def grab_numpy_testdata(shape=(3e3, 128), dtype=np.uint8):
ndata = utool.get_arg('--ndata', type_=int, default=2)
print('[TEST] build ndata=%d numpy arrays with shape=%r' % (ndata, shape))
print(' * expected_memory(table_list) = %s' % utool.byte_str2(ndata * np.product(shape)))
table_list = [np.empty(shape, dtype=dtype) for i in xrange(ndata)]
print(' * memory+overhead(table_list) = %s' % utool.byte_str2(utool.get_object_size(table_list)))
return table_list
def change_names(ibs, qaid_list):
#next_name = utool.get_arg('--name', str, default='<name>_the_<species>')
next_name = utool.get_arg('--name', str, default='glob')
for aid in qaid_list:
ibs.print_name_table()
#(nid,) = ibs.add_names((next_name,))
ibs.set_annot_names(aid, next_name)
ibs.print_name_table()
ibs.print_annotation_table()
def TEST_INTERACT(ibs):
valid_gids = ibs.get_valid_gids()
valid_aids = ibs.get_valid_aids()
print('''
* len(valid_aids) = %r
* len(valid_gids) = %r
''' % (len(valid_aids), len(valid_gids)))
assert len(valid_gids) > 0, 'database images cannot be empty for test'
gindex = int(utool.get_arg('--gx', default=0))
cindex = int(utool.get_arg('--rx', default=0))
gid = valid_gids[gindex]
aid_list = ibs.get_image_aids(gid)
aid = aid_list[cindex]
#----------------------
#print('Show Image')
aids = aid_list[1:3]
interact.ishow_image(ibs, gid, aids=aids, fnum=1)
#----------------------
#print('Show Chip')
interact.ishow_chip(ibs, aid, in_image=False, fnum=2)
#interact.ishow_chip(ibs, aid, in_image=True, fnum=3)
#----------------------
#print('Show Query')
#aid1 = aid
#qcid2_qres = ibs.query_all([aid1])
#qres = qcid2_qres.values()[0]
#top_cids = qres.get_top_cids(ibs)
#assert len(top_cids) > 0, 'there does not seem to be results'
#cid2 = top_cids[0] # 294
#viz.show_matches(ibs, qres, cid2, fnum=4)
#viz.show_qres(ibs, qres, fnum=5)
return locals()
def TEST_QUERY_COMP(ibs):
print('[TEST_QUERY_COMP]')
qaid_list = ibs.get_valid_aids()[0:1]
print('[TEST_QUERY_COMP] len(qaid_list)=%r' % (qaid_list))
ibs._init_query_requestor()
qreq = ibs.qreq
#query_helpers.find_matchable_chips(ibs)
aids = ibs.get_recognition_database_aids()
index = 0
index = utool.get_arg('--index', type_=int, default=index)
qaid_list = utool.safe_slice(aids, index, index + 1)
comp_locals_ = query_helpers.get_query_components(ibs, qaid_list)
qres_dict = OrderedDict([
('ORIG', comp_locals_['qres_ORIG']),
('FILT', comp_locals_['qres_FILT']),
('SVER', comp_locals_['qres_SVER']),
])
top_aids = qres_dict['SVER'].get_top_aids(ibs)
top_aids = utool.safe_slice(top_aids, 3)
aid2 = top_aids[0]
for px, (lbl, qres) in enumerate(six.iteritems(qres_dict)):
print(lbl)
fnum = df2.next_fnum()
df2.figure(fnum=fnum, doclf=True)
#viz_matches.show_matches(ibs, qres, aid2, fnum=fnum, in_image=True)
#viz.show_qres(ibs, qres, fnum=fnum, top_aids=top_aids, ensure=False)
interact.ishow_qres(ibs, qres, fnum=fnum, top_aids=top_aids, ensure=False)
df2.set_figtitle(lbl)
df2.adjust_subplots_safe(top=.8)
fnum = df2.next_fnum()
qaid2_svtups = comp_locals_['qaid2_svtups']
qaid2_chipmatch_FILT = comp_locals_['qaid2_chipmatch_FILT']
aid1 = qaid = comp_locals_['qaid']
aid2_svtup = qaid2_svtups[aid1]
chipmatch_FILT = qaid2_chipmatch_FILT[aid1]
viz.show_sver(ibs, aid1, aid2, chipmatch_FILT, aid2_svtup, fnum=fnum)
return locals()
def show_descriptors_match_distances(orgres2_distance, fnum=1, db_name='', **kwargs):
disttype_list = orgres2_distance.itervalues().next().keys()
orgtype_list = orgres2_distance.keys()
(nRow, nCol) = len(orgtype_list), len(disttype_list)
nColors = nRow * nCol
color_list = df2.distinct_colors(nColors)
df2.figure(fnum=fnum, docla=True, doclf=True)
pnum_ = lambda px: (nRow, nCol, px + 1)
plot_type = utool.get_arg('--plot-type', default='plot')
# Remember min and max val for each distance type (l1, emd...)
distkey2_min = {distkey: np.uint64(-1) for distkey in disttype_list}
distkey2_max = {distkey: 0 for distkey in disttype_list}
def _distplot(dists, color, label, distkey, plot_type=plot_type):
data = sorted(dists)
ax = df2.gca()
min_ = distkey2_min[distkey]
max_ = distkey2_max[distkey]
if plot_type == 'plot':
df2.plot(data, color=color, label=label, yscale='linear')
#xticks = np.linspace(np.min(data), np.max(data), 3)
#yticks = np.linspace(0, len(data), 5)
#ax.set_xticks(xticks)
#ax.set_yticks(yticks)
ax.set_ylim(min_, max_)
ax.set_xlim(0, len(dists))
ax.set_ylabel('distance')
ax.set_xlabel('matches indexes (sorted by distance)')
df2.legend(loc='lower right')
if plot_type == 'pdf':
df2.plot_pdf(data, color=color, label=label)
ax.set_ylabel('pr')
ax.set_xlabel('distance')
ax.set_xlim(min_, max_)
df2.legend(loc='upper left')
df2.dark_background(ax)
df2.small_xticks(ax)
df2.small_yticks(ax)
px = 0
for orgkey in orgtype_list:
for distkey in disttype_list:
dists = orgres2_distance[orgkey][distkey]
if len(dists) == 0:
continue
min_ = dists.min()
max_ = dists.max()
distkey2_min[distkey] = min(distkey2_min[distkey], min_)
distkey2_max[distkey] = max(distkey2_max[distkey], max_)
for count, orgkey in enumerate(orgtype_list):
for distkey in disttype_list:
printDBG('[allres-viz] plotting: %r' % ((orgkey, distkey),))
dists = orgres2_distance[orgkey][distkey]
df2.figure(fnum=fnum, pnum=pnum_(px))
color = color_list[px]
title = distkey + ' ' + orgkey
label = 'P(%s | %s)' % (distkey, orgkey)
_distplot(dists, color, label, distkey, **kwargs)
if count == 0:
ax = df2.gca()
ax.set_title(distkey)
px += 1
subtitle = 'the matching distances between sift descriptors'
title = '(sift) matching distances'
if db_name != '':
title = db_name + ' ' + title
df2.set_figtitle(title, subtitle)
df2.adjust_subplots_safe()
res.show_topN(hs, fnum=fnum, query_cfg=query_cfg)
fnum += 1
return fnum
def plot_feature_distances(allres, orgres_list=None, fnum=1):
print('[dev] plot_feature_distances()')
orgres2_distance = allres.get_orgres2_distances(orgres_list=orgres_list)
db_name = allres.hs.get_db_name()
allres_viz.show_descriptors_match_distances(orgres2_distance,
db_name=db_name, fnum=fnum)
fnum += 1
return fnum
YSCALE = utool.get_arg('--yscale', default='symlog') # 'symlog'
XSCALE = 'linear'
def plot_seperability(hs, qcx_list, fnum=1):
print('[dev] plot_seperability(fnum=%r)' % fnum)
qcx2_res = get_qcx2_res(hs, qcx_list)
qcx2_separability = get_seperatbility(hs, qcx2_res)
sep_score_list = qcx2_separability.values()
df2.figure(fnum=fnum, doclf=True, docla=True)
print('[dev] seperability stats: ' + utool.stats_str(sep_score_list))
sorted_sepscores = sorted(sep_score_list)
df2.plot(sorted_sepscores, color=df2.DEEP_PINK, label='seperation score',
yscale=YSCALE)
df2.set_xlabel('true chipmatch index (%d)' % len(sep_score_list))
df2.set_logyscale_from_data(sorted_sepscores)
return aid_list
def list_ingestable_images(img_dir, fullpath=True, recursive=True):
ignore_list = ['_hsdb', '.hs_internals', '_ibeis_cache', '_ibsdb']
gpath_list = utool.list_images(img_dir,
fullpath=fullpath,
recursive=recursive,
ignore_list=ignore_list)
# Ensure in unix format
gpath_list = map(utool.unixpath, gpath_list)
return gpath_list
if __name__ == '__main__':
import multiprocessing
multiprocessing.freeze_support() # win32
print('__main__ = ingest_database.py')
print(utool.unindent(
'''
usage:
./ibeis/ingest/ingest_database.py --db [dbname]
Valid dbnames:''') + utool.indentjoin(STANDARD_INGEST_FUNCS.keys(), '\n * '))
db = utool.get_arg('--db', str, None)
ibs = ingest_standard_database(db)
#img_dir = join(ibeis.sysres.get_workdir(), 'polar_bears')
#main_locals = ibeis.main(dbdir=img_dir, gui=False)
#ibs = main_locals['ibs']
#ingest_rawdata(ibs, img_dir)
from __future__ import absolute_import, division, print_function
import numpy as np
from itertools import izip
import plottool.draw_func2 as df2
from plottool import plot_helpers as ph
import utool
import vtool.keypoint as ktool
from ibeis.dev import ibsfuncs
from ibeis.control.accessor_decors import getter, getter_vector_output
(print, print_, printDBG, rrr, profile) = utool.inject(__name__, '[viz_helpers]', DEBUG=False)
NO_LBL_OVERRIDE = utool.get_arg('--no-lbl-override', type_=bool, default=None)
FNUMS = dict(image=1, chip=2, res=3, inspect=4, special=5, name=6)
IN_IMAGE_OVERRIDE = utool.get_arg('--in-image-override', type_=bool, default=None)
SHOW_QUERY_OVERRIDE = utool.get_arg('--show-query-override', type_=bool, default=None)
NO_LBL_OVERRIDE = utool.get_arg('--no-lbl-override', type_=bool, default=None)
SIFT_OR_VECFIELD = ph.SIFT_OR_VECFIELD
def register_FNUMS(FNUMS_):
# DEPREICATE
global FNUMS
FNUMS = FNUMS_
draw = ph.draw
get_square_row_cols = ph.get_square_row_cols
for repo in TPL_REPO_DIRS:
utool.util_git.std_build_command(repo) # Executes {plat}_build.{ext}
# Build only IBEIS repos with setup.py
utool.set_project_repos(IBEIS_REPO_URLS, IBEIS_REPO_DIRS)
utool.gg_command('sudo {pythoncmd} setup.py build'.format(**envcmds))
if utool.get_flag('--develop'):
utool.set_project_repos(IBEIS_REPO_URLS, IBEIS_REPO_DIRS)
utool.gg_command('sudo {pythoncmd} setup.py develop'.format(**envcmds))
if utool.get_flag('--tag-status'):
utool.gg_command('git tag')
# Tag everything
tag_name = utool.get_arg('--newtag', type_=str, default=None)
if tag_name is not None:
utool.gg_command('git tag -a "{tag_name}" -m "super_setup autotag {tag_name}"'.format(**locals()))
utool.gg_command('git push --tags')
if utool.get_flag('--test'):
import ibeis
print('found ibeis=%r' % (ibeis,))
if utool.get_flag('--push'):
utool.gg_command('git push')
if utool.get_flag('--bext'):
def show_descriptors_match_distances(orgres2_distance,
fnum=1,
db_name='',
**kwargs):
disttype_list = orgres2_distance.itervalues().next().keys()
orgtype_list = orgres2_distance.keys()
(nRow, nCol) = len(orgtype_list), len(disttype_list)
nColors = nRow * nCol
color_list = df2.distinct_colors(nColors)
df2.figure(fnum=fnum, docla=True, doclf=True)
pnum_ = lambda px: (nRow, nCol, px + 1)
plot_type = utool.get_arg('--plot-type', default='plot')
# Remember min and max val for each distance type (l1, emd...)
distkey2_min = {distkey: np.uint64(-1) for distkey in disttype_list}
distkey2_max = {distkey: 0 for distkey in disttype_list}
def _distplot(dists, color, label, distkey, plot_type=plot_type):
data = sorted(dists)
ax = df2.gca()
min_ = distkey2_min[distkey]
max_ = distkey2_max[distkey]
if plot_type == 'plot':
df2.plot(data, color=color, label=label, yscale='linear')
#xticks = np.linspace(np.min(data), np.max(data), 3)
#yticks = np.linspace(0, len(data), 5)
#ax.set_xticks(xticks)
#ax.set_yticks(yticks)
ax.set_ylim(min_, max_)
ax.set_xlim(0, len(dists))
ax.set_ylabel('distance')
ax.set_xlabel('matches indexes (sorted by distance)')
df2.legend(loc='lower right')
if plot_type == 'pdf':
df2.plot_pdf(data, color=color, label=label)
ax.set_ylabel('pr')
ax.set_xlabel('distance')
ax.set_xlim(min_, max_)
df2.legend(loc='upper left')
df2.dark_background(ax)
df2.small_xticks(ax)
df2.small_yticks(ax)
px = 0
for orgkey in orgtype_list:
for distkey in disttype_list:
dists = orgres2_distance[orgkey][distkey]
if len(dists) == 0:
continue
min_ = dists.min()
max_ = dists.max()
distkey2_min[distkey] = min(distkey2_min[distkey], min_)
distkey2_max[distkey] = max(distkey2_max[distkey], max_)
for count, orgkey in enumerate(orgtype_list):
for distkey in disttype_list:
printDBG('[allres-viz] plotting: %r' % ((orgkey, distkey), ))
dists = orgres2_distance[orgkey][distkey]
df2.figure(fnum=fnum, pnum=pnum_(px))
color = color_list[px]
title = distkey + ' ' + orgkey
label = 'P(%s | %s)' % (distkey, orgkey)
_distplot(dists, color, label, distkey, **kwargs)
if count == 0:
ax = df2.gca()
ax.set_title(distkey)
px += 1
subtitle = 'the matching distances between sift descriptors'
title = '(sift) matching distances'
if db_name != '':
title = db_name + ' ' + title
df2.set_figtitle(title, subtitle)
df2.adjust_subplots_safe()
"""
from __future__ import absolute_import, division, print_function
import multiprocessing
import ibeis
import utool
from os.path import join
if __name__ == '__main__':
multiprocessing.freeze_support() # for win32
# Create a directory for the demo database
workdir = ibeis.get_workdir()
demodir = join(workdir, 'demo')
if utool.get_arg('--reset'):
# Remove the previous demo if it exists
utool.delete(demodir)
# Start a new database there
main_locals = ibeis.main(dbdir=demodir)
# Get a handle to the GUIBackend Control
back = main_locals['back']
# Get a directory with some images in it
testurl = 'https://www.dropbox.com/s/s4gkjyxjgghr18c/testdata_detect.zip'
testdir = utool.grab_zipped_url(testurl)
execstr = ibeis.main_loop(main_locals)