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))

我读到这个错误：

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

我认为建立图像阵列是错误的？在

Answer 1

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

这里您尝试在Python列表上应用reshape方法。在

但是，imgs应该是numpy array。因此，您应该更换

^{2}$

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')]])