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%matplotlib inline
%pylab inline



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repeat = 1

Load test data



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

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

test_images = []
for i in mio.import_images(path + 'PhD/DataBases/faces/cofw/testset/', 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')
    test_images.append(i)



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from menpo.visualize import visualize_images

visualize_images(test_images)

CLMs

Load



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from alabortcvpr2015.utils import pickle_load

clm = pickle_load(path + 'PhD/Models/clm_cofw_fast_dsift')

RLMS



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from alabortcvpr2015.clm import CLMFitter, RLMS
from alabortcvpr2015.utils import pickle_dump
from alabortcvpr2015.result import SerializableResult


fitter = CLMFitter(clm, algorithm_cls=RLMS, n_shape=[3, 12])

fitter_results = []

for seed in xrange(repeat):
    
    np.random.seed(seed=seed)

    for j, i in enumerate(test_images):
    

        gt_s = i.landmarks[group].lms
        s = fitter.perturb_shape(gt_s, noise_std=0.05)

        fr = fitter.fit(i, s, gt_shape=gt_s, max_iters=20, prior=False)

        fitter_results.append(fr)
        fr.downscale = 0.5

        print 'Image: ', j
        print fr
        
results = [SerializableResult('none', fr.shapes(), fr.n_iters, 'RLMS', fr.gt_shape) 
           for fr in fitter_results]

pickle_dump(results, path + 'PhD/Results/clm_rlms_cofw_fast_dsift')

Visualize Results



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np.mean([fr.final_error(error_type='rmse') for fr in fitter_results])



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from menpofit.visualize import visualize_fitting_results
    
visualize_fitting_results(fitter_results)