pca特征脸python
时间: 2023-12-05 07:41:52 浏览: 36
PCA(Principal Component Analysis)是一种常用的数据降维方法,而特征脸是PCA在图像处理中的应用。下面是PCA特征脸的Python实现:
1.导入必要的库和数据
```python
import numpy as np
import cv2
import os
# 读取图像数据
def read_images(path, sz=None):
c = 0
X, y = [], []
for dirname, dirnames, filenames in os.walk(path):
for subdirname in dirnames:
subject_path = os.path.join(dirname, subdirname)
for filename in os.listdir(subject_path):
try:
if not filename.endswith('.pgm'):
continue
filepath = os.path.join(subject_path, filename)
im = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
if sz is not None:
im = cv2.resize(im, sz)
X.append(np.asarray(im, dtype=np.uint8))
y.append(c)
except IOError as e:
print("I/O error({0}): {1}".format(e.errno, e.strerror))
except:
print("Unexpected error:", sys.exc_info()[0])
raise
c = c+1
return [X,y]
```
2.计算特征脸
```python
# 计算特征脸
def compute_eigenface(X):
# 将图像数据转换为矩阵
X = np.asarray(X)
# 计算均值脸
mean_face = X.mean(axis=0)
# 将每个样本向量减去均值脸
X = X - mean_face
# 计算协方差矩阵
cov = np.dot(X.T, X)
# 计算特征值和特征向量
eigenvalues, eigenvectors = np.linalg.eig(cov)
# 将特征向量按照特征值从大到小排序
idx = eigenvalues.argsort()[::-1]
eigenvectors = eigenvectors[:,idx]
# 取前k个特征向量
k = 10
topk_evecs = eigenvectors[:,:k]
# 计算特征脸
eigenface = np.dot(X, topk_evecs)
return eigenface, topk_evecs, mean_face
```
3.计算训练样本在特征脸空间的投影
```python
# 计算训练样本在特征脸空间的投影
def compute_eigen_train_sample(normTrainFaceMat, eigenface):
eigen_train_sample = np.dot(normTrainFaceMat, eigenface)
return eigen_train_sample
```
4.计算测试样本在特征脸空间的投影
```python
# 计算测试样本在特征脸空间的投影
def compute_eigen_test_sample(normTestFaceMat, eigenface):
eigen_test_sample = np.dot(normTestFaceMat, eigenface)
return eigen_test_sample
```
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