gradle导入:
implementation("org.openpnp:opencv:4.5.1-2")
import nu.pattern.OpenCV;
import org.opencv.core.*;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import javax.imageio.ImageIO;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
public class TimetableUtils {
private static TimetableUtils timetableUtils = null;
private TimetableUtils() {
}
public static TimetableUtils getTimetableUtils() {
if (timetableUtils == null) {
timetableUtils = new TimetableUtils();
}
return timetableUtils;
}
/**
* 获取两张滑动验证码的距离
*
* @param bFile
* @param sFile
* @return
*/
public double getDistance(File bFile, File sFile) {
OpenCV.loadShared();
try {
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
binarizeTheImage(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();
Core.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
*/
private void pictureBrightnessAdjustment(BufferedImage image, int param) throws IOException {
if (image != null) {
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;
return Math.max(rgb, 0);
}
/**
* 生成半透明小图并裁剪
*/
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
pictureBrightnessAdjustment(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
*/
private void binarizeTheImage(Mat mat) {
int BLACK = 0;
int WHITE = 255;
int ucThre = 0, ucThreNew = 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 != ucThreNew) {
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 > ucThreNew) {
nBack_sum += nValue;
nBack_count++;
} else {
nData_sum += nValue;
nData_count++;
}
}
}
nBack_sum = nBack_sum / nBack_count;
nData_sum = nData_sum / nData_count;
ucThre = ucThreNew;
ucThreNew = (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 > ucThreNew) {
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);
}
}
}
}
}
}