/*
* To change this template, choose Tools | Templates
* and open the template in the editor.
*/
package snailocr.util;
import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.util.logging.Level;
import java.util.logging.Logger;
import javax.imageio.ImageIO;
/**
*
* @author Administrator
*/
public class ImageTool {
private BufferedImage image;
private int width;
private int height;
/**
* 变图像为黑白色 提示: 黑白化之前最好灰色化以便得到好的灰度平均值,利于获得好的黑白效果
*
* @return
*/
public ImageTool changeToBlackWhiteImage() {
int avgGrayValue = getAvgValue();
int whitePoint = getWhitePoint(), blackPoint = getBlackPoint();
Color point;
for (int i = 0; i < height; i++) {
for (int j = 0; j < width; j++) {
point = new Color(image.getRGB(j, i));
image.setRGB(j, i, (point.getRed() < avgGrayValue ? blackPoint : whitePoint));
}
}
return this;
}
/**
*
*
* @param whiteAreaPercent 过滤之后白色区域面积占整个图片面积的最小百分比
* @param removeLighter true:过滤比中值颜色轻的,false:过滤比中值颜色重的,一般都是true
* @return
*/
public ImageTool midddleValueFilter(int whiteAreaMinPercent, boolean removeLighter) {
int modify = 0;
int avg = getAvgValue();
Color point;
while (getWhitePercent() < whiteAreaMinPercent) {
for (int i = 0; i < height; i++) {
for (int j = 0; j < width; j++) {
point = new Color(image.getRGB(j, i));
if (removeLighter) {
if (((point.getRed() + point.getGreen() + point.getBlue()) / 3) > avg - modify) {
// System.out.println(((point.getRed() + point.getGreen() + point.getBlue()) / 3)+"--"+(avg - modify));
image.setRGB(j, i, getWhitePoint());
}
} else {
if (((point.getRed() + point.getGreen() + point.getBlue()) / 3) < avg + modify) {
// System.out.println(((point.getRed() + point.getGreen() + point.getBlue()) / 3)+"--"+(avg - modify));
image.setRGB(j, i, getWhitePoint());
}
}
}
}
modify++;
}
// System.out.println(getWhitePercent());
return this;
}
private int getWhitePercent() {
Color point;
int white = 0;
for (int i = 0; i < height; i++) {
for (int j = 0; j < width; j++) {
point = new Color(image.getRGB(j, i));
if (((point.getRed() + point.getGreen() + point.getBlue()) / 3) == 255) {
white++;
}
}
}
return (int) Math.ceil(((float) white * 100 / (width * height)));
}
/**
* @param 变图像为灰色 取像素点的rgb三色平均值作为灰度值
*
* @return
*/
public ImageTool changeToGrayImage() {
int gray;
Color point;
for (int i = 0; i < height; i++) {
for (int j = 0; j < width; j++) {
point = new Color(image.getRGB(j, i));
gray = (point.getRed() + point.getGreen() + point.getBlue()) / 3;
image.setRGB(j, i, new Color(gray, gray, gray).getRGB());
}
}
return this;
}
/**
*
* 去除噪点和单点组成的干扰线 注意: 去除噪点之前应该对图像黑白化
*
* @param neighborhoodMinCount 每个点最少的邻居数
* @return
*/
public ImageTool removeBadBlock(int blockWidth, int blockHeight, int neighborhoodMinCount) {
int val;
int whitePoint = getWhitePoint();
int counter, topLeftXIndex, topLeftYIndex;
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
//初始化邻居数为0
counter = 0;
topLeftXIndex = x - 1;
topLeftYIndex = y - 1;
//x1 y1是以x,y左上角点为顶点的矩形,该矩形包围在传入的矩形的外围,计算传入的矩形的有效邻居数目
if (isBlackBlock(x, y, blockWidth, blockHeight)) {//只有当块是全黑色才计算
for (int x1 = topLeftXIndex; x1 <= topLeftXIndex + blockWidth + 1; x1++) {
for (int y1 = topLeftYIndex; y1 <= topLeftYIndex + blockHeight + 1; y1++) {
//判断这个点是否存在
if (x1 < width && x1 >= 0 && y1 < height && y1 >= 0) {
//判断这个点是否是传入矩形的外围点
if (x1 == topLeftXIndex || x1 == topLeftXIndex + blockWidth + 1
|| y1 == topLeftYIndex || y1 == topLeftYIndex + blockHeight + 1) {
//这里假定图像已经被黑白化,取Red值认为不是0就是255
val = new Color(image.getRGB(x1, y1)).getRed();
// System.out.println(val + "--" + (centerVal));
//如果这个邻居是黑色,就把中心点的有效邻居数目加一
if (val == 0) {
counter++;
}
}
}
}
}
// System.out.println("-------------------");
// System.out.println(x+"-"+y+"-"+counter);
if (counter < neighborhoodMinCount) {
image.setRGB(x, y, whitePoint);
}
}
}
}
return this;
}
/**
* 如果点周围的黑点数达到补偿值就把这个点变为黑色
*
* @param addFlag 补偿阀值,通过观察处理过的图像确定,一般为2即可
* @return
*/
public ImageTool modifyBlank(int addFlag) {
int val, counter = 0, topLeftXIndex, topLeftYIndex, blackPoint = getBlackPoint();
Color point;
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
//初始化邻居数为0
counter = 0;
topLeftXIndex = x - 1;
topLeftYIndex = y - 1;
point = new Color(image.getRGB(x, y));
//这里假定图像已经被黑白化,取Red值认为不是0就是255
val = point.getRed();
//只有白点才进行补偿
if (val == 255) {
for (int x1 = topLeftXIndex; x1 <= topLeftXIndex + 2; x1++) {
for (int y1 = topLeftYIndex; y1 <= topLeftYIndex + 2; y1++) {
//判断这个点是否存在
if (x1 < width && x1 >= 0 && y1 < height && y1 >= 0) {
//判断这个点是否是传入点的外围点
if (x1 == topLeftXIndex || x1 == topLeftXIndex + 2
|| y1 == topLeftYIndex || y1 == topLeftYIndex + 2) {
//这里假定图像已经被黑白化,取Red值认为不是0就是255
val = new Color(image.getRGB(x1, y1)).getRed();
// System.out.println(val + "--" + (centerVal));
//如果这个邻居是黑色,就把中心点的补偿数目加一
if (val == 0) {
counter++;
}
}
}
}
}
//如果这个点周围的黑点数达到补偿值就把这个点变为黑色
if (counter >= addFlag) {
image.setRGB(x, y, blackPoint);
}
}
}
}
return this;
}
public BufferedImage getBufferedImage(String filename) {
File file = new File(filename);
try {
return ImageIO.read(file);
} catch (IOException ex) {
Logger.getLogger(ImageTool.class.getName()).log(Level.SEVERE, null, ex);
return null;
}
}
private boolean isBlackBlock(int startX, int startY, int blockWidth, int blockHeight) {
int counter = 0;//统计黑色像素点的个数
int total = 0;//统计有效像素点的个数
int val;
for (int x1 = startX; x1 <= startX + blockWidth - 1; x1++) {
for (int y1 = startY; y1 <= startY + blockHeight - 1; y1++) {
//判断这个点是否存在
if (x1 < width && x1 >= 0 && y1 < height && y1 >= 0) {
total++;//有效像素点的个数
//这里假定图像已经被黑白化,取Red值认为不是0就是255
val = new Color(image.getRGB(x1, y1)).getRed();
//如果这个点是黑色,就把黑色像素点的数目加一
if (val == 0) {
counter++;
}
}
}
}
// System.out.println(startX + "--" + startY + "" + (counter == total&&total!=0));
return counter == total && total != 0;
}
private int getWhitePoint() {
return (new Color(255, 255, 255).getRGB() & 0xffffffff);
}
private int getBlackPoint() {
return (new Color(0, 0, 0).getRGB() & 0xffffffff);
}
private int getAvgValue() {
Color point;
int total = 0;
for (int i = 0; i < height; i++) {
for (int j = 0; j < width; j++) {
point = new Color(image.getRGB(j, i));
total += (point.getRed() + point.getGreen() + point.getBlue()) / 3;
}
}
return total / (width * height);
}
public void saveToFile(String filePath) {
try {
String ext = filePath.substring(filePath.lastIndexOf(".") + 1);
File newFile = new File(filePath);
ImageIO.write(image, ext, newFile);
} catch (IOException ex) {
Logger.getLogger(ImageTool.class.getName()).log(Level.SEVERE, null, ex);
}
}
public BufferedImage getImage() {
return image;
}
public void setImage(BufferedImage image) {
this.image = image;
width = image.getWidth();
height = image.getHeight();
}
}
本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
