Python K-means实现简单图像聚类的示例代码

这里直接给出第一个版本的直接实现:

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

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)

clt = KMeans(n_clusters=2)
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)

主要需要注意的问题是对K-Means原理的理解。K-means做的是对向量的聚类,也就是说,假设要处理的是224×224×3的RGB图像,那么就得先将其转为1维的向量。在上面的做法里,我们是直接对其展平:

image = image.reshape(-1, )

那么这么做的缺陷也是十分明显的。例如,对于两张一模一样的图像,我们将前者向左平移一个像素。这么做下来后两张图像在感官上几乎没有任何区别,但由于整体平移会导致两者的图像矩阵逐像素比较的结果差异巨大。以橘子汽车聚类为例,实验结果如下:

在这里插入图片描述

在这里插入图片描述

可以看到结果是比较差的。因此,我们进行改进,利用ResNet-50进行图像特征的提取(embedding),在特征的基础上聚类而非直接在像素上聚类,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 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, )
    all_images.append(image.detach().numpy())

clt = KMeans(n_clusters=2)
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)

可以发现结果明显改善:

在这里插入图片描述

在这里插入图片描述

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