OpenCV provides the cv2.kmeans() function, which implements a k-means clustering algorithm, which finds centers of clusters and groups input samples around the clusters.
The objective of the k-means clustering algorithm is to partition (or cluster) n samples into K clusters where each sample will belong to the cluster with the nearest mean. The signature of the cv2.kmeans() function is as follows:
retval, bestLabels, centers=cv.kmeans(data, K, bestLabels, criteria, attempts, flags[, centers])
data represents the input data for clustering. It should be of np.float32 data type, and each feature should be placed in a single column. K specifies the number of clusters required at the end. The algorithm-termination criteria are specified with the criteria parameter, which sets the maximum number of iterations and/or the desired accuracy. When these criteria are...
