main.cpp

#include <iostream>
#include <opencv2\opencv.hpp>
#include <opencv2\imgproc\imgproc.hpp>
#include <algorithm>

using namespace std;
using namespace cv;

cv::Point2f center(0, 0);


bool sort_corners(std::vector<cv::Point2f>& corners)
{
    std::vector<cv::Point2f> top, bot;
    cv::Point2f tmp_pt;
    std::vector<cv::Point2f> olddata = corners;

    if (corners.size() != 4)
    {
        return false;
    }

    for (size_t i = 0; i < corners.size(); i++)
    {
        for (size_t j = i + 1; j<corners.size(); j++)
        {
            if (corners[i].y < corners[j].y)
            {
                tmp_pt = corners[i];
                corners[i] = corners[j];
                corners[j] = tmp_pt;
            }
        }
    }
    top.push_back(corners[0]);
    top.push_back(corners[1]);
    bot.push_back(corners[2]);
    bot.push_back(corners[3]);
    if (top.size() == 2 && bot.size() == 2) {
        corners.clear();
        cv::Point2f tl = top[0].x > top[1].x ? top[1] : top[0];
        cv::Point2f tr = top[0].x > top[1].x ? top[0] : top[1];
        cv::Point2f bl = bot[0].x > bot[1].x ? bot[1] : bot[0];
        cv::Point2f br = bot[0].x > bot[1].x ? bot[0] : bot[1];
        corners.push_back(tl);
        corners.push_back(tr);
        corners.push_back(br);
        corners.push_back(bl);
        return true;
    }
    else
    {
        corners = olddata;
        return false;
    }
}

cv::Point2f computeIntersect(cv::Vec4i a, cv::Vec4i b)
{
    int x1 = a[0], y1 = a[1], x2 = a[2], y2 = a[3];
    int x3 = b[0], y3 = b[1], x4 = b[2], y4 = b[3];

    if (float d = ((float)(x1 - x2) * (y3 - y4)) - ((y1 - y2) * (x3 - x4)))
    {
        cv::Point2f pt;
        pt.x = ((x1*y2 - y1*x2) * (x3 - x4) - (x1 - x2) * (x3*y4 - y3*x4)) / d;
        pt.y = ((x1*y2 - y1*x2) * (y3 - y4) - (y1 - y2) * (x3*y4 - y3*x4)) / d;
        return pt;
    }
    else
        return cv::Point2f(-1, -1);
}

bool IsBadLine(int a, int b)
{
    if (a * a + b * b < 100)
    {
        return true;
    }
    else
    {
        return false;
    }
}

bool x_sort(const Point2f & m1, const Point2f & m2)
{
    return m1.x < m2.x;
}

//确定四个点的中心线
void sortCorners(std::vector<cv::Point2f>& corners,
    cv::Point2f center)
{
    std::vector<cv::Point2f> top, bot;
    vector<Point2f> backup = corners;

    sort(corners, x_sort);  //注意先按x的大小给4个点排序

    for (int i = 0; i < corners.size(); i++)
    {
        if (corners[i].y < center.y && top.size() < 2)    //这里的小于2是为了避免三个顶点都在top的情况
            top.push_back(corners[i]);
        else
            bot.push_back(corners[i]);
    }
    corners.clear();

    if (top.size() == 2 && bot.size() == 2) 
    {
        cout << "log" << endl;
        cv::Point2f tl = top[0].x > top[1].x ? top[1] : top[0];
        cv::Point2f tr = top[0].x > top[1].x ? top[0] : top[1];
        cv::Point2f bl = bot[0].x > bot[1].x ? bot[1] : bot[0];
        cv::Point2f br = bot[0].x > bot[1].x ? bot[0] : bot[1];


        corners.push_back(tl);
        corners.push_back(tr);
        corners.push_back(br);
        corners.push_back(bl);
    }
    else
    {
        corners = backup;
    }
}

int g_dst_hight;  //最终图像的高度
int g_dst_width; //最终图像的宽度

void CalcDstSize(const vector<cv::Point2f>& corners)
{
    int h1 = sqrt((corners[0].x - corners[3].x)*(corners[0].x - corners[3].x) + (corners[0].y - corners[3].y)*(corners[0].y - corners[3].y));
    int h2 = sqrt((corners[1].x - corners[2].x)*(corners[1].x - corners[2].x) + (corners[1].y - corners[2].y)*(corners[1].y - corners[2].y));
    g_dst_hight = MAX(h1, h2);

    int w1 = sqrt((corners[0].x - corners[1].x)*(corners[0].x - corners[1].x) + (corners[0].y - corners[1].y)*(corners[0].y - corners[1].y));
    int w2 = sqrt((corners[2].x - corners[3].x)*(corners[2].x - corners[3].x) + (corners[2].y - corners[3].y)*(corners[2].y - corners[3].y));
    g_dst_width = MAX(w1, w2);
}

int main()
{
    Mat src = imread("30.png");
    imshow("src img", src);
    Mat source = src.clone();

    Mat bkup = src.clone();

    Mat img = src.clone();
    cvtColor(img, img, CV_RGB2GRAY);   //二值化
    imshow("gray", img);
    //equalizeHist(img, img);
    //imshow("equal", img);
    GaussianBlur(img, img, Size(5, 5), 0, 0);  //高斯滤波

    //获取自定义核
    Mat element = getStructuringElement(MORPH_RECT, Size(3, 3)); //第一个参数MORPH_RECT表示矩形的卷积核，当然还可以选择椭圆形的、交叉型的
    //膨胀操作
    dilate(img, img, element);  //实现过程中发现，适当的膨胀很重要
    imshow("dilate", img);
    Canny(img, img, 30, 120, 3);   //边缘提取
    imshow("get contour", img);

    vector<vector<Point> > contours;
    vector<vector<Point> > f_contours;
    std::vector<cv::Point> approx2;
    //注意第5个参数为CV_RETR_EXTERNAL，只检索外框  
    findContours(img, f_contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE); //找轮廓

    //求出面积最大的轮廓
    int max_area = 0;
    int index;
    for (int i = 0; i < f_contours.size(); i++)
    {
        double tmparea = fabs(contourArea(f_contours[i]));
        if (tmparea > max_area)
        {
            index = i;
            max_area = tmparea;
        }

    }
    contours.push_back(f_contours[index]);

    cout << contours.size() << endl;  //因为我写的是找出最外层轮廓，所以理论上只有一个轮廓

    vector<Point> tmp = contours[0];

    for (int line_type = 1; line_type <= 3; line_type++)
    {
        cout << "line_type: " << line_type << endl;
        Mat black = img.clone();
        black.setTo(0);
        drawContours(black, contours, 0, Scalar(255), line_type);  //注意线的厚度，不要选择太细的
        imshow("show contour", black);


        std::vector<Vec4i> lines;
        std::vector<cv::Point2f> corners;
        std::vector<cv::Point2f> approx;
        
        int para = 10;
        int flag = 0;
        int round = 0;
        for (; para < 300; para++)
        {
            cout << "round: " << ++round << endl;
            lines.clear();
            corners.clear();
            approx.clear();
            center = Point2f(0, 0);

            cv::HoughLinesP(black, lines, 1, CV_PI / 180, para, 30, 10);

            //过滤距离太近的直线
            std::set<int> ErasePt;
            for (int i = 0; i < lines.size(); i++)
            {
                for (int j = i + 1; j < lines.size(); j++)
                {
                    if (IsBadLine(abs(lines[i][0] - lines[j][0]), abs(lines[i][1] - lines[j][1])) && (IsBadLine(abs(lines[i][2] - lines[j][2]), abs(lines[i][3] - lines[j][3]))))
                    {
                        ErasePt.insert(j);//将该坏线加入集合
                    }
                }
            }

            int Num = lines.size();
            while (Num != 0)
            {
                std::set<int>::iterator j = ErasePt.find(Num);
                if (j != ErasePt.end())
                {
                    lines.erase(lines.begin() + Num - 1);
                }
                Num--;
            }
            if (lines.size() != 4)
            {
                continue;
            }

            //计算直线的交点，保存在图像范围内的部分
            for (int i = 0; i < lines.size(); i++)
            {
                for (int j = i + 1; j < lines.size(); j++)
                {
                    cv::Point2f pt = computeIntersect(lines[i], lines[j]);
                    if (pt.x >= 0 && pt.y >= 0 && pt.x <= src.cols && pt.y <= src.rows)             //保证交点在图像的范围之内
                        corners.push_back(pt);
                }
            }
            if (corners.size() != 4)
            {
                continue;
            }
#if 1
            bool IsGoodPoints = true;

            //保证点与点的距离足够大以排除错误点
            for (int i = 0; i < corners.size(); i++)
            {
                for (int j = i + 1; j < corners.size(); j++)
                {
                    int distance = sqrt((corners[i].x - corners[j].x)*(corners[i].x - corners[j].x) + (corners[i].y - corners[j].y)*(corners[i].y - corners[j].y));
                    if (distance < 5)
                    {
                        IsGoodPoints = false;
                    }
                }
            }

            if (!IsGoodPoints) continue;
#endif
            cv::approxPolyDP(cv::Mat(corners), approx, cv::arcLength(cv::Mat(corners), true) * 0.02, true);

            if (lines.size() == 4 && corners.size() == 4 && approx.size() == 4)
            {
                flag = 1;
                break;
            }
        }

        // Get mass center  
        for (int i = 0; i < corners.size(); i++)
            center += corners[i];
        center *= (1. / corners.size());

        if (flag)
        {
            cout << "we found it!" << endl;
            cv::circle(bkup, corners[0], 3, CV_RGB(255, 0, 0), -1);
            cv::circle(bkup, corners[1], 3, CV_RGB(0, 255, 0), -1);
            cv::circle(bkup, corners[2], 3, CV_RGB(0, 0, 255), -1);
            cv::circle(bkup, corners[3], 3, CV_RGB(255, 255, 255), -1);
            cv::circle(bkup, center, 3, CV_RGB(255, 0, 255), -1);
            imshow("backup", bkup);
            cout << "corners size" << corners.size() << endl;
          // cv::waitKey();

           // bool sort_flag = sort_corners(corners);
           // if (!sort_flag) cout << "fail to sort" << endl;

            sortCorners(corners, center);
            cout << "corners size" << corners.size() << endl;
            cout << "tl:" << corners[0] << endl;
            cout << "tr:" << corners[1] << endl;
            cout << "br:" << corners[2] << endl;
            cout << "bl:" << corners[3] << endl;

            CalcDstSize(corners);

            cv::Mat quad = cv::Mat::zeros(g_dst_hight, g_dst_width, CV_8UC3);
            std::vector<cv::Point2f> quad_pts;
            quad_pts.push_back(cv::Point2f(0, 0));
            quad_pts.push_back(cv::Point2f(quad.cols, 0));
            quad_pts.push_back(cv::Point2f(quad.cols, quad.rows));

            quad_pts.push_back(cv::Point2f(0, quad.rows));

            cv::Mat transmtx = cv::getPerspectiveTransform(corners, quad_pts);
            cv::warpPerspective(source, quad, transmtx, quad.size());

            imshow("find", bkup);
            cv::imshow("quadrilateral", quad);

            /*如果需要二值化就解掉注释把*/
            /*
            Mat local,gray;
            cvtColor(quad, gray, CV_RGB2GRAY);
            int blockSize = 25;
            int constValue = 10;
            adaptiveThreshold(gray, local, 255, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, blockSize, constValue);

            imshow("二值化", local);

            */


            cv::waitKey();
            return 0;
        }
    }

    cout << "can not transform!" << endl;
    waitKey();
}