In [1]:

    

%matplotlib inline
%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

from alabortcvpr2015.utils import pickle_load

path = '/data/'
exp = 'cofw_fast_dsift'

legend_entries = ['Initial',
                  'AAM-PIC',
                  'AAM-AIC',
                  'CLM-RLMS',
                  'UNI-PIC-RLMS',
                  'UNI-AIC-RLMS']

aam_pic = pickle_load(path + 'PhD/Results/aam_pic_' + exp)
aam_aic = pickle_load(path + 'PhD/Results/aam_aic_' + exp)

clm_rlms = pickle_load(path + 'PhD/Results/clm_rlms_' + exp)

unified_pic_rlms = pickle_load(path + 'PhD/Results/unified_picrlms_' + exp)
unified_aic_rlms = pickle_load(path + 'PhD/Results/unified_aicrlms_' + exp)

results = [aam_pic, aam_aic, clm_rlms, unified_pic_rlms, unified_aic_rlms]


    

In [8]:

    

initial_errors = [fr.initial_error() for fr in aam_aic]

final_errors = []
for fitter_results in results:
    final_errors.append([fr.final_error() for fr in fitter_results])
    
errors = [initial_errors] + final_errors


    

In [9]:

    

from menpofit.visualize import plot_ced

plot_ced(errors, legend_entries=legend_entries)


    

In [10]:

    

from __future__ import division

print '\t\t', 'Mean \t', 'STD \t', 'Median \t', 'Convergence \t'

for err, method in zip(errors, legend_entries):
    print method, '\t', 
    print np.round(np.mean(err), decimals=4), '\t', 
    print np.round(np.std(err), decimals=4), '\t', 
    print np.round(np.median(err), decimals=4), '\t',
    
    c = 0
    for e, ini_e in zip(err, errors[0]):
        if e < ini_e:
            c+=1
        
    print np.round(c / len(err), decimals=4)


    

    




		Mean 	STD 	Median 	Convergence 	
Initial 	0.0917 	0.0382 	0.0844 	0.0
AAM-PIC 	0.0559 	0.0334 	0.0454 	0.856
AAM-AIC 	0.0406 	0.0261 	0.0346 	0.9606
CLM-RLMS 	0.0454 	0.0244 	0.0395 	0.9566
UNI-PIC-RLMS 	0.0425 	0.0247 	0.0366 	0.9882
UNI-AIC-RLMS 	0.0408 	0.0314 	0.0353 	0.97

In [10]:

    




    

In [ ]: