java实现识别二维码图片功能方法详解与实例源码
啊猿 人气:0首先添加依赖
<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>
java识别二维码代码实现
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;
/**
* 作用：二维码识别（图片）
* 类名：QRCodeUtils
**/
public class QRCodeUtils {
/**
* 解析二维码,此方法解析一个路径的二维码图片
* path:图片路径
*/
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
}
Java二维码图片调优
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;
/**
* @author weijianxing
* @description: TODO
* @date 2020/11/26 9:28
*/
public class ImageOptimizationUtil {
// 阈值0-255
public static int YZ = 150;
/**
* 图像二值化处理
*
* @param filePath 要处理的图片路径
* @param fileOutputPath 处理后的图片输出路径
*/
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));
}
/**
* 图像的灰度处理
* 利用浮点算法：Gray = R*0.3 + G*0.59 + B*0.11;
*
* @param rgb 该点的RGB值
* @return 返回处理后的灰度值
*/
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;
}
/**
* 自己加周围8个灰度值再除以9，算出其相对灰度值
*
* @param gray
* @param x 要计算灰度的点的横坐标
* @param y 要计算灰度的点的纵坐标
* @param w 图像的宽度
* @param h 图像的高度
* @return
*/
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;
}
/**
* 二值化后的图像的开运算：先腐蚀再膨胀（用于去除图像的小黑点）
*
* @param filePath 要处理的图片路径
* @param fileOutputPath 处理后的图片输出路径
* @throws IOException
*/
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++) {
/*
* 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
* 由于公司图片态模糊，完全达到9个点全为黑的点太少，最后效果很差，故改为了小于30
* （写30的原因是，当只有一个点为白，即总共255，调用getGray方法后得到255/9 = 28）
*/
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;
}
第二种方法：
package com.ghl.magicbox.qrcode.b;
import cn.hutool.core.util.IdUtil;
import com.google.zxing.*;
import com.google.zxing.client.j2se.BufferedImageLuminanceSource;
import com.google.zxing.common.HybridBinarizer;
import lombok.SneakyThrows;
import lombok.extern.slf4j.Slf4j;
import org.opencv.core.*;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.CLAHE;
import org.opencv.imgproc.Imgproc;
import org.springframework.util.ResourceUtils;
import org.springframework.web.multipart.MultipartFile;
import javax.imageio.ImageIO;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.net.URL;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
/**
* @Author: GHL
* @Date: 2022/2/18
* @Description:
*/
@Slf4j
public class QRCodeUtil {
/**
* 默认放大倍数
*/
private final static int TIMES = 4;
static {
// 加载Opencv的dll文件
URL url = ClassLoader.getSystemResource("lib/opencv_java3416.dll");
System.load(url.getPath());
}
/**
* 复杂图片二维码解析
*
* @param file
* @return
*/
public static String complexDecode(File file) {
String tempFilePath = null;
try {
log.debug("QRCodeUtil -> complexDecode() fileName:{}",file.getName());
tempFilePath = getFilePath(file.getName());
//第一次解析：直接解析
log.debug("QRCodeUtil -> complexDecode() firstDecode begin by:{}",file.getName());
String codeDataByFirst = simpleDecode(file);
if (codeDataByFirst != null) {
return codeDataByFirst;
}
//第二次解析：定位图中二维码，截图放大
log.debug("QRCodeUtil -> complexDecode() secondDecode begin by:{}",file.getName());
piz(file.getAbsolutePath(),tempFilePath);
String codeDataBySecond = simpleDecode(tempFilePath);
if (codeDataBySecond != null) {
return codeDataBySecond;
}
//第三次解析：将截图后二维码二值化
log.debug("QRCodeUtil -> complexDecode() thirdDecode begin by:{}",file.getName());
Mat mat = binarization(tempFilePath);
String codeDataByThird = simpleDecode(tempFilePath);
if (codeDataByThird != null) {
return codeDataByThird;
}
//第四次解析: 进行限制对比度的自适应直方图均衡化处理
log.debug("QRCodeUtil -> complexDecode() fourthDecode begin by:{}",file.getName());
limitContrast(tempFilePath,mat);
String codeDataByFourth = simpleDecode(tempFilePath);
if (codeDataByFourth != null) {
log.debug("QRCodeUtil -> complexDecode() fileName:{} state:{} result:{}",file.getName(),Boolean.TRUE,codeDataByFourth);
return codeDataByFourth;
}
log.debug("QRCodeUtil -> complexDecode() fileName:{} state:{}",file.getName(), Boolean.FALSE);
} finally {
file.deleteOnExit();
if (tempFilePath != null){
file = new File(tempFilePath);
file.deleteOnExit();
}
}
return null;
}
/**
* 复杂图片二维码解析
*
* @param path
* @return
*/
public static String complexDecode(String path) {
return complexDecode(new File(path));
}
/**
* 复杂图片二维码解析
*
* @param originalFile
* @return
*/
public static String complexDecode(MultipartFile originalFile) {
String filePath = getFilePath(originalFile.getOriginalFilename());
File mkFile = new File(filePath);
if (!mkFile.exists()){
mkFile.mkdir();
log.debug("QRCodeUtil -> complexDecode() create temp file ready by:{}",originalFile.getOriginalFilename());
}
try {
originalFile.transferTo(mkFile);
} catch (IOException e) {
e.printStackTrace();
}
return complexDecode(mkFile);
}
/**
* 简单二维码解析
*
* @param path
* @return
*/
public static String simpleDecode(String path) {
return simpleDecode(new File(path));
}
/**
* 简单二维码解析
*
* @param file
* @return zxing解析率实测与opencv差不多。所以直接使用zxing解析
* zxing版本高能提高识别率
*/
public static String simpleDecode(File file) {
try {
BufferedImage image = ImageIO.read(file);
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);
return result.getText();
} catch (Exception e) {
return null;
}
}
/**
* 获取临时文件存储地址
*/
@SneakyThrows
private static String getFilePath(String fileName) {
String path = ResourceUtils.getFile("classpath:").getPath() + "/static/decodeWork/";
File folder = new File(path);
if (!folder.exists()){
folder.mkdirs();
}
String contentType = fileName.contains(".") ? fileName.substring(fileName.lastIndexOf(".") + 1) : null;
String newFileName = IdUtil.getSnowflake(0, 0).nextId() + "." + contentType;
return path + newFileName;
}
/**
* 定位 - > 截取 -> 放大
* @param filePath
* @param tempFilePath
*/
private static void piz(String filePath, String tempFilePath) {
Mat srcGray = new Mat();
Mat src = Imgcodecs.imread(filePath, 1);
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
List<MatOfPoint> markContours = new ArrayList<MatOfPoint>();
//System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
// URL url = ClassLoader.getSystemResource("lib/opencv_java3416.dll");
// System.load(url.getPath());
//图片太小就放大
if (src.width() * src.height() < 90000) {
Imgproc.resize(src, src, new Size(800, 600));
}
// 彩色图转灰度图
Imgproc.cvtColor(src, srcGray, Imgproc.COLOR_RGB2GRAY);
// 对图像进行平滑处理
Imgproc.GaussianBlur(srcGray, srcGray, new Size(3, 3), 0);
Imgproc.Canny(srcGray, srcGray, 112, 255);
Mat hierarchy = new Mat();
Imgproc.findContours(srcGray, contours, hierarchy, Imgproc.RETR_TREE, Imgproc.CHAIN_APPROX_NONE);
for (int i = 0; i < contours.size(); i++) {
MatOfPoint2f newMtx = new MatOfPoint2f(contours.get(i).toArray());
RotatedRect rotRect = Imgproc.minAreaRect(newMtx);
double w = rotRect.size.width;
double h = rotRect.size.height;
double rate = Math.max(w, h) / Math.min(w, h);
// 长短轴比小于1.3，总面积大于60
if (rate < 1.3 && w < srcGray.cols() / 4 && h < srcGray.rows() / 4 && Imgproc.contourArea(contours.get(i)) > 60) {
// 计算层数，二维码角框有五层轮廓（有说六层），这里不计自己这一层，有4个以上子轮廓则标记这一点
double[] ds = hierarchy.get(0, i);
if (ds != null && ds.length > 3) {
int count = 0;
if (ds[3] == -1) {
//最外层轮廓排除
continue;
}
// 计算所有子轮廓数量
while ((int) ds[2] != -1) {
++count;
ds = hierarchy.get(0, (int) ds[2]);
}
if (count >= 4) {
markContours.add(contours.get(i));
}
}
}
}
/*
* 二维码有三个角轮廓，正常需要定位三个角才能确定坐标，本工具当识别到两个点的时候也将二维码定位出来；
* 当识别到两个及两个以上点时，取两个点中间点，往四周扩散截取 当小于两个点时，直接返回
*/
if (markContours.size() == 0) {
return;
} else if (markContours.size() == 1) {
capture(markContours.get(0), src ,tempFilePath);
} else {
List<MatOfPoint> threePointList = new ArrayList<>();
threePointList.add(markContours.get(0));
threePointList.add(markContours.get(1));
capture(threePointList, src,tempFilePath);
}
}
/**
* 当只识别到二维码的两个定位点时，根据两个点的中点进行定位
* @param threePointList
* @param src
*/
private static void capture(List<MatOfPoint> threePointList, Mat src, String tempFilePath) {
Point p1 = centerCal(threePointList.get(0));
Point p2 = centerCal(threePointList.get(1));
Point centerPoint = new Point((p1.x + p2.x) / 2, (p1.y + p2.y) / 2);
double width = Math.abs(p1.x - p2.x) + Math.abs(p1.y - p2.y) + 50;
// 设置截取规则
Rect roiArea = new Rect((int) (centerPoint.x - width) > 0 ? (int) (centerPoint.x - width) : 0,
(int) (centerPoint.y - width) > 0 ? (int) (centerPoint.y - width) : 0, (int) (2 * width),
(int) (2 * width));
// 截取二维码
Mat dstRoi = new Mat(src, roiArea);
// 放大图片
Imgproc.resize(dstRoi, dstRoi, new Size(TIMES * width, TIMES * width));
Imgcodecs.imwrite(tempFilePath, dstRoi);
}
/**
* 针对对比度不高的图片，只能识别到一个角的，直接以该点为中心截取
* @param matOfPoint
* @param src
* @param tempFilePath
*/
private static void capture(MatOfPoint matOfPoint, Mat src, String tempFilePath) {
Point centerPoint = centerCal(matOfPoint);
int width = 200;
Rect roiArea = new Rect((int) (centerPoint.x - width) > 0 ? (int) (centerPoint.x - width) : 0,
(int) (centerPoint.y - width) > 0 ? (int) (centerPoint.y - width) : 0, (int) (2 * width),
(int) (2 * width));
// 截取二维码
Mat dstRoi = new Mat(src, roiArea);
// 放大图片
Imgproc.resize(dstRoi, dstRoi, new Size(TIMES * width, TIMES * width));
Imgcodecs.imwrite(tempFilePath, dstRoi);
}
/**
* 获取轮廓的中心坐标
* @param matOfPoint
* @return
*/
private static Point centerCal(MatOfPoint matOfPoint) {
double centerx = 0, centery = 0;
MatOfPoint2f mat2f = new MatOfPoint2f(matOfPoint.toArray());
RotatedRect rect = Imgproc.minAreaRect(mat2f);
Point vertices[] = new Point[4];
rect.points(vertices);
centerx = ((vertices[0].x + vertices[1].x) / 2 + (vertices[2].x + vertices[3].x) / 2) / 2;
centery = ((vertices[0].y + vertices[1].y) / 2 + (vertices[2].y + vertices[3].y) / 2) / 2;
Point point = new Point(centerx, centery);
return point;
}
/**
* 二值化图像
* @param filePath 图像地址
*/
private static Mat binarization(String filePath){
Mat mat = Imgcodecs.imread(filePath, 1);
// 彩色图转灰度图
Imgproc.cvtColor(mat, mat, Imgproc.COLOR_RGB2GRAY);
// 对图像进行平滑处理
Imgproc.blur(mat, mat, new Size(3, 3));
// 中值去噪
Imgproc.medianBlur(mat, mat, 5);
// 这里定义一个新的Mat对象，主要是为了保留原图，未下次处理做准备
Mat mat2 = new Mat();
// 根据OTSU算法进行二值化
Imgproc.threshold(mat, mat2, 205, 255, Imgproc.THRESH_OTSU);
// 生成二值化后的图像
Imgcodecs.imwrite(filePath, mat2);
return mat;
}
/**
* 图像进行限制对比度的自适应直方图均衡化处理
* @param filePath
*/
public static void limitContrast(String filePath,Mat mat){
CLAHE clahe = Imgproc.createCLAHE(2, new Size(8, 8));
clahe.apply(mat, mat);
Imgcodecs.imwrite(filePath, mat);
}
public static void main(String[] args) {
String s = complexDecode("C:\\Users\\ghl\\Desktop\\b.jpg");
System.out.println(s);
}
}更多关于java实现识别二维码图片功能文章请查看下面的相关链接
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