kmeans图片聚类算法python

k-means图片聚类算法是一种常用的无监督学习算法，用于将一组图片聚类成k个簇。下面给出了一个基于Python的k-means图片聚类算法的实现:

import os
import numpy as np
from sklearn.cluster import KMeans
import cv2
from imutils import build_montages
import matplotlib.image as imgplt

# 读取所有图片
image_path = []
all_images = []
images = os.listdir('./images')
for image_name in images:
    image_path.append('./images/' + image_name)
for path in image_path:
    image = imgplt.imread(path)
    image = image.reshape(-1, )
    all_images.append(image)

# 使用KMeans算法进行聚类
clt = KMeans(n_clusters=2)
clt.fit(all_images)

# 获取每个簇的标签
labelIDs = np.unique(clt.labels_)

# 随机选择每个簇中的一些图片进行展示
for labelID in labelIDs:
    idxs = np.where(clt.labels_ == labelID)[0]
    idxs = np.random.choice(idxs, size=min(25, len(idxs)), replace=False)
    show_box = []
    for i in idxs:
        image = cv2.imread(image_path[i])
        image = cv2.resize(image, (96, 96))
        show_box.append(image)
    montage = build_montages(show_box, (96, 96), (5, 5))[0]
    title = "Type {}".format(labelID)
    cv2.imshow(title, montage)
    cv2.waitKey(0)

这个代码使用了`sklearn.cluster.KMeans`类来进行图片聚类，并使用了OpenCV库来处理图片。首先，将所有图片读取并转换为一维数组的形式，然后使用KMeans算法将图片聚类成k个簇。最后，随机选择每个簇中的一些图片进行展示。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [利用Python K-means实现简单图像聚类](https://blog.csdn.net/qq_40714949/article/details/120854418)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
- *2* *3* [python使用k-means算法代码案例-K-means聚类算法及python代码实现](https://blog.csdn.net/weixin_37988176/article/details/109394754)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
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