使用java + selenium + OpenCV破解网易易盾滑动验证码的示例

太阳不下山

网易易盾：dun.163.com

* 验证码地址：https://dun.163.com/trial/jigsaw
* 使用OpenCv模板匹配
* Java + Selenium + OpenCV

产品样例

接下来就是见证奇迹的时刻！

注意！！！
· 在模拟滑动时不能按照相同速度或者过快的速度滑动，需要向人滑动时一样先快后慢，这样才不容易被识别。
模拟滑动代码↓↓↓

/**
   * 模拟人工移动
   * @param driver
   * @param element页面滑块
   * @param distance需要移动距离
   */
  public static void move(WebDriver driver, WebElement element, int distance) throws InterruptedException {
    int randomTime = 0;
    if (distance > 90) {
      randomTime = 250;
    } else if (distance > 80 && distance <= 90) {
      randomTime = 150;
    }
    List<Integer> track = getMoveTrack(distance - 2);
    int moveY = 1;
    try {
      Actions actions = new Actions(driver);
      actions.clickAndHold(element).perform();
      Thread.sleep(200);
      for (int i = 0; i < track.size(); i++) {
        actions.moveByOffset(track.get(i), moveY).perform();
        Thread.sleep(new Random().nextInt(300) + randomTime);
      }
      Thread.sleep(200);
      actions.release(element).perform();
    } catch (Exception e) {
      e.printStackTrace();
    }
  }
  /**
   * 根据距离获取滑动轨迹
   * @param distance需要移动的距离
   * @return
   */
  public static List<Integer> getMoveTrack(int distance) {
    List<Integer> track = new ArrayList<>();// 移动轨迹
    Random random = new Random();
    int current = 0;// 已经移动的距离
    int mid = (int) distance * 4 / 5;// 减速阈值
    int a = 0;
    int move = 0;// 每次循环移动的距离
    while (true) {
      a = random.nextInt(10);
      if (current <= mid) {
        move += a;// 不断加速
      } else {
        move -= a;
      }
      if ((current + move) < distance) {
        track.add(move);
      } else {
        track.add(distance - current);
        break;
      }
      current += move;
    }
    return track;
  }

操作过程

/**
   * 获取网易验证滑动距离
   * 
   * @return
   */
  public static String dllPath = "C://chrome//opencv_java440.dll";
 
  public double getDistance(String bUrl, String sUrl) {
    System.load(dllPath);
    File bFile = new File("C:/EasyDun_b.png");
    File sFile = new File("C:/EasyDun_s.png");
    try {
      FileUtils.copyURLToFile(new URL(bUrl), bFile);
      FileUtils.copyURLToFile(new URL(sUrl), sFile);
      BufferedImage bgBI = ImageIO.read(bFile);
      BufferedImage sBI = ImageIO.read(sFile);
      // 裁剪
      cropImage(bgBI, sBI, bFile, sFile);
      Mat s_mat = Imgcodecs.imread(sFile.getPath());
      Mat b_mat = Imgcodecs.imread(bFile.getPath());
      
      //阴影部分为黑底时需要转灰度和二值化，为白底时不需要
      // 转灰度图像
      Mat s_newMat = new Mat();
      Imgproc.cvtColor(s_mat, s_newMat, Imgproc.COLOR_BGR2GRAY);
      // 二值化图像
      binaryzation(s_newMat);
      Imgcodecs.imwrite(sFile.getPath(), s_newMat);
 
      int result_rows = b_mat.rows() - s_mat.rows() + 1;
      int result_cols = b_mat.cols() - s_mat.cols() + 1;
      Mat g_result = new Mat(result_rows, result_cols, CvType.CV_32FC1);
      Imgproc.matchTemplate(b_mat, s_mat, g_result, Imgproc.TM_SQDIFF); // 归一化平方差匹配法TM_SQDIFF 相关系数匹配法TM_CCOEFF
                                        
      Core.normalize(g_result, g_result, 0, 1, Core.NORM_MINMAX, -1, new Mat());
      Point matchLocation = new Point();
      MinMaxLocResult mmlr = Core.minMaxLoc(g_result);
      matchLocation = mmlr.maxLoc; // 此处使用maxLoc还是minLoc取决于使用的匹配算法
      Imgproc.rectangle(b_mat, matchLocation, new Point(matchLocation.x + s_mat.cols(), matchLocation.y + s_mat.rows()), new Scalar(0, 255, 0, 0));
      Imgcodecs.imwrite(bFile.getPath(), b_mat);
      return matchLocation.x + s_mat.cols() - sBI.getWidth() + 12;
    } catch (Throwable e) {
      e.printStackTrace();
      return 0;
    } finally {
       bFile.delete();
       sFile.delete();
    }
  }
 
  /**
   * 图片亮度调整
   * 
   * @param image
   * @param param
   * @throws IOException
   */
  public void bloding(BufferedImage image, int param) throws IOException {
    if (image == null) {
      return;
    } else {
      int rgb, R, G, B;
      for (int i = 0; i < image.getWidth(); i++) {
        for (int j = 0; j < image.getHeight(); j++) {
          rgb = image.getRGB(i, j);
          R = ((rgb >> 16) & 0xff) - param;
          G = ((rgb >> 8) & 0xff) - param;
          B = (rgb & 0xff) - param;
          rgb = ((clamp(255) & 0xff) << 24) | ((clamp(R) & 0xff) << 16) | ((clamp(G) & 0xff) << 8) | ((clamp(B) & 0xff));
          image.setRGB(i, j, rgb);
 
        }
      }
    }
  }
 
  // 判断a,r,g,b值，大于256返回256，小于0则返回0,0到256之间则直接返回原始值
  private int clamp(int rgb) {
    if (rgb > 255)
      return 255;
    if (rgb < 0)
      return 0;
    return rgb;
  }
 
  /**
   * 生成半透明小图并裁剪
   * 
   * @param image
   * @return
   */
  private void cropImage(BufferedImage bigImage, BufferedImage smallImage, File bigFile, File smallFile) {
    int y = 0;
    int h_ = 0;
    try {
      // 2 生成半透明图片
      bloding(bigImage, 75);
      for (int w = 0; w < smallImage.getWidth(); w++) {
        for (int h = smallImage.getHeight() - 2; h >= 0; h--) {
          int rgb = smallImage.getRGB(w, h);
          int A = (rgb & 0xFF000000) >>> 24;
          if (A >= 100) {
            rgb = (127 << 24) | (rgb & 0x00ffffff);
            smallImage.setRGB(w, h, rgb);
          }
        }
      }
      for (int h = 1; h < smallImage.getHeight(); h++) {
        for (int w = 1; w < smallImage.getWidth(); w++) {
          int rgb = smallImage.getRGB(w, h);
          int A = (rgb & 0xFF000000) >>> 24;
          if (A > 0) {
            if (y == 0)
              y = h;
            h_ = h - y;
            break;
          }
        }
      }
      smallImage = smallImage.getSubimage(0, y, smallImage.getWidth(), h_);
      bigImage = bigImage.getSubimage(0, y, bigImage.getWidth(), h_);
      ImageIO.write(bigImage, "png", bigFile);
      ImageIO.write(smallImage, "png", smallFile);
    } catch (Throwable e) {
      System.out.println(e.toString());
    }
  }
 
  /**
   * 
   * @param mat
   *   二值化图像
   */
  public static void binaryzation(Mat mat) {
    int BLACK = 0;
    int WHITE = 255;
    int ucThre = 0, ucThre_new = 127;
    int nBack_count, nData_count;
    int nBack_sum, nData_sum;
    int nValue;
    int i, j;
    int width = mat.width(), height = mat.height();
    // 寻找最佳的阙值
    while (ucThre != ucThre_new) {
      nBack_sum = nData_sum = 0;
      nBack_count = nData_count = 0;
 
      for (j = 0; j < height; ++j) {
        for (i = 0; i < width; i++) {
          nValue = (int) mat.get(j, i)[0];
 
          if (nValue > ucThre_new) {
            nBack_sum += nValue;
            nBack_count++;
          } else {
            nData_sum += nValue;
            nData_count++;
          }
        }
      }
      nBack_sum = nBack_sum / nBack_count;
      nData_sum = nData_sum / nData_count;
      ucThre = ucThre_new;
      ucThre_new = (nBack_sum + nData_sum) / 2;
    }
    // 二值化处理
    int nBlack = 0;
    int nWhite = 0;
    for (j = 0; j < height; ++j) {
      for (i = 0; i < width; ++i) {
        nValue = (int) mat.get(j, i)[0];
        if (nValue > ucThre_new) {
          mat.put(j, i, WHITE);
          nWhite++;
        } else {
          mat.put(j, i, BLACK);
          nBlack++;
        }
      }
    }
    // 确保白底黑字
    if (nBlack > nWhite) {
      for (j = 0; j < height; ++j) {
        for (i = 0; i < width; ++i) {
          nValue = (int) (mat.get(j, i)[0]);
          if (nValue == 0) {
            mat.put(j, i, WHITE);
          } else {
            mat.put(j, i, BLACK);
          }
        }
      }
    }
  }
  // 延时加载
  private static WebElement waitWebElement(WebDriver driver, By by, int count) throws Exception {
    WebElement webElement = null;
    boolean isWait = false;
    for (int k = 0; k < count; k++) {
      try {
        webElement = driver.findElement(by);
        if (isWait)
          System.out.println(" ok!");
        return webElement;
      } catch (org.openqa.selenium.NoSuchElementException ex) {
        isWait = true;
        if (k == 0)
          System.out.print("waitWebElement(" + by.toString() + ")");
        else
          System.out.print(".");
        Thread.sleep(50);
      }
    }
    if (isWait)
      System.out.println(" outTime!");
    return null;
  }

注意:有一个问题还没有解决，还无法区分阴影部分是黑色还是白色。 因为两种的情况不同，所以处理方式也不同。阴影部分为黑底时需要转灰度和二值化，为白底时不需要。黑底使用归一化平方差匹配算法 TM_SQDIFF ，而白底使用相关系数匹配算法 TM_CCOEFF。
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原文链接：https://blog.csdn.net/weixin_49701447/article/details/109740160