def show_single_coverage_mask(qreq_, cm, weight_mask_m, weight_mask, daids, fnum=None):
import plottool as pt
from ibeis import viz
fnum = pt.ensure_fnum(fnum)
idx_list = ut.dict_take(cm.daid2_idx, daids)
nPlots = len(idx_list) + 1
nRows, nCols = pt.get_square_row_cols(nPlots)
pnum_ = pt.make_pnum_nextgen(nRows, nCols)
pt.figure(fnum=fnum, pnum=(1, 2, 1))
# Draw coverage masks with bbox
# <FlipHack>
#weight_mask_m = np.fliplr(np.flipud(weight_mask_m))
#weight_mask = np.fliplr(np.flipud(weight_mask))
# </FlipHack>
stacked_weights, offset_tup, sf_tup = vt.stack_images(weight_mask_m, weight_mask, return_sf=True)
(woff, hoff) = offset_tup[1]
wh1 = weight_mask_m.shape[0:2][::-1]
wh2 = weight_mask.shape[0:2][::-1]
pt.imshow(255 * (stacked_weights), fnum=fnum, pnum=pnum_(0), title='(query image) What did match vs what should match')
pt.draw_bbox(( 0, 0) + wh1, bbox_color=(0, 0, 1))
pt.draw_bbox((woff, hoff) + wh2, bbox_color=(0, 0, 1))
# Get contributing matches
qaid = cm.qaid
daid_list = daids
fm_list = ut.take(cm.fm_list, idx_list)
fs_list = ut.take(cm.fs_list, idx_list)
# Draw matches
for px, (daid, fm, fs) in enumerate(zip(daid_list, fm_list, fs_list), start=1):
viz.viz_matches.show_matches2(qreq_.ibs, qaid, daid, fm, fs,
draw_pts=False, draw_lines=True,
draw_ell=False, fnum=fnum, pnum=pnum_(px),
darken=.5)
coverage_score = score_matching_mask(weight_mask_m, weight_mask)
pt.set_figtitle('score=%.4f' % (coverage_score,))
def show_page(self):
if self.fig is None:
raise AssertionError('fig is None, did you run interction.start()?')
import plottool as pt
fig = ih.begin_interaction('expandable', self.fnum)
if not any(self.pnum_list) and self.nRows is None and self.nRows is None:
# Hack if no pnum was given
self.nRows, self.nCols = pt.get_num_rc(len(self.pnum_list),
nRows=self.nRows,
nCols=self.nCols)
nSubplots = len(self.func_list)
pnum_ = pt.make_pnum_nextgen(self.nRows, self.nCols, nSubplots=nSubplots)
self.pnum_list = [pnum_() for _ in self.pnum_list]
for index, (pnum, func) in enumerate(zip(self.pnum_list, self.func_list)):
if check_if_subinteract(func):
# Hack
interclass = func
interclass.static_plot(fnum=self.fnum, pnum=pnum)
elif hasattr(func, 'plot'):
inter = func
inter.plot(fnum=self.fnum, pnum=pnum)
else:
func(fnum=self.fnum, pnum=pnum)
ax = pt.gca()
pt.set_plotdat(ax, 'plot_func', func)
pt.set_plotdat(ax, 'expandable_index', index)
#if self.interactive is None or self.interactive:
# ih.connect_callback(fig, 'button_press_event', self.onclick)
self.connect_callbacks()
self.fig = fig
return fig
def show_power_law_plots():
"""
CommandLine:
python -m ibeis.algo.hots.devcases --test-show_power_law_plots --show
Example:
>>> # DISABLE_DOCTEST
>>> #%pylab qt4
>>> from ibeis.all_imports import * # NOQA
>>> from ibeis.algo.hots.devcases import * # NOQA
>>> show_power_law_plots()
>>> pt.show_if_requested()
"""
import numpy as np
import plottool as pt
xdata = np.linspace(0, 1, 1000)
ydata = xdata
fnum = 1
powers = [.01, .1, .5, 1, 2, 30, 70, 100, 1000]
nRows, nCols = pt.get_square_row_cols(len(powers), fix=True)
pnum_next = pt.make_pnum_nextgen(nRows, nCols)
for p in powers:
plotkw = dict(
fnum=fnum,
marker='g-',
linewidth=2,
pnum=pnum_next(),
title='p=%r' % (p,)
)
ydata_ = ydata ** p
pt.plot2(xdata, ydata_, **plotkw)
pt.set_figtitle('power laws y = x ** p')
def show_power_law_plots():
"""
CommandLine:
python -m ibeis.algo.hots.devcases --test-show_power_law_plots --show
Example:
>>> # DISABLE_DOCTEST
>>> #%pylab qt4
>>> from ibeis.all_imports import * # NOQA
>>> from ibeis.algo.hots.devcases import * # NOQA
>>> show_power_law_plots()
>>> pt.show_if_requested()
"""
import numpy as np
import plottool as pt
xdata = np.linspace(0, 1, 1000)
ydata = xdata
fnum = 1
powers = [.01, .1, .5, 1, 2, 30, 70, 100, 1000]
nRows, nCols = pt.get_square_row_cols(len(powers), fix=True)
pnum_next = pt.make_pnum_nextgen(nRows, nCols)
for p in powers:
plotkw = dict(fnum=fnum,
marker='g-',
linewidth=2,
pnum=pnum_next(),
title='p=%r' % (p, ))
ydata_ = ydata**p
pt.plot2(xdata, ydata_, **plotkw)
pt.set_figtitle('power laws y = x ** p')
def show_internals(self, fnum=None):
import plottool as pt
pt.qtensure()
pnum_ = pt.make_pnum_nextgen(nRows=1, nCols=len(self.forests))
for level, forest in enumerate(self.forests):
pt.show_nx(forest.to_networkx(), title='level=%r' % (level,),
fnum=fnum, pnum=pnum_())
def show_single_coverage_mask(qreq_,
cm,
weight_mask_m,
weight_mask,
daids,
fnum=None):
import plottool as pt
from ibeis import viz
fnum = pt.ensure_fnum(fnum)
idx_list = ut.dict_take(cm.daid2_idx, daids)
nPlots = len(idx_list) + 1
nRows, nCols = pt.get_square_row_cols(nPlots)
pnum_ = pt.make_pnum_nextgen(nRows, nCols)
pt.figure(fnum=fnum, pnum=(1, 2, 1))
# Draw coverage masks with bbox
# <FlipHack>
#weight_mask_m = np.fliplr(np.flipud(weight_mask_m))
#weight_mask = np.fliplr(np.flipud(weight_mask))
# </FlipHack>
stacked_weights, offset_tup, sf_tup = vt.stack_images(weight_mask_m,
weight_mask,
return_sf=True)
(woff, hoff) = offset_tup[1]
wh1 = weight_mask_m.shape[0:2][::-1]
wh2 = weight_mask.shape[0:2][::-1]
pt.imshow(255 * (stacked_weights),
fnum=fnum,
pnum=pnum_(0),
title='(query image) What did match vs what should match')
pt.draw_bbox((0, 0) + wh1, bbox_color=(0, 0, 1))
pt.draw_bbox((woff, hoff) + wh2, bbox_color=(0, 0, 1))
# Get contributing matches
qaid = cm.qaid
daid_list = daids
fm_list = ut.take(cm.fm_list, idx_list)
fs_list = ut.take(cm.fs_list, idx_list)
# Draw matches
for px, (daid, fm, fs) in enumerate(zip(daid_list, fm_list, fs_list),
start=1):
viz.viz_matches.show_matches2(qreq_.ibs,
qaid,
daid,
fm,
fs,
draw_pts=False,
draw_lines=True,
draw_ell=False,
fnum=fnum,
pnum=pnum_(px),
darken=.5)
coverage_score = score_matching_mask(weight_mask_m, weight_mask)
pt.set_figtitle('score=%.4f' % (coverage_score, ))
def sanity_checks(offset_list, Y_list, query_annots, ibs):
nfeat_list = np.diff(offset_list)
for Y, nfeat in ut.ProgIter(zip(Y_list, nfeat_list), 'checking'):
assert nfeat == sum(ut.lmap(len, Y.fxs_list))
if False:
# Visualize queries
# Look at the standard query images here
# http://www.robots.ox.ac.uk:5000/~vgg/publications/2007/Philbin07/philbin07.pdf
from ibeis.viz import viz_chip
import plottool as pt
pt.qt4ensure()
fnum = 1
pnum_ = pt.make_pnum_nextgen(len(query_annots.aids) // 5, 5)
for aid in ut.ProgIter(query_annots.aids):
pnum = pnum_()
viz_chip.show_chip(ibs,
aid,
in_image=True,
annote=False,
notitle=True,
draw_lbls=False,
fnum=fnum,
pnum=pnum)
def test_siamese_performance(model, data, labels, flat_metadata, dataname=''):
r"""
CommandLine:
utprof.py -m ibeis_cnn --tf pz_patchmatch --db liberty --test --weights=liberty:current --arch=siaml2_128 --test
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --test --ensure
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --test --ensure --weights=new
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --train --weights=new
python -m ibeis_cnn --tf netrun --db pzmtest --weights=liberty:current --arch=siaml2_128 --test # NOQA
python -m ibeis_cnn --tf netrun --db pzmtest --weights=liberty:current --arch=siaml2_128
"""
import vtool as vt
import plottool as pt
# TODO: save in model.trainind_dpath/diagnostics/figures
ut.colorprint('\n[siam_perf] Testing Siamese Performance', 'white')
#epoch_dpath = model.get_epoch_diagnostic_dpath()
epoch_dpath = model.arch_dpath
ut.vd(epoch_dpath)
dataname += ' ' + model.get_history_hashid() + '\n'
history_text = ut.list_str(model.era_history, newlines=True)
ut.write_to(ut.unixjoin(epoch_dpath, 'era_history.txt'), history_text)
#if True:
# import matplotlib as mpl
# mpl.rcParams['agg.path.chunksize'] = 100000
#data = data[::50]
#labels = labels[::50]
#from ibeis_cnn import utils
#data, labels = utils.random_xy_sample(data, labels, 10000, model.data_per_label_input)
FULL = not ut.get_argflag('--quick')
fnum_gen = pt.make_fnum_nextgen()
ut.colorprint('[siam_perf] Show era history', 'white')
fig = model.show_era_loss(fnum=fnum_gen())
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
# hack
ut.colorprint('[siam_perf] Show weights image', 'white')
fig = model.show_weights_image(fnum=fnum_gen())
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
#model.draw_all_conv_layer_weights(fnum=fnum_gen())
#model.imwrite_weights(1)
#model.imwrite_weights(2)
# Compute each type of score
ut.colorprint('[siam_perf] Building Scores', 'white')
test_outputs = model.predict2(model, data)
network_output = test_outputs['network_output_determ']
# hack converting network output to distances for non-descriptor networks
if len(network_output.shape) == 2 and network_output.shape[1] == 1:
cnn_scores = network_output.T[0]
elif len(network_output.shape) == 1:
cnn_scores = network_output
elif len(network_output.shape) == 2 and network_output.shape[1] > 1:
assert model.data_per_label_output == 2
vecs1 = network_output[0::2]
vecs2 = network_output[1::2]
cnn_scores = vt.L2(vecs1, vecs2)
else:
assert False
cnn_scores = cnn_scores.astype(np.float64)
# Segfaults with the data passed in is large (AND MEMMAPPED apparently)
# Fixed in hesaff implementation
SIFT = FULL
if SIFT:
sift_scores, sift_list = test_sift_patchmatch_scores(data, labels)
sift_scores = sift_scores.astype(np.float64)
ut.colorprint('[siam_perf] Learning Encoders', 'white')
# Learn encoders
encoder_kw = {
#'monotonize': False,
'monotonize': True,
}
cnn_encoder = vt.ScoreNormalizer(**encoder_kw)
cnn_encoder.fit(cnn_scores, labels)
if SIFT:
sift_encoder = vt.ScoreNormalizer(**encoder_kw)
sift_encoder.fit(sift_scores, labels)
# Visualize
ut.colorprint('[siam_perf] Visualize Encoders', 'white')
viz_kw = dict(
with_scores=False,
with_postbayes=False,
with_prebayes=False,
target_tpr=.95,
)
inter_cnn = cnn_encoder.visualize(
figtitle=dataname + ' CNN scores. #data=' + str(len(data)),
fnum=fnum_gen(), **viz_kw)
if SIFT:
inter_sift = sift_encoder.visualize(
figtitle=dataname + ' SIFT scores. #data=' + str(len(data)),
fnum=fnum_gen(), **viz_kw)
# Save
pt.save_figure(fig=inter_cnn.fig, dpath=epoch_dpath)
if SIFT:
pt.save_figure(fig=inter_sift.fig, dpath=epoch_dpath)
# Save out examples of hard errors
#cnn_fp_label_indicies, cnn_fn_label_indicies =
#cnn_encoder.get_error_indicies(cnn_scores, labels)
#sift_fp_label_indicies, sift_fn_label_indicies =
#sift_encoder.get_error_indicies(sift_scores, labels)
with_patch_examples = FULL
if with_patch_examples:
ut.colorprint('[siam_perf] Visualize Confusion Examples', 'white')
cnn_indicies = cnn_encoder.get_confusion_indicies(cnn_scores, labels)
if SIFT:
sift_indicies = sift_encoder.get_confusion_indicies(sift_scores, labels)
warped_patch1_list, warped_patch2_list = list(zip(*ut.ichunks(data, 2)))
samp_args = (warped_patch1_list, warped_patch2_list, labels)
_sample = functools.partial(draw_results.get_patch_sample_img, *samp_args)
cnn_fp_img = _sample({'fs': cnn_scores}, cnn_indicies.fp)[0]
cnn_fn_img = _sample({'fs': cnn_scores}, cnn_indicies.fn)[0]
cnn_tp_img = _sample({'fs': cnn_scores}, cnn_indicies.tp)[0]
cnn_tn_img = _sample({'fs': cnn_scores}, cnn_indicies.tn)[0]
if SIFT:
sift_fp_img = _sample({'fs': sift_scores}, sift_indicies.fp)[0]
sift_fn_img = _sample({'fs': sift_scores}, sift_indicies.fn)[0]
sift_tp_img = _sample({'fs': sift_scores}, sift_indicies.tp)[0]
sift_tn_img = _sample({'fs': sift_scores}, sift_indicies.tn)[0]
#if ut.show_was_requested():
#def rectify(arr):
# return np.flipud(arr)
SINGLE_FIG = False
if SINGLE_FIG:
def dump_img(img_, lbl, fnum):
fig, ax = pt.imshow(img_, figtitle=dataname + ' ' + lbl, fnum=fnum)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
dump_img(cnn_fp_img, 'cnn_fp_img', fnum_gen())
dump_img(cnn_fn_img, 'cnn_fn_img', fnum_gen())
dump_img(cnn_tp_img, 'cnn_tp_img', fnum_gen())
dump_img(cnn_tn_img, 'cnn_tn_img', fnum_gen())
dump_img(sift_fp_img, 'sift_fp_img', fnum_gen())
dump_img(sift_fn_img, 'sift_fn_img', fnum_gen())
dump_img(sift_tp_img, 'sift_tp_img', fnum_gen())
dump_img(sift_tn_img, 'sift_tn_img', fnum_gen())
#vt.imwrite(dataname + '_' + 'cnn_fp_img.png', (cnn_fp_img))
#vt.imwrite(dataname + '_' + 'cnn_fn_img.png', (cnn_fn_img))
#vt.imwrite(dataname + '_' + 'sift_fp_img.png', (sift_fp_img))
#vt.imwrite(dataname + '_' + 'sift_fn_img.png', (sift_fn_img))
else:
print('Drawing TP FP TN FN')
fnum = fnum_gen()
pnum_gen = pt.make_pnum_nextgen(4, 2)
fig = pt.figure(fnum)
pt.imshow(cnn_fp_img, title='CNN FP', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_fp_img, title='SIFT FP', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_fn_img, title='CNN FN', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_fn_img, title='SIFT FN', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_tp_img, title='CNN TP', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_tp_img, title='SIFT TP', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_tn_img, title='CNN TN', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_tn_img, title='SIFT TN', fnum=fnum, pnum=pnum_gen())
pt.set_figtitle(dataname + ' confusions')
pt.adjust_subplots(left=0, right=1.0, bottom=0., wspace=.01, hspace=.05)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180, figsize=(9, 18))
with_patch_desc = FULL
if with_patch_desc:
ut.colorprint('[siam_perf] Visualize Patch Descriptors', 'white')
fnum = fnum_gen()
fig = pt.figure(fnum=fnum, pnum=(1, 1, 1))
num_rows = 7
pnum_gen = pt.make_pnum_nextgen(num_rows, 3)
# Compare actual output descriptors
for index in ut.random_indexes(len(sift_list), num_rows):
vec_sift = sift_list[index]
vec_cnn = network_output[index]
patch = data[index]
pt.imshow(patch, fnum=fnum, pnum=pnum_gen())
pt.plot_descriptor_signature(vec_cnn, 'cnn vec', fnum=fnum, pnum=pnum_gen())
pt.plot_sift_signature(vec_sift, 'sift vec', fnum=fnum, pnum=pnum_gen())
pt.set_figtitle('Patch Descriptors')
pt.adjust_subplots(left=0, right=0.95, bottom=0., wspace=.1, hspace=.15)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180, figsize=(9, 18))
def sift_test():
"""
Play with SIFT equations using python so I can see and compare results.
"""
import numpy as np
# Sample measurement from lena
sift_raw = np.array([
48.0168,
130.017,
159.065,
54.5727,
63.7103,
14.3629,
27.0228,
15.3527,
40.5067,
165.721,
511.036,
196.888,
4.72748,
8.85093,
15.9457,
14.4198,
49.7571,
209.104,
452.047,
223.972,
2.66391,
16.8975,
21.7488,
13.6855,
0.700244,
10.2518,
312.483,
282.647,
1.82898,
3.01759,
0.448028,
0,
144.834,
300.438,
131.837,
40.3284,
11.1998,
9.68647,
7.68484,
29.166,
425.953,
386.903,
352.388,
267.883,
12.9652,
18.833,
8.55462,
71.7924,
112.282,
295.512,
678.599,
419.405,
21.3151,
91.9408,
22.8681,
9.83749,
3.06347,
97.6562,
458.799,
221.873,
68.1473,
410.764,
48.9493,
2.01682,
194.794,
43.7171,
16.2078,
17.5604,
48.8504,
48.3823,
45.7636,
299.432,
901.565,
188.732,
32.6512,
23.6874,
55.379,
272.264,
68.2334,
221.37,
159.631,
44.1475,
126.636,
95.1978,
74.1097,
1353.24,
239.319,
33.5368,
5.62254,
69.0013,
51.7629,
9.55458,
26.4599,
699.623,
208.78,
2.09156,
135.278,
19.5378,
52.0265,
51.8445,
49.1938,
9.04161,
11.6605,
87.4498,
604.012,
85.6801,
42.9738,
75.8549,
183.65,
206.912,
34.2781,
95.0146,
13.4201,
83.7426,
440.322,
83.0038,
125.663,
457.333,
52.6424,
4.93713,
0.38947,
244.762,
291.113,
7.50165,
8.16208,
73.2169,
21.9674,
0.00429259,
])
import vtool as vt
# CONFIRMED: One normalization followed by another does not do anything
#sift_root1 = vt.normalize(sift_root1, ord=2)
#sift_root1 = vt.normalize(sift_root1, ord=1)
sift_clip = sift_raw.copy()
sift_clip = vt.normalize(sift_clip, ord=2)
sift_clip[sift_clip > .2] = .2
sift_clip = vt.normalize(sift_clip, ord=2)
siff_ell2 = vt.normalize(sift_raw, ord=2)
siff_ell1 = vt.normalize(sift_raw, ord=1)
# Two versions of root SIFT
# They are equlivalent
# taken from https://hal.inria.fr/hal-00840721/PDF/RR-8325.pdf
normalize1 = lambda x: vt.normalize(x, ord=1) # NOQA
normalize2 = lambda x: vt.normalize(x, ord=2) # NOQA
assert np.all(
np.isclose(np.sqrt(normalize1(sift_raw)),
normalize2(np.sqrt(sift_raw))))
# How do we genralize this for alpha != .5?
# Just always L2 normalize afterwords?
alpha = .2
powerlaw = lambda x: np.power(x, alpha) # NOQA
sift_root1 = normalize2(powerlaw(normalize1(sift_raw)))
sift_root2 = normalize2(powerlaw(sift_raw))
flags = np.isclose(sift_root1, sift_root2)
print(flags)
assert np.all(flags)
#sift_root_quant = np.clip((sift_root1 * 512), 0, 255).astype(np.uint8)
#p = (np.bincount(sift_root_quant) / 128)
#entropy = -np.nansum(p * np.log2(p))
s = sift_raw[0:10]
np.sqrt(s) / (np.sqrt(s).sum()**2)
np.power(normalize1(s), 2)
#b = powerlaw(normalize1(s))
#print(np.isclose(a, b))
np.isclose(normalize1(s), normalize1(normalize2(s)))
# Another root SIFT version from
# https://hal.inria.fr/hal-00688169/document
# but this doesnt seem to work with uint8 representations
sift_root3 = np.sqrt(sift_raw)
sift_root3 = sift_root3 / np.sqrt(np.linalg.norm(sift_root3))
import plottool as pt
import utool as ut
ut.qtensure()
fig = pt.figure(fnum=1, pnum=None)
def draw_sift(sift, pnum, title, **kwargs):
ax = fig.add_subplot(*pnum)
pt.draw_sifts(ax, sift[None, :], **kwargs)
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.grid(False)
ax.set_aspect('equal')
ax.set_xticks([])
ax.set_yticks([])
if title:
ax.set_title(title)
fig.clf()
pnum_ = pt.make_pnum_nextgen(2, 4)
draw_sift(sift_raw, pnum_(), 'raw/max(raw)', fidelity=sift_raw.max())
draw_sift(sift_clip, pnum_(), 'clip', fidelity=1.0)
draw_sift(siff_ell2, pnum_(), 'l2', fidelity=1.0)
draw_sift(siff_ell1, pnum_(), 'l1', fidelity=1.0)
draw_sift(sift_root1, pnum_(), 'root1', fidelity=1.0)
draw_sift(sift_root2, pnum_(), 'root2', fidelity=1.0)
draw_sift(sift_root3, pnum_(), 'root3', fidelity=2.0)
def test_rot_invar():
r"""
CommandLine:
python -m pyhesaff test_rot_invar --show --rebuild-hesaff --no-rmbuild
python -m pyhesaff test_rot_invar --show --nocpp
python -m vtool.tests.dummy testdata_ratio_matches --show --ratio_thresh=1.0 --rotation_invariance --rebuild-hesaff
python -m vtool.tests.dummy testdata_ratio_matches --show --ratio_thresh=1.1 --rotation_invariance --rebuild-hesaff
Example:
>>> # DISABLE_DODCTEST
>>> from pyhesaff._pyhesaff import * # NOQA
>>> test_rot_invar()
"""
import cv2
import vtool as vt
import plottool as pt
TAU = 2 * np.pi
fnum = pt.next_fnum()
NUM_PTS = 5 # 9
theta_list = np.linspace(0, TAU, NUM_PTS, endpoint=False)
nRows, nCols = pt.get_square_row_cols(len(theta_list), fix=True)
next_pnum = pt.make_pnum_nextgen(nRows, nCols)
# Expand the border a bit around star.png
pad_ = 100
img_fpath = grab_test_imgpath('star.png')
img_fpath2 = vt.pad_image_ondisk(img_fpath, pad_, value=26)
for theta in theta_list:
print('-----------------')
print('theta = %r' % (theta, ))
img_fpath = vt.rotate_image_ondisk(img_fpath2,
theta,
border_mode=cv2.BORDER_REPLICATE)
if not ub.argflag('--nocpp'):
(kpts_list_ri, vecs_list2) = detect_feats(img_fpath,
rotation_invariance=True)
kpts_ri = kpts_list_ri[0:2]
(kpts_list_gv, vecs_list1) = detect_feats(img_fpath,
rotation_invariance=False)
kpts_gv = kpts_list_gv[0:2]
# find_kpts_direction
imgBGR = vt.imread(img_fpath)
kpts_ripy = vt.find_kpts_direction(imgBGR,
kpts_gv,
DEBUG_ROTINVAR=False)
# Verify results stdout
#print('nkpts = %r' % (len(kpts_gv)))
#print(vt.kpts_repr(kpts_gv))
#print(vt.kpts_repr(kpts_ri))
#print(vt.kpts_repr(kpts_ripy))
# Verify results plot
pt.figure(fnum=fnum, pnum=next_pnum())
pt.imshow(imgBGR)
#if len(kpts_gv) > 0:
# pt.draw_kpts2(kpts_gv, ori=True, ell_color=pt.BLUE, ell_linewidth=10.5)
ell = False
rect = True
if not ub.argflag('--nocpp'):
if len(kpts_ri) > 0:
pt.draw_kpts2(kpts_ri,
rect=rect,
ell=ell,
ori=True,
ell_color=pt.RED,
ell_linewidth=5.5)
if len(kpts_ripy) > 0:
pt.draw_kpts2(kpts_ripy,
rect=rect,
ell=ell,
ori=True,
ell_color=pt.GREEN,
ell_linewidth=3.5)
pt.set_figtitle('green=python, red=C++')
pt.show_if_requested()
def fourier_devtest(img):
r"""
Args:
img (ndarray[uint8_t, ndim=2]): image data
CommandLine:
python -m vtool.quality_classifier --test-fourier_devtest --show
References:
http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/py_fourier_transform.html
http://cns-alumni.bu.edu/~slehar/fourier/fourier.html
Example:
>>> # DISABLE_DOCTEST
>>> from vtool.quality_classifier import * # NOQA
>>> import vtool as vt
>>> # build test data
>>> img_fpath = ut.grab_test_imgpath('lena.png')
>>> img = vt.imread(img_fpath, grayscale=True)
>>> # execute function
>>> magnitude_spectrum = fourier_devtest(img)
"""
import plottool as pt
def pad_img(img):
rows, cols = img.shape
nrows = cv2.getOptimalDFTSize(rows)
ncols = cv2.getOptimalDFTSize(cols)
right = ncols - cols
bottom = nrows - rows
bordertype = cv2.BORDER_CONSTANT
nimg = cv2.copyMakeBorder(img, 0, bottom, 0, right, bordertype, value=0)
return nimg
def convert_to_fdomain(img):
dft = cv2.dft(img.astype(np.float32), flags=cv2.DFT_COMPLEX_OUTPUT)
#dft_shift = np.fft.fftshift(dft)
return dft
def convert_from_fdomain(dft):
img = cv2.idft(dft)
img = cv2.magnitude(img[:, :, 0], img[:, :, 1])
img /= img.max()
return img * 255.0
def get_fdomain_mag(dft_shift):
magnitude_spectrum = np.log(cv2.magnitude(dft_shift[:, :, 0], dft_shift[:, :, 1]))
return magnitude_spectrum
def imgstats(img):
print('stats:')
print(' dtype = %r ' % (img.dtype,))
print(' ' + ut.get_stats_str(img, axis=None))
nimg = pad_img(img)
dft = convert_to_fdomain(nimg)
#freq_domain = np.fft.fft2(img)
#freq_domain_shift = np.fft.fftshift(freq_domain)
rows, cols = nimg.shape
crow, ccol = rows / 2 , cols / 2
# create a mask first, center square is 1, remaining all zeros
mask = np.zeros((rows, cols, 2), np.uint8)
mask[crow - 30:crow + 30, ccol - 30:ccol + 30] = 1
dft_mask = np.fft.ifftshift(np.fft.fftshift(dft) * mask)
img_back = convert_from_fdomain(dft_mask)
imgstats(dft)
imgstats(mask)
imgstats(nimg)
imgstats(nimg)
print('nimg.shape = %r' % (nimg.shape,))
print('dft_shift.shape = %r' % (dft.shape,))
if ut.show_was_requested():
#import plottool as pt
next_pnum = pt.make_pnum_nextgen(nRows=3, nCols=2)
pt.imshow(nimg, pnum=next_pnum(), title='nimg')
pt.imshow(20 * get_fdomain_mag(dft), pnum=next_pnum(), title='mag(f)')
pt.imshow(20 * get_fdomain_mag(dft_mask), pnum=next_pnum(), title='dft_mask')
pt.imshow(img_back, pnum=next_pnum(), title='img_back')
pt.show_if_requested()
def start_new_viz(simp, nRows, nCols, fnum=None):
import plottool as pt
rchip1, rchip2, kpts1, vecs1, kpts2, vecs2, dlen_sqrd2 = simp.testtup
fm_ORIG, fs_ORIG, fm_RAT, fs_RAT, fm_SV, fs_SV, H_RAT = simp.basetup
fm_SC, fs_SC, fm_SCR, fs_SCR, fm_SCRSV, fs_SCRSV, H_SCR = simp.nexttup
fm_norm_RAT, fm_norm_SV = simp.base_meta
fm_norm_SC, fm_norm_SCR, fm_norm_SVSCR = simp.next_meta
locals_ = ut.delete_dict_keys(locals(), ['title'])
keytitle_tups = [
('ORIG', 'initial neighbors'),
('RAT', 'ratio filtered'),
('SV', 'ratio filtered + SV'),
('SC', 'spatially constrained'),
('SCR', 'spatially constrained + ratio'),
('SCRSV', 'spatially constrained + SV'),
]
keytitle_dict = dict(keytitle_tups)
key_list = ut.get_list_column(keytitle_tups, 0)
matchtup_dict = {
key: (locals_['fm_' + key], locals_['fs_' + key])
for key in key_list
}
normtup_dict = {
key: locals_.get('fm_norm_' + key, None)
for key in key_list
}
next_pnum = pt.make_pnum_nextgen(nRows=nRows, nCols=nCols)
if fnum is None:
fnum = pt.next_fnum()
INTERACTIVE = True
if INTERACTIVE:
from plottool import interact_helpers as ih
fig = ih.begin_interaction('qres', fnum)
ih.connect_callback(fig, 'button_press_event', on_single_match_clicked)
else:
pt.figure(fnum=fnum, doclf=True, docla=True)
def show_matches_(key, **kwargs):
assert key in key_list, 'unknown key=%r' % (key,)
showkw = locals_.copy()
pnum = next_pnum()
showkw['pnum'] = pnum
showkw['fnum'] = fnum
showkw.update(kwargs)
_fm, _fs = matchtup_dict[key]
title = keytitle_dict[key]
if kwargs.get('coverage'):
from vtool import coverage_kpts
kpts2, rchip2 = ut.dict_get(locals_, ('kpts2', 'rchip2'))
kpts2_m = kpts2.take(_fm.T[1], axis=0)
chipshape2 = rchip2.shape
chipsize2 = chipshape2[0:2][::-1]
coverage_mask = coverage_kpts.make_kpts_coverage_mask(kpts2_m, chipsize2, fx2_score=_fs, resize=True, return_patch=False)
pt.imshow(coverage_mask * 255, pnum=pnum, fnum=fnum)
else:
if kwargs.get('norm', False):
_fm = normtup_dict[key]
assert _fm is not None, key
showkw['cmap'] = 'cool'
title += ' normalizers'
show_matches(_fm, _fs, title=title, key=key, **showkw)
# state hack
#show_matches_.next_pnum = next_pnum
return show_matches_
def compare_featscores():
"""
CommandLine:
ibeis --tf compare_featscores --db PZ_MTEST \
--nfscfg :disttype=[L2_sift,lnbnn],top_percent=[None,.5,.1] -a timectrl \
-p default:K=[1,2],normalizer_rule=name \
--save featscore{db}.png --figsize=13,20 --diskshow
ibeis --tf compare_featscores --db PZ_MTEST \
--nfscfg :disttype=[L2_sift,normdist,lnbnn],top_percent=[None,.5] -a timectrl \
-p default:K=[1],normalizer_rule=name,sv_on=[True,False] \
--save featscore{db}.png --figsize=13,10 --diskshow
ibeis --tf compare_featscores --nfscfg :disttype=[L2_sift,normdist,lnbnn] \
-a timectrl -p default:K=1,normalizer_rule=name --db PZ_Master1 \
--save featscore{db}.png --figsize=13,13 --diskshow
ibeis --tf compare_featscores --nfscfg :disttype=[L2_sift,normdist,lnbnn] \
-a timectrl -p default:K=1,normalizer_rule=name --db GZ_ALL \
--save featscore{db}.png --figsize=13,13 --diskshow
ibeis --tf compare_featscores --db GIRM_Master1 \
--nfscfg ':disttype=fg,L2_sift,normdist,lnbnn' \
-a timectrl -p default:K=1,normalizer_rule=name \
--save featscore{db}.png --figsize=13,13
ibeis --tf compare_featscores --nfscfg :disttype=[L2_sift,normdist,lnbnn] \
-a timectrl -p default:K=[1,2,3],normalizer_rule=name,sv_on=False \
--db PZ_Master1 --save featscore{db}.png \
--dpi=128 --figsize=15,20 --diskshow
ibeis --tf compare_featscores --show --nfscfg :disttype=[L2_sift,normdist] -a timectrl -p :K=1 --db PZ_MTEST
ibeis --tf compare_featscores --show --nfscfg :disttype=[L2_sift,normdist] -a timectrl -p :K=1 --db GZ_ALL
ibeis --tf compare_featscores --show --nfscfg :disttype=[L2_sift,normdist] -a timectrl -p :K=1 --db PZ_Master1
ibeis --tf compare_featscores --show --nfscfg :disttype=[L2_sift,normdist] -a timectrl -p :K=1 --db GIRM_Master1
ibeis --tf compare_featscores --db PZ_MTEST \
--nfscfg :disttype=[L2_sift,normdist,lnbnn],top_percent=[None,.5,.2] -a timectrl \
-p default:K=[1],normalizer_rule=name \
--save featscore{db}.png --figsize=13,20 --diskshow
ibeis --tf compare_featscores --db PZ_MTEST \
--nfscfg :disttype=[L2_sift,normdist,lnbnn],top_percent=[None,.5,.2] -a timectrl \
-p default:K=[1],normalizer_rule=name \
--save featscore{db}.png --figsize=13,20 --diskshow
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.scorenorm import * # NOQA
>>> result = compare_featscores()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import plottool as pt
import ibeis
nfs_cfg_list = NormFeatScoreConfig.from_argv_cfgs()
learnkw = {}
ibs, testres = ibeis.testdata_expts(
defaultdb='PZ_MTEST', a=['default'], p=['default:K=1'])
print('nfs_cfg_list = ' + ut.repr3(nfs_cfg_list))
encoder_list = []
lbl_list = []
varied_nfs_lbls = ut.get_varied_cfg_lbls(nfs_cfg_list)
varied_qreq_lbls = ut.get_varied_cfg_lbls(testres.cfgdict_list)
#varies_qreq_lbls
#func = ut.cached_func(cache_dir='.')(learn_featscore_normalizer)
for datakw, nlbl in zip(nfs_cfg_list, varied_nfs_lbls):
for qreq_, qlbl in zip(testres.cfgx2_qreq_, varied_qreq_lbls):
lbl = qlbl + ' ' + nlbl
cfgstr = '_'.join([datakw.get_cfgstr(), qreq_.get_full_cfgstr()])
try:
encoder = vt.ScoreNormalizer()
encoder.load(cfgstr=cfgstr)
except IOError:
print('datakw = %r' % (datakw,))
encoder = learn_featscore_normalizer(qreq_, datakw, learnkw)
encoder.save(cfgstr=cfgstr)
encoder_list.append(encoder)
lbl_list.append(lbl)
fnum = 1
# next_pnum = pt.make_pnum_nextgen(nRows=len(encoder_list), nCols=3)
next_pnum = pt.make_pnum_nextgen(nRows=len(encoder_list) + 1, nCols=3, start=3)
iconsize = 94
if len(encoder_list) > 3:
iconsize = 64
icon = qreq_.ibs.get_database_icon(max_dsize=(None, iconsize), aid=qreq_.qaids[0])
score_range = (0, .6)
for encoder, lbl in zip(encoder_list, lbl_list):
#encoder.visualize(figtitle=encoder.get_cfgstr(), with_prebayes=False, with_postbayes=False)
encoder._plot_score_support_hist(fnum, pnum=next_pnum(), titlesuf='\n' + lbl, score_range=score_range)
encoder._plot_prebayes(fnum, pnum=next_pnum())
encoder._plot_roc(fnum, pnum=next_pnum())
if icon is not None:
pt.overlay_icon(icon, coords=(1, 0), bbox_alignment=(1, 0))
nonvaried_lbl = ut.get_nonvaried_cfg_lbls(nfs_cfg_list)[0]
figtitle = qreq_.__str__() + '\n' + nonvaried_lbl
pt.set_figtitle(figtitle)
pt.adjust_subplots(hspace=.5, top=.92, bottom=.08, left=.1, right=.9)
pt.update_figsize()
pt.plt.tight_layout()
def test_featweight_worker():
"""
test function
python -m ibeis.algo.preproc.preproc_featweight --test-gen_featweight_worker --show --cnn
"""
import ibeis
qreq_ = ibeis.main_helpers.testdata_qreq_(defaultdb='PZ_MTEST', p=['default:fw_detector=cnn'], qaid_override=[1])
ibs = qreq_.ibs
config2_ = qreq_.qparams
lazy = True
aid_list = qreq_.get_external_qaids()
#aid_list = ibs.get_valid_aids()[0:30]
kpts_list = ibs.get_annot_kpts(aid_list)
chipsize_list = ibs.get_annot_chip_sizes(aid_list, config2_=config2_)
probchip_fpath_list = preproc_probchip.compute_and_write_probchip(ibs,
aid_list,
lazy=lazy,
config2_=config2_)
print('probchip_fpath_list = %r' % (probchip_fpath_list,))
probchip_list = [vt.imread(fpath, grayscale=True) if exists(fpath) else None
for fpath in probchip_fpath_list]
_iter = list(zip(aid_list, kpts_list, probchip_list, chipsize_list))
_iter = ut.InteractiveIter(_iter, enabled=ut.get_argflag('--show'))
for aid, kpts, probchip, chipsize in _iter:
#kpts = kpts_list[0]
#aid = aid_list[0]
#probchip = probchip_list[0]
#chipsize = chipsize_list[0]
tup = (aid, kpts, probchip, chipsize)
(aid, weights) = gen_featweight_worker(tup)
if aid == 3 and ibs.get_dbname() == 'testdb1':
# Run Asserts if not interactive
weights_03_test = weights[0:3]
print('weights[0:3] = %r' % (weights_03_test,))
#weights_03_target = [ 0.098, 0.155, 0.422]
#weights_03_target = [ 0.324, 0.407, 0.688]
#weights_thresh = [ 0.09, 0.09, 0.09]
#ut.assert_almost_eq(weights_03_test, weights_03_target, weights_thresh)
ut.assert_inbounds(weights_03_test, 0, 1)
if not ut.show_was_requested():
break
if ut.show_was_requested():
import plottool as pt
#sfx, sfy = (probchip.shape[1] / chipsize[0], probchip.shape[0] / chipsize[1])
#kpts_ = vt.offset_kpts(kpts, (0, 0), (sfx, sfy))
pnum_ = pt.make_pnum_nextgen(1, 3) # *pt.get_square_row_cols(4))
fnum = 1
pt.figure(fnum=fnum, doclf=True)
###
pt.imshow(ibs.get_annot_chips(aid, config2_=config2_), pnum=pnum_(0), fnum=fnum)
if ut.get_argflag('--numlbl'):
pt.gca().set_xlabel('(1)')
###
pt.imshow(probchip, pnum=pnum_(2), fnum=fnum)
if ut.get_argflag('--numlbl'):
pt.gca().set_xlabel('(2)')
#pt.draw_kpts2(kpts_, ell_alpha=.4, color_list=pt.ORANGE)
###
#pt.imshow(probchip, pnum=pnum_(3), fnum=fnum)
#color_list = pt.draw_kpts2(kpts_, weights=weights, ell_alpha=.7, cmap_='jet')
#cb = pt.colorbar(weights, color_list)
#cb.set_label('featweights')
###
pt.imshow(ibs.get_annot_chips(aid, config2_=qreq_.qparams), pnum=pnum_(1), fnum=fnum)
#color_list = pt.draw_kpts2(kpts, weights=weights, ell_alpha=.3, cmap_='jet')
color_list = pt.draw_kpts2(kpts, weights=weights, ell_alpha=.3)
cb = pt.colorbar(weights, color_list)
cb.set_label('featweights')
if ut.get_argflag('--numlbl'):
pt.gca().set_xlabel('(3)')
#pt.draw_kpts2(kpts, ell_alpha=.4)
pt.draw()
pt.show_if_requested()
def start_new_viz(simp, nRows, nCols, fnum=None):
import plottool as pt
rchip1, rchip2, kpts1, vecs1, kpts2, vecs2, dlen_sqrd2 = simp.testtup
fm_ORIG, fs_ORIG, fm_RAT, fs_RAT, fm_SV, fs_SV, H_RAT = simp.basetup
fm_SC, fs_SC, fm_SCR, fs_SCR, fm_SCRSV, fs_SCRSV, H_SCR = simp.nexttup
fm_norm_RAT, fm_norm_SV = simp.base_meta
fm_norm_SC, fm_norm_SCR, fm_norm_SVSCR = simp.next_meta
locals_ = ut.delete_dict_keys(locals(), ['title'])
keytitle_tups = [
('ORIG', 'initial neighbors'),
('RAT', 'ratio filtered'),
('SV', 'ratio filtered + SV'),
('SC', 'spatially constrained'),
('SCR', 'spatially constrained + ratio'),
('SCRSV', 'spatially constrained + SV'),
]
keytitle_dict = dict(keytitle_tups)
key_list = ut.get_list_column(keytitle_tups, 0)
matchtup_dict = {
key: (locals_['fm_' + key], locals_['fs_' + key])
for key in key_list
}
normtup_dict = {
key: locals_.get('fm_norm_' + key, None)
for key in key_list
}
next_pnum = pt.make_pnum_nextgen(nRows=nRows, nCols=nCols)
if fnum is None:
fnum = pt.next_fnum()
INTERACTIVE = True
if INTERACTIVE:
from plottool import interact_helpers as ih
fig = ih.begin_interaction('qres', fnum)
ih.connect_callback(fig, 'button_press_event',
on_single_match_clicked)
else:
pt.figure(fnum=fnum, doclf=True, docla=True)
def show_matches_(key, **kwargs):
assert key in key_list, 'unknown key=%r' % (key, )
showkw = locals_.copy()
pnum = next_pnum()
showkw['pnum'] = pnum
showkw['fnum'] = fnum
showkw.update(kwargs)
_fm, _fs = matchtup_dict[key]
title = keytitle_dict[key]
if kwargs.get('coverage'):
from vtool import coverage_kpts
kpts2, rchip2 = ut.dict_get(locals_, ('kpts2', 'rchip2'))
kpts2_m = kpts2.take(_fm.T[1], axis=0)
chipshape2 = rchip2.shape
chipsize2 = chipshape2[0:2][::-1]
coverage_mask = coverage_kpts.make_kpts_coverage_mask(
kpts2_m,
chipsize2,
fx2_score=_fs,
resize=True,
return_patch=False)
pt.imshow(coverage_mask * 255, pnum=pnum, fnum=fnum)
else:
if kwargs.get('norm', False):
_fm = normtup_dict[key]
assert _fm is not None, key
showkw['cmap'] = 'cool'
title += ' normalizers'
show_matches(_fm, _fs, title=title, key=key, **showkw)
# state hack
#show_matches_.next_pnum = next_pnum
return show_matches_
def test_rot_invar():
r"""
CommandLine:
python -m pyhesaff test_rot_invar --show --rebuild-hesaff --no-rmbuild
python -m pyhesaff test_rot_invar --show --nocpp
python -m vtool.tests.dummy testdata_ratio_matches --show --ratio_thresh=1.0 --rotation_invariance --rebuild-hesaff
python -m vtool.tests.dummy testdata_ratio_matches --show --ratio_thresh=1.1 --rotation_invariance --rebuild-hesaff
Example:
>>> # DISABLE_DODCTEST
>>> from pyhesaff._pyhesaff import * # NOQA
>>> test_rot_invar()
"""
import cv2
import utool as ut
import vtool as vt
import plottool as pt
TAU = 2 * np.pi
fnum = pt.next_fnum()
NUM_PTS = 5 # 9
theta_list = np.linspace(0, TAU, NUM_PTS, endpoint=False)
nRows, nCols = pt.get_square_row_cols(len(theta_list), fix=True)
next_pnum = pt.make_pnum_nextgen(nRows, nCols)
# Expand the border a bit around star.png
pad_ = 100
img_fpath = ut.grab_test_imgpath('star.png')
img_fpath2 = vt.pad_image_ondisk(img_fpath, pad_, value=26)
for theta in theta_list:
print('-----------------')
print('theta = %r' % (theta,))
#theta = ut.get_argval('--theta', type_=float, default=TAU * 3 / 8)
img_fpath = vt.rotate_image_ondisk(img_fpath2, theta, borderMode=cv2.BORDER_REPLICATE)
if not ut.get_argflag('--nocpp'):
(kpts_list_ri, vecs_list2) = detect_feats(img_fpath, rotation_invariance=True)
kpts_ri = ut.strided_sample(kpts_list_ri, 2)
(kpts_list_gv, vecs_list1) = detect_feats(img_fpath, rotation_invariance=False)
kpts_gv = ut.strided_sample(kpts_list_gv, 2)
# find_kpts_direction
imgBGR = vt.imread(img_fpath)
kpts_ripy = vt.find_kpts_direction(imgBGR, kpts_gv, DEBUG_ROTINVAR=False)
# Verify results stdout
#print('nkpts = %r' % (len(kpts_gv)))
#print(vt.kpts_repr(kpts_gv))
#print(vt.kpts_repr(kpts_ri))
#print(vt.kpts_repr(kpts_ripy))
# Verify results plot
pt.figure(fnum=fnum, pnum=next_pnum())
pt.imshow(imgBGR)
#if len(kpts_gv) > 0:
# pt.draw_kpts2(kpts_gv, ori=True, ell_color=pt.BLUE, ell_linewidth=10.5)
ell = False
rect = True
if not ut.get_argflag('--nocpp'):
if len(kpts_ri) > 0:
pt.draw_kpts2(kpts_ri, rect=rect, ell=ell, ori=True,
ell_color=pt.RED, ell_linewidth=5.5)
if len(kpts_ripy) > 0:
pt.draw_kpts2(kpts_ripy, rect=rect, ell=ell, ori=True,
ell_color=pt.GREEN, ell_linewidth=3.5)
#print('\n'.join(vt.get_ori_strs(np.vstack([kpts_gv, kpts_ri, kpts_ripy]))))
#ut.embed(exec_lines=['pt.update()'])
pt.set_figtitle('green=python, red=C++')
pt.show_if_requested()
def fourier_devtest(img):
r"""
Args:
img (ndarray[uint8_t, ndim=2]): image data
CommandLine:
python -m vtool.quality_classifier --test-fourier_devtest --show
References:
http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/py_fourier_transform.html
http://cns-alumni.bu.edu/~slehar/fourier/fourier.html
Example:
>>> # DISABLE_DOCTEST
>>> from vtool.quality_classifier import * # NOQA
>>> import vtool as vt
>>> # build test data
>>> img_fpath = ut.grab_test_imgpath('lena.png')
>>> img = vt.imread(img_fpath, grayscale=True)
>>> # execute function
>>> magnitude_spectrum = fourier_devtest(img)
"""
import plottool as pt
def pad_img(img):
rows, cols = img.shape
nrows = cv2.getOptimalDFTSize(rows)
ncols = cv2.getOptimalDFTSize(cols)
right = ncols - cols
bottom = nrows - rows
bordertype = cv2.BORDER_CONSTANT
nimg = cv2.copyMakeBorder(img,
0,
bottom,
0,
right,
bordertype,
value=0)
return nimg
def convert_to_fdomain(img):
dft = cv2.dft(img.astype(np.float32), flags=cv2.DFT_COMPLEX_OUTPUT)
#dft_shift = np.fft.fftshift(dft)
return dft
def convert_from_fdomain(dft):
img = cv2.idft(dft)
img = cv2.magnitude(img[:, :, 0], img[:, :, 1])
img /= img.max()
return img * 255.0
def get_fdomain_mag(dft_shift):
magnitude_spectrum = np.log(
cv2.magnitude(dft_shift[:, :, 0], dft_shift[:, :, 1]))
return magnitude_spectrum
def imgstats(img):
print('stats:')
print(' dtype = %r ' % (img.dtype, ))
print(' ' + ut.get_stats_str(img, axis=None))
nimg = pad_img(img)
dft = convert_to_fdomain(nimg)
#freq_domain = np.fft.fft2(img)
#freq_domain_shift = np.fft.fftshift(freq_domain)
rows, cols = nimg.shape
crow, ccol = rows / 2, cols / 2
# create a mask first, center square is 1, remaining all zeros
mask = np.zeros((rows, cols, 2), np.uint8)
mask[crow - 30:crow + 30, ccol - 30:ccol + 30] = 1
dft_mask = np.fft.ifftshift(np.fft.fftshift(dft) * mask)
img_back = convert_from_fdomain(dft_mask)
imgstats(dft)
imgstats(mask)
imgstats(nimg)
imgstats(nimg)
print('nimg.shape = %r' % (nimg.shape, ))
print('dft_shift.shape = %r' % (dft.shape, ))
if ut.show_was_requested():
#import plottool as pt
next_pnum = pt.make_pnum_nextgen(nRows=3, nCols=2)
pt.imshow(nimg, pnum=next_pnum(), title='nimg')
pt.imshow(20 * get_fdomain_mag(dft), pnum=next_pnum(), title='mag(f)')
pt.imshow(20 * get_fdomain_mag(dft_mask),
pnum=next_pnum(),
title='dft_mask')
pt.imshow(img_back, pnum=next_pnum(), title='img_back')
pt.show_if_requested()
def show_augmented_patches(Xb, Xb_, yb, yb_, data_per_label=1, shadows=None):
"""
from ibeis_cnn.augment import * # NOQA
std_ = center_std
mean_ = center_mean
"""
import plottool as pt
import vtool as vt
Xb_old = vt.rectify_to_float01(Xb)
Xb_new = vt.rectify_to_float01(Xb_)
# only look at ones that were actually augmented
sample1 = Xb_old[0::data_per_label]
sample2 = Xb_new[0::data_per_label]
diff = np.abs((sample1 - sample2))
diff_batches = diff.sum(-1).sum(-1).sum(-1) > 0
modified_indexes = np.where(diff_batches > 0)[0]
print('modified_indexes = %r' % (modified_indexes,))
#modified_indexes = np.arange(num_examples)
Xb_old = vt.rectify_to_uint8(Xb_old)
Xb_new = vt.rectify_to_uint8(Xb_new)
# Group data into n-tuples
grouped_idxs = [np.arange(n, len(Xb_), data_per_label)
for n in range(data_per_label)]
data_lists_old = vt.apply_grouping(Xb_old, grouped_idxs, axis=0)
data_lists_new = vt.apply_grouping(Xb_new, grouped_idxs, axis=0)
import six
#chunck_sizes = (4, 10)
import utool
with utool.embed_on_exception_context:
chunk_sizes = pt.get_square_row_cols(len(modified_indexes), max_cols=10,
fix=False, inclusive=False)
_iter = ut.iter_multichunks(modified_indexes, chunk_sizes)
multiindices = six.next(_iter)
from ibeis_cnn import draw_results
tup = draw_results.get_patch_multichunks(data_lists_old, yb, {},
multiindices)
orig_stack = tup[0]
#stacked_img, stacked_offsets, stacked_sfs = tup
tup = draw_results.get_patch_multichunks(data_lists_new, yb_, {},
multiindices)
warp_stack = tup[0]
#stacked_img, stacked_offsets, stacked_sfs = tup
#orig_stack = stacked_img_pairs(Xb_old, modified_indexes, yb)
#warp_stack = stacked_img_pairs(Xb_new, modified_indexes, yb_)
if shadows is not None:
# hack
shadow_stack = stacked_img_pairs(shadows, modified_indexes, yb_)
fnum = None
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum)
#next_pnum = pt.make_pnum_nextgen(nRows=2 + (shadows is not None), nCols=1)
next_pnum = pt.make_pnum_nextgen(nCols=2 + (shadows is not None), nRows=1)
pt.imshow(orig_stack, pnum=next_pnum(), title='before')
pt.imshow(warp_stack, pnum=next_pnum(), title='after')
if shadows is not None:
pt.imshow(shadow_stack, pnum=next_pnum(), title='shadow_stack')
def demo_classes(pblm):
r"""
CommandLine:
python -m ibeis.algo.verif.vsone demo_classes --saveparts --save=classes.png --clipwhite
python -m ibeis.algo.verif.vsone demo_classes --saveparts --save=figures/classes.png --clipwhite --dpath=~/latex/crall-iccv-2017
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.verif.vsone import * # NOQA
>>> pblm = OneVsOneProblem.from_empty(defaultdb='PZ_PB_RF_TRAIN')
>>> pblm.load_features()
>>> pblm.load_samples()
>>> pblm.build_feature_subsets()
>>> pblm.demo_classes()
>>> ut.show_if_requested()
"""
task_key = 'match_state'
labels = pblm.samples.subtasks[task_key]
pb_labels = pblm.samples.subtasks['photobomb_state']
classname_offset = {
POSTV: 0,
NEGTV: 0,
INCMP: 0,
}
class_name = POSTV
class_name = NEGTV
class_name = INCMP
feats = pblm.samples.X_dict['learn(sum,glob)']
offset = 0
class_to_edge = {}
for class_name in labels.class_names:
print('Find example of %r' % (class_name, ))
# Find an example of each class (that is not a photobomb)
pbflags = pb_labels.indicator_df['notpb']
flags = labels.indicator_df[class_name]
assert np.all(pbflags.index == flags.index)
flags = flags & pbflags
ratio = feats['sum(ratio)']
if class_name == INCMP:
# flags &= feats['global(delta_yaw)'] > 3
flags &= feats['global(delta_view)'] > 2
# flags &= feats['sum(ratio)'] > 0
if class_name == NEGTV:
low = ratio[flags].max()
flags &= feats['sum(ratio)'] >= low
if class_name == POSTV:
low = ratio[flags].median() / 2
high = ratio[flags].median()
flags &= feats['sum(ratio)'] < high
flags &= feats['sum(ratio)'] > low
# flags &= pblm.samples.simple_scores[flags]['score_lnbnn_1vM'] > 0
idxs = np.where(flags)[0]
print('Found %d candidates' % (len(idxs)))
offset = classname_offset[class_name]
idx = idxs[offset]
series = labels.indicator_df.iloc[idx]
assert series[class_name]
edge = series.name
class_to_edge[class_name] = edge
import plottool as pt
import guitool as gt
gt.ensure_qapp()
pt.qtensure()
fnum = 1
pt.figure(fnum=fnum, pnum=(1, 3, 1))
pnum_ = pt.make_pnum_nextgen(1, 3)
# classname_alias = {
# POSTV: 'positive',
# NEGTV: 'negative',
# INCMP: 'incomparable',
# }
ibs = pblm.infr.ibs
for class_name in class_to_edge.keys():
edge = class_to_edge[class_name]
aid1, aid2 = edge
# alias = classname_alias[class_name]
print('class_name = %r' % (class_name, ))
annot1 = ibs.annots([aid1])[0]._make_lazy_dict()
annot2 = ibs.annots([aid2])[0]._make_lazy_dict()
vt.matching.ensure_metadata_normxy(annot1)
vt.matching.ensure_metadata_normxy(annot2)
match = vt.PairwiseMatch(annot1, annot2)
cfgdict = pblm.hyper_params.vsone_match.asdict()
match.apply_all(cfgdict)
pt.figure(fnum=fnum, pnum=pnum_())
match.show(show_ell=False, show_ori=False)
