

In [ ]:

    
%matplotlib inline
%pylab inline

Load training data



In [ ]:

    
import menpo.io as mio
from menpo.landmark import labeller, lfpw_face
from menpofast.utils import convert_from_menpo

path = '/data/'
group = 'lfpw_face'

training_images = []
for i in mio.import_images(path + 'PhD/DataBases/faces/cofw/trainset/',
                           verbose=True, max_images=None):
    
    # convert the image from menpo Image to menpofast Image (channels at front)
    i = convert_from_menpo(i)
    
    labeller(i, 'PTS', eval(group))
    i.crop_to_landmarks_proportion_inplace(0.5, group=group)
    i = i.rescale_landmarks_to_diagonal_range(200, group=group)
    
    if i.n_channels == 3:
        i = i.as_greyscale(mode='average')
    training_images.append(i)



In [ ]:

    
from menpo.visualize import visualize_images

visualize_images(training_images)

Active Appearance Models

Build and Save



In [ ]:

    
from menpofast.feature import no_op, fast_dsift
from alabortcvpr2015.aam import PartsAAMBuilder

unified = PartsAAMBuilder(parts_shape=(21, 21),
                          features=fast_dsift,
                          diagonal=100,
                          normalize_parts=False,
                          scales=(1, .5),
                          max_shape_components=25,
                          max_appearance_components=500).build(training_images,
                                                               group=group,
                                                               verbose=True)



In [ ]:

    
from menpofast.image import Image

Image(unified.appearance_models[0].mean().pixels[18, 0]).view()

Save



In [ ]:

    
from alabortcvpr2015.utils import pickle_dump

pickle_dump(unified, path + 'PhD/Models/aam_cofw_fast_dsift')