Templates 二维点数据集中的OpenCV模板

我在想，在二维点阵列中检测“图形”的最佳方法是什么

在这个例子中，我有两个“模板”。图1是一个模板，图2是一个模板。 这些模板中的每一个都仅作为具有x、y坐标的点向量存在

假设我们有第三个向量，其中的点是x，y坐标

找出并隔离与第三个阵列中前两个阵列之一匹配的点的最佳方法是什么。（包括缩放、旋转）

我一直在尝试最近的邻居（FlannBasedMatcehr）甚至SVM实现，但它似乎没有得到任何结果，我认为模板匹配似乎也不是解决问题的方法。我不是在图像上工作，而是在内存中的2D点上工作

特别是因为输入向量总是比要比较的原始数据集有更多的点

它所需要做的就是在数组中找到与模板匹配的点

我不是机器学习或opencv方面的“专家”。我想我从一开始就忽略了一些事情

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非常感谢您的帮助/建议

只是为了好玩，我试了一下：

选择点数据集中的两个点，并计算将前两个模式点映射到这些点的转换

测试是否可以在数据集中找到所有转换的模式点

这种方法非常简单，复杂性为

O（m*n²）

，有n个数据点和一个大小为m（点）的单一模式。对于某些最近邻搜索方法，这种复杂性可能会增加。所以你必须考虑它是否对你的应用效率不够。

一些改进可能包括一些启发式方法，不选择所有的n²点组合，但您需要最大模式缩放的背景信息或类似信息

对于评估，我首先创建了一个模式：

然后我创建随机点，并在其中的某个位置添加图案（缩放、旋转和平移）：

经过一些计算后，这种方法可以识别模式。红线显示用于变换计算的选定点

代码如下：

// draw a set of points on a given destination image
void drawPoints(cv::Mat & image, std::vector<cv::Point2f> points, cv::Scalar color = cv::Scalar(255,255,255), float size=10)
{
    for(unsigned int i=0; i<points.size(); ++i)
    {
        cv::circle(image, points[i], 0, color, size);
    }
}

// assumes a 2x3 (affine) transformation (CV_32FC1). does not change the input points
std::vector<cv::Point2f> applyTransformation(std::vector<cv::Point2f> points, cv::Mat transformation)
{
    for(unsigned int i=0; i<points.size(); ++i)
    {
        const cv::Point2f tmp = points[i];
        points[i].x = tmp.x * transformation.at<float>(0,0) + tmp.y * transformation.at<float>(0,1) + transformation.at<float>(0,2) ;
        points[i].y = tmp.x * transformation.at<float>(1,0) + tmp.y * transformation.at<float>(1,1) + transformation.at<float>(1,2) ;
    }

    return points;
}

const float PI = 3.14159265359;

// similarity transformation uses same scaling along both axes, rotation and a translation part
cv::Mat composeSimilarityTransformation(float s, float r, float tx, float ty)
{
    cv::Mat transformation = cv::Mat::zeros(2,3,CV_32FC1);

    // compute rotation matrix and scale entries
    float rRad = PI*r/180.0f;
    transformation.at<float>(0,0) = s*cosf(rRad);
    transformation.at<float>(0,1) = s*sinf(rRad);
    transformation.at<float>(1,0) = -s*sinf(rRad);
    transformation.at<float>(1,1) = s*cosf(rRad);

    // translation
    transformation.at<float>(0,2) = tx;
    transformation.at<float>(1,2) = ty;

    return transformation;

}

// create random points
std::vector<cv::Point2f> createPointSet(cv::Size2i imageSize, std::vector<cv::Point2f> pointPattern, unsigned int nRandomDots = 50)
{
    // subtract center of gravity to allow more intuitive rotation
    cv::Point2f centerOfGravity(0,0);
    for(unsigned int i=0; i<pointPattern.size(); ++i)
    {
        centerOfGravity.x += pointPattern[i].x;
        centerOfGravity.y += pointPattern[i].y;
    }
    centerOfGravity.x /= (float)pointPattern.size();
    centerOfGravity.y /= (float)pointPattern.size();
    pointPattern = applyTransformation(pointPattern, composeSimilarityTransformation(1,0,-centerOfGravity.x, -centerOfGravity.y));

    // create random points
    //unsigned int nRandomDots = 0;
    std::vector<cv::Point2f> pointset;
    srand (time(NULL));
    for(unsigned int i =0; i<nRandomDots; ++i)
    {
        pointset.push_back( cv::Point2f(rand()%imageSize.width, rand()%imageSize.height) );
    }

    cv::Mat image = cv::Mat::ones(imageSize,CV_8UC3);
    image = cv::Scalar(255,255,255);

    drawPoints(image, pointset, cv::Scalar(0,0,0));
    cv::namedWindow("pointset"); cv::imshow("pointset", image);

    // add point pattern to a random location

    float scaleFactor = rand()%30 + 10.0f;
    float translationX = rand()%(imageSize.width/2)+ imageSize.width/4;
    float translationY = rand()%(imageSize.height/2)+ imageSize.height/4;
    float rotationAngle = rand()%360;

    std::cout << "s: " << scaleFactor << " r: " << rotationAngle << " t: " << translationX << "/" << translationY << std::endl;


    std::vector<cv::Point2f> transformedPattern = applyTransformation(pointPattern,composeSimilarityTransformation(scaleFactor,rotationAngle,translationX,translationY));
    //std::vector<cv::Point2f> transformedPattern = applyTransformation(pointPattern,trans);

    drawPoints(image, transformedPattern, cv::Scalar(0,0,0));
    drawPoints(image, transformedPattern, cv::Scalar(0,255,0),3);
    cv::imwrite("dataPoints.png", image);
    cv::namedWindow("pointset + pattern"); cv::imshow("pointset + pattern", image);



    for(unsigned int i=0; i<transformedPattern.size(); ++i)
        pointset.push_back(transformedPattern[i]);

    return pointset;

}

void programDetectPointPattern()
{
    cv::Size2i imageSize(640,480);

    // create a point pattern, this can be in any scale and any relative location
    std::vector<cv::Point2f> pointPattern;
    pointPattern.push_back(cv::Point2f(0,0));
    pointPattern.push_back(cv::Point2f(2,0));
    pointPattern.push_back(cv::Point2f(4,0));
    pointPattern.push_back(cv::Point2f(1,2));
    pointPattern.push_back(cv::Point2f(3,2));
    pointPattern.push_back(cv::Point2f(2,4));

    // transform the pattern so it can be drawn
    cv::Mat trans = cv::Mat::ones(2,3,CV_32FC1);
    trans.at<float>(0,0) = 20.0f;   // scale x
    trans.at<float>(1,1) = 20.0f;   // scale y
    trans.at<float>(0,2) = 20.0f;   // translation x
    trans.at<float>(1,2) = 20.0f;   // translation y

    // draw the pattern
    cv::Mat drawnPattern = cv::Mat::ones(cv::Size2i(128,128),CV_8U);
    drawnPattern *= 255;
    drawPoints(drawnPattern,applyTransformation(pointPattern, trans), cv::Scalar(0),5);

    // display and save pattern
    cv::imwrite("patternToDetect.png", drawnPattern);
    cv::namedWindow("pattern"); cv::imshow("pattern", drawnPattern);

    // draw the points and the included pattern
    std::vector<cv::Point2f> pointset = createPointSet(imageSize, pointPattern);
    cv::Mat image = cv::Mat(imageSize, CV_8UC3);
    image = cv::Scalar(255,255,255);
    drawPoints(image,pointset, cv::Scalar(0,0,0));


    // normally we would have to use some nearest neighbor distance computation, but to make it easier here,
    // we create a small area around every point, which allows to test for point existence in a small neighborhood very efficiently (for small images)
    // in the real application this "inlier" check should be performed by k-nearest neighbor search and threshold the distance,
    // efficiently evaluated by a kd-tree
    cv::Mat pointImage = cv::Mat::zeros(imageSize,CV_8U);
    float maxDist = 3.0f; // how exact must the pattern be recognized, can there be some "noise" in the position of the data points?
    drawPoints(pointImage, pointset, cv::Scalar(255),maxDist);
    cv::namedWindow("pointImage"); cv::imshow("pointImage", pointImage);

    // choose two points from the pattern (can be arbitrary so just take the first two)
    cv::Point2f referencePoint1 = pointPattern[0];
    cv::Point2f referencePoint2 = pointPattern[1];
    cv::Point2f diff1;  // difference vector
    diff1.x = referencePoint2.x - referencePoint1.x;
    diff1.y = referencePoint2.y - referencePoint1.y;
    float referenceLength = sqrt(diff1.x*diff1.x + diff1.y*diff1.y);
    diff1.x = diff1.x/referenceLength; diff1.y = diff1.y/referenceLength;

    std::cout << "reference: " << std::endl;
    std::cout << referencePoint1 << std::endl;

    // now try to find the pattern
    for(unsigned int j=0; j<pointset.size(); ++j)
    {
        cv::Point2f targetPoint1 =  pointset[j];

        for(unsigned int i=0; i<pointset.size(); ++i)
        {
            cv::Point2f targetPoint2 =  pointset[i];

            cv::Point2f diff2;
            diff2.x = targetPoint2.x - targetPoint1.x;
            diff2.y = targetPoint2.y - targetPoint1.y;
            float targetLength = sqrt(diff2.x*diff2.x + diff2.y*diff2.y);
            diff2.x = diff2.x/targetLength; diff2.y = diff2.y/targetLength;

            // with nearest-neighborhood search this line will be similar or the maximal neighbor distance must be relative to targetLength!
            if(targetLength < maxDist) continue;

            // scale:
            float s = targetLength/referenceLength;

            // rotation:
            float r = -180.0f/PI*(atan2(diff2.y,diff2.x) + atan2(diff1.y,diff1.x));

            // scale and rotate the reference point to compute the translation needed
            std::vector<cv::Point2f> origin;
            origin.push_back(referencePoint1);
            origin = applyTransformation(origin, composeSimilarityTransformation(s,r,0,0));
            // compute the translation which maps the two reference points on the two target points
            float tx = targetPoint1.x - origin[0].x;
            float ty = targetPoint1.y - origin[0].y;

            std::vector<cv::Point2f> transformedPattern = applyTransformation(pointPattern,composeSimilarityTransformation(s,r,tx,ty));


            // now test if all transformed pattern points can be found in the dataset
            bool found = true;
            for(unsigned int i=0; i<transformedPattern.size(); ++i)
            {
                cv::Point2f curr = transformedPattern[i];
                // here we check whether there is a point drawn in the image. If you have no image you will have to perform a nearest neighbor search.
                // this can be done with a balanced kd-tree in O(log n) time
                // building such a balanced kd-tree has to be done once for the whole dataset and needs O(n*(log n)) afair
                if((curr.x >= 0)&&(curr.x <= pointImage.cols-1)&&(curr.y>=0)&&(curr.y <= pointImage.rows-1))
                {
                    if(pointImage.at<unsigned char>(curr.y, curr.x) == 0) found = false;
                    // if working with kd-tree: if nearest neighbor distance > maxDist => found = false;
                }
                else found = false;

            }



            if(found)
            {
                std::cout << composeSimilarityTransformation(s,r,tx,ty) << std::endl;
                cv::Mat currentIteration;
                image.copyTo(currentIteration);
                cv::circle(currentIteration,targetPoint1,5, cv::Scalar(255,0,0),1);
                cv::circle(currentIteration,targetPoint2,5, cv::Scalar(255,0,255),1);
                cv::line(currentIteration,targetPoint1,targetPoint2,cv::Scalar(0,0,255));
                drawPoints(currentIteration, transformedPattern, cv::Scalar(0,0,255),4);

                cv::imwrite("detectedPattern.png", currentIteration);
                cv::namedWindow("iteration"); cv::imshow("iteration", currentIteration); cv::waitKey(-1);
            }

        }
    }


}

//在给定的目标图像上绘制一组点
空心绘图点（cv:：Mat&image、std:：vector points、cv:：Scalar color=cv:：Scalar（255255255）、浮点大小=10）
{
对于（unsigned int i=0；iPoint Set Pattern Matching under righed Motion-Arijit Bishnu、Sandip Das、Subhas C.Nandy和Bhargab B.Bhattacharya感谢这篇文章。虽然这篇论文有点不适合我，但现在我知道搜索“点集模式匹配”
特征点匹配/注册
将是另一个可以搜索的术语，但是您必须记住，许多特征点匹配方法都使用点的描述符（纹理）邻里，你没有。Thx Mika。有没有办法使用我自己的2D特征？筛选/旋转所有其他检测器都使用图像。我没有图像。我可以尝试推送我自己的特征，如点的平均距离、它们相互关联的向量的角度等等……但我不知道如何实现这一点或者这是一个好方法吗？你想找到一种使用空间分布信息来找到特征模式的特征匹配方法。只需将你所有的数据点解释为特征。或者发明某种描述符来减少信息量米卡，这是很棒的东西！我已经有一段时间没有再次参与这个项目了，但我没有在一个Openframeworks项目（Openframeworks.cc）中使用了你的代码，而且它是开箱即用的。太棒了！如果你把你的地址发到wim@nocomputer.be我会送你鲜花和饮料！