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# -*- coding: utf-8 -* |
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import numpy as np |
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import os |
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import matplotlib.pyplot as plt |
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from scipy import misc |
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scale = 0.4 |
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k = 4 |
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feature_face = 0 |
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principal_percent = 0.3 |
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# covert image to sole vector |
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def img2vector(filename): |
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imgVector = misc.imresize(plt.imread(filename), scale).flatten() |
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#imgVector = plt.imread(filename).flatten() |
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return imgVector.astype(np.float) |
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# load image from diretion |
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def loadimage(dataSetDir): |
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train_face = np.zeros((40 * k, int(112 * scale) * int(92 * scale))) # image size:112*92 |
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train_face_number = np.zeros(40 * k).astype(np.int8) |
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test_face = np.zeros((40 * (10 - k), int(112 * scale) * int(92 * scale))) |
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test_face_number = np.zeros(40 * (10 - k)).astype(np.int8) |
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for i in np.linspace(1, 40, 40).astype(np.int8): #40 sample people |
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people_num = i |
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for j in np.linspace(1, 10, 10).astype(np.int8): #everyone has 10 different face |
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if j <= k: |
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filename = dataSetDir+'/s'+str(people_num)+'/'+str(j)+'.pgm' |
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img = img2vector(filename) |
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train_face[(i-1)*k+(j-1),:] = img |
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train_face_number[(i-1)*k+(j-1)] = people_num |
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else: |
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filename = dataSetDir+'/s'+str(people_num)+'/'+str(j)+'.pgm' |
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img = img2vector(filename) |
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test_face[(i-1)*(10-k)+(j-k)-1,:] = img |
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test_face_number[(i-1)*(10-k)+(j-k)-1] = people_num |
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return train_face,train_face_number,test_face,test_face_number |
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40
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# subtract a vector from a matrex |
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def subvector(target_matrex, target_vector): |
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vector4matrex = np.repeat(target_vector, target_matrex.shape[0],axis = 0) |
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target_matrex = target_matrex - vector4matrex |
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return target_matrex |
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# both data subtract mean data of train data |
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def submean(train_data, test_data): |
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mean_data = train_data.mean(axis = 0).reshape(1, train_data.shape[1]) |
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train_data = subvector(train_data, mean_data) |
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test_data = subvector(test_data, mean_data) |
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return train_data,test_data |
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# main program |
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train_face,train_face_number,test_face,test_face_number = loadimage(os.getcwd()+'/att_faces') |
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train_face, test_face = submean(train_face, test_face) |
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cov = np.dot(train_face.T, train_face) |
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print("Calculate eigenvalues & eigenvectors") |
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l, v = np.linalg.eig(cov) |
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print("Sort eigenvectors by the value of eigenvalues") |
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mix = np.vstack((l,v)) |
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mix = mix.T[np.lexsort(mix[::-1,:])].T[:,::-1] |
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v = np.delete(mix, 0, axis = 0) |
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#show feature maps |
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if feature_face == 1: |
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plt.figure('Feature Map') |
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r, c = (4, 10) |
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for i in np.linspace(1, r * c, r * c).astype(np.int8): |
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plt.subplot(r,c,i) |
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plt.imshow(v[:, i-1].real.reshape(int(112 * scale), int(92 * scale)), cmap='gray') |
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plt.axis('off') |
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v = v[:,0:int(v.shape[1]*principal_percent)] |
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train_face = np.dot(train_face, v) |
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test_face = np.dot(test_face , v) |
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count = 0 |
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for i in np.linspace(0, test_face.shape[0] - 1, test_face.shape[0]).astype(np.int64): |
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sub = subvector(train_face,test_face[i, :].reshape((1,test_face.shape[1]))) |
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dis = np.linalg.norm(sub, axis = 1) |
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fig = np.argmin(dis) |
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if train_face_number[fig] == test_face_number[i]: |
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count = count + 1 |
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correct_rate = count / test_face.shape[0] |
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print("Principal =", principal_percent * 100, "%, count for", int(v.shape[1]*principal_percent), "principal eigenvectors") |
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print("Correct rate =", correct_rate * 100 , "%") |
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print("Finish...") |
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SilvesterHsu /
ORLFaceRecognition-PCA
