def face(index=0, fit=True, N=67, K=12, interpolation=True):
from absfile import AbsFile
from face import Face
import algorithms
file = ['test_16/04371d164.abs', 'test_same/04203d350.abs', 'test_same/04203d352.abs', 'test_same/04203d354.abs', 'test_8/04316d216.abs', 'test_4/04374d193.abs'][index]
face = Face(AbsFile(file))
if interpolation:
algorithms.repair(face)
algorithms.smooth(face)
algorithms.crop(face)
algorithms.zoom(face)
algorithms.key_points(face)
algorithms.rotate(face)
if fit:
algorithms.key_points(face)
algorithms.fit(face)
# Extract features
algorithms.features_histogram(face, N, K)
return face
def face(index=0, fit=True, N=67, K=12, interpolation=True):
from Absfile import AbsFile
from face import Face
import algorithms
file = ['test_16/04371d164.abs', 'test_same/04203d350.abs', 'test_same/04203d352.abs', 'test_same/04203d354.abs',
'test_8/04316d216.abs', 'test_4/04374d193.abs'][index]
face = Face(AbsFile(file))
if interpolation:
algorithms.repair(face)
algorithms.smooth(face)
algorithms.crop(face)
algorithms.zoom(face)
algorithms.key_points(face)
algorithms.rotate(face)
if fit:
algorithms.key_points(face)
algorithms.fit(face)
# Extract features
algorithms.features_histogram(face, N, K)
return face
def evaluate_rotate(rotations=[-5.0, -2.5, -1.0, 1, 2.5, 5.0], index=4, output_file='rotations.pdf'):
from scipy.ndimage import interpolation
import algorithms
original = face(index)
other = face(1)
faces = []
for rotation in rotations:
f = face(index)
f.abs_file.data['X'] = interpolation.rotate(f.abs_file.data['X'], rotation, mode='nearest', prefilter=False,
reshape=False)
f.abs_file.data['Y'] = interpolation.rotate(f.abs_file.data['Y'], rotation, mode='nearest', prefilter=False,
reshape=False)
f.abs_file.data['Z'] = interpolation.rotate(f.abs_file.data['Z'], rotation, mode='nearest', prefilter=False,
reshape=False)
algorithms.features_histogram(f)
faces.append(f)
pyplot.figure()
subplot = pyplot.subplot(1, 2 + len(rotations), 1)
subplot.imshow(original.Z)
subplot.title.set_text("Original")
subplot.title.set_fontsize(10)
subplot.xaxis.set_visible(False)
subplot.yaxis.set_visible(False)
for rotation, f, i in zip(rotations, faces, range(len(rotations))):
subplot = pyplot.subplot(1, 2 + len(rotations), 2 + i)
subplot.imshow(f.Z)
subplot.title.set_text("%.1f deg" % rotation)
subplot.title.set_fontsize(10)
subplot.xaxis.set_visible(False)
subplot.yaxis.set_visible(False)
subplot = pyplot.subplot(1, 2 + len(rotations), len(rotations) + 2)
subplot.imshow(other.Z)
subplot.title.set_text("Other")
subplot.title.set_fontsize(10)
subplot.xaxis.set_visible(False)
subplot.yaxis.set_visible(False)
pyplot.savefig(output_file, format='pdf', dpi=600, orientation='landscape', bbox_inches="tight")
return algorithms.similarity_matrix([original] + faces + [other], methods=[algorithms.distance_histogram_euclidean,
algorithms.distance_histogram_city_block,
algorithms.distance_histogram_correlation],
normalizers=[False, False, False])
def evaluate_rotate(rotations=[-5.0, -2.5, -1.0, 1, 2.5, 5.0], index=4, output_file='rotations.pdf'):
from scipy.ndimage import interpolation
import algorithms
original = face(index)
other = face(1)
faces = []
for rotation in rotations:
f = face(index)
f.abs_file.data['X'] = interpolation.rotate(f.abs_file.data['X'], rotation, mode='nearest', prefilter=False, reshape=False)
f.abs_file.data['Y'] = interpolation.rotate(f.abs_file.data['Y'], rotation, mode='nearest', prefilter=False, reshape=False)
f.abs_file.data['Z'] = interpolation.rotate(f.abs_file.data['Z'], rotation, mode='nearest', prefilter=False, reshape=False)
algorithms.features_histogram(f)
faces.append(f)
pyplot.figure()
subplot = pyplot.subplot(1, 2+len(rotations), 1)
subplot.imshow(original.Z)
subplot.title.set_text("Original")
subplot.title.set_fontsize(10)
subplot.xaxis.set_visible(False)
subplot.yaxis.set_visible(False)
for rotation, f, i in zip(rotations, faces, range(len(rotations))):
subplot = pyplot.subplot(1, 2+len(rotations), 2 + i)
subplot.imshow(f.Z)
subplot.title.set_text("%.1f deg" % rotation)
subplot.title.set_fontsize(10)
subplot.xaxis.set_visible(False)
subplot.yaxis.set_visible(False)
subplot = pyplot.subplot(1, 2+len(rotations), len(rotations) + 2)
subplot.imshow(other.Z)
subplot.title.set_text("Other")
subplot.title.set_fontsize(10)
subplot.xaxis.set_visible(False)
subplot.yaxis.set_visible(False)
pyplot.savefig(output_file, format='pdf', dpi=600, orientation='landscape', bbox_inches="tight")
return algorithms.similarity_matrix([original] + faces + [other], methods=[algorithms.distance_histogram_euclidean, algorithms.distance_histogram_city_block, algorithms.distance_histogram_correlation], normalizers=[False, False, False])
