python - 通过在 python 中的相似性进行图像聚类

Question

我有一组照片，我想区分相似照片的簇。我应该使用图像的哪些特征和哪种算法来解决我的任务？

Answer 1

我遇到了同样的问题，我想出了这个解决方案:

1. 使用 Keras 导入预训练模型(此处为 VGG16)
2. 提取每张图片的特征
3. 做 kmeans
4. 复制带簇标签导出

这是我的代码，部分原因是 this post .

from keras.preprocessing import image
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
import numpy as np
from sklearn.cluster import KMeans
import os, shutil, glob, os.path
from PIL import Image as pil_image
image.LOAD_TRUNCATED_IMAGES = True 
model = VGG16(weights='imagenet', include_top=False)

# Variables
imdir = 'C:/indir/'
targetdir = "C:/outdir/"
number_clusters = 3

# Loop over files and get features
filelist = glob.glob(os.path.join(imdir, '*.jpg'))
filelist.sort()
featurelist = []
for i, imagepath in enumerate(filelist):
    print("    Status: %s / %s" %(i, len(filelist)), end="\r")
    img = image.load_img(imagepath, target_size=(224, 224))
    img_data = image.img_to_array(img)
    img_data = np.expand_dims(img_data, axis=0)
    img_data = preprocess_input(img_data)
    features = np.array(model.predict(img_data))
    featurelist.append(features.flatten())

# Clustering
kmeans = KMeans(n_clusters=number_clusters, random_state=0).fit(np.array(featurelist))

# Copy images renamed by cluster 
# Check if target dir exists
try:
    os.makedirs(targetdir)
except OSError:
    pass
# Copy with cluster name
print("\n")
for i, m in enumerate(kmeans.labels_):
    print("    Copy: %s / %s" %(i, len(kmeans.labels_)), end="\r")
    shutil.copy(filelist[i], targetdir + str(m) + "_" + str(i) + ".jpg")

02/2022 更新:

在某些情况下(例如未知数量的集群)使用 Affinity Propagation可能是比 kmeans 更好的选择。在这种情况下，将 kmeans 替换为:

from sklearn.cluster import AffinityPropagation
affprop = AffinityPropagation(affinity="euclidean", damping=0.5).fit(np.array(featurelist))

并遍历 affprop.labels_ 以访问结果。