c++ - 如何获取属于轮廓特定一侧的点？

Question

我正在使用 OpenCV 2.9 c++，在带有 findContours 的图像中找到了一个轮廓及其边界框:

下一步是获取所有轮廓点，这些点存储在 vector<Point> 中，在 S 和 E 之间，同样在对边(轮廓箭头指向的地方)。我不知道如何提取这些点，如果你能在这里帮助我，那就太好了。

提前致谢!

编辑:

点S和E没有预先给定，它们是与边界框相交的轮廓点。

Answer 1

这是一种适用于综合数据的方法，但可能不适用于真实轮廓。问题是找到角落，我只是没有找到正确的方法，虽然我觉得我应该知道怎么做。这个想法是找到某种“局部极值”，它描述了与其邻居相比的角落。

正如我所说，肯定有问题，但我现在不明白......

我生成这个输入:

使用 findContours 并提取“极值”(仅适用于我的综合数据):

然后，找到极值和边界框角之间的对应关系，并将其间的所有轮廓点排序为边界框线段:

如果有人解释了如何正确测量轮廓中的拐角/极值，也许您可​​以使用该代码的某些部分，也许可以使用整个代码……代码没有任何优化，许多模运算等……没有封装。 ..

int main()
{
    cv::Mat input;
    input = cv::Mat::zeros(512,512,CV_8UC1);

    // generate something similar to the question:
    cv::Point2f topLeft  = 2*cv::Point2f(100,50);
    cv::Point2f topRight = 2*cv::Point2f(180,55);
    cv::Point2f bottomLeft  = 2*cv::Point2f(50,150);
    cv::Point2f bottomRight = 2*cv::Point2f(200,145);

    int lineWidth = 1;
    cv::line(input, topLeft, topRight, cv::Scalar(255),lineWidth);
    cv::line(input, topRight, bottomRight, cv::Scalar(255),lineWidth);
    cv::line(input, bottomRight, bottomLeft, cv::Scalar(255),lineWidth);
    cv::line(input, bottomLeft, topLeft, cv::Scalar(255),lineWidth);

    cv::Mat colored;
    cv::cvtColor(input,colored, CV_GRAY2BGR);


    std::vector<std::vector<cv::Point> > contours;
    cv::findContours(input,contours,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_NONE);


    if(contours.size() == 0) return 0;

    std::vector<cv::Point> relevantContour = contours[0];
    cv::Rect bb = cv::boundingRect(relevantContour);

    // display bb
    //cv::rectangle(colored, bb, cv::Scalar(0,0,255));


    // you can encapusalte that easily!

    // first "derivative" of conour
    std::vector<cv::Point2f> contourDifferences;
    std::vector<cv::Point> contourDifferencesPoints;
    for(unsigned int i=0; i<relevantContour.size(); ++i)
    {
        contourDifferences.push_back(relevantContour[i] - relevantContour[(i+2)%relevantContour.size()]);
        contourDifferencesPoints.push_back(relevantContour[(i+1)%relevantContour.size()]);
    }

    // second "derivative"
    std::vector<cv::Point2f> contourDifferences2;
    std::vector<cv::Point> contourDifferences2Points;
    for(unsigned int i=0; i<contourDifferences.size(); ++i)
    {
        contourDifferences2.push_back(contourDifferences[i] - contourDifferences[(i+2)%contourDifferences.size()]);
        contourDifferences2Points.push_back(contourDifferencesPoints[(i+1)%contourDifferencesPoints.size()]);
    }


    // local optima:
    std::vector<cv::Point> localOptima;
    float delta = 0.0f; // add some delta to overcome noise?!? not necessary for my example, but maybe good for real contours...
    for(unsigned int i=0; i<contourDifferences2.size(); ++i)
    {
        int cIdx = (i+1)%contourDifferences2.size();
        if(cv::norm(contourDifferences2[cIdx]) - delta > cv::norm(contourDifferences2[i]) 
            && cv::norm(contourDifferences2[cIdx]) - delta > cv::norm(contourDifferences2[(i+2)%contourDifferences2.size()]))
        {
            std::cout << cv::norm(contourDifferences2[cIdx]) << std::endl;
            cv::circle(colored, contourDifferences2Points[cIdx], 5, cv::Scalar(0,255,0));
            localOptima.push_back(contourDifferences2Points[cIdx]);
        }
    }


    // this should be 4 local optima in your setting if everything works fine.
    std::cout << "number of local Optima: " << localOptima.size() << std::endl;

    // these are the 4 bounding box corners.
    std::vector<cv::Point> cornersBB;
    cornersBB.push_back(cv::Point(bb.x, bb.y));
    cornersBB.push_back(cv::Point(bb.x+bb.width, bb.y));
    cornersBB.push_back(cv::Point(bb.x+bb.width, bb.y+bb.height));
    cornersBB.push_back(cv::Point(bb.x, bb.y+bb.height));

    std::vector<int> matchingBB2Contour(cornersBB.size(), -1);
    //std::vector<int> matchingContour2BB(localOptima.size(), -1);

    for(unsigned int i=0; i<cornersBB.size(); ++i)
    {
        float bestDist = FLT_MAX;
        for(unsigned int j=0; j<localOptima.size(); ++j)
        {
            float cDist = cv::norm(cornersBB[i] - localOptima[j]);
            if(cDist < bestDist)
            {
                bestDist = cDist;
                matchingBB2Contour[i] = j;
            }
        }
    }

    // todo: compute the best matching from contour optima to bounding box corners too and compare them.

    float contourOrientation = cv::contourArea(relevantContour, true); // negative = counter-clockwise
    std::cout << contourOrientation << std::endl;
    // now visualize the result:
    for(unsigned int i=0; i<cornersBB.size(); ++i)
    {
        cv::Scalar segmentColor(i*100, 255-(i*100), (i==3)?255:0);

        // color the bounding box segment too
        cv::line(colored, cornersBB[i], cornersBB[(i+1) % cornersBB.size()], segmentColor, 1);

        cv::Point startPoint = localOptima[matchingBB2Contour[i]];
        cv::Point endPoint = localOptima[matchingBB2Contour[(i+1)%matchingBB2Contour.size()] % localOptima.size()];
        int startIndex = -1;
        int endIndex = -1;
        for(unsigned int j=0; j<relevantContour.size(); ++j)
        {
            if(startPoint == relevantContour[j])
            {
                startIndex = j;
            }
            if(endPoint == relevantContour[j])
            {
                endIndex = j;
            }
        }

        // swap start and end if orientation is negative = contour is counter clockwise
        if(contourOrientation < 0)
        {
            int tmp = startIndex;
            startIndex = endIndex;
            endIndex = tmp;
        }

        std::cout << "start index: " << startIndex << std::endl;
        std::cout << "end index: " << endIndex << std::endl;

        //while(relevantContour[startIndex%relevantContour.size()] != endPoint)
        while(startIndex % relevantContour.size() != endIndex)
        {
            cv::circle(colored, relevantContour[startIndex%relevantContour.size()], 1, segmentColor);
            startIndex++;
        }

    }

    // show bb-to-contour correspondences:
    /*
    for(unsigned int i=0; i<matchingBB2Contour.size(); ++i)
    {
        cv::line(colored, localOptima[matchingBB2Contour[i]], cornersBB[i], cv::Scalar(0,0,255),2);
    }
    */


    cv::imshow("colored", colored);
    cv::waitKey(0);
    return 0;
}