def testdata_matcher(fname1='easy1.png', fname2='easy2.png'):
""""
fname1 = 'easy1.png'
fname2 = 'hard3.png'
python -m vtool.test_constrained_matching --test-visualize_matches --show
Args:
fname1 (str): (default = 'easy1.png')
fname2 (str): (default = 'easy2.png')
Returns:
?: testtup
CommandLine:
python -m vtool.test_constrained_matching --test-testdata_matcher
Example:
>>> # DISABLE_DOCTEST
>>> from vtool.test_constrained_matching import * # NOQA
>>> fname1 = 'easy1.png'
>>> fname2 = 'easy2.png'
>>> testtup = testdata_matcher(fname1, fname2)
>>> result = ('testtup = %s' % (str(testtup),))
>>> print(result)
"""
import utool as ut
#import vtool as vt
from vtool import image as gtool
from vtool import features as feattool
fpath1 = ut.grab_test_imgpath(fname1)
fpath2 = ut.grab_test_imgpath(fname2)
featkw = dict(rotation_invariance=True)
kpts1, vecs1 = feattool.extract_features(fpath1, **featkw)
kpts2, vecs2 = feattool.extract_features(fpath2, **featkw)
#if featkw['rotation_invariance']:
# print('ori stats 1 ' + ut.get_stats_str(vt.get_oris(kpts2)))
# print('ori stats 2 ' + ut.get_stats_str(vt.get_oris(kpts1)))
rchip1 = gtool.imread(fpath1)
rchip2 = gtool.imread(fpath2)
#chip1_shape = vt.gtool.open_image_size(fpath1)
chip2_shape = gtool.open_image_size(fpath2)
dlen_sqrd2 = chip2_shape[0] ** 2 + chip2_shape[1]
testtup = (rchip1, rchip2, kpts1, vecs1, kpts2, vecs2, dlen_sqrd2)
return testtup
def testdata_matcher(fname1='easy1.png', fname2='easy2.png'):
""""
fname1 = 'easy1.png'
fname2 = 'hard3.png'
python -m vtool.test_constrained_matching --test-visualize_matches --show
Args:
fname1 (str): (default = 'easy1.png')
fname2 (str): (default = 'easy2.png')
Returns:
?: testtup
CommandLine:
python -m vtool.test_constrained_matching --test-testdata_matcher
Example:
>>> # DISABLE_DOCTEST
>>> from vtool.test_constrained_matching import * # NOQA
>>> fname1 = 'easy1.png'
>>> fname2 = 'easy2.png'
>>> testtup = testdata_matcher(fname1, fname2)
>>> result = ('testtup = %s' % (str(testtup),))
>>> print(result)
"""
import utool as ut
#import vtool as vt
from vtool import image as gtool
from vtool import features as feattool
fpath1 = ut.grab_test_imgpath(fname1)
fpath2 = ut.grab_test_imgpath(fname2)
featkw = dict(rotation_invariance=True)
kpts1, vecs1 = feattool.extract_features(fpath1, **featkw)
kpts2, vecs2 = feattool.extract_features(fpath2, **featkw)
#if featkw['rotation_invariance']:
# print('ori stats 1 ' + ut.get_stats_str(vt.get_oris(kpts2)))
# print('ori stats 2 ' + ut.get_stats_str(vt.get_oris(kpts1)))
rchip1 = gtool.imread(fpath1)
rchip2 = gtool.imread(fpath2)
#chip1_shape = vt.gtool.open_image_size(fpath1)
chip2_shape = gtool.open_image_size(fpath2)
dlen_sqrd2 = chip2_shape[0]**2 + chip2_shape[1]
testtup = (rchip1, rchip2, kpts1, vecs1, kpts2, vecs2, dlen_sqrd2)
return testtup
def get_dummy_test_vars1(fname1='easy1.png', fname2='easy2.png'):
import utool as ut
from vtool import image as gtool
from vtool import features as feattool
fpath1 = ut.grab_test_imgpath(fname1)
fpath2 = ut.grab_test_imgpath(fname2)
kpts1, vecs1 = feattool.extract_features(fpath1)
kpts2, vecs2 = feattool.extract_features(fpath2)
chip1 = gtool.imread(fpath1)
chip2 = gtool.imread(fpath2)
#chip1_shape = vt.gtool.open_image_size(fpath1)
#chip2_shape = gtool.open_image_size(fpath2)
#dlen_sqrd2 = chip2_shape[0] ** 2 + chip2_shape[1]
#testtup = (rchip1, rchip2, kpts1, vecs1, kpts2, vecs2, dlen_sqrd2)
import vtool as vt
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)
import pyflann
try:
fx2_to_fx1, _fx2_to_dist = flann.nn_index(vecs2, num_neighbors=2, 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)
fx2_to_ratio = np.divide(fx2_to_dist.T[0], fx2_to_dist.T[1])
ratio_thresh = .625
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
fm = fm_RAT
return chip1, chip2, kpts1, kpts2, fm
def get_dummy_test_vars1(fname1='easy1.png', fname2='easy2.png'):
import utool as ut
from vtool import image as gtool
from vtool import features as feattool
fpath1 = ut.grab_test_imgpath(fname1)
fpath2 = ut.grab_test_imgpath(fname2)
kpts1, vecs1 = feattool.extract_features(fpath1)
kpts2, vecs2 = feattool.extract_features(fpath2)
chip1 = gtool.imread(fpath1)
chip2 = gtool.imread(fpath2)
#chip1_shape = vt.gtool.open_image_size(fpath1)
#chip2_shape = gtool.open_image_size(fpath2)
#dlen_sqrd2 = chip2_shape[0] ** 2 + chip2_shape[1]
#testtup = (rchip1, rchip2, kpts1, vecs1, kpts2, vecs2, dlen_sqrd2)
import vtool as vt
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)
import pyflann
try:
fx2_to_fx1, _fx2_to_dist = flann.nn_index(vecs2,
num_neighbors=2,
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)
fx2_to_ratio = np.divide(fx2_to_dist.T[0], fx2_to_dist.T[1])
ratio_thresh = .625
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
fm = fm_RAT
return chip1, chip2, kpts1, kpts2, fm
def load(annot):
from vtool import image as gtool
from vtool import features as feattool
kpts, vecs = feattool.extract_features(annot.fpath)
annot.kpts = kpts
annot.vecs = vecs
annot.rchip = gtool.imread(annot.fpath)
annot.dstncvs = compute_distinctivness([annot.vecs], annot.species)[0]
annot.fgweights = compute_forgroundness(annot.fpath, annot.kpts, annot.species)
annot.chipshape = annot.rchip.shape
annot.dlen_sqrd = annot.chipshape[0] ** 2 + annot.chipshape[1] ** 2
def load(annot):
from vtool import image as gtool
from vtool import features as feattool
kpts, vecs = feattool.extract_features(annot.fpath)
annot.kpts = kpts
annot.vecs = vecs
annot.rchip = gtool.imread(annot.fpath)
annot.dstncvs = compute_distinctivness([annot.vecs], annot.species)[0]
annot.fgweights = compute_forgroundness(annot.fpath, annot.kpts,
annot.species)
annot.chipshape = annot.rchip.shape
annot.dlen_sqrd = annot.chipshape[0]**2 + annot.chipshape[1]**2
def get_testdata_kpts(fname=None, with_vecs=False):
if fname is None:
kpts = get_dummy_kpts()
vecs = (np.random.rand(len(kpts), 128) * 255).astype(np.uint8)
# TODO: dummy vecs
else:
from vtool import features as feattool
import utool as ut
fpath = ut.grab_test_imgpath(fname)
kpts, vecs = feattool.extract_features(fpath)
if with_vecs:
return kpts, vecs
else:
return kpts
def testdata_matcher(fname1='easy1.png', fname2='easy2.png'):
""""
fname1 = 'easy1.png'
fname2 = 'hard3.png'
annot1 = Annot(fpath1)
annot2 = Annot(fpath2)
"""
import utool as ut
from vtool import image as gtool
from vtool import features as feattool
fpath1 = ut.grab_test_imgpath(fname1)
fpath2 = ut.grab_test_imgpath(fname2)
kpts1, vecs1 = feattool.extract_features(fpath1)
kpts2, vecs2 = feattool.extract_features(fpath2)
rchip1 = gtool.imread(fpath1)
rchip2 = gtool.imread(fpath2)
#chip1_shape = vt.gtool.open_image_size(fpath1)
chip2_shape = gtool.open_image_size(fpath2)
dlen_sqrd2 = chip2_shape[0]**2 + chip2_shape[1]**2
testtup = (rchip1, rchip2, kpts1, vecs1, kpts2, vecs2, dlen_sqrd2)
return testtup
def get_testdata_kpts(fname=None, with_vecs=False):
if fname is None:
kpts = get_dummy_kpts()
vecs = (np.random.rand(len(kpts), 128) * 255).astype(np.uint8)
# TODO: dummy vecs
else:
from vtool import features as feattool
import utool as ut
fpath = ut.grab_test_imgpath(fname)
kpts, vecs = feattool.extract_features(fpath)
if with_vecs:
return kpts, vecs
else:
return kpts
def testdata_matcher(fname1="easy1.png", fname2="easy2.png"):
""""
fname1 = 'easy1.png'
fname2 = 'hard3.png'
annot1 = Annot(fpath1)
annot2 = Annot(fpath2)
"""
import utool as ut
from vtool import image as gtool
from vtool import features as feattool
fpath1 = ut.grab_test_imgpath(fname1)
fpath2 = ut.grab_test_imgpath(fname2)
kpts1, vecs1 = feattool.extract_features(fpath1)
kpts2, vecs2 = feattool.extract_features(fpath2)
rchip1 = gtool.imread(fpath1)
rchip2 = gtool.imread(fpath2)
# chip1_shape = vt.gtool.open_image_size(fpath1)
chip2_shape = gtool.open_image_size(fpath2)
dlen_sqrd2 = chip2_shape[0] ** 2 + chip2_shape[1] ** 2
testtup = (rchip1, rchip2, kpts1, vecs1, kpts2, vecs2, dlen_sqrd2)
return 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
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