我知道这已经很老了,但也许这个答案可能会有所帮助。
您可以使用分割 http://docs.opencv.org/2.4/modules/core/doc/clustering.html#partition,它将把一个元素集分割成等价类。
您可以将等价类定义为给定欧氏距离内的所有点。这可以是 lambda 函数 (C++11) 或函子(请参阅代码中的两个示例)。
从这张图片开始(我手动删除了紫色边框):
使用 20 的欧氏距离我得到:
您可以看到欧几里德距离内的所有白色像素都分配给同一簇(相同颜色)。圆圈表示每个簇的质心。
Code:
#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 distance between clusters
int euclidean_distance = 20;
// Apply partition
// All pixels within the the given distance will belong to the same cluster
vector<int> labels;
// With functor
//int n_labels = partition(pts, labels, EuclideanDistanceFunctor(euclidean_distance));
// With lambda function
int th2 = euclidean_distance * euclidean_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;
});
// Store all points in same cluster, and compute centroids
vector<vector<Point>> clusters(n_labels);
vector<Point> centroids(n_labels, Point(0,0));
for (int i = 0; i < pts.size(); ++i)
{
clusters[labels[i]].push_back(pts[i]);
centroids[labels[i]] += pts[i];
}
for (int i = 0; i < n_labels; ++i)
{
centroids[i].x /= clusters[i].size();
centroids[i].y /= clusters[i].size();
}
// 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 points
Mat3b res(img.rows, img.cols, Vec3b(0, 0, 0));
for (int i = 0; i < pts.size(); ++i)
{
res(pts[i]) = colors[labels[i]];
}
// Draw centroids
for (int i = 0; i < n_labels; ++i)
{
circle(res, centroids[i], 3, Scalar(colors[i][0], colors[i][1], colors[i][2]), CV_FILLED);
circle(res, centroids[i], 6, Scalar(255 - colors[i][0], 255 - colors[i][1], 255 - colors[i][2]));
}
imshow("Clusters", res);
waitKey();
return 0;
}