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package com.acts.opencv.base; |
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import java.text.DecimalFormat; |
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import java.text.NumberFormat; |
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import java.util.ArrayList; |
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import java.util.Collections; |
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import java.util.Comparator; |
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import java.util.Date; |
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import java.util.List; |
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import javax.servlet.http.HttpServletResponse; |
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import org.opencv.core.Core; |
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import org.opencv.core.CvType; |
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import org.opencv.core.Mat; |
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import org.opencv.core.MatOfInt; |
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import org.opencv.core.MatOfPoint; |
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import org.opencv.core.MatOfPoint2f; |
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import org.opencv.core.Point; |
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import org.opencv.core.Rect; |
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import org.opencv.core.Scalar; |
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import org.opencv.core.Size; |
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import org.opencv.highgui.Highgui; |
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import org.opencv.imgproc.Imgproc; |
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import org.slf4j.Logger; |
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import org.slf4j.LoggerFactory; |
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import org.springframework.stereotype.Controller; |
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import org.springframework.web.bind.annotation.RequestMapping; |
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import com.acts.opencv.common.utils.Constants; |
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import com.acts.opencv.common.web.BaseController; |
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@Controller |
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@RequestMapping(value = "card2") |
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public class Card2Controller extends BaseController { |
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private static final Logger logger = LoggerFactory.getLogger(Card2Controller.class); |
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/** |
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* 答题卡识别 |
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* @Author Songer |
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* @param response |
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* @param imagefile void |
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* @Date 2018年10月22日 |
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* 更新日志 |
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* 2018年10月22日 Songer 首次创建 |
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* |
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*/ |
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@RequestMapping(value = "cardMarking") |
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public void cardMarking(HttpServletResponse response, String imagefile,Integer picno) { |
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try { |
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System.loadLibrary(Core.NATIVE_LIBRARY_NAME); |
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long t1 = new Date().getTime(); |
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//String sourceimage1 = "D:\\test\\abc\\card\\test4.jpg"; |
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String sourceimage = Constants.PATH + imagefile; |
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//表格检测，获取到表格内容，是查找识别区域的部分 |
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Mat mat = markingArea(sourceimage); |
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String destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "cardResult_3.png"; |
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Highgui.imwrite(destPath, mat); |
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// Highgui.imwrite("D:\\test\\abc\\card\\card111.png", mat); |
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//具体的答题卡识别过程，主要就是答案识别部分了 |
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String result = cardResult(mat); |
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renderString(response, result); |
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long t2 = new Date().getTime(); |
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logger.info("===耗时"+(t2-t1)); |
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} catch (Exception e) { |
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e.printStackTrace(); |
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logger.error("答题卡识别异常！", e); |
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} |
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} |
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/** |
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* 此方法主要是通过边缘检测凸包，查找识别区域。即客观题的框 |
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* @Author Songer |
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* @Date 2018年9月21日 |
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* 更新日志 |
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* 2018年9月21日 Songer 首次创建 |
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* |
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*/ |
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public static Mat markingArea(String path){ |
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Mat source = Highgui.imread(path, Highgui.CV_LOAD_IMAGE_COLOR); |
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Mat img = new Mat();; |
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// 彩色转灰度 |
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Mat result= source.clone(); |
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Imgproc.cvtColor(source, img, Imgproc.COLOR_BGR2GRAY); |
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// //方式1：通过高斯滤波然后边缘检测膨胀来链接边缘，将轮廓连通便于轮廓识别 |
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// // 高斯滤波，降噪 |
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// Imgproc.GaussianBlur(img, img, new Size(3,3), 2, 2); |
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// |
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// // Canny边缘检测 |
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// Imgproc.Canny(img, img, 20, 60, 3, false); |
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// // 膨胀，连接边缘 |
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// Imgproc.dilate(img, img, new Mat(), new Point(-1,-1), 3, 1, new Scalar(1)); |
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// |
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//方式2:使用形态学梯度算法，此算法来保留物体的边缘轮廓很有效果 |
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Mat element = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5)); |
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Imgproc.morphologyEx(img, img, Imgproc.MORPH_GRADIENT, element); |
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Imgproc.threshold(img,img, 170, 255, Imgproc.THRESH_BINARY|Imgproc.THRESH_OTSU); |
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String destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "cardResult_1.png"; |
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Highgui.imwrite(destPath, img); |
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// Highgui.imwrite("D:\\test\\abc\\card\\card1.png", img); |
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List<MatOfPoint> contours = new ArrayList<>(); |
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Mat hierarchy = new Mat(); |
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Imgproc.findContours(img, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE); |
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// 找出轮廓对应凸包的四边形拟合 |
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List<MatOfPoint> squares = new ArrayList<>(); |
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List<MatOfPoint> hulls = new ArrayList<>(); |
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MatOfInt hull = new MatOfInt(); |
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MatOfPoint2f approx = new MatOfPoint2f(); |
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approx.convertTo(approx, CvType.CV_32F); |
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for (MatOfPoint contour: contours) { |
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// 边框的凸包 |
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Imgproc.convexHull(contour, hull); |
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// 用凸包计算出新的轮廓点 |
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Point[] contourPoints = contour.toArray(); |
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int[] indices = hull.toArray(); |
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List<Point> newPoints = new ArrayList<>(); |
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for (int index : indices) { |
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newPoints.add(contourPoints[index]); |
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} |
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MatOfPoint2f contourHull = new MatOfPoint2f(); |
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contourHull.fromList(newPoints); |
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// 多边形拟合凸包边框(此时的拟合的精度较低) |
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Imgproc.approxPolyDP(contourHull, approx, Imgproc.arcLength(contourHull, true)*0.02, true); |
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// 筛选出面积大于某一阈值的，且四边形的各个角度都接近直角的凸四边形 |
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MatOfPoint approxf1 = new MatOfPoint(); |
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approx.convertTo(approxf1, CvType.CV_32S); |
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if (approx.rows() == 4 && Math.abs(Imgproc.contourArea(approx)) > 40000 && |
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Imgproc.isContourConvex(approxf1)) { |
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double maxCosine = 0; |
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for (int j = 2; j < 5; j++) { |
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double cosine = Math.abs(getAngle(approxf1.toArray()[j%4], approxf1.toArray()[j-2], approxf1.toArray()[j-1])); |
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maxCosine = Math.max(maxCosine, cosine); |
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} |
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// 角度大概72度 |
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if (maxCosine < 0.3) { |
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MatOfPoint tmp = new MatOfPoint(); |
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contourHull.convertTo(tmp, CvType.CV_32S); |
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squares.add(approxf1); |
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hulls.add(tmp); |
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} |
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} |
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} |
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// 找出外接矩形最大的四边形 |
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int index = findLargestSquare(squares); |
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MatOfPoint largest_square = squares.get(index); |
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if (largest_square.rows() == 0 || largest_square.cols() == 0) |
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return result; |
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// 找到这个最大的四边形对应的凸边框，再次进行多边形拟合，此次精度较高，拟合的结果可能是大于4条边的多边形 |
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MatOfPoint contourHull = hulls.get(index); |
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MatOfPoint2f tmp = new MatOfPoint2f(); |
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contourHull.convertTo(tmp, CvType.CV_32F); |
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Imgproc.approxPolyDP(tmp, approx, 3, true); |
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List<Point> newPointList = new ArrayList<>(); |
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double maxL = Imgproc.arcLength(approx, true) * 0.02; |
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// 找到高精度拟合时得到的顶点中 距离小于低精度拟合得到的四个顶点maxL的顶点，排除部分顶点的干扰 |
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for (Point p : approx.toArray()) { |
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if (!(getSpacePointToPoint(p, largest_square.toList().get(0)) > maxL && |
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getSpacePointToPoint(p, largest_square.toList().get(1)) > maxL && |
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getSpacePointToPoint(p, largest_square.toList().get(2)) > maxL && |
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getSpacePointToPoint(p, largest_square.toList().get(3)) > maxL)) { |
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newPointList.add(p); |
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} |
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} |
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// 找到剩余顶点连线中，边长大于 2 * maxL的四条边作为四边形物体的四条边 |
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List<double[]> lines = new ArrayList<>(); |
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for (int i = 0; i < newPointList.size(); i++) { |
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Point p1 = newPointList.get(i); |
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Point p2 = newPointList.get((i+1) % newPointList.size()); |
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if (getSpacePointToPoint(p1, p2) > 2 * maxL) { |
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lines.add(new double[]{p1.x, p1.y, p2.x, p2.y}); |
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logger.info("p1x:"+p1.x+" p1y:"+p1.y+" p2x:"+p2.x+" p2y:"+p2.y); |
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Core.line(source, new Point(p1.x, p1.y), new Point( p2.x, p2.y), new Scalar(255, 0, 0),4); |
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} |
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} |
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destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "cardResult_2.png"; |
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Highgui.imwrite(destPath, source); |
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// Highgui.imwrite("D:\\test\\abc\\card\\card2.png", source); |
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// 计算出这四条边中 相邻两条边的交点，即物体的四个顶点 |
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List<Point> corners = new ArrayList<>(); |
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for (int i = 0; i < lines.size(); i++) { |
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Point corner = computeIntersect(lines.get(i),lines.get((i+1) % lines.size())); |
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corners.add(corner); |
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} |
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// 对顶点顺时针排序 |
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sortCorners(corners); |
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// 计算目标图像的尺寸 |
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Point p0 = corners.get(0); |
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Point p1 = corners.get(1); |
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Point p2 = corners.get(2); |
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Point p3 = corners.get(3); |
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logger.info(" "+p0.x+" "+p0.y); |
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logger.info(" "+p1.x+" "+p1.y); |
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logger.info(" "+p2.x+" "+p2.y); |
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logger.info(" "+p3.x+" "+p3.y); |
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double space0 = getSpacePointToPoint(p0, p1); |
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double space1 = getSpacePointToPoint(p1, p2); |
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double space2 = getSpacePointToPoint(p2, p3); |
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double space3 = getSpacePointToPoint(p3, p0); |
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double imgWidth = space1 > space3 ? space1 : space3; |
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double imgHeight = space0 > space2 ? space0 : space2; |
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logger.info("imgWidth:"+imgWidth+" imgHeight:"+imgHeight); |
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// 如果提取出的图片宽小于高，则旋转90度 |
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if (imgWidth > imgHeight) { |
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logger.info("----in"); |
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double temp = imgWidth; |
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imgWidth = imgHeight; |
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imgHeight = temp; |
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Point tempPoint = p0.clone(); |
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p0 = p1.clone(); |
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p1 = p2.clone(); |
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p2 = p3.clone(); |
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p3 = tempPoint.clone(); |
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} |
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// Mat quad = Mat.zeros((int)imgHeight * 2, (int)imgWidth * 2, CvType.CV_8UC3); |
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Mat quad = Mat.zeros((int)imgHeight, (int)imgWidth, CvType.CV_8UC3); |
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MatOfPoint2f cornerMat = new MatOfPoint2f(p0, p1, p2, p3); |
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// MatOfPoint2f quadMat = new MatOfPoint2f(new Point(imgWidth*0.4, imgHeight*1.6), |
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// new Point(imgWidth*0.4, imgHeight*0.4), |
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// new Point(imgWidth*1.6, imgHeight*0.4), |
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// new Point(imgWidth*1.6, imgHeight*1.6)); |
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MatOfPoint2f quadMat = new MatOfPoint2f(new Point(0, 0), |
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new Point(imgWidth, 0), |
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new Point(imgWidth, imgHeight), |
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new Point(0, imgHeight)); |
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// 提取图像 |
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Mat transmtx = Imgproc.getPerspectiveTransform(cornerMat, quadMat); |
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Imgproc.warpPerspective(result, quad, transmtx, quad.size()); |
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return quad; |
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} |
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// 根据三个点计算中间那个点的夹角 pt1 pt0 pt2 |
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private static double getAngle(Point pt1, Point pt2, Point pt0){ |
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double dx1 = pt1.x - pt0.x; |
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double dy1 = pt1.y - pt0.y; |
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double dx2 = pt2.x - pt0.x; |
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double dy2 = pt2.y - pt0.y; |
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return (dx1*dx2 + dy1*dy2)/Math.sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10); |
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} |
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// 找到最大的正方形轮廓 |
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private static int findLargestSquare(List<MatOfPoint> squares) { |
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if (squares.size() == 0) |
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return -1; |
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int max_width = 0; |
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int max_height = 0; |
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int max_square_idx = 0; |
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int currentIndex = 0; |
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for (MatOfPoint square : squares) { |
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Rect rectangle = Imgproc.boundingRect(square); |
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if (rectangle.width >= max_width && rectangle.height >= max_height) { |
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max_width = rectangle.width; |
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max_height = rectangle.height; |
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max_square_idx = currentIndex; |
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} |
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currentIndex++; |
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} |
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return max_square_idx; |
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} |
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// 点到点的距离 |
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private static double getSpacePointToPoint(Point p1, Point p2) { |
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double a = p1.x - p2.x; |
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double b = p1.y - p2.y; |
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return Math.sqrt(a * a + b * b); |
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} |
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// 两直线的交点 |
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private static Point computeIntersect(double[] a, double[] b) { |
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if (a.length != 4 || b.length != 4) |
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throw new ClassFormatError(); |
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double x1 = a[0], y1 = a[1], x2 = a[2], y2 = a[3], x3 = b[0], y3 = b[1], x4 = b[2], y4 = b[3]; |
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double d = ((x1 - x2) * (y3 - y4)) - ((y1 - y2) * (x3 - x4)); |
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if (d != 0) { |
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Point pt = new Point(); |
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pt.x = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / d; |
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pt.y = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / d; |
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return pt; |
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} |
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else |
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return new Point(-1, -1); |
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} |
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// 对多个点按顺时针排序 |
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private static void sortCorners(List<Point> corners) { |
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if (corners.size() == 0) return; |
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Point p1 = corners.get(0); |
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int index = 0; |
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for (int i = 1; i < corners.size(); i++) { |
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Point point = corners.get(i); |
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if (p1.x > point.x) { |
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p1 = point; |
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index = i; |
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} |
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} |
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corners.set(index, corners.get(0)); |
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corners.set(0, p1); |
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Point lp = corners.get(0); |
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for (int i = 1; i < corners.size(); i++) { |
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for (int j = i + 1; j < corners.size(); j++) { |
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Point point1 = corners.get(i); |
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Point point2 = corners.get(j); |
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if ((point1.y-lp.y*1.0)/(point1.x-lp.x)>(point2.y-lp.y*1.0)/(point2.x-lp.x)) { |
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Point temp = point1.clone(); |
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corners.set(i, corners.get(j)); |
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corners.set(j, temp); |
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} |
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} |
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} |
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} |
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//答题卡识别 |
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public String cardResult(Mat mat){ |
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int cutsize = 20; |
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Mat img_cut = mat.submat(cutsize,mat.rows()-cutsize,cutsize,mat.cols()-cutsize);new Mat(); |
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Mat img_gray = img_cut.clone(); |
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Imgproc.cvtColor(img_cut, img_gray, Imgproc.COLOR_BGR2GRAY); |
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Imgproc.threshold(img_gray,img_gray, 170, 255, Imgproc.THRESH_BINARY_INV|Imgproc.THRESH_OTSU); |
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Mat temp = img_gray.clone(); |
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// //方式1：通过高斯滤波然后边缘检测膨胀来链接边缘，将轮廓连通便于轮廓识别 |
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// // 高斯滤波，降噪 |
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// Imgproc.GaussianBlur(temp, temp, new Size(3,3), 2, 2); |
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// // Canny边缘检测 |
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// Imgproc.Canny(temp, temp, 20, 60, 3, false); |
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// // 膨胀，连接边缘 |
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// Imgproc.dilate(temp, temp, new Mat(), new Point(-1,-1), 3, 1, new Scalar(1)); |
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//方式2:使用形态学梯度算法，此算法来保留物体的边缘轮廓很有效果 |
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Mat element = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5)); |
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Imgproc.morphologyEx(temp, temp, Imgproc.MORPH_GRADIENT, element); |
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Imgproc.threshold(temp, temp, 170, 255, Imgproc.THRESH_BINARY|Imgproc.THRESH_OTSU); |
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String destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "cardResult_4.png"; |
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Highgui.imwrite(destPath, temp); |
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// Highgui.imwrite("D:\\test\\abc\\card\\card3.png", temp); |
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// logger.info(temp.cols()); |
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Mat cut1 = temp.submat(0,temp.rows(),0,(int)(0.275*temp.cols())); |
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Mat cut_gray1 = img_gray.submat(0,img_gray.rows(),0,(int)(0.275*img_gray.cols())); |
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Mat cut2 = temp.submat(0,temp.rows(),(int)(0.275*temp.cols()),(int)(0.518*temp.cols())); |
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Mat cut_gray2 = img_gray.submat(0,img_gray.rows(),(int)(0.275*img_gray.cols()),(int)(0.518*img_gray.cols())); |
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Mat cut3 = temp.submat(0,temp.rows(),(int)(0.518*temp.cols()),(int)(0.743*temp.cols())); |
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Mat cut_gray3 = img_gray.submat(0,img_gray.rows(),(int)(0.518*img_gray.cols()),(int)(0.743*img_gray.cols())); |
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Mat cut4 = temp.submat((int)(0.387*temp.rows()),temp.rows(),(int)(0.743*temp.cols()),temp.cols()); |
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Mat cut_gray4 = img_gray.submat((int)(0.387*img_gray.rows()),img_gray.rows(),(int)(0.743*img_gray.cols()),img_gray.cols()); |
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//学号 |
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Mat cut5 = temp.submat(0,(int)(0.387*temp.rows()),(int)(0.743*temp.cols()),temp.cols()); |
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Mat cut_gray5 = img_gray.submat(0,(int)(0.387*img_gray.rows()),(int)(0.743*img_gray.cols()),img_gray.cols()); |
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// Highgui.imwrite("D:\\test\\abc\\card\\card_cut1.png", cut1); |
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// Highgui.imwrite("D:\\test\\abc\\card\\card_cut1_gary.png", cut_gray1); |
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// Highgui.imwrite("D:\\test\\abc\\card\\card_cut2.png", cut2); |
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// Highgui.imwrite("D:\\test\\abc\\card\\card_cut3.png", cut3); |
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// Highgui.imwrite("D:\\test\\abc\\card\\card_cut4.png", cut4); |
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// Highgui.imwrite("D:\\test\\abc\\card\\card_cut5.png", cut5); |
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List<String> resultList = new ArrayList<String>(); |
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List<String> list1 = processByCol(cut1,cut_gray1,img_cut,5); |
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List<String> list2 = processByCol(cut2,cut_gray2,img_cut,5); |
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List<String> list3 = processByCol(cut3,cut_gray3,img_cut,4); |
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List<String> list4 = processByCol(cut4,cut_gray4,img_cut,4); |
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//学号单独处理 |
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List<String> list5 = processByCol(cut5,cut_gray5,img_cut,4); |
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resultList.addAll(list1); |
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resultList.addAll(list2); |
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resultList.addAll(list3); |
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resultList.addAll(list4); |
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String studentNo = getStudentNo(list5); |
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logger.info("学生学号为："+studentNo); |
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StringBuffer result = new StringBuffer("学生学号为："+studentNo); |
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for (int i = 0; i < resultList.size(); i++) { |
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result.append(resultList.get(i)); |
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logger.info(resultList.get(i)); |
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} |
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return result.toString(); |
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} |
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/** |
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* 根据列单独处理 |
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|
|
* @Author Songer |
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* @param cut1 传入的答案列，一般1-20一列 |
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* @param cut_gary 传入的答案列未处理过 |
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* @param temp 表格范围mat |
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* @param toIndex 列答案数，即每几个答案一组 |
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|
* @Date 2018年9月20日 |
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|
|
* 更新日志 |
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|
|
* 2018年9月20日 Songer 首次创建 |
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* |
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|
*/ |
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|
private static List<String> processByCol(Mat cut1,Mat cut_gray,Mat temp,int answerCols) { |
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|
List<String> result = new ArrayList<String>(); |
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List<MatOfPoint> contours = new ArrayList<MatOfPoint>(); |
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|
List<MatOfPoint> answerList = new ArrayList<MatOfPoint>(); |
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|
Mat hierarchy = new Mat(); |
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|
Imgproc.findContours(cut1.clone(), contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE); |
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// logger.info(contours.size()); |
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// logger.info("-----w------"+(temp.width()*80/2693-20)); |
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// logger.info("-----h------"+(temp.height()*80/2764-20)); |
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for(int i = 0;i<contours.size();i++){ |
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MatOfPoint mop= contours.get(i); |
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Rect rect = Imgproc.boundingRect(mop); |
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|
// logger.info("-----------"+rect.width+" "+rect.height); |
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|
//一个填图区域大概占整个表格的w:80/2733 h:0/2804,，所以排除掉太小的轮廓和过大的轮廓 |
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// Imgproc.drawContours(cut1, contours, i, new Scalar(170), 2); |
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if(rect.width>(temp.width()*80/2693-20) && rect.height>(temp.height()*80/2764-20) && rect.width<temp.width()*0.05 && rect.height<temp.height()*0.05){ |
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// if(rect.width>50&&rect.height>50&&rect.area()>2500&&rect.area()<10000){ |
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// Core.rectangle(img_cut, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y |
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// + rect.height), new Scalar(0, 255, 0), 2); |
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answerList.add(mop); |
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} |
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} |
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|
Collections.sort(answerList, new Comparator<MatOfPoint>() { |
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|
//按照y坐标升序排列 |
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|
|
@Override |
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|
|
public int compare(MatOfPoint o1, MatOfPoint o2) { |
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|
|
Rect rect1 = Imgproc.boundingRect(o1); |
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|
|
Rect rect2 = Imgproc.boundingRect(o2); |
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|
if(rect1.y<rect2.y){ |
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|
return -1; |
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|
}else if(rect1.y>rect2.y){ |
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|
return 1; |
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|
}else{ |
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|
return 0; |
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|
} |
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|
} |
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|
}); |
|
|
|
//每4/5个一组组成新list，并按x坐标排序 |
|
|
|
int queno = 1; |
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|
|
int totoSize = answerList.size(); |
|
|
|
for (int i = 0; i < totoSize; i+=answerCols) { |
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|
|
int toIndex = i+answerCols; |
|
|
|
if(toIndex>totoSize){ |
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|
|
toIndex = totoSize-1; |
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|
|
} |
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|
|
List<MatOfPoint> newList = answerList.subList(i,toIndex); |
|
|
|
Collections.sort(newList, new Comparator<MatOfPoint>() { |
|
|
|
//按照y坐标升序排列 |
|
|
|
@Override |
|
|
|
public int compare(MatOfPoint o1, MatOfPoint o2) { |
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|
|
Rect rect1 = Imgproc.boundingRect(o1); |
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|
|
Rect rect2 = Imgproc.boundingRect(o2); |
|
|
|
if(rect1.x<rect2.x){ |
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|
|
return -1; |
|
|
|
}else if(rect1.x>rect2.x){ |
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|
|
return 1; |
|
|
|
}else{ |
|
|
|
return 0; |
|
|
|
} |
|
|
|
} |
|
|
|
}); |
|
|
|
String resultChoose = ""; |
|
|
|
for (int j = 0; j < newList.size(); j++) { |
|
|
|
Imgproc.drawContours(cut_gray, newList, j, new Scalar(170), 2); |
|
|
|
//掩模提取出轮廓 |
|
|
|
Mat mask = Mat.zeros(cut_gray.size(), CvType.CV_8UC1); |
|
|
|
Imgproc.drawContours(mask, newList, j, new Scalar(255), -1); |
|
|
|
Mat dst = new Mat(); |
|
|
|
Core.bitwise_and(cut_gray, mask, dst); |
|
|
|
//获取填涂百分比 |
|
|
|
double p100 = Core.countNonZero(dst) * 100 / Core.countNonZero(mask); |
|
|
|
String anno = index2ColName(j); |
|
|
|
if(p100>80){ |
|
|
|
resultChoose += anno; |
|
|
|
logger.info(p100+" 第"+queno+"行:选项（"+anno+"）填涂"); |
|
|
|
}else{ |
|
|
|
logger.info(p100+" 第"+queno+"行:选项（"+anno+"）未填涂"); |
|
|
|
} |
|
|
|
// Highgui.imwrite("D:\\test\\abc\\card\\card_x"+i+j+".png", dst); |
|
|
|
if(i==0&&j==0){ |
|
|
|
String destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "cardResult_5.png"; |
|
|
|
Highgui.imwrite(destPath, dst); |
|
|
|
} |
|
|
|
} |
|
|
|
result.add(resultChoose); |
|
|
|
queno++; |
|
|
|
} |
|
|
|
|
|
|
|
// Highgui.imwrite("D:\\test\\abc\\card\\card4.png", cut1); |
|
|
|
return result; |
|
|
|
} |
|
|
|
|
|
|
|
//编号转答案0-A 1-B |
|
|
|
public static String index2ColName(int index){ |
|
|
|
if (index < 0) { |
|
|
|
return null; |
|
|
|
} |
|
|
|
int num = 65;// A的Unicode码 |
|
|
|
String colName = ""; |
|
|
|
do { |
|
|
|
if (colName.length() > 0) { |
|
|
|
index--; |
|
|
|
} |
|
|
|
int remainder = index % 26; |
|
|
|
colName = ((char) (remainder + num)) + colName; |
|
|
|
index = (int) ((index - remainder) / 26); |
|
|
|
} while (index > 0); |
|
|
|
return colName; |
|
|
|
} |
|
|
|
|
|
|
|
/**根据表元的列名转换为列号 |
|
|
|
* @param colName 列名, 从A开始 |
|
|
|
* @return A1->0; B1->1...AA1->26 |
|
|
|
*/ |
|
|
|
public static int colName2Index(String colName) { |
|
|
|
int index = -1; |
|
|
|
int num = 65;// A的Unicode码 |
|
|
|
int length = colName.length(); |
|
|
|
for (int i = 0; i < length; i++) { |
|
|
|
char c = colName.charAt(i); |
|
|
|
if (Character.isDigit(c)) break;// 确定指定的char值是否为数字 |
|
|
|
index = (index + 1) * 26 + (int)c - num; |
|
|
|
} |
|
|
|
return index; |
|
|
|
} |
|
|
|
|
|
|
|
/** |
|
|
|
* 根据返回的结果集转换为学号 |
|
|
|
* 因为学号部分的处理跟答案一样是一横行进行处理的，同时返回的是ABCDE等选项结果 |
|
|
|
* 转换公式为：学生学号=遍历list的index值*（A=1000,B=100,C=10,D=1）相加 |
|
|
|
* @Author Songer |
|
|
|
* @param resultList |
|
|
|
* @return String |
|
|
|
* @Date 2018年9月20日 |
|
|
|
* 更新日志 |
|
|
|
* 2018年9月20日 Songer 首次创建 |
|
|
|
* |
|
|
|
*/ |
|
|
|
public static String getStudentNo(List<String> resultList){ |
|
|
|
int studentNo = 0; |
|
|
|
for (int i = 0; i < resultList.size(); i++) { |
|
|
|
String result = resultList.get(i); |
|
|
|
if ("A".equals(result)) { |
|
|
|
studentNo += 1000 * i; |
|
|
|
} else if ("B".equals(result)) { |
|
|
|
studentNo += 100 * i; |
|
|
|
} else if ("C".equals(result)) { |
|
|
|
studentNo += 10 * i; |
|
|
|
} else if ("D".equals(result)) { |
|
|
|
studentNo += i; |
|
|
|
} |
|
|
|
} |
|
|
|
NumberFormat formatter = new DecimalFormat("0000"); |
|
|
|
String number = formatter.format(studentNo); |
|
|
|
return number; |
|
|
|
} |
|
|
|
|
|
|
|
} |
songer
5 years ago
