我正在尝试使用 python 通过主成分分析 (PCA) 实现人脸识别。我正在按照本教程中的步骤操作:http://onionesquereality.wordpress.com/2009/02/11/face-recognition-using-eigenfaces-and-distance-classifiers-a-tutorial/
这是我的代码:
import os
from PIL import Image
import numpy as np
import glob
import numpy.linalg as linalg
#Step1: put database images into a 2D array
filenames = glob.glob('C:\\Users\\Karim\\Downloads\\att_faces\\New folder/*.pgm')
filenames.sort()
img = [Image.open(fn).convert('L').resize((90, 90)) for fn in filenames]
images = np.asarray([np.array(im).flatten() for im in img])
#Step 2: find the mean image and the mean-shifted input images
mean_image = images.mean(axis=0)
shifted_images = images - mean_image
#Step 3: Covariance
c = np.cov(shifted_images)
#Step 4: Sorted eigenvalues and eigenvectors
eigenvalues,eigenvectors = linalg.eig(c)
idx = np.argsort(-eigenvalues)
eigenvalues = eigenvalues[idx]
eigenvectors = eigenvectors[:, idx]
#Step 5: Only keep the top 'num_eigenfaces' eigenvectors
num_components = 20
eigenvalues = eigenvalues[0:num_components].copy()
eigenvectors = eigenvectors[:, 0:num_components].copy()
#Step 6: Finding weights
w = eigenvectors.T * np.asmatrix(shifted_images)
#Step 7: Input image
input_image = Image.open('C:\\Users\\Karim\\Downloads\\att_faces\\1.pgm').convert('L').resize((90, 90))
input_image = np.asarray(input_image).flatten()
#Step 8: get the normalized image, covariance, eigenvalues and eigenvectors for input image
shifted_in = input_image - mean_image
c = np.cov(input_image)
cmat = c.reshape(1,1)
eigenvalues_in, eigenvectors_in = linalg.eig(cmat)
#Step 9: Fing weights of input image
w_in = eigenvectors_in.T * np.asmatrix(shifted_in)
print w_in
print w_in.shape
#Step 10: Euclidean distance
d = np.sqrt(np.sum((w - w_in)**2))
idx = np.argmin(d)
match = images[idx]
我在第 10 步遇到问题,因为我收到此错误:
Traceback (most recent call last):
File "C:/Users/Karim/Desktop/Bachelor 2/New folder/new3.py", line 59, in <module>
d = np.sqrt(np.sum((w - w_in)**2))
File "C:\Python27\lib\site-packages\numpy\matrixlib\defmatrix.py", line 343, in __pow__
return matrix_power(self, other)
File "C:\Python27\lib\site-packages\numpy\matrixlib\defmatrix.py", line 160, in matrix_power
raise ValueError("input must be a square array")
ValueError: input must be a square array
谁能帮忙??
最佳答案
我认为您想将计算 d
的行更改为如下所示:
#Step 10: Euclidean distance
d = np.sqrt(np.sum(np.asarray(w - w_in)**2, axis=1)
这为您提供了一个长度为 M
(训练图像的数量)的列表,其中包含每个图像像素之间的平方、求和、求根距离。我相信您不想要矩阵乘积,您想要每个值的元素平方,因此 np.asarray
使其不是矩阵。这为您提供了 w_in
和每个 w
矩阵之间的“欧氏”差异。
关于python - 错误 - 在 python 中计算 PCA 的欧几里得距离,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/16026437/