def get_keypoint_stats(ibs):
"""
kp info
"""
# from ut import util_latex
#from hsdev import dev_consistency
#dev_consistency.check_keypoint_consistency(ibs)
# Keypoint stats
#ibs.refresh_features()
import vtool as vt
from ibeis.control.IBEISControl import IBEISController
assert(isinstance(ibs, IBEISController))
valid_aids = np.array(ibs.get_valid_aids())
cx2_kpts = ibs.get_annot_kpts(valid_aids)
#cx2_kpts = ibs.feats.cx2_kpts
# Check cx2_kpts
cx2_nFeats = list(map(len, cx2_kpts))
kpts = np.vstack(cx2_kpts)
print('[dbinfo] --- LaTeX --- ')
#_printopts = np.get_printoptions()
#np.set_printoptions(precision=3)
scales = vt.get_scales(kpts)
scales = np.array(sorted(scales))
tex_scale_stats = util_latex.latex_get_stats(r'kpt scale', scales)
tex_nKpts = util_latex.latex_scalar(r'\# kpts', len(kpts))
tex_kpts_stats = util_latex.latex_get_stats(r'\# kpts/chip', cx2_nFeats)
print(tex_nKpts)
print(tex_kpts_stats)
print(tex_scale_stats)
#np.set_printoptions(**_printopts)
print('[dbinfo] ---/LaTeX --- ')
return (tex_nKpts, tex_kpts_stats, tex_scale_stats)
def print_feat_stats(kpts, vecs):
assert len(vecs) == len(kpts), 'disagreement'
print('keypoints and vecs agree')
flat_kpts = np.vstack(kpts)
num_kpts = list(map(len, kpts))
kpt_scale = vt.get_scales(flat_kpts)
num_kpts_stats = ut.get_stats(num_kpts)
scale_kpts_stats = ut.get_stats(kpt_scale)
print('Number of ' + prefix + ' keypoints: ' + ut.repr3(num_kpts_stats, nl=0, precision=2))
print('Scale of ' + prefix + ' keypoints: ' + ut.repr3(scale_kpts_stats, nl=0, precision=2))
def warp_patch_onto_kpts(
kpts, patch, chipshape,
weights=None,
out=None,
cov_scale_factor=.2,
cov_agg_mode='max',
cov_remove_shape=False,
cov_remove_scale=False,
cov_size_penalty_on=True,
cov_size_penalty_power=.5,
cov_size_penalty_frac=.1):
r"""
Overlays the source image onto a destination image in each keypoint location
Args:
kpts (ndarray[float32_t, ndim=2]): keypoints
patch (ndarray): patch to warp (like gaussian)
chipshape (tuple):
weights (ndarray): score for every keypoint
Kwargs:
cov_scale_factor (float):
Returns:
ndarray: mask
CommandLine:
python -m vtool.coverage_kpts --test-warp_patch_onto_kpts
python -m vtool.coverage_kpts --test-warp_patch_onto_kpts --show
python -m vtool.coverage_kpts --test-warp_patch_onto_kpts --show --hole
python -m vtool.coverage_kpts --test-warp_patch_onto_kpts --show --square
python -m vtool.coverage_kpts --test-warp_patch_onto_kpts --show --square --hole
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.coverage_kpts import * # NOQA
>>> import vtool as vt
>>> import pyhesaff
>>> img_fpath = ut.grab_test_imgpath('carl.jpg')
>>> (kpts, vecs) = pyhesaff.detect_feats(img_fpath)
>>> kpts = kpts[::15]
>>> chip = vt.imread(img_fpath)
>>> chipshape = chip.shape
>>> weights = np.ones(len(kpts))
>>> cov_scale_factor = 1.0
>>> srcshape = (19, 19)
>>> radius = srcshape[0] / 2.0
>>> sigma = 0.4 * radius
>>> SQUARE = ut.get_argflag('--square')
>>> HOLE = ut.get_argflag('--hole')
>>> if SQUARE:
>>> patch = np.ones(srcshape)
>>> else:
>>> patch = ptool.gaussian_patch(shape=srcshape, sigma=sigma) #, norm_01=False)
>>> patch = patch / patch.max()
>>> if HOLE:
>>> patch[int(patch.shape[0] / 2), int(patch.shape[1] / 2)] = 0
>>> # execute function
>>> dstimg = warp_patch_onto_kpts(kpts, patch, chipshape, weights, cov_scale_factor=cov_scale_factor)
>>> # verify results
>>> print('dstimg stats %r' % (ut.get_stats_str(dstimg, axis=None)),)
>>> print('patch stats %r' % (ut.get_stats_str(patch, axis=None)),)
>>> #print(patch.sum())
>>> assert np.all(ut.inbounds(dstimg, 0, 1, eq=True))
>>> # show results
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> mask = dstimg
>>> show_coverage_map(chip, mask, patch, kpts)
>>> pt.show_if_requested()
"""
import vtool as vt
#if len(kpts) == 0:
# return None
chip_scale_h = int(np.ceil(chipshape[0] * cov_scale_factor))
chip_scale_w = int(np.ceil(chipshape[1] * cov_scale_factor))
if len(kpts) == 0:
dstimg = np.zeros((chip_scale_h, chip_scale_w))
return dstimg
if weights is None:
weights = np.ones(len(kpts))
dsize = (chip_scale_w, chip_scale_h)
# Allocate destination image
patch_shape = patch.shape
# Scale keypoints into destination image
# <HACK>
if cov_remove_shape:
# disregard affine information in keypoints
# i still dont understand why we are trying this
(patch_h, patch_w) = patch_shape
half_width = (patch_w / 2.0) # - .5
half_height = (patch_h / 2.0) # - .5
# Center src image
T1 = vt.translation_mat3x3(-half_width + .5, -half_height + .5)
# Scale src to the unit circle
if not cov_remove_scale:
S1 = vt.scale_mat3x3(1.0 / half_width, 1.0 / half_height)
# Transform the source image to the keypoint ellipse
kpts_T = np.array([vt.translation_mat3x3(x, y) for (x, y) in vt.get_xys(kpts).T])
if not cov_remove_scale:
kpts_S = np.array([vt.scale_mat3x3(np.sqrt(scale))
for scale in vt.get_scales(kpts).T])
# Adjust for the requested scale factor
S2 = vt.scale_mat3x3(cov_scale_factor, cov_scale_factor)
#perspective_list = [S2.dot(A).dot(S1).dot(T1) for A in invVR_aff2Ds]
if not cov_remove_scale:
M_list = reduce(vt.matrix_multiply, (S2, kpts_T, kpts_S, S1, T1))
else:
M_list = reduce(vt.matrix_multiply, (S2, kpts_T, T1))
# </HACK>
else:
M_list = ktool.get_transforms_from_patch_image_kpts(kpts, patch_shape,
cov_scale_factor)
affmat_list = M_list[:, 0:2, :]
weight_list = weights
# For each keypoint warp a gaussian scaled by the feature score into the image
warped_patch_iter = warped_patch_generator(
patch, dsize, affmat_list, weight_list,
cov_size_penalty_on=cov_size_penalty_on,
cov_size_penalty_power=cov_size_penalty_power,
cov_size_penalty_frac=cov_size_penalty_frac)
# Either max or sum
if cov_agg_mode == 'max':
dstimg = vt.iter_reduce_ufunc(np.maximum, warped_patch_iter, out=out)
elif cov_agg_mode == 'sum':
dstimg = vt.iter_reduce_ufunc(np.add, warped_patch_iter, out=out)
# HACK FOR SUM: DO NOT DO THIS FOR MAX
dstimg[dstimg > 1.0] = 1.0
else:
raise AssertionError('Unknown cov_agg_mode=%r' % (cov_agg_mode,))
return dstimg
def scale(self):
return vt.get_scales(self.kparr)
def get_support_data(qreq_, daid_list):
"""
CommandLine:
python -m wbia.algo.hots.neighbor_index get_support_data --show
Example:
>>> # xdoctest: +REQUIRES(module:wbia_cnn)
>>> from wbia.algo.hots.neighbor_index import * # NOQA
>>> import wbia
>>> qreq_ = wbia.testdata_qreq_(defaultdb='PZ_MTEST', p=':fgw_thresh=.9,maxscale_thresh=10', a=':size=2')
>>> daid_list = qreq_.daids
>>> tup = get_support_data(qreq_, daid_list)
>>> vecs_list, fgws_list, fxs_list = tup
>>> assert all([np.all(fgws > .9) for fgws in fgws_list])
>>> result = ('depth_profile = %r' % (ut.depth_profile(tup),))
>>> print(result)
depth_profile = [[(128, 128), (174, 128)], [128, 174], [128, 174]]
I can't figure out why this tests isn't determenistic all the time and
I can't get it to reproduce non-determenism.
This could be due to theano.
depth_profile = [[(39, 128), (22, 128)], [39, 22], [39, 22]]
depth_profile = [[(35, 128), (24, 128)], [35, 24], [35, 24]]
depth_profile = [[(34, 128), (31, 128)], [34, 31], [34, 31]]
depth_profile = [[(83, 128), (129, 128)], [83, 129], [83, 129]]
depth_profile = [[(13, 128), (104, 128)], [13, 104], [13, 104]]
"""
config2_ = qreq_.get_internal_data_config2()
vecs_list = qreq_.ibs.get_annot_vecs(daid_list, config2_=config2_)
# Create corresponding feature indicies
fxs_list = [np.arange(len(vecs)) for vecs in vecs_list]
# <HACK:featweight>
# hack to get feature weights. returns None if feature weights are turned
# off in config settings
if config2_.minscale_thresh is not None or config2_.maxscale_thresh is not None:
min_ = -np.inf if config2_.minscale_thresh is None else config2_.minscale_thresh
max_ = np.inf if config2_.maxscale_thresh is None else config2_.maxscale_thresh
kpts_list = qreq_.ibs.get_annot_kpts(daid_list, config2_=config2_)
# kpts_list = vt.ziptake(kpts_list, fxs_list, axis=0) # not needed for first filter
scales_list = [vt.get_scales(kpts) for kpts in kpts_list]
# Remove data under the threshold
flags_list = [
np.logical_and(scales >= min_, scales <= max_)
for scales in scales_list
]
vecs_list = vt.zipcompress(vecs_list, flags_list, axis=0)
fxs_list = vt.zipcompress(fxs_list, flags_list, axis=0)
if qreq_.qparams.fg_on:
# I've found that the call to get_annot_fgweights is different on
# different machines. Something must be configured differently.
fgws_list = qreq_.ibs.get_annot_fgweights(daid_list,
config2_=config2_,
ensure=True)
fgws_list = vt.ziptake(fgws_list, fxs_list, axis=0)
# assert list(map(len, fgws_list)) == list(map(len, vecs_list)), 'bad corresponding vecs'
if config2_.fgw_thresh is not None and config2_.fgw_thresh > 0:
flags_list = [fgws > config2_.fgw_thresh for fgws in fgws_list]
# Remove data under the threshold
fgws_list = vt.zipcompress(fgws_list, flags_list, axis=0)
vecs_list = vt.zipcompress(vecs_list, flags_list, axis=0)
fxs_list = vt.zipcompress(fxs_list, flags_list, axis=0)
else:
fgws_list = None
# </HACK:featweight>
return vecs_list, fgws_list, fxs_list
def test_sver_wrapper():
"""
Test to ensure cpp and python agree and that cpp is faster
CommandLine:
python -m vtool.sver_c_wrapper --test-test_sver_wrapper
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --rebuild-sver
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --dummy
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=easy1.png --fname2=easy2.png
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=easy1.png --fname2=hard3.png
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=carl.jpg --fname2=hard3.png
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.sver_c_wrapper import * # NOQA
>>> test_sver_wrapper()
Ignore:
%timeit call_python_version(*args)
%timeit get_affine_inliers_cpp(*args)
"""
import vtool.spatial_verification as sver
import vtool.tests.dummy as dummy
xy_thresh_sqrd = ktool.KPTS_DTYPE(.4)
scale_thresh_sqrd = ktool.KPTS_DTYPE(2.0)
ori_thresh = ktool.KPTS_DTYPE(TAU / 4.0)
keys = 'xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh'.split(', ')
print(ut.dict_str(ut.dict_subset(locals(), keys)))
def report_errors():
pass
if ut.get_argflag('--dummy'):
testtup = dummy.testdata_dummy_matches()
(kpts1, kpts2, fm_input, fs_input, rchip1, rchip2) = testtup
fm_input = fm_input.astype(fm_dtype)
#fm_input = fm_input[0:10].astype(fm_dtype)
#fs_input = fs_input[0:10].astype(np.float32)
else:
fname1 = ut.get_argval('--fname1', type_=str, default='easy1.png')
fname2 = ut.get_argval('--fname2', type_=str, default='easy2.png')
testtup = dummy.testdata_ratio_matches(fname1, fname2)
(kpts1, kpts2, fm_input, fs_input, rchip1, rchip2) = testtup
# pack up call to aff hypothesis
import vtool as vt
import scipy.stats.mstats
scales1 = vt.get_scales(kpts1.take(fm_input.T[0], axis=0))
scales2 = vt.get_scales(kpts2.take(fm_input.T[1], axis=0))
#fs_input = 1 / scipy.stats.mstats.gmean(np.vstack((scales1, scales2)))
fs_input = scipy.stats.mstats.gmean(np.vstack((scales1, scales2)))
print('fs_input = ' + ut.numpy_str(fs_input))
#fs_input[0:-9] = 0
#fs_input = np.ones(len(fm_input), dtype=fs_dtype)
#ut.embed()
#fs_input = scales1 * scales2
args = (kpts1, kpts2, fm_input, fs_input, xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh)
ex_list = []
try:
with ut.Indenter('[TEST1] '):
inlier_tup = vt.compare_implementations(
sver.get_affine_inliers,
get_affine_inliers_cpp,
args, lbl1='py', lbl2='c',
output_lbl=('aff_inliers_list', 'aff_errors_list', 'Aff_mats')
)
out_inliers, out_errors, out_mats = inlier_tup
except AssertionError as ex:
ex_list.append(ex)
raise
try:
import functools
with ut.Indenter('[TEST2] '):
bestinlier_tup = vt.compare_implementations(
functools.partial(sver.get_best_affine_inliers, forcepy=True),
get_best_affine_inliers_cpp,
args, show_output=True, lbl1='py', lbl2='c',
output_lbl=('bestinliers', 'besterror', 'bestmat')
)
bestinliers, besterror, bestmat = bestinlier_tup
except AssertionError as ex:
ex_list.append(ex)
raise
if len(ex_list) > 0:
raise AssertionError('some tests failed. see previous stdout')
#num_inliers_list = np.array(map(len, out_inliers_c))
#best_argx = num_inliers_list.argmax()
##best_inliers_py = out_inliers_py[best_argx]
#best_inliers_c = out_inliers_c[best_argx]
if ut.show_was_requested():
import plottool as pt
fm_output = fm_input.take(bestinliers, axis=0)
fnum = pt.next_fnum()
pt.figure(fnum=fnum, doclf=True, docla=True)
pt.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm_input, ell_linewidth=5, fnum=fnum, pnum=(2, 1, 1))
pt.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm_output, ell_linewidth=5, fnum=fnum, pnum=(2, 1, 2))
pt.show_if_requested()
def warp_patch_onto_kpts(kpts,
patch,
chipshape,
weights=None,
out=None,
cov_scale_factor=.2,
cov_agg_mode='max',
cov_remove_shape=False,
cov_remove_scale=False,
cov_size_penalty_on=True,
cov_size_penalty_power=.5,
cov_size_penalty_frac=.1):
r"""
Overlays the source image onto a destination image in each keypoint location
Args:
kpts (ndarray[float32_t, ndim=2]): keypoints
patch (ndarray): patch to warp (like gaussian)
chipshape (tuple):
weights (ndarray): score for every keypoint
Kwargs:
cov_scale_factor (float):
Returns:
ndarray: mask
CommandLine:
python -m vtool.coverage_kpts --test-warp_patch_onto_kpts
python -m vtool.coverage_kpts --test-warp_patch_onto_kpts --show
python -m vtool.coverage_kpts --test-warp_patch_onto_kpts --show --hole
python -m vtool.coverage_kpts --test-warp_patch_onto_kpts --show --square
python -m vtool.coverage_kpts --test-warp_patch_onto_kpts --show --square --hole
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.coverage_kpts import * # NOQA
>>> import vtool as vt
>>> import pyhesaff
>>> img_fpath = ut.grab_test_imgpath('carl.jpg')
>>> (kpts, vecs) = pyhesaff.detect_feats(img_fpath)
>>> kpts = kpts[::15]
>>> chip = vt.imread(img_fpath)
>>> chipshape = chip.shape
>>> weights = np.ones(len(kpts))
>>> cov_scale_factor = 1.0
>>> srcshape = (19, 19)
>>> radius = srcshape[0] / 2.0
>>> sigma = 0.4 * radius
>>> SQUARE = ut.get_argflag('--square')
>>> HOLE = ut.get_argflag('--hole')
>>> if SQUARE:
>>> patch = np.ones(srcshape)
>>> else:
>>> patch = ptool.gaussian_patch(shape=srcshape, sigma=sigma) #, norm_01=False)
>>> patch = patch / patch.max()
>>> if HOLE:
>>> patch[int(patch.shape[0] / 2), int(patch.shape[1] / 2)] = 0
>>> # execute function
>>> dstimg = warp_patch_onto_kpts(kpts, patch, chipshape, weights, cov_scale_factor=cov_scale_factor)
>>> # verify results
>>> print('dstimg stats %r' % (ut.get_stats_str(dstimg, axis=None)),)
>>> print('patch stats %r' % (ut.get_stats_str(patch, axis=None)),)
>>> #print(patch.sum())
>>> assert np.all(ut.inbounds(dstimg, 0, 1, eq=True))
>>> # show results
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> mask = dstimg
>>> show_coverage_map(chip, mask, patch, kpts)
>>> pt.show_if_requested()
"""
import vtool as vt
#if len(kpts) == 0:
# return None
chip_scale_h = int(np.ceil(chipshape[0] * cov_scale_factor))
chip_scale_w = int(np.ceil(chipshape[1] * cov_scale_factor))
if len(kpts) == 0:
dstimg = np.zeros((chip_scale_h, chip_scale_w))
return dstimg
if weights is None:
weights = np.ones(len(kpts))
dsize = (chip_scale_w, chip_scale_h)
# Allocate destination image
patch_shape = patch.shape
# Scale keypoints into destination image
# <HACK>
if cov_remove_shape:
# disregard affine information in keypoints
# i still dont understand why we are trying this
(patch_h, patch_w) = patch_shape
half_width = (patch_w / 2.0) # - .5
half_height = (patch_h / 2.0) # - .5
# Center src image
T1 = vt.translation_mat3x3(-half_width + .5, -half_height + .5)
# Scale src to the unit circle
if not cov_remove_scale:
S1 = vt.scale_mat3x3(1.0 / half_width, 1.0 / half_height)
# Transform the source image to the keypoint ellipse
kpts_T = np.array(
[vt.translation_mat3x3(x, y) for (x, y) in vt.get_xys(kpts).T])
if not cov_remove_scale:
kpts_S = np.array([
vt.scale_mat3x3(np.sqrt(scale))
for scale in vt.get_scales(kpts).T
])
# Adjust for the requested scale factor
S2 = vt.scale_mat3x3(cov_scale_factor, cov_scale_factor)
#perspective_list = [S2.dot(A).dot(S1).dot(T1) for A in invVR_aff2Ds]
if not cov_remove_scale:
M_list = reduce(vt.matrix_multiply, (S2, kpts_T, kpts_S, S1, T1))
else:
M_list = reduce(vt.matrix_multiply, (S2, kpts_T, T1))
# </HACK>
else:
M_list = ktool.get_transforms_from_patch_image_kpts(
kpts, patch_shape, cov_scale_factor)
affmat_list = M_list[:, 0:2, :]
weight_list = weights
# For each keypoint warp a gaussian scaled by the feature score into the image
warped_patch_iter = warped_patch_generator(
patch,
dsize,
affmat_list,
weight_list,
cov_size_penalty_on=cov_size_penalty_on,
cov_size_penalty_power=cov_size_penalty_power,
cov_size_penalty_frac=cov_size_penalty_frac)
# Either max or sum
if cov_agg_mode == 'max':
dstimg = vt.iter_reduce_ufunc(np.maximum, warped_patch_iter, out=out)
elif cov_agg_mode == 'sum':
dstimg = vt.iter_reduce_ufunc(np.add, warped_patch_iter, out=out)
# HACK FOR SUM: DO NOT DO THIS FOR MAX
dstimg[dstimg > 1.0] = 1.0
else:
raise AssertionError('Unknown cov_agg_mode=%r' % (cov_agg_mode, ))
return dstimg
def scale(self):
return vt.get_scales(self.kparr)
def test_sver_wrapper():
"""
Test to ensure cpp and python agree and that cpp is faster
CommandLine:
python -m vtool.sver_c_wrapper --test-test_sver_wrapper
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --rebuild-sver
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --dummy
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=easy1.png --fname2=easy2.png
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=easy1.png --fname2=hard3.png
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=carl.jpg --fname2=hard3.png
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.sver_c_wrapper import * # NOQA
>>> test_sver_wrapper()
Ignore:
%timeit call_python_version(*args)
%timeit get_affine_inliers_cpp(*args)
"""
import vtool.spatial_verification as sver
import vtool.tests.dummy as dummy
xy_thresh_sqrd = ktool.KPTS_DTYPE(.4)
scale_thresh_sqrd = ktool.KPTS_DTYPE(2.0)
ori_thresh = ktool.KPTS_DTYPE(TAU / 4.0)
keys = 'xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh'.split(', ')
print(ut.dict_str(ut.dict_subset(locals(), keys)))
def report_errors():
pass
if ut.get_argflag('--dummy'):
testtup = dummy.testdata_dummy_matches()
(kpts1, kpts2, fm_input, fs_input, rchip1, rchip2) = testtup
fm_input = fm_input.astype(fm_dtype)
#fm_input = fm_input[0:10].astype(fm_dtype)
#fs_input = fs_input[0:10].astype(np.float32)
else:
fname1 = ut.get_argval('--fname1', type_=str, default='easy1.png')
fname2 = ut.get_argval('--fname2', type_=str, default='easy2.png')
testtup = dummy.testdata_ratio_matches(fname1, fname2)
(kpts1, kpts2, fm_input, fs_input, rchip1, rchip2) = testtup
# pack up call to aff hypothesis
import vtool as vt
import scipy.stats.mstats
scales1 = vt.get_scales(kpts1.take(fm_input.T[0], axis=0))
scales2 = vt.get_scales(kpts2.take(fm_input.T[1], axis=0))
#fs_input = 1 / scipy.stats.mstats.gmean(np.vstack((scales1, scales2)))
fs_input = scipy.stats.mstats.gmean(np.vstack((scales1, scales2)))
print('fs_input = ' + ut.numpy_str(fs_input))
#fs_input[0:-9] = 0
#fs_input = np.ones(len(fm_input), dtype=fs_dtype)
#ut.embed()
#fs_input = scales1 * scales2
args = (kpts1, kpts2, fm_input, fs_input, xy_thresh_sqrd,
scale_thresh_sqrd, ori_thresh)
ex_list = []
try:
with ut.Indenter('[TEST1] '):
inlier_tup = vt.compare_implementations(
sver.get_affine_inliers,
get_affine_inliers_cpp,
args,
lbl1='py',
lbl2='c',
output_lbl=('aff_inliers_list', 'aff_errors_list', 'Aff_mats'))
out_inliers, out_errors, out_mats = inlier_tup
except AssertionError as ex:
ex_list.append(ex)
raise
try:
import functools
with ut.Indenter('[TEST2] '):
bestinlier_tup = vt.compare_implementations(
functools.partial(sver.get_best_affine_inliers, forcepy=True),
get_best_affine_inliers_cpp,
args,
show_output=True,
lbl1='py',
lbl2='c',
output_lbl=('bestinliers', 'besterror', 'bestmat'))
bestinliers, besterror, bestmat = bestinlier_tup
except AssertionError as ex:
ex_list.append(ex)
raise
if len(ex_list) > 0:
raise AssertionError('some tests failed. see previous stdout')
#num_inliers_list = np.array(map(len, out_inliers_c))
#best_argx = num_inliers_list.argmax()
##best_inliers_py = out_inliers_py[best_argx]
#best_inliers_c = out_inliers_c[best_argx]
if ut.show_was_requested():
import plottool as pt
fm_output = fm_input.take(bestinliers, axis=0)
fnum = pt.next_fnum()
pt.figure(fnum=fnum, doclf=True, docla=True)
pt.show_chipmatch2(rchip1,
rchip2,
kpts1,
kpts2,
fm_input,
ell_linewidth=5,
fnum=fnum,
pnum=(2, 1, 1))
pt.show_chipmatch2(rchip1,
rchip2,
kpts1,
kpts2,
fm_output,
ell_linewidth=5,
fnum=fnum,
pnum=(2, 1, 2))
pt.show_if_requested()
def _annotate_kpts(kpts_, sel_fx=None, **kwargs):
r"""
Args:
kpts_ (ndarray): keypoints
sel_fx (None):
Keywords:
color: 3/4-tuple, ndarray, or str
Returns:
None
Example:
>>> from plottool.viz_keypoints import * # NOQA
>>> sel_fx = None
>>> kpts = np.array([[ 92.9246, 17.5453, 7.8103, -3.4594, 10.8566, 0. ],
... [ 76.8585, 24.7918, 11.4412, -3.2634, 9.6287, 0. ],
... [ 140.6303, 24.9027, 10.4051, -10.9452, 10.5991, 0. ],])
"""
if len(kpts_) == 0:
print('len(kpts_) == 0...')
return
#color = kwargs.get('color', 'distinct' if sel_fx is None else df2.ORANGE)
color = kwargs.get('color', 'scale' if sel_fx is None else df2.ORANGE)
if color == 'distinct':
# hack for distinct colors
color = df2.distinct_colors(len(kpts_)) # , randomize=True)
elif color == 'scale':
# hack for distinct colors
import vtool as vt
#color = df2.scores_to_color(vt.get_scales(kpts_), cmap_='inferno', score_range=(0, 50))
color = df2.scores_to_color(vt.get_scales(kpts_), cmap_='viridis', score_range=(5, 30), cmap_range=None)
#df2.distinct_colors(len(kpts_)) # , randomize=True)
# Keypoint drawing kwargs
drawkpts_kw = {
'ell': True,
'pts': False,
'ell_alpha': .4,
'ell_linewidth': 2,
'ell_color': color,
}
drawkpts_kw.update(kwargs)
# draw all keypoints
if sel_fx is None:
df2.draw_kpts2(kpts_, **drawkpts_kw)
else:
# dont draw the selected keypoint in this batch
nonsel_kpts_ = np.vstack((kpts_[0:sel_fx], kpts_[sel_fx + 1:]))
# Draw selected keypoint
sel_kpts = kpts_[sel_fx:sel_fx + 1]
import utool as ut
if ut.isiterable(color) and ut.isiterable(color[0]):
# hack for distinct colors
drawkpts_kw['ell_color'] = color[0:sel_fx] + color[sel_fx + 1:]
drawkpts_kw
drawkpts_kw2 = drawkpts_kw.copy()
drawkpts_kw2.update({
'ell_color': df2.BLUE,
'eig': True,
'rect': True,
'ori': True,
})
df2.draw_kpts2(nonsel_kpts_, **drawkpts_kw)
df2.draw_kpts2(sel_kpts, **drawkpts_kw2)