live_kmeans_segmentation.py

import cv2
import numpy as np

cap = cv2.VideoCapture(0)
k = 5

# define stopping criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)

while True:
    # read the image
    _, image = cap.read()

    # reshape the image to a 2D array of pixels and 3 color values (RGB)
    pixel_values = image.reshape((-1, 3))
    # convert to float
    pixel_values = np.float32(pixel_values)

    # number of clusters (K)
    _, labels, (centers) = cv2.kmeans(pixel_values, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)

    # convert back to 8 bit values
    centers = np.uint8(centers)

    # convert all pixels to the color of the centroids
    segmented_image = centers[labels.flatten()]

    # reshape back to the original image dimension
    segmented_image = segmented_image.reshape(image.shape)

    # reshape labels too
    labels = labels.reshape(image.shape[0], image.shape[1])

    cv2.imshow("segmented_image", segmented_image)
    # visualize each segment

    if cv2.waitKey(1) == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()