PCA人脸识别的python实现

     这几天看了看PCA及其人脸识别的流程，并在网络上搜相应的python代码，有，但代码质量不好，于是自己就重新写了下，对于att_faces数据集的识别率能达到92.5%~98.0%（40种类型，每种随机选5张训练，5张识别），全部代码如下，不到50行哦。

# -*- coding: utf-8 -*-
import numpy as np
import os, glob, random, cv2

def pca(data,k):
data = np.float32(np.mat(data))
rows,cols = data.shape                              #取大小
data_mean = np.mean(data,0)                         #求均值
Z = data - np.tile(data_mean,(rows,1))
D,V = np.linalg.eig(Z*Z.T )                         #特征值与特征向量
V1 = V[:, :k]                                       #取前k个特征向量
V1 = Z.T*V1
for i in xrange(k):                                 #特征向量归一化
V1[:,i] /= np.linalg.norm(V1[:,i])
return np.array(Z*V1),data_mean,V1

def loadImageSet(folder=u'E:/迅雷下载/faceProcess/att_faces', sampleCount=5): #加载图像集，随机选择sampleCount张图片用于训练
trainData = []; testData = []; yTrain=[]; yTest = [];
for k in range(40):
folder2 = os.path.join(folder, 's%d' % (k+1))
data = [cv2.imread(d.encode('gbk'),0) for d in glob.glob(os.path.join(folder2, '*.pgm'))]
sample = random.sample(range(10), sampleCount)
trainData.extend([data[i].ravel() for i in range(10) if i in sample])
testData.extend([data[i].ravel() for i in range(10) if i not in sample])
yTest.extend([k]* (10-sampleCount))
yTrain.extend([k]* sampleCount)
return np.array(trainData),  np.array(yTrain), np.array(testData), np.array(yTest)

def main():
xTrain_, yTrain, xTest_, yTest = loadImageSet()
num_train, num_test = xTrain_.shape[0], xTest_.shape[0]

xTrain,data_mean,V = pca(xTrain_, 50)
xTest = np.array((xTest_-np.tile(data_mean,(num_test,1))) * V)  #得到测试脸在特征向量下的数据

yPredict =[yTrain[np.sum((xTrain-np.tile(d,(num_train,1)))**2, 1).argmin()] for d in xTest]
print u'欧式距离法识别率: %.2f%%'% ((yPredict == np.array(yTest)).mean()*100)

svm = cv2.SVM()                              #支持向量机方法
svm.train(np.float32(xTrain), np.float32(yTrain), params = {'kernel_type':cv2.SVM_LINEAR})
yPredict = [svm.predict(d) for d in np.float32(xTest)]
#yPredict = svm.predict_all(xTest.astype(np.float64))
print u'支持向量机识别率: %.2f%%' % ((yPredict == np.array(yTest)).mean()*100)

if __name__ =='__main__':
main()