本文实例为大家分享了java实现识别二维码图片功能，供大家参考，具体内容如下
所需maven依赖
<dependency>
<groupId>com.google.zxing</groupId>
<artifactId>javase</artifactId>
<version>3.2.1</version>
</dependency>
<dependency>
<groupId>com.google.zxing</groupId>
<artifactId>core</artifactId>
<version>3.3.3</version>
</dependency>实现的java类
import com.google.zxing.*;
import com.google.zxing.client.j2se.BufferedImageLuminanceSource;
import com.google.zxing.common.HybridBinarizer;
import sun.misc.BASE64Decoder;
import javax.imageio.ImageIO;
import java.awt.image.BufferedImage;
import java.io.ByteArrayInputStream;
import java.io.File;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
public class QRCodeUtils {
public static String deEncodeByPath(String path) {
String content = null;
BufferedImage image;
try {
image = ImageIO.read(new File(path));
LuminanceSource source = new BufferedImageLuminanceSource(image);
Binarizer binarizer = new HybridBinarizer(source);
BinaryBitmap binaryBitmap = new BinaryBitmap(binarizer);
Map<DecodeHintType, Object> hints = new HashMap<DecodeHintType, Object>();
hints.put(DecodeHintType.CHARACTER_SET, "UTF-8");
Result result = new MultiFormatReader().decode(binaryBitmap, hints);//解码
System.out.println("图片中内容： ");
System.out.println("content： " + result.getText());
content = result.getText();
} catch (IOException e) {
e.printStackTrace();
} catch (NotFoundException e) {
//这里判断如果识别不了带LOGO的图片，重新添加上一个属性
try {
image = ImageIO.read(new File(path));
LuminanceSource source = new BufferedImageLuminanceSource(image);
Binarizer binarizer = new HybridBinarizer(source);
BinaryBitmap binaryBitmap = new BinaryBitmap(binarizer);
Map<DecodeHintType, Object> hints = new HashMap<DecodeHintType, Object>();
//设置编码格式
hints.put(DecodeHintType.CHARACTER_SET, "UTF-8");
//设置优化精度
hints.put(DecodeHintType.TRY_HARDER, Boolean.TRUE);
//设置复杂模式开启（我使用这种方式就可以识别微信的二维码了）
hints.put(DecodeHintType.PURE_BARCODE,Boolean.TYPE);
Result result = new MultiFormatReader().decode(binaryBitmap, hints);//解码
System.out.println("图片中内容： ");
System.out.println("content： " + result.getText());
content = result.getText();
} catch (IOException e) {
e.printStackTrace();
} catch (NotFoundException e) {
e.printStackTrace();
}
}
return content;
}
}测试
public static void main(String [] args){
deEncodeByPath("D:\\Users/admin/Desktop/erweima/timg (5).jpg");//二维码图片路径
}输出结果：
图片中内容：
content: http://qrcode.online
如果上述不能识别的话，那么就需要对图片处理一次，然后再进行识别，这里是个调优图片的工具类。
package com.face.ele.common.utils;
import javax.imageio.ImageIO;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
public class ImageOptimizationUtil {
// 阈值0-255
public static int YZ = 150;
public static void binarization(String filePath, String fileOutputPath) throws IOException {
File file = new File(filePath);
BufferedImage bi = ImageIO.read(file);
// 获取当前图片的高,宽,ARGB
int h = bi.getHeight();
int w = bi.getWidth();
int arr[][] = new int[w][h];
// 获取图片每一像素点的灰度值
for (int i = 0; i < w; i++) {
for (int j = 0; j < h; j++) {
// getRGB()返回默认的RGB颜色模型(十进制)
arr[i][j] = getImageGray(bi.getRGB(i, j));// 该点的灰度值
}
}
// 构造一个类型为预定义图像类型,BufferedImage
BufferedImage bufferedImage = new BufferedImage(w, h, BufferedImage.TYPE_BYTE_BINARY);
// 和预先设置的阈值大小进行比较，大的就显示为255即白色，小的就显示为0即黑色
for (int i = 0; i < w; i++) {
for (int j = 0; j < h; j++) {
if (getGray(arr, i, j, w, h) > YZ) {
int white = new Color(255, 255, 255).getRGB();
bufferedImage.setRGB(i, j, white);
} else {
int black = new Color(0, 0, 0).getRGB();
bufferedImage.setRGB(i, j, black);
}
}
}
ImageIO.write(bufferedImage, "jpg", new File(fileOutputPath));
}
private static int getImageGray(int rgb) {
String argb = Integer.toHexString(rgb);// 将十进制的颜色值转为十六进制
// argb分别代表透明,红,绿,蓝 分别占16进制2位
int r = Integer.parseInt(argb.substring(2, 4), 16);// 后面参数为使用进制
int g = Integer.parseInt(argb.substring(4, 6), 16);
int b = Integer.parseInt(argb.substring(6, 8), 16);
int gray = (int) (r*0.28 + g*0.95 + b*0.11);
return gray;
}
public static int getGray(int gray[][], int x, int y, int w, int h) {
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;
}
public static void opening(String filePath, String fileOutputPath) throws IOException {
File file = new File(filePath);
BufferedImage bi = ImageIO.read(file);
// 获取当前图片的高,宽,ARGB
int h = bi.getHeight();
int w = bi.getWidth();
int arr[][] = new int[w][h];
// 获取图片每一像素点的灰度值
for (int i = 0; i < w; i++) {
for (int j = 0; j < h; j++) {
// getRGB()返回默认的RGB颜色模型(十进制)
arr[i][j] = getImageGray(bi.getRGB(i, j));// 该点的灰度值
}
}
int black = new Color(0, 0, 0).getRGB();
int white = new Color(255, 255, 255).getRGB();
BufferedImage bufferedImage = new BufferedImage(w, h, BufferedImage.TYPE_BYTE_BINARY);
// 临时存储腐蚀后的各个点的亮度
int temp[][] = new int[w][h];
// 1.先进行腐蚀操作
for (int i = 0; i < w; i++) {
for (int j = 0; j < h; j++) {
if (getGray(arr, i, j, w, h) < 30) {
temp[i][j] = 0;
} else{
temp[i][j] = 255;
}
}
}
// 2.再进行膨胀操作
for (int i = 0; i < w; i++) {
for (int j = 0; j < h; j++) {
bufferedImage.setRGB(i, j, white);
}
}
for (int i = 0; i < w; i++) {
for (int j = 0; j < h; j++) {
// 为0表示改点和周围8个点都是黑，则该点腐蚀操作后为黑
if (temp[i][j] == 0) {
bufferedImage.setRGB(i, j, black);
if(i > 0) {
bufferedImage.setRGB(i-1, j, black);
}
if (j > 0) {
bufferedImage.setRGB(i, j-1, black);
}
if (i > 0 && j > 0) {
bufferedImage.setRGB(i-1, j-1, black);
}
if (j < h-1) {
bufferedImage.setRGB(i, j+1, black);
}
if (i < w-1) {
bufferedImage.setRGB(i+1, j, black);
}
if (i < w-1 && j > 0) {
bufferedImage.setRGB(i+1, j-1, black);
}
if (i < w-1 && j < h-1) {
bufferedImage.setRGB(i+1, j+1, black);
}
if (i > 0 && j < h-1) {
bufferedImage.setRGB(i-1, j+1, black);
}
}
}
}
ImageIO.write(bufferedImage, "jpg", new File(fileOutputPath));
}
public static void main(String[] args) {
String fullPath="E:\\weijianxing\\img\\微信图片_20201202160240.jpg";
String newPath="E:\\weijianxing\\img\\1new_微信图片_20201202160240.jpg";
try {
ImageOptimizationUtil.binarization(fullPath,newPath);
} catch (IOException e) {
e.printStackTrace();
}
}
}可以手动测试，然后对改代码的部分进行调正对应的参数-- gray变量里的计算进行灰度调整
private static int getImageGray(int rgb) {
String argb = Integer.toHexString(rgb);// 将十进制的颜色值转为十六进制
// argb分别代表透明,红,绿,蓝 分别占16进制2位
int r = Integer.parseInt(argb.substring(2, 4), 16);// 后面参数为使用进制
int g = Integer.parseInt(argb.substring(4, 6), 16);
int b = Integer.parseInt(argb.substring(6, 8), 16);
int gray = (int) (r*0.28 + g*0.95 + b*0.11);
return gray;
}等调整之后，在对图片进行识别即可。
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持编程网。
