人脸识别 - 的Python [英] Face recognition - Python

问题描述

我想通过的进行人脸识别使用python主成分分析法的（PCA）。

I am trying to make face recognition by Principal Component Analysis (PCA) using python.

现在我能够得到训练图像图片和输入图像 input_image 之间的最小欧氏距离。这里是我的code：

Now I am able to get the minimum euclidean distance between the training images images and the input image input_image. Here is my code:

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，我要回的图像（或数组索引图片）的最小欧几里得距离不是它指数距离 D

My problem now is that I want to return the image (or its index in the array images) with the minimum euclidean distance not its index in the array of distances d

推荐答案

我不相信你已经相比修改的图像存储在是W 的顺序在图片，因此， IDX 从 np.argmin（D）应该是图像相同指数列表，因此

I don't believe that you have modified the order that the images are stored in w compared to in images, therefore, the idx from np.argmin(d) should be the same index of the images list, so

images[idx]

应该是你想要的图像。

should be the image you want.

当然，

images[idx].shape

会给（1800），因为它仍然夷为平地。如果你想unflatten它，你可以这样做：

will give (1800,) because it's still flattened. If you want to unflatten it, you can do:

images[idx].reshape(90,90)

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