java读取bmp（java读取文件内容并输出）
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如何用java实现1bit bmp位图读取
import java.awt.Image;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
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.SwingUtilities;
public class TestWin extends JFrame implements ActionListener {
private JLabel imgLabel = new JLabel();
public TestWin() {
try {
Image img=ImageIO.read(new File("1bit.bmp"));
imgLabel.setIcon(new ImageIcon(img));
} catch (IOException e) {
e.printStackTrace();
}
add(imgLabel);
pack();
setLocationRelativeTo(null);
setDefaultCloseOperation(EXIT_ON_CLOSE);
}
@Override
public void actionPerformed(ActionEvent e) {
}
public static void main(String[] args) {
SwingUtilities.invokeLater(new Runnable() {
@Override
public void run() {
new TestWin().setVisible(true);
}
});
}
}
Java如何读取BMP的每个像素点，输出到一个二维数组
楼上的基本正确，
问题一：
int[] rgb = new int[3];最好用二维数组
int[] rgb = new int[3][width*height]
问题二：
rgb[0] = (pixel 0xff0000 ) 16 ;
rgb[1] = (pixel 0xff00 ) 8 ;
rgb[2] = (pixel 0xff );
会把数组内的值覆盖，获得就是最后像素点的RGB值；
我写了一个希望可以帮助到你
package imageReadAndWrite;
import java.awt.image.BufferedImage;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.io.IOException;
import com.sun.image.codec.jpeg.JPEGCodec;
import com.sun.image.codec.jpeg.JPEGImageDecoder;
import com.sun.image.codec.jpeg.JPEGImageEncoder;
/**
* JPG File reader/writer. Uses native com.sun libraries (which may deprecate at
* any time)
*
*
* @author PhoenixZJG
* @version 1.0
*/
public class JPGFile implements xxxFile {
private int[] ints = null;
private byte bytes[] = null; // bytes which make up binary PPM image
private double doubles[] = null;
private int[][] imageRGB = null;
private String filename = null; // filename for PPM image
private int height = 0;
private int width = 0;
/**
* Read the PPM File.
*
* @throws FileNotFoundException
* if the directory/image specified is wrong
* @throws IOException
* if there are problems reading the file.
*/
public JPGFile(String filename) throws FileNotFoundException, IOException {
this.filename = filename;
readImage();
}
/**
* Get the height of the PPM image.
*
* @return the height of the image.
*/
public int getHeight() {
return height;
}
/**
* Get the width of the PPM image.
*
* @return the width of the image.
*/
public int getWidth() {
return width;
}
/**
* Get the data as byte array. Data is of any type that has been read from
* the file (usually 8bit RGB)
*
* @return The data of the image.
*/
public byte[] getBytes() {
return bytes;
}
/**
* Get the data as double array. Data is of any type that has been read from
* the file (usually 8bit RGB put into an 64bit double)
*
* @return The data of the image.
*/
public double[] getDouble() {
return doubles;
}
/**
* Get the data as double array. Data is of any type that has been read from
* the file (usually 8bit RGB put into an 64bit double)
*
* @return The data of the image.
*/
public int[] getInt() {
return ints;
}
/**
* Get the data as integer array. Data is of any type that has been read from
* the file (usually 8bit RGB put into an 64bit double)
*
* @return The data of the image.
*/
public int[][] getImageRGB() {
return imageRGB;
}
/**
* Write to codefn/code file the codedata/code using the
* codewidth, height/code variables. Data is assumed to be 8bit RGB.
*
* @throws FileNotFoundException
* if the directory/image specified is wrong
* @throws IOException
* if there are problems reading the file.
*/
public static void writeImage(String fn, int[] data, int width, int height)
throws FileNotFoundException, IOException {
FileOutputStream fOut = new FileOutputStream(fn);
JPEGImageEncoder jpeg_encode = JPEGCodec.createJPEGEncoder(fOut);
BufferedImage image = new BufferedImage(width, height,
BufferedImage.TYPE_INT_RGB);
image.setRGB(0, 0, width, height, data, 0, width);
jpeg_encode.encode(image);
fOut.close();
}
/**
* Read the image from the specified file.
*
* @throws FileNotFoundException
* pretty obvious
* @throws IOException
* filesystem related problems
*/
private void readImage() throws FileNotFoundException, IOException {
FileInputStream fIn = new FileInputStream(filename);
JPEGImageDecoder jpeg_decode = JPEGCodec.createJPEGDecoder(fIn);
BufferedImage image = jpeg_decode.decodeAsBufferedImage();
width = image.getWidth();
height = image.getHeight();
int[] rgbdata = new int[width * height];
image.getRGB(0, 0, width, height, rgbdata, 0, width);
ints = rgbdata;
bytes = new byte[rgbdata.length];
doubles = new double[rgbdata.length];
imageRGB = new int[3][rgbdata.length];
for (int i = 0; i bytes.length; i++) {
bytes[i] = (byte) (rgbdata[i] 0xFF);
doubles[i] = (double) (rgbdata[i]);
imageRGB[0][i] = (rgbdata[i] 16711680) 16;
imageRGB[1][i] = (rgbdata[i] 65280) 8;
imageRGB[2][i] = (rgbdata[i] 255);
}
}
}
上述代码可以复制，粘贴使用，有方法的注视，getImageRGB() 就可以获得所有像素的RGB值，你就可以在其他方法里处理这个二维数组，得到你想要的平均值了，处理后的值，记得把值逆运算，再转换到Int[]里，就可以用，writeImage（）输出图像了。
java数字图像处理常用算法
前些时候做毕业设计 用java做的数字图像处理方面的东西 这方面的资料ms比较少 发点东西上来大家共享一下 主要就是些算法 有自己写的 有人家的 还有改人家的 有的算法写的不好 大家不要见笑
一 读取bmp图片数据
// 获取待检测图像 数据保存在数组 nData[] nB[] nG[] nR[]中
public void getBMPImage(String source) throws Exception { clearNData(); //清除数据保存区 FileInputStream fs = null; try { fs = new FileInputStream(source); int bfLen = ; byte bf[] = new byte[bfLen]; fs read(bf bfLen); // 读取 字节BMP文件头 int biLen = ; byte bi[] = new byte[biLen]; fs read(bi biLen); // 读取 字节BMP信息头
// 源图宽度 nWidth = (((int) bi[ ] xff) ) | (((int) bi[ ] xff) ) | (((int) bi[ ] xff) ) | (int) bi[ ] xff;
// 源图高度 nHeight = (((int) bi[ ] xff) ) | (((int) bi[ ] xff) ) | (((int) bi[ ] xff) ) | (int) bi[ ] xff;
// 位数 nBitCount = (((int) bi[ ] xff) ) | (int) bi[ ] xff;
// 源图大小 int nSizeImage = (((int) bi[ ] xff) ) | (((int) bi[ ] xff) ) | (((int) bi[ ] xff) ) | (int) bi[ ] xff;
// 对 位BMP进行解析 if (nBitCount == ){ int nPad = (nSizeImage / nHeight) nWidth * ; nData = new int[nHeight * nWidth]; nB=new int[nHeight * nWidth]; nR=new int[nHeight * nWidth]; nG=new int[nHeight * nWidth]; byte bRGB[] = new byte[(nWidth + nPad) * * nHeight]; fs read(bRGB (nWidth + nPad) * * nHeight); int nIndex = ; for (int j = ; j nHeight; j++){ for (int i = ; i nWidth; i++) { nData[nWidth * (nHeight j ) + i] = ( xff) | (((int) bRGB[nIndex + ] xff) ) | (((int) bRGB[nIndex + ] xff) ) | (int) bRGB[nIndex] xff; nB[nWidth * (nHeight j ) + i]=(int) bRGB[nIndex] xff; nG[nWidth * (nHeight j ) + i]=(int) bRGB[nIndex+ ] xff; nR[nWidth * (nHeight j ) + i]=(int) bRGB[nIndex+ ] xff; nIndex += ; } nIndex += nPad; } // Toolkit kit = Toolkit getDefaultToolkit(); // image = kit createImage(new MemoryImageSource(nWidth nHeight // nData nWidth));
/* //调试数据的读取
FileWriter fw = new FileWriter( C:\\Documents and Settings\\Administrator\\My Documents\\nDataRaw txt );//创建新文件 PrintWriter out = new PrintWriter(fw); for(int j= ;jnHeight;j++){ for(int i= ;inWidth;i++){ out print(( * +nData[nWidth * (nHeight j ) + i])+ _ +nR[nWidth * (nHeight j ) + i]+ _ +nG[nWidth * (nHeight j ) + i]+ _ +nB[nWidth * (nHeight j ) + i]+ ); } out println( ); } out close();*/ } } catch (Exception e) { e printStackTrace(); throw new Exception(e); } finally { if (fs != null) { fs close(); } } // return image; }
二 由r g b 获取灰度数组
public int[] getBrightnessData(int rData[] int gData[] int bData[]){ int brightnessData[]=new int[rData length]; if(rData length!=gData length || rData length!=bData length || bData length!=gData length){ return brightnessData; } else { for(int i= ;ibData length;i++){ double temp= *rData[i]+ *gData[i]+ *bData[i]; brightnessData[i]=(int)(temp)+((temp (int)(temp)) ? : ); } return brightnessData; } }
三 直方图均衡化
public int [] equilibrateGray(int[] PixelsGray int width int height) { int gray; int length=PixelsGray length; int FrequenceGray[]=new int[length]; int SumGray[]=new int[ ]; int ImageDestination[]=new int[length]; for(int i = ; i length ;i++) { gray=PixelsGray[i]; FrequenceGray[gray]++; } // 灰度均衡化 SumGray[ ]=FrequenceGray[ ]; for(int i= ;i ;i++){ SumGray[i]=SumGray[i ]+FrequenceGray[i]; } for(int i= ;i ;i++) { SumGray[i]=(int)(SumGray[i]* /length); } for(int i= ;iheight;i++) { for(int j= ;jwidth;j++) { int k=i*width+j; ImageDestination[k]= xFF | ((SumGray[PixelsGray[k]] ) | (SumGray[PixelsGray[k]] ) | SumGray[PixelsGray[k]]); } } return ImageDestination; }
四 laplace 阶滤波 增强边缘 图像锐化
public int[] laplace DFileter(int []data int width int height){ int filterData[]=new int[data length]; int min= ; int max= ; for(int i= ;iheight;i++){ for(int j= ;jwidth;j++){ if(i== || i==height || j== || j==width ) filterData[i*width+j]=data[i*width+j]; else filterData[i*width+j]= *data[i*width+j] data[i*width+j ] data[i*width+j+ ] data[(i )*width+j] data[(i )*width+j ] data[(i )*width+j+ ] data[(i+ )*width+j] data[(i+ )*width+j ] data[(i+ )*width+j+ ]; if(filterData[i*width+j]min) min=filterData[i*width+j]; if(filterData[i*width+j]max) max=filterData[i*width+j]; } }// System out println( max: +max);// System out println( min: +min); for(int i= ;iwidth*height;i++){ filterData[i]=(filterData[i] min)* /(max min); } return filterData; }
五 laplace 阶增强滤波 增强边缘 增强系数delt
public int[] laplaceHigh DFileter(int []data int width int height double delt){ int filterData[]=new int[data length]; int min= ; int max= ; for(int i= ;iheight;i++){ for(int j= ;jwidth;j++){ if(i== || i==height || j== || j==width ) filterData[i*width+j]=(int)(( +delt)*data[i*width+j]); else filterData[i*width+j]=(int)(( +delt)*data[i*width+j] data[i*width+j ]) data[i*width+j+ ] data[(i )*width+j] data[(i )*width+j ] data[(i )*width+j+ ] data[(i+ )*width+j] data[(i+ )*width+j ] data[(i+ )*width+j+ ]; if(filterData[i*width+j]min) min=filterData[i*width+j]; if(filterData[i*width+j]max) max=filterData[i*width+j]; } } for(int i= ;iwidth*height;i++){ filterData[i]=(filterData[i] min)* /(max min); } return filterData; } 六 局部阈值处理 值化
// 局部阈值处理 值化 niblack s method /*原理 T(x y)=m(x y) + k*s(x y) 取一个宽度为w的矩形框 (x y)为这个框的中心 统计框内数据 T(x y)为阈值 m(x y)为均值 s(x y)为均方差 k为参数(推荐 )计算出t再对(x y)进行切割 / 这个算法的优点是 速度快 效果好 缺点是 niblack s method会产生一定的噪声 */ public int[] localThresholdProcess(int []data int width int height int w int h double coefficients double gate){ int[] processData=new int[data length]; for(int i= ;idata length;i++){ processData[i]= ; } if(data length!=width*height) return processData; int wNum=width/w; int hNum=height/h; int delt[]=new int[w*h]; //System out println( w; +w+ h: +h+ wNum: +wNum+ hNum: +hNum); for(int j= ;jhNum;j++){ for(int i= ;iwNum;i++){ //for(int j= ;j ;j++){ // for(int i= ;i ;i++){ for(int n= ;nh;n++) for(int k= ;kw;k++){ delt[n*w+k]=data[(j*h+n)*width+i*w+k]; //System out print( delt[ +(n*w+k)+ ]: +delt[n*w+k]+ ); } //System out println(); /* for(int n= ;nh;n++) for(int k= ;kw;k++){ System out print( data[ +((j*h+n)*width+i*w+k)+ ]: +data[(j*h+n)*width+i*w+k]+ ); } System out println(); */ delt=thresholdProcess(delt w h coefficients gate); for(int n= ;nh;n++) for(int k= ;kw;k++){ processData[(j*h+n)*width+i*w+k]=delt[n*w+k]; // System out print( delt[ +(n*w+k)+ ]: +delt[n*w+k]+ ); } //System out println(); /* for(int n= ;nh;n++) for(int k= ;kw;k++){ System out print( processData[ +((j*h+n)*width+i*w+k)+ ]: +processData[(j*h+n)*width+i*w+k]+ ); } System out println(); */ } } return processData; }
七 全局阈值处理 值化
public int[] thresholdProcess(int []data int width int height double coefficients double gate){ int [] processData=new int[data length]; if(data length!=width*height) return processData; else{ double sum= ; double average= ; double variance= ; double threshold; if( gate!= ){ threshold=gate; } else{ for(int i= ;iwidth*height;i++){ sum+=data[i]; } average=sum/(width*height); for(int i= ;iwidth*height;i++){ variance+=(data[i] average)*(data[i] average); } variance=Math sqrt(variance); threshold=average coefficients*variance; } for(int i= ;iwidth*height;i++){ if(data[i]threshold) processData[i]= ; else processData[i]= ; } return processData; } }
八 垂直边缘检测 sobel算子
public int[] verticleEdgeCheck(int []data int width int height int sobelCoefficients) throws Exception{ int filterData[]=new int[data length]; int min= ; int max= ; if(data length!=width*height) return filterData; try{ for(int i= ;iheight;i++){ for(int j= ;jwidth;j++){ if(i== || i== || i==height || i==height ||j== || j== || j==width || j==width ){ filterData[i*width+j]=data[i*width+j]; } else{ double average; //中心的九个像素点 //average=data[i*width+j] Math sqrt( )*data[i*width+j ]+Math sqrt( )*data[i*width+j+ ] average=data[i*width+j] sobelCoefficients*data[i*width+j ]+sobelCoefficients*data[i*width+j+ ] data[(i )*width+j ]+data[(i )*width+j+ ] data[(i+ )*width+j ]+data[(i+ )*width+j+ ]; filterData[i*width+j]=(int)(average); } if(filterData[i*width+j]min) min=filterData[i*width+j]; if(filterData[i*width+j]max) max=filterData[i*width+j]; } } for(int i= ;iwidth*height;i++){ filterData[i]=(filterData[i] min)* /(max min); } } catch (Exception e) { e printStackTrace(); throw new Exception(e); } return filterData; }
九 图像平滑 * 掩模处理（平均处理） 降低噪声
lishixinzhi/Article/program/Java/hx/201311/26286
java 怎么实现读取8位的bmp图片文件
import java.awt.image.BufferedImage;
import javax.imageio.ImageIO;
import java.io.*;
public class Test{
public static void main(String args[]) {
int[] rgb = new int[3];
File file = new File("a.bmp");
BufferedImage bi=null;
try{
bi = ImageIO.read(file);
}catch(Exception e){
e.printStackTrace();
}
int width=bi.getWidth();
int height=bi.getHeight();
int minx=bi.getMinX();
int miny=bi.getMinY();
System.out.println("width="+width+",height="+height+".");
System.out.println("minx="+minx+",miniy="+miny+".");
for(int i=minx;iwidth;i++){
for(int j=miny;jheight;j++){
//System.out.print(bi.getRGB(jw, ih));
int pixel=bi.getRGB(i, j);
rgb[0] = (pixel 0xff0000 ) 16 ;
rgb[1] = (pixel 0xff00 ) 8 ;
rgb[2] = (pixel 0xff );
System.out.println("i="+i+",j="+j+":("+rgb[0]+","+rgb[1]+","+rgb[2]+")");
}
}
}
}
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