In [1]:
%matplotlib inline
%pylab inline
In [2]:
import menpo.io as mio
from menpo.landmark import labeller, streetscene_car_view_0
from menpofast.utils import convert_from_menpo
group = 'streetscene_car_view_0'
training_images = []
for i in mio.import_images('/data/PhD/DataBases/cars/cmu_car_data1/view0/',
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(1, 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 [9]:
training_images = training_images[::2]
In [10]:
from menpo.visualize import visualize_images
visualize_images(training_images)
In [15]:
from menpofast.feature import no_op, fast_dsift, fast_daisy
from alabortcvpr2015.aam import PartsAAMBuilder
aam = PartsAAMBuilder(parts_shape=(17, 17),
features=fast_dsift,
diagonal=100,
normalize_parts=False,
scales=(1, .5),
max_shape_components=25,
max_appearance_components=250).build(training_images,
group=group,
verbose=True)
In [17]:
from menpofast.image import Image
Image(aam.appearance_models[0].mean().pixels[5, 0]).view()
Out[17]:
In [18]:
from alabortcvpr2015.utils import pickle_dump
pickle_dump(aam, '/data/PhD/Models/aam_view0_fast_dsift')