Scikit学习图像分类

Question

我写了这个代码：

# Import datasets, classifiers and performance metrics 
from sklearn import datasets, svm, metrics 
import matplotlib.image as mpimg 

imgs=[[mpimg.imread('sci/img/1.jpg'),mpimg.imread('sci/img/2.jpg')],[mpimg.imread('sci/img/3.jpg'),mpimg.imread('sci/img/4.jpg')]] 
targ=[1,2] 

# To apply a classifier on this data, we need to flatten the image, to 
# turn the data in a (samples, feature) matrix: 
n_samples = len(imgs) 
data = imgs.reshape((n_samples, -1)) 

# Create a classifier: a support vector classifier 
classifier = svm.SVC(gamma=0.001) 

# We learn the digits on the first half of the digits 
classifier.fit(data, targ) 

# Now predict the value of the digit on the second half: 
expected = targ 
predicted = classifier.predict(data) 

print("Classification report for classifier %s:\n%s\n" 
     % (classifier, metrics.classification_report(expected, predicted))) 
print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted)) 

和我读到这个错误：

AttributeError: 'list' object has no attribute 'reshape'

我想我是错了建立图像阵列，因为它解决？

Answer 1

data = imgs.reshape((n_samples, -1)) 

在这里，你想申请的方法reshape Python列表上。

但是，imgs应该是numpy array。因此，你应该更换

imgs = [[mpimg.imread('sci/img/1.jpg'), mpimg.imread('sci/img/2.jpg')],[mpimg.imread('sci/img/3.jpg'), mpimg.imread('sci/img/4.jpg')]] 

与

import numpy as np 
imgs = np.array([[mpimg.imread('sci/img/1.jpg'), mpimg.imread('sci/img/2.jpg')], [mpimg.imread('sci/img/3.jpg'), mpimg.imread('sci/img/4.jpg')]])