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%matplotlib inline
%pylab inline


    

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import menpo.io as mio
from menpo.landmark import labeller, ibug_face_66
from menpofast.utils import convert_from_menpo

training_images = []
for i in mio.import_images('/data/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', ibug_face_66)
    i.crop_to_landmarks_proportion_inplace(0.5, group='ibug_face_66')
    i = i.rescale_landmarks_to_diagonal_range(200, group='ibug_face_66')
    
    if i.n_channels == 3:
        i = i.as_greyscale(mode='average')
    training_images.append(i)


    

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import menpo.io as mio
from menpo.landmark import labeller, streetscene_car_view_1
from menpofast.utils import convert_from_menpo

training_images_color = []
training_images = []
for i in mio.import_images('/data/PhD/DataBases/cars/cmu_car_data1/view1/', verbose=True, 
                           max_images=20):
    
    # convert the image from menpo Image to menpofast Image (channels at front)
    i = convert_from_menpo(i)
    
    labeller(i, 'PTS', streetscene_car_view_1)
    i.crop_to_landmarks_proportion_inplace(0.5, group='streetscene_car_view_1')
    i = i.rescale_landmarks_to_diagonal_range(200, group='streetscene_car_view_1')
    
    training_images_color.append(i)
    
    if i.n_channels == 3:
        ii = i.as_greyscale(mode='average')
    training_images.append(ii)


    

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from menpo.visualize import visualize_images

visualize_images(training_images_color, colours='r', linewidths=2)


    

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img = training_images_color[11]

img.view_widget(colours='r', linewidths=2)


    

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from menpofast.feature import aam_dsift

img = training_images[11]

masked_img = img.as_masked()
masked_img.build_mask_around_landmarks((34, 34), group='streetscene_car_view_1')

dsift_img = aam_dsift(masked_img)

dsift_img.view_widget(colours='r', linewidths=2)