def get_patches(inva, wx, ibs, verbose=True):
"""
Loads the patches assigned to a particular word in this stack
>>> inva.wx_to_aids = inva.compute_inverted_list()
>>> verbose=True
"""
config = inva.config
aid_list = inva.wx_to_aids[wx]
X_list = [inva.get_annot(aid) for aid in aid_list]
fxs_groups = [X.fxs(wx) for X in X_list]
all_kpts_list = ibs.depc.d.get_feat_kpts(aid_list, config=config)
sub_kpts_list = vt.ziptake(all_kpts_list, fxs_groups, axis=0)
total_patches = sum(ut.lmap(len, fxs_groups))
chip_list = ibs.depc_annot.d.get_chips_img(aid_list, config=config)
# 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
shape = (total_patches, patch_size, patch_size, 3)
_prog = ut.ProgPartial(enabled=verbose, lbl='warping patches', bs=True)
_patchiter = ut.iflatten(
[
vt.get_warped_patches(chip, kpts, patch_size=patch_size)[0]
# vt.get_warped_patches(chip, kpts, patch_size=patch_size, use_cpp=True)[0]
for chip, kpts in _prog(
zip(chip_list, sub_kpts_list), length=len(aid_list)
)
]
)
word_patches = vt.fromiter_nd(_patchiter, shape, dtype=np.uint8)
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_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_name_shortlist_aids(
daid_list,
dnid_list,
annot_score_list,
name_score_list,
nid2_nidx,
nNameShortList,
nAnnotPerName,
):
r"""
CommandLine:
python -m wbia.algo.hots.scoring --test-get_name_shortlist_aids
Example:
>>> # ENABLE_DOCTEST
>>> from wbia.algo.hots.scoring import * # NOQA
>>> daid_list = np.array([11, 12, 13, 14, 15, 16, 17])
>>> dnid_list = np.array([21, 21, 21, 22, 22, 23, 24])
>>> annot_score_list = np.array([ 6, 2, 3, 5, 6, 3, 2])
>>> name_score_list = np.array([ 8, 9, 5, 4])
>>> nid2_nidx = {21:0, 22:1, 23:2, 24:3}
>>> nNameShortList, nAnnotPerName = 3, 2
>>> args = (daid_list, dnid_list, annot_score_list, name_score_list,
... nid2_nidx, nNameShortList, nAnnotPerName)
>>> top_daids = get_name_shortlist_aids(*args)
>>> result = str(top_daids)
>>> print(result)
[15, 14, 11, 13, 16]
"""
unique_nids, groupxs = vt.group_indices(np.array(dnid_list))
grouped_annot_scores = vt.apply_grouping(annot_score_list, groupxs)
grouped_daids = vt.apply_grouping(np.array(daid_list), groupxs)
# Ensure name score list is aligned with the unique_nids
aligned_name_score_list = name_score_list.take(
ut.dict_take(nid2_nidx, unique_nids))
# Sort each group by the name score
group_sortx = aligned_name_score_list.argsort()[::-1]
_top_daid_groups = ut.take(grouped_daids, group_sortx)
_top_annot_score_groups = ut.take(grouped_annot_scores, group_sortx)
top_daid_groups = ut.listclip(_top_daid_groups, nNameShortList)
top_annot_score_groups = ut.listclip(_top_annot_score_groups,
nNameShortList)
# Sort within each group by the annotation score
top_daid_sortx_groups = [
annot_score_group.argsort()[::-1]
for annot_score_group in top_annot_score_groups
]
top_sorted_daid_groups = vt.ziptake(top_daid_groups, top_daid_sortx_groups)
top_clipped_daids = [
ut.listclip(sorted_daid_group, nAnnotPerName)
for sorted_daid_group in top_sorted_daid_groups
]
top_daids = ut.flatten(top_clipped_daids)
return top_daids
def get_name_shortlist_aids(daid_list, dnid_list, annot_score_list,
name_score_list, nid2_nidx,
nNameShortList, nAnnotPerName):
r"""
CommandLine:
python -m ibeis.algo.hots.scoring --test-get_name_shortlist_aids
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.hots.scoring import * # NOQA
>>> # build test data
>>> daid_list = np.array([11, 12, 13, 14, 15, 16, 17])
>>> dnid_list = np.array([21, 21, 21, 22, 22, 23, 24])
>>> annot_score_list = np.array([ 6, 2, 3, 5, 6, 3, 2])
>>> name_score_list = np.array([ 8, 9, 5, 4])
>>> nid2_nidx = {21:0, 22:1, 23:2, 24:3}
>>> nNameShortList, nAnnotPerName = 3, 2
>>> # execute function
>>> args = (daid_list, dnid_list, annot_score_list, name_score_list,
... nid2_nidx, nNameShortList, nAnnotPerName)
>>> top_daids = get_name_shortlist_aids(*args)
>>> # verify results
>>> result = str(top_daids)
>>> print(result)
[15, 14, 11, 13, 16]
"""
unique_nids, groupxs = vt.group_indices(np.array(dnid_list))
grouped_annot_scores = vt.apply_grouping(annot_score_list, groupxs)
grouped_daids = vt.apply_grouping(np.array(daid_list), groupxs)
# Ensure name score list is aligned with the unique_nids
aligned_name_score_list = name_score_list.take(ut.dict_take(nid2_nidx, unique_nids))
# Sort each group by the name score
group_sortx = aligned_name_score_list.argsort()[::-1]
_top_daid_groups = ut.take(grouped_daids, group_sortx)
_top_annot_score_groups = ut.take(grouped_annot_scores, group_sortx)
top_daid_groups = ut.listclip(_top_daid_groups, nNameShortList)
top_annot_score_groups = ut.listclip(_top_annot_score_groups, nNameShortList)
# Sort within each group by the annotation score
top_daid_sortx_groups = [annot_score_group.argsort()[::-1]
for annot_score_group in top_annot_score_groups]
top_sorted_daid_groups = vt.ziptake(top_daid_groups, top_daid_sortx_groups)
top_clipped_daids = [ut.listclip(sorted_daid_group, nAnnotPerName)
for sorted_daid_group in top_sorted_daid_groups]
top_daids = ut.flatten(top_clipped_daids)
return top_daids
def hackshow_names(ibs, aid_list, fnum=None):
r"""
Args:
ibs (IBEISController): wbia controller object
aid_list (list):
CommandLine:
python -m wbia.other.dbinfo --exec-hackshow_names --show
python -m wbia.other.dbinfo --exec-hackshow_names --show --db PZ_Master1
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.other.dbinfo import * # NOQA
>>> import wbia
>>> ibs = wbia.opendb(defaultdb='PZ_MTEST')
>>> aid_list = ibs.get_valid_aids()
>>> result = hackshow_names(ibs, aid_list)
>>> print(result)
>>> ut.show_if_requested()
"""
import wbia.plottool as pt
import vtool as vt
grouped_aids, nid_list = ibs.group_annots_by_name(aid_list)
grouped_aids = [aids for aids in grouped_aids if len(aids) > 1]
unixtimes_list = ibs.unflat_map(ibs.get_annot_image_unixtimes_asfloat, grouped_aids)
yaws_list = ibs.unflat_map(ibs.get_annot_yaws, grouped_aids)
# markers_list = [[(1, 2, yaw * 360 / (np.pi * 2)) for yaw in yaws] for yaws in yaws_list]
unixtime_list = ut.flatten(unixtimes_list)
timemax = np.nanmax(unixtime_list)
timemin = np.nanmin(unixtime_list)
timerange = timemax - timemin
unixtimes_list = [
((unixtimes[:] - timemin) / timerange) for unixtimes in unixtimes_list
]
for unixtimes in unixtimes_list:
num_nan = sum(np.isnan(unixtimes))
unixtimes[np.isnan(unixtimes)] = np.linspace(-1, -0.5, num_nan)
# ydata_list = [np.arange(len(aids)) for aids in grouped_aids]
sortx_list = vt.argsort_groups(unixtimes_list, reverse=False)
# markers_list = ut.list_ziptake(markers_list, sortx_list)
yaws_list = ut.list_ziptake(yaws_list, sortx_list)
ydatas_list = vt.ziptake(unixtimes_list, sortx_list)
# ydatas_list = sortx_list
# ydatas_list = vt.argsort_groups(unixtimes_list, reverse=False)
# Sort by num members
# ydatas_list = ut.take(ydatas_list, np.argsort(list(map(len, ydatas_list))))
xdatas_list = [
np.zeros(len(ydatas)) + count for count, ydatas in enumerate(ydatas_list)
]
# markers = ut.flatten(markers_list)
# yaws = np.array(ut.flatten(yaws_list))
y_data = np.array(ut.flatten(ydatas_list))
x_data = np.array(ut.flatten(xdatas_list))
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum=fnum)
ax = pt.gca()
# unique_yaws, groupxs = vt.group_indices(yaws)
ax.scatter(x_data, y_data, color=[1, 0, 0], s=1, marker='.')
# pt.draw_stems(x_data, y_data, marker=markers, setlims=True, linestyle='')
pt.dark_background()
ax = pt.gca()
ax.set_xlim(min(x_data) - 0.1, max(x_data) + 0.1)
ax.set_ylim(min(y_data) - 0.1, max(y_data) + 0.1)
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 hackshow_names(ibs, aid_list, fnum=None):
r"""
Args:
ibs (IBEISController): ibeis controller object
aid_list (list):
CommandLine:
python -m ibeis.other.dbinfo --exec-hackshow_names --show
python -m ibeis.other.dbinfo --exec-hackshow_names --show --db PZ_Master1
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.other.dbinfo import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='PZ_MTEST')
>>> aid_list = ibs.get_valid_aids()
>>> result = hackshow_names(ibs, aid_list)
>>> print(result)
>>> ut.show_if_requested()
"""
import plottool as pt
import vtool as vt
grouped_aids, nid_list = ibs.group_annots_by_name(aid_list)
grouped_aids = [aids for aids in grouped_aids if len(aids) > 1]
unixtimes_list = ibs.unflat_map(ibs.get_annot_image_unixtimes_asfloat, grouped_aids)
yaws_list = ibs.unflat_map(ibs.get_annot_yaws, grouped_aids)
#markers_list = [[(1, 2, yaw * 360 / (np.pi * 2)) for yaw in yaws] for yaws in yaws_list]
unixtime_list = ut.flatten(unixtimes_list)
timemax = np.nanmax(unixtime_list)
timemin = np.nanmin(unixtime_list)
timerange = timemax - timemin
unixtimes_list = [((unixtimes[:] - timemin) / timerange) for unixtimes in unixtimes_list]
for unixtimes in unixtimes_list:
num_nan = sum(np.isnan(unixtimes))
unixtimes[np.isnan(unixtimes)] = np.linspace(-1, -.5, num_nan)
#ydata_list = [np.arange(len(aids)) for aids in grouped_aids]
sortx_list = vt.argsort_groups(unixtimes_list, reverse=False)
#markers_list = ut.list_ziptake(markers_list, sortx_list)
yaws_list = ut.list_ziptake(yaws_list, sortx_list)
ydatas_list = vt.ziptake(unixtimes_list, sortx_list)
#ydatas_list = sortx_list
#ydatas_list = vt.argsort_groups(unixtimes_list, reverse=False)
# Sort by num members
#ydatas_list = ut.take(ydatas_list, np.argsort(list(map(len, ydatas_list))))
xdatas_list = [np.zeros(len(ydatas)) + count for count, ydatas in enumerate(ydatas_list)]
#markers = ut.flatten(markers_list)
#yaws = np.array(ut.flatten(yaws_list))
y_data = np.array(ut.flatten(ydatas_list))
x_data = np.array(ut.flatten(xdatas_list))
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum=fnum)
ax = pt.gca()
#unique_yaws, groupxs = vt.group_indices(yaws)
ax.scatter(x_data, y_data, color=[1, 0, 0], s=1, marker='.')
#pt.draw_stems(x_data, y_data, marker=markers, setlims=True, linestyle='')
pt.dark_background()
ax = pt.gca()
ax.set_xlim(min(x_data) - .1, max(x_data) + .1)
ax.set_ylim(min(y_data) - .1, max(y_data) + .1)
