def dump_analysis(allres):
print('[rr2] dump analysis')
greater1_cxs = allres.greater1_cxs
#qcx = greater5_cxs[0]
for qcx in greater1_cxs:
viz.show_chip(allres, qcx, 'analysis', 'analysis')
viz.show_chip(allres, qcx, 'analysis', 'analysis', annotations=False, title_aug=' noanote')
def dump_all_queries(allres):
test_cxs = allres.hs.test_sample_cx
print('[rr2] dumping all %r queries' % len(test_cxs))
for qcx in test_cxs:
viz.show_chip(allres, qcx, 'analysis', subdir='allqueries',
annotations=False, title_aug=' noanote')
viz.show_chip(allres, qcx, 'analysis', subdir='allqueries')
def dump_missed_top5(allres):
#print('\n---DUMPING MISSED TOP 5---')
'Displays the top5 matches for all queries'
greater5_cxs = allres.greater5_cxs
#qcx = greater5_cxs[0]
for qcx in greater5_cxs:
viz.show_chip(allres, qcx, 'top5', 'missed_top5')
viz.show_chip(allres, qcx, 'gt_matches', 'missed_top5')
def dump_missed_top5(allres):
#print('\n---DUMPING MISSED TOP 5---')
'Displays the top5 matches for all queries'
greater5_cxs = allres.greater5_cxs
#qcx = greater5_cxs[0]
for qcx in greater5_cxs:
viz.show_chip(allres, qcx, 'top5', 'missed_top5')
viz.show_chip(allres, qcx, 'gt_matches', 'missed_top5')
def dump_all_queries(allres):
test_cxs = allres.hs.test_sample_cx
print('[rr2] dumping all %r queries' % len(test_cxs))
for qcx in test_cxs:
viz.show_chip(allres,
qcx,
'analysis',
subdir='allqueries',
annotations=False,
title_aug=' noanote')
viz.show_chip(allres, qcx, 'analysis', subdir='allqueries')
def show_chip(back, cx, **kwargs):
fnum = FNUMS['chip']
did_exist = df2.plt.fignum_exists(fnum)
df2.figure(fnum=fnum, docla=True, doclf=True)
INTERACTIVE_CHIPS = True # This should always be True
if INTERACTIVE_CHIPS:
interact_fn = interact.interact_chip
interact_fn(back.hs, cx, fnum=fnum, figtitle='Chip View')
else:
viz.show_chip(back.hs, cx, fnum=fnum, figtitle='Chip View')
if not did_exist:
back.layout_figures()
def dump_analysis(allres):
print('[rr2] dump analysis')
greater1_cxs = allres.greater1_cxs
#qcx = greater5_cxs[0]
for qcx in greater1_cxs:
viz.show_chip(allres, qcx, 'analysis', 'analysis')
viz.show_chip(allres,
qcx,
'analysis',
'analysis',
annotations=False,
title_aug=' noanote')
def show_chip(back, cx, **kwargs):
fnum = FNUMS['chip']
did_exist = df2.plt.fignum_exists(fnum)
df2.figure(fnum=fnum, docla=True, doclf=True)
INTERACTIVE_CHIPS = True # This should always be True
if INTERACTIVE_CHIPS:
interact_fn = interact.interact_chip
interact_fn(back.hs, cx, fnum=fnum, figtitle='Chip View')
else:
viz.show_chip(back.hs, cx, fnum=fnum, figtitle='Chip View')
if not did_exist:
back.layout_figures()
def dump_gt_matches(allres):
#print('\n---DUMPING GT MATCHES ---')
'Displays the matches to ground truth for all queries'
qcx2_res = allres.qcx2_res
for qcx in xrange(0, len(qcx2_res)):
viz.show_chip(allres, qcx, 'gt_matches')
def dump_gt_matches(allres):
#print('\n---DUMPING GT MATCHES ---')
'Displays the matches to ground truth for all queries'
qcx2_res = allres.qcx2_res
for qcx in xrange(0, len(qcx2_res)):
viz.show_chip(allres, qcx, 'gt_matches')
nTop = 2
for tx in xrange(nTop):
cx2 = res.topN_cxs(hs)[tx]
dstimg1, dstimg2, args_, kwargs_ = test_result_coverage(
hs, res, cx2, scale_factor)
test_find_coverage_score(hs, res)
res.show_chipres(hs, cx2, fnum=fnum)
df2.set_figtitle('matching viz' + str(tx), incanvas=False)
fnum += 1
df2.show_chipmatch2(dstimg1, dstimg2, *args_, fnum=fnum, **kwargs_)
df2.set_figtitle('matching coverage' + str(tx))
fnum += 1
df2.imshow(srcimg, fnum=fnum, heatmap=True)
df2.set_figtitle('gaussian weights')
fnum += 1
df2.imshow(dstimg, fnum=fnum, heatmap=True)
df2.set_figtitle('chip coverage map')
fnum += 1
df2.imshow(dstimg_thresh, fnum=fnum, heatmap=True)
df2.set_figtitle('thresholded chip coverage map')
fnum += 1
viz.show_chip(hs, cx, fnum=fnum)
df2.set_figtitle('chip', incanvas=False)
fnum += 1
exec(viz.df2.present())
def show_query(res, hs, **kwargs):
from hsviz import viz
print('[res] show_query')
viz.show_chip(hs, res=res, **kwargs)
from hscom import helpers
from hscom import helpers as util
from hsviz import viz
import multiprocessing
import numpy as np # NOQA
if __name__ == '__main__':
multiprocessing.freeze_support()
# Debugging vars
chip_cfg = None
#l')=103.7900s
cx_list = None
kwargs = {}
# --- LOAD TABLES --- #
args = argparse2.parse_arguments(defaultdb='NAUTS')
hs = api.HotSpotter(args)
hs.load_tables()
hs.update_samples()
# --- LOAD CHIPS --- #
force_compute = helpers.get_flag('--force', default=False)
cc2.load_chips(hs, force_compute=force_compute)
cx = helpers.get_arg('--cx', type_=int)
if not cx is None:
#tau = np.pi * 2
#hs.change_theta(cx, tau / 8)
viz.show_chip(hs, cx, draw_kpts=False, fnum=1)
viz.show_image(hs, hs.cx2_gx(cx), fnum=2)
else:
print('usage: feature_compute.py --cx [cx]')
exec(viz.df2.present())
multiprocessing.freeze_support()
print('[fc2] __main__ = feature_compute2.py')
# Read Args
cx = helpers.get_arg('--cx', type_=int)
delete_features = helpers.get_flag('--delete-features', default=False)
nRandKpts = helpers.get_arg('--nRandKpts', type_=int)
# Debugging vars
feat_cfg = None
cx_list = None
kwargs = {}
# --- LOAD TABLES --- #
args = argparse2.parse_arguments(db='NAUTS')
hs = api.HotSpotter(args)
hs.load_tables()
# --- LOAD CHIPS --- #
hs.update_samples()
hs.load_chips()
# Delete features if needed
if delete_features:
fc2.clear_feature_cache(hs)
# --- LOAD FEATURES --- #
fc2.load_features(hs)
if not cx is None:
viz.show_chip(hs, cx, nRandKpts=nRandKpts)
else:
print(
'usage: feature_compute.py --cx [cx] --nRandKpts [num] [--delete-features]'
)
exec(viz.df2.present())
def show_query(res, hs, **kwargs):
from hsviz import viz
print('[res] show_query')
viz.show_chip(hs, res=res, **kwargs)