AttributeError:“LinearSVC”对象没有属性“classes”

Question

这段代码是用来检测字符和绘制矩形，然后预测字符的，但是每次都会出现错误！在

for rect in rects:
# Draw the rectangles
cv2.rectangle(im, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3) 
# Make the rectangular region around the digit
leng = int(rect[3] * 1.6)
pt1 = int(rect[1] + rect[3] // 2 - leng // 2)
pt2 = int(rect[0] + rect[2] // 2 - leng // 2)
roi = im_th[pt1:pt1+leng, pt2:pt2+leng]
# Resize the image
roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)
roi = cv2.dilate(roi, (3, 3))
# Calculate the HOG features
roi_hog_fd = hog(roi, orientations=9, pixels_per_cell=(14, 14), cells_per_block=(1, 1), visualise=False)
ar=np.array([roi_hog_fd], 'float64')
nbr = clf.predict(ar)

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Answer 1

@Wineartist显然这个错误出现是因为我没有执行下面的Generate Classifier代码：

# Import the modules
from sklearn.externals import joblib
from sklearn import datasets
from skimage.feature import hog
from sklearn.svm import LinearSVC
import numpy as np
from collections import Counter

# Load the dataset
dataset = datasets.fetch_mldata("MNIST Original")

# Extract the features and labels
features = np.array(dataset.data, 'int16') 
labels = np.array(dataset.target, 'int')

# Extract the hog features
list_hog_fd = []
for feature in features:
    fd = hog(feature.reshape((28, 28)), orientations=9, pixels_per_cell=(14, 14), cells_per_block=(1, 1), visualise=False)
    list_hog_fd.append(fd)
hog_features = np.array(list_hog_fd, 'float64')

print "Count of digits in dataset", Counter(labels)

# Create a linear SVM object
clf = LinearSVC()

# Perform the training
clf.fit(hog_features, labels)

# Save the classifier
joblib.dump(clf, "digits_cls.pkl", compress=3)