def get_namescore_nonvoting_feature_flags(fm_list, fs_list, dnid_list, name_groupxs, kpts1=None):
r"""
fm_list = [fm[:min(len(fm), 10)] for fm in fm_list]
fs_list = [fs[:min(len(fs), 10)] for fs in fs_list]
"""
fx1_list = [fm.T[0] for fm in fm_list]
# Group annotation matches by name
name_grouped_fx1_list = vt.apply_grouping_(fx1_list, name_groupxs)
name_grouped_fs_list = vt.apply_grouping_(fs_list, name_groupxs)
# Stack up all matches to a particular name, keep track of original indicies via offets
name_invertable_flat_fx1_list = list(map(ut.invertible_flatten2_numpy, name_grouped_fx1_list))
name_grouped_fx1_flat = ut.get_list_column(name_invertable_flat_fx1_list, 0)
name_grouped_invertable_cumsum_list = ut.get_list_column(name_invertable_flat_fx1_list, 1)
name_grouped_fs_flat = list(map(np.hstack, name_grouped_fs_list))
if kpts1 is not None:
xys1_ = vt.get_xys(kpts1).T
kpts_xyid_list = vt.compute_unique_data_ids(xys1_)
# Make nested group for every name by query feature index (accounting for duplicate orientation)
name_grouped_xyid_flat = list(kpts_xyid_list.take(fx1) for fx1 in name_grouped_fx1_flat)
xyid_groupxs_list = list(vt.group_indices(xyid_flat)[1] for xyid_flat in name_grouped_xyid_flat)
name_group_fx1_groupxs_list = xyid_groupxs_list
else:
# Make nested group for every name by query feature index
fx1_groupxs_list = [vt.group_indices(fx1_flat)[1] for fx1_flat in name_grouped_fx1_flat]
name_group_fx1_groupxs_list = fx1_groupxs_list
name_grouped_fid_grouped_fs_list = [
vt.apply_grouping(fs_flat, fid_groupxs)
for fs_flat, fid_groupxs in zip(name_grouped_fs_flat, name_group_fx1_groupxs_list)
]
# Flag which features are valid in this grouped space. Only one keypoint should be able to vote
# for each group
name_grouped_fid_grouped_isvalid_list = [
np.array([fs_group.max() == fs_group for fs_group in fid_grouped_fs_list])
for fid_grouped_fs_list in name_grouped_fid_grouped_fs_list
]
# Go back to being grouped only in name space
#dtype = np.bool
name_grouped_isvalid_flat_list = [
vt.invert_apply_grouping2(fid_grouped_isvalid_list, fid_groupxs, dtype=np.bool)
for fid_grouped_isvalid_list, fid_groupxs in zip(name_grouped_fid_grouped_isvalid_list, name_group_fx1_groupxs_list)
]
name_grouped_isvalid_unflat_list = [
ut.unflatten2(isvalid_flat, invertable_cumsum_list)
for isvalid_flat, invertable_cumsum_list in zip(name_grouped_isvalid_flat_list, name_grouped_invertable_cumsum_list)
]
# Reports which features were valid in name scoring for every annotation
featflag_list = vt.invert_apply_grouping(name_grouped_isvalid_unflat_list, name_groupxs)
return featflag_list
def compute_and_write_probchip(ibs, aid_list, config2_=None, lazy=True):
""" Computes probability chips using pyrf
CommandLine:
python -m ibeis.algo.preproc.preproc_probchip --test-compute_and_write_probchip:0 --show
python -m ibeis.algo.preproc.preproc_probchip --test-compute_and_write_probchip:1
python -m ibeis.algo.preproc.preproc_probchip --test-compute_and_write_probchip:2 --show --cnn
Example0:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.preproc.preproc_probchip import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb('PZ_MTEST')
>>> config2_ = None
>>> lazy = True
>>> aid_list = ibs.get_valid_aids(species=ibeis.const.TEST_SPECIES.ZEB_PLAIN)[0:4]
>>> probchip_fpath_list_ = compute_and_write_probchip(ibs, aid_list, config2_, lazy=lazy)
>>> result = ut.list_str(probchip_fpath_list_)
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> iteract_obj = pt.interact_multi_image.MultiImageInteraction(probchip_fpath_list_, nPerPage=4)
>>> ut.show_if_requested()
Example1:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.preproc.preproc_probchip import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb('testdb1')
>>> config2_ = None
>>> lazy = False
>>> aid_list = ibs.get_valid_aids(species=ibeis.const.TEST_SPECIES.ZEB_PLAIN)
>>> probchip_fpath_list_ = compute_and_write_probchip(ibs, aid_list, config2_, lazy=lazy)
>>> result = ut.list_str(probchip_fpath_list_)
>>> print(result)
Example2:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.preproc.preproc_probchip import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb('testdb1')
>>> config2_ = ibs.new_query_params({'featweight_detector': 'cnn'})
>>> lazy = True
>>> aid_list = ibs.get_valid_aids()
>>> probchip_fpath_list_ = compute_and_write_probchip(ibs, aid_list, config2_, lazy=lazy)
>>> result = ut.list_str(probchip_fpath_list_)
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> iteract_obj = pt.interact_multi_image.MultiImageInteraction(probchip_fpath_list_, nPerPage=4)
>>> ut.show_if_requested()
Dev::
#ibs.delete_annot_chips(aid_list)
#probchip_fpath_list = get_annot_probchip_fpath_list(ibs, aid_list)
"""
# Get probchip dest information (output path)
# TODO; properly ungroup output
grouped_aids, unique_species, groupxs = group_aids_by_featweight_species(
ibs, aid_list, config2_)
nSpecies = len(unique_species)
nTasks = len(aid_list)
print(('[preproc_probchip.compute_and_write_probchip] '
'Preparing to compute %d probchips of %d species')
% (nTasks, nSpecies))
cachedir = ibs.get_probchip_dir()
ut.ensuredir(cachedir)
grouped_probchip_fpath_list = []
if ut.VERBOSE:
print('[preproc_probchip] +--------------------')
for aids, species in zip(grouped_aids, unique_species):
if ut.VERBOSE:
print('[preproc_probchip] Computing probchips for species=%r' % species)
print('[preproc_probchip] |--------------------')
if len(aids) == 0:
continue
probchip_fpaths = get_annot_probchip_fpath_list(ibs, aids,
config2_=config2_,
species=species)
if lazy:
# Filter out probchips that are already on disk
# pyrf used to do this, now we need to do it
# caching should be implicit due to using the visual_annot_uuid in
# the filename
isdirty_list = ut.not_list(map(exists, probchip_fpaths))
dirty_aids = ut.compress(aids, isdirty_list)
dirty_probchip_fpath_list = ut.compress(probchip_fpaths, isdirty_list)
print(('[preproc_probchip.compute_and_write_probchip]'
' Lazy compute of to compute %d/%d of species=%s') %
(len(dirty_aids), len(aids), species))
else:
# No filtering
dirty_aids = aids
dirty_probchip_fpath_list = probchip_fpaths
if len(dirty_aids) > 0:
write_dirty_aids(ibs, dirty_probchip_fpath_list, dirty_aids, config2_, species)
grouped_probchip_fpath_list.append(probchip_fpaths)
if ut.VERBOSE:
print('[preproc_probchip] Done computing probability images')
print('[preproc_probchip] L_______________________')
probchip_fpath_list = vt.invert_apply_grouping2(
grouped_probchip_fpath_list, groupxs, dtype=object)
return probchip_fpath_list
def compute_and_write_probchip(ibs, aid_list, config2_=None, lazy=True):
""" Computes probability chips using pyrf
CommandLine:
python -m ibeis.algo.preproc.preproc_probchip --test-compute_and_write_probchip:0 --show
python -m ibeis.algo.preproc.preproc_probchip --test-compute_and_write_probchip:1
python -m ibeis.algo.preproc.preproc_probchip --test-compute_and_write_probchip:2 --show --cnn
Example0:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.preproc.preproc_probchip import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb('PZ_MTEST')
>>> config2_ = None
>>> lazy = True
>>> aid_list = ibs.get_valid_aids(species=ibeis.const.TEST_SPECIES.ZEB_PLAIN)[0:4]
>>> probchip_fpath_list_ = compute_and_write_probchip(ibs, aid_list, config2_, lazy=lazy)
>>> result = ut.list_str(probchip_fpath_list_)
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> iteract_obj = pt.interact_multi_image.MultiImageInteraction(probchip_fpath_list_, nPerPage=4)
>>> ut.show_if_requested()
Example1:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.preproc.preproc_probchip import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb('testdb1')
>>> config2_ = None
>>> lazy = False
>>> aid_list = ibs.get_valid_aids(species=ibeis.const.TEST_SPECIES.ZEB_PLAIN)
>>> probchip_fpath_list_ = compute_and_write_probchip(ibs, aid_list, config2_, lazy=lazy)
>>> result = ut.list_str(probchip_fpath_list_)
>>> print(result)
Example2:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.preproc.preproc_probchip import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb('testdb1')
>>> config2_ = ibs.new_query_params({'fw_detector': 'cnn'})
>>> lazy = True
>>> aid_list = ibs.get_valid_aids()
>>> probchip_fpath_list_ = compute_and_write_probchip(ibs, aid_list, config2_, lazy=lazy)
>>> result = ut.list_str(probchip_fpath_list_)
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> iteract_obj = pt.interact_multi_image.MultiImageInteraction(probchip_fpath_list_, nPerPage=4)
>>> ut.show_if_requested()
Dev::
#ibs.delete_annot_chips(aid_list)
#probchip_fpath_list = get_annot_probchip_fpath_list(ibs, aid_list)
"""
# Get probchip dest information (output path)
# TODO; properly ungroup output
grouped_aids, unique_species, groupxs = group_aids_by_featweight_species(
ibs, aid_list, config2_)
nSpecies = len(unique_species)
nTasks = len(aid_list)
print(('[preproc_probchip.compute_and_write_probchip] '
'Preparing to compute %d probchips of %d species') %
(nTasks, nSpecies))
cachedir = ibs.get_probchip_dir()
ut.ensuredir(cachedir)
grouped_probchip_fpath_list = []
if ut.VERBOSE:
print('[preproc_probchip] +--------------------')
for aids, species in zip(grouped_aids, unique_species):
if ut.VERBOSE:
print('[preproc_probchip] Computing probchips for species=%r' %
species)
print('[preproc_probchip] |--------------------')
if len(aids) == 0:
continue
probchip_fpaths = get_annot_probchip_fpath_list(ibs,
aids,
config2_=config2_,
species=species)
if lazy:
# Filter out probchips that are already on disk
# pyrf used to do this, now we need to do it
# caching should be implicit due to using the visual_annot_uuid in
# the filename
isdirty_list = ut.not_list(map(exists, probchip_fpaths))
dirty_aids = ut.compress(aids, isdirty_list)
dirty_probchip_fpath_list = ut.compress(probchip_fpaths,
isdirty_list)
print(('[preproc_probchip.compute_and_write_probchip]'
' Lazy compute of to compute %d/%d of species=%s') %
(len(dirty_aids), len(aids), species))
else:
# No filtering
dirty_aids = aids
dirty_probchip_fpath_list = probchip_fpaths
if len(dirty_aids) > 0:
write_dirty_aids(ibs, dirty_probchip_fpath_list, dirty_aids,
config2_, species)
grouped_probchip_fpath_list.append(probchip_fpaths)
if ut.VERBOSE:
print('[preproc_probchip] Done computing probability images')
print('[preproc_probchip] L_______________________')
probchip_fpath_list = vt.invert_apply_grouping2(
grouped_probchip_fpath_list, groupxs, dtype=object)
return probchip_fpath_list
