我正在尝试使用 python 通过主成分分析 (PCA) 进行人脸识别。我正在使用pca. matplotlib这是它的文档：
类 matplotlib.mlab.PCA(a) 计算 a 的 SVD 并为 PCA 存储数据。使用 project 将数据投影到一组缩减的维度上
Inputs: a: a numobservations x numdims array Attrs: a a centered unit sigma version of input a numrows, numcols: the dimensions of a mu : a numdims array of means of a sigma : a numdims array of atandard deviation of a fracs : the proportion of variance of each of the principal components Wt : the weight vector for projecting a numdims point or array into PCA space Y : a projected into PCA space
因子载荷在 Wt 因子中，即第一个主成分的因子载荷由 Wt[0] 给出
这是我的代码：
import os
from PIL import Image
import numpy as np
import glob
import numpy.linalg as linalg
from matplotlib.mlab import PCA
#Step 1: 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: database PCA
results = PCA(images.T)
w = results.Wt
#Step 3: input image
input_image = Image.open('C:\\Users\\Karim\\Downloads\\att_faces\\1.pgm').convert('L')
input_image = np.asarray(input_image)
#Step 4: input image PCA
results_in = PCA(input_image)
w_in = results_in.Wt
#Step 5: Euclidean distance
d = np.sqrt(np.sum(np.asarray(w - w_in)**2, axis=1))
但我收到一个错误：
Traceback (most recent call last):
File "C:/Users/Karim/Desktop/Bachelor 2/New folder/matplotlib_pca.py", line 32, in <module>
d = np.sqrt(np.sum(np.asarray(w - w_in)**2, axis=1))
ValueError: operands could not be broadcast together with shapes (30,30) (92,92)
- 谁能帮我纠正错误？
- 这是人脸识别的正确方法吗？