检测表格中的行和单元格以搜索复选标记

Question

我需要检测文档图像中的表格，并为每个表格检测行号并识别带有复选标记的单元格。

这是我的源图片：

我已经关注了这个example，我可以获得三个单独的图像，每个图像包含一个表格。

现在我需要有关代码的帮助来处理单个表以检测每行的复选标记单元格。

以下是代码：

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;
int main(int ac, char **av)
{
// Load source image
string filename = "questions.png";
Mat src = imread(filename);

// Check if image is loaded fine
if(!src.data)
    cerr << "Problem loading image!!!" << endl;

 // Show source image
  imshow("src", src);

// resizing for practical reasons
Mat rsz;
Size size(800, 900);
resize(src, rsz, size);

imshow("rsz", rsz);

// Transform source image to gray if it is not
Mat gray;

if (rsz.channels() == 3)
{
    cvtColor(rsz, gray, CV_BGR2GRAY);
}
else
{
    gray = rsz;
}

 // Show gray image
 imshow("gray", gray);

// Apply adaptiveThreshold at the bitwise_not of gray, notice the ~ symbol
Mat bw;
adaptiveThreshold(~gray, bw, 255, CV_ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, 15, -2);

// Show binary image
imshow("binary", bw);

 // Create the images that will use to extract the horizonta and vertical lines
Mat horizontal = bw.clone();
Mat vertical = bw.clone();

int scale = 15; // play with this variable in order to increase/decrease the amount of lines to be detected

// Specify size on horizontal axis
int horizontalsize = horizontal.cols / scale;

// Create structure element for extracting horizontal lines through morphology operations
Mat horizontalStructure = getStructuringElement(MORPH_RECT, Size(horizontalsize,1));

// Apply morphology operations
erode(horizontal, horizontal, horizontalStructure, Point(-1, -1));
dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1));


// Show extracted horizontal lines
imshow("horizontal", horizontal);

// Specify size on vertical axis
int verticalsize = vertical.rows / scale;

// Create structure element for extracting vertical lines through morphology operations
Mat verticalStructure = getStructuringElement(MORPH_RECT, Size( 1,verticalsize));

// Apply morphology operations
erode(vertical, vertical, verticalStructure, Point(-1, -1));
dilate(vertical, vertical, verticalStructure, Point(-1, -1));

// Show extracted vertical lines
// imshow("vertical", vertical);

// create a mask which includes the tables
Mat mask = horizontal + vertical;
//imshow("mask", mask);
// waitKey(5000);
// detectCells(mask);


    // find the joints between the lines of the tables, we will use this information in order to descriminate tables from pictures (tables will contain more than 4 joints while a picture only 4 (i.e. at the corners))
Mat joints;
bitwise_and(horizontal, vertical, joints);
imshow("joints", joints);

  // Find external contours from the mask, which most probably will belong to tables or to images
vector<Vec4i> hierarchy;
std::vector<std::vector<cv::Point> > contours;
cv::findContours(mask, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));

vector<vector<Point> > contours_poly( contours.size() );
vector<Rect> boundRect( contours.size() );
vector<Mat> rois;

for (size_t i = 0; i < contours.size(); i++)
{
    // find the area of each contour
    double area = contourArea(contours[i]);

    // filter individual lines of blobs that might exist and they do not represent a table
    if(area < 100) // value is randomly chosen, you will need to find that by yourself with trial and error procedure
        continue;

    approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );
    boundRect[i] = boundingRect( Mat(contours_poly[i]) );

    // find the number of joints that each table has
    Mat roi = joints(boundRect[i]);

    vector<vector<Point> > joints_contours;
    findContours(roi, joints_contours, RETR_CCOMP, CHAIN_APPROX_SIMPLE);

    // if the number is not more than 5 then most likely it not a table

    if(joints_contours.size() <= 4)
        continue;

    rois.push_back(rsz(boundRect[i]).clone());

    //drawContours( rsz, contours, i, Scalar(0, 0, 255), CV_FILLED, 8, vector<Vec4i>(), 0, Point() );
    rectangle( rsz, boundRect[i].tl(), boundRect[i].br(), Scalar(0, 255, 0), 1, 8, 0 );
}

for(size_t i = 0; i < rois.size(); ++i)
{

    /* Now I should detect cells inside each table
     * detecting checkmarks                             */
       imshow("roi", rois[i]);

       waitKey();

}
imshow("contours", rsz);
    waitKey();
return 0;
}

我的想法是找到从左到右，从上到下的方形区域，并以这种方式跳过第一列和第二列，然后测量右边其余方块内的像素密度，以确定单元格是否已勾选或不。

问题是我无法用openCV做到这一点，我是图像处理中的新手:-)因此我被要求帮助。