Eigenface与PCA人脸识别算法实验

  1 import numpy as np
  2 
  3 from numpy import *
  4 
  5 from numpy import linalg as la
  6 
  7 from PIL import Image
  8 
  9 import glob
 10 
 11 from matplotlib import pyplot as plt
 12 
 13 def loadImageSet(add):
 14 
 15  filenames = glob.glob(‘face/pgm/*.pgm‘)
 16 
 17  filenames.sort()
 18 
 19  img = [Image.open(fn).convert(‘L‘).resize((98, 116)) for fn in filenames]
 20 
 21  FaceMat = np.asarray([np.array(im).flatten() for im in img])
 22 
 23  return FaceMat
 24 
 25 def ReconginitionVector(selecthr = 0.8):
 26 
 27  # step1: load the face image data ,get the matrix consists of all image
 28 
 29  FaceMat = loadImageSet(‘face/yalefaces/‘)
 30 
 31  print(‘-----------FaceMat.shape--------‘)
 32 
 33  print(FaceMat.shape)
 34 
 35  # step2: average the FaceMat
 36 
 37  avgImg = mean(FaceMat,0)
 38 
 39  # step3: calculate the difference of avgimg and all image data(FaceMat)
 40 
 41  diffTrain = FaceMat-avgImg
 42 
 43  covMat =np.asmatrix(diffTrain) * np.asmatrix(diffTrain.T)
 44 
 45  eigvals,eigVects = linalg.eig(covMat) #la.linalg.eig(np.mat(covMat))
 46 
 47  #step4: calculate eigenvector of covariance matrix (because covariance matrix will cause memory error)
 48 
 49  eigSortIndex = argsort(-eigvals)
 50 
 51  for i in range(shape(FaceMat)[1]):
 52 
 53  if (eigvals[eigSortIndex[:i]]/eigvals.sum()).sum() >= selecthr:
 54 
 55  eigSortIndex = eigSortIndex[:i]
 56 
 57  break
 58 
 59  covVects = diffTrain.T * eigVects[:,eigSortIndex] # covVects is the eigenvector of covariance matrix
 60 
 61  # avgImg 是均值图像，covVects是协方差矩阵的特征向量，diffTrain是偏差矩阵
 62 
 63  return avgImg,covVects,diffTrain
 64 
 65 def judgeFace(judgeImg,FaceVector,avgImg,diffTrain):
 66 
 67  diff = judgeImg - avgImg
 68 
 69  weiVec = FaceVector.T* diff.T
 70 
 71  res = 0
 72 
 73  resVal = inf
 74 
 75 #==============================================================================
 76 
 77 # plt.imshow(avgImg.reshape(98,116))
 78 
 79 # plt.show()
 80 
 81 #==============================================================================
 82 
 83  for i in range(15):
 84 
 85  TrainVec = (diffTrain[i]*FaceVector).T
 86 
 87  if (array(weiVec-TrainVec)**2).sum() < resVal:
 88 
 89  res = i
 90 
 91  resVal = (array(weiVec-TrainVec)**2).sum()
 92 
 93  return res+1
 94 
 95 if __name__ == ‘__main__‘:
 96 
 97  avgImg,FaceVector,diffTrain = ReconginitionVector(selecthr = 0.8)
 98 
 99  nameList = [‘01‘,‘02‘,‘03‘,‘04‘,‘05‘,‘06‘,‘07‘,‘08‘,‘09‘,‘10‘,‘11‘,‘12‘,‘13‘,‘14‘,‘15‘]
100 
101  characteristic = [‘centerlight‘,‘glasses‘,‘happy‘,‘leftlight‘,‘noglasses‘,‘rightlight‘,‘sad‘,‘sleepy‘,‘surprised‘,‘wink‘]
102 
103  for c in characteristic:
104 
105  count = 0
106 
107  for i in range(len(nameList)):
108 
109  # 这里的loadname就是我们要识别的未知人脸图，我们通过15张未知人脸找出的对应训练人脸进行对比来求出正确率
110 
111  loadname = ‘face/yalefaces/subject‘+nameList[i]+‘.‘+c+‘.pgm‘
112 
113  judgeImg = Image.open(loadname).convert(‘L‘).resize((98, 116))
114 
115  #print(loadname)
116 
117  if judgeFace(mat(judgeImg).flatten(),FaceVector,avgImg,diffTrain) == int(nameList[i]):
118 
119  count += 1
120 
121 print(‘accuracy of %s is %f‘%(c, float(count)/len(nameList))) # 求出正确率

 1 accuracy of centerlight is 0.400000
 2 
 3 accuracy of glasses is 0.533333
 4 
 5 accuracy of happy is 0.866667
 6 
 7 accuracy of leftlight is 0.133333
 8 
 9 accuracy of noglasses is 0.800000
10 
11 accuracy of rightlight is 0.133333
12 
13 accuracy of sad is 0.800000
14 
15 accuracy of sleepy is 0.800000
16 
17 accuracy of surprised is 0.666667
18 
19 accuracy of wink is 0.600000