網(wǎng)易易盾：dun.163.com

* 驗證碼地址：https://dun.163.com/trial/jigsaw* 使用OpenCv模板匹配* Java + Selenium + OpenCV

產(chǎn)品樣例

接下來就是見證奇跡的時刻！

注意！！！· 在模擬滑動時不能按照相同速度或者過快的速度滑動，需要向人滑動時一樣先快后慢，這樣才不容易被識別。模擬滑動代碼↓↓↓

/** * 模擬人工移動 * @param driver * @param element頁面滑塊 * @param distance需要移動距離 */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();}}/** * 根據(jù)距離獲取滑動軌跡 * @param distance需要移動的距離 * @return */public static List<Integer> getMoveTrack(int distance) {List<Integer> track = new ArrayList<>();// 移動軌跡Random random = new Random();int current = 0;// 已經(jīng)移動的距離int mid = (int) distance * 4 / 5;// 減速閾值int a = 0;int move = 0;// 每次循環(huán)移動的距離while (true) {a = random.nextInt(10);if (current <= mid) {move += a;// 不斷加速} else {move -= a;}if ((current + move) < distance) {track.add(move);} else {track.add(distance - current);break;}current += move;}return track;}

操作過程

/** * 獲取網(wǎng)易驗證滑動距離 * * @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());//陰影部分為黑底時需要轉灰度和二值化，為白底時不需要// 轉灰度圖像Mat s_newMat = new Mat();Imgproc.cvtColor(s_mat, s_newMat, Imgproc.COLOR_BGR2GRAY);// 二值化圖像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); // 歸一化平方差匹配法TM_SQDIFF 相關系數(shù)匹配法TM_CCOEFFCore.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; // 此處使用maxLoc還是minLoc取決于使用的匹配算法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();}}/** * 圖片亮度調(diào)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);}}}}// 判斷a,r,g,b值，大于256返回256，小于0則返回0,0到256之間則直接返回原始值private int clamp(int rgb) {if (rgb > 255)return 255;if (rgb < 0)return 0;return rgb;}/** * 生成半透明小圖并裁剪 * * @param image * @return */private void cropImage(BufferedImage bigImage, BufferedImage smallImage, File bigFile, File smallFile) {int y = 0;int h_ = 0;try {// 2 生成半透明圖片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 * 二值化圖像 */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();// 尋找最佳的闕值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;}// 二值化處理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++;}}}// 確保白底黑字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);}}}}}// 延時加載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() + ')');elseSystem.out.print('.');Thread.sleep(50);}}if (isWait)System.out.println(' outTime!');return null;}

注意:有一個問題還沒有解決，還無法區(qū)分陰影部分是黑色還是白色。 因為兩種的情況不同，所以處理方式也不同。陰影部分為黑底時需要轉灰度和二值化，為白底時不需要。黑底使用歸一化平方差匹配算法 TM_SQDIFF ，而白底使用相關系數(shù)匹配算法 TM_CCOEFF。

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