拆分图像并比较每个部分

Question

我试图将图像分成n个相等的部分，然后比较每个块以定义一个＆＃34;均衡＆＃34;在插图中。 例如，在下图所示的图像中，与顶部相比，底部往往相似。

我写过这篇文章，但是我被困住了，不知道该怎么做，有什么帮助吗？

import math
import io
import numpy as np
from sklearn.cluster import KMeans
from PIL import Image
import image_slicer
from scipy import sum, average


tiles = image_slicer.slice('img/eq1.jpg', 2, save=False)
vectors = []
for tile in tiles:
    image = tile.image
    image = image.convert('RGB')

    colors = np.array(image).tolist()
    colors = [average(x, -1) for x in colors][0]

    vectors.append(colors)

#lista = np.array(tile.image)
#print np.array(tile.image)   

    image.show()

from sklearn.metrics.pairwise import cosine_similarity

print cosine_similarity(vectors)

Answer 1

好吧我解决了这个问题：

import math
import io
import numpy as np
from sklearn.cluster import KMeans
from PIL import Image
import image_slicer
from scipy import sum, average
from scipy.linalg import norm
import sklearn.metrics.pairwise


tiles = image_slicer.slice('img/ad.jpg', 4, save=False)
vectors = []
for tile in tiles:
    image = tile.image
    image = image.convert('RGB')

    colors = np.array(image).tolist()
    colors = [average(x, -1) for x in colors][0]

    vectors.append(colors)

#lista = np.array(tile.image)
#print np.array(tile.image)   

#    image.show()

from sklearn.metrics.pairwise import cosine_similarity

print np.around(sklearn.metrics.pairwise.manhattan_distances(vectors))