Color-Quantization-using-K-Means

Cluster and show one or more color from an image using K-means clustering algorithm.

The goal is to partition n data points into k clusters. Each of the n data points will be assigned to a cluster with the nearest mean. The mean of each cluster is called its “centroid” or “center” Overall, applying k-means yields k separate clusters of the original n data points. Data points inside a particular cluster are considered to be “more similar” to each other than data points that belong to other clusters. In our case, we will be clustering the pixel intensities of a RGB image. Given a MxN size image, we thus have MxN pixels, each consisting of three components: Red, Green, and Blue respectively. We will treat these MxN pixels as our data points and cluster them using k-means. Pixels that belong to a given cluster will be more similar in color than pixels belonging to a separate cluster. One caveat of k-means is that we need to specify the number of clusters we want to generate ahead of time. I used Spyder for this project.

#OpenCV and Python K-Means Color Clustering:

clustering pixel intensities using OpenCV, Python, and k-means:

importing the packages

import numpy as np from sklearn.cluster import KMeans import matplotlib.pyplot as plt import argparse import cv2

creating centroid and clustering the colors

def centroid_histogram(clt): numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1) (hist, ) = np.histogram(clt.labels, bins = numLabels) hist = hist.astype("float") hist /= hist.sum() return hist

def plot_colors(hist, centroids): bar = np.zeros((50, 300, 3), dtype = "uint8") startX = 0 for (percent, color) in zip(hist, centroids): endX = startX + (percent * 300) cv2.rectangle(bar, (int(startX), 0), (int(endX), 50), color.astype("uint8").tolist(), -1) startX = endX return bar

construct the argument parser and parse the arguments

ap = argparse.ArgumentParser() ap.add_argument("image", help = "Path to the image") ap.add_argument("clusters", type = int,help = "# of clusters") args = ap.parse_args()

load the image and convert it from BGR to RGB so that

we can dispaly it with matplotlib

image = cv2.imread(args.image) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

show our image

plt.figure() plt.axis("off") plt.imshow(image)

reshape the image to be a list of pixel

image = image.reshape((image.shape[0] * image.shape[1], 3))

cluster the pixel intensities

clt = KMeans(n_clusters = args["clusters"]) clt.fit(image)

hist = centroid_histogram(clt) bar = plot_colors(hist, clt.cluster_centers_) plt.figure() plt.axis("off") plt.imshow(bar) plt.show()

CHANDAN7SHAW/Color-Quantization-using-K-Means