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

时间:2021-08-02 11:43     来源/作者:香芋味的猫

网易易盾:dun.163.com

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* 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

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

接下来就是见证奇迹的时刻!

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

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

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↓↓↓

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/**
     * 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

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

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/**
     * 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

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