本文尝试用python进行主成分分析(PCA)进行人脸识别。在
现在我可以得到训练图像images
和输入图像input_image
之间的最小欧几里德距离。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,我想返回最小欧几里德距离的图像(或它在数组images
中的索引),而不是它在距离数组中的索引{
我不认为您修改了图像在}应该与{}列表的索引相同,所以
w
中的存储顺序,因此,np.argmin(d)
中的{应该是你想要的形象。在
当然了
^{pr2}$会给出
^{3}$(1800,)
,因为它仍然是扁平的。如果您想解开它,可以执行以下操作:相关问题 更多 >
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