Example #1
0
def get_varied_pipecfg_lbls(cfgdict_list, pipecfg_list=None):
    if pipecfg_list is None:
        from ibeis.algo import Config
        cfg_default_dict = dict(Config.QueryConfig().parse_items())
        cfgx2_lbl = ut.get_varied_cfg_lbls(cfgdict_list, cfg_default_dict)
    else:
        # TODO: group cfgdict by config type and then get varied labels
        cfg_default_dict = None
        cfgx2_lbl = ut.get_varied_cfg_lbls(cfgdict_list, cfg_default_dict)
    return cfgx2_lbl
Example #2
0
def get_varied_pipecfg_lbls(cfgdict_list, pipecfg_list=None):
    if pipecfg_list is None:
        from ibeis.algo import Config
        #cls_list = [Config] * len(cfgdict_list)
        cfg_default_dict = dict(Config.QueryConfig().parse_items())
        cfgx2_lbl = ut.get_varied_cfg_lbls(cfgdict_list, cfg_default_dict)
    else:
        # TODO: group cfgdict by config type and then get varied labels
        cfg_default_dict = None
        cfgx2_lbl = ut.get_varied_cfg_lbls(cfgdict_list, cfg_default_dict)
    return cfgx2_lbl
Example #3
0
def compare_featscores():
    """
    CommandLine:

        ibeis --tf compare_featscores  --db PZ_MTEST \
            --nfscfg :disttype=[L2_sift,lnbnn],top_percent=[None,.5,.1] -a timectrl \
            -p default:K=[1,2],normalizer_rule=name \
            --save featscore{db}.png --figsize=13,20 --diskshow

        ibeis --tf compare_featscores  --db PZ_MTEST \
            --nfscfg :disttype=[L2_sift,normdist,lnbnn],top_percent=[None,.5] -a timectrl \
            -p default:K=[1],normalizer_rule=name,sv_on=[True,False] \
            --save featscore{db}.png --figsize=13,10 --diskshow

        ibeis --tf compare_featscores --nfscfg :disttype=[L2_sift,normdist,lnbnn] \
            -a timectrl -p default:K=1,normalizer_rule=name --db PZ_Master1 \
            --save featscore{db}.png  --figsize=13,13 --diskshow

        ibeis --tf compare_featscores --nfscfg :disttype=[L2_sift,normdist,lnbnn] \
            -a timectrl -p default:K=1,normalizer_rule=name --db GZ_ALL \
            --save featscore{db}.png  --figsize=13,13 --diskshow

        ibeis --tf compare_featscores  --db GIRM_Master1 \
            --nfscfg ':disttype=fg,L2_sift,normdist,lnbnn' \
            -a timectrl -p default:K=1,normalizer_rule=name \
            --save featscore{db}.png  --figsize=13,13

        ibeis --tf compare_featscores --nfscfg :disttype=[L2_sift,normdist,lnbnn] \
            -a timectrl -p default:K=[1,2,3],normalizer_rule=name,sv_on=False \
            --db PZ_Master1 --save featscore{db}.png  \
                --dpi=128 --figsize=15,20 --diskshow

        ibeis --tf compare_featscores --show --nfscfg :disttype=[L2_sift,normdist] -a timectrl -p :K=1 --db PZ_MTEST
        ibeis --tf compare_featscores --show --nfscfg :disttype=[L2_sift,normdist] -a timectrl -p :K=1 --db GZ_ALL
        ibeis --tf compare_featscores --show --nfscfg :disttype=[L2_sift,normdist] -a timectrl -p :K=1 --db PZ_Master1
        ibeis --tf compare_featscores --show --nfscfg :disttype=[L2_sift,normdist] -a timectrl -p :K=1 --db GIRM_Master1

        ibeis --tf compare_featscores  --db PZ_MTEST \
            --nfscfg :disttype=[L2_sift,normdist,lnbnn],top_percent=[None,.5,.2] -a timectrl \
            -p default:K=[1],normalizer_rule=name \
            --save featscore{db}.png --figsize=13,20 --diskshow

        ibeis --tf compare_featscores  --db PZ_MTEST \
            --nfscfg :disttype=[L2_sift,normdist,lnbnn],top_percent=[None,.5,.2] -a timectrl \
            -p default:K=[1],normalizer_rule=name \
            --save featscore{db}.png --figsize=13,20 --diskshow

    Example:
        >>> # DISABLE_DOCTEST
        >>> from ibeis.algo.hots.scorenorm import *  # NOQA
        >>> result = compare_featscores()
        >>> print(result)
        >>> ut.quit_if_noshow()
        >>> import plottool as pt
        >>> ut.show_if_requested()
    """
    import plottool as pt
    import ibeis
    nfs_cfg_list = NormFeatScoreConfig.from_argv_cfgs()
    learnkw = {}
    ibs, testres = ibeis.testdata_expts(
        defaultdb='PZ_MTEST', a=['default'], p=['default:K=1'])
    print('nfs_cfg_list = ' + ut.repr3(nfs_cfg_list))

    encoder_list = []
    lbl_list = []

    varied_nfs_lbls = ut.get_varied_cfg_lbls(nfs_cfg_list)
    varied_qreq_lbls = ut.get_varied_cfg_lbls(testres.cfgdict_list)
    #varies_qreq_lbls

    #func = ut.cached_func(cache_dir='.')(learn_featscore_normalizer)
    for datakw, nlbl in zip(nfs_cfg_list, varied_nfs_lbls):
        for qreq_, qlbl in zip(testres.cfgx2_qreq_, varied_qreq_lbls):
            lbl = qlbl + ' ' + nlbl
            cfgstr = '_'.join([datakw.get_cfgstr(), qreq_.get_full_cfgstr()])
            try:
                encoder = vt.ScoreNormalizer()
                encoder.load(cfgstr=cfgstr)
            except IOError:
                print('datakw = %r' % (datakw,))
                encoder = learn_featscore_normalizer(qreq_, datakw, learnkw)
                encoder.save(cfgstr=cfgstr)
            encoder_list.append(encoder)
            lbl_list.append(lbl)

    fnum = 1
    # next_pnum = pt.make_pnum_nextgen(nRows=len(encoder_list), nCols=3)
    next_pnum = pt.make_pnum_nextgen(nRows=len(encoder_list) + 1, nCols=3, start=3)

    iconsize = 94
    if len(encoder_list) > 3:
        iconsize = 64

    icon = qreq_.ibs.get_database_icon(max_dsize=(None, iconsize), aid=qreq_.qaids[0])
    score_range = (0, .6)
    for encoder, lbl in zip(encoder_list, lbl_list):
        #encoder.visualize(figtitle=encoder.get_cfgstr(), with_prebayes=False, with_postbayes=False)
        encoder._plot_score_support_hist(fnum, pnum=next_pnum(), titlesuf='\n' + lbl, score_range=score_range)
        encoder._plot_prebayes(fnum, pnum=next_pnum())
        encoder._plot_roc(fnum, pnum=next_pnum())
        if icon is not None:
            pt.overlay_icon(icon, coords=(1, 0), bbox_alignment=(1, 0))

    nonvaried_lbl = ut.get_nonvaried_cfg_lbls(nfs_cfg_list)[0]
    figtitle = qreq_.__str__() + '\n' + nonvaried_lbl

    pt.set_figtitle(figtitle)
    pt.adjust_subplots(hspace=.5, top=.92, bottom=.08, left=.1, right=.9)
    pt.update_figsize()
    pt.plt.tight_layout()