opencv-python kmeans

K-means clustering is a popular unsupervised machine learning algorithm used for clustering data points into distinct groups or clusters. In OpenCV-Python, you can perform K-means clustering using the `kmeans` function from the `cv2` module.

Here is an example code snippet to demonstrate how to use K-means clustering in OpenCV-Python:

import cv2
import numpy as np

# Load the image
image = cv2.imread('image.jpg')

# Reshape the image to a 2D array of pixels
pixels = image.reshape(-1, 3)

# Convert the pixel values to float32
pixels = np.float32(pixels)

# Define the criteria for the algorithm (number of iterations and epsilon)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

# Set the number of clusters
k = 5

# Perform K-means clustering
_, labels, centers = cv2.kmeans(pixels, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)

# Convert the centers to uint8
centers = np.uint8(centers)

# Map each pixel to its corresponding cluster center
segmented_image = centers[labels.flatten()]

# Reshape the segmented image back to its original shape
segmented_image = segmented_image.reshape(image.shape)

# Display the original image and the segmented image
cv2.imshow('Original Image', image)
cv2.imshow('Segmented Image', segmented_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Make sure to replace `'image.jpg'` with the path to your own image. The code reads an image, reshapes it into a 2D array of pixels, performs K-means clustering, maps each pixel to its corresponding cluster center, and displays the original image and the segmented image.

Feel free to ask if you have any further questions!