def get_cfgstr(nnindexer, noquery=False):
r""" returns string which uniquely identified configuration and support data
Args:
noquery (bool): if True cfgstr is only relevant to building the
index. No search params are returned (default = False)
Returns:
str: flann_cfgstr
CommandLine:
python -m ibeis.algo.hots.neighbor_index --test-get_cfgstr
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.neighbor_index import * # NOQA
>>> import ibeis
>>> cfgdict = dict(fg_on=False)
>>> qreq_ = ibeis.testdata_qreq_(defaultdb='testdb1', p='default:fg_on=False')
>>> qreq_.load_indexer()
>>> nnindexer = qreq_.indexer
>>> noquery = True
>>> flann_cfgstr = nnindexer.get_cfgstr(noquery)
>>> result = ('flann_cfgstr = %s' % (str(flann_cfgstr),))
>>> print(result)
flann_cfgstr = _FLANN((algo=kdtree,seed=42,t=8,))_VECS((11260,128)gj5nea@ni0%f3aja)
"""
flann_cfgstr_list = []
use_params_hash = True
use_data_hash = True
if use_params_hash:
flann_defaults = vt.get_flann_params(
nnindexer.flann_params['algorithm'])
#flann_params_clean = flann_defaults.copy()
flann_params_clean = ut.sort_dict(flann_defaults)
ut.updateif_haskey(flann_params_clean, nnindexer.flann_params)
if noquery:
ut.delete_dict_keys(flann_params_clean, ['checks'])
shortnames = dict(algorithm='algo',
checks='chks',
random_seed='seed',
trees='t')
short_params = ut.odict([
(shortnames.get(key, key), str(val)[0:7])
for key, val in six.iteritems(flann_params_clean)
])
flann_valsig_ = ut.dict_str(short_params,
nl=False,
explicit=True,
strvals=True)
flann_valsig_ = flann_valsig_.lstrip('dict').replace(' ', '')
#flann_valsig_ = str(list(flann_params.values()))
#flann_valsig = ut.remove_chars(flann_valsig_, ', \'[]')
flann_cfgstr_list.append('_FLANN(' + flann_valsig_ + ')')
if use_data_hash:
vecs_hashstr = ut.hashstr_arr(nnindexer.idx2_vec, '_VECS')
flann_cfgstr_list.append(vecs_hashstr)
flann_cfgstr = ''.join(flann_cfgstr_list)
return flann_cfgstr
def get_cfgstr(nnindexer, noquery=False):
r""" returns string which uniquely identified configuration and support data
Args:
noquery (bool): if True cfgstr is only relevant to building the
index. No search params are returned (default = False)
Returns:
str: flann_cfgstr
CommandLine:
python -m ibeis.algo.hots.neighbor_index --test-get_cfgstr
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.neighbor_index import * # NOQA
>>> import ibeis
>>> cfgdict = dict(fg_on=False)
>>> qreq_ = ibeis.testdata_qreq_(defaultdb='testdb1', p='default:fg_on=False')
>>> qreq_.load_indexer()
>>> nnindexer = qreq_.indexer
>>> noquery = True
>>> flann_cfgstr = nnindexer.get_cfgstr(noquery)
>>> result = ('flann_cfgstr = %s' % (str(flann_cfgstr),))
>>> print(result)
flann_cfgstr = _FLANN((algo=kdtree,seed=42,t=8,))_VECS((11260,128)gj5nea@ni0%f3aja)
"""
flann_cfgstr_list = []
use_params_hash = True
use_data_hash = True
if use_params_hash:
flann_defaults = vt.get_flann_params(nnindexer.flann_params['algorithm'])
#flann_params_clean = flann_defaults.copy()
flann_params_clean = ut.sort_dict(flann_defaults)
ut.updateif_haskey(flann_params_clean, nnindexer.flann_params)
if noquery:
ut.delete_dict_keys(flann_params_clean, ['checks'])
shortnames = dict(algorithm='algo', checks='chks', random_seed='seed', trees='t')
short_params = ut.odict([(shortnames.get(key, key), str(val)[0:7])
for key, val in six.iteritems(flann_params_clean)])
flann_valsig_ = ut.dict_str(
short_params, nl=False, explicit=True, strvals=True)
flann_valsig_ = flann_valsig_.lstrip('dict').replace(' ', '')
#flann_valsig_ = str(list(flann_params.values()))
#flann_valsig = ut.remove_chars(flann_valsig_, ', \'[]')
flann_cfgstr_list.append('_FLANN(' + flann_valsig_ + ')')
if use_data_hash:
vecs_hashstr = ut.hashstr_arr(nnindexer.idx2_vec, '_VECS')
flann_cfgstr_list.append(vecs_hashstr)
flann_cfgstr = ''.join(flann_cfgstr_list)
return flann_cfgstr
def __init__(self, ibs, qaid2_qres, **kwargs):
# Initialize variables. No logic
self.fnum = None
self.nPerPage = None
self.ranks_lt = None
self.on_change_callback = None
self.ibs = None
self.nCands = 0 # number of candidate matches
self.qaid2_qres = {}
self.cand_match_list = []
self.start_index = 0
self.current_pagenum = -1
self.current_match_aids = None
self.current_qres = None
self.scope = [] # for keeping those widgets alive!
self.nPages = 0
self.stop_index = -1
self.interactkw = {
'draw_fmatches': False,
'draw_ell': True,
'draw_rect': True,
'draw_lines': True,
'in_image': False,
'draw_lbl': True,
'show_timedelta': False,
}
self.toggleable_kws = odict([
('TOG: fmatch', 'draw_fmatches'),
('TOG: in_image', 'in_image'),
('TOG: timedelta', 'show_timedelta'),
('TOG: lbl', 'draw_lbl'),
])
# Initialize Logic
# main data
self.ibs = ibs
self.qaid2_qres = qaid2_qres
# update keyword args
params = default_interact_qres_params()
ut.updateif_haskey(params, kwargs)
self.__dict__.update(**params)
# initialize matches
self.init_candidates(qaid2_qres)
# show first page
self.show_page(0)
def __init__(self, ibs, qaid2_qres, **kwargs):
# Initialize variables. No logic
self.fnum = None
self.nPerPage = None
self.ranks_lt = None
self.on_change_callback = None
self.ibs = None
self.nCands = 0 # number of candidate matches
self.qaid2_qres = {}
self.cand_match_list = []
self.start_index = 0
self.current_pagenum = -1
self.current_match_aids = None
self.current_qres = None
self.scope = [] # for keeping those widgets alive!
self.nPages = 0
self.stop_index = -1
self.interactkw = {
'draw_fmatches': False,
'draw_ell': True,
'draw_rect': True,
'draw_lines': True,
'in_image': False,
'draw_lbl': True,
'show_timedelta': False,
}
self.toggleable_kws = odict([
('TOG: fmatch', 'draw_fmatches'),
('TOG: in_image', 'in_image'),
('TOG: timedelta', 'show_timedelta'),
('TOG: lbl', 'draw_lbl'),
])
# Initialize Logic
# main data
self.ibs = ibs
self.qaid2_qres = qaid2_qres
# update keyword args
params = default_interact_qres_params()
ut.updateif_haskey(params, kwargs)
self.__dict__.update(**params)
# initialize matches
self.init_candidates(qaid2_qres)
# show first page
self.show_page(0)
def testdata_ratio_matches(fname1='easy1.png', fname2='easy2.png', **kwargs):
r"""
Runs simple ratio-test matching between two images.
Technically this is not dummy data.
Args:
fname1 (str):
fname2 (str):
Returns:
tuple : matches_testtup
CommandLine:
python -m vtool.tests.dummy --test-testdata_ratio_matches
python -m vtool.tests.dummy --test-testdata_ratio_matches --help
python -m vtool.tests.dummy --test-testdata_ratio_matches --show
python -m vtool.tests.dummy --test-testdata_ratio_matches --show --ratio_thresh=1.1 --rotation_invariance
python -m vtool.tests.dummy --test-testdata_ratio_matches --show --ratio_thresh=.625 --rotation_invariance --fname1 easy1.png --fname2 easy3.png
python -m vtool.tests.dummy --test-testdata_ratio_matches --show --ratio_thresh=.625 --no-rotation_invariance --fname1 easy1.png --fname2 easy3.png
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.tests.dummy import * # NOQA
>>> import vtool as vt
>>> # build test data
>>> fname1 = ut.get_argval('--fname1', type_=str, default='easy1.png')
>>> fname2 = ut.get_argval('--fname2', type_=str, default='easy2.png')
>>> # execute function
>>> default_dict = vt.get_extract_features_default_params()
>>> default_dict['ratio_thresh'] = .625
>>> kwargs = ut.argparse_dict(default_dict)
>>> matches_testtup = testdata_ratio_matches(fname1, fname2, **kwargs)
>>> (kpts1, kpts2, fm_RAT, fs_RAT, rchip1, rchip2) = matches_testtup
>>> if ut.show_was_requested():
>>> import plottool as pt
>>> pt.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm_RAT, fs_RAT, ori=True)
>>> num_matches = len(fm_RAT)
>>> score_sum = sum(fs_RAT)
>>> title = 'Simple matches using the Lowe\'s ratio test'
>>> title += '\n num_matches=%r, score_sum=%.2f' % (num_matches, score_sum)
>>> pt.set_figtitle(title)
>>> pt.show_if_requested()
"""
import utool as ut
import vtool as vt
from vtool import image as gtool
from vtool import features as feattool
import pyflann
# Get params
ratio_thresh = kwargs.get('ratio_thresh', .625)
print('ratio_thresh=%r' % (ratio_thresh, ))
featkw = vt.get_extract_features_default_params()
ut.updateif_haskey(featkw, kwargs)
# Read Images
fpath1 = ut.grab_test_imgpath(fname1)
fpath2 = ut.grab_test_imgpath(fname2)
# Extract Features
kpts1, vecs1 = feattool.extract_features(fpath1, **featkw)
kpts2, vecs2 = feattool.extract_features(fpath2, **featkw)
rchip1 = gtool.imread(fpath1)
rchip2 = gtool.imread(fpath2)
# Run Algorithm
def assign_nearest_neighbors(vecs1, vecs2, K=2):
checks = 800
flann_params = {'algorithm': 'kdtree', 'trees': 8}
#pseudo_max_dist_sqrd = (np.sqrt(2) * 512) ** 2
pseudo_max_dist_sqrd = 2 * (512**2)
flann = vt.flann_cache(vecs1, flann_params=flann_params)
try:
fx2_to_fx1, _fx2_to_dist = flann.nn_index(vecs2,
num_neighbors=K,
checks=checks)
except pyflann.FLANNException:
print('vecs1.shape = %r' % (vecs1.shape, ))
print('vecs2.shape = %r' % (vecs2.shape, ))
print('vecs1.dtype = %r' % (vecs1.dtype, ))
print('vecs2.dtype = %r' % (vecs2.dtype, ))
raise
fx2_to_dist = np.divide(_fx2_to_dist, pseudo_max_dist_sqrd)
return fx2_to_fx1, fx2_to_dist
def ratio_test(fx2_to_fx1, fx2_to_dist, ratio_thresh):
fx2_to_ratio = np.divide(fx2_to_dist.T[0], fx2_to_dist.T[1])
fx2_to_isvalid = fx2_to_ratio < ratio_thresh
fx2_m = np.where(fx2_to_isvalid)[0]
fx1_m = fx2_to_fx1.T[0].take(fx2_m)
fs_RAT = np.subtract(1.0, fx2_to_ratio.take(fx2_m))
fm_RAT = np.vstack((fx1_m, fx2_m)).T
# return normalizer info as well
fx1_m_normalizer = fx2_to_fx1.T[1].take(fx2_m)
fm_norm_RAT = np.vstack((fx1_m_normalizer, fx2_m)).T
return fm_RAT, fs_RAT, fm_norm_RAT
# GET NEAREST NEIGHBORS
fx2_to_fx1, fx2_to_dist = assign_nearest_neighbors(vecs1, vecs2, K=2)
#fx2_m = np.arange(len(fx2_to_fx1))
#fx1_m = fx2_to_fx1.T[0]
#fm_ORIG = np.vstack((fx1_m, fx2_m)).T
#fs_ORIG = fx2_to_dist.T[0]
#fs_ORIG = 1 - np.divide(fx2_to_dist.T[0], fx2_to_dist.T[1])
#np.ones(len(fm_ORIG))
# APPLY RATIO TEST
#ratio_thresh = .625
fm_RAT, fs_RAT, fm_norm_RAT = ratio_test(fx2_to_fx1, fx2_to_dist,
ratio_thresh)
kpts1 = kpts1.astype(np.float64)
kpts2 = kpts2.astype(np.float64)
matches_testtup = (kpts1, kpts2, fm_RAT, fs_RAT, rchip1, rchip2)
return matches_testtup
def testdata_ratio_matches(fname1='easy1.png', fname2='easy2.png', **kwargs):
r"""
Runs simple ratio-test matching between two images.
Technically this is not dummy data.
Args:
fname1 (str):
fname2 (str):
Returns:
tuple : matches_testtup
CommandLine:
python -m vtool.tests.dummy --test-testdata_ratio_matches
python -m vtool.tests.dummy --test-testdata_ratio_matches --help
python -m vtool.tests.dummy --test-testdata_ratio_matches --show
python -m vtool.tests.dummy --test-testdata_ratio_matches --show --ratio_thresh=1.1 --rotation_invariance
python -m vtool.tests.dummy --test-testdata_ratio_matches --show --ratio_thresh=.625 --rotation_invariance --fname1 easy1.png --fname2 easy3.png
python -m vtool.tests.dummy --test-testdata_ratio_matches --show --ratio_thresh=.625 --no-rotation_invariance --fname1 easy1.png --fname2 easy3.png
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.tests.dummy import * # NOQA
>>> import vtool as vt
>>> # build test data
>>> fname1 = ut.get_argval('--fname1', type_=str, default='easy1.png')
>>> fname2 = ut.get_argval('--fname2', type_=str, default='easy2.png')
>>> # execute function
>>> default_dict = vt.get_extract_features_default_params()
>>> default_dict['ratio_thresh'] = .625
>>> kwargs = ut.argparse_dict(default_dict)
>>> matches_testtup = testdata_ratio_matches(fname1, fname2, **kwargs)
>>> (kpts1, kpts2, fm_RAT, fs_RAT, rchip1, rchip2) = matches_testtup
>>> if ut.show_was_requested():
>>> import plottool as pt
>>> pt.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm_RAT, fs_RAT, ori=True)
>>> num_matches = len(fm_RAT)
>>> score_sum = sum(fs_RAT)
>>> title = 'Simple matches using the Lowe\'s ratio test'
>>> title += '\n num_matches=%r, score_sum=%.2f' % (num_matches, score_sum)
>>> pt.set_figtitle(title)
>>> pt.show_if_requested()
"""
import utool as ut
import vtool as vt
from vtool import image as gtool
from vtool import features as feattool
import pyflann
# Get params
ratio_thresh = kwargs.get('ratio_thresh', .625)
print('ratio_thresh=%r' % (ratio_thresh,))
featkw = vt.get_extract_features_default_params()
ut.updateif_haskey(featkw, kwargs)
# Read Images
fpath1 = ut.grab_test_imgpath(fname1)
fpath2 = ut.grab_test_imgpath(fname2)
# Extract Features
kpts1, vecs1 = feattool.extract_features(fpath1, **featkw)
kpts2, vecs2 = feattool.extract_features(fpath2, **featkw)
rchip1 = gtool.imread(fpath1)
rchip2 = gtool.imread(fpath2)
# Run Algorithm
def assign_nearest_neighbors(vecs1, vecs2, K=2):
checks = 800
flann_params = {
'algorithm': 'kdtree',
'trees': 8
}
#pseudo_max_dist_sqrd = (np.sqrt(2) * 512) ** 2
pseudo_max_dist_sqrd = 2 * (512 ** 2)
flann = vt.flann_cache(vecs1, flann_params=flann_params)
try:
fx2_to_fx1, _fx2_to_dist = flann.nn_index(vecs2, num_neighbors=K, checks=checks)
except pyflann.FLANNException:
print('vecs1.shape = %r' % (vecs1.shape,))
print('vecs2.shape = %r' % (vecs2.shape,))
print('vecs1.dtype = %r' % (vecs1.dtype,))
print('vecs2.dtype = %r' % (vecs2.dtype,))
raise
fx2_to_dist = np.divide(_fx2_to_dist, pseudo_max_dist_sqrd)
return fx2_to_fx1, fx2_to_dist
def ratio_test(fx2_to_fx1, fx2_to_dist, ratio_thresh):
fx2_to_ratio = np.divide(fx2_to_dist.T[0], fx2_to_dist.T[1])
fx2_to_isvalid = fx2_to_ratio < ratio_thresh
fx2_m = np.where(fx2_to_isvalid)[0]
fx1_m = fx2_to_fx1.T[0].take(fx2_m)
fs_RAT = np.subtract(1.0, fx2_to_ratio.take(fx2_m))
fm_RAT = np.vstack((fx1_m, fx2_m)).T
# return normalizer info as well
fx1_m_normalizer = fx2_to_fx1.T[1].take(fx2_m)
fm_norm_RAT = np.vstack((fx1_m_normalizer, fx2_m)).T
return fm_RAT, fs_RAT, fm_norm_RAT
# GET NEAREST NEIGHBORS
fx2_to_fx1, fx2_to_dist = assign_nearest_neighbors(vecs1, vecs2, K=2)
#fx2_m = np.arange(len(fx2_to_fx1))
#fx1_m = fx2_to_fx1.T[0]
#fm_ORIG = np.vstack((fx1_m, fx2_m)).T
#fs_ORIG = fx2_to_dist.T[0]
#fs_ORIG = 1 - np.divide(fx2_to_dist.T[0], fx2_to_dist.T[1])
#np.ones(len(fm_ORIG))
# APPLY RATIO TEST
#ratio_thresh = .625
fm_RAT, fs_RAT, fm_norm_RAT = ratio_test(fx2_to_fx1, fx2_to_dist, ratio_thresh)
kpts1 = kpts1.astype(np.float64)
kpts2 = kpts2.astype(np.float64)
matches_testtup = (kpts1, kpts2, fm_RAT, fs_RAT, rchip1, rchip2)
return matches_testtup
