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硬币图像的结构化Ward层次聚类演示#

使用Ward层次聚类计算二维图像的分割。聚类在空间上受到约束，以确保每个分割区域都是一个整体。

# 作者：scikit-learn 开发者
# SPDX-License-Identifier：BSD-3-Clause

生成数据#

from skimage.data import coins

orig_coins = coins()

将其调整为原始大小的20%以加快处理速度在缩小之前应用高斯滤波进行平滑处理可以减少混叠伪影。

import numpy as np
from scipy.ndimage import gaussian_filter
from skimage.transform import rescale

smoothened_coins = gaussian_filter(orig_coins, sigma=2)
rescaled_coins = rescale(
    smoothened_coins,
    0.2,
    mode="reflect",
    anti_aliasing=False,
)

X = np.reshape(rescaled_coins, (-1, 1))

定义数据结构#

像素与其邻居相连。

from sklearn.feature_extraction.image import grid_to_graph

connectivity = grid_to_graph(*rescaled_coins.shape)

计算聚类#

import time as time

from sklearn.cluster import AgglomerativeClustering

print("Compute structured hierarchical clustering...")
st = time.time()
n_clusters = 27  # number of regions
ward = AgglomerativeClustering(
    n_clusters=n_clusters, linkage="ward", connectivity=connectivity
)
ward.fit(X)
label = np.reshape(ward.labels_, rescaled_coins.shape)
print(f"Elapsed time: {time.time() - st:.3f}s")
print(f"Number of pixels: {label.size}")
print(f"Number of clusters: {np.unique(label).size}")

Compute structured hierarchical clustering...
Elapsed time: 0.088s
Number of pixels: 4697
Number of clusters: 27

在图像上绘制结果#

凝聚聚类能够分割每个硬币，然而，由于分割在背景中找到了一个较大的区域，我们不得不使用比硬币数量更多的 n_cluster 。

import matplotlib.pyplot as plt

plt.figure(figsize=(5, 5))
plt.imshow(rescaled_coins, cmap=plt.cm.gray)
for l in range(n_clusters):
    plt.contour(
        label == l,
        colors=[
            plt.cm.nipy_spectral(l / float(n_clusters)),
        ],
    )
plt.axis("off")
plt.show()