opencv欧几里得聚类vs findContours [英] opencv euclidean clustering vs findContours

问题描述

我有以下图像遮罩:

我想应用类似于cv::findContours的方法，但是该算法仅将相同组中的连接点连接在一起.我想以一定的公差来做到这一点，即我想在给定的半径公差内将彼此靠近的像素相加:这类似于欧几里得距离层次聚类.

I want to apply something similar to cv::findContours, but that algorithm only joins connected points in the same groups. I want to do this with some tolerance, i.e., I want to add the pixels near each other within a given radius tolerance: this is similar to Euclidean distance hierarchical clustering.

这是在OpenCV中实现的吗?还是有任何快速的方法来实现这一点?

Is this implemented in OpenCV? Or is there any fast approach for implementing this?

我想要的东西与此类似，

What I want is something similar to this,

http://www.pointclouds.org/documentation/tutorials/cluster_extraction.php

应用于此蒙版的白色像素.

applied to the white pixels of this mask.

谢谢.

推荐答案

您可以使用分区:

partition 将元素集划分为等效类.您可以将等效类定义为给定欧氏距离(半径公差)内的所有点

partition splits an element set into equivalency classes. You can define your equivalence class as all points within a given euclidean distance (radius tolerance)

如果您具有C ++ 11，则可以简单地使用lambda函数:

If you have C++11, you can simply use a lambda function:

int th_distance = 18; // radius tolerance

int th2 = th_distance * th_distance; // squared radius tolerance
vector<int> labels;

int n_labels = partition(pts, labels, [th2](const Point& lhs, const Point& rhs) {
    return ((lhs.x - rhs.x)*(lhs.x - rhs.x) + (lhs.y - rhs.y)*(lhs.y - rhs.y)) < th2; 
});

否则，您可以只构建一个仿函数(请参见下面的代码中的详细信息).

otherwise, you can just build a functor (see details in the code below).

以适当的半径距离(我发现这张图片有18个效果很好)，我得到了:

With appropriate radius distance (I found 18 works good on this image), I got:

完整代码:

#include <opencv2\opencv.hpp>
#include <vector>
#include <algorithm>

using namespace std;
using namespace cv;

struct EuclideanDistanceFunctor
{
    int _dist2;
    EuclideanDistanceFunctor(int dist) : _dist2(dist*dist) {}

    bool operator()(const Point& lhs, const Point& rhs) const
    {
        return ((lhs.x - rhs.x)*(lhs.x - rhs.x) + (lhs.y - rhs.y)*(lhs.y - rhs.y)) < _dist2;
    }
};

int main()
{
    // Load the image (grayscale)
    Mat1b img = imread("path_to_image", IMREAD_GRAYSCALE);

    // Get all non black points
    vector<Point> pts;
    findNonZero(img, pts);

    // Define the radius tolerance
    int th_distance = 18; // radius tolerance

    // Apply partition 
    // All pixels within the radius tolerance distance will belong to the same class (same label)
    vector<int> labels;

    // With functor
    //int n_labels = partition(pts, labels, EuclideanDistanceFunctor(th_distance));

    // With lambda function (require C++11)
    int th2 = th_distance * th_distance;
    int n_labels = partition(pts, labels, [th2](const Point& lhs, const Point& rhs) {
        return ((lhs.x - rhs.x)*(lhs.x - rhs.x) + (lhs.y - rhs.y)*(lhs.y - rhs.y)) < th2;
    });

    // You can save all points in the same class in a vector (one for each class), just like findContours
    vector<vector<Point>> contours(n_labels);
    for (int i = 0; i < pts.size(); ++i)
    {
        contours[labels[i]].push_back(pts[i]);
    }

    // Draw results

    // Build a vector of random color, one for each class (label)
    vector<Vec3b> colors;
    for (int i = 0; i < n_labels; ++i)
    {
        colors.push_back(Vec3b(rand() & 255, rand() & 255, rand() & 255));
    }

    // Draw the labels
    Mat3b lbl(img.rows, img.cols, Vec3b(0, 0, 0));
    for (int i = 0; i < pts.size(); ++i)
    {
        lbl(pts[i]) = colors[labels[i]];
    }

    imshow("Labels", lbl);
    waitKey();

    return 0;
}

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