基于Java BufferedImage实现识别图片中的黑色矩形

前言：在项目中需要实现将图片中的黑色矩形替换为其他图形，其中的难点在于图片中存在其他黑点或者黑色小方块。

实现思路：

二值化，将纯黑的区域保留下来，其他区域编程白色。

去噪：去除噪点

转为矩阵，将黑色像素点的位置的值设为1，其他位置的值设为0

求极大全为1的子矩阵，使用悬吊法求极大全为1的子矩阵

二值化代码：

public static void binaryImage(String filePath,double threshold){

try {

BufferedImage image = ImageIO.read(new File(filePath));

int minX = 0;//图片起始点X

int minY = 0;//图片起始点Y

int width = image.getWidth();//图片的宽度

m = width;

int height = image.getHeight();//图片的高度

n = height;

//将黑色区域化为1,其他为0

for (int i = minX; i < width; i++) {

for (int j = minY; j < height; j++) {

Object data = image.getRaster().getDataElements(i, j, null);//获取该点像素，并以object类型表示

int red = image.getColorModel().getRed(data);

int blue = image.getColorModel().getBlue(data);

int green = image.getColorModel().getGreen(data);

if(red==0&&green==0&&blue==0){

a[i+1 ][j+1 ] = 1;

}

}

}

}catch (IOException e) {

e.printStackTrace();

}

}

去噪：

public static void removeNoise(int whiteThreshold,int blackThreshold){

int i,j,nValue,nCount,m,n;

int nWidth=Calculate.maxn;

int nHeight=Calculate.maxn;

//对图像上下边缘去噪

for(i=0;i

a[i][0]=0;

a[i][nHeight-1]=0;

}

//对图像上下边缘去噪

for( i=0;i

a[0][i]=0;

a[0][nWidth-1]=0;

}

//根据周围点的颜色去噪

//遍历所有的点 //j是y, i是x

for(i=1;i

for( j=1;j

nValue=a[i][j];

if(nValue==1&&whiteThreshold!=0){//如果一点是黑点

nCount=0;

//遍历他周围的八个点，如果他

for(m=j-1;m<=j+1;m++){

for(n=i-1;n<=i+1;n++){

if(a[n][m]==0){//周围白点的个数

nCount++;

}

}

}

if(nCount>=whiteThreshold){//周围白点的个数大于阈值则变为白色

a[i][j]=0;

}

}else{//如果一个点是白色的点,周围的点是黑色

nCount=0;

for(m=j-1;m<=j+1;m++){

for(n=i-1;n<=i+1;n++){

if(a[n][m]==1){

nCount++;

}

}

}

if(nCount>=blackThreshold){//周围黑点的个数大于阈值就变成黑色

a[i][j]=1;

}

}

}

}

}

找到极大全为1的矩形矩阵:

public static void findMaxBlackRect(){

int ans = 0;

//初始化

for(int i=1;i<=m;++i) {

l[0][i] = 1;

r[0][i] = m;

}

for(int i=1; i<=n; i++){

int maxl=1, maxr=m;

//计算h和l

for(int j=1; j<=m; j++){

if(a[i][j]==0){

h[i][j] = 0;

l[i][j] = 1;

maxl = j+1;

}else{

l[i][j] = Math.max(l[i-1][j],maxl);

h[i][j] = h[i-1][j]+1;

}

}

//计算r

for(int j=m; j>=1; j--){

if(a[i][j]==0){

r[i][j] = m;

maxr = j-1;

}else{

r[i][j] = Math.min(r[i-1][j],maxr);

int temp=ans;

ans = Math.max(ans,(r[i][j]-l[i][j]+1)*h[i][j]);

if(temp!=ans){

y1=l[i][j];

x1=i-h[i][j]+1;

rwidth=r[i][j]-l[i][j]+1;

rheight=h[i][j];

// System.out.println("i: "+i+" j:"+j+" l:"+l[i][j]+" r:"+r[i][j]+" h:"+h[i][j]);

}

}

}

}

//由于在二值化时将所有的点横纵坐标都加了1，找到矩形起点时要减去1

x1=x1-1;

y1=y1-1;

System.out.println("x1: "+x1+" y1: "+y1+" width: "+rwidth+" height: "+rheight);

}

粘贴图片：

public static void stickImage(String bigImagePath,String smallImagePath,String outImagePath) {

try {

BufferedImage bigImage = ImageIO.read(new File(bigImagePath));

BufferedImage smallImage = ImageIO.read(new File(smallImagePath));

Graphics2D gh=bigImage.createGraphics();

gh.drawImage(smallImage,null,x1,y1);

gh.dispose();

ImageIO.write(bigImage,"jpg",new File(outImagePath));

} catch (IOException ex) {

ex.printStackTrace();

}

}

没有输出路劲就随机产生一个：

public static void stickImage(String bigImagePath,String smallImagePath) {

try {

BufferedImage bigImage = ImageIO.read(new File(bigImagePath));

BufferedImage smallImage = ImageIO.read(new File(smallImagePath));

Graphics2D gh=bigImage.createGraphics();

gh.drawImage(smallImage,null,x1,y1);

gh.dispose();

ImageIO.write(bigImage,"jpg",new File(Generate.randomOutPutPath()));

} catch (IOException ex) {

ex.printStackTrace();

}

}

全文程序：

package com.edupt;

import javax.imageio.ImageIO;

import java.awt.*;

import java.awt.image.BufferedImage;

import java.io.File;

import java.io.IOException;

public class Calculate {

static int maxn = 2000;

static int n,m;

static int[][]a=new int[maxn][maxn]; //二值化结果

// static int[][]pixels;

static int[][]h=new int[maxn][maxn]; //高度

static int[][]l=new int[maxn][maxn];//向左能移动到的终点

static int[][]r=new int [maxn][maxn];//向右能移动到的终点

static int x1;

static int y1;

static int x2;

static int y2;

static int rwidth;

static int rheight;

static final int FZ=130;

public static void main(String[] args) {

String filePath="C:\\Users\\86130\\Desktop\\素材\\2019-12-30-17-56.jpg";

String filePath2="C:\\Users\\86130\\Desktop\\素材\\BigHead.jpg";

// String outPath="C:\\Users\\86130\\Desktop\\素材\\result1.jpg";

binaryImage(filePath,7);

// removeNoise(0,3);

print();

// findMaxBlackRect();

//stickImage(filePath,filePath2);

}

//二值化图像

public static void binaryImage(String filePath,double threshold){

try {

BufferedImage image = ImageIO.read(new File(filePath));

int minX = 0;//图片起始点X

int minY = 0;//图片起始点Y

int width = image.getWidth();//图片的宽度

m = width;

int height = image.getHeight();//图片的高度

n = height;

//pixels=new int[height][width];

//将黑色区域化为1,其他为0

for (int i = minX; i < width; i++) {

for (int j = minY; j < height; j++) {

Object data = image.getRaster().getDataElements(i, j, null);//获取该点像素，并以object类型表示

int red = image.getColorModel().getRed(data);

int blue = image.getColorModel().getBlue(data);

int green = image.getColorModel().getGreen(data);

if(red==0&&green==0&&blue==0){

a[i+1 ][j+1 ] = 1;

}

}

}

}catch (IOException e) {

e.printStackTrace();

}

}

/**

*

* @param whiteThreshold 默认为0

* @param blackThreshold

*/

public static void removeNoise(int whiteThreshold,int blackThreshold){

int i,j,nValue,nCount,m,n;

int nWidth=Calculate.maxn;

int nHeight=Calculate.maxn;

//对图像上下边缘去噪

for(i=0;i

a[i][0]=0;

a[i][nHeight-1]=0;

}

//对图像上下边缘去噪

for( i=0;i

a[0][i]=0;

a[0][nWidth-1]=0;

}

//根据周围点的颜色去噪

//遍历所有的点 //j是y, i是x

for(i=1;i

for( j=1;j

nValue=a[i][j];

if(nValue==1&&whiteThreshold!=0){//如果一点是黑点

nCount=0;

//遍历他周围的八个点，如果他

for(m=j-1;m<=j+1;m++){

for(n=i-1;n<=i+1;n++){

if(a[n][m]==0){//周围白点的个数

nCount++;

}

}

}

if(nCount>=whiteThreshold){//周围白点的个数大于阈值则变为白色

a[i][j]=0;

}

}else{//如果一个点是白色的点,周围的点是黑色

nCount=0;

for(m=j-1;m<=j+1;m++){

for(n=i-1;n<=i+1;n++){

if(a[n][m]==1){

nCount++;

}

}

}

if(nCount>=blackThreshold){//周围黑点的个数大于阈值就变成黑色

a[i][j]=1;

}

}

}

}

}

//找到极大全为1的矩形矩阵

public static void findMaxBlackRect(){

int ans = 0;

//初始化

for(int i=1;i<=m;++i) {

l[0][i] = 1;

r[0][i] = m;

}

for(int i=1; i<=n; i++){

int maxl=1, maxr=m;

//计算h和l

for(int j=1; j<=m; j++){

if(a[i][j]==0){

h[i][j] = 0;

l[i][j] = 1;

maxl = j+1;

}else{

l[i][j] = Math.max(l[i-1][j],maxl);

h[i][j] = h[i-1][j]+1;

}

}

//计算r

for(int j=m; j>=1; j--){

if(a[i][j]==0){

r[i][j] = m;

maxr = j-1;

}else{

r[i][j] = Math.min(r[i-1][j],maxr);

int temp=ans;

ans = Math.max(ans,(r[i][j]-l[i][j]+1)*h[i][j]);

if(temp!=ans){

y1=l[i][j];

x1=i-h[i][j]+1;

rwidth=r[i][j]-l[i][j]+1;

rheight=h[i][j];

// System.out.println("i: "+i+" j:"+j+" l:"+l[i][j]+" r:"+r[i][j]+" h:"+h[i][j]);

}

}

}

}

//由于在二值化时将所有的点横纵坐标都加了1，找到矩形起点时要减去1

x1=x1-1;

y1=y1-1;

System.out.println("x1: "+x1+" y1: "+y1+" width: "+rwidth+" height: "+rheight);

}

//粘贴图片

public static void stickImage(String bigImagePath,String smallImagePath,String outImagePath) {

try {

BufferedImage bigImage = ImageIO.read(new File(bigImagePath));

BufferedImage smallImage = ImageIO.read(new File(smallImagePath));

Graphics2D gh=bigImage.createGraphics();

gh.drawImage(smallImage,null,x1,y1);

gh.dispose();

ImageIO.write(bigImage,"jpg",new File(outImagePath));

} catch (IOException ex) {

ex.printStackTrace();

}

}

//没有输出路劲就随机产生一个

public static void stickImage(String bigImagePath,String smallImagePath) {

try {

BufferedImage bigImage = ImageIO.read(new File(bigImagePath));

BufferedImage smallImage = ImageIO.read(new File(smallImagePath));

Graphics2D gh=bigImage.createGraphics();

gh.drawImage(smallImage,null,x1,y1);

gh.dispose();

ImageIO.write(bigImage,"jpg",new File(Generate.randomOutPutPath()));

} catch (IOException ex) {

ex.printStackTrace();

}

}

public static void print(){

String filePath="C:\\Users\\86130\\Desktop\\素材\\2019-12-30-14-30.jpg";

try {

BufferedImage image=ImageIO.read(new File(filePath));

for(int i=501;i<=1000;i++){

for(int j=501;j<=1000;j++){

if(a[i][j]==0){

Object data = image.getRaster().getDataElements(i-1, j-1, null);//获取该点像素，并以object类型表示

int red = image.getColorModel().getRed(data);

int blue = image.getColorModel().getBlue(data);

int green = image.getColorModel().getGreen(data);

System.out.print("[ red "+red+" blue "+blue+" green "+green+" ]");

}

}

System.out.println();

}

} catch (IOException e) {

e.printStackTrace();

}

}

}