Created
January 30, 2019 05:35
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//Make good representation for clustering | |
Mat points = Mat::zeros(sum(img_zeroone)[0], 2, CV_32F); | |
for (int i = 0, k = 0; i < img_zeroone.rows; i++){ | |
for (int j = 0; j < img_zeroone.cols; j++){ | |
if ((int)img_zeroone.at<char>(i, j) == 255){ | |
points.at<float>(k, 0) = i; | |
points.at<float>(k, 1) = j; | |
k++; | |
} | |
} | |
} | |
Mat kCenters, kLabels; //Clustering | |
int clusterCount = 5, attempts = 10, iterationNumber = 1e40; | |
kmeans(points, clusterCount, kLabels, TermCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, iterationNumber, 1e-4), attempts, KMEANS_PP_CENTERS, kCenters); | |
for (int i = 0; i < kCenters.rows; i++){ | |
float x = kCenters.at<float>(i, 1), y = kCenters.at<float>(i, 0); | |
circle(img_sharp_not, Point(x, y), 2, (0, 0, 255), -1); | |
rectangle(img_sharp_not, Rect(x - 13, y - 13, 26, 26), Scalar(255, 255, 255)); | |
} |
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