python实现人脸识别经典算法（一） 特征脸法

#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')