def get_patches(invassign, wx):
ax_list = invassign.wx2_axs[wx]
fx_list = invassign.wx2_fxs[wx]
config = invassign.fstack.config
ibs = invassign.fstack.ibs
unique_axs, groupxs = vt.group_indices(ax_list)
fxs_groups = vt.apply_grouping(fx_list, groupxs)
unique_aids = ut.take(invassign.fstack.ax2_aid, unique_axs)
all_kpts_list = ibs.depc.d.get_feat_kpts(unique_aids, config=config)
sub_kpts_list = vt.ziptake(all_kpts_list, fxs_groups, axis=0)
chip_list = ibs.depc_annot.d.get_chips_img(unique_aids)
# convert to approprate colorspace
#if colorspace is not None:
# chip_list = vt.convert_image_list_colorspace(chip_list, colorspace)
# ut.print_object_size(chip_list, 'chip_list')
patch_size = 64
grouped_patches_list = [
vt.get_warped_patches(chip, kpts, patch_size=patch_size)[0]
for chip, kpts in ut.ProgIter(zip(chip_list, sub_kpts_list),
nTotal=len(unique_aids),
lbl='warping patches')
]
# Make it correspond with original fx_list and ax_list
word_patches = vt.invert_apply_grouping(grouped_patches_list, groupxs)
return word_patches
def get_patches(invassign, wx):
ax_list = invassign.wx2_axs[wx]
fx_list = invassign.wx2_fxs[wx]
config = invassign.fstack.config
ibs = invassign.fstack.ibs
unique_axs, groupxs = vt.group_indices(ax_list)
fxs_groups = vt.apply_grouping(fx_list, groupxs)
unique_aids = ut.take(invassign.fstack.ax2_aid, unique_axs)
all_kpts_list = ibs.depc.d.get_feat_kpts(unique_aids, config=config)
sub_kpts_list = vt.ziptake(all_kpts_list, fxs_groups, axis=0)
chip_list = ibs.depc_annot.d.get_chips_img(unique_aids)
# convert to approprate colorspace
#if colorspace is not None:
# chip_list = vt.convert_image_list_colorspace(chip_list, colorspace)
# ut.print_object_size(chip_list, 'chip_list')
patch_size = 64
grouped_patches_list = [
vt.get_warped_patches(chip, kpts, patch_size=patch_size)[0]
for chip, kpts in ut.ProgIter(zip(chip_list, sub_kpts_list),
nTotal=len(unique_aids),
lbl='warping patches')
]
# Make it correspond with original fx_list and ax_list
word_patches = vt.invert_apply_grouping(grouped_patches_list, groupxs)
return word_patches
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 get_annot_kpts_distinctiveness(ibs, aid_list, config2_=None, **kwargs):
"""
very hacky, but cute way to cache keypoint distinctivness
Args:
ibs (IBEISController): ibeis controller object
aid_list (list):
dstncvs_normer (None):
Returns:
list: dstncvs_list
CommandLine:
python -m ibeis.control.manual_ibeiscontrol_funcs --test-get_annot_kpts_distinctiveness
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.control.manual_ibeiscontrol_funcs import * # NOQA
>>> from ibeis.algo.hots import distinctiveness_normalizer
>>> import ibeis
>>> import numpy as np
>>> config2_ = None
>>> # build test data
>>> ibs = ibeis.opendb('testdb1')
>>> aid_list = ibs.get_valid_aids(species=const.TEST_SPECIES.ZEB_PLAIN)
>>> # execute function
>>> aid_list1 = aid_list[::2]
>>> aid_list2 = aid_list[1::3]
>>> dstncvs_list1 = get_annot_kpts_distinctiveness(ibs, aid_list1)
>>> dstncvs_list2 = get_annot_kpts_distinctiveness(ibs, aid_list2)
>>> dstncvs_list = get_annot_kpts_distinctiveness(ibs, aid_list)
>>> print(ut.depth_profile(dstncvs_list1))
>>> stats_dict = ut.dict_stack([ut.get_stats(dstncvs) for dstncvs in dstncvs_list])
>>> print(ut.dict_str(stats_dict))
>>> assert np.all(np.array(stats_dict['min']) >= 0), 'distinctiveness was out of bounds'
>>> assert np.all(np.array(stats_dict['max']) <= 1), 'distinctiveness was out of bounds'
"""
from ibeis.algo.hots import distinctiveness_normalizer as dcvs_normer
# per-species disinctivness wrapper around ibeis cached function
# get feature rowids
aid_list = np.array(aid_list)
fid_list = np.array(
ibs.get_annot_feat_rowids(aid_list,
ensure=True,
eager=True,
nInput=None,
config2_=config2_))
species_rowid_list = np.array(ibs.get_annot_species_rowids(aid_list))
# Compute distinctivness separately for each species
unique_sids, groupxs = vt.group_indices(species_rowid_list)
fids_groups = vt.apply_grouping(fid_list, groupxs)
species_text_list = ibs.get_species_texts(unique_sids)
# Map distinctivness computation
normer_list = [
dcvs_normer.request_species_distinctiveness_normalizer(species)
for species in species_text_list
]
# Reduce to get results
dstncvs_groups = [
get_feat_kpts_distinctiveness(ibs,
fids,
dstncvs_normer=dstncvs_normer,
species_rowid=sid,
**kwargs) for dstncvs_normer, fids, sid
in zip(normer_list, fids_groups, unique_sids)
]
dstncvs_list = vt.invert_apply_grouping(dstncvs_groups, groupxs)
return dstncvs_list
def get_annot_kpts_distinctiveness(ibs, aid_list, config2_=None, **kwargs):
"""
very hacky, but cute way to cache keypoint distinctivness
Args:
ibs (IBEISController): ibeis controller object
aid_list (list):
dstncvs_normer (None):
Returns:
list: dstncvs_list
CommandLine:
python -m ibeis.control.manual_ibeiscontrol_funcs --test-get_annot_kpts_distinctiveness
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.control.manual_ibeiscontrol_funcs import * # NOQA
>>> from ibeis.algo.hots import distinctiveness_normalizer
>>> import ibeis
>>> import numpy as np
>>> config2_ = None
>>> # build test data
>>> ibs = ibeis.opendb('testdb1')
>>> aid_list = ibs.get_valid_aids(species=const.TEST_SPECIES.ZEB_PLAIN)
>>> # execute function
>>> aid_list1 = aid_list[::2]
>>> aid_list2 = aid_list[1::3]
>>> dstncvs_list1 = get_annot_kpts_distinctiveness(ibs, aid_list1)
>>> dstncvs_list2 = get_annot_kpts_distinctiveness(ibs, aid_list2)
>>> dstncvs_list = get_annot_kpts_distinctiveness(ibs, aid_list)
>>> print(ut.depth_profile(dstncvs_list1))
>>> stats_dict = ut.dict_stack([ut.get_stats(dstncvs) for dstncvs in dstncvs_list])
>>> print(ut.dict_str(stats_dict))
>>> assert np.all(np.array(stats_dict['min']) >= 0), 'distinctiveness was out of bounds'
>>> assert np.all(np.array(stats_dict['max']) <= 1), 'distinctiveness was out of bounds'
"""
from ibeis.algo.hots import distinctiveness_normalizer as dcvs_normer
# per-species disinctivness wrapper around ibeis cached function
# get feature rowids
aid_list = np.array(aid_list)
fid_list = np.array(ibs.get_annot_feat_rowids(aid_list, ensure=True,
eager=True, nInput=None,
config2_=config2_))
species_rowid_list = np.array(ibs.get_annot_species_rowids(aid_list))
# Compute distinctivness separately for each species
unique_sids, groupxs = vt.group_indices(species_rowid_list)
fids_groups = vt.apply_grouping(fid_list, groupxs)
species_text_list = ibs.get_species_texts(unique_sids)
# Map distinctivness computation
normer_list = [dcvs_normer.request_species_distinctiveness_normalizer(species)
for species in species_text_list]
# Reduce to get results
dstncvs_groups = [
get_feat_kpts_distinctiveness(ibs, fids, dstncvs_normer=dstncvs_normer,
species_rowid=sid, **kwargs)
for dstncvs_normer, fids, sid in zip(normer_list, fids_groups, unique_sids)
]
dstncvs_list = vt.invert_apply_grouping(dstncvs_groups, groupxs)
return dstncvs_list
