UnboundLocalError在scipy中使用Kmeans

Question

我试图学习有关Python中图像处理的更多信息，作为该过程的一部分，我正在阅读的书中进行一些练习。 在一个练习中，我试图对图像中平均像素颜色进行kmeans聚类。 下面的代码几乎完全来自该示例，但是我不断收到错误消息（如下所示）。

文件“ C：/Users/xxx/gitStuff/version-control/image/data/practiceCh6.py”，第31行，质心，方差= kmeans（功能，3）

文件“ C：\\ Users \\ xxx \\ AppData \\ Local \\ Continuum \\ Anaconda \\ Lib \\ site-packages \\ scipy \\ cluster \\ vq.py”，第524行，以kmeans表示= best_book，best_dist

UnboundLocalError：分配前已引用局部变量“ best_book”

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()

任何关于什么是错的建议将不胜感激。

Answer 1

我对图像处理不熟悉，但是通过在代码中添加简单的打印语句，您将看到数组中的值是“ 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

返回值：

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

您不能在nan上运行K-means，我会回去检查您如何分配R，G，B。