Python normalize_image 예제들

예제 #1

파일: demo3.py 프로젝트: HamiltonChris/Facial-Recognition

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)

예제 #2

파일: test.py 프로젝트: HamiltonChris/Facial-Recognition

# test.py
# used to quickly test functionality of code
# requires orl_faces images to be saved in data folder
import utilities as utils
import pca

components = 400
rows = 112
columns = 92

M = utils.load_images('orl_faces')
img = utils.create_image(rows, columns, 2)

eigenvalues, W, mu = pca.create_subspace(M,components)
print W.shape, img.shape, mu.shape
x = pca.project_image(img,W,mu)
print x.shape
y = pca.reverse_projection(x,W,mu)
print y.shape 
image = utils.normalize_image(utils.unflatten_image(y,rows,columns))
for i in range(1):
    print 'eigenface ' + str(i) + ': ', utils.normalize_image(utils.unflatten_image(W[:,i],rows,columns)).shape
    utils.display_image(utils.normalize_image(utils.unflatten_image(W[:,i],rows,columns)))
utils.display_image(image)
utils.display_image(utils.unflatten_image(img,rows,columns))

예제 #3

파일: demo5.py 프로젝트: HamiltonChris/Facial-Recognition

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])

예제 #4

파일: demo1.py 프로젝트: HamiltonChris/Facial-Recognition

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)

for i in range(15):
    ut.display_image(ut.unflatten_image(ut.normalize_image(W[:,i]), rows, columns), str(i))