基于Kmeans、Kmeans++和二分K均值算法的图像分割代码

以下是基于Kmeans、Kmeans++和二分K均值算法的图像分割代码的实现：

import numpy as np
import cv2

class Kmeans:
    def __init__(self, k=3, max_iter=10):
        self.k = k
        self.max_iter = max_iter

    def fit(self, X):
        self.centroids = X[np.random.choice(X.shape[0], self.k, replace=False)]
        for i in range(self.max_iter):
            distances = np.sqrt(((X - self.centroids[:, np.newaxis])**2).sum(axis=2))
            self.labels = distances.argmin(axis=0)
            for j in range(self.k):
                self.centroids[j] = X[self.labels == j].mean(axis=0)

class KmeansPlusPlus(Kmeans):
    def fit(self, X):
        self.centroids = np.zeros((self.k, X.shape[1]))
        self.centroids[0] = X[np.random.randint(X.shape[0])]
        for i in range(1, self.k):
            distances = np.sqrt(((X - self.centroids[:i, np.newaxis])**2).sum(axis=2))
            probs = distances.min(axis=0) ** 2 / (distances.min(axis=0) ** 2).sum()
            self.centroids[i] = X[np.random.choice(X.shape[0], p=probs)]
        for i in range(self.max_iter):
            distances = np.sqrt(((X - self.centroids[:, np.newaxis])**2).sum(axis=2))
            self.labels = distances.argmin(axis=0)
            for j in range(self.k):
                self.centroids[j] = X[self.labels == j].mean(axis=0)

class BisectingKmeans:
    def __init__(self, k=3, max_iter=10):
        self.k = k
        self.max_iter = max_iter

    def fit(self, X):
        self.clusters = [np.arange(X.shape[0])]
        for i in range(self.k-1):
            max_sse = -1
            for j, cluster in enumerate(self.clusters):
                kmeans = Kmeans(k=2, max_iter=self.max_iter).fit(X[cluster])
                sse = ((X[cluster] - kmeans.centroids[kmeans.labels])**2).sum()
                if sse > max_sse:
                    max_sse = sse
                    max_cluster = j
                    max_kmeans = kmeans
            self.clusters.pop(max_cluster)
            self.clusters.append(cluster[max_kmeans.labels == 0])
            self.clusters.append(cluster[max_kmeans.labels == 1])
        self.labels = np.zeros(X.shape[0])
        for i, cluster in enumerate(self.clusters):
            self.labels[cluster] = i

def image_segmentation(image_path, algorithm='kmeans', k=3, max_iter=10):
    image = cv2.imread(image_path)
    X = image.reshape(-1, 3)
    if algorithm == 'kmeans':
        kmeans = Kmeans(k=k, max_iter=max_iter).fit(X)
        segmented_image = kmeans.centroids[kmeans.labels].reshape(image.shape)
    elif algorithm == 'kmeans++':
        kmeans_plus_plus = KmeansPlusPlus(k=k, max_iter=max_iter).fit(X)
        segmented_image = kmeans_plus_plus.centroids[kmeans_plus_plus.labels].reshape(image.shape)
    elif algorithm == 'bisecting_kmeans':
        bisecting_kmeans = BisectingKmeans(k=k, max_iter=max_iter).fit(X)
        segmented_image = bisecting_kmeans.centroids[bisecting_kmeans.labels].reshape(image.shape)
    else:
        raise ValueError('Invalid algorithm.')
    return segmented_image

该代码实现了三种不同的K均值算法：基本K均值、K均值++和二分K均值。在图像分割中，我们将图像的像素视为数据点，将其重新组织为一个二维数组，其中每行代表一个像素，每列代表该像素的颜色通道值。我们可以使用上述算法对这些数据点进行聚类，然后使用聚类中心的颜色通道值来对图像进行分割。

以下是使用该代码进行图像分割的示例：

import matplotlib.pyplot as plt

image_path = 'example.jpg'

# K均值算法
segmented_image = image_segmentation(image_path, algorithm='kmeans', k=3, max_iter=10)
plt.imshow(segmented_image)
plt.show()

# K均值++算法
segmented_image = image_segmentation(image_path, algorithm='kmeans++', k=3, max_iter=10)
plt.imshow(segmented_image)
plt.show()

# 二分K均值算法
segmented_image = image_segmentation(image_path, algorithm='bisecting_kmeans', k=3, max_iter=10)
plt.imshow(segmented_image)
plt.show()