获取错误消息，
预期的2D数组，取而代之的是1D数组： 数组= [0.00127552 0.00286695 0.00135289 ... 0.00611554 0.02416051 0.00977264]。 如果数据具有单个功能，则使用array.reshape（-1，1）来重塑数据；如果包含单个样本，则使用array.reshape（1，-1）来重塑数据。
尝试过tstArray.reshape(1,-1)，但没有运气。
import numpy as np
import matplotlib.pyplot as plt
import skimage.feature
from sklearn.decomposition import PCA
trnImages = np.load('trnImage.npy')
tstImages = np.load('tstImage.npy')
trnLabels = np.load('trnLabel.npy')
tstLabels = np.load('tstLabel.npy')
trnidx = 20
trnImages.shape
from sklearn.svm import SVC
import tensorflow.keras as keras
def computeFeatures(image):
# This function computes the HOG features with the parsed hyperparameters and returns the features as hog_feature.
# By setting visualize=True we obtain an image, hog_as_image, which can be plotted for insight into extracted HOG features.
hog_feature, hog_as_image = skimage.feature.hog(image, visualize=True, block_norm='L2-Hys')
return hog_feature
rnArray = np.zeros([10000,324])
tstArray = np.zeros([1000,324])
for i in range (0, 10000 ):
trnFeatures = computeFeatures(trnImages[:,:,:,i])
trnArray[i,:] = trnFeatures
ValueError: Expected 2D array, got 1D array instead:
array=[0.00127552 0.00286695 0.00135289 ... 0.00611554 0.02416051 0.00977264].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.