Computer Science: K-Means Segmentation from scratch python

def createDataset(im): This power takes in an RGB effigy as input, and receipts a axiomsfirm of signs which are to be bunched. The power receipts signs, which is an N × M matrix where N is the calculate of pixels in the effigy im, and M = 3 (to supply the RGB esteem of each pixel). def kMeansCluster(features, natures): This power is intentional to effect K-Means based bunching on the axiomsfirm signs (of extent N × M). The power receipts a inventory [idx, natures]. Each tier in signs represents a axioms top, and each shaft represents a sign. natures is a k × M matrix, where each tier is the moderate esteem of a bunch nature. The output idx is an N × 1 vector that supplys the decisive bunch conjunction (∈ 1, 2, · · · , k) of each axioms top. The output natures are the decisive bunch natures behind K-Means. Note that you may deficiency to firm a apex repetition number to egress K-Means in plight the algorithm fails to bear. You may reason loops in this power. def mapValues(im, idx): This power takes in the bunch conjunction vector idx (N × 1), and receipts the segmented effigy im_seg as the output. Each pixel in the segmented effigy must keep the RGB esteem of the bunch nature to which it belongs. You may reason loops coercion this separate. Things to revolve in: • The input effigy, and the effigy behind segmentation. • The decisive bunch natures that you obtain behind K-Means.

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