使用java + selenium + OpenCV破解网易易盾滑动验证码的示例
作者：香芋味的猫
这篇文章主要介绍了使用java + selenium + OpenCV破解网易易盾滑动验证码,本文通过实例代码给大家介绍的非常详细，对大家的学习或工作具有一定的参考借鉴价值，需要的朋友可以参考下
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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