Springboot集成selemiumtess4jopencv-实现验证码识别去干扰线模拟用户登录-二

上一章节已经搭建好了基础架构，接下来就是图像处理和识别的代码了。

1 ： 通过selemium获取页面验证码元素

WebElement element = SeleniumUtil.findElement(driver,szsbEnum);
return new ByteArrayInputStream(((TakesScreenshot) element).getScreenshotAs(OutputType.BYTES));

2 ：对验证码图片进行处理

直接贴图片处理的代码

public BufferedImage cleanLinesInImage(BufferedImage oriBufferedImage)  throws IOException{
        BufferedImage bufferedImage = oriBufferedImage;
        int h = bufferedImage.getHeight();
        int w = bufferedImage.getWidth();

        // 灰度化
        int[][] gray = new int[w][h];
        for (int x = 0; x < w; x++)
        {
            for (int y = 0; y < h; y++)
            {
                int argb = bufferedImage.getRGB(x, y);
                // 图像加亮（调整亮度识别率非常高）
                int r = (int) (((argb >> 16) & 0xFF) * 1.1 + 30);
                int g = (int) (((argb >> 8) & 0xFF) * 1.1 + 30);
                int b = (int) (((argb >> 0) & 0xFF) * 1.1 + 30);
                if (r >= 255)
                {
                    r = 255;
                }
                if (g >= 255)
                {
                    g = 255;
                }
                if (b >= 255)
                {
                    b = 255;
                }
                gray[x][y] = (int) Math
                        .pow((Math.pow(r, 2.2) * 0.2973 + Math.pow(g, 2.2)
                                * 0.6274 + Math.pow(b, 2.2) * 0.0753), 1 / 2.2);
            }
        }

        // 二值化
        int threshold = ostu(gray, w, h);
        BufferedImage binaryBufferedImage = new BufferedImage(w, h, BufferedImage.TYPE_BYTE_BINARY);
        for (int x = 0; x < w; x++)
        {
            for (int y = 0; y < h; y++)
            {
                if (gray[x][y] > threshold)
                {
                    gray[x][y] |= 0x00FFFF;
                } else
                {
                    gray[x][y] &= 0xFF0000;
                }
                binaryBufferedImage.setRGB(x, y, gray[x][y]);
            }
        }
        File file = FileUtil.file(IdUtil.fastUUID()+".png");
        ImageIO.write(binaryBufferedImage,"png",file);

        //这里开始是利用opencv的api进行处理
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        Mat imread = Imgcodecs.imread(file.getAbsolutePath());
        Mat target = new Mat();
//        Core.bitwise_not(imread,target);
        Mat kelner = Imgproc.getStructuringElement(MORPH_RECT, new Size(3, 3), new Point(-1, -1));

        //膨胀
        Imgproc.dilate(imread,target,kelner);
        //腐蚀
        Imgproc.erode(target,target,kelner);
        oriBufferedImage = mat2BufImg(target,".png");
        file.delete();
        return oriBufferedImage;
    }

顺便放一下图片处理过程每个步骤的细节~~~~
原始验证码 ：
灰度和二值化 ：
膨胀 ：
腐蚀 ：

3 ：调用OCR进行识别

public String getAuthCodeByOpencv(WebDriver driver, SeleniumUtil.SzsbEnum szsbEnum){
        String code = "";
        //获取网页验证码图片
        try (InputStream inputStream = AuthCodeScreenShotUtil.getAuthCodeImageById(driver, szsbEnum)) {

            try {
                code = tesseract.doOCR(ImageCleanPlanOpencv.INSTANCE.clean(ImageIO.read(inputStream)));
            } catch (TesseractException e) {
                e.printStackTrace();
            }
        } catch (IOException e) {
            logger.warn(e.getMessage());
        }
        //当前验证码是数字类型 直接去除数字以外所有值
        code = code.replaceAll("\\D", "");
        return code;
    }

参考文章
https://blog.csdn.net/liziqin4/article/details/83085635
https://blog.csdn.net/hechaojie_com/article/details/82057411