Question

我试图在Python中学习更多有关图像处理的知识，作为整个过程的一部分，我正在阅读一本书中的一些练习。在一个练习中，我尝试对图像中的平均像素颜色进行kmeans聚类。下面的代码几乎是从示例中逐字逐句，但我不断收到错误（堆栈如下）。

文件＆＃34; C：/Users/xxx/gitStuff/version-control/image/data/practiceCh6.py" ;,第31行，在质心，方差= kmeans（特征，3）

文件＆＃34; C：\ Users \ xxx \ AppData \ Local \ Continuum \ Anaconda \ Lib \ site-packages \ scipy \ cluster \ vq.py＆＃34;，第524行，在kmeans中 result = best_book，best_dist

UnboundLocalError：本地变量＆＃39; best_book＆＃39;在分配前引用

代码如下：

from PIL import Image
from scipy.cluster.vq import kmeans,vq
from scipy.misc import imresize
from numpy import *


steps = 50
im = array(Image.open('frontside.jpg'))

dx = im.shape[0]
dy = im.shape[1]

#compute color features for each region
features =[]
for x in range(steps):
    for y in range(steps):
        R = mean(im[x*dx:(x+1)*dx,y*dy:(y+1)*dy,0])
        G = mean(im[x*dx:(x+1)*dx,y*dy:(y+1)*dy,1])
        B = mean(im[x*dx:(x+1)*dx,y*dy:(y+1)*dy,2])
        features.append([R,G,B])

features = array(features,'f') #make into array

#cluster
centroids,variance = kmeans(features,3)
code,distance = vq(features,centroids)

#create image with clulster labels
codeim = code.reshape(steps,steps)
codeim = imresize(codeim,im.shape[:2],interp='nearest')

figure()
imshow(codeim)
show()

非常感谢任何关于可能出错的建议。

Answer 1

我不熟悉图像处理，但通过在代码中添加一个简单的print语句，您会发现数组中的值是'nan'。

for x in range(steps):
    for y in range(steps):
        R = mean(im[x*dx:(x+1)*dx,y*dy:(y+1)*dy,0])
        G = mean(im[x*dx:(x+1)*dx,y*dy:(y+1)*dy,1])
        B = mean(im[x*dx:(x+1)*dx,y*dy:(y+1)*dy,2])
        features.append([R,G,B])

features = array(features,'f') #make into array
print features

返回：

[[ 186.93768311  159.18690491  157.92678833]
 [          nan           nan           nan]
 [          nan           nan           nan]
 ...,
 [          nan           nan           nan]
 [          nan           nan           nan]
 [          nan           nan           nan]]

你不能在nan上运行K-means，我会回去查看你如何分配R，G，B。

在scipy中使用Kmeans的UnboundLocalError

1 个答案: