In [7]:

    

import site
site.addsitedir('..')
import menpo.io as mio
import menpodetect
import numpy as np
import facefit
%matplotlib inline
from facefit import esr, ert, lbf
from facefit.test import fit_all
from facefit import util


    

In [8]:

    

MODEL_NAME = "lbf-fast_lfpw+helen.hkl"


    

In [9]:

    

lfpw_train = util.read_images("../datasets/lfpw/trainset/", normalise=True)
lfpw_test = util.read_images("../datasets/lfpw/testset/", normalise=True)
helen_train = util.read_images("../datasets/helen_cropped/trainset_cropped", normalise=True)
helen_test = util.read_images("../datasets/helen_cropped/testset_cropped/", normalise=True)


    

    




- Loading 330 assets: [====================] 100%

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train_images = np.concatenate([lfpw_train, helen_train])
test_images = np.concatenate([lfpw_test, helen_test])


    

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builder = lbf.LBFBuilder(n_stages=5, n_trees=300, tree_depth=5, n_perturbations=20, MU=0)

initial_errors, final_errors, initial_shapes, final_shapes, model = fit_all(builder, train_images, test_images, num_init=1)


    

    




Detecting face 833/2812

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In [15]:

    

print "Mean initial error: {}".format(np.mean(initial_errors))
print "Mean final error: {}".format(np.mean(final_errors))


    

    




Mean initial error: 0.10884353332
Mean final error: 0.0254884393181

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from menpofit.visualize import plot_ced
plot_ced(final_errors)


    

In [19]:

    

from facefit.test.end2end import test_model


    

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initial_errors, final_errors, initial_shapes, final_shapes = test_model(model, test_images, num_init=5)


    

    




554/554

In [22]:

    

print "Mean initial error: {}".format(np.mean(initial_errors))
print "Mean final error: {}".format(np.mean(final_errors))


    

    




Mean initial error: 0.10884353332
Mean final error: 0.0264041762659

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from menpofit.visualize import plot_ced
plot_ced(final_errors)


    

In [24]:

    

initial_errors, final_errors, initial_shapes, final_shapes = test_model(model, lfpw_test, num_init=1)


    

    




224/224

In [25]:

    

print "Mean initial error: {}".format(np.mean(initial_errors))
print "Mean final error: {}".format(np.mean(final_errors))


    

    




Mean initial error: 0.102570194775
Mean final error: 0.0234881140285

In [26]:

    

from menpofit.visualize import plot_ced
plot_ced(final_errors)


    

In [27]:

    

initial_errors, final_errors, initial_shapes, final_shapes = test_model(model, lfpw_test, num_init=5)


    

    




224/224

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print "Mean initial error: {}".format(np.mean(initial_errors))
print "Mean final error: {}".format(np.mean(final_errors))


    

    




Mean initial error: 0.102570194775
Mean final error: 0.0237487028208

In [29]:

    

from menpofit.visualize import plot_ced
plot_ced(final_errors)


    

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