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增加无定位点识别注释说明 

页面说明默认展开方便查看
fetches/jiege0525_admin/master
songer 5 years ago
parent
4e7ab390c3
commit
f3131bce68
20 changed files with 68 additions and 38 deletions
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  1. +46
    -19
      src/main/java/com/acts/opencv/base/Card2Controller.java
  2. +3
    -0
      src/main/java/com/acts/opencv/demo/DemoController.java
  3. +1
    -1
      src/main/webapp/view/base/adaptiveBinary.jsp
  4. +1
    -1
      src/main/webapp/view/base/binary.jsp
  5. +1
    -1
      src/main/webapp/view/base/canny.jsp
  6. +1
    -1
      src/main/webapp/view/base/contours.jsp
  7. +1
    -1
      src/main/webapp/view/base/erosion_dilation.jsp
  8. +1
    -1
      src/main/webapp/view/base/findcolor.jsp
  9. +2
    -2
      src/main/webapp/view/base/findtemplate.jsp
  10. +1
    -1
      src/main/webapp/view/base/floodfill.jsp
  11. +1
    -1
      src/main/webapp/view/base/gaussian.jsp
  12. +1
    -1
      src/main/webapp/view/base/grayHistogram.jsp
  13. +1
    -1
      src/main/webapp/view/base/houghcircle.jsp
  14. +1
    -1
      src/main/webapp/view/base/houghline.jsp
  15. +1
    -1
      src/main/webapp/view/base/morphologyEx.jsp
  16. +1
    -1
      src/main/webapp/view/base/resize.jsp
  17. +1
    -1
      src/main/webapp/view/base/sharpness.jsp
  18. +1
    -1
      src/main/webapp/view/base/zxing.jsp
  19. +1
    -1
      src/main/webapp/view/card/cardPlus.jsp
  20. +1
    -1
      src/main/webapp/view/card/recognition.jsp

+ 46
- 19
src/main/java/com/acts/opencv/base/Card2Controller.java View File

@@ -53,7 +53,7 @@ public class Card2Controller extends BaseController {
long t1 = new Date().getTime();
//String sourceimage1 = "D:\\test\\abc\\card\\test4.jpg";
String sourceimage = Constants.PATH + imagefile;
//表格检测，获取到表格内容，是查找识别区域的部分
//表格检测，获取到表格内容，是查找识别区域的部分，返回的是校正之后的图像
Mat mat = markingArea(sourceimage);
String destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "cardResult_3.png";
Highgui.imwrite(destPath, mat);
@@ -84,13 +84,13 @@ public class Card2Controller extends BaseController {
public static Mat markingArea(String path){
Mat source = Highgui.imread(path, Highgui.CV_LOAD_IMAGE_COLOR);
Mat img = new Mat();;
// 彩色转灰度
Mat result= source.clone();
// 彩色转灰度
Imgproc.cvtColor(source, img, Imgproc.COLOR_BGR2GRAY);
//此处图像预处理，可以使用方式1也可以使用方式2都可以，自己也可以测试下2种方式的差异
// //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
// // 高斯滤波，降噪
// Imgproc.GaussianBlur(img, img, new Size(3,3), 2, 2);
//
// // Canny边缘检测
// Imgproc.Canny(img, img, 20, 60, 3, false);
// // 膨胀，连接边缘
@@ -99,6 +99,9 @@ public class Card2Controller extends BaseController {
//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
Mat element = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5));
Imgproc.morphologyEx(img, img, Imgproc.MORPH_GRADIENT, element);
//图像二值化，使用的是OTSU二值化，阈值为170，也可以使用自适用二值化
// Imgproc.adaptiveThreshold(img,img, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY_INV, 51, 10);
Imgproc.threshold(img,img, 170, 255, Imgproc.THRESH_BINARY|Imgproc.THRESH_OTSU);
String destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "cardResult_1.png";
@@ -107,10 +110,9 @@ public class Card2Controller extends BaseController {
List<MatOfPoint> contours = new ArrayList<>();
Mat hierarchy = new Mat();
//轮廓查找，主要就是找最外表格框
Imgproc.findContours(img, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
// 找出轮廓对应凸包的四边形拟合
List<MatOfPoint> squares = new ArrayList<>();
List<MatOfPoint> hulls = new ArrayList<>();
@@ -138,6 +140,7 @@ public class Card2Controller extends BaseController {
// 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
MatOfPoint approxf1 = new MatOfPoint();
approx.convertTo(approxf1, CvType.CV_32S);
//此处是筛选表格框，面积大于40000
if (approx.rows() == 4 && Math.abs(Imgproc.contourArea(approx)) > 40000 &&
Imgproc.isContourConvex(approxf1)) {
double maxCosine = 0;
@@ -145,7 +148,7 @@ public class Card2Controller extends BaseController {
double cosine = Math.abs(getAngle(approxf1.toArray()[j%4], approxf1.toArray()[j-2], approxf1.toArray()[j-1]));
maxCosine = Math.max(maxCosine, cosine);
}
// 角度大概72度
// 考虑到图片倾斜等情况，角度大概72度
if (maxCosine < 0.3) {
MatOfPoint tmp = new MatOfPoint();
contourHull.convertTo(tmp, CvType.CV_32S);
@@ -165,7 +168,6 @@ public class Card2Controller extends BaseController {
// 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
MatOfPoint contourHull = hulls.get(index);
MatOfPoint2f tmp = new MatOfPoint2f();
contourHull.convertTo(tmp, CvType.CV_32F);
Imgproc.approxPolyDP(tmp, approx, 3, true);
@@ -190,6 +192,7 @@ public class Card2Controller extends BaseController {
if (getSpacePointToPoint(p1, p2) > 2 * maxL) {
lines.add(new double[]{p1.x, p1.y, p2.x, p2.y});
logger.info("p1x:"+p1.x+" p1y:"+p1.y+" p2x:"+p2.x+" p2y:"+p2.y);
//画出4条边线，真正识别过程中这些都是可以注释掉的，只是为了方便观察
Core.line(source, new Point(p1.x, p1.y), new Point( p2.x, p2.y), new Scalar(255, 0, 0),4);
}
}
@@ -221,11 +224,11 @@ public class Card2Controller extends BaseController {
double space1 = getSpacePointToPoint(p1, p2);
double space2 = getSpacePointToPoint(p2, p3);
double space3 = getSpacePointToPoint(p3, p0);
// 使用最宽和最长的边作为进行图像矫正目标图像的长宽
double imgWidth = space1 > space3 ? space1 : space3;
double imgHeight = space0 > space2 ? space0 : space2;
logger.info("imgWidth:"+imgWidth+" imgHeight:"+imgHeight);
// 如果提取出的图片宽小于高，则旋转90度
// 如果提取出的图片宽小于高，则旋转90度，因为示例中的矩形框是宽>高的，如果宽小于高应该是图片旋转了
if (imgWidth > imgHeight) {
logger.info("----in");
double temp = imgWidth;
@@ -246,12 +249,14 @@ public class Card2Controller extends BaseController {
// new Point(imgWidth*0.4, imgHeight*0.4),
// new Point(imgWidth*1.6, imgHeight*0.4),
// new Point(imgWidth*1.6, imgHeight*1.6));
//quadMat目标图像的点设置，以之前取出的最长的长宽作为新图像的长宽，创建一个图层
MatOfPoint2f quadMat = new MatOfPoint2f(new Point(0, 0),
new Point(imgWidth, 0),
new Point(imgWidth, imgHeight),
new Point(0, imgHeight));

// 提取图像
// 提取图像，使用warpPerspective做图像的透视变换
Mat transmtx = Imgproc.getPerspectiveTransform(cornerMat, quadMat);
Imgproc.warpPerspective(result, quad, transmtx, quad.size());
return quad;
@@ -341,14 +346,28 @@ public class Card2Controller extends BaseController {
}
//答题卡识别
/**
* 答题卡识别，增加注释
* 已得到矫正后的图像后，进行后续答案识别算法过程
* @Author Songer
* @param mat
* @return String
* @Date 2018年12月19日
* 更新日志
* 2018年12月19日 王嵩 首次创建
*
*/
public String cardResult(Mat mat){
//设置剪切的边距，目的是裁剪表格边框，防止边框影响轮廓查找，这里设置为20像素
int cutsize = 20;
Mat img_cut = mat.submat(cutsize,mat.rows()-cutsize,cutsize,mat.cols()-cutsize);new Mat();
Mat img_gray = img_cut.clone();
//图像灰度化
Imgproc.cvtColor(img_cut, img_gray, Imgproc.COLOR_BGR2GRAY);
//图像二值化，注意是反向二值化以及OTSU算法
Imgproc.threshold(img_gray,img_gray, 170, 255, Imgproc.THRESH_BINARY_INV|Imgproc.THRESH_OTSU);
Mat temp = img_gray.clone();
//此处使用的是方式2，形态学梯度算法保留填图选项的边框
// //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
// // 高斯滤波，降噪
// Imgproc.GaussianBlur(temp, temp, new Size(3,3), 2, 2);
@@ -357,15 +376,14 @@ public class Card2Controller extends BaseController {
// // 膨胀，连接边缘
// Imgproc.dilate(temp, temp, new Mat(), new Point(-1,-1), 3, 1, new Scalar(1));
//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
Mat element = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5));
Imgproc.morphologyEx(temp, temp, Imgproc.MORPH_GRADIENT, element);
//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
Mat element = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5,5));
Imgproc.morphologyEx(temp, temp, Imgproc.MORPH_GRADIENT, element);
Imgproc.threshold(temp, temp, 170, 255, Imgproc.THRESH_BINARY|Imgproc.THRESH_OTSU);
String destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "cardResult_4.png";
Highgui.imwrite(destPath, temp);
// Highgui.imwrite("D:\\test\\abc\\card\\card3.png", temp);
// logger.info(temp.cols());
//按比例截取，此处是根据原答题卡的各列的比例截取成4列，学号那列还要横向分隔一下，上半部分是学号下半部分是答题卡
Mat cut1 = temp.submat(0,temp.rows(),0,(int)(0.275*temp.cols()));
Mat cut_gray1 = img_gray.submat(0,img_gray.rows(),0,(int)(0.275*img_gray.cols()));
@@ -389,6 +407,7 @@ public class Card2Controller extends BaseController {
// Highgui.imwrite("D:\\test\\abc\\card\\card_cut5.png", cut5);

List<String> resultList = new ArrayList<String>();
//按列处理
List<String> list1 = processByCol(cut1,cut_gray1,img_cut,5);
List<String> list2 = processByCol(cut2,cut_gray2,img_cut,5);
List<String> list3 = processByCol(cut3,cut_gray3,img_cut,4);
@@ -427,16 +446,20 @@ public class Card2Controller extends BaseController {
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
List<MatOfPoint> answerList = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
//进行轮廓查找，注意因为该答题卡特征是闭合填图区域，所以用这种方式，如果是非闭合填涂区域，则不适用
Imgproc.findContours(cut1.clone(), contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
// logger.info(contours.size());
// logger.info("-----w------"+(temp.width()*80/2693-20));
// logger.info("-----h------"+(temp.height()*80/2764-20));
for(int i = 0;i<contours.size();i++){
MatOfPoint mop= contours.get(i);
//每个轮廓给出外包矩形，便于操作
Rect rect = Imgproc.boundingRect(mop);
// logger.info("-----------"+rect.width+" "+rect.height);
//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
// //绘制每个轮廓图
// Imgproc.drawContours(cut1, contours, i, new Scalar(170), 2);
//此处是为了排除杂点，较小或较大的轮廓都是非填图选项，可以排除，可以按实际情况灵活变动限制条件，最好输出出轮廓便于观察
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){
// if(rect.width>50&&rect.height>50&&rect.area()>2500&&rect.area()<10000){
// Core.rectangle(img_cut, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y
@@ -462,6 +485,8 @@ public class Card2Controller extends BaseController {
}
});
//每4/5个一组组成新list，并按x坐标排序
//该方式依赖于预处理后的图片没有干扰轮廓。如果图像处理不佳，干扰项没排除，那么此处的分组就会错乱。
//提供一个优化思路：对于闭合填涂区域的可以使用水平垂直投影进行坐标定位点的判定。
int queno = 1;
int totoSize = answerList.size();
for (int i = 0; i < totoSize; i+=answerCols) {
@@ -471,7 +496,7 @@ public class Card2Controller extends BaseController {
}
List<MatOfPoint> newList = answerList.subList(i,toIndex);
Collections.sort(newList, new Comparator<MatOfPoint>() {
//按照y坐标升序排列
//按照x坐标升序排列
@Override
public int compare(MatOfPoint o1, MatOfPoint o2) {
Rect rect1 = Imgproc.boundingRect(o1);
@@ -490,12 +515,14 @@ public class Card2Controller extends BaseController {
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);
//绘制轮廓便于观察
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
//获取填涂百分比,填涂区域的二值化后取出非0点/掩模的轮廓面积
double p100 = Core.countNonZero(dst) * 100 / Core.countNonZero(mask);
String anno = index2ColName(j);
//认为非0像素超过80%的算是填涂
if(p100>80){
resultChoose += anno;
logger.info(p100+" 第"+queno+"行:选项（"+anno+"）填涂");
@@ -503,7 +530,7 @@ public class Card2Controller extends BaseController {
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){
if(i==0&&j==0){//输出一下第一个掩模
String destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "cardResult_5.png";
Highgui.imwrite(destPath, dst);
}


+ 3
- 0
src/main/java/com/acts/opencv/demo/DemoController.java View File

@@ -32,6 +32,7 @@ public class DemoController extends BaseController {
@RequestMapping(value = "detectFace")
public void detectFace(HttpServletResponse response, HttpServletRequest request, String url) {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
System.out.println("===========java.library.path:" + System.getProperty("java.library.path"));
logger.info("\nRunning DetectFaceDemo");
String resourcePath = getClass().getResource("/lbpcascade_frontalface.xml").getPath().substring(1);
logger.info("resourcePath============" + resourcePath);
@@ -62,6 +63,8 @@ public class DemoController extends BaseController {
System.out.println("Hello, OpenCV");
// Load the native library.
System.loadLibrary("opencv_java2413");
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
System.out.println("===========java.library.path:" + System.getProperty("java.library.path"));

}
}

+ 1
- 1
src/main/webapp/view/base/adaptiveBinary.jsp View File

@@ -91,7 +91,7 @@
</a>
</h4>
</div>
<div id="collapseOne" class="panel-collapse collapse"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div id="collapseOne" class="panel-collapse collapse in"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div class="box-body">
<h4>参考资料：<br>
<a href="https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/miscellaneous_transformations.html#adaptivethreshold">https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/miscellaneous_transformations.html#adaptivethreshold</a> <br>


+ 1
- 1
src/main/webapp/view/base/binary.jsp View File

@@ -93,7 +93,7 @@
</a>
</h4>
</div>
<div id="collapseOne" class="panel-collapse collapse"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div id="collapseOne" class="panel-collapse collapse in"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div class="box-body">
<h4>参考资料：<br>
<a href="https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/miscellaneous_transformations.html#threshold">https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/miscellaneous_transformations.html#threshold</a> <br>


+ 1
- 1
src/main/webapp/view/base/canny.jsp View File

@@ -100,7 +100,7 @@
</a>
</h4>
</div>
<div id="collapseOne" class="panel-collapse collapse"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div id="collapseOne" class="panel-collapse collapse in"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div class="box-body">
<h4>参考资料：<br>
<a href="https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/feature_detection.html#canny">https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/feature_detection.html#canny</a><br>


+ 1
- 1
src/main/webapp/view/base/contours.jsp View File

@@ -94,7 +94,7 @@
</a>
</h4>
</div>
<div id="collapseOne" class="panel-collapse collapse"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div id="collapseOne" class="panel-collapse collapse in"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div class="box-body">
<h4>参考资料<br>
<a href="https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html#findcontours">https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html#findcontours</a><br>


+ 1
- 1
src/main/webapp/view/base/erosion_dilation.jsp View File

@@ -107,7 +107,7 @@
</a>
</h4>
</div>
<div id="collapseOne" class="panel-collapse collapse"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div id="collapseOne" class="panel-collapse collapse in"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div class="box-body">
<h4>参考资料：<br>
<a href="https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/filtering.html#erode">https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/filtering.html#erode</a> <br>


+ 1
- 1
src/main/webapp/view/base/findcolor.jsp View File

@@ -79,7 +79,7 @@
</a>
</h4>
</div>
<div id="collapseOne" class="panel-collapse collapse"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div id="collapseOne" class="panel-collapse collapse in"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div class="box-body">
备注知识：所谓RGB就是：红（Red）、绿（Green）、蓝（Blue）三种色光原色。RGB色彩模型的混色属于加法混色。每种原色的数值越高，色彩越明亮。 R、G、B都为0时是黑色，都为255时是白色。RGB是电脑设计中最直接的色彩表示方法。电脑中的24位真彩图像，就是采用RGB模型来精确记录色彩。所以，在电脑中利用RGB数值可以精确取得某种颜色。
RGB虽然表示直接，但是R、G、B数值和色彩的三属性没有直接的联系，不能揭示色彩之间的关系。所以在进行配色设计时，RGB模型就不是那么合适了。在所有美术软件中，都可以直接设置RGB的数值。有些软件还提供了直观的"RGB三维色彩模型"来设置RGB色彩值。<br>


+ 2
- 2
src/main/webapp/view/base/findtemplate.jsp View File

@@ -181,7 +181,7 @@
</a>
</h4>
</div>
<div id="collapseOne" class="panel-collapse collapse"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div id="collapseOne" class="panel-collapse collapse in"><!--class="panel-collapse collapse in"中的 in 控制展开 -->
<div class="box-body">
<h4>
参考资料:<br>
@@ -231,7 +231,7 @@
</a>
</h4>
</div>
<div id="collapseTwo" class="panel-collapse collapse">
<div id="collapseTwo" class="panel-collapse collapse in">
<div class="box-body">
<h4>模板匹配方法是利用原图与模板图之间重叠的部分进行对比，来得出最近似的结果集。具体的匹配算法如下Method参数所示。Imgproc.TM_SQDIFF=0;标准平方差匹配：Imgproc.TM_SQDIFF_NORMED=1；相关性匹配：Imgproc.TM_CCORR=2；
标准相关匹配：Imgproc.TM_CCORR_NORMED=3；相关性系数匹配：Imgproc.TM_CCOEFF=4；标准相关性系数匹配：Imgproc.TM_CCOEFF_NORMED=5<br>


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<h4>参考资料：<br>
<a href="https://docs.opencv.org/2.4.13.6/modules/core/doc/operations_on_arrays.html#addweighted">Opencv官方文档：addweighted</a> <br>


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<h4>参考资料：<br>
<a href="https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/filtering.html#gaussianblur">Opencv官方文档：gaussianblur</a> <br>


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<h4>参考资料：<br>
<a href="https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/histograms.html">https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/histograms.html</a> <br>


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<h4>参考资料：<br>
<a href="https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/feature_detection.html#houghcircles">https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/feature_detection.html#houghcircles</a><br>


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<h4>参考资料：<br>
<a href="https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/feature_detection.html#houghlines">https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/feature_detection.html#houghlines</a><br>


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<h4>参考资料：<br>
<a href="http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/imgproc/opening_closing_hats/opening_closing_hats.html#morphology-2">官网形态学进阶demo</a><br>


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参考资料：<a href="https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/geometric_transformations.html#resize">https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/geometric_transformations.html#resize</a>
<br>public static void resize(Mat src, Mat dst, Size dsize, double fx, double fy, int interpolation); 各参数说明：</h4>


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<h4>参考资料：<br>
<a href="https://docs.opencv.org/2.4.13.6/modules/core/doc/operations_on_arrays.html#addweighted">Opencv官方文档：addweighted</a> <br>


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<h4>参考资料：<br>
<a href="https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/miscellaneous_transformations.html#adaptivethreshold">https://docs.opencv.org/2.4.13.6/modules/imgproc/doc/miscellaneous_transformations.html#adaptivethreshold</a> <br>


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图片是拍摄的一张常规答题卡，在此基础上进行了改进，增加了底部和侧边的标记块，用于定位答题卡各选项的坐标。识别思路如下：<br>
1、图形预处理：腐蚀膨胀、二值化、高斯模糊等<br>


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本demo先进行填图区域的识别，之前肯定先需要坐标点定位，目前先写死。实际中应该提前利用定位点确定坐标位置。思路如下：<br>
1、先进行图片预处理，由于是扫描的图片，像素渣，噪点很明显，所以先除噪。<br>


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