怎么用Python K-means实现简单图像聚类

这篇文章主要介绍"怎么用Python K-means实现简单图像聚类"，在日常操作中，相信很多人在怎么用Python K-means实现简单图像聚类问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答"怎么用Python K-means实现简单图像聚类"的疑惑有所帮助！接下来，请跟着小编一起来学习吧！

这里直接给出第一个版本的直接实现：

import osimport numpy as npfrom sklearn.cluster import KMeansimport cv2from imutils import build_montagesimport matplotlib.image as imgpltimage_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：

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 osimport numpy as npfrom sklearn.cluster import KMeansimport cv2from imutils import build_montagesimport torch.nn as nnimport torchvision.models as modelsfrom PIL import Imagefrom torchvision import transformsclass 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 xnet = 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：

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