Python实现人脸识别经典算法：特征脸法（Eigenface）详解

#encoding=utf-8 
import numpy as np 
import cv2 
import os 
 
class EigenFace(object): 
 def __init__(self,threshold,dimNum,dsize): 
 self.threshold = threshold # 阈值暂未使用 
 self.dimNum = dimNum 
 self.dsize = dsize 
 
 def loadImg(self,fileName,dsize): 
 ''''' 
 载入图像，灰度化处理，统一尺寸，直方图均衡化 
 :param fileName: 图像文件名 
 :param dsize: 统一尺寸大小。元组形式 
 :return: 图像矩阵 
 ''' 
 img = cv2.imread(fileName) 
 retImg = cv2.resize(img,dsize) 
 retImg = cv2.cvtColor(retImg,cv2.COLOR_RGB2GRAY) 
 retImg = cv2.equalizeHist(retImg) 
 # cv2.imshow('img',retImg) 
 # cv2.waitKey() 
 return retImg 
 
 
 def createImgMat(self,dirName): 
 ''''' 
 生成图像样本矩阵，组织形式为行为属性，列为样本 
 :param dirName: 包含训练数据集的图像文件夹路径 
 :return: 样本矩阵，标签矩阵 
 ''' 
 dataMat = np.zeros((10,1)) 
 label = [] 
 for parent,dirnames,filenames in os.walk(dirName): 
  # print parent 
  # print dirnames 
  # print filenames 
  index = 0 
  for dirname in dirnames: 
  for subParent,subDirName,subFilenames in os.walk(parent+'/'+dirname): 
   for filename in subFilenames: 
   img = self.loadImg(subParent+'/'+filename,self.dsize) 
   tempImg = np.reshape(img,(-1,1)) 
   if index == 0 : 
    dataMat = tempImg 
   else: 
    dataMat = np.column_stack((dataMat,tempImg)) 
   label.append(subParent+'/'+filename) 
   index += 1 
 return dataMat,label 
 
 
 def PCA(self,dataMat,dimNum): 
 ''''' 
 PCA函数，用于数据降维 
 :param dataMat: 样本矩阵 
 :param dimNum: 降维后的目标维度 
 :return: 降维后的样本矩阵和变换矩阵 
 ''' 
 # 均值化矩阵 
 meanMat = np.mat(np.mean(dataMat,1)).T 
 print '平均值矩阵维度',meanMat.shape 
 diffMat = dataMat-meanMat 
 # 求协方差矩阵，由于样本维度远远大于样本数目，所以不直接求协方差矩阵，采用下面的方法 
 covMat = (diffMat.T*diffMat)/float(diffMat.shape[1]) # 归一化 
 #covMat2 = np.cov(dataMat,bias=True) 
 #print '基本方法计算协方差矩阵为',covMat2 
 print '协方差矩阵维度',covMat.shape 
 eigVals, eigVects = np.linalg.eig(np.mat(covMat)) 
 print '特征向量维度',eigVects.shape 
 print '特征值',eigVals 
 eigVects = diffMat*eigVects 
 eigValInd = np.argsort(eigVals) 
 eigValInd = eigValInd[::-1] 
 eigValInd = eigValInd[:dimNum] # 取出指定个数的前n大的特征值 
 print '选取的特征值',eigValInd 
 eigVects = eigVects/np.linalg.norm(eigVects,axis=0) #归一化特征向量 
 redEigVects = eigVects[:,eigValInd] 
 print '选取的特征向量',redEigVects.shape 
 print '均值矩阵维度',diffMat.shape 
 lowMat = redEigVects.T*diffMat 
 print '低维矩阵维度',lowMat.shape 
 return lowMat,redEigVects 
 
 def compare(self,dataMat,testImg,label): 
 ''''' 
 比较函数，这里只是用了最简单的欧氏距离比较，还可以使用KNN等方法，如需修改修改此处即可 
 :param dataMat: 样本矩阵 
 :param testImg: 测试图像矩阵，最原始形式 
 :param label: 标签矩阵 
 :return: 与测试图片最相近的图像文件名 
 ''' 
 testImg = cv2.resize(testImg,self.dsize) 
 testImg = cv2.cvtColor(testImg,cv2.COLOR_RGB2GRAY) 
 testImg = np.reshape(testImg,(-1,1)) 
 lowMat,redVects = self.PCA(dataMat,self.dimNum) 
 testImg = redVects.T*testImg 
 print '检测样本变换后的维度',testImg.shape 
 disList = [] 
 testVec = np.reshape(testImg,(1,-1)) 
 for sample in lowMat.T: 
  disList.append(np.linalg.norm(testVec-sample)) 
 print disList 
 sortIndex = np.argsort(disList) 
 return label[sortIndex[0]] 
 
 
 def predict(self,dirName,testFileName): 
 ''''' 
 预测函数 
 :param dirName: 包含训练数据集的文件夹路径 
 :param testFileName: 测试图像文件名 
 :return: 预测结果 
 ''' 
 testImg = cv2.imread(testFileName) 
 dataMat,label = self.createImgMat(dirName) 
 print '加载图片标签',label 
 ans = self.compare(dataMat,testImg,label) 
 return ans 
 
 
if __name__ == '__main__': 
 eigenface = EigenFace(20,50,(50,50)) 
 print eigenface.predict('d:/face','D:/face_test/1.bmp')