Python K-means實(shí)現(xiàn)簡(jiǎn)單圖像聚類的示例代碼
這里直接給出第一個(gè)版本的直接實(shí)現(xiàn)：
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)
主要需要注意的問題是對(duì)K-Means原理的理解。K-means做的是對(duì)向量的聚類，也就是說，假設(shè)要處理的是224×224×3的RGB圖像，那么就得先將其轉(zhuǎn)為1維的向量。在上面的做法里，我們是直接對(duì)其展平：
image = image.reshape(-1, )
那么這么做的缺陷也是十分明顯的。例如，對(duì)于兩張一模一樣的圖像，我們將前者向左平移一個(gè)像素。這么做下來后兩張圖像在感官上幾乎沒有任何區(qū)別，但由于整體平移會(huì)導(dǎo)致兩者的圖像矩陣逐像素比較的結(jié)果差異巨大。以橘子汽車聚類為例，實(shí)驗(yàn)結(jié)果如下：
可以看到結(jié)果是比較差的。因此，我們進(jìn)行改進(jìn)，利用ResNet-50進(jìn)行圖像特征的提取(embedding)，在特征的基礎(chǔ)上聚類而非直接在像素上聚類，代碼如下：
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)
可以發(fā)現(xiàn)結(jié)果明顯改善：
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