Eigenface and Fisherface

2015200548_KimJeyeon_Assignment/Eigenface/EigenfaceRecognition.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jun 14 16:39:01 2015
+
+@author: JYKIM
+"""
+
+import numpy as np
+from PIL import Image
+from pylab import *
+
+
+
+img_height = 68
+img_width = 90
+img_sample_num = 15
+
+img_mat = np.zeros(img_height * img_width)
+
+for h in range(img_height):
+    for w in range(img_width):
+        temp = Image.open('ImageTestset/other2.bmp')
+        img_mat[img_height * w + h] = array(temp.convert('L'))[h, w]
+
+
+img_mean_file = open('meanface.txt')
+
+img_mean_mat = np.zeros((img_height * img_width))
+
+
+for index in range(img_height * img_width):
+    img_temp = img_mean_file.readline()
+    img_mean_mat[index] = float(img_temp)
+
+img_mean_file.close()
+
+img_subtract_mat = np.zeros((img_height * img_width))
+
+for index in range(img_height * img_width):
+    img_subtract_mat[index] = img_mat[index] - img_mean_mat[index]
+
+
+
+img_eigenvector_sample = np.zeros((img_sample_num, img_height * img_width))
+
+for sample_num in range(img_sample_num):
+
+    img_eigen_temp = open('ImageDatasetEigenvector/{0:d}.txt'.format(sample_num + 1))
+
+    for index in range(img_height * img_width):
+        img_temp_eigen = img_eigen_temp.readline()
+        img_eigenvector_sample[sample_num, index] = float(img_temp_eigen)
+
+    img_eigen_temp.close()    
+
+img_test_weight = np.zeros((img_sample_num))
+
+img_test_weight = np.dot(img_eigenvector_sample, img_subtract_mat.T)  
+
+img_weight_sample = np.zeros((img_sample_num, img_sample_num))
+img_weight_distance = np.zeros((img_sample_num))
+
+for sample_num in range(img_sample_num):
+
+    img_weight_temp = open('ImageDatasetWeight/{0:d}.txt'.format(sample_num + 1))
+
+
+
+    for index in range(img_sample_num):
+        img_temp_weight = img_weight_temp.readline()
+        img_weight_sample[sample_num, index] = float(img_temp_weight)
+
+    img_weight_temp.close()
+
+
+for sample_num in range(img_sample_num):     
+    for index in range(img_sample_num):
+        img_weight_distance[sample_num] = img_weight_distance[sample_num] + pow(img_weight_sample[sample_num, index] - img_test_weight[index], 2)
+
+    img_weight_distance[sample_num] = sqrt(img_weight_distance[sample_num])
+
+
+
+min_distance = 10000
+classify = 0
+
+for sample_num in range(img_sample_num):
+    temp = min_distance
+    min_distance = min(min_distance, img_weight_distance[sample_num])
+    if temp != min_distance:
+        classify = sample_num
+
+
+classnum = int(classify / 5) + 1
+
+if img_weight_distance[classify] <= 1800: 
+    print 'Error : '
+    print img_weight_distance[classify]
+    print '\n'
+    print 'Subset : '
+    print classnum
+else:
+    print 'Error : '
+    print img_weight_distance[classify]
+    print '\n'
+    print 'Subset : '
+    print 'This image is a new image!'
+
+print '\n'    
+print 'Eigen Face'
+
+img_eigenface = np.zeros((img_height, img_width))
+img_temp_eigenface = np.zeros((img_height * img_width))
+
+num = 0
+for img_sample in range(img_sample_num):
+    num = num + 1
+
+    for h in range(img_height):
+        for w in range(img_width):
+            img_eigenface[h, w] = abs(img_eigenvector_sample[img_sample, img_height * w + h])
+    subplot(3, 5, num)
+    imshow(img_eigenface)      

2015200548_KimJeyeon_Assignment/Eigenface/GetEigenfaceFunction.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jun 13 14:37:59 2015
+
+@author: jykim15
+"""
+
+import numpy as np
+from pylab import *
+
+#import _imaging
+
+def GetSubtractImage(img_mat, average_img_vector, img_height, img_width):
+
+
+    img_subtract_mat = np.zeros((img_height * img_width))
+
+    for index in range(img_height * img_width):
+        img_subtract_mat[index] = img_mat[index] - average_img_vector[index]
+
+
+
+    return img_subtract_mat
+
+
+
+def GetWeight(img_subtract_mat, img_num, img_height, img_width):
+
+    L_mat = np.zeros((img_num, img_num)) 
+    L_mat = np.dot(img_subtract_mat, img_subtract_mat.T)
+
+    eigenvalue, eigenvector = linalg.eigh(L_mat)
+
+    covar_mat_eigenvector = np.zeros((img_height * img_width, img_num))
+    covar_mat_eigenvector = np.dot(img_subtract_mat.T, eigenvector)
+
+    sum_eigen = np.zeros(img_num)
+
+    for sample_num in range(img_num):
+        for index in range(img_height * img_width):
+            sum_eigen[sample_num] = sum_eigen[sample_num] + pow(covar_mat_eigenvector[index, sample_num], 2)
+
+        sum_eigen[sample_num] = sqrt(sum_eigen[sample_num])
+
+        for index in range(img_height * img_width):
+            covar_mat_eigenvector[index, sample_num] = covar_mat_eigenvector[index, sample_num] / sum_eigen[sample_num]
+
+
+    for sample_num in range(img_num):
+        temp_file = open('ImageDatasetEigenvector/{0:d}.txt'.format(sample_num + 1), 'w')
+        for index in range(img_height * img_width):
+            temp_file.write(str(covar_mat_eigenvector[index, sample_num]))
+            temp_file.write('\n')
+        temp_file.close()
+
+
+
+    weight = np.zeros((img_num, img_num))
+
+
+    weight = np.dot(covar_mat_eigenvector.T, img_subtract_mat.T)
+
+    return weight

2015200548_KimJeyeon_Assignment/Eigenface/GetSampleEigenface.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jun 12 17:27:12 2015
+
+@author: jykim15
+"""
+
+import numpy as np
+from PIL import Image
+from pylab import *
+import GetEigenfaceFunction
+
+img_height = 68
+img_width = 90
+img_sample_num = 15
+
+img_sample = np.zeros((img_sample_num, img_height * img_width))
+for sample_num in range(5):
+    for h in range(img_height):
+        for w in range(img_width):
+            temp1 = Image.open('ImageDataset/subset1/{0:d}.bmp'.format(sample_num + 1))
+            temp2 = Image.open('ImageDataset/subset2/{0:d}.bmp'.format(sample_num + 1))
+            temp3 = Image.open('ImageDataset/subset3/{0:d}.bmp'.format(sample_num + 1))
+
+            img_sample[sample_num, img_height * w + h] = array(temp1.convert('L'))[h, w] 
+            img_sample[5 + sample_num, img_height * w + h] = array(temp2.convert('L'))[h, w]
+            img_sample[10 + sample_num, img_height * w + h] = array(temp3.convert('L'))[h, w]
+
+
+img_average_mat = np.zeros((img_height * img_width))
+
+for index in range(img_sample_num):
+    img_average_mat[:] = img_average_mat[:] + img_sample[index,:] 
+
+img_average_mat[:] = img_average_mat[:] / img_sample_num    
+
+temp_img = np.zeros((img_height, img_width))
+for h in range(img_height):
+    for w in range(img_width):
+        temp_img[h, w] = img_average_mat[img_height * w + h]
+
+
+
+img_mean_subtract_mat = np.zeros((img_sample_num, img_height * img_width))
+temp1_mat = np.zeros((img_height * img_width))
+temp2_mat = np.zeros((img_height * img_width))
+
+for index in range(img_sample_num):
+    for sample1_num in range(img_height * img_width):
+        temp1_mat[sample1_num] = img_sample[index, sample1_num]
+
+    temp2_mat = GetEigenfaceFunction.GetSubtractImage(temp1_mat, img_average_mat, img_height, img_width)
+    for sample3_num in range(img_height * img_width):
+        img_mean_subtract_mat[index, sample3_num] = temp2_mat[sample3_num]
+
+
+eigenface_weight = np.zeros((img_sample_num, img_sample_num))
+
+eigenface_weight = GetEigenfaceFunction.GetWeight(img_mean_subtract_mat, img_sample_num, img_height, img_width)
+
+
+
+for sample_num in range(img_sample_num):
+    temp_file = open('ImageDatasetWeight/{0:d}.txt'.format(sample_num + 1), 'w')
+    for index in range(img_sample_num):
+        temp_file.write(str(eigenface_weight[index, sample_num]))
+        temp_file.write('\n')
+    temp_file.close()
+
+temp_meanface_file = open('meanface.txt', 'w')
+for index in range(img_height * img_width):
+    temp_meanface_file.write(str(img_average_mat[index]))
+    temp_meanface_file.write('\n')
+temp_meanface_file.close()