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This article introduces the relevant knowledge of "how to identify QR code pictures by java". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Required maven dependency

Com.google.zxing javase 3.2.1 com.google.zxing core 3.3.3

Implemented java class

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 / * function: QR code recognition (picture) * class name: QRCodeUtils * / public class QRCodeUtils {/ * parse the QR code. This method parses the QR code picture of a path * path: picture path * / 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 hints = new HashMap (); hints.put (DecodeHintType.CHARACTER_SET, "UTF-8"); Result result = new MultiFormatReader (). Decode (binaryBitmap, hints); / / decode System.out.println ("content in the picture:") System.out.println ("content:" + result.getText ()); content = result.getText ();} catch (IOException e) {e.printStackTrace () } catch (NotFoundException e) {/ / it is judged here that if the picture with LOGO cannot be recognized, add an attribute try {image = ImageIO.read (new File (path)); LuminanceSource source = new BufferedImageLuminanceSource (image); Binarizer binarizer = new HybridBinarizer (source); BinaryBitmap binaryBitmap = new BinaryBitmap (binarizer) Map hints = new HashMap (); / / set encoding format hints.put (DecodeHintType.CHARACTER_SET, "UTF-8"); / / set optimization precision hints.put (DecodeHintType.TRY_HARDER, Boolean.TRUE) / / set complex mode on (I can recognize the Wechat's QR code in this way) hints.put (DecodeHintType.PURE_BARCODE,Boolean.TYPE); Result result = new MultiFormatReader (). Decode (binaryBitmap, hints); / / decode System.out.println ("content in the picture:") System.out.println ("content:" + result.getText ()); content = result.getText ();} catch (IOException e) {e.printStackTrace ();} catch (NotFoundException e) {e.printStackTrace ();}} return content;}}

test

Public static void main (String [] args) {deEncodeByPath ("D:\\ Users/admin/Desktop/erweima/timg (5) .jpg"); / / QR code image path}

Output result:

The content in the picture:

Content: http://qrcode.online

If the above can not be recognized, then you need to process the image once, and then identify it. Here is a tool class for tuning the image.

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 {/ / threshold 0-255 public static int YZ = 150 / * * Image binarization processing * * @ param filePath Image path to be processed * @ Image output path after param fileOutputPath processing * / public static void binarization (String filePath, String fileOutputPath) throws IOException {File file = new File (filePath); BufferedImage bi = ImageIO.read (file) / / get the height and width of the current picture, ARGB int h = bi.getHeight (); int w = bi.getWidth (); int arr [] [] = new int [w] [h]; / / get the gray value for (int I = 0; I) of each pixel of the picture

< 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)); grayscale processing of} / * * image * using floating-point algorithm: Gray = Run0.3 + Gloss0.59 + Bron0.11 * * @ param rgb the RGB value of this point * @ return returns the processed grayscale value * / private static int getImageGray (int rgb) {String argb = Integer.toHexString (rgb); / / convert the decimal color value to hexadecimal / / argb represents transparency, red, green and blue, respectively, int r = Integer.parseInt (argb.substring (2,4), 16) / / the following parameters are: using binary int g = Integer.parseInt (argb.substring (4,6), 16); int b = Integer.parseInt (argb.substring (6,8), 16); int gray = (int) (rang 0.28 + grub 0.95 + bang 0.11); return gray } / * add 8 surrounding grayscale values and divide by 9 Calculate its relative gray value * * @ param gray * @ param x Abscissa of the point to be calculated * @ param y the ordinate of the point to be calculated * @ param w image width * @ param h image height * @ 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 = 0255: 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-1255: gray [x + 1] [y + 1]) Open operation of return rs / 9;} / * binary image: first corrode and then inflate (used to remove small black spots of the image) * * @ param filePath image path to be processed * @ param fileOutputPath image output path * @ throws IOException * / public static void opening (String filePath, String fileOutputPath) throws IOException {File file = new File (filePath) BufferedImage bi = ImageIO.read (file); / / get the height and width of the current picture, ARGB int h = bi.getHeight (); int w = bi.getWidth (); int arr [] [] = new int [w] [h]; / get the gray value for (int I = 0; I) of each pixel of the picture

< 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 (iMur1, j, black);} if (j > 0) {bufferedImage.setRGB (I, jmur1, black) } if (I > 0 & & j > 0) {bufferedImage.setRGB (imur1, jmur1, 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 (iTun1, jmur1, black);} if (I

< w-1 && j < h-1) { bufferedImage.setRGB(i+1, j+1, black); } if (i >

0 & & j < hmur1) {bufferedImage.setRGB (iMuth1, juli1, black);} ImageIO.write (bufferedImage, "jpg", new File (fileOutputPath)) } public static void main (String [] args) {String fullPath= "E:\\ weijianxing\\ img\\ Wechat picture _ 20201202160240.jpg"; String newPath= "E:\\ weijianxing\\ img\\ 1new_ Wechat picture _ 20201202160240.jpg"; try {ImageOptimizationUtil.binarization (fullPath,newPath);} catch (IOException e) {e.printStackTrace ();}

You can test it manually, and then adjust the corresponding parameter-- the calculation in the gray variable-- to adjust the grayscale of the changed code.

Private static int getImageGray (int rgb) {String argb = Integer.toHexString (rgb); / / convert the decimal color value to hexadecimal / / argb represents transparent, red, green and blue respectively 2 bits int r = Integer.parseInt (argb.substring (2,4), 16); / / the latter parameter is to use the binary int g = Integer.parseInt (argb.substring (4,6), 16) Int b = Integer.parseInt (argb.substring (6,8), 16); int gray = (int) (rang 0.28 + gr 0.95 + bang 0.11); return gray;}

After the adjustment, you can identify the picture.

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