OpenCV 3 Computer Vision with Python Cookbook

How to do it...

1. Import the necessary modules:

import cv2import numpy as npimport matplotlib.pyplot as plt

2. Open an image and convert it to Lab color space:

image = cv2.imread('../data/Lena.png').astype(np.float32) / 255.image_lab = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)

3. Reshape the image into a vector:

data = image_lab.reshape((-1, 3))

4. Define the number of clusters and the criteria to finish the segmentation process. Then, perform k-means clusterization:

num_classes = 4criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 50, 0.1)_, labels, centers = cv2.kmeans(data, num_classes, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)

5. Apply the colors of centroids to all pixels that are relevant to these centroids. Afterwards, reshape ...