def get_coverage_kpts_gridsearch_configs():
""" testing function """
varied_dict = {
'cov_agg_mode': ['max', 'sum'],
#'cov_blur_ksize' : [(19, 19), (5, 5)],
'cov_blur_ksize': [(5, 5)],
'cov_blur_on': [True, False],
'cov_blur_sigma': [5.0],
'cov_remove_scale': [True],
'cov_remove_shape': [False, True],
'cov_scale_factor': [.3],
'cov_size_penalty_frac': [.1],
'cov_size_penalty_on': [True],
'cov_size_penalty_power': [.5],
}
slice_dict = {
'cov_scale_factor': slice(0, 3),
'cov_agg_mode': slice(0, 2),
'cov_blur_ksize': slice(0, 2),
#'grid_sigma' : slice(0, 4),
}
slice_dict = None
# Make configuration for every parameter setting
def constrain_func(cfgdict):
if cfgdict['cov_remove_shape']:
cfgdict['cov_remove_scale'] = False
cfgdict['cov_size_penalty_on'] = False
if not cfgdict['cov_size_penalty_on']:
cfgdict['cov_size_penalty_power'] = None
cfgdict['cov_size_penalty_frac'] = None
if not cfgdict['cov_blur_on']:
cfgdict['cov_blur_ksize'] = None
cfgdict['cov_blur_sigma'] = None
return cfgdict
cfgdict_list, cfglbl_list = ut.make_constrained_cfg_and_lbl_list(
varied_dict, constrain_func, slice_dict)
return cfgdict_list, cfglbl_list
def get_coverage_kpts_gridsearch_configs():
""" testing function """
varied_dict = {
'cov_agg_mode' : ['max', 'sum'],
#'cov_blur_ksize' : [(19, 19), (5, 5)],
'cov_blur_ksize' : [(5, 5)],
'cov_blur_on' : [True, False],
'cov_blur_sigma' : [5.0],
'cov_remove_scale' : [True],
'cov_remove_shape' : [False, True],
'cov_scale_factor' : [.3],
'cov_size_penalty_frac' : [.1],
'cov_size_penalty_on' : [True],
'cov_size_penalty_power' : [.5],
}
slice_dict = {
'cov_scale_factor' : slice(0, 3),
'cov_agg_mode' : slice(0, 2),
'cov_blur_ksize' : slice(0, 2),
#'grid_sigma' : slice(0, 4),
}
slice_dict = None
# Make configuration for every parameter setting
def constrain_func(cfgdict):
if cfgdict['cov_remove_shape']:
cfgdict['cov_remove_scale'] = False
cfgdict['cov_size_penalty_on'] = False
if not cfgdict['cov_size_penalty_on']:
cfgdict['cov_size_penalty_power'] = None
cfgdict['cov_size_penalty_frac'] = None
if not cfgdict['cov_blur_on']:
cfgdict['cov_blur_ksize'] = None
cfgdict['cov_blur_sigma'] = None
return cfgdict
cfgdict_list, cfglbl_list = ut.make_constrained_cfg_and_lbl_list(varied_dict, constrain_func, slice_dict)
return cfgdict_list, cfglbl_list
def tst_single_annot_distinctiveness_params(ibs, aid):
r"""
CommandLine:
python -m wbia.algo.hots.distinctiveness_normalizer --test-test_single_annot_distinctiveness_params --show
python -m wbia.algo.hots.distinctiveness_normalizer --test-test_single_annot_distinctiveness_params --show --db GZ_ALL
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots.distinctiveness_normalizer import * # NOQA
>>> import wbia.plottool as pt
>>> import wbia
>>> # build test data
>>> ibs = wbia.opendb(ut.get_argval('--db', type_=str, default='PZ_MTEST'))
>>> aid = ut.get_argval('--aid', type_=int, default=1)
>>> # execute function
>>> test_single_annot_distinctiveness_params(ibs, aid)
>>> pt.show_if_requested()
"""
####
# TODO: Also paramatarize the downweighting based on the keypoint size
####
# HACK IN ABILITY TO SET CONFIG
from wbia.init.main_commands import postload_commands
from wbia.algo import Config
postload_commands(ibs, None)
import wbia.plottool as pt
# cfglbl_list = cfgdict_list
# ut.all_dict_combinations_lbls(varied_dict)
# Get info to find distinctivness of
species_text = ibs.get_annot_species(aid)
# FIXME; qreq_ params for config rowid
vecs = ibs.get_annot_vecs(aid)
kpts = ibs.get_annot_kpts(aid)
chip = ibs.get_annot_chips(aid)
# Paramater space to search
# TODO: use slicing to control the params being varied
# Use GridSearch class to modify paramaters as you go.
varied_dict = Config.DCVS_DEFAULT.get_varydict()
logger.info('Varied Dict: ')
logger.info(ut.repr2(varied_dict))
cfgdict_list, cfglbl_list = ut.make_constrained_cfg_and_lbl_list(
varied_dict)
# Get groundtruthish distinctivness map
# for objective function
# Load distinctivness normalizer
with ut.Timer('Loading Distinctivness Normalizer for %s' % (species_text)):
dstcvnss_normer = request_species_distinctiveness_normalizer(
species_text)
# Get distinctivness over all params
dstncvs_list = [
dstcvnss_normer.get_distinctiveness(vecs, **cfgdict)
for cfgdict in ut.ProgIter(cfgdict_list, lbl='get dstcvns')
]
# fgweights = ibs.get_annot_fgweights([aid])[0]
# dstncvs_list = [x * fgweights for x in dstncvs_list]
fnum = 1
import functools
show_func = functools.partial(show_chip_distinctiveness_plot, chip, kpts)
ut.interact_gridsearch_result_images(
show_func,
cfgdict_list,
cfglbl_list,
dstncvs_list,
score_list=None,
fnum=fnum,
figtitle='dstncvs gridsearch',
)
pt.present()
def test_single_annot_distinctiveness_params(ibs, aid):
r"""
CommandLine:
python -m ibeis.algo.hots.distinctiveness_normalizer --test-test_single_annot_distinctiveness_params --show
python -m ibeis.algo.hots.distinctiveness_normalizer --test-test_single_annot_distinctiveness_params --show --db GZ_ALL
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.distinctiveness_normalizer import * # NOQA
>>> import plottool as pt
>>> import ibeis
>>> # build test data
>>> ibs = ibeis.opendb(ut.get_argval('--db', type_=str, default='PZ_MTEST'))
>>> aid = ut.get_argval('--aid', type_=int, default=1)
>>> # execute function
>>> test_single_annot_distinctiveness_params(ibs, aid)
>>> pt.show_if_requested()
"""
####
# TODO: Also paramatarize the downweighting based on the keypoint size
####
# HACK IN ABILITY TO SET CONFIG
from ibeis.init.main_commands import postload_commands
from ibeis.algo import Config
postload_commands(ibs, None)
import plottool as pt
#cfglbl_list = cfgdict_list
#ut.all_dict_combinations_lbls(varied_dict)
# Get info to find distinctivness of
species_text = ibs.get_annot_species(aid)
# FIXME; qreq_ params for config rowid
vecs = ibs.get_annot_vecs(aid)
kpts = ibs.get_annot_kpts(aid)
chip = ibs.get_annot_chips(aid)
# Paramater space to search
# TODO: use slicing to control the params being varied
# Use GridSearch class to modify paramaters as you go.
varied_dict = Config.DCVS_DEFAULT.get_varydict()
print('Varied Dict: ')
print(ut.dict_str(varied_dict))
cfgdict_list, cfglbl_list = ut.make_constrained_cfg_and_lbl_list(varied_dict)
# Get groundtruthish distinctivness map
# for objective function
# Load distinctivness normalizer
with ut.Timer('Loading Distinctivness Normalizer for %s' % (species_text)):
dstcvnss_normer = request_species_distinctiveness_normalizer(species_text)
# Get distinctivness over all params
dstncvs_list = [dstcvnss_normer.get_distinctiveness(vecs, **cfgdict)
for cfgdict in ut.ProgIter(cfgdict_list, lbl='get dstcvns')]
#fgweights = ibs.get_annot_fgweights([aid])[0]
#dstncvs_list = [x * fgweights for x in dstncvs_list]
fnum = 1
import functools
show_func = functools.partial(show_chip_distinctiveness_plot, chip, kpts)
ut.interact_gridsearch_result_images(
show_func, cfgdict_list, cfglbl_list, dstncvs_list,
score_list=None, fnum=fnum, figtitle='dstncvs gridsearch')
pt.present()
