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
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↓↓↓
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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
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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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