[二维码]读取二维码

dev-zxing/src/main/java/com/tykj/dev/device/zxing/util/Base64Util.java

+package com.tykj.dev.device.zxing.util;
+
+import sun.misc.BASE64Encoder;
+
+import javax.imageio.stream.FileImageOutputStream;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.util.ArrayList;
+import java.util.Base64;
+
+/**
+ * DATE:2021-7-21
+ * Author:zsp
+ */
+public class Base64Util {
+    public static String getImageStr(String imgFile) {
+        InputStream inputStream = null;
+        byte[] data = null;
+        try {
+            inputStream = new FileInputStream(imgFile);
+            data = new byte[inputStream.available()];
+            inputStream.read(data);
+            inputStream.close();
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+        // 加密
+        Base64.Encoder encoder = Base64.getEncoder();
+        return encoder.encodeToString(data);
+    }
+
+    public static byte[] getFileBytes(String path) {
+        ArrayList bytesList = new ArrayList();
+        byte[] bytes = new byte[1];
+        FileInputStream inStream = null;
+        try {
+            inStream = new FileInputStream(path);
+            while (inStream.read(bytes) > 0) {
+                bytesList.add(bytes[0]);
+            }
+            bytes = new byte[bytesList.size()];
+            for (int i = 0; i < bytes.length; i++) {
+                bytes[i] = (byte) bytesList.get(i);
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        } finally {
+            try {
+                if (inStream != null) {
+                    inStream.close();
+                }
+            } catch (Exception e) {
+                e.printStackTrace();
+            }
+        }
+        return bytes;
+    }
+
+    public static void byte2image(byte[] data, String path) {
+        if (data.length < 3 || path.equals("")) {
+            return;
+        }
+        try {
+            FileImageOutputStream imageOutput = new FileImageOutputStream(new File(path));
+            imageOutput.write(data, 0, data.length);
+            imageOutput.close();
+            System.out.println("Make Picture success,Please find image in " + path);
+        }
+        catch (Exception ex) {
+            System.out.println("Exception: " + ex);
+            ex.printStackTrace();
+        }
+    }
+}

dev-zxing/src/main/java/com/tykj/dev/device/zxing/util/ImageUtils.java

+package com.tykj.dev.device.zxing.util;
+
+import java.awt.*;
+import java.awt.image.BufferedImage;
+import java.io.IOException;
+
+public class ImageUtils {
+    // 阈值0-255
+    public static int YZ = 150;
+
+    /**
+     * 图像二值化处理
+     *
+     * @param bi       要处理的图片路径
+     * @throws IOException
+     */
+    public static BufferedImage binarization(BufferedImage bi) throws IOException {
+        // 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);
+                }
+            }
+
+        }
+        return bufferedImage;
+    }
+
+    /**
+     * 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 * 0.3 + g * 0.59 + 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 bi       要处理的图片流
+     * @throws IOException
+     */
+    public static BufferedImage opening(BufferedImage bi) throws IOException {
+        // 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);
+                    }
+                }
+            }
+        }
+        return bufferedImage;
+    }
+}

dev-zxing/src/main/java/com/tykj/dev/device/zxing/util/PdfToImageUtil.java

+package com.tykj.dev.device.zxing.util;
+
+import org.apache.pdfbox.pdmodel.PDDocument;
+import org.apache.pdfbox.rendering.PDFRenderer;
+
+import javax.imageio.ImageIO;
+import java.awt.image.BufferedImage;
+import java.io.ByteArrayOutputStream;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * @author ldtianzhe
+ * @description
+ * @date 2021/7/21
+ */
+public class PdfToImageUtil {
+    /**
+     * dpi越大转换后越清晰，相对转换速度越慢
+     */
+    private static final Integer DPI = 300;
+
+    /**
+     * 转换后的图片类型
+     */
+    private static final String IMG_TYPE = "png";
+
+    /**
+     * PDF转图片
+     *
+     * @param fileContent PDF文件的二进制流
+     * @return 图片文件的二进制流
+     */
+    public static List<byte[]> pdfToImage(byte[] fileContent) throws IOException {
+        List<byte[]> result = new ArrayList<>();
+        try (PDDocument document = PDDocument.load(fileContent)) {
+            PDFRenderer renderer = new PDFRenderer(document);
+            for (int i = 0; i < document.getNumberOfPages(); ++i) {
+                BufferedImage bufferedImage = renderer.renderImageWithDPI(i, DPI);
+                ByteArrayOutputStream out = new ByteArrayOutputStream();
+                ImageIO.write(bufferedImage, IMG_TYPE, out);
+                result.add(out.toByteArray());
+            }
+        }
+        return result;
+    }
+}

dev-zxing/src/main/java/com/tykj/dev/device/zxing/util/QRCodeUtils.java

+package com.tykj.dev.device.zxing.util;
+
+import com.google.zxing.*;
+import com.google.zxing.client.j2se.BufferedImageLuminanceSource;
+import com.google.zxing.common.HybridBinarizer;
+import com.tykj.dev.device.task.repository.TaskDao;
+import com.tykj.dev.device.task.subject.domin.Task;
+import com.tykj.dev.device.zxing.vo.ZxingTaskVo;
+import org.springframework.beans.factory.annotation.Autowired;
+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.Base64;
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * 解析二维码
+ **/
+public class QRCodeUtils {
+
+   @Autowired
+   private TaskDao taskDao;
+    /**
+     * 解析二维码解析,此方法是解析Base64格式二维码图片
+     * baseStr:base64字符串,data:image/png;base64开头的
+     */
+    public ZxingTaskVo deEncodeByBase64(String baseStr) {
+        String content = null;
+        BufferedImage image;
+        BASE64Decoder decoder = new BASE64Decoder();
+        byte[] b=null;
+        try {
+//            int i = baseStr.indexOf("data:image/png;base64,");
+//            baseStr = baseStr.substring(i+"data:image/png;base64,".length());//去掉base64图片的data:image/png;base64,部分才能转换为byte[]
+            b = decoder.decodeBuffer(baseStr);//baseStr转byte[]
+            ByteArrayInputStream byteArrayInputStream=new ByteArrayInputStream(b);//byte[] 转BufferedImage
+            image = ImageIO.read(byteArrayInputStream);
+            BufferedImage temp = ImageUtils.opening(ImageUtils.binarization(image));
+            LuminanceSource source = new BufferedImageLuminanceSource(temp);
+            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
+            //返回taskId
+            ZxingTaskVo zxingTaskVo = null;
+            if (result.getText() != null){
+                Task task = taskDao.findByParentTaskId(Integer.valueOf(result.getText()));
+                zxingTaskVo = new ZxingTaskVo(task.getParentTaskId(), task.getId());
+            }
+            return zxingTaskVo;
+//            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 null;
+    }
+
+    /**
+     * 解析二维码,此方法解析一个字节数组的二维码图片
+     * path:图片路径
+     */
+    public static String deEncodeByBtyes(byte[] bytes) {
+        String content = null;
+        BufferedImage image;
+        try {
+            ByteArrayInputStream in = new ByteArrayInputStream(bytes);
+            image = ImageIO.read(in);
+            BufferedImage temp = ImageUtils.opening(ImageUtils.binarization(image));
+            LuminanceSource source = new BufferedImageLuminanceSource(temp);
+            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("二维码内容：  ");
+            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 {
+                ByteArrayInputStream in = new ByteArrayInputStream(bytes);
+                image = ImageIO.read(in);
+                BufferedImage temp = ImageUtils.opening(ImageUtils.binarization(image));
+                LuminanceSource source = new BufferedImageLuminanceSource(temp);
+                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("二维码内容：  ");
+                System.out.println("content： " + result.getText());
+                content = result.getText();
+            } catch (IOException e1) {
+                e1.printStackTrace();
+            } catch (NotFoundException e1) {
+                e1.printStackTrace();
+            }
+        }
+        return content;
+    }
+
+}