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

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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