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项目中需要用到opencv，先了解后做仿照别人做了两个 关于java Opencv 答题卡扫描 银行卡号码截取 的 小程序。
Opencv的安装下载，就不多介绍，主要是贴代码，希望大家能多多指教。
答题卡
import org.opencv.core.*;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import java.util.*;
import static org.opencv.core.CvType.CV_8U;
import static org.opencv.imgproc.Imgproc.MORPH_RECT;
/**
* @author lsw
* @email [email protected]
*/
public class OpenCv {
static {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
}
public static void main(String []args) {
String sheet = "D://A4.jpg";
//A4 二值化膨胀后生成的图片路径
String results = "E://result.jpg";
String msg = rowsAndCols(sheet, results);
System.out.println(msg);
}
public static void Canny(String oriImg, String dstImg, int threshold) {
//装载图片
Mat img = Imgcodecs.imread(oriImg);
Mat srcImage2 = new Mat();
Mat srcImage3 = new Mat();
Mat srcImage4 = new Mat();
Mat srcImage5 = new Mat();
//图片变成灰度图片
Imgproc.cvtColor(img, srcImage2, Imgproc.COLOR_RGB2GRAY);
//图片二值化
Imgproc.adaptiveThreshold(srcImage2, srcImage3, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY_INV, 255, 1);
//确定腐蚀和膨胀核的大小
Mat element = Imgproc.getStructuringElement(MORPH_RECT, new Size(1, 6));
//腐蚀操作
Imgproc.erode(srcImage3, srcImage4, element);
//膨胀操作
Imgproc.dilate(srcImage4, srcImage5, element);
//Imgcodecs.imwrite("E:/picpool/card/enresults.jpg", srcImage4);
//确定每张答题卡的ROI区域
Mat imag_ch1 = srcImage4.submat(new Rect(200, 1065, 1930, 2210));
//识别所有轮廓
Vector<MatOfPoint> chapter1 = new Vector<>();
Imgproc.findContours(imag_ch1, chapter1, new Mat(), 2, 3);
Mat result = new Mat(imag_ch1.size(), CV_8U, new Scalar(255));
Imgproc.drawContours(result, chapter1, -1, new Scalar(0), 2);
Imgcodecs.imwrite("E://result.jpg", result);
//new一个 矩形集合 用来装 轮廓
List<RectComp> RectCompList = new ArrayList<>();
for (int i = 0; i < chapter1.size(); i++) {
Rect rm = Imgproc.boundingRect(chapter1.get(i));
RectComp ti = new RectComp(rm);
//把轮廓宽度区间在 50 - 80 范围内的轮廓装进矩形集合
if (ti.rm.width > 60 && ti.rm.width < 85) {
RectCompList.add(ti);
}
}
//new一个 map 用来存储答题卡上填的答案 (A\B\C\D)
TreeMap<Integer, String> listenAnswer = new TreeMap<>();
//按 X轴 对listenAnswer进行排序
RectCompList.sort((o1, o2) -> {
if (o1.rm.x > o2.rm.x) {
return 1;
}
if (o1.rm.x == o2.rm.x) {
return 0;
}
if (o1.rm.x < o2.rm.x) {
return -1;
}
return -1;
});
/*
如果精度高，可以通过像素计算
for (RectComp rc : RectCompList) {
int x = RectCompList.get(t).getRm().x - 16;
int y = RectCompList.get(t).getRm().y - 94;
//计算x轴上的分割 如果超过5题，那么还会有一个大分割
int xSplit = x/85 /5;
//因为第一题 x=21 计算机中题目从0开始算，现实是从1开始 所以+1
int xTitleNum = x/85 + 1;
//由于精度问题 x轴会慢慢递减 递减到上一个答案去 如果不跨过两个答案以上，都没问题 如果答题卡x轴40题左右 会出问题
if(x%85>20){
System.out.println("x轴递减程度" + x%85);
xTitleNum++;
}
xTitleNum = xTitleNum - xSplit;
System.out.println(xTitleNum);
}
*/
//根据 Y轴 确定被选择答案 (A\B\C\D)
for (RectComp rc : RectCompList) {
for (int h = 0; h < 7; h++) {
if ((rc.rm.contains(new Point(rc.rm.x + 20, 115 + (320 * h))))) {
for (int w = 0; w < 4; w++) {
if (rc.rm.contains(new Point(55 + (500 * w), rc.rm.y))) {
listenAnswer.put(1 + (20 * h) + (5 * w), "A");
} else if (rc.rm.contains(new Point(135 + (500 * w), rc.rm.y))) {
listenAnswer.put(2 + (20 * h) + (5 * w), "A");
} else if (rc.rm.contains(new Point(215 + (500 * w), rc.rm.y))) {
listenAnswer.put(3 + (20 * h) + (5 * w), "A");
} else if (rc.rm.contains(new Point(300 + (500 * w), rc.rm.y))) {
listenAnswer.put(4 + (20 * h) + (5 * w), "A");
} else if (rc.rm.contains(new Point(380 + (500 * w), rc.rm.y))) {
listenAnswer.put(5 + (20 * h) + (5 * w), "A");
}
}
} else if ((rc.rm.contains(new Point(rc.rm.x + 20, 165 + (320 * h))))) {
for (int w = 0; w < 4; w++) {
if (rc.rm.contains(new Point(55 + (500 * w), rc.rm.y))) {
listenAnswer.put(1 + (20 * h) + (5 * w), "B");
} else if (rc.rm.contains(new Point(135 + (500 * w), rc.rm.y))) {
listenAnswer.put(2 + (20 * h) + (5 * w), "B");
} else if (rc.rm.contains(new Point(215 + (500 * w), rc.rm.y))) {
listenAnswer.put(3 + (20 * h) + (5 * w), "B");
} else if (rc.rm.contains(new Point(300 + (500 * w), rc.rm.y))) {
listenAnswer.put(4 + (20 * h) + (5 * w), "B");
} else if (rc.rm.contains(new Point(380 + (500 * w), rc.rm.y))) {
listenAnswer.put(5 + (20 * h) + (5 * w), "B");
}
}
} else if ((rc.rm.contains(new Point(rc.rm.x + 20, 220 + (320 * h))))) {
for (int w = 0; w < 4; w++) {
if (rc.rm.contains(new Point(55 + (500 * w), rc.rm.y))) {
listenAnswer.put(1 + (20 * h) + (5 * w), "C");
} else if (rc.rm.contains(new Point(135 + (500 * w), rc.rm.y))) {
listenAnswer.put(2 + (20 * h) + (5 * w), "C");
} else if (rc.rm.contains(new Point(215 + (500 * w), rc.rm.y))) {
listenAnswer.put(3 + (20 * h) + (5 * w), "C");
} else if (rc.rm.contains(new Point(300 + (500 * w), rc.rm.y))) {
listenAnswer.put(4 + (20 * h) + (5 * w), "C");
} else if (rc.rm.contains(new Point(380 + (500 * w), rc.rm.y))) {
listenAnswer.put(5 + (20 * h) + (5 * w), "C");
}
}
} else if ((rc.rm.contains(new Point(rc.rm.x + 20, 275 + (320 * h))))) {
for (int w = 0; w < 4; w++) {
if (rc.rm.contains(new Point(55 + (500 * w), rc.rm.y))) {
listenAnswer.put(1 + (20 * h) + (5 * w), "D");
} else if (rc.rm.contains(new Point(135 + (500 * w), rc.rm.y))) {
listenAnswer.put(2 + (20 * h) + (5 * w), "D");
} else if (rc.rm.contains(new Point(215 + (500 * w), rc.rm.y))) {
listenAnswer.put(3 + (20 * h) + (5 * w), "D");
} else if (rc.rm.contains(new Point(300 + (500 * w), rc.rm.y))) {
listenAnswer.put(4 + (20 * h) + (5 * w), "D");
} else if (rc.rm.contains(new Point(380 + (500 * w), rc.rm.y))) {
listenAnswer.put(5 + (20 * h) + (5 * w), "D");
}
}
}
}
}
Iterator iter = listenAnswer.entrySet().iterator();
while (iter.hasNext()) {
Map.Entry entry = (Map.Entry) iter.next();
Object key = entry.getKey();
Object val = entry.getValue();
System.out.println("第" + key + "题,分数:" + val);
}
}
public static String rowsAndCols(String oriImg, String dstImg) {
String msg = "";
Canny(oriImg, dstImg, 50);
Mat mat = Imgcodecs.imread(dstImg);
msg += "\n行数:" + mat.rows();
msg += "\n列数:" + mat.cols();
msg += "\nheight:" + mat.height();
msg += "\nwidth:" + mat.width();
msg += "\nelemSide:" + mat.elemSize();
//CvType contourSeq = null;
return msg;
}
}核心代码如上图： 还有另外一个类，也很简单 我就不贴出来了，大家可以去我的github上面找得到 通过核心代码就能理解出大概的思路。
github地址 : https://github.com/shiwenlin/opencv
银行卡也很简单最后是也在github上 项目中截取到银行卡的图片，我们也可以用别的插件转成数字，