Question

我正在尝试使用主要组件分析（PCA）使用python实现面部识别。我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]

我在步骤10中遇到问题，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

任何人都可以提供帮助??

Answer 1

我认为您要将计算d的行更改为以下内容：

#Step 10: Euclidean distance
d = np.sqrt(np.sum(np.asarray(w - w_in)**2, axis=1)

这为您提供了每个图像像素之间的平方，求和的根距离的长度M（训练图像的数量）列表。我相信你不需要矩阵乘积，你想要每个值的元素方形，因此np.asarray使它不矩阵。这为您提供w_in与每个w矩阵之间的“欧几里德”差异。

Answer 2

当您转到(w - w_in)时，结果不是方阵。要将矩阵乘以它自己，它必须是正方形（这只是矩阵乘法的一个属性）。因此，您构建的w和w_in矩阵是错误的，或者您实际要做的是将矩阵(w - w_in)中的每个元素对齐，这是一个不同的操作。搜索逐元素乘法以找到numpy语法。

错误 - 计算python中PCA的欧氏距离

2 个答案: