Question

我正在尝试使用python通过主成分分析（PCA）进行人脸识别。

现在，我可以获得训练图像images和输入图像input_image之间的最小欧氏距离。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\\me\\Downloads\\/*.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.asmatrix(shifted_images) * np.asmatrix(shifted_images.T)

#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) 
# check eigenvectors.T/eigenvectors 

#Step 7: Input image
input_image = Image.open('C:\\Users\\me\\Test\\5.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: Find weights of input image
w_in = eigenvectors_in.T * np.asmatrix(shifted_in) 
# check eigenvectors/eigenvectors_in

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

我现在的问题是我希望以最小的欧氏距离返回图像（或其在数组images中的索引）而不是距离数组中的索引{{1 }}

Answer 1

我不相信您修改了w中图片的存储顺序与images相比，因此，来自idx的{{1}}应该是np.argmin(d)列表的相同索引，所以

images

应该是您想要的图像。

当然，

images[idx]

会给images[idx].shape，因为它仍然是扁平化的。如果你想取消它，你可以这样做：

(1800,)

人脸识别 - Python

1 个答案: