐标的ARGB的像素值 int rgb2 = bi2.getRGB(0, 0); int[][] gray=new int[w][h]; for (int x = 0; x < w; x++) { for (int y = 0; y < h; y++) { gray[x][y]=getGray(bi1.getRGB(x, y)); } } /* * 两幅图像求和相加 */ int[][] add = new int[w][h]; int[][] sub = new int[w][h]; int[][] mul = new int[w][h]; int[][] div = new int[w][h]; for(int x = 0; x < w; ++ x) { for(int y = 0; y < h; ++ y) { add[x][y] = (getGray(bi1.getRGB(x, y)) + getGray(bi2.getRGB(x, y)))%256; sub[x][y] = (getGray(bi1.getRGB(x, y)) - getGray(bi2.getRGB(x, y)))%256; mul[x][y] = (getGray(bi1.getRGB(x, y)) * getGray(bi2.getRGB(x, y)))%256; //div[x][y] = (getGray(bi1.getRGB(x, y)) / getGray(bi2.getRGB(x, y)))%256; } } BufferedImage nbi=new BufferedImage(w,h,BufferedImage.TYPE_BYTE_BINARY); BufferedImage nbi1=new BufferedImage(w,h,BufferedImage.TYPE_INT_RGB); BufferedImage nbi2=new BufferedImage(w,h,BufferedImage.TYPE_INT_RGB); BufferedImage nbi3=new BufferedImage(w,h,BufferedImage.TYPE_INT_RGB); BufferedImage nbi4=new BufferedImage(w,h,BufferedImage.TYPE_INT_RGB); /* * 二值化 */ int SW=160; for (int x = 0; x < w; x++) { for (int y = 0; y < h; y++) { if(getAverageColor(gray, x, y, w, h)>SW) { int max=new Color(255,255,255).getRGB(); nbi.setRGB(x, y, max); }else { int min=new Color(0,0,0).getRGB(); nbi.setRGB(x, y, min); } } } for(int x = 0; x < w; ++ x) { for(int y = 0; y < h; ++ y) { nbi1.setRGB(x, y, add[x][y]); nbi2.setRGB(x, y, sub[x][y]); nbi3.setRGB(x, y, mul[x][y]); nbi4.setRGB(x, y, div[x][y]); } } ImageIO.write(nbi, "jpg", new File("./bin.jpg")); ImageIO.write(nbi1, "jpg", new File("./add.jpg")); ImageIO.write(nbi2, "jpg", new File("./sub.jpg")); ImageIO.write(nbi3, "jpg", new File("./mul.jpg")); ImageIO.write(nbi4, "jpg", new File("./div.jpg")); Image img = new Image(); img.ShowImage("add.jpg", "sub.jpg", "mul.jpg", "div.jpg", "bin.jpg"); } /* * 每个像素多有Red Green Blue 三原色 */ public static int getGray(int rgb) { String str=Integer.toHexString(rgb); int r=Integer.parseInt(str.substring(2,4),16); int g=Integer.parseInt(str.substring(4,6),16); int b=Integer.parseInt(str.substring(6,8),16); //or 直接new个color对象 Color c=new Color(rgb); r=c.getRed(); g=c.getGreen(); b=c.getBlue(); int top=(r+g+b)/3; return (int)(top); } /** * 自己加周围8个灰度值再除以9,算出其相对灰度值 */ public static int getAverageColor(int[][] gray, int x, int y, int w, int h) { /* * 相加九次取均值 */ int rs = gray[x][y] + (x == 0 ? 255 : gray[x - 1][y]) + (x == 0 || y == 0 ? 255 : gray[x - 1][y - 1]) + (x == 0 || y == h - 1 ? 255 : gray[x - 1][y + 1]) + (y == 0 ? 255 : gray[x][y - 1]) + (y == h - 1 ? 255 : gray[x][y + 1]) + (x == w - 1 ? 255 : gray[x + 1][ y]) + (x == w - 1 || y == 0 ? 255 : gray[x + 1][y - 1]) + (x == w - 1 || y == h - 1 ? 255 : gray[x + 1][y + 1]); return rs / 9; } }
用Java做图像处理似乎还不错,呵呵。 加油封尘浪!
原文:http://blog.csdn.net/qqzwp/article/details/25832539