本篇内容介绍了“java怎么实现识别二维码图片功能”的有关知识,在实际案例的操作过程中,不少人都会遇到这样的困境,接下来就让小编带领大家学习一下如何处理这些情况吧!希望大家仔细阅读,能够学有所成!
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。
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