K-Means Clustering in OpenCV

Goal

• Learn to use cv.kmeans() function in OpenCV for data clustering

Understanding Parameters

Input parameters

1. samples: It should be of np.float32 data type.
2. nclusters (K): Number of clusters required at end
3. criteria: It is the iteration termination criteria — (type, max_iter, epsilon):
   • cv.TERM_CRITERIA_EPS — stop if specified accuracy epsilon is reached
   • cv.TERM_CRITERIA_MAX_ITER — stop after specified number of iterations
   • cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER — stop when any condition is met
4. attempts: Number of times the algorithm is executed using different initial labellings
5. flags: Specifies how initial centers are taken: cv.KMEANS_PP_CENTERS or cv.KMEANS_RANDOM_CENTERS

Output parameters

1. compactness: Sum of squared distance from each point to their corresponding centers
2. labels: Label array where each element marked ‘0’, ‘1’ etc.
3. centers: Array of centers of clusters.

1. Data with Only One Feature

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt

x = np.random.randint(25, 100, 25)
y = np.random.randint(175, 255, 25)
z = np.hstack((x, y))
z = z.reshape((50, 1))
z = np.float32(z)

# Define criteria
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 10, 1.0)
flags = cv.KMEANS_RANDOM_CENTERS

# Apply KMeans
compactness, labels, centers = cv.kmeans(z, 2, None, criteria, 10, flags)

A = z[labels == 0]
B = z[labels == 1]

plt.hist(A, 256, [0, 256], color='r')
plt.hist(B, 256, [0, 256], color='b')
plt.hist(centers, 32, [0, 256], color='y')
plt.show()

2. Data with Multiple Features

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt

X = np.random.randint(25, 50, (25, 2))
Y = np.random.randint(60, 85, (25, 2))
Z = np.vstack((X, Y))
Z = np.float32(Z)

criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 10, 1.0)
ret, label, center = cv.kmeans(Z, 2, None, criteria, 10, cv.KMEANS_RANDOM_CENTERS)

A = Z[label.ravel() == 0]
B = Z[label.ravel() == 1]

plt.scatter(A[:, 0], A[:, 1])
plt.scatter(B[:, 0], B[:, 1], c='r')
plt.scatter(center[:, 0], center[:, 1], s=80, c='y', marker='s')
plt.xlabel('Height'), plt.ylabel('Weight')
plt.show()

3. Color Quantization

Color Quantization is the process of reducing number of colors in an image. Here we will use K-Means clustering for color quantization:

import numpy as np
import cv2 as cv

img = cv.imread('home.jpg')
Z = img.reshape((-1, 3))
Z = np.float32(Z)

criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 10, 1.0)
K = 8
ret, label, center = cv.kmeans(Z, K, None, criteria, 10, cv.KMEANS_RANDOM_CENTERS)

center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((img.shape))

cv.imshow('res2', res2)
cv.waitKey(0)
cv.destroyAllWindows()

Source: OpenCV Python Tutorials — Original Documentation