Machine Learning for OpenCV 4 - Second Edition

Running k-means

Setting up k-means works exactly the same as in the previous examples. We tell the algorithm to perform at most 10 iterations and stop the process if our prediction of the cluster centers does not improve within a distance of 1.0:

In [2]: import cv2...     criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,...                 10, 1.0)...     flags = cv2.KMEANS_RANDOM_CENTERS

Then, we apply k-means to the data as we did before. Since there are 10 different digits (0-9), we tell the algorithm to look for 10 distinct clusters:

In [3]: import numpy as np...     digits.data = digits.data.astype(np.float32)...     compactness, clusters, centers = cv2.kmeans(digits.data, 10, None,...                                                 criteria, 10, flags)

And we're done!

Similar to the N x 3 matrices ...