Merge remote-tracking branch 'origin/wn' into ywx

smarking/pom.xml

@@ -31,6 +31,12 @@
             <artifactId>poi</artifactId>
             <version>3.17</version>
         </dependency>
+        <!--答题卡识别-->
+        <dependency>
+            <groupId>org.opencv</groupId>
+            <artifactId>opencv</artifactId>
+            <version>4.6.0</version>
+        </dependency>
 
     </dependencies>
     <dependencyManagement>

smarking/src/main/java/com/xhkjedu/smarking/controller/exam/MsExamController.java

@@ -190,20 +190,6 @@ public class MsExamController {
         return new ResultVo(0, "查询成功", msExamService.findById(examid));
     }
 
-    /**
-     * @Description 发布成绩
-     * @Date 2024/11/26 9:10
-     * @Author YWX
-     * @Param [msExam]
-     * @Return com.xhkjedu.vo.ResultVo
-     **/
-    @PostMapping("/fb_cj")
-    public ResultVo fbCj(@RequestBody MsExam msExam) {
-        Integer examid = msExam.getExamid();
-        N_Utils.validation(new Object[]{examid, "考试id", 1});
-        msExamService.updateExamState(examid,4);
-        return new ResultVo(0, "发布成绩成功");
-    }
 
     /**
      * @Description 获取考试关联学校
@@ -246,4 +232,65 @@ public class MsExamController {
         N_Utils.validation(new Object[]{examid, "考试id", 1});
         return new ResultVo(0, "查询成功", msExamService.getExamstate(examid));
     }
+
+    /*
+     * @Description 一键统分
+     * @Date 2025/1/8 16:49:38
+     * @Author WN
+     * @Param [msExam]
+     * @Return com.xhkjedu.vo.ResultVo
+     **/
+    @PostMapping("/yjtf")
+    public ResultVo updateExamStateForYjtf(@RequestBody MsExam msExam) {
+        Integer examid = msExam.getExamid();
+        N_Utils.validation(new Object[]{examid, "考试id", 1});
+        msExamService.updateExamStateForYjtf(examid);
+        return new ResultVo(0, "保存成功");
+    }
+    /*
+     * @Description 取消一键统分
+     * @Date 2025/1/8 16:49:38
+     * @Author WN
+     * @Param [msExam]
+     * @Return com.xhkjedu.vo.ResultVo
+     **/
+    @PostMapping("/unyjtf")
+    public ResultVo updateExamStateForUnYjtf(@RequestBody MsExam msExam) {
+        Integer examid = msExam.getExamid();
+        N_Utils.validation(new Object[]{examid, "考试id", 1});
+        msExamService.updateExamStateForUnYjtf(examid);
+        return new ResultVo(0, "取消成功");
+    }
+
+    /*
+     * @Description 发布成绩
+     * @Date 2025/1/8 16:52:37
+     * @Author WN
+     * @Param [msExam]
+     * @Return com.xhkjedu.vo.ResultVo
+     **/
+    @PostMapping("/fbcj")
+    public ResultVo updateExamStateForfbcj(@RequestBody MsExam msExam) {
+        Integer examid = msExam.getExamid();
+        N_Utils.validation(new Object[]{examid, "考试id", 1});
+        msExamService.updateExamStateForfbcj(examid);
+        return new ResultVo(0, "发布成功");
+    }
+
+    /*
+     * @Description 取消发布成绩
+     * @Date 2025/1/8 16:53:05
+     * @Author WN
+     * @Param [msExam]
+     * @Return com.xhkjedu.vo.ResultVo
+     **/
+    @PostMapping("/unfbcj")
+    public ResultVo updateExamStateForUnfbcj(@RequestBody MsExam msExam) {
+        Integer examid = msExam.getExamid();
+        N_Utils.validation(new Object[]{examid, "考试id", 1});
+        msExamService.updateExamStateForUnfbcj(examid);
+        return new ResultVo(0, "取消成功");
+    }
+
+
 }

smarking/src/main/java/com/xhkjedu/smarking/mapper/exam/MsExamMapper.java

@@ -39,4 +39,6 @@ public interface MsExamMapper extends TkMapper<MsExam> {
 
     //获取考试学校id
     Integer getSchoolIdByExamId(@Param("examid") Integer examid);
+    //批量结束考试
+    Integer updateBatchExamState(@Param("examids") List<Integer> examids);
 }

smarking/src/main/java/com/xhkjedu/smarking/mapper/exam/MsSubjectMapper.java

@@ -58,6 +58,8 @@ public interface MsSubjectMapper extends TkMapper<MsSubject> {
     int updateStartSubject(@Param("strtime") String strtime);
     //定时任务-科目结束考试-获取待结束考试
     List<Integer> listMsidsForEndSubject(@Param("strtime") String strtime);
+    //定时任务-科目对应考试下所有科目
+    List<MsSubject> listSubjectAndStateByMsids(@Param("list") List<Integer> list);
     //定时任务-科目结束考试
     int updateEndSubject(@Param("timestamp") Integer timestamp,@Param("msids")List<Integer> msids);
 

smarking/src/main/java/com/xhkjedu/smarking/mapper/stupaper/MsPaperStudentMapper.java

@@ -66,14 +66,15 @@ public interface MsPaperStudentMapper extends TkMapper<MsPaperStudent> {
 
     //学生考试-获取开始考试学生数量
     Integer getStartNumByExamIdExamMode(@Param("examid") Integer examid,@Param("exammode") Integer exammode);
-    //清空试卷学生信息
+    //删除试卷学生信息
     int deletePaperStuByMpid(@Param("mpid") Integer mpid);
 
     //扫描异常-清空学生扫描试卷信息
     int updateStuPaperForScanErrorClear(@Param("mpsid")Integer mpsid);
     //扫描异常-缺考异常-更改状态
     int updateBatchStuPaperSstate(@Param("list") List<MsPaperStudent> paperStudents);
-
+    //扫描-清空学生试卷扫描信息
+    int updateStuPaperForScanClearByMpid(@Param("mpid") Integer mpid);
     //补录成绩-学生科目成绩
     List<MsPaperStudent> listPaperStudentForFillScore(@Param("examid") Integer examid,@Param("studentid") Integer studentid);
     //补录成绩-补录单科

smarking/src/main/java/com/xhkjedu/smarking/service/exam/MsExamService.java

@@ -389,16 +389,6 @@ public class MsExamService {
         return map;
     }
 
-    /**
-     * @Description 更新考试状态
-     * @Date 2024/11/26 9:12
-     * @Author YWX
-     * @Param [examid, examstate]
-     * @Return void
-     **/
-    public void updateExamState(Integer examid, int examstate) {
-        msExamMapper.updateExamState(examid, examstate);
-    }
 
     /**
      * @Description 获取考试关联学校
@@ -435,4 +425,39 @@ public class MsExamService {
     public Integer getExamstate(Integer examid){
         return msExamMapper.getExamstate(examid);
     }
+
+    //改状态-一键统分
+    public void updateExamStateForYjtf(Integer examid){
+        Integer examstateOld = msExamMapper.getExamstate(examid);
+        if(examstateOld < 3){
+            msExamMapper.updateExamState(examid,3);
+        }else{
+            throw new ServiceException("已统分，禁止操作");
+        }
+    }
+
+    //改状态-取消一键统分
+    public void updateExamStateForUnYjtf(Integer examid){
+        Integer examstateOld = msExamMapper.getExamstate(examid);
+        if(examstateOld > 3){
+            msExamMapper.updateExamState(examid,2);
+        }
+    }
+
+    //该状态-发布成绩
+    public void updateExamStateForfbcj(Integer examid){
+        Integer examstateOld = msExamMapper.getExamstate(examid);
+        if(examstateOld == 4){
+            msExamMapper.updateExamState(examid,5);
+        }else if(examstateOld < 4){
+            throw new ServiceException("未生成报告，无法发布成绩");
+        }
+    }
+    //该状态-取消发布成绩
+    public void updateExamStateForUnfbcj(Integer examid){
+        Integer examstateOld = msExamMapper.getExamstate(examid);
+        if(examstateOld == 5){
+            msExamMapper.updateExamState(examid,4);
+        }
+    }
 }

smarking/src/main/java/com/xhkjedu/smarking/service/scan/MsScanbatchPaperService.java

@@ -1,5 +1,7 @@
 package com.xhkjedu.smarking.service.scan;
 
+import com.xhkjedu.exception.ServiceException;
+import com.xhkjedu.smarking.mapper.exam.MsExamMapper;
 import com.xhkjedu.smarking.mapper.paper.MsPaperBlockQuestionMapper;
 import com.xhkjedu.smarking.mapper.paper.MsTemplateMapper;
 import com.xhkjedu.smarking.mapper.scan.MsScanbatchMapper;
@@ -64,6 +66,8 @@ public class MsScanbatchPaperService {
     private SchoolMapper schoolMapper;
     @Resource
     private MsTemplateMapper msTemplateMapper;
+    @Resource
+    private MsExamMapper msExamMapper;
 
     /*
      * @Description 扫描记录-查看原卷
@@ -100,11 +104,11 @@ public class MsScanbatchPaperService {
      * @Date 2024-10-15
      **/
     @Transactional(rollbackFor = Exception.class)
-    public void savePaperIdentify(MsScanbatchPaper msScanbatchPaper) throws Exception {
+    public void savePaperIdentify(MsScanbatchPaper msScanbatchPaper){
 
         Integer examstate  = msScanbatchMapper.getExamStateByBatchid(msScanbatchPaper.getBatchid());
         if(examstate == 3){
-            throw new Exception("考试已统分，禁止扫描");
+            throw new ServiceException("考试已统分，禁止扫描");
         }
 
         //更改扫描出错次数
@@ -632,7 +636,17 @@ public class MsScanbatchPaperService {
         msScanbatchPaperMapper.updateBatchScanPaperMspstate(list);
     }
 
+    @Transactional(rollbackFor = Exception.class)
     public void reIdentifyPaper(Integer examid,String subjectid,Integer mpid) {
+        Integer examstate  = msExamMapper.getExamstate(examid);
+        if(examstate >= 3){
+            throw new ServiceException("考试已统分，禁止重新识别");
+        }
+
+        //清空学生题块试题信息和学生试卷扫描信息
+        msPaperStudentBlockMapper.updateStudentBlockQuestionByMpid(mpid);
+        msPaperStudentMapper.updateStuPaperForScanClearByMpid(mpid);
+
         //获取考试对应的试卷模板及坐标信息
         List<MsTemplate> templates = msTemplateMapper.listTemplateBlockQueByMpid(mpid);
 

smarking/src/main/java/com/xhkjedu/smarking/task/ExamSubjectTask.java

@@ -1,6 +1,8 @@
 package com.xhkjedu.smarking.task;
 
+import com.xhkjedu.smarking.mapper.exam.MsExamMapper;
 import com.xhkjedu.smarking.mapper.exam.MsSubjectMapper;
+import com.xhkjedu.smarking.model.exam.MsSubject;
 import com.xhkjedu.smarking.service.stupaper.MsPaperStudentQuestionService;
 import com.xhkjedu.utils.N_Utils;
 import lombok.extern.slf4j.Slf4j;
@@ -9,7 +11,10 @@ import org.springframework.scheduling.annotation.Scheduled;
 import org.springframework.stereotype.Component;
 
 import javax.annotation.Resource;
+import java.util.ArrayList;
 import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
 
 /**
  * @Description：考试科目状态定时任务
@@ -24,6 +29,8 @@ public class ExamSubjectTask {
     private MsSubjectMapper msSubjectMapper;
     @Resource
     private MsPaperStudentQuestionService msPaperStudentQuestionService;
+    @Resource
+    private MsExamMapper msExamMapper;
 
     /*
      * @Description 单科开始考试
@@ -54,6 +61,21 @@ public class ExamSubjectTask {
         List<Integer> msids = msSubjectMapper.listMsidsForEndSubject(strtime);
         if(N_Utils.isListNotEmpty(msids)){
             msSubjectMapper.updateEndSubject(timestamp, msids);
+            //获取科目对应的考试下所有科目
+            List<MsSubject> subjects = msSubjectMapper.listSubjectAndStateByMsids(msids);
+            Map<Integer,List<MsSubject>> subjectMap = subjects.stream().collect(Collectors.groupingBy(MsSubject::getExamid));
+            List<Integer> examids = new ArrayList<>();
+            for(Map.Entry<Integer,List<MsSubject>> entry : subjectMap.entrySet()){
+                List<MsSubject> subList = entry.getValue();
+                List<MsSubject> endSubList=subList.stream().filter(ms -> ms.getMsstate() == 2).collect(Collectors.toList());
+                if(!endSubList.isEmpty() && endSubList.size() == subList.size()){
+                    examids.add(entry.getKey());
+                }
+            }
+            //考试下所有科目已经结束考试，更改考试状态为已结束
+            if(!examids.isEmpty()){
+                msExamMapper.updateBatchExamState(examids);
+            }
         }
     }
 

smarking/src/main/java/com/xhkjedu/smarking/utils/opencv/CvCommonUtils.java

@@ -0,0 +1,447 @@
+package com.xhkjedu.smarking.utils.opencv;
+
+import org.opencv.core.*;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.imgproc.Imgproc;
+
+import java.util.Base64;
+import java.util.List;
+
+public class CvCommonUtils {
+    /**
+     * 逆时针旋转90度
+     *
+     * @param source 原Mat
+     * @return Mat
+     */
+    public static Mat rotate90Counter(Mat source) {
+        Mat dst = new Mat();
+        Core.rotate(
+                source,
+                dst,
+                Core.ROTATE_90_COUNTERCLOCKWISE
+        );
+        return dst;
+    }
+
+    /**
+     * 顺时针旋转90度
+     *
+     * @param source 原Mat
+     * @return Mat
+     */
+    public static Mat rotate90(Mat source) {
+        Mat dst = new Mat();
+        Core.rotate(
+                source,
+                dst,
+                Core.ROTATE_90_CLOCKWISE
+        );
+        return dst;
+    }
+
+    /**
+     * 旋转180度
+     *
+     * @param source 原Mat
+     * @return Mat
+     */
+    public static Mat rotate180(Mat source) {
+        Mat dst = new Mat();
+        Core.rotate(
+                source,
+                dst,
+                Core.ROTATE_180
+        );
+        return dst;
+    }
+
+    /**
+     * 旋转任意角度
+     *
+     * @param source 原Mat
+     * @param angle  角度
+     * @return Mat
+     */
+    public static Mat rotate(Mat source, int angle) {
+        angle = -angle;
+        // 获取图像的尺寸
+        Size size = source.size();
+        int width = (int) size.width;
+        int height = (int) size.height;
+        // 创建一个新的Mat对象来存储旋转后的图像
+        Mat dst;
+        int tempAngle = Math.abs(angle) % 180;
+        if (tempAngle > 45) {
+            dst = new Mat(
+                    width,
+                    height,
+                    source.type()
+            );
+        } else {
+            dst = new Mat(
+                    height,
+                    width,
+                    source.type()
+            );
+        }
+        // 获取图像的中心点
+        Point center = new Point((double) width / 2, (double) height / 2);
+        // 计算旋转矩阵
+        Mat rotMat = Imgproc.getRotationMatrix2D(
+                center,
+                angle,
+                1.0
+        );
+        // 调整旋转矩阵的平移部分以保持图像在目标矩阵中心
+        rotMat.put(
+                0,
+                2,
+                rotMat.get(
+                        0,
+                        2
+                )[0] + (dst.size().width / 2.0 - center.x)
+        );
+        rotMat.put(
+                1,
+                2,
+                rotMat.get(1, 2)[0] + (dst.size().height / 2.0 - center.y)
+        );
+        // 执行仿射变换（旋转）
+        Imgproc.warpAffine(
+                source,
+                dst,
+                rotMat,
+                dst.size()
+        );
+        return dst;
+    }
+
+    /**
+     * 缩放
+     *
+     * @param source 原Mat
+     * @param width  目标宽度
+     * @param height 目标高度
+     * @return Mat
+     */
+    public static Mat resize(Mat source, int width, int height) {
+        // 检查输入图像是否有效
+        if (source.empty()) {
+            return new Mat();
+        }
+        // 获取源图像的宽高
+        int sWidth = source.cols();
+        int sHeight = source.rows();
+        // 若目标宽度和高度同时为0，直接返回源图像
+        if (width == 0 && height == 0) {
+            return source.clone(); // 深拷贝源图像
+        }
+        // 计算目标宽高
+        double aspectRatio = (double) sWidth / sHeight; // 原始宽高比
+        int targetWidth = (width > 0) ? width : (int) (height * aspectRatio);
+        int targetHeight = (height > 0) ? height : (int) (width / aspectRatio);
+        // 创建结果Mat并进行缩放
+        Mat resultMat = new Mat();
+        Imgproc.resize(source, resultMat, new Size(targetWidth, targetHeight));
+        return resultMat;
+    }
+
+    /**
+     * 以灰度化读取图片
+     *
+     * @param imgPath 图片路径
+     * @return Mat
+     */
+    public static Mat gray(String imgPath) {
+        return Imgcodecs.imread(
+                imgPath,
+                Imgcodecs.IMREAD_GRAYSCALE
+        );
+    }
+
+    // 灰度化
+    public static Mat gray(Mat inputImage) {
+        Mat grayImage = new Mat();
+        Imgproc.cvtColor(
+                inputImage,
+                grayImage,
+                Imgproc.COLOR_BGR2GRAY
+        );
+        return grayImage;
+    }
+
+    /**
+     * 二值化 输入源必须是灰度化的图片
+     *
+     * @param grayImage 灰度化Mat
+     * @return Mat
+     */
+    public static Mat binary(Mat grayImage) {
+        Mat binaryImage = new Mat();
+        Imgproc.threshold(
+                grayImage,
+                binaryImage,
+                0,
+                255,
+                Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU
+        );
+        return binaryImage;
+    }
+
+    /**
+     * 腐蚀（黑色区域变大）
+     *
+     * @param source 原Mat
+     * @return Mat
+     */
+    public static Mat eroding(Mat source) {
+        return eroding(
+                source,
+                1
+        );
+    }
+
+    /**
+     * 腐蚀（黑色区域变大）
+     *
+     * @param source       原Mat
+     * @param erosion_size 腐蚀大小
+     * @return Mat
+     */
+    public static Mat eroding(Mat source, double erosion_size) {
+        Mat resultMat = new Mat(
+                source.rows(),
+                source.cols(),
+                source.type()
+        );
+        Mat element = Imgproc.getStructuringElement(
+                Imgproc.MORPH_RECT,
+                new Size(
+                        erosion_size + 1,
+                        erosion_size + 1
+                )
+        );
+        Imgproc.erode(
+                source,
+                resultMat,
+                element
+        );
+        return resultMat;
+    }
+
+    /**
+     * 膨胀（白色区域变大）
+     *
+     * @param source 原Mat
+     * @return Mat
+     */
+    public static Mat dilation(Mat source) {
+        return dilation(
+                source,
+                1
+        );
+    }
+
+    /**
+     * 膨胀（白色区域变大）
+     *
+     * @param source        原Mat
+     * @param dilation_size 膨胀因子2*x+1 里的x
+     * @return Mat
+     */
+    public static Mat dilation(Mat source, double dilation_size) {
+        Mat resultMat = new Mat(
+                source.rows(),
+                source.cols(),
+                source.type()
+        );
+        Mat element = Imgproc.getStructuringElement(
+                Imgproc.MORPH_RECT,
+                new Size(
+                        2 * dilation_size + 1,
+                        2 * dilation_size + 1
+                )
+        );
+        Imgproc.dilate(
+                source,
+                resultMat,
+                element
+        );
+        return resultMat;
+    }
+
+    /**
+     * 获取区域Mat
+     *
+     * @param source 原Mat
+     * @param rect   区域
+     * @return Mat
+     */
+    public static Mat getRectMat(Mat source, Rect rect) {
+        return new Mat(
+                source,
+                rect
+        );
+    }
+
+    /**
+     * 获取填涂率
+     *
+     * @param source 原Mat
+     * @param rect   区域
+     * @return Mat
+     */
+    public static double getSmearRate(Mat source, Rect rect) {
+        // 创建一个新的Mat对象，它表示源图像中的指定区域
+        Mat matTemp = new Mat(
+                source,
+                rect
+        );
+        // 计算非零像素的数量
+        int count = Core.countNonZero(matTemp);
+        // 计算总像素数
+        int total = matTemp.cols() * matTemp.rows();
+        // 计算零像素的比率
+        double rate = (double) (total - count) / total;
+        // 释放临时Mat对象的资源
+        matTemp.release();
+        // 返回比率是否大于最大值
+        return rate;
+    }
+
+    /**
+     * 是否涂卡
+     *
+     * @param source 图片
+     * @param rect   区域
+     * @param min    最小的填涂率
+     * @return 是否填涂
+     */
+    public static boolean isSmearCard(Mat source, Rect rect, double min) {
+        // 计算零像素的比率
+        double rate = getSmearRate(
+                source,
+                rect
+        );
+        // 返回比率是否大于最大值
+        return rate > min;
+    }
+
+    /**
+     * 颜色反转
+     *
+     * @param source 原Mat
+     * @return Mat
+     */
+    public static Mat bitwiseNot(Mat source) {
+        Mat resultMat = new Mat();
+        Core.bitwise_not(
+                source,
+                resultMat
+        );
+        return resultMat;
+    }
+
+    /**
+     * 垂直合并多个Mat
+     *
+     * @param matList 图片列表
+     * @return Mat
+     */
+    public static Mat jointMatV(List<Mat> matList) {
+        if (matList.isEmpty()) {
+            return new Mat();
+        }
+        int rows = 0;
+        int cols = 0;
+        for (Mat imgTemp : matList) {
+            rows += imgTemp.rows();
+            cols = Math.max(cols, imgTemp.cols());
+        }
+        // 创建一个白色背景的Mat对象
+        Mat result = new Mat(rows, cols, matList.get(0).type(), new Scalar(255, 255, 255));
+        int tempRows = 0;
+        for (Mat itemMat : matList) {
+            // 定义复制的区域
+            Mat roi = result.submat(new Rect(0, tempRows, itemMat.cols(), itemMat.rows()));
+            // 将itemMat的内容复制到ROI中
+            itemMat.copyTo(roi);
+            tempRows += itemMat.rows();
+        }
+        return result;
+    }
+
+    /**
+     * 水平合并多个Mat
+     *
+     * @param matList 图片列表
+     * @return Mat
+     */
+    public static Mat jointMatH(List<Mat> matList) {
+        if (matList.isEmpty()) {
+            return new Mat();
+        }
+        int rows = 0;
+        int cols = 0;
+        for (Mat imgTemp : matList) {
+            rows = Math.max(rows, imgTemp.rows());
+            cols += imgTemp.cols();
+        }
+        // 创建一个白色背景的Mat对象
+        Mat result = new Mat(rows, cols, matList.get(0).type(), new Scalar(255, 255, 255));
+        int tempCols = 0;
+        for (Mat itemMat : matList) {
+            // 定义复制的区域
+            Mat roi = result.submat(new Rect(tempCols, 0, itemMat.cols(), itemMat.rows()));
+            // 将itemMat的内容复制到ROI中
+            itemMat.copyTo(roi);
+            tempCols += itemMat.cols();
+        }
+        return result;
+    }
+
+    /**
+     * Mat转Base64字符串
+     *
+     * @param mat mat
+     * @return Base64字符串
+     */
+    public static String matToBase64(Mat mat) {
+        return matToBase64(mat, true);
+    }
+
+    /**
+     * Mat转Base64字符串
+     *
+     * @param mat       mat
+     * @param addPrefix 是否添加前缀
+     * @return Base64字符串
+     */
+    public static String matToBase64(Mat mat, boolean addPrefix) {
+        // 将Mat对象转换为PNG格式的字节数组
+        MatOfByte matOfByte = new MatOfByte();
+        Imgcodecs.imencode(".jpeg", mat, matOfByte);
+        // 将字节数组转换为Base64字符串
+        byte[] byteArray = matOfByte.toArray();
+        String imgBase64Str = Base64.getEncoder().encodeToString(byteArray);
+        if (addPrefix) {
+            String prefix = "data:image/jpeg;base64,";
+            return prefix + imgBase64Str;
+        }
+        return imgBase64Str;
+    }
+
+    /**
+     * 保存图片
+     *
+     * @param img  图片Mat
+     * @param path 保存路径
+     */
+    public static void saveImg(Mat img, String path) {
+        Imgcodecs.imwrite(
+                path,
+                img
+        );
+    }
+}

smarking/src/main/java/com/xhkjedu/smarking/utils/opencv/CvContoursUtils.java

@@ -0,0 +1,123 @@
+package com.xhkjedu.smarking.utils.opencv;
+
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfPoint;
+import org.opencv.core.Point;
+import org.opencv.imgproc.Imgproc;
+
+import java.util.Vector;
+
+/**
+ * 轮廓工具类
+ */
+public class CvContoursUtils {
+
+    /**
+     * 获取图片的四个顶点
+     *
+     * @param img 原图
+     * @return Point[]
+     */
+    public static Point[] getAllPoints(Mat img) {
+        Point[] potArr = new Point[4];
+        for (int i = 0; i < 4; i++) {
+            potArr[i] = new Point(-1, -1);
+        }
+
+        int[] spaceArr = new int[]{-1, -1, -1, -1};
+        int cols = img.cols();
+        int rows = img.rows();
+        int x1 = cols / 3;
+        int x2 = cols * 2 / 3;
+        int y1 = rows / 3;
+        int y2 = rows * 2 / 3;
+        for (int x = 0; x < cols; x++) {
+            for (int y = 0; y < rows; y++) {
+                if (x > x1 && x < x2 && y > y1 && y < y2) {
+                    continue;
+                }
+                double[] darr = img.get(y, x);
+                if (darr != null && darr.length >= 1 && darr[0] == 0) {
+                    if (spaceArr[0] == -1) {
+                        potArr[0].x = x;
+                        potArr[0].y = y;
+                        potArr[1].x = x;
+                        potArr[1].y = y;
+                        potArr[2].x = x;
+                        potArr[2].y = y;
+                        potArr[3].x = x;
+                        potArr[3].y = y;
+                        spaceArr[0] = getSpace(0, 0, x, y);
+                        spaceArr[1] = getSpace(cols, 0, x, y);
+                        spaceArr[2] = getSpace(cols, rows, x, y);
+                        spaceArr[3] = getSpace(0, rows, x, y);
+                    } else {
+                        int s0 = getSpace(0, 0, x, y);
+                        int s1 = getSpace(cols, 0, x, y);
+                        int s2 = getSpace(cols, rows, x, y);
+                        int s3 = getSpace(0, rows, x, y);
+                        if (s0 < spaceArr[0]) {
+                            spaceArr[0] = s0;
+                            potArr[0].x = x;
+                            potArr[0].y = y;
+                        }
+                        if (s1 < spaceArr[1]) {
+                            spaceArr[1] = s1;
+                            potArr[1].x = x;
+                            potArr[1].y = y;
+                        }
+                        if (s2 < spaceArr[2]) {
+                            spaceArr[2] = s2;
+                            potArr[2].x = x;
+                            potArr[2].y = y;
+                        }
+                        if (s3 < spaceArr[3]) {
+                            spaceArr[3] = s3;
+                            potArr[3].x = x;
+                            potArr[3].y = y;
+                        }
+                    }
+
+                }
+            }
+        }
+        return potArr;
+    }
+
+    /**
+     * 轮廓识别，使用最外轮廓发抽取轮廓RETR_EXTERNAL，轮廓识别方法为CHAIN_APPROX_SIMPLE
+     *
+     * @param source 传入进来的图片Mat对象
+     * @return 返回轮廓结果集
+     */
+    public static Vector<MatOfPoint> findContours(Mat source) {
+        Mat rs = new Mat();
+
+        /*
+          定义轮廓识别方法
+          边缘近似方法(除了RETR_RUNS使用内置的近似，其他模式均使用此设定的近似算法)。可取值如下:
+         CV_CHAIN_CODE:以Freeman链码的方式输出轮廓，所有其他方法输出多边形(顶点的序列)。
+         CHAIN_APPROX_NONE:将所有的连码点，转换成点。
+         CHAIN_APPROX_SIMPLE:压缩水平的、垂直的和斜的部分，也就是，函数只保留他们的终点部分。
+         CHAIN_APPROX_TC89_L1，CV_CHAIN_APPROX_TC89_KCOS:使用the flavors of Teh-Chin chain近似算法的一种。
+         LINK_RUNS:通过连接水平段的1，使用完全不同的边缘提取算法。使用CV_RETR_LIST检索模式能使用此方法。
+         */
+        Vector<MatOfPoint> contours = new Vector<>();
+        Imgproc.findContours(
+                source,
+                contours,
+                rs,
+                Imgproc.RETR_LIST,
+                Imgproc.CHAIN_APPROX_SIMPLE
+        );
+        return contours;
+    }
+
+
+    private static int getSpace(int x1, int y1, int x2, int y2) {
+        int xspace = Math.abs(x1 - x2);
+        int yspace = Math.abs(y1 - y2);
+        return (int) Math.sqrt(Math.pow(xspace, 2) + Math.pow(yspace, 2));
+    }
+
+}

smarking/src/main/java/com/xhkjedu/smarking/utils/opencv/CvWarpPerspectiveUtils.java

@@ -0,0 +1,63 @@
+package com.xhkjedu.smarking.utils.opencv;
+
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.utils.Converters;
+
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * 透视变换工具类
+ * 因为我透视变换做的也不是很好，就仅提供一个大概的函数...
+ */
+public class CvWarpPerspectiveUtils {
+
+    /**
+     * 执行透视变换
+     *
+     * @param src    输入图像
+     * @param points 变换所需的四个角点
+     * @return 透视变换后的图像
+     */
+    public static Mat warpPerspective(Mat src, Point[] points) {
+        // 点的顺序[左上 ，右上 ，右下 ，左下]
+        List<Point> listSrcs = Arrays.asList(
+                points[0],
+                points[1],
+                points[2],
+                points[3]
+        );
+        Mat srcPoints = Converters.vector_Point_to_Mat(listSrcs, CvType.CV_32F);
+
+        List<Point> listDsts = Arrays.asList(
+                new Point(0, 0),
+                new Point(src.width(), 0),
+                new Point(src.width(), src.height()),
+                new Point(0, src.height())
+        );
+
+
+        Mat dstPoints = Converters.vector_Point_to_Mat(listDsts, CvType.CV_32F);
+
+        Mat perspectiveMmat = Imgproc.getPerspectiveTransform(dstPoints, srcPoints);
+
+        Mat dst = new Mat();
+
+        Imgproc.warpPerspective(
+                src,
+                dst,
+                perspectiveMmat,
+                src.size(),
+                Imgproc.INTER_LINEAR + Imgproc.WARP_INVERSE_MAP,
+                1,
+                new Scalar(0)
+        );
+
+        return dst;
+    }
+
+}

smarking/src/main/resources/mapper/exam/MsExamMapper.xml

@@ -75,4 +75,12 @@
     <select id="getSchoolIdByExamId" resultType="java.lang.Integer">
         select schoolid from ms_exam where examid=#{examid}
     </select>
+
+    <!--批量结束考试-->
+    <update id="updateBatchExamState">
+        update ms_exam set examstate=2 where examid in
+        <foreach collection="examids" item="item" open="(" separator="," close=")">
+            #{item}
+        </foreach>
+    </update>
 </mapper>

smarking/src/main/resources/mapper/exam/MsSubjectMapper.xml

@@ -117,11 +117,17 @@
         and ms.msstate=0 and concat(ms.sdate,' ',ms.begintime)<=#{strtime}
     </update>
     <!--定时任务-科目结束考试-获取待结束考试-->
-    <select id="listMsidsForEndSubject">
+    <select id="listMsidsForEndSubject" resultType="javj.lang.Integer">
         select ms.msid from ms_subject ms left join ms_exam e on ms.examid=e.examid
         where e.examstate>=1 and e.deleted=1 and e.exammode=3 and ms.msstate=1
         and adddate(concat(ms.sdate,' ',ms.endtime),interval 5 minute) &lt;#{strtime}
     </select>
+    <!--定时任务-科目对应考试下所有科目-->
+    <select id="listSubjectAndStateByMsids" resultType="com.xhkjedu.smarking.model.exam.MsSubject">
+        select distinct s.msid,s.msstate,s.examid from ms_subject s where s.examid in
+        (select t.examid from ms_subject t where t.msid in
+        <foreach collection="list" item="msid" open="(" close=")" separator=",">#{msid}</foreach>
+    </select>
     <!--获取考试科目ids-->
     <select id="listSubjectIdsByExamId" resultType="java.lang.String">
         select subjectid from ms_subject where examid=#{examid}

smarking/src/main/resources/mapper/stupaper/MsPaperStudentMapper.xml

@@ -249,6 +249,11 @@
             update ms_paper_student set sstate=#{p.sstate} where mspid=#{p.mspid} and mpid=#{p.mpid}
         </foreach>
     </update>
+    <!--扫描-清空学生试卷扫描信息-->
+    <update id="updateStuPaperForScanClearByMpid">
+        update ms_paper_student set sstate=0,stuscore=0.0,stuscoretype=0,hasbad=0,batchid=null,mspid=null,
+                                    paperfront=null,paperback=null,pagenum=0 where mpid=#{mpid}
+    </update>
 
     <!--补录成绩-学生科目成绩-->
     <select id="listPaperStudentForFillScore" resultType="com.xhkjedu.smarking.model.stupaper.MsPaperStudent">