opencv::KMeans图像分割
#include <opencv2/opencv.hpp> #include <iostream> using namespace cv; using namespace std; int main(int argc, char** argv) { Mat src = imread("D:/vcprojects/images/toux.jpg"); if (src.empty()) { printf("could not load image...\n"); return -1; } namedWindow("input image", CV_WINDOW_AUTOSIZE); imshow("input image", src); Scalar colorTab[] = { Scalar(0, 0, 255), Scalar(0, 255, 0), Scalar(255, 0, 0), Scalar(0, 255, 255), Scalar(255, 0, 255) }; int width = src.cols; int height = src.rows; int dims = src.channels(); // 像素点个数 int sampleCount = width*height; int clusterCount = 4; //将数据装载到一行 Mat points(sampleCount, dims, CV_32F, Scalar(10)); Mat labels; Mat centers(clusterCount, 1, points.type()); // RGB 数据转换到样本数据 int index = 0; for (int row = 0; row < height; row++) { for (int col = 0; col < width; col++) { index = row*width + col; Vec3b bgr = src.at<Vec3b>(row, col); points.at<float>(index, 0) = static_cast<int>(bgr[0]); points.at<float>(index, 1) = static_cast<int>(bgr[1]); points.at<float>(index, 2) = static_cast<int>(bgr[2]); } } // 运行K-Means TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1); kmeans(points, clusterCount, labels, criteria, 3, KMEANS_PP_CENTERS, centers); // 显示图像分割结果 Mat result = Mat::zeros(src.size(), src.type()); for (int row = 0; row < height; row++) { for (int col = 0; col < width; col++) { index = row*width + col; int label = labels.at<int>(index, 0); result.at<Vec3b>(row, col)[0] = colorTab[label][0]; result.at<Vec3b>(row, col)[1] = colorTab[label][1]; result.at<Vec3b>(row, col)[2] = colorTab[label][2]; } } for (int i = 0; i < centers.rows; i++) { int x = centers.at<float>(i, 0); int y = centers.at<float>(i, 1); printf("center %d = c.x : %d, c.y : %d\n", i, x, y); } imshow("KMeans Image Segmentation Demo", result); waitKey(0); return 0; }