二分-k均值聚类算法进行图像分割完整代码

以下是使用Python实现二分-k均值聚类算法进行图像分割的完整代码：

import numpy as np
from PIL import Image

def load_image(filename):
    img = Image.open(filename)
    return np.array(img)

def save_image(filename, data):
    img = Image.fromarray(np.uint8(data))
    img.save(filename)

def kmeans(data, k, max_iter=100):
    centers = np.random.randint(256, size=(k,))
    for i in range(max_iter):
        clusters = [[] for _ in range(k)]
        for x in data:
            distances = np.abs(centers - x)
            idx = np.argmin(distances)
            clusters[idx].append(x)
        new_centers = [np.mean(cluster) for cluster in clusters]
        if np.allclose(new_centers, centers):
            break
        centers = new_centers
    return centers, clusters

def bisecting_kmeans(data, k, max_iter=100):
    clusters = [data]
    while len(clusters) < k:
        max_sse = -1
        for i, cluster in enumerate(clusters):
            centers, subclusters = kmeans(cluster, 2, max_iter)
            sse = 0
            for subcluster in subclusters:
                distances = np.abs(subcluster - centers[0])
                sse += np.sum(distances ** 2)
            if sse > max_sse:
                max_sse = sse
                max_idx = i
                max_centers = centers
                max_subclusters = subclusters
        clusters.pop(max_idx)
        clusters.extend(max_subclusters)
    return clusters

if __name__ == '__main__':
    img = load_image('input.jpg')
    data = img.reshape(-1, 3)
    clusters = bisecting_kmeans(data, 4)
    for i, cluster in enumerate(clusters):
        centers, _ = kmeans(cluster, 1)
        distances = np.abs(cluster - centers[0])
        mask = np.argmin(distances, axis=1) == 0
        data[mask] = centers[0]
    img = data.reshape(img.shape)
    save_image('output.jpg', img)

该代码使用PIL库读取和保存图像，numpy库进行数组操作。首先，将输入图像转换成数据数组。然后使用二分-k均值聚类算法将数据分成四个簇。接下来，对于每个簇，使用k均值聚类算法将其压缩为一个点，并使用该点将簇中所有像素点赋值为该点的颜色。最后，将数据数组转换回图像并保存输出图像。

注意，这里默认输入图像为RGB格式，如果输入图像为其他格式，需要根据实际情况进行修改。