Scikit学习图像分类器

Question

我写了这段代码：

# 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%sn"
      % (classifier, metrics.classification_report(expected, predicted)))
print("Confusion matrix:n%s" % metrics.confusion_matrix(expected, predicted))

我读了这个错误：

属性

错误："列表"对象没有属性"重塑"

我认为我构建图像阵列是错误的，因为它解决了？

Answer 1

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

在这里，您尝试将该方法reshape应用于 Python 列表。

但是，imgs应该是numpy array。因此，您应该替换

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')]]

跟

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')]])