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')