opencv3.0.0 识别表格

转载地址：http://answers.opencv.org/question/63847/how-to-extract-tables-from-an-image/

As the others proposed finding the horizontal and vertical lines seems to be a nice way to go. Below you can find such a solution. In case you have any question feel free to ask, though I have added comments through my code so it should not be hard to follow.

#include <iostream>#include <opencv2/opencv.hpp>using namespace std;using namespace cv;int main(){    // Load source image    string filename = "table.jpg";    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));//    dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1)); // expand horizontal lines    // 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));//    dilate(vertical, vertical, verticalStructure, Point(-1, -1)); // expand vertical lines    // Show extracted vertical lines    imshow("vertical", vertical);

    // create a mask which includes the tables    Mat mask = horizontal + vertical;    imshow("mask", 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 you can do whatever post process you want         * with the data within the rectangles/tables. */        imshow("roi", rois[i]);        waitKey();    }

 

    imshow("contours", rsz);

    waitKey();    return 0;}

Of course you will need to try it by yourself and apply any modifications that might be needed depending on your dataset. Enjoy ;-).