

In [2]:

    
%matplotlib inline
%pylab inline









    



Populating the interactive namespace from numpy and matplotlib

Load training data



In [3]:

    
import menpo.io as mio
from menpo.landmark import labeller, ibug_face_49
from menpofast.utils import convert_from_menpo

path = '/data/'
group = 'ibug_face_49'

training_images = []
for i in mio.import_images(path + 'PhD/DataBases/faces/lfpw/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='PTS')
    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)









    



- Loading 811 assets: [====================] 100%



In [ ]:

    
for i in mio.import_images(path + 'PhD/DataBases/faces/helen/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='PTS')
    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 [ ]:

    
for i in mio.import_images(path + 'PhD/DataBases/faces/ibug/',
                           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='PTS')
    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 [4]:

    
from menpo.visualize import visualize_images

visualize_images(training_images)

Constrained Local Models

Build



In [5]:

    
from menpofast.feature import no_op, fast_dsift, fast_daisy
from alabortcvpr2015.clm import CLMBuilder
from alabortcvpr2015.clm.classifier import MCF

offsets = np.meshgrid(range(-0, 1, 1), range(-0, 1, 1))
offsets = np.asarray([offsets[0].flatten(), offsets[1].flatten()]).T 

clm = CLMBuilder(parts_shape=(17, 17),
                 features=fast_dsift,
                 diagonal=100,
                 classifier=MCF,
                 normalize_parts=False,
                 covariance=3,
                 scales=(1, .5),
                 offsets=offsets).build(training_images, 
                                        group=group, 
                                        verbose=True)









    



- Building models
  - Level 0: Done
  - Level 1: Done



In [6]:

    
clm.parts_filters()[1][37].view()









    Out[6]:





<menpo.visualize.viewmatplotlib.MatplotlibImageSubplotsViewer2d at 0x7f9455355a50>

Save



In [7]:

    
from alabortcvpr2015.utils import pickle_dump

pickle_dump(clm, path + 'PhD/Models/clm_lfpw_fast_dsift')