

In [1]:

    
%matplotlib inline
%pylab inline









    



Populating the interactive namespace from numpy and matplotlib



In [2]:

    
repeat = 1

Load test 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'

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









    



- Loading 337 assets: [====================] 100%



In [4]:

    
from menpo.visualize import visualize_images

visualize_images(test_images)

Unified HDMs and HPDMs

AIC + RLMS



In [5]:

    
from alabortcvpr2015.utils import pickle_load

unified = pickle_load(path + 'PhD/Models/unified_lfpw_fast_dsift0')

sampling_mask = np.require(np.zeros(unified.parts_shape), dtype=np.bool)
sampling_mask[2::6, 2::6] = True

imshow(sampling_mask)









    Out[5]:





<matplotlib.image.AxesImage at 0x7f0e181c7d90>



In [6]:

    
from alabortcvpr2015.utils import pickle_load
from alabortcvpr2015.unified import PartsUnifiedFitter, AICRLMS
from alabortcvpr2015.utils import pickle_dump
from alabortcvpr2015.result import SerializableResult

for k in xrange(7, 8):

    unified = pickle_load(path + 'PhD/Models/unified_lfpw_fast_dsift' + str(k))
    
    n_a = np.minimum(50, unified.appearance_models[1].n_components) - 1
    
    fitter = PartsUnifiedFitter(unified, algorithm_cls=AICRLMS, n_shape=[3, 12],
                                n_appearance=[25, n_a], sampling_mask=sampling_mask)
    
    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, 'AICRLMS', fr.gt_shape) 
               for fr in fitter_results]

    pickle_dump(results, path + 'PhD/Results/unified_aicrlms_afw_fast_dsift' + str(k))









    



Image:  0
Initial error: 0.1908
Final error: 0.0172
Image:  1
Initial error: 0.1166
Final error: 0.0190
Image:  2
Initial error: 0.1387
Final error: 0.0448
Image:  3
Initial error: 0.0942
Final error: 0.0239
Image:  4
Initial error: 0.0722
Final error: 0.0184
Image:  5
Initial error: 0.1358
Final error: 0.0145
Image:  6
Initial error: 0.0966
Final error: 0.0178
Image:  7
Initial error: 0.0792
Final error: 0.0189
Image:  8
Initial error: 0.0847
Final error: 0.0541
Image:  9
Initial error: 0.0493
Final error: 0.0258
Image:  10
Initial error: 0.1396
Final error: 0.0251
Image:  11
Initial error: 0.1332
Final error: 0.0247
Image:  12
Initial error: 0.1621
Final error: 0.0430
Image:  13
Initial error: 0.1072
Final error: 0.0388
Image:  14
Initial error: 0.0678
Final error: 0.0637
Image:  15
Initial error: 0.1480
Final error: 0.0347
Image:  16
Initial error: 0.0874
Final error: 0.0256
Image:  17
Initial error: 0.0455
Final error: 0.0183
Image:  18
Initial error: 0.0779
Final error: 0.0251
Image:  19
Initial error: 0.0739
Final error: 0.0216
Image:  20
Initial error: 0.1331
Final error: 0.0249
Image:  21
Initial error: 0.1206
Final error: 0.0197
Image:  22
Initial error: 0.0855
Final error: 0.0220
Image:  23
Initial error: 0.0561
Final error: 0.0420
Image:  24
Initial error: 0.0724
Final error: 0.0348
Image:  25
Initial error: 0.1538
Final error: 0.0503
Image:  26
Initial error: 0.1075
Final error: 0.0222
Image:  27
Initial error: 0.1895
Final error: 0.2010
Image:  28
Initial error: 0.0716
Final error: 0.0270
Image:  29
Initial error: 0.1385
Final error: 0.0440
Image:  30
Initial error: 0.1037
Final error: 0.0406
Image:  31
Initial error: 0.0958
Final error: 0.0328
Image:  32
Initial error: 0.0816
Final error: 0.0326
Image:  33
Initial error: 0.0970
Final error: 0.0453
Image:  34
Initial error: 0.1045
Final error: 0.0346
Image:  35
Initial error: 0.0891
Final error: 0.0277
Image:  36
Initial error: 0.1333
Final error: 0.0264
Image:  37
Initial error: 0.0691
Final error: 0.0169
Image:  38
Initial error: 0.0953
Final error: 0.0399
Image:  39
Initial error: 0.0744
Final error: 0.0336
Image:  40
Initial error: 0.0564
Final error: 0.0266
Image:  41
Initial error: 0.1577
Final error: 0.0289
Image:  42
Initial error: 0.0864
Final error: 0.0549
Image:  43
Initial error: 0.0683
Final error: 0.0220
Image:  44
Initial error: 0.0910
Final error: 0.0160
Image:  45
Initial error: 0.1634
Final error: 0.0589
Image:  46
Initial error: 0.0756
Final error: 0.0202
Image:  47
Initial error: 0.1189
Final error: 0.0549
Image:  48
Initial error: 0.0544
Final error: 0.0263
Image:  49
Initial error: 0.1533
Final error: 0.0240
Image:  50
Initial error: 0.0650
Final error: 0.0723
Image:  51
Initial error: 0.0424
Final error: 0.0213
Image:  52
Initial error: 0.0584
Final error: 0.0328
Image:  53
Initial error: 0.0781
Final error: 0.0258
Image:  54
Initial error: 0.1218
Final error: 0.0180
Image:  55
Initial error: 0.1241
Final error: 0.0422
Image:  56
Initial error: 0.0820
Final error: 0.0140
Image:  57
Initial error: 0.1319
Final error: 0.0317
Image:  58
Initial error: 0.0823
Final error: 0.0329
Image:  59
Initial error: 0.1025
Final error: 0.0330
Image:  60
Initial error: 0.0630
Final error: 0.0223
Image:  61
Initial error: 0.0886
Final error: 0.0189
Image:  62
Initial error: 0.0696
Final error: 0.0159
Image:  63
Initial error: 0.0761
Final error: 0.0176
Image:  64
Initial error: 0.1079
Final error: 0.0152
Image:  65
Initial error: 0.0921
Final error: 0.0135
Image:  66
Initial error: 0.0967
Final error: 0.0253
Image:  67
Initial error: 0.1600
Final error: 0.0303
Image:  68
Initial error: 0.0491
Final error: 0.0186
Image:  69
Initial error: 0.1672
Final error: 0.0311
Image:  70
Initial error: 0.1052
Final error: 0.0519
Image:  71
Initial error: 0.0836
Final error: 0.0223
Image:  72
Initial error: 0.0564
Final error: 0.0196
Image:  73
Initial error: 0.1353
Final error: 0.0255
Image:  74
Initial error: 0.0861
Final error: 0.0325
Image:  75
Initial error: 0.1000
Final error: 0.0188
Image:  76
Initial error: 0.0857
Final error: 0.0336
Image:  77
Initial error: 0.0872
Final error: 0.0335
Image:  78
Initial error: 0.1003
Final error: 0.0462
Image:  79
Initial error: 0.0648
Final error: 0.0451
Image:  80
Initial error: 0.0953
Final error: 0.0218
Image:  81
Initial error: 0.0960
Final error: 0.0206
Image:  82
Initial error: 0.0977
Final error: 0.0285
Image:  83
Initial error: 0.1158
Final error: 0.0296
Image:  84
Initial error: 0.0942
Final error: 0.0218
Image:  85
Initial error: 0.0763
Final error: 0.0296
Image:  86
Initial error: 0.0590
Final error: 0.0267
Image:  87
Initial error: 0.0752
Final error: 0.0356
Image:  88
Initial error: 0.0772
Final error: 0.0238
Image:  89
Initial error: 0.1005
Final error: 0.0508
Image:  90
Initial error: 0.1256
Final error: 0.0267
Image:  91
Initial error: 0.0877
Final error: 0.0361
Image:  92
Initial error: 0.0682
Final error: 0.0206
Image:  93
Initial error: 0.0661
Final error: 0.0197
Image:  94
Initial error: 0.1105
Final error: 0.0180
Image:  95
Initial error: 0.1893
Final error: 0.1126
Image:  96
Initial error: 0.1012
Final error: 0.0172
Image:  97
Initial error: 0.0901
Final error: 0.0188
Image:  98
Initial error: 0.1195
Final error: 0.0402
Image:  99
Initial error: 0.1410
Final error: 0.0374
Image:  100
Initial error: 0.0610
Final error: 0.0291
Image:  101
Initial error: 0.1252
Final error: 0.0277
Image:  102
Initial error: 0.1080
Final error: 0.0258
Image:  103
Initial error: 0.0985
Final error: 0.0992
Image:  104
Initial error: 0.1488
Final error: 0.0267
Image:  105
Initial error: 0.1223
Final error: 0.0273
Image:  106
Initial error: 0.1551
Final error: 0.0464
Image:  107
Initial error: 0.1195
Final error: 0.0151
Image:  108
Initial error: 0.0560
Final error: 0.0186
Image:  109
Initial error: 0.0699
Final error: 0.0182
Image:  110
Initial error: 0.0394
Final error: 0.0213
Image:  111
Initial error: 0.1464
Final error: 0.0326
Image:  112
Initial error: 0.0813
Final error: 0.0420
Image:  113
Initial error: 0.1235
Final error: 0.0249
Image:  114
Initial error: 0.0377
Final error: 0.0337
Image:  115
Initial error: 0.0626
Final error: 0.0299
Image:  116
Initial error: 0.1192
Final error: 0.0258
Image:  117
Initial error: 0.2063
Final error: 0.0765
Image:  118
Initial error: 0.1226
Final error: 0.0236
Image:  119
Initial error: 0.1173
Final error: 0.0260
Image:  120
Initial error: 0.1689
Final error: 0.1586
Image:  121
Initial error: 0.1076
Final error: 0.0177
Image:  122
Initial error: 0.0748
Final error: 0.0245
Image:  123
Initial error: 0.1528
Final error: 0.0173
Image:  124
Initial error: 0.1059
Final error: 0.0301
Image:  125
Initial error: 0.0785
Final error: 0.0356
Image:  126
Initial error: 0.1551
Final error: 0.0764
Image:  127
Initial error: 0.0569
Final error: 0.0294
Image:  128
Initial error: 0.1112
Final error: 0.0284
Image:  129
Initial error: 0.1097
Final error: 0.0166
Image:  130
Initial error: 0.1723
Final error: 0.1902
Image:  131
Initial error: 0.1004
Final error: 0.0315
Image:  132
Initial error: 0.1862
Final error: 0.0566
Image:  133
Initial error: 0.0523
Final error: 0.0161
Image:  134
Initial error: 0.0502
Final error: 0.0295
Image:  135
Initial error: 0.0908
Final error: 0.0233
Image:  136
Initial error: 0.1576
Final error: 0.0174
Image:  137
Initial error: 0.0558
Final error: 0.0204
Image:  138
Initial error: 0.0959
Final error: 0.0209
Image:  139
Initial error: 0.1829
Final error: 0.1771
Image:  140
Initial error: 0.1409
Final error: 0.0642
Image:  141
Initial error: 0.0771
Final error: 0.0680
Image:  142
Initial error: 0.1097
Final error: 0.0350
Image:  143
Initial error: 0.0679
Final error: 0.0262
Image:  144
Initial error: 0.1067
Final error: 0.0181
Image:  145
Initial error: 0.1365
Final error: 0.0359
Image:  146
Initial error: 0.0784
Final error: 0.0195
Image:  147
Initial error: 0.0766
Final error: 0.0272
Image:  148
Initial error: 0.1250
Final error: 0.0685
Image:  149
Initial error: 0.1049
Final error: 0.0273
Image:  150
Initial error: 0.1120
Final error: 0.0164
Image:  151
Initial error: 0.1488
Final error: 0.0526
Image:  152
Initial error: 0.1270
Final error: 0.0158
Image:  153
Initial error: 0.0515
Final error: 0.0189
Image:  154
Initial error: 0.1090
Final error: 0.0293
Image:  155
Initial error: 0.0418
Final error: 0.0189
Image:  156
Initial error: 0.0789
Final error: 0.0195
Image:  157
Initial error: 0.0581
Final error: 0.0159
Image:  158
Initial error: 0.0475
Final error: 0.0158
Image:  159
Initial error: 0.0560
Final error: 0.0209
Image:  160
Initial error: 0.1039
Final error: 0.0195
Image:  161
Initial error: 0.0929
Final error: 0.0548
Image:  162
Initial error: 0.1711
Final error: 0.0251
Image:  163
Initial error: 0.0767
Final error: 0.0294
Image:  164
Initial error: 0.0939
Final error: 0.0241
Image:  165
Initial error: 0.0633
Final error: 0.0297
Image:  166
Initial error: 0.0921
Final error: 0.0168
Image:  167
Initial error: 0.1894
Final error: 0.0129
Image:  168
Initial error: 0.1466
Final error: 0.0340
Image:  169
Initial error: 0.0997
Final error: 0.0355
Image:  170
Initial error: 0.0544
Final error: 0.0224
Image:  171
Initial error: 0.1386
Final error: 0.0498
Image:  172
Initial error: 0.0537
Final error: 0.0228
Image:  173
Initial error: 0.0871
Final error: 0.0282
Image:  174
Initial error: 0.0556
Final error: 0.0246
Image:  175
Initial error: 0.1034
Final error: 0.0233
Image:  176
Initial error: 0.0741
Final error: 0.0355
Image:  177
Initial error: 0.0757
Final error: 0.0149
Image:  178
Initial error: 0.0850
Final error: 0.0222
Image:  179
Initial error: 0.1097
Final error: 0.0270
Image:  180
Initial error: 0.0484
Final error: 0.0249
Image:  181
Initial error: 0.1651
Final error: 0.1351
Image:  182
Initial error: 0.0480
Final error: 0.0224
Image:  183
Initial error: 0.1777
Final error: 0.0243
Image:  184
Initial error: 0.0606
Final error: 0.0279
Image:  185
Initial error: 0.0718
Final error: 0.0213
Image:  186
Initial error: 0.0569
Final error: 0.0169
Image:  187
Initial error: 0.1528
Final error: 0.0218
Image:  188
Initial error: 0.0534
Final error: 0.0180
Image:  189
Initial error: 0.0816
Final error: 0.0179
Image:  190
Initial error: 0.0908
Final error: 0.0215
Image:  191
Initial error: 0.0573
Final error: 0.0343
Image:  192
Initial error: 0.0667
Final error: 0.0221
Image:  193
Initial error: 0.1489
Final error: 0.0223
Image:  194
Initial error: 0.0962
Final error: 0.0168
Image:  195
Initial error: 0.0868
Final error: 0.0347
Image:  196
Initial error: 0.0485
Final error: 0.0138
Image:  197
Initial error: 0.0984
Final error: 0.0190
Image:  198
Initial error: 0.1219
Final error: 0.0245
Image:  199
Initial error: 0.0872
Final error: 0.0189
Image:  200
Initial error: 0.0875
Final error: 0.0156
Image:  201
Initial error: 0.0772
Final error: 0.0177
Image:  202
Initial error: 0.1170
Final error: 0.0237
Image:  203
Initial error: 0.1497
Final error: 0.0383
Image:  204
Initial error: 0.0924
Final error: 0.0330
Image:  205
Initial error: 0.0712
Final error: 0.0239
Image:  206
Initial error: 0.0936
Final error: 0.0171
Image:  207
Initial error: 0.1222
Final error: 0.0206
Image:  208
Initial error: 0.0779
Final error: 0.0191
Image:  209
Initial error: 0.0382
Final error: 0.0158
Image:  210
Initial error: 0.1334
Final error: 0.0328
Image:  211
Initial error: 0.0792
Final error: 0.0278
Image:  212
Initial error: 0.1859
Final error: 0.0308
Image:  213
Initial error: 0.1045
Final error: 0.0304
Image:  214
Initial error: 0.0962
Final error: 0.0148
Image:  215
Initial error: 0.1036
Final error: 0.0844
Image:  216
Initial error: 0.0925
Final error: 0.0167
Image:  217
Initial error: 0.0915
Final error: 0.0237
Image:  218
Initial error: 0.0750
Final error: 0.0193
Image:  219
Initial error: 0.1082
Final error: 0.0247
Image:  220
Initial error: 0.1344
Final error: 0.0407
Image:  221
Initial error: 0.0676
Final error: 0.0220
Image:  222
Initial error: 0.0670
Final error: 0.0186
Image:  223
Initial error: 0.1560
Final error: 0.0586
Image:  224
Initial error: 0.1490
Final error: 0.0217
Image:  225
Initial error: 0.2057
Final error: 0.0362
Image:  226
Initial error: 0.1137
Final error: 0.0197
Image:  227
Initial error: 0.0868
Final error: 0.0231
Image:  228
Initial error: 0.0917
Final error: 0.0210
Image:  229
Initial error: 0.0558
Final error: 0.0271
Image:  230
Initial error: 0.0820
Final error: 0.0415
Image:  231
Initial error: 0.0404
Final error: 0.0313
Image:  232
Initial error: 0.1113
Final error: 0.0236
Image:  233
Initial error: 0.0828
Final error: 0.0463
Image:  234
Initial error: 0.1071
Final error: 0.0452
Image:  235
Initial error: 0.1547
Final error: 0.0321
Image:  236
Initial error: 0.0785
Final error: 0.0458
Image:  237
Initial error: 0.0592
Final error: 0.0328
Image:  238
Initial error: 0.1585
Final error: 0.0457
Image:  239
Initial error: 0.1400
Final error: 0.0186
Image:  240
Initial error: 0.1244
Final error: 0.0656
Image:  241
Initial error: 0.0671
Final error: 0.0202
Image:  242
Initial error: 0.0980
Final error: 0.0291
Image:  243
Initial error: 0.1044
Final error: 0.1123
Image:  244
Initial error: 0.0515
Final error: 0.0202
Image:  245
Initial error: 0.0714
Final error: 0.0257
Image:  246
Initial error: 0.0704
Final error: 0.0186
Image:  247
Initial error: 0.1363
Final error: 0.0247
Image:  248
Initial error: 0.1299
Final error: 0.0299
Image:  249
Initial error: 0.1013
Final error: 0.0334
Image:  250
Initial error: 0.1102
Final error: 0.0290
Image:  251
Initial error: 0.1336
Final error: 0.0203
Image:  252
Initial error: 0.1397
Final error: 0.0256
Image:  253
Initial error: 0.0675
Final error: 0.0212
Image:  254
Initial error: 0.0598
Final error: 0.0186
Image:  255
Initial error: 0.0442
Final error: 0.0229
Image:  256
Initial error: 0.0701
Final error: 0.0354
Image:  257
Initial error: 0.0702
Final error: 0.0280
Image:  258
Initial error: 0.1081
Final error: 0.0499
Image:  259
Initial error: 0.0996
Final error: 0.0222
Image:  260
Initial error: 0.1357
Final error: 0.0960
Image:  261
Initial error: 0.0947
Final error: 0.0694
Image:  262
Initial error: 0.0755
Final error: 0.0235
Image:  263
Initial error: 0.1904
Final error: 0.0187
Image:  264
Initial error: 0.1209
Final error: 0.0290
Image:  265
Initial error: 0.0670
Final error: 0.0310
Image:  266
Initial error: 0.0718
Final error: 0.0241
Image:  267
Initial error: 0.0941
Final error: 0.0240
Image:  268
Initial error: 0.0556
Final error: 0.0284
Image:  269
Initial error: 0.0680
Final error: 0.0138
Image:  270
Initial error: 0.0796
Final error: 0.0188
Image:  271
Initial error: 0.1281
Final error: 0.0387
Image:  272
Initial error: 0.0473
Final error: 0.0186
Image:  273
Initial error: 0.0921
Final error: 0.0233
Image:  274
Initial error: 0.0740
Final error: 0.0258
Image:  275
Initial error: 0.1008
Final error: 0.0272
Image:  276
Initial error: 0.0924
Final error: 0.0285
Image:  277
Initial error: 0.1111
Final error: 0.0403
Image:  278
Initial error: 0.1860
Final error: 0.1034
Image:  279
Initial error: 0.0914
Final error: 0.0496
Image:  280
Initial error: 0.0629
Final error: 0.0316
Image:  281
Initial error: 0.0957
Final error: 0.0195
Image:  282
Initial error: 0.1015
Final error: 0.0227
Image:  283
Initial error: 0.0559
Final error: 0.0402
Image:  284
Initial error: 0.0907
Final error: 0.0582
Image:  285
Initial error: 0.1344
Final error: 0.0280
Image:  286
Initial error: 0.0393
Final error: 0.0169
Image:  287
Initial error: 0.0689
Final error: 0.0205
Image:  288
Initial error: 0.0765
Final error: 0.0181
Image:  289
Initial error: 0.1240
Final error: 0.0138
Image:  290
Initial error: 0.0645
Final error: 0.0250
Image:  291
Initial error: 0.1447
Final error: 0.0171
Image:  292
Initial error: 0.0770
Final error: 0.0175
Image:  293
Initial error: 0.1223
Final error: 0.0185
Image:  294
Initial error: 0.1146
Final error: 0.0224
Image:  295
Initial error: 0.0556
Final error: 0.0288
Image:  296
Initial error: 0.0700
Final error: 0.0302
Image:  297
Initial error: 0.0939
Final error: 0.0179
Image:  298
Initial error: 0.0767
Final error: 0.0287
Image:  299
Initial error: 0.1080
Final error: 0.0300
Image:  300
Initial error: 0.0980
Final error: 0.0251
Image:  301
Initial error: 0.1019
Final error: 0.0363
Image:  302
Initial error: 0.0817
Final error: 0.0244
Image:  303
Initial error: 0.0685
Final error: 0.0345
Image:  304
Initial error: 0.0977
Final error: 0.0374
Image:  305
Initial error: 0.0909
Final error: 0.0795
Image:  306
Initial error: 0.0885
Final error: 0.0217
Image:  307
Initial error: 0.1006
Final error: 0.0667
Image:  308
Initial error: 0.1471
Final error: 0.0148
Image:  309
Initial error: 0.0582
Final error: 0.0139
Image:  310
Initial error: 0.1210
Final error: 0.0208
Image:  311
Initial error: 0.0656
Final error: 0.0197
Image:  312
Initial error: 0.0829
Final error: 0.0213
Image:  313
Initial error: 0.0537
Final error: 0.0124
Image:  314
Initial error: 0.1645
Final error: 0.0358
Image:  315
Initial error: 0.1012
Final error: 0.0294
Image:  316
Initial error: 0.1442
Final error: 0.0205
Image:  317
Initial error: 0.1482
Final error: 0.0596
Image:  318
Initial error: 0.1000
Final error: 0.0242
Image:  319
Initial error: 0.1182
Final error: 0.0211
Image:  320
Initial error: 0.0825
Final error: 0.0282
Image:  321
Initial error: 0.1488
Final error: 0.1247
Image:  322
Initial error: 0.0841
Final error: 0.0206
Image:  323
Initial error: 0.0906
Final error: 0.0247
Image:  324
Initial error: 0.1037
Final error: 0.0261
Image:  325
Initial error: 0.0743
Final error: 0.0252
Image:  326
Initial error: 0.0902
Final error: 0.0272
Image:  327
Initial error: 0.0757
Final error: 0.0165
Image:  328
Initial error: 0.0993
Final error: 0.0289
Image:  329
Initial error: 0.1342
Final error: 0.1649
Image:  330
Initial error: 0.0781
Final error: 0.0288
Image:  331
Initial error: 0.0585
Final error: 0.0181
Image:  332
Initial error: 0.0919
Final error: 0.0360
Image:  333
Initial error: 0.0630
Final error: 0.0169
Image:  334
Initial error: 0.1222
Final error: 0.0232
Image:  335
Initial error: 0.0624
Final error: 0.0215
Image:  336
Initial error: 0.0866
Final error: 0.0318

PIC + RLMS



In [7]:

    
from alabortcvpr2015.utils import pickle_load

unified = pickle_load(path + 'PhD/Models/unified_lfpw_fast_dsift0')

sampling_mask = np.require(np.zeros(unified.parts_shape), dtype=np.bool)
sampling_mask[2::6, 2::6] = True

imshow(sampling_mask)









    Out[7]:





<matplotlib.image.AxesImage at 0x7f0e15d43b90>



In [8]:

    
from alabortcvpr2015.utils import pickle_load
from alabortcvpr2015.unified import PartsUnifiedFitter, PICRLMS
from alabortcvpr2015.utils import pickle_dump
from alabortcvpr2015.result import SerializableResult

for k in xrange(7, 8):

    unified = pickle_load(path + 'PhD/Models/unified_lfpw_fast_dsift' + str(k))
    
    n_a = np.minimum(50, unified.appearance_models[1].n_components) - 1
    
    fitter = PartsUnifiedFitter(unified, algorithm_cls=PICRLMS, n_shape=[3, 12],
                                n_appearance=[25, n_a], sampling_mask=sampling_mask)
    
    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, 'PICRLMS', fr.gt_shape) 
               for fr in fitter_results]

    pickle_dump(results, path + 'PhD/Results/unified_picrlms_afw_fast_dsift' + str(k))









    



Image:  0
Initial error: 0.1908
Final error: 0.0341
Image:  1
Initial error: 0.1166
Final error: 0.0389
Image:  2
Initial error: 0.1387
Final error: 0.0798
Image:  3
Initial error: 0.0942
Final error: 0.0958
Image:  4
Initial error: 0.0722
Final error: 0.0325
Image:  5
Initial error: 0.1358
Final error: 0.0254
Image:  6
Initial error: 0.0966
Final error: 0.0333
Image:  7
Initial error: 0.0792
Final error: 0.0452
Image:  8
Initial error: 0.0847
Final error: 0.0491
Image:  9
Initial error: 0.0493
Final error: 0.0332
Image:  10
Initial error: 0.1396
Final error: 0.0304
Image:  11
Initial error: 0.1332
Final error: 0.0747
Image:  12
Initial error: 0.1621
Final error: 0.1341
Image:  13
Initial error: 0.1072
Final error: 0.0954
Image:  14
Initial error: 0.0678
Final error: 0.0721
Image:  15
Initial error: 0.1480
Final error: 0.1096
Image:  16
Initial error: 0.0874
Final error: 0.0346
Image:  17
Initial error: 0.0455
Final error: 0.0257
Image:  18
Initial error: 0.0779
Final error: 0.0490
Image:  19
Initial error: 0.0739
Final error: 0.0286
Image:  20
Initial error: 0.1331
Final error: 0.0766
Image:  21
Initial error: 0.1206
Final error: 0.0281
Image:  22
Initial error: 0.0855
Final error: 0.0471
Image:  23
Initial error: 0.0561
Final error: 0.0485
Image:  24
Initial error: 0.0724
Final error: 0.0385
Image:  25
Initial error: 0.1538
Final error: 0.0740
Image:  26
Initial error: 0.1075
Final error: 0.0388
Image:  27
Initial error: 0.1895
Final error: 0.0987
Image:  28
Initial error: 0.0716
Final error: 0.0425
Image:  29
Initial error: 0.1385
Final error: 0.0599
Image:  30
Initial error: 0.1037
Final error: 0.0864
Image:  31
Initial error: 0.0958
Final error: 0.0721
Image:  32
Initial error: 0.0816
Final error: 0.0746
Image:  33
Initial error: 0.0970
Final error: 0.0491
Image:  34
Initial error: 0.1045
Final error: 0.0436
Image:  35
Initial error: 0.0891
Final error: 0.0331
Image:  36
Initial error: 0.1333
Final error: 0.0243
Image:  37
Initial error: 0.0691
Final error: 0.0146
Image:  38
Initial error: 0.0953
Final error: 0.0502
Image:  39
Initial error: 0.0744
Final error: 0.0445
Image:  40
Initial error: 0.0564
Final error: 0.0280
Image:  41
Initial error: 0.1577
Final error: 0.0252
Image:  42
Initial error: 0.0864
Final error: 0.0225
Image:  43
Initial error: 0.0683
Final error: 0.0260
Image:  44
Initial error: 0.0910
Final error: 0.0205
Image:  45
Initial error: 0.1634
Final error: 0.0690
Image:  46
Initial error: 0.0756
Final error: 0.0280
Image:  47
Initial error: 0.1189
Final error: 0.0481
Image:  48
Initial error: 0.0544
Final error: 0.0319
Image:  49
Initial error: 0.1533
Final error: 0.0414
Image:  50
Initial error: 0.0650
Final error: 0.0665
Image:  51
Initial error: 0.0424
Final error: 0.0221
Image:  52
Initial error: 0.0584
Final error: 0.0388
Image:  53
Initial error: 0.0781
Final error: 0.0347
Image:  54
Initial error: 0.1218
Final error: 0.0237
Image:  55
Initial error: 0.1241
Final error: 0.0426
Image:  56
Initial error: 0.0820
Final error: 0.0224
Image:  57
Initial error: 0.1319
Final error: 0.0476
Image:  58
Initial error: 0.0823
Final error: 0.0426
Image:  59
Initial error: 0.1025
Final error: 0.0333
Image:  60
Initial error: 0.0630
Final error: 0.0297
Image:  61
Initial error: 0.0886
Final error: 0.0283
Image:  62
Initial error: 0.0696
Final error: 0.0211
Image:  63
Initial error: 0.0761
Final error: 0.0227
Image:  64
Initial error: 0.1079
Final error: 0.0138
Image:  65
Initial error: 0.0921
Final error: 0.0270
Image:  66
Initial error: 0.0967
Final error: 0.0332
Image:  67
Initial error: 0.1600
Final error: 0.0663
Image:  68
Initial error: 0.0491
Final error: 0.0215
Image:  69
Initial error: 0.1672
Final error: 0.0387
Image:  70
Initial error: 0.1052
Final error: 0.0560
Image:  71
Initial error: 0.0836
Final error: 0.0311
Image:  72
Initial error: 0.0564
Final error: 0.0241
Image:  73
Initial error: 0.1353
Final error: 0.0276
Image:  74
Initial error: 0.0861
Final error: 0.0433
Image:  75
Initial error: 0.1000
Final error: 0.0303
Image:  76
Initial error: 0.0857
Final error: 0.0249
Image:  77
Initial error: 0.0872
Final error: 0.0560
Image:  78
Initial error: 0.1003
Final error: 0.0504
Image:  79
Initial error: 0.0648
Final error: 0.0377
Image:  80
Initial error: 0.0953
Final error: 0.0404
Image:  81
Initial error: 0.0960
Final error: 0.0262
Image:  82
Initial error: 0.0977
Final error: 0.0450
Image:  83
Initial error: 0.1158
Final error: 0.0216
Image:  84
Initial error: 0.0942
Final error: 0.0231
Image:  85
Initial error: 0.0763
Final error: 0.0341
Image:  86
Initial error: 0.0590
Final error: 0.0278
Image:  87
Initial error: 0.0752
Final error: 0.0379
Image:  88
Initial error: 0.0772
Final error: 0.0307
Image:  89
Initial error: 0.1005
Final error: 0.0522
Image:  90
Initial error: 0.1256
Final error: 0.0508
Image:  91
Initial error: 0.0877
Final error: 0.0351
Image:  92
Initial error: 0.0682
Final error: 0.0218
Image:  93
Initial error: 0.0661
Final error: 0.0212
Image:  94
Initial error: 0.1105
Final error: 0.0187
Image:  95
Initial error: 0.1893
Final error: 0.0243
Image:  96
Initial error: 0.1012
Final error: 0.0188
Image:  97
Initial error: 0.0901
Final error: 0.0230
Image:  98
Initial error: 0.1195
Final error: 0.0428
Image:  99
Initial error: 0.1410
Final error: 0.0855
Image:  100
Initial error: 0.0610
Final error: 0.0336
Image:  101
Initial error: 0.1252
Final error: 0.0484
Image:  102
Initial error: 0.1080
Final error: 0.0321
Image:  103
Initial error: 0.0985
Final error: 0.0386
Image:  104
Initial error: 0.1488
Final error: 0.0793
Image:  105
Initial error: 0.1223
Final error: 0.0349
Image:  106
Initial error: 0.1551
Final error: 0.0654
Image:  107
Initial error: 0.1195
Final error: 0.0261
Image:  108
Initial error: 0.0560
Final error: 0.0235
Image:  109
Initial error: 0.0699
Final error: 0.0224
Image:  110
Initial error: 0.0394
Final error: 0.0193
Image:  111
Initial error: 0.1464
Final error: 0.1011
Image:  112
Initial error: 0.0813
Final error: 0.0517
Image:  113
Initial error: 0.1235
Final error: 0.0312
Image:  114
Initial error: 0.0377
Final error: 0.0297
Image:  115
Initial error: 0.0626
Final error: 0.0317
Image:  116
Initial error: 0.1192
Final error: 0.0796
Image:  117
Initial error: 0.2063
Final error: 0.0712
Image:  118
Initial error: 0.1226
Final error: 0.0458
Image:  119
Initial error: 0.1173
Final error: 0.0430
Image:  120
Initial error: 0.1689
Final error: 0.0722
Image:  121
Initial error: 0.1076
Final error: 0.0227
Image:  122
Initial error: 0.0748
Final error: 0.0373
Image:  123
Initial error: 0.1528
Final error: 0.0171
Image:  124
Initial error: 0.1059
Final error: 0.0494
Image:  125
Initial error: 0.0785
Final error: 0.0459
Image:  126
Initial error: 0.1551
Final error: 0.0727
Image:  127
Initial error: 0.0569
Final error: 0.0360
Image:  128
Initial error: 0.1112
Final error: 0.0431
Image:  129
Initial error: 0.1097
Final error: 0.0232
Image:  130
Initial error: 0.1723
Final error: 0.1470
Image:  131
Initial error: 0.1004
Final error: 0.0384
Image:  132
Initial error: 0.1862
Final error: 0.0254
Image:  133
Initial error: 0.0523
Final error: 0.0178
Image:  134
Initial error: 0.0502
Final error: 0.0310
Image:  135
Initial error: 0.0908
Final error: 0.0312
Image:  136
Initial error: 0.1576
Final error: 0.0220
Image:  137
Initial error: 0.0558
Final error: 0.0233
Image:  138
Initial error: 0.0959
Final error: 0.0271
Image:  139
Initial error: 0.1829
Final error: 0.0713
Image:  140
Initial error: 0.1409
Final error: 0.1311
Image:  141
Initial error: 0.0771
Final error: 0.0437
Image:  142
Initial error: 0.1097
Final error: 0.0339
Image:  143
Initial error: 0.0679
Final error: 0.0377
Image:  144
Initial error: 0.1067
Final error: 0.0262
Image:  145
Initial error: 0.1365
Final error: 0.1166
Image:  146
Initial error: 0.0784
Final error: 0.0222
Image:  147
Initial error: 0.0766
Final error: 0.0251
Image:  148
Initial error: 0.1250
Final error: 0.0928
Image:  149
Initial error: 0.1049
Final error: 0.0463
Image:  150
Initial error: 0.1120
Final error: 0.0249
Image:  151
Initial error: 0.1488
Final error: 0.0491
Image:  152
Initial error: 0.1270
Final error: 0.0337
Image:  153
Initial error: 0.0515
Final error: 0.0208
Image:  154
Initial error: 0.1090
Final error: 0.0400
Image:  155
Initial error: 0.0418
Final error: 0.0260
Image:  156
Initial error: 0.0789
Final error: 0.0210
Image:  157
Initial error: 0.0581
Final error: 0.0206
Image:  158
Initial error: 0.0475
Final error: 0.0167
Image:  159
Initial error: 0.0560
Final error: 0.0330
Image:  160
Initial error: 0.1039
Final error: 0.0240
Image:  161
Initial error: 0.0929
Final error: 0.0527
Image:  162
Initial error: 0.1711
Final error: 0.0871
Image:  163
Initial error: 0.0767
Final error: 0.0474
Image:  164
Initial error: 0.0939
Final error: 0.0443
Image:  165
Initial error: 0.0633
Final error: 0.0449
Image:  166
Initial error: 0.0921
Final error: 0.0303
Image:  167
Initial error: 0.1894
Final error: 0.0392
Image:  168
Initial error: 0.1466
Final error: 0.0550
Image:  169
Initial error: 0.0997
Final error: 0.0460
Image:  170
Initial error: 0.0544
Final error: 0.0284
Image:  171
Initial error: 0.1386
Final error: 0.0549
Image:  172
Initial error: 0.0537
Final error: 0.0204
Image:  173
Initial error: 0.0871
Final error: 0.0358
Image:  174
Initial error: 0.0556
Final error: 0.0232
Image:  175
Initial error: 0.1034
Final error: 0.0304
Image:  176
Initial error: 0.0741
Final error: 0.0509
Image:  177
Initial error: 0.0757
Final error: 0.0243
Image:  178
Initial error: 0.0850
Final error: 0.0319
Image:  179
Initial error: 0.1097
Final error: 0.0273
Image:  180
Initial error: 0.0484
Final error: 0.0366
Image:  181
Initial error: 0.1651
Final error: 0.0901
Image:  182
Initial error: 0.0480
Final error: 0.0216
Image:  183
Initial error: 0.1777
Final error: 0.0699
Image:  184
Initial error: 0.0606
Final error: 0.0314
Image:  185
Initial error: 0.0718
Final error: 0.0207
Image:  186
Initial error: 0.0569
Final error: 0.0246
Image:  187
Initial error: 0.1528
Final error: 0.0194
Image:  188
Initial error: 0.0534
Final error: 0.0155
Image:  189
Initial error: 0.0816
Final error: 0.0178
Image:  190
Initial error: 0.0908
Final error: 0.0371
Image:  191
Initial error: 0.0573
Final error: 0.0408
Image:  192
Initial error: 0.0667
Final error: 0.0358
Image:  193
Initial error: 0.1489
Final error: 0.0376
Image:  194
Initial error: 0.0962
Final error: 0.0195
Image:  195
Initial error: 0.0868
Final error: 0.0512
Image:  196
Initial error: 0.0485
Final error: 0.0159
Image:  197
Initial error: 0.0984
Final error: 0.0250
Image:  198
Initial error: 0.1219
Final error: 0.0720
Image:  199
Initial error: 0.0872
Final error: 0.0262
Image:  200
Initial error: 0.0875
Final error: 0.0228
Image:  201
Initial error: 0.0772
Final error: 0.0341
Image:  202
Initial error: 0.1170
Final error: 0.0554
Image:  203
Initial error: 0.1497
Final error: 0.0996
Image:  204
Initial error: 0.0924
Final error: 0.0494
Image:  205
Initial error: 0.0712
Final error: 0.0245
Image:  206
Initial error: 0.0936
Final error: 0.0270
Image:  207
Initial error: 0.1222
Final error: 0.0346
Image:  208
Initial error: 0.0779
Final error: 0.0196
Image:  209
Initial error: 0.0382
Final error: 0.0234
Image:  210
Initial error: 0.1334
Final error: 0.0288
Image:  211
Initial error: 0.0792
Final error: 0.0406
Image:  212
Initial error: 0.1859
Final error: 0.1193
Image:  213
Initial error: 0.1045
Final error: 0.0427
Image:  214
Initial error: 0.0962
Final error: 0.0173
Image:  215
Initial error: 0.1036
Final error: 0.0341
Image:  216
Initial error: 0.0925
Final error: 0.0226
Image:  217
Initial error: 0.0915
Final error: 0.0236
Image:  218
Initial error: 0.0750
Final error: 0.0219
Image:  219
Initial error: 0.1082
Final error: 0.0377
Image:  220
Initial error: 0.1344
Final error: 0.0933
Image:  221
Initial error: 0.0676
Final error: 0.0285
Image:  222
Initial error: 0.0670
Final error: 0.0199
Image:  223
Initial error: 0.1560
Final error: 0.0554
Image:  224
Initial error: 0.1490
Final error: 0.0639
Image:  225
Initial error: 0.2057
Final error: 0.1447
Image:  226
Initial error: 0.1137
Final error: 0.0280
Image:  227
Initial error: 0.0868
Final error: 0.0302
Image:  228
Initial error: 0.0917
Final error: 0.0236
Image:  229
Initial error: 0.0558
Final error: 0.0247
Image:  230
Initial error: 0.0820
Final error: 0.0517
Image:  231
Initial error: 0.0404
Final error: 0.0257
Image:  232
Initial error: 0.1113
Final error: 0.0261
Image:  233
Initial error: 0.0828
Final error: 0.0543
Image:  234
Initial error: 0.1071
Final error: 0.0402
Image:  235
Initial error: 0.1547
Final error: 0.0336
Image:  236
Initial error: 0.0785
Final error: 0.0312
Image:  237
Initial error: 0.0592
Final error: 0.0313
Image:  238
Initial error: 0.1585
Final error: 0.0767
Image:  239
Initial error: 0.1400
Final error: 0.0293
Image:  240
Initial error: 0.1244
Final error: 0.0708
Image:  241
Initial error: 0.0671
Final error: 0.0232
Image:  242
Initial error: 0.0980
Final error: 0.0345
Image:  243
Initial error: 0.1044
Final error: 0.0273
Image:  244
Initial error: 0.0515
Final error: 0.0215
Image:  245
Initial error: 0.0714
Final error: 0.0288
Image:  246
Initial error: 0.0704
Final error: 0.0244
Image:  247
Initial error: 0.1363
Final error: 0.0679
Image:  248
Initial error: 0.1299
Final error: 0.0644
Image:  249
Initial error: 0.1013
Final error: 0.0726
Image:  250
Initial error: 0.1102
Final error: 0.0645
Image:  251
Initial error: 0.1336
Final error: 0.0632
Image:  252
Initial error: 0.1397
Final error: 0.0499
Image:  253
Initial error: 0.0675
Final error: 0.0188
Image:  254
Initial error: 0.0598
Final error: 0.0288
Image:  255
Initial error: 0.0442
Final error: 0.0254
Image:  256
Initial error: 0.0701
Final error: 0.0377
Image:  257
Initial error: 0.0702
Final error: 0.0335
Image:  258
Initial error: 0.1081
Final error: 0.0940
Image:  259
Initial error: 0.0996
Final error: 0.0293
Image:  260
Initial error: 0.1357
Final error: 0.0302
Image:  261
Initial error: 0.0947
Final error: 0.1009
Image:  262
Initial error: 0.0755
Final error: 0.0291
Image:  263
Initial error: 0.1904
Final error: 0.0430
Image:  264
Initial error: 0.1209
Final error: 0.0381
Image:  265
Initial error: 0.0670
Final error: 0.0413
Image:  266
Initial error: 0.0718
Final error: 0.0247
Image:  267
Initial error: 0.0941
Final error: 0.0325
Image:  268
Initial error: 0.0556
Final error: 0.0339
Image:  269
Initial error: 0.0680
Final error: 0.0229
Image:  270
Initial error: 0.0796
Final error: 0.0274
Image:  271
Initial error: 0.1281
Final error: 0.0524
Image:  272
Initial error: 0.0473
Final error: 0.0272
Image:  273
Initial error: 0.0921
Final error: 0.0389
Image:  274
Initial error: 0.0740
Final error: 0.0345
Image:  275
Initial error: 0.1008
Final error: 0.0252
Image:  276
Initial error: 0.0924
Final error: 0.0497
Image:  277
Initial error: 0.1111
Final error: 0.0581
Image:  278
Initial error: 0.1860
Final error: 0.1163
Image:  279
Initial error: 0.0914
Final error: 0.0515
Image:  280
Initial error: 0.0629
Final error: 0.0309
Image:  281
Initial error: 0.0957
Final error: 0.0260
Image:  282
Initial error: 0.1015
Final error: 0.0397
Image:  283
Initial error: 0.0559
Final error: 0.0430
Image:  284
Initial error: 0.0907
Final error: 0.0289
Image:  285
Initial error: 0.1344
Final error: 0.0394
Image:  286
Initial error: 0.0393
Final error: 0.0174
Image:  287
Initial error: 0.0689
Final error: 0.0220
Image:  288
Initial error: 0.0765
Final error: 0.0228
Image:  289
Initial error: 0.1240
Final error: 0.0187
Image:  290
Initial error: 0.0645
Final error: 0.0304
Image:  291
Initial error: 0.1447
Final error: 0.0507
Image:  292
Initial error: 0.0770
Final error: 0.0321
Image:  293
Initial error: 0.1223
Final error: 0.0306
Image:  294
Initial error: 0.1146
Final error: 0.0399
Image:  295
Initial error: 0.0556
Final error: 0.0402
Image:  296
Initial error: 0.0700
Final error: 0.0317
Image:  297
Initial error: 0.0939
Final error: 0.0184
Image:  298
Initial error: 0.0767
Final error: 0.0320
Image:  299
Initial error: 0.1080
Final error: 0.0334
Image:  300
Initial error: 0.0980
Final error: 0.0479
Image:  301
Initial error: 0.1019
Final error: 0.0680
Image:  302
Initial error: 0.0817
Final error: 0.0287
Image:  303
Initial error: 0.0685
Final error: 0.0346
Image:  304
Initial error: 0.0977
Final error: 0.0564
Image:  305
Initial error: 0.0909
Final error: 0.0294
Image:  306
Initial error: 0.0885
Final error: 0.0375
Image:  307
Initial error: 0.1006
Final error: 0.0508
Image:  308
Initial error: 0.1471
Final error: 0.0224
Image:  309
Initial error: 0.0582
Final error: 0.0202
Image:  310
Initial error: 0.1210
Final error: 0.0289
Image:  311
Initial error: 0.0656
Final error: 0.0326
Image:  312
Initial error: 0.0829
Final error: 0.0239
Image:  313
Initial error: 0.0537
Final error: 0.0183
Image:  314
Initial error: 0.1645
Final error: 0.0401
Image:  315
Initial error: 0.1012
Final error: 0.0366
Image:  316
Initial error: 0.1442
Final error: 0.0233
Image:  317
Initial error: 0.1482
Final error: 0.0314
Image:  318
Initial error: 0.1000
Final error: 0.0334
Image:  319
Initial error: 0.1182
Final error: 0.0359
Image:  320
Initial error: 0.0825
Final error: 0.0360
Image:  321
Initial error: 0.1488
Final error: 0.1680
Image:  322
Initial error: 0.0841
Final error: 0.0212
Image:  323
Initial error: 0.0906
Final error: 0.0386
Image:  324
Initial error: 0.1037
Final error: 0.0294
Image:  325
Initial error: 0.0743
Final error: 0.0233
Image:  326
Initial error: 0.0902
Final error: 0.0311
Image:  327
Initial error: 0.0757
Final error: 0.0244
Image:  328
Initial error: 0.0993
Final error: 0.0727
Image:  329
Initial error: 0.1342
Final error: 0.1137
Image:  330
Initial error: 0.0781
Final error: 0.0430
Image:  331
Initial error: 0.0585
Final error: 0.0289
Image:  332
Initial error: 0.0919
Final error: 0.0616
Image:  333
Initial error: 0.0630
Final error: 0.0176
Image:  334
Initial error: 0.1222
Final error: 0.0320
Image:  335
Initial error: 0.0624
Final error: 0.0231
Image:  336
Initial error: 0.0866
Final error: 0.0414