导入类库
1 import numpy as np
2 import cv2
3 import matplotlib.pyplot as plt
4 from sklearn.cluster import KMeans
5 from sklearn.utils import shuffle
6 from time import time
7 from skimage import io
提取和存储图像数据
1 n_colors = 64
2 # 读取图片像素数据
3 tiger = cv2.imread("tiger.jpg")
4 # print('tiger >>>>',tiger)
5 login = cv2.imread('login.png')
6 # 将图片像素数据标准化为0-1的数据并保存至数组中
7 china = np.array(tiger, dtype=np.float64) / 255
8 # print('china >>>>',china)
9 w, h, d = original_shape = tuple(china.shape)
10 print('original_shape >>>>', original_shape)
11 print('w,h,d >>>>', w, h, d) # w：层数，h行数，d列数
12
13 image_array = np.reshape(china, (w * h, d))
14 # 每个点作为一个样本，维数为3，将三维数组china化为二维数组，列数不变，行数变为原行数乘层数
15 # print('image_array >>>>', image_array)
16 print('image_array shape>>>>', image_array.shape)训练图像数据
1 t0 = time()
2 # 将所有点打乱顺序，取前1000个点
3 # 不使用所有点主要是为了训练模型的速度
4 image_array_sample = shuffle(image_array, random_state=0)[:1000]
5
6 # 训练图片像素数据，将像素数据分为64类
7 # random_state = 0用来重现同样的结果，不设置则每次都是不同的随机结果
8 kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(image_array_sample)
9 print("done in %0.3fs." % (time() - t0)) # 查看训练时间预测
1 print("Predicting color indices on the full image (k-means)")
2 t0 = time()
3 # 预测数据分类,image_array(921600,3)二维数组预测完毕后的结果labels是一维数组
4 labels = kmeans.predict(image_array)
5 print("done in %0.3fs." % (time() - t0)) # 查看预测时间
6 print(labels)
7 print(labels.shape)
8 # 将labels从一维数组化为二维数组
9 labels = labels.reshape(w, h)
10 print(labels)
11 print(labels.shape)保存像素查询本和处理后的图像
1 def save_compress_data():
2 np.save('codebook_tiger.npy', kmeans.cluster_centers_)
3 io.imsave('compressed_tiger.png', labels)还原图像
1 def recreate_image(codebook, labels, w, h):
2 # Recreate the (compressed) image from the code book & labels
3 # 每个像素查询码本（对应0~63），取得其对应的像素值
4 d = codebook.shape[1]
5 image = np.zeros((w, h, d))
6 label_idx = 0
7 for i in range(w):
8 for j in range(h):
9 image[i][j] = codebook[labels[i, j]]
10 label_idx += 1
11 print('还原出的图像 >>>>', image)
12 return image执行代码
1 # save_compress_data()
2 centers = np.load('codebook_tiger.npy') # 像素查询码本
3 c_image = io.imread('compressed_tiger.png') # 这张图片里的像素已经过分类
4 print('像素查询本 >>>>', centers)
5 print(centers.shape)
6 print(centers.shape[1]) # 0是行数，1是列数
7 print('压缩的图像 >>>>', c_image)
8 print(c_image.shape)
9
10 cv2.imshow("new", recreate_image(centers, c_image, w, h))
11 cv2.waitKey(0)'''
取出压缩后图像的每一个数据即像素分类id(labels[i, j])，在像素查询本中查找该分类对应的三位像素一行数据(codebook[labels[i, j]])，
赋予新的image对象(无需指定列数，三位像素即3列)
original_shape >>>> (720, 1280, 3)
w,h,d >>>> 720 1280 3
image_array shape>>>> (921600, 3)
done in 0.583s.
Predicting color indices on the full image (k-means)
done in 0.740s.
[10 10 10 ... 60 60 60]
(921600,)
[[10 10 10 ... 42 8 42]
[10 10 10 ... 42 42 8]
[10 10 10 ... 42 42 8]
...
[ 3 3 36 ... 60 60 60]
[ 3 3 36 ... 60 60 60]
[ 3 3 36 ... 60 60 60]]
(720, 1280)
像素查询本 >>>> [[0.26901961 0.27607843 0.35686275]
[0.67189542 0.66887883 0.66747109]
[0.09971989 0.09271709 0.10028011]
[0.38901961 0.38184874 0.39383754]
[0.83529412 0.8285205 0.83333333]
[0.4402852 0.57468806 0.71764706]
[0.46849673 0.49947712 0.47712418]
[0.19622926 0.26651584 0.24494721]
[0.30539216 0.45 0.60441176]
[0.14444444 0.50359477 0.22581699]
[0.64325609 0.63850267 0.62923351]
[0.75148874 0.74524328 0.7405955 ]
[0.18221289 0.18585434 0.20770308]
[0.55294118 0.69233512 0.8631016 ]
[0.09656863 0.28039216 0.43823529]
[0.59155354 0.58924082 0.57797888]
[0.53202614 0.38039216 0.7124183 ]
[0.90756303 0.90364146 0.90140056]
[0.3713555 0.46683717 0.54339301]
[0.31215686 0.54352941 0.43372549]
[0.23504902 0.32254902 0.30563725]
[0.29694989 0.37342048 0.42461874]
[0.19117647 0.32622549 0.4495098 ]
[0.18431373 0.55757576 0.30516934]
[0.70718954 0.76601307 0.83529412]
[0.51328976 0.50544662 0.51568627]
[0.36705882 0.23921569 0.61098039]
[0.44416027 0.42983802 0.43631714]
[0.10053476 0.16363636 0.23600713]
[0.15022624 0.14434389 0.15806938]
[0.40452489 0.50497738 0.59457014]
[0.51265597 0.63030303 0.78324421]
[0.29215686 0.30392157 0.30056022]
[0.06876751 0.10294118 0.15644258]
[0.0455243 0.05268542 0.06479113]
[0.95294118 0.95294118 0.95803922]
[0.34232026 0.35065359 0.33970588]
[0.56254902 0.55431373 0.54196078]
[0.2745098 0.49063181 0.27973856]
[0.78676471 0.7814951 0.78112745]
[0.21411765 0.20627451 0.43176471]
[0.34196078 0.55843137 0.35803922]
[0.36470588 0.5027451 0.66352941]
[0.47189542 0.67973856 0.56078431]
[0.49084967 0.55294118 0.63300654]
[0.10889894 0.20301659 0.32488688]
[0.65228758 0.78823529 0.92222222]
[0.3372549 0.53411765 0.7427451 ]
[0.71036415 0.70672269 0.69677871]
[0.17019608 0.26431373 0.3696732 ]
[0.39063181 0.44814815 0.40217865]
[0.57303922 0.34656863 0.81617647]
[0.28039216 0.37385621 0.49477124]
[0.33440285 0.42816399 0.49411765]
[0.43137255 0.61019608 0.81882353]
[0.25743945 0.24705882 0.24313725]
[0.27088989 0.34901961 0.36651584]
[0.52990196 0.59215686 0.54166667]
[0.39607843 0.55196078 0.51470588]
[0.87511312 0.87179487 0.87722474]
[0.41137255 0.46196078 0.46745098]
[0.23627451 0.26470588 0.29362745]
[0.1254902 0.44248366 0.18431373]
[0.61265597 0.6197861 0.60463458]]
(64, 3)
3
压缩的图像 >>>> [[10 10 10 ... 42 8 42]
[10 10 10 ... 42 42 8]
[10 10 10 ... 42 42 8]
...
[ 3 3 36 ... 60 60 60]
[ 3 3 36 ... 60 60 60]
[ 3 3 36 ... 60 60 60]]
(720, 1280)
还原出的图像 >>>> [[[0.64325609 0.63850267 0.62923351]
[0.64325609 0.63850267 0.62923351]
[0.64325609 0.63850267 0.62923351]
...
[0.36470588 0.5027451 0.66352941]
[0.30539216 0.45 0.60441176]
[0.36470588 0.5027451 0.66352941]]
[[0.64325609 0.63850267 0.62923351]
[0.64325609 0.63850267 0.62923351]
[0.64325609 0.63850267 0.62923351]
...
[0.36470588 0.5027451 0.66352941]
[0.36470588 0.5027451 0.66352941]
[0.30539216 0.45 0.60441176]]
[[0.64325609 0.63850267 0.62923351]
[0.64325609 0.63850267 0.62923351]
[0.64325609 0.63850267 0.62923351]
...
[0.36470588 0.5027451 0.66352941]
[0.36470588 0.5027451 0.66352941]
[0.30539216 0.45 0.60441176]]
...
[[0.38901961 0.38184874 0.39383754]
[0.38901961 0.38184874 0.39383754]
[0.34232026 0.35065359 0.33970588]
...
[0.41137255 0.46196078 0.46745098]
[0.41137255 0.46196078 0.46745098]
[0.41137255 0.46196078 0.46745098]]
[[0.38901961 0.38184874 0.39383754]
[0.38901961 0.38184874 0.39383754]
[0.34232026 0.35065359 0.33970588]
...
[0.41137255 0.46196078 0.46745098]
[0.41137255 0.46196078 0.46745098]
[0.41137255 0.46196078 0.46745098]]
[[0.38901961 0.38184874 0.39383754]
[0.38901961 0.38184874 0.39383754]
[0.34232026 0.35065359 0.33970588]
...
[0.41137255 0.46196078 0.46745098]
[0.41137255 0.46196078 0.46745098]
[0.41137255 0.46196078 0.46745098]]]
'''