projections = 'orl_projections_' + str(components) + '.npz'
image_path = database + 's10/1.pgm'
if not os.path.exists(path + subspace):
M = ut.load_images(database)
eigenvalues, W, mu = pca.create_subspace(M, components)
pca.save_subspace(path + subspace, eigenvalues, W, mu)
else:
eigenvalues, W, mu = pca.load_subspace(path + subspace)
if not os.path.exists(path + projections):
for j in range(40):
index = j + 1
img = ut.load_image(database + 's' + str(index) + '/9.pgm')
x = pca.project_image(img,W,mu)
ut.save_projection('face ' + str(index), x, projections)
print index
img = ut.load_image(image_path)
x = pca.project_image(img,W,mu)
y = pca.reverse_projection(x,W,mu)
image = ut.normalize_image(ut.unflatten_image(y,rows,columns))
ut.display_image(image, str(400) + ' components')
name, projection = pca.find_closest(x, path + projections)
closest_img = pca.reverse_projection(projection,W,mu)
closest_image = ut.normalize_image(ut.unflatten_image(closest_img,rows,columns))
print name
ut.display_image(closest_image, name[0])
import utilities as ut import pca import os path = '../data/' database = 'orl_faces' subspace = 'orl_subspace.npz' components = 400 rows = 112 columns = 92 if not os.path.exists(path + subspace): M = ut.load_images(database) eigenvalues, W, mu = pca.create_subspace(M, components) pca.save_subspace(path + subspace, eigenvalues, W, mu) else: eigenvalues, W, mu = pca.load_subspace(path + subspace) img = ut.create_image(rows,columns, 3) x = pca.project_image(img,W,mu) y = pca.reverse_projection(x,W,mu) image = ut.normalize_image(ut.unflatten_image(y,rows,columns)) original = ut.unflatten_image(img,rows,columns) ut.display_image(image) ut.display_image(original)
import pca
import os
path = '../data/'
database = 'orl_faces/'
subspace = 'orl_subspace.npz'
image_path = database + 's10/1.pgm'
components = 400
rows = 112
columns = 92
if not os.path.exists(path + subspace):
M = ut.load_images(database)
eigenvalues, W, mu = pca.create_subspace(M, components)
pca.save_subspace(path + subspace, eigenvalues, W, mu)
else:
eigenvalues, W, mu = pca.load_subspace(path + subspace)
img = ut.load_image(image_path)
num_components = [1,2,3,4,5,6,7,8,9,10,15,20,50,75,100,150,200,400]
for i in num_components:
x = pca.project_image(img,W[:,1:i],mu)
y = pca.reverse_projection(x,W[:,1:i],mu)
image = ut.normalize_image(ut.unflatten_image(y,rows,columns))
original = ut.unflatten_image(img,rows,columns)
ut.display_image(image, str(i) + ' components')
ut.display_image(original, 'original')
