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%matplotlib inline
%pylab inline


    

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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

path = '/data/'
group = 'streetscene_car_view_1'

training_images = []
for i in mio.import_images(path + 'PhD/DataBases/cars/cmu_car_data1/view1/',
                           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(1.5, 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)


    

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training_images = training_images[::2]


    

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from menpo.visualize import visualize_images

visualize_images(training_images)


    

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img = training_images[0].copy()
img = img.rescale_to_diagonal(50)
img = img.as_masked()
img.build_mask_around_landmarks((15, 15), group=group)
img.view_widget()


    

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img2 = img.copy()
img2 = img2.rescale(0.5)
img2 = img2.as_masked()
img2.build_mask_around_landmarks((15, 15), group=group)
img2.view_widget()


    

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from menpofast.feature import no_op, fast_dsift

parts_shape = (15, 15)
features = fast_dsift
diagonal = 50
normalize_parts = False
covariance = 3
scales = (1, .5)
max_shape_components = 25
max_appearance_components = 500


    

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from alabortcvpr2015.aam import PartsAAMBuilder

parts_aam = PartsAAMBuilder(
    parts_shape=parts_shape,
    features=features,
    diagonal=diagonal,
    normalize_parts=normalize_parts,
    scales=scales,
    max_shape_components=max_shape_components,
    max_appearance_components=max_appearance_components).build(training_images,
                                                               group=group,
                                                               verbose=True)


    

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from menpofast.image import Image

Image(parts_aam.appearance_models[1].mean().pixels[8, 0]).view()


    

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from alabortcvpr2015.utils import pickle_dump

pickle_dump(parts_aam, path + 'PhD/Models/parts_aam_view1_fast_dsift')


    

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from alabortcvpr2015.aam import GlobalAAMBuilder

global_aam = GlobalAAMBuilder(
    features=features,
    diagonal=diagonal,
    scales=scales,
    max_shape_components=max_shape_components,
    max_appearance_components=max_appearance_components).build(training_images,
                                                               group=group,
                                                               verbose=True)


    

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global_aam.appearance_models[1].mean().view()


    

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from alabortcvpr2015.utils import pickle_dump

pickle_dump(global_aam, path + 'PhD/Models/global_aam_view1_fast_dsift')


    

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from alabortcvpr2015.clm import CLMBuilder

clm = CLMBuilder(
    parts_shape=parts_shape,
    features=features,
    diagonal=diagonal,
    normalize_parts=normalize_parts,
    covariance=covariance,
    scales=scales,
    max_shape_components=max_shape_components).build(training_images,
                                                     group=group,
                                                     verbose=True)


    

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clm.parts_filters()[1][8].view()


    

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from alabortcvpr2015.utils import pickle_dump

pickle_dump(clm, path + 'PhD/Models/clm_view1_fast_dsift')


    

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from alabortcvpr2015.unified import PartsUnifiedBuilder

parts_unified = PartsUnifiedBuilder(
    parts_shape=parts_shape,
    features=features,
    diagonal=diagonal,
    normalize_parts=normalize_parts,
    covariance=covariance,
    scales=scales,
    max_shape_components=max_shape_components,
    max_appearance_components=max_appearance_components).build(training_images,
                                                               group=group,
                                                               verbose=True)


    

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from menpofast.image import Image

Image(parts_unified.appearance_models[1].mean().pixels[8, 0]).view()


    

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parts_unified.parts_filters()[1][8].view()


    

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from alabortcvpr2015.utils import pickle_dump

pickle_dump(parts_unified, path + 'PhD/Models/parts_unified_view1_fast_dsift')


    

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from menpofast.feature import no_op, fast_dsift, fast_daisy
from alabortcvpr2015.unified import GlobalUnifiedBuilder

global_unified = GlobalUnifiedBuilder(
    parts_shape=parts_shape,
    features=features,
    diagonal=diagonal,
    normalize_parts=normalize_parts,
    covariance=covariance,
    scales=scales,
    max_shape_components=max_shape_components,
    max_appearance_components=max_appearance_components).build(training_images,
                                                               group=group,
                                                               verbose=True)


    

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global_unified.appearance_models[1].mean().view()


    

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parts_unified.parts_filters()[1][8].view()


    

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from alabortcvpr2015.utils import pickle_dump

pickle_dump(global_unified, path + 'PhD/Models/global_unified_view1_fast_dsift')