本文是利用Python K-means实现简单图像聚类的后续分析。

上文我们提到过，利用ResNet可以进行图像特征的抽取，进而帮助我们去进行聚类。但是其实这里面有个问题，拿resnet提取到的特征高达114688维，如果样本数量上去来的话，会变得非常耗时。

容易想到，那么多维特征，并不是每种特征都"有用"，那么这个时候就可以对图像的特征进行一定的降维，这里我们使用PCA进行处理：

pca = PCA(n_components=10)
all_images = pca.fit_transform(all_images)

由于本文的例子里是对十张图像进行个二聚类，样本数总共就10，因此维数只能降到10及以下。但是呢，我们可以发现，哪怕是10维，效果也和原114688维差不多：


可以做到100%的分类准确率(足球&其他球)。实验证明，在维度大于6的情况下，性能都是高度可用的，这也侧面印证了视觉图像中存在大量的冗余信息。代码如下：

import os
import numpy as np
from sklearn.cluster import KMeans
import cv2
from imutils import build_montages
import torch.nn as nn
import torchvision.models as models
from PIL import Image
from sklearn.decomposition import PCA
from torchvision import transforms
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        resnet50 = models.resnet50(pretrained=True)
        self.resnet = nn.Sequential(resnet50.conv1,
                                    resnet50.bn1,
                                    resnet50.relu,
                                    resnet50.maxpool,
                                    resnet50.layer1,
                                    resnet50.layer2,
                                    resnet50.layer3,
                                    resnet50.layer4)

    def forward(self, x):
        x = self.resnet(x)
        return x

net = Net().eval()

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 = Image.open(path).convert('RGB')
    image = transforms.Resize([224,244])(image)
    image = transforms.ToTensor()(image)
    image = image.unsqueeze(0)
    image = net(image)
    image = image.reshape(-1, )
    print(image.shape)
    all_images.append(image.detach().numpy())

print("starting pca")
pca = PCA(n_components=10)
all_images = pca.fit_transform(all_images)
print(pca.explained_variance_ratio_)
print("finish pca")
print(all_images)

clt = KMeans(n_clusters=2, random_state=1234)
clt.fit(all_images)
labelIDs = np.unique(clt.labels_)

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)