C#使用OpenCV识别答题卡填涂区域(方形圆形都可)

参考这位大佬用c++写的，和这位大佬用EmguCv写的,一步一步翻译成c#OpenCv

具体逻辑为
1.先读取到包裹选项的框(这里是把包裹选项的框的面积设为最大，再获取面积最大的框)
2.用选项的行数和列数获取第一步获取到的框中所有选项的位置(拿列来说，程序可以获取到x最小和x最大的选项，再把最大x和最小x的差除以列数，就可以得到选项之间的间隔，就能获取到每个选项的x,y也同理，所以也有每个选项间的x间隔和y间隔必须相同)
3.选项位置获取到了后，拿每个选项的xy获取到对应点的选项，再根据提供的选项宽高获取此点所在的矩形和矩形所填涂的面积，再根据提供的判断是否填涂参数对比来判断是否选中

限制：1.选项的宽高必须固定；2.图片分辨率最好为1200*1700左右；3.包裹选项的框的粗细要小于选项的框)；4.包裹选项的框面积要最大(不最大的话要修改获取框的逻辑)；5.扫描的图片不能太倾斜;

直接上代码

具体demo可以去这里拿 https://github.com/cstajj/Scann

调用示例

List<Point[]> selectOption;
int[,] resultArray;
MatchAnswer(fileTextBox.Text, int.Parse(rowTextBox.Text), int.Parse(celTextBox.Text), int.Parse(yzMinTextBox.Text), int.Parse(yzMaxTextBox.Text), int.Parse(heightTextBox.Text), int.Parse(widthTextBox.Text), int.Parse(ttTextBox.Text), out resultArray, out selectOption, out int fzfgCount);

原图

结果

引用

类库
OpenCvSharp4，版本为4.7.0.20230115
OpenCvSharp4.runtime.win ，版本为4.7.0.20230115

using OpenCvSharp;
using Point = OpenCvSharp.Point;
using Size = OpenCvSharp.Size;
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
using System.Numerics;
using System.Security.Cryptography;
using System.Net.Http;
using System.IO;

方法

     private static int imgShowIndex = 1;private static string showIndex = "";//显示步骤 1234private static string showSuqIndex = "";//显示检测到的矩形下标(每个答题卡有很多框) 123/// <summary>/// 匹配答案/// </summary>/// <param name="path">文件路径</param>/// <param name="row">答案行数</param>/// <param name="column">答案列数</param>/// <param name="par1">阈值Min可以默认给150</param>/// <param name="par2">阈值Max</param>/// <param name="height">填涂框的高度 px</param>/// <param name="width">填涂框的宽度 px</param>/// <param name="judgeSize">填涂覆盖值，达到这个值才为选中</param>/// <param name="resultArray">返回数据</param>/// <param name="selectOption">选中数据</param>/// <param name="fzfgCount">阈值调整次数</param>/// <returns></returns>public static Mat MatchAnswer(string path, int? row, int? column, int? par1, int? par2, int? height, int? width, int? judgeSize, out int[,] resultArray, out List<Point[]> selectOption, out int fzfgCount){JudgeHasValueAndSet(row, 4);JudgeHasValueAndSet(column, 6);JudgeHasValueAndSet(par1, 150);JudgeHasValueAndSet(par2, 255);JudgeHasValueAndSet(height, 35);JudgeHasValueAndSet(width, 50);JudgeHasValueAndSet(judgeSize, 1500);resultArray = new int[,] { };selectOption = new List<Point[]>();Mat answerSheet = Cv2.ImRead(path);Point[] result_contour = GetBoundaryOfPic(answerSheet);Mat birdMat = WarpPerspective(answerSheet, result_contour);ShowImg(birdMat, "鸟瞰");//OTSU阈值分割Mat target = new Mat();int selectOptionCount = 0;List<Point[]> selected_contour = new List<Point[]>();fzfgCount = 0;//自动调整阈值，取到填涂框才继续while (selectOptionCount <= 1){if (fzfgCount >= 100)return null;if (fzfgCount > 0)par1--;fzfgCount++;Cv2.Threshold(birdMat, target, par1.Value, par2.Value, ThresholdTypes.BinaryInv);//修改thresh或maxval可以调整轮廓取值范围(调的不好会直接取外面的大轮廓)//ShowImg(target, "阈值分割");selected_contour = SelectedContour(target, height.Value, width.Value);selectOptionCount = selected_contour.Count();}selectOption = selected_contour;//3.验证结果Mat answerSheet_con = target.Clone();Cv2.CvtColor(answerSheet_con, answerSheet_con, ColorConversionCodes.GRAY2BGR);Cv2.DrawContours(answerSheet_con, selected_contour, -1, new Scalar(0, 0, 255), 2);ShowImg(answerSheet_con, "选项");List<Point>[,] classed_contours = ClassedOfContours(selected_contour, row.Value, column.Value);//5.绘制并验证List<Scalar> color = new List<Scalar>();color.Add(new Scalar(0, 0, 255));color.Add(new Scalar(255, 0, 255));color.Add(new Scalar(0, 255, 255));color.Add(new Scalar(255, 0, 0));color.Add(new Scalar(0, 255, 0));Mat groupMap = target.Clone();Cv2.CvtColor(groupMap, groupMap, ColorConversionCodes.GRAY2BGR);for (int i = 0; i < row; i++){List<List<Point>> tempGroupPoints = new List<List<Point>>();for (int j = 0; j < column; j++){if (classed_contours[i, j].Count > 0)tempGroupPoints.Add(classed_contours[i, j]);}if (tempGroupPoints.Count > 0)Cv2.DrawContours(groupMap, tempGroupPoints, -1, color[(i >= 5 ? i % 5 : i)], 2);}ShowImg(groupMap, "分组");//检测答题者的选项resultArray = GetResultArray(target, classed_contours, row.Value, column.Value, judgeSize.Value);Mat resultMat = new Mat();Cv2.CvtColor(target, resultMat, ColorConversionCodes.GRAY2BGR);List<List<Point>> tempPoints = new List<List<Point>>();for (int i = 0; i < row; i++){for (int j = 0; j < column; j++){if (resultArray[i, j] == 1){tempPoints.Add(classed_contours[i, j]);}}}Cv2.DrawContours(resultMat, tempPoints, -1, new Scalar(255, 0, 0), 2);ShowImg(resultMat, "结果");return resultMat;}public static void JudgeHasValueAndSet(int? value,int defaultValue = 0) {if (value == null)value = defaultValue;}/// <summary>/// 寻找边界/// </summary>/// <param name="mat"></param>/// <returns></returns>public static Point[] GetBoundaryOfPic(Mat mat, string size = "0,90,3,true"){//灰度转化Mat gray = new Mat();Cv2.CvtColor(mat, gray, ColorConversionCodes.RGB2GRAY);//进行高斯滤波Mat blurred = new Mat();Cv2.GaussianBlur(gray, blurred, new Size(3, 3), 0);//进行canny边缘检测Mat canny = new Mat();//Cv2.Canny(blurred, canny, 0, 180);string[] str = size.Split(",");Cv2.Canny(blurred, canny, int.Parse(str[0]), int.Parse(str[1]), int.Parse(str[2]), bool.Parse(str[3]));ShowImg(canny, "canny");//寻找矩形边界Point[][] contours;HierarchyIndex[] hierarchly;Cv2.FindContours(canny, out contours, out hierarchly, RetrievalModes.External, ContourApproximationModes.ApproxSimple);//Cv2.FindContours(canny, out contours, out hierarchly, RetrievalModes.External, ContourApproximationModes.ApproxNone);//Cv2.FindContours(canny, out contours, out hierarchly, RetrievalModes.External, ContourApproximationModes.ApproxTC89KCOS);//Cv2.FindContours(canny, out contours, out hierarchly, RetrievalModes.External, ContourApproximationModes.ApproxTC89L1);Point[] result_contour;if (contours.Length == 1){result_contour = contours[0];}else{double max = -1;int index = -1;for (int i = 0; i < contours.Length; i++){if (contours[i].Length < 4){continue;}double tem = Cv2.ArcLength(contours[i], true);bool pass = IsGreatArc(contours[i]);if (tem > max && pass){max = tem;index = i;}if (!string.IsNullOrEmpty(showSuqIndex)){Mat birdMat = WarpPerspective(mat, contours[i]);ShowImg2(birdMat, "鸟瞰" + i + "|" + tem.ToString("f2") + (pass ? "|通过" : ""));}}//取面积最大的矩形为检测填涂区域，所以在做答题卡时需要把填涂区的框做最大，或者自己调整，但上面的判断也需要一并调整result_contour = contours[index];}return result_contour;}class TempXY{public int X { get; set; }public int Y { get; set; }}public static bool IsGreatArc(Point[] contours){TempXY minXmaxY = new TempXY() { X = -1 };TempXY minXminY = new TempXY() { X = -1 };TempXY maxXmaxY = new TempXY() { X = -1 };TempXY maxXminY = new TempXY() { X = -1 };int minX = -1, minY = -1, maxX = -1, maxY = -1;foreach (var item in contours){if (item.X < minX || minX == -1)minX = item.X;if (item.X > maxX || maxX == -1)maxX = item.X;if (item.Y < minY || minY == -1)minY = item.Y;if (item.Y > maxY || maxY == -1)maxY = item.Y;}foreach (var item in contours){if (Math.Abs(item.X - minX) + Math.Abs(item.Y - maxY) < Math.Abs(minXmaxY.X - minX) + Math.Abs(minXmaxY.Y - maxY) || minXmaxY.X == -1){minXmaxY.X = item.X;minXmaxY.Y = item.Y;}if (Math.Abs(item.X - minX) + Math.Abs(item.Y - minY) < Math.Abs(minXminY.X - minX) + Math.Abs(minXminY.Y - minY) || minXminY.X == -1){minXminY.X = item.X;minXminY.Y = item.Y;}if (Math.Abs(item.X - maxX) + Math.Abs(item.Y - maxY) < Math.Abs(maxXmaxY.X - maxX) + Math.Abs(maxXmaxY.Y - maxY) || maxXmaxY.X == -1){maxXmaxY.X = item.X;maxXmaxY.Y = item.Y;}if (Math.Abs(item.X - maxX) + Math.Abs(item.Y - minY) < Math.Abs(maxXminY.X - maxX) + Math.Abs(maxXminY.Y - minY) || maxXminY.X == -1){maxXminY.X = item.X;maxXminY.Y = item.Y;}}if (Math.Abs(minXminY.X - minXmaxY.X) < 30 && Math.Abs(maxXminY.X - maxXmaxY.X) < 30 && minXmaxY.X > 5 && minXminY.Y > 5 && maxXminY.X - minXminY.X > 50 && minXmaxY.Y - minXminY.Y > 50){if (Math.Abs(minXminY.Y - maxXminY.Y) < 30 && Math.Abs(minXmaxY.Y - maxXmaxY.Y) < 30){return true;}}return false;}/// <summary>/// 对图像进行矫正(转为鸟瞰图，删除多余边界)/// </summary>/// <param name="mat"></param>/// <param name="result_contour"></param>/// <returns></returns>public static Mat WarpPerspective(Mat mat, Point[] result_contour){//使用DP算法拟合答题卡的几何轮廓,保存点集pts并顺时针排序double result_length = Cv2.ArcLength(result_contour, true);Point[] pts = Cv2.ApproxPolyDP(result_contour, result_length * 0.02, true);int width = 0;int height = 0;if (pts.Length == 4){if (pts[1].X < pts[3].X){//说明当前为逆时针存储，改为顺时针存储（交换第2、4点）Point p = new Point();p = pts[1];pts[1] = pts[3];pts[3] = p;}if (Math.Abs(pts[0].X - pts[3].X) > 100){Point temp = pts[pts.Length - 1];for (int i = pts.Length - 1; i >= 0; i--){if (i == 0)pts[i] = temp;elsepts[i] = pts[i - 1];}}//进行透视变换//1.确定变化尺寸的宽度float width1 = (pts[0].X - pts[1].X) * (pts[0].X - pts[1].X) + (pts[0].Y - pts[1].Y) * (pts[0].Y - pts[1].Y);float width2 = (pts[2].X - pts[3].X) * (pts[2].X - pts[3].X) + (pts[2].Y - pts[3].Y) * (pts[2].Y - pts[3].Y);width = width1 > width2 ? (int)Math.Sqrt(width1) : (int)Math.Sqrt(width2);//2.确定变化尺寸的高度float height1 = (pts[0].X - pts[3].X) * (pts[0].X - pts[3].X) + (pts[0].Y - pts[3].Y) * (pts[0].Y - pts[3].Y);float height2 = (pts[2].X - pts[1].X) * (pts[2].X - pts[1].X) + (pts[2].Y - pts[1].Y) * (pts[2].Y - pts[1].Y);height = height1 > height2 ? (int)Math.Sqrt(height1) : (int)Math.Sqrt(height2);}Point2f[] pts_src = Array.ConvertAll(pts.ToArray(), new Converter<Point, Point2f>(PointToPointF));Point2f[] pts_target = new Point2f[] { new Point2f(0, 0), new Point2f(width - 1, 0), new Point2f(width - 1, height - 1), new Point2f(0, height - 1) };//4.计算透视变换矩阵//4.1类型转化Mat data = Cv2.GetPerspectiveTransform(pts_src, pts_target);//5.进行透视变换Mat birdMat = new Mat();//进行透视操作Mat mat_Perspective = new Mat();Mat src_gray = new Mat();Cv2.CvtColor(mat, src_gray, ColorConversionCodes.BGR2GRAY);Cv2.WarpPerspective(src_gray, birdMat, data, new Size(width, height));return birdMat;}/// <summary>/// 获取所有选项位置/// </summary>/// <param name="target">矫正和OTSU阈值分割后的mat</param>/// <returns></returns>public static List<Point[]> SelectedContour(Mat target, int height, int width){//轮廓筛选//1.改善轮廓Mat element = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(1, 1));Cv2.Dilate(target, target, element);//ShowImg(target);//2.筛选轮廓Point[][] target_contour;List<Point[]> selected_contour = new List<Point[]>();HierarchyIndex[] hierarchly2;Cv2.FindContours(target, out target_contour, out hierarchly2, RetrievalModes.External, ContourApproximationModes.ApproxSimple);foreach (var m in target_contour){Rect rect = Cv2.BoundingRect(m);double k = (double)rect.Height / rect.Width;if (rect.Height > height && rect.Width > width && rect.Width < 100){selected_contour.Add(m);}}return selected_contour;}/// <summary>/// 把选项分为有序的行列数据/// </summary>/// <param name="selected_contour">位置</param>/// <param name="countOfRow">选项行数</param>/// <param name="countOfColumn">列数</param>/// <returns></returns>public static List<Point>[,] ClassedOfContours(List<Point[]> selected_contour, int countOfRow, int countOfColumn){//依据圆心的位置来确认答题卡轮廓的位置//1.计算所有外接圆基本数据float[] radius = new float[selected_contour.Count];Point2f[] center = new Point2f[selected_contour.Count];for (int i = 0; i < selected_contour.Count; i++){float radiusItem;Point2f centerItem;Cv2.MinEnclosingCircle(selected_contour[i], out centerItem, out radiusItem);//最小外接圆center[i] = centerItem;radius[i] = radiusItem;}//2.计算x轴、y轴分割间隔float x_min = 999, y_min = 999;float x_max = -1, y_max = -1;float x_interval = 0, y_interval = 0;//相邻圆心的间距foreach (Point2f pf in center){//获取所有圆心中的坐标最值if (pf.X < x_min) x_min = pf.X;if (pf.X > x_max) x_max = pf.X;if (pf.Y < y_min) y_min = pf.Y;if (pf.Y > y_max) y_max = pf.Y;}x_interval = (x_max - x_min) / (countOfColumn - 1);//答题卡每列x个，即x-1个间隔y_interval = (y_max - y_min) / (countOfRow - 1);//答题卡每行y个圆，即y-1个间隔//4.分类List<Point>[,] classed_contours = new List<Point>[countOfRow, countOfColumn];//初始化VectorOfVectorOfPoint二维数组for (int i = 0; i < countOfRow; i++){for (int j = 0; j < countOfColumn; j++){classed_contours[i, j] = new List<Point>();}}if (x_interval == 0 || y_interval == 0)return classed_contours;for (int i = 0; i < selected_contour.Count; i++){Point2f pf = center[i];int index_r = (int)Math.Round((pf.Y - y_min) / y_interval);//行号int index_c = (int)Math.Round((pf.X - x_min) / x_interval);//列号Point[] temp = selected_contour[i];classed_contours[index_r, index_c].AddRange(temp.ToList());}return classed_contours;}/// <summary>/// 检测答题者的选项，获取涂选的结果数组/// </summary>/// <param name="mat_threshold">经阈值处理后的图像</param>/// <param name="classed_contours">经排序分类后的轮廓数组</param>/// <param name="countOfRow">一行中轮廓的个数</param>/// <param name="countOfColumn">一列中轮廓的个数</param>/// <returns></returns>public static int[,] GetResultArray(Mat mat_threshold, List<Point>[,] classed_contours, int countOfRow, int countOfColumn, int judgeSize){int[,] result_count = new int[countOfRow, countOfColumn];//结果数组//统计所有答题圆圈外接矩形内非零像素个数Rect[,] re_rect = new Rect[countOfRow, countOfColumn];//外接矩形数组int[,] count_roi = new int[countOfRow, countOfColumn];//外接矩形内非零像素个数int min_count = 999;//非零像素个数最大值，作为已涂选的参照int max_count = -1;//非零像素个数最小值，作为未涂选的参照for (int i = 0; i < countOfRow; i++){for (int j = 0; j < countOfColumn; j++){List<Point> countour = classed_contours[i, j];re_rect[i, j] = Cv2.BoundingRect(countour);Mat temp = new Mat(mat_threshold, re_rect[i, j]);//提取ROI矩形区域int count = Cv2.CountNonZero(temp);//计算图像内非零像素个数count_roi[i, j] = count;if (count > max_count) max_count = count;if (count < min_count) min_count = count;}}if (judgeSize > 0){max_count = judgeSize * 2;}//比对涂选的答案，以涂满圆圈一半以上为标准for (int i = 0; i < countOfRow; i++){for (int j = 0; j < countOfColumn; j++){if (count_roi[i, j] > max_count / 2){result_count[i, j] = 1;}}}return result_count;}public static void ShowImg(Mat mat, string name = ""){bool show = false;foreach (var item in showIndex){int i = int.Parse(item.ToString());int i2 = (int)item;if (imgShowIndex == int.Parse(item.ToString())){show = true;}}if (!show){imgShowIndex++;return;}Cv2.ImShow(!string.IsNullOrEmpty(name) ? name : imgShowIndex.ToString(), mat);imgShowIndex++;}private static int suqShowIndex = 1;public static void ShowImg2(Mat mat, string name = ""){bool show = false;if (showSuqIndex == "0"){Cv2.ImShow(name, mat);return;}else if (!string.IsNullOrEmpty(showSuqIndex)){foreach (var item in showSuqIndex){int i = int.Parse(item.ToString());int i2 = (int)item;if (suqShowIndex == int.Parse(item.ToString())){show = true;}}if (!show){suqShowIndex++;return;}Cv2.ImShow(!string.IsNullOrEmpty(name) ? name : suqShowIndex.ToString(), mat);suqShowIndex++;}}/// <summary>/// Point转换为PointF类型/// </summary>/// <param name="p"></param>/// <returns></returns>public static Point2f PointToPointF(Point p){return new Point2f(p.X, p.Y);}

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C#使用OpenCV识别答题卡填涂区域(方形圆形都可)

调用示例

引用

方法

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