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BufferedImage image;
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int w = image.getWidth();
int h = image.getHeight();
int color;
for (int i = 0; i w; i++) {
for (int j = 0; j h; j++) {
color = image.getRGB(i, j);
}
}
不知道你到底要做什么,这个只是帮你拿到图上的点的颜色。那些提取特征点的算法就是相当复杂了,比如透过值,颜色分布值,对比度,亮度,甚至要多做因素综合考虑起来,难度不小的。java做图形是越来越少了,qq282052309
摘要图像识别是目前很热门的研究领域,涉及的知识很广,包括信息论、模式识别、模糊数学、图像编码、内容分类等等。本文仅对使用Java实现了一个简单的图像文本二值处理,关于识别并未实现。
步骤
建立文本字符模板二值矩阵
对测试字符进行二值矩阵化处理
代码
/*
* @(#)StdModelRepository.java
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Library General Public License for more details.
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
*/
package cn.edu.ynu.sei.recognition.util;import java.awt.Image;import java.awt.image.BufferedImage;import java.io.File;import java.io.IOException;import java.util.ArrayList;import java.util.List;import java.util.logging.Level;import java.util.logging.Logger;import javax.imageio.ImageIO;/** * Hold character charImgs as standard model repository.
* @author 88250
* @version 1.0.0.0, Mar 20, 2008
*/
public class StdModelRepository {
/** * hold character images
*/ List charImgs = new ArrayList();
/** * default width of a character
*/ static int width = 16 /** * default height of a character
*/ static int height = 28 /** * standard character model matrix
*/ public int[][][] stdCharMatrix = new int[27][width][height];
/** * Default constructor.
*/ public StdModelRepository() {
BufferedImage lowercase = null try {
lowercase = ImageIO.read(new File("lowercase.png"));
} catch (IOException ex) {
Logger.getLogger(StdModelRepository.class.getName()).
log(Level.SEVERE, null, ex);
}
for (int i = 0 i 26 i++) {
charImgs.add(lowercase.getSubimage(i * width,
0,
width,
height));
}
for (int i = 0 i charImgs.size(); i++) {
Image image = charImgs.get(i);
int[] pixels = ImageUtils.getPixels(image,
image.getWidth(null),
image.getHeight(null));
stdCharMatrix[i] = ImageUtils.getSymbolMatrix(pixels, 0).clone();
ImageUtils.displayMatrix(stdCharMatrix[i]);
}
}
}
/*
* @(#)ImageUtils.java
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Library General Public License for more details.
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
*/
package cn.edu.ynu.sei.recognition.util;import java.awt.Image;import java.awt.image.PixelGrabber;import java.util.logging.Level;import java.util.logging.Logger;/** * Mainipulation of image data.
* @author 88250
* @version 1.0.0.3, Mar 20, 2008
*/
public class ImageUtils {
/** * Return all of the pixel values of sepecified codeimage .* @param image the sepecified image
* @param width width of the image
* @param height height of the image
* @return */ public static int[] getPixels(Image image, int width, int height) {
int[] pixels = new int[width * height];
try {
new PixelGrabber(image, 0, 0, width, height, pixels, 0, width).grabPixels();
} catch (InterruptedException ex) {
Logger.getLogger(ImageUtils.class.getName()).
log(Level.SEVERE, null, ex);
}
return pixels;
}
资源来自:
Java检测人脸图片是否高清可以通过以下步骤实现。
1、对人脸图片进行图像处理,以提取出图像中的人脸特征。
2、使用支持向量机SVM分类算法,建立一个高清人脸图像与模糊人脸图像的分类模型,用来区分高清图像和模糊图像。
3、将待测人脸图像和模型进行比较,并判断其属于高清图像还是模糊图像。
Java图像处理技巧四则
下面代码中用到的sourceImage是一个已经存在的Image对象
图像剪切
对于一个已经存在的Image对象,要得到它的一个局部图像,可以使用下面的步骤:
//import java.awt.*;
//import java.awt.image.*;
Image croppedImage;
ImageFilter cropFilter;
CropFilter =new CropImageFilter(25,30,75,75); //四个参数分别为图像起点坐标和宽高,即CropImageFilter(int x,int y,int width,int height),详细情况请参考API
CroppedImage= Toolkit.getDefaultToolkit().createImage(new FilteredImageSource(sourceImage.getSource(),cropFilter));
如果是在Component的子类中使用,可以将上面的Toolkit.getDefaultToolkit().去掉。FilteredImageSource是一个ImageProducer对象。
图像缩放
对于一个已经存在的Image对象,得到它的一个缩放的Image对象可以使用Image的getScaledInstance方法:
Image scaledImage=sourceImage. getScaledInstance(100,100, Image.SCALE_DEFAULT); //得到一个100X100的图像
Image doubledImage=sourceImage. getScaledInstance(sourceImage.getWidth(this)*2,sourceImage.getHeight(this)*2, Image.SCALE_DEFAULT); //得到一个放大两倍的图像,这个程序一般在一个swing的组件中使用,而类Jcomponent实现了图像观察者接口ImageObserver,所有可以使用this。
//其它情况请参考API
灰度变换
下面的程序使用三种方法对一个彩色图像进行灰度变换,变换的效果都不一样。一般而言,灰度变换的算法是将象素的三个颜色分量使用R*0.3+G*0.59+ B*0.11得到灰度值,然后将之赋值给红绿蓝,这样颜色取得的效果就是灰度的。另一种就是取红绿蓝三色中的最大值作为灰度值。java核心包也有一种算法,但是没有看源代码,不知道具体算法是什么样的,效果和上述不同。
/* GrayFilter.java*/
/*@author:cherami */
/*email:cherami@163.net*/
import java.awt.image.*;
public class GrayFilter extends RGBImageFilter {
int modelStyle;
public GrayFilter() {
modelStyle=GrayModel.CS_MAX;
canFilterIndexColorModel=true;
}
public GrayFilter(int style) {
modelStyle=style;
canFilterIndexColorModel=true;
}
public void setColorModel(ColorModel cm) {
if (modelStyle==GrayModel
else if (modelStyle==GrayModel
}
public int filterRGB(int x,int y,int pixel) {
return pixel;
}
}
/* GrayModel.java*/
/*@author:cherami */
/*email:cherami@163.net*/
import java.awt.image.*;
public class GrayModel extends ColorModel {
public static final int CS_MAX=0;
public static final int CS_FLOAT=1;
ColorModel sourceModel;
int modelStyle;
public GrayModel(ColorModel sourceModel) {
super(sourceModel.getPixelSize());
this.sourceModel=sourceModel;
modelStyle=0;
}
public GrayModel(ColorModel sourceModel,int style) {
super(sourceModel.getPixelSize());
this.sourceModel=sourceModel;
modelStyle=style;
}
public void setGrayStyle(int style) {
modelStyle=style;
}
protected int getGrayLevel(int pixel) {
if (modelStyle==CS_MAX) {
return Math.max(sourceModel.getRed(pixel),Math.max(sourceModel.getGreen(pixel),sourceModel.getBlue(pixel)));
}
else if (modelStyle==CS_FLOAT){
return (int)(sourceModel.getRed(pixel)*0.3+sourceModel.getGreen(pixel)*0.59+sourceModel.getBlue(pixel)*0.11);
}
else {
return 0;
}
}
public int getAlpha(int pixel) {
return sourceModel.getAlpha(pixel);
}
public int getRed(int pixel) {
return getGrayLevel(pixel);
}
public int getGreen(int pixel) {
return getGrayLevel(pixel);
}
public int getBlue(int pixel) {
return getGrayLevel(pixel);
}
public int getRGB(int pixel) {
int gray=getGrayLevel(pixel);
return (getAlpha(pixel)24)+(gray16)+(gray8)+gray;
}
}
如果你有自己的算法或者想取得特殊的效果,你可以修改类GrayModel的方法getGrayLevel()。
色彩变换
根据上面的原理,我们也可以实现色彩变换,这样的效果就很多了。下面是一个反转变换的例子:
/* ReverseColorModel.java*/
/*@author:cherami */
/*email:cherami@163.net*/
import java.awt.image.*;
public class ReverseColorModel extends ColorModel {
ColorModel sourceModel;
public ReverseColorModel(ColorModel sourceModel) {
super(sourceModel.getPixelSize());
this.sourceModel=sourceModel;
}
public int getAlpha(int pixel) {
return sourceModel.getAlpha(pixel);
}
public int getRed(int pixel) {
return ~sourceModel.getRed(pixel);
}
public int getGreen(int pixel) {
return ~sourceModel.getGreen(pixel);
}
public int getBlue(int pixel) {
return ~sourceModel.getBlue(pixel);
}
public int getRGB(int pixel) {
return (getAlpha(pixel)24)+(getRed(pixel)16)+(getGreen(pixel)8)+getBlue(pixel);
}
}
/* ReverseColorModel.java*/
/*@author:cherami */
/*email:cherami@163.net*/
import java.awt.image.*;
public class ReverseFilter extends RGBImageFilter {
public ReverseFilter() {
canFilterIndexColorModel=true;
}
public void setColorModel(ColorModel cm) {
substituteColorModel(cm,new ReverseColorModel(cm));
}
public int filterRGB(int x,int y,int pixel) {
return pixel;
}
}
要想取得自己的效果,需要修改ReverseColorModel.java中的三个方法,getRed、getGreen、getBlue。
下面是上面的效果的一个总的演示程序。
/*GrayImage.java*/
/*@author:cherami */
/*email:cherami@163.net*/
import java.awt.*;
import java.awt.image.*;
import javax.swing.*;
import java.awt.color.*;
public class GrayImage extends JFrame{
Image source,gray,gray3,clip,bigimg;
BufferedImage bimg,gray2;
GrayFilter filter,filter2;
ImageIcon ii;
ImageFilter cropFilter;
int iw,ih;
public GrayImage() {
ii=new ImageIcon(\"images/11.gif\");
source=ii.getImage();
iw=source.getWidth(this);
ih=source.getHeight(this);
filter=new GrayFilter();
filter2=new GrayFilter(GrayModel.CS_FLOAT);
gray=createImage(new FilteredImageSource(source.getSource(),filter));
gray3=createImage(new FilteredImageSource(source.getSource(),filter2));
cropFilter=new CropImageFilter(5,5,iw-5,ih-5);
clip=createImage(new FilteredImageSource(source.getSource(),cropFilter));
bigimg=source.getScaledInstance(iw*2,ih*2,Image.SCALE_DEFAULT);
MediaTracker mt=new MediaTracker(this);
mt.addImage(gray,0);
try {
mt.waitForAll();
} catch (Exception e) {
}
当然可以。
一、纯JAVA开发的技术可行性,即JAVA是否能够实现图像识别的各种算法。
二、如果第一点没有问题,纯JAVA与C++相比,开发效率上的差异。效率要低很多,和具体问题有关。
三、如果第一点没有问题且第二点差异不太大时,纯JAVA与C++相比,相同算法的情况下,软件运行效率的差异。运行效率的差异也很大,也是和具体的算法有关。