def show_normalizers(match, fnum=None, pnum=None, update=True):
import plottool as pt
from plottool import plot_helpers as ph
# hack keys out of namespace
keys = ['rchip', 'kpts']
rchip1, kpts1 = ut.dict_take(match.annot1.__dict__, keys)
rchip2, kpts2 = ut.dict_take(match.annot2.__dict__, keys)
fs, fm = match.fs, match.fm_norm
cmap = 'cool'
draw_lines = True
if fnum is None:
fnum = pt.next_fnum()
pt.figure(fnum=fnum, pnum=pnum)
#doclf=True, docla=True)
ax, xywh1, xywh2 = pt.show_chipmatch2(rchip1,
rchip2,
kpts1,
kpts2,
fm=fm,
fs=fs,
fnum=fnum,
cmap=cmap,
draw_lines=draw_lines)
ph.set_plotdat(ax, 'viztype', 'matches')
ph.set_plotdat(ax, 'key', match.key)
title = match.key + '\n num=%d, sum=%.2f' % (len(fm), sum(fs))
pt.set_title(title)
if update:
pt.update()
return ax, xywh1, xywh2
def visualize_probs(normalizer, update=True, fnum=None, pnum=(1, 1, 1)):
plot_postbayes_pdf(normalizer.score_domain, 1 - normalizer.p_tp_given_score,
normalizer.p_tp_given_score,
cfgstr=normalizer.get_cfgstr(), fnum=fnum, pnum=pnum)
if update:
import plottool as pt
pt.update()
def show_normalizers(match, fnum=None, pnum=None, update=True):
import plottool as pt
from plottool import plot_helpers as ph
# hack keys out of namespace
keys = ["rchip", "kpts"]
rchip1, kpts1 = ut.dict_take(match.annot1.__dict__, keys)
rchip2, kpts2 = ut.dict_take(match.annot2.__dict__, keys)
fs, fm = match.fs, match.fm_norm
cmap = "cool"
draw_lines = True
if fnum is None:
fnum = pt.next_fnum()
pt.figure(fnum=fnum, pnum=pnum)
# doclf=True, docla=True)
ax, xywh1, xywh2 = pt.show_chipmatch2(
rchip1, rchip2, kpts1, kpts2, fm=fm, fs=fs, fnum=fnum, cmap=cmap, draw_lines=draw_lines
)
ph.set_plotdat(ax, "viztype", "matches")
ph.set_plotdat(ax, "key", match.key)
title = match.key + "\n num=%d, sum=%.2f" % (len(fm), sum(fs))
pt.set_title(title)
if update:
pt.update()
return ax, xywh1, xywh2
def visualize(normalizer, update=True, verbose=True, fnum=None):
"""
CommandLine:
python -m ibeis.algo.hots.score_normalization --test-visualize --index 0
--cmd
Example:
>>> # DISABLE_DOCTEST
>>> import plottool as pt
>>> from ibeis.algo.hots.score_normalization import * # NOQA
>>> #import ibeis
>>> index = ut.get_argval('--index', type_=int, default=0)
>>> normalizer = load_precomputed_normalizer(index, with_global=False)
>>> normalizer.visualize()
>>> six.exec_(pt.present(), globals(), locals())
"""
import plottool as pt
if verbose:
print(normalizer.get_infostr())
if normalizer.score_domain is None:
return
if fnum is None:
fnum = pt.next_fnum()
pt.figure(fnum=fnum, pnum=(2, 1, 1), doclf=True, docla=True)
normalizer.visualize_probs(fnum=fnum, pnum=(2, 1, 1), update=False)
normalizer.visualize_support(fnum=fnum, pnum=(2, 1, 2), update=False)
if update:
pt.update()
def show_selected(self, event):
import plottool as pt
print('show_selected')
from ibeis.viz import viz_chip
fnum = pt.ensure_fnum(None)
print('fnum = %r' % (fnum,))
pt.figure(fnum=fnum)
viz_chip.show_many_chips(self.ibs, self.selected_aids)
pt.update()
def show_selected(self, event):
import plottool as pt
print('show_selected')
from ibeis.viz import viz_chip
fnum = pt.ensure_fnum(None)
print('fnum = %r' % (fnum, ))
pt.figure(fnum=fnum)
viz_chip.show_many_chips(self.ibs, self.selected_aids)
pt.update()
def plot_weights(self, event=None):
scalars = self.infr.get_scalars()
import plottool as pt
inter = pt.ExpandableInteraction(fnum=1)
for px, (key, vals) in enumerate(scalars.items()):
print(key + ' = ' + ut.get_stats_str(vals, use_nan=True))
args = (np.arange(len(vals)), sorted(vals))
kw = dict(title=key, y_label=key, marker='-o', equal_aspect=False)
inter.append_partial(pt.plot2, *args, **kw)
inter.start()
inter.show_page()
pt.update()
def __init__(self,
ibs,
gid,
next_callback=None,
prev_callback=None,
rows_updated_callback=None,
reset_window=True,
dodraw=True,
fnum=None):
"""
TODO: rename to interact image annotations?
"""
self.ibs = ibs
self.gid = gid
self.rows_updated_callback = rows_updated_callback
img = ibs.get_images(self.gid)
self.aid_list = ibs.get_image_aids(self.gid)
bbox_list = ibs.get_annot_bboxes(self.aid_list)
#verts_list = ibs.get_annot_verts(self.aid_list) # TODO
theta_list = ibs.get_annot_thetas(self.aid_list)
species_list = ibs.get_annot_species_texts(self.aid_list)
#valid_species = ibs.get_all_species_texts()
valid_species = [tup[1] for tup in ibs.get_working_species()]
metadata_list = [ibs.get_annot_lazy_dict(aid) for aid in self.aid_list]
for metadata in metadata_list:
# eager eval on name
metadata['name']
if True:
interact_annotations.rrr()
self.interact_ANNOTATIONS = interact_annotations.ANNOTATIONInteraction(
img,
bbox_list=bbox_list,
theta_list=theta_list,
species_list=species_list,
metadata_list=metadata_list,
commit_callback=self.commit_callback,
# TODO: get default species in a better way
default_species=self.ibs.cfg.detect_cfg.species_text,
next_callback=next_callback,
prev_callback=prev_callback,
fnum=fnum,
valid_species=valid_species,
#figure_to_use=None if reset_window else self.interact_ANNOTATIONS.fig,
)
if dodraw:
pt.update()
def test_sharpness():
import ibeis
defaltdb = 'seaturtles'
a = ['default']
ibs = ibeis.opendb(defaultdb=defaltdb)
ibs, qaids, daids = ibeis.testdata_expanded_aids(ibs=ibs, a=a)
from vtool import quality_classifier
contrast_list = [quality_classifier.compute_average_contrast(chip) for chip in ibs.get_annot_chips(qaids)]
sortx = ut.argsort(contrast_list)[::-1]
sharpest_qaids = ut.take(qaids, sortx)
aid = sharpest_qaids[0]
ut.ensure_pylab_qt4()
from ibeis import viz
import plottool as pt
for aid in ut.InteractiveIter(sharpest_qaids):
viz.show_chip(ibs, aid, annot=False, nokpts=True)
pt.update()
def __init__(self, ibs, gid, next_callback=None, prev_callback=None,
rows_updated_callback=None, reset_window=True, dodraw=True,
fnum=None):
"""
TODO: rename to interact image annotations?
"""
self.ibs = ibs
self.gid = gid
self.rows_updated_callback = rows_updated_callback
img = ibs.get_images(self.gid)
self.aid_list = ibs.get_image_aids(self.gid)
bbox_list = ibs.get_annot_bboxes(self.aid_list)
#verts_list = ibs.get_annot_verts(self.aid_list) # TODO
theta_list = ibs.get_annot_thetas(self.aid_list)
species_list = ibs.get_annot_species_texts(self.aid_list)
#valid_species = ibs.get_all_species_texts()
valid_species = [tup[1] for tup in
ibs.get_working_species()]
metadata_list = [ibs.get_annot_lazy_dict(aid) for aid in self.aid_list]
for metadata in metadata_list:
# eager eval on name
metadata['name']
if True:
interact_annotations.rrr()
self.interact_ANNOTATIONS = interact_annotations.ANNOTATIONInteraction(
img,
bbox_list=bbox_list,
theta_list=theta_list,
species_list=species_list,
metadata_list=metadata_list,
commit_callback=self.commit_callback,
# TODO: get default species in a better way
default_species=self.ibs.cfg.detect_cfg.species_text,
next_callback=next_callback,
prev_callback=prev_callback,
fnum=fnum,
valid_species=valid_species,
#figure_to_use=None if reset_window else self.interact_ANNOTATIONS.fig,
)
if dodraw:
pt.update()
def test_sharpness():
import ibeis
defaltdb = 'seaturtles'
a = ['default']
ibs = ibeis.opendb(defaultdb=defaltdb)
ibs, qaids, daids = ibeis.testdata_expanded_aids(ibs=ibs, a=a)
from vtool import quality_classifier
contrast_list = [
quality_classifier.compute_average_contrast(chip)
for chip in ibs.get_annot_chips(qaids)
]
sortx = ut.argsort(contrast_list)[::-1]
sharpest_qaids = ut.take(qaids, sortx)
aid = sharpest_qaids[0]
ut.ensure_pylab_qt4()
from ibeis import viz
import plottool as pt
for aid in ut.InteractiveIter(sharpest_qaids):
viz.show_chip(ibs, aid, annot=False, nokpts=True)
pt.update()
def view_training_data(ibs, **kwargs):
"""
CommandLine:
python -m ibeis_cnn.ibsplugin --test-view_training_data --db NNP_Master3
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_cnn.ibsplugin import * # NOQA
>>> import ibeis
>>> # build test data
>>> ibs = ibeis.opendb(defaultdb='testdb1')
>>> view_training_data(ibs)
"""
data_fpath, labels_fpath, training_dpath = get_identify_training_fpaths(
ibs, **kwargs)
data = np.load(data_fpath, mmap_mode='r')
#width = height = base_size * 2 # HACK FIXME
#channels = 3
#img_list = utils.convert_data_to_imglist(data, width, height, channels)
import plottool as pt
for img in ut.InteractiveIter(data, display_item=False):
pt.imshow(img)
pt.update()
def show_single_namematch():
import plottool as pt
ax = cm.show_single_namematch(qreq_, aid2, mode=0)
ax = pt.gca()
ax.figure.canvas.draw()
pt.update()
def export_testset_for_chuck(ibs, min_num_annots):
"""
Exports a set with some number of annotations that has good demo examples.
multiple annotations per name and large time variation within names.
Args:
ibs (IBEISController): ibeis controller object
CommandLine:
python -m ibeis.other.ibsfuncs --test-export_testset_for_chuck --dbdir /raid/work2/Turk/PZ_Master --min-num-annots 100
python -m ibeis.other.ibsfuncs --test-export_testset_for_chuck --dbdir /raid/work2/Turk/PZ_Master --min-num-annots 500
python -m ibeis.other.ibsfuncs --test-export_testset_for_chuck --dbdir /raid/work2/Turk/GZ_Master --min-num-annots 100
python -m ibeis.other.ibsfuncs --test-export_testset_for_chuck --dbdir
/raid/work2/Turk/GZ_Master --min-num-annots 500_DOCTEST
python -m ibeis.other.ibsfuncs --test-export_testset_for_chuck --db GIR_Tanya --min-num-annots 100
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.other.ibsfuncs import * # NOQA
>>> import ibeis
>>> # build test data
>>> #dbdir = ut.get_argval(('--dbdir',), type_=str, default='testdb1')
>>> min_num_annots = ut.get_argval(('--min-num-annots',), type_=int, default=500)
>>> #ibs = ibeis.opendb('testdb1')
>>> #ibs = ibeis.opendb(dbdir='/raid/work2/Turk/PZ_Master')
>>> ibs = ibeis.opendb() # dbdir=dbdir)
>>> #ibs = ibeis.opendb(dbdir='/raid/work2/Turk/GZ_Master')
>>> print(ibs.get_dbinfo_str())
>>> #ibs = ibeis.opendb('testdb1')
>>> # execute function
>>> result = export_testset_for_chuck(ibs, min_num_annots)
>>> # verify results
>>> print(result)
min_num_annots = 500
"""
import numpy as np
min_num_annots_per_name = 3
max_annot_per_image = 5
#3
#min_num_annots_per_name = 1
#max_annot_per_image = 3000
nid_list = ibs.get_valid_nids()
aids_list = ibs.get_name_aids(nid_list)
nAids_list = list(map(len, aids_list))
nOther_aids_list = ibs.unflat_map(ibs.get_annot_num_contact_aids,
aids_list)
invalid_by_num_annots = [
num < min_num_annots_per_name for num in nAids_list
]
invalid_by_num_others = [
any([num > max_annot_per_image for num in nums])
for nums in nOther_aids_list
]
invalid_list = ut.or_lists(invalid_by_num_annots, invalid_by_num_others)
valid_nids = ut.filterfalse_items(nid_list, invalid_list)
def get_name_time_variation(ibs, nid_list):
aids_list = ibs.get_name_aids(nid_list)
unixtimes_list = ibs.unflat_map(ibs.get_annot_image_unixtimes,
aids_list)
unixtimes_arrs = list(map(np.array, unixtimes_list))
fixtimes_list = [arr[arr > 0] for arr in unixtimes_arrs]
std_list = [
np.std(arr) if len(arr) > 1 else 0 for arr in fixtimes_list
]
return std_list
std_list = get_name_time_variation(ibs, valid_nids)
sorted_nids = ut.sortedby(valid_nids, std_list, reverse=True)
# Find which names to include
num_annot_cumsum = np.cumsum(ibs.get_name_num_annotations(sorted_nids))
pos_list = np.where(num_annot_cumsum >= min_num_annots)[0]
assert len(pos_list) > 0
nid_list_chosen = sorted_nids[:pos_list[0] + 1]
print('using names:')
print(ibs.get_name_texts(nid_list_chosen))
aids_list_chosen = ibs.get_name_aids(nid_list_chosen)
aid_list_chosen = ut.flatten(aids_list_chosen)
gid_list_chosen = ibs.get_annot_gids(aid_list_chosen)
#ut.debug_duplicate_items(gid_list_chosen)
# make sure not too many other annots are along for the ride
other_aids = ibs.get_annot_contact_aids(aid_list_chosen)
unexpected_aids = list(
set(ut.flatten(other_aids)).difference(set(aid_list_chosen)))
print('got %d unexpected_aids' % (len(unexpected_aids), ))
from ibeis.dbio import export_subset
def new_nonconflicting_dbpath(ibs):
dpath, dbname = split(ibs.get_dbdir())
base_fmtstr = dbname + '_demo' + str(min_num_annots) + '_export%d'
new_dbpath = ut.get_nonconflicting_path(base_fmtstr, dpath)
return new_dbpath
#ut.embed()
dbpath = new_nonconflicting_dbpath(ibs)
ibs_dst = ibeis.opendb(dbdir=dbpath, allow_newdir=True)
ibs_src = ibs
gid_list = gid_list_chosen
export_subset.merge_databases(ibs_src, ibs_dst, gid_list=gid_list)
DEBUG_NAME = False
if DEBUG_NAME:
ibs.get_name_num_annotations(sorted_nids[0:10])
import plottool as pt
ibeis.viz.viz_name.show_name(ibs, sorted_nids[0])
pt.update()
def toggle_samefig(self):
self.same_fig = not self.same_fig
if self.mx is not None:
self.select_ith_match(self.mx)
pt.update()
def param_interaction():
r"""
CommandLine:
python -m vtool.test_constrained_matching --test-param_interaction
Notes:
python -m vtool.test_constrained_matching --test-param_interaction
setparam normalizer_mode=nearby
setparam normalizer_mode=far
setparam ratio_thresh=.625
setparam ratio_thresh=.5
setparam ratio_thresh2=.625
normalizer_mode=plus
Example:
>>> # DISABLE_DOCTEST
>>> from vtool.test_constrained_matching import * # NOQA
>>> # build test data
>>> # execute function
>>> testtup = param_interaction()
>>> # verify results
>>> result = str(testtup)
>>> print(result)
"""
import plottool as pt
USE_IBEIS = False and ut.is_developer()
if USE_IBEIS:
from ibeis.algo.hots import devcases
index = 2
fpath1, fpath2, fpath3 = devcases.get_dev_test_fpaths(index)
testtup1 = testdata_matcher(fpath1, fpath2)
testtup2 = testdata_matcher(fpath1, fpath3)
else:
testtup1 = testdata_matcher('easy1.png', 'easy2.png')
testtup2 = testdata_matcher('easy1.png', 'hard3.png')
testtup_list = [testtup1, testtup2]
simp_list = [SimpleMatcher(testtup) for testtup in testtup_list]
varied_dict = dict([
('sver_xy_thresh', .1),
('ratio_thresh', .625),
('search_K', 7),
('ratio_thresh2', .625),
('sver_xy_thresh2', .01),
('normalizer_mode', ['nearby', 'far', 'plus'][1]),
('match_xy_thresh', .1),
])
cfgdict_list = ut.all_dict_combinations(varied_dict)
tried_configs = []
# DEFINE CUSTOM INTRACTIONS
custom_actions, valid_vizmodes, viz_index_, offset_fnum_ = make_custom_interactions(
simp_list)
# /DEFINE CUSTOM INTRACTIONS
for cfgdict in ut.InteractiveIter(
cfgdict_list,
#default_action='reload',
custom_actions=custom_actions,
wraparound=True):
for simp in simp_list:
simp.run_matching(cfgdict=cfgdict)
vizkey = valid_vizmodes[viz_index_[0]].replace('visualize_', '')
print('vizkey = %r' % (vizkey, ))
for fnum_, simp in enumerate(simp_list):
fnum = fnum_ + offset_fnum_[0]
simp.visualize(vizkey, fnum=fnum)
tried_configs.append(cfgdict.copy())
print('Current Config = ')
print(ut.dict_str(cfgdict))
pt.present()
pt.update()
def test_mser():
import cv2
import vtool as vt
import plottool as pt
import numpy as np
pt.qt4ensure()
class Keypoints(ut.NiceRepr):
"""
Convinence class for dealing with keypoints
"""
def __init__(self, kparr, info=None):
self.kparr = kparr
if info is None:
info = {}
self.info = info
def add_info(self, key, val):
self.info[key] = val
def __nice__(self):
return ' ' + str(len(self.kparr))
@property
def scale(self):
return vt.get_scales(self.kparr)
@property
def eccentricity(self):
return vt.get_kpts_eccentricity(self.kparr)
def compress(self, flags, inplace=False):
subarr = self.kparr.compress(flags, axis=0)
info = {
key: ut.compress(val, flags)
for key, val in self.info.items()
}
return Keypoints(subarr, info)
img_fpath = ut.grab_test_imgpath(
ut.get_argval('--fname', default='zebra.png'))
imgBGR = vt.imread(img_fpath)
imgGray = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2GRAY)
# http://docs.opencv.org/master/d3/d28/classcv_1_1MSER.html#gsc.tab=0
# http://stackoverflow.com/questions/17647500/exact-meaning-of-the-parameters-given-to-initialize-mser-in-opencv-2-4-x
factory = cv2.MSER_create
img_area = np.product(np.array(vt.get_size(imgGray)))
_max_area = (img_area // 10)
_delta = 8
_min_diversity = .5
extractor = factory(_delta=_delta,
_max_area=_max_area,
_min_diversity=_min_diversity)
# bboxes are x,y,w,h
regions, bboxes = extractor.detectRegions(imgGray)
# ellipse definition from [Fitzgibbon95]
# http://www.bmva.org/bmvc/1995/bmvc-95-050.pdf p518
# ell = [c_x, c_y, R_x, R_y, theta]
# (cx, cy) = conic center
# Rx and Ry = conic radii
# theta is the counterclockwise angle
fitz_ellipses = [cv2.fitEllipse(mser) for mser in regions]
# http://answers.opencv.org/question/19015/how-to-use-mser-in-python/
#hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]
#hull_ells = [cv2.fitEllipse(hull[:, 0]) for hull in hulls]
invVR_mats = []
for ell in fitz_ellipses:
((cx, cy), (dx, dy), degrees) = ell
theta = np.radians(degrees) # opencv lives in radians
# Convert diameter to radians
rx = dx / 2
ry = dy / 2
S = vt.scale_mat3x3(rx, ry)
T = vt.translation_mat3x3(cx, cy)
R = vt.rotation_mat3x3(theta)
invVR = T.dot(R.dot(S))
invVR_mats.append(invVR)
invVR_mats = np.array(invVR_mats)
#_oris = vt.get_invVR_mats_oris(invVR_mats)
kpts2_ = vt.flatten_invV_mats_to_kpts(invVR_mats)
self = Keypoints(kpts2_)
self.add_info('regions', regions)
flags = (self.eccentricity < .9)
#flags = self.scale < np.mean(self.scale)
#flags = self.scale < np.median(self.scale)
self = self.compress(flags)
import plottool as pt
#pt.interact_keypoints.ishow_keypoints(imgBGR, self.kparr, None, ell_alpha=.4, color='distinct', fnum=2)
#import plottool as pt
vis = imgBGR.copy()
for region in self.info['regions']:
vis[region.T[1], region.T[0], :] = 0
#regions, bbox = mser.detectRegions(gray)
#hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in self.info['regions']]
#cv2.polylines(vis, hulls, 1, (0, 255, 0))
#for region in self.info['regions']:
# ell = cv2.fitEllipse(region)
# cv2.ellipse(vis, ell, (255))
pt.interact_keypoints.ishow_keypoints(vis,
self.kparr,
None,
ell_alpha=.4,
color='distinct',
fnum=2)
#pt.imshow(vis, fnum=2)
pt.update()
#extractor = extract_factory['DAISY']()
#desc_type_to_dtype = {
# cv2.CV_8U: np.uint8,
# cv2.CV_8s: np.uint,
#}
#def alloc_desc(extractor):
# desc_type = extractor.descriptorType()
# desc_size = extractor.descriptorSize()
# dtype = desc_type_to_dtype[desc_type]
# shape = (len(cv2_kpts), desc_size)
# desc = np.empty(shape, dtype=dtype)
# return desc
#ut.search_module(cv2, 'array', recursive=True)
#ut.search_module(cv2, 'freak', recursive=True)
#ut.search_module(cv2, 'out', recursive=True)
#cv2_kpts = cv2_kpts[0:2]
#for key, factory in just_desc_factory_.items():
# extractor = factory()
# desc = alloc_desc(extractor)
# desc = extractor.compute(imgGray, cv2_kpts)
# feats[key] = (desc,)
# #extractor.compute(imgGray, cv2_kpts, desc)
# pass
#kpts = np.array(list(map(from_cv2_kpts, cv2_kpts)))
#orb = cv2.ORB()
#kp1, des1 = orb.detectAndCompute(imgGray, None)
#blober = cv2.SimpleBlobDetector_create()
#haris_kpts = cv2.cornerHarris(imgGray, 2, 3, 0.04)
#[name for name in dir(cv2) if 'mat' in name.lower()]
#[name for name in dir(cv2.xfeatures2d) if 'desc' in name.lower()]
#[name for name in dir(cv2) if 'detect' in name.lower()]
#[name for name in dir(cv2) if 'extract' in name.lower()]
#[name for name in dir(cv2) if 'ellip' in name.lower()]
#sift = cv2.xfeatures2d.SIFT_create()
#cv2_kpts = sift.detect(imgGray)
#desc = sift.compute(imgGray, cv2_kpts)[1]
#freak = cv2.xfeatures2d.FREAK_create()
#cv2_kpts = freak.detect(imgGray)
#desc = freak.compute(imgGray, cv2_kpts)[1]
pass
def visualize_support(normalizer, update=True, fnum=None, pnum=(1, 1, 1)):
plot_support(normalizer.tn_support, normalizer.tp_support, fnum=fnum, pnum=pnum)
if update:
import plottool as pt
pt.update()
def export_testset_for_chuck(ibs, min_num_annots):
"""
Exports a set with some number of annotations that has good demo examples.
multiple annotations per name and large time variation within names.
Args:
ibs (IBEISController): ibeis controller object
CommandLine:
python -m ibeis.other.ibsfuncs --test-export_testset_for_chuck --dbdir /raid/work2/Turk/PZ_Master --min-num-annots 100
python -m ibeis.other.ibsfuncs --test-export_testset_for_chuck --dbdir /raid/work2/Turk/PZ_Master --min-num-annots 500
python -m ibeis.other.ibsfuncs --test-export_testset_for_chuck --dbdir /raid/work2/Turk/GZ_Master --min-num-annots 100
python -m ibeis.other.ibsfuncs --test-export_testset_for_chuck --dbdir
/raid/work2/Turk/GZ_Master --min-num-annots 500_DOCTEST
python -m ibeis.other.ibsfuncs --test-export_testset_for_chuck --db GIR_Tanya --min-num-annots 100
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.other.ibsfuncs import * # NOQA
>>> import ibeis
>>> # build test data
>>> #dbdir = ut.get_argval(('--dbdir',), type_=str, default='testdb1')
>>> min_num_annots = ut.get_argval(('--min-num-annots',), type_=int, default=500)
>>> #ibs = ibeis.opendb('testdb1')
>>> #ibs = ibeis.opendb(dbdir='/raid/work2/Turk/PZ_Master')
>>> ibs = ibeis.opendb() # dbdir=dbdir)
>>> #ibs = ibeis.opendb(dbdir='/raid/work2/Turk/GZ_Master')
>>> print(ibs.get_dbinfo_str())
>>> #ibs = ibeis.opendb('testdb1')
>>> # execute function
>>> result = export_testset_for_chuck(ibs, min_num_annots)
>>> # verify results
>>> print(result)
min_num_annots = 500
"""
import numpy as np
min_num_annots_per_name = 3
max_annot_per_image = 5
#3
#min_num_annots_per_name = 1
#max_annot_per_image = 3000
nid_list = ibs.get_valid_nids()
aids_list = ibs.get_name_aids(nid_list)
nAids_list = list(map(len, aids_list))
nOther_aids_list = ibs.unflat_map(ibs.get_annot_num_contact_aids, aids_list)
invalid_by_num_annots = [num < min_num_annots_per_name for num in nAids_list]
invalid_by_num_others = [any([num > max_annot_per_image for num in nums])
for nums in nOther_aids_list]
invalid_list = ut.or_lists(invalid_by_num_annots, invalid_by_num_others)
valid_nids = ut.filterfalse_items(nid_list, invalid_list)
def get_name_time_variation(ibs, nid_list):
aids_list = ibs.get_name_aids(nid_list)
unixtimes_list = ibs.unflat_map(ibs.get_annot_image_unixtimes, aids_list)
unixtimes_arrs = list(map(np.array, unixtimes_list))
fixtimes_list = [arr[arr > 0] for arr in unixtimes_arrs]
std_list = [np.std(arr) if len(arr) > 1 else 0 for arr in fixtimes_list]
return std_list
std_list = get_name_time_variation(ibs, valid_nids)
sorted_nids = ut.sortedby(valid_nids, std_list, reverse=True)
# Find which names to include
num_annot_cumsum = np.cumsum(ibs.get_name_num_annotations(sorted_nids))
pos_list = np.where(num_annot_cumsum >= min_num_annots)[0]
assert len(pos_list) > 0
nid_list_chosen = sorted_nids[:pos_list[0] + 1]
print('using names:')
print(ibs.get_name_texts(nid_list_chosen))
aids_list_chosen = ibs.get_name_aids(nid_list_chosen)
aid_list_chosen = ut.flatten(aids_list_chosen)
gid_list_chosen = ibs.get_annot_gids(aid_list_chosen)
#ut.debug_duplicate_items(gid_list_chosen)
# make sure not too many other annots are along for the ride
other_aids = ibs.get_annot_contact_aids(aid_list_chosen)
unexpected_aids = list(set(ut.flatten(other_aids)).difference(set(aid_list_chosen)))
print('got %d unexpected_aids' % (len(unexpected_aids),))
from ibeis.dbio import export_subset
def new_nonconflicting_dbpath(ibs):
dpath, dbname = split(ibs.get_dbdir())
base_fmtstr = dbname + '_demo' + str(min_num_annots) + '_export%d'
new_dbpath = ut.get_nonconflicting_path(base_fmtstr, dpath)
return new_dbpath
#ut.embed()
dbpath = new_nonconflicting_dbpath(ibs)
ibs_dst = ibeis.opendb(dbdir=dbpath, allow_newdir=True)
ibs_src = ibs
gid_list = gid_list_chosen
export_subset.merge_databases(ibs_src, ibs_dst, gid_list=gid_list)
DEBUG_NAME = False
if DEBUG_NAME:
ibs.get_name_num_annotations(sorted_nids[0:10])
import plottool as pt
ibeis.viz.viz_name.show_name(ibs, sorted_nids[0])
pt.update()
def param_interaction():
r"""
CommandLine:
python -m vtool.test_constrained_matching --test-param_interaction
Notes:
python -m vtool.test_constrained_matching --test-param_interaction
setparam normalizer_mode=nearby
setparam normalizer_mode=far
setparam ratio_thresh=.625
setparam ratio_thresh=.5
setparam ratio_thresh2=.625
normalizer_mode=plus
Example:
>>> # DISABLE_DOCTEST
>>> from vtool.test_constrained_matching import * # NOQA
>>> # build test data
>>> # execute function
>>> testtup = param_interaction()
>>> # verify results
>>> result = str(testtup)
>>> print(result)
"""
import plottool as pt
USE_IBEIS = False and ut.is_developer()
if USE_IBEIS:
from ibeis.algo.hots import devcases
index = 2
fpath1, fpath2, fpath3 = devcases.get_dev_test_fpaths(index)
testtup1 = testdata_matcher(fpath1, fpath2)
testtup2 = testdata_matcher(fpath1, fpath3)
else:
testtup1 = testdata_matcher('easy1.png', 'easy2.png')
testtup2 = testdata_matcher('easy1.png', 'hard3.png')
testtup_list = [testtup1, testtup2]
simp_list = [SimpleMatcher(testtup) for testtup in testtup_list]
varied_dict = dict([
('sver_xy_thresh', .1),
('ratio_thresh', .625),
('search_K', 7),
('ratio_thresh2', .625),
('sver_xy_thresh2', .01),
('normalizer_mode', ['nearby', 'far', 'plus'][1]),
('match_xy_thresh', .1),
])
cfgdict_list = ut.all_dict_combinations(varied_dict)
tried_configs = []
# DEFINE CUSTOM INTRACTIONS
custom_actions, valid_vizmodes, viz_index_, offset_fnum_ = make_custom_interactions(simp_list)
# /DEFINE CUSTOM INTRACTIONS
for cfgdict in ut.InteractiveIter(cfgdict_list,
#default_action='reload',
custom_actions=custom_actions,
wraparound=True):
for simp in simp_list:
simp.run_matching(cfgdict=cfgdict)
vizkey = valid_vizmodes[viz_index_[0]].replace('visualize_', '')
print('vizkey = %r' % (vizkey,))
for fnum_, simp in enumerate(simp_list):
fnum = fnum_ + offset_fnum_[0]
simp.visualize(vizkey, fnum=fnum)
tried_configs.append(cfgdict.copy())
print('Current Config = ')
print(ut.dict_str(cfgdict))
pt.present()
pt.update()
def test_mser():
import cv2
import vtool as vt
import plottool as pt
import numpy as np
pt.qt4ensure()
class Keypoints(ut.NiceRepr):
"""
Convinence class for dealing with keypoints
"""
def __init__(self, kparr, info=None):
self.kparr = kparr
if info is None:
info = {}
self.info = info
def add_info(self, key, val):
self.info[key] = val
def __nice__(self):
return ' ' + str(len(self.kparr))
@property
def scale(self):
return vt.get_scales(self.kparr)
@property
def eccentricity(self):
return vt.get_kpts_eccentricity(self.kparr)
def compress(self, flags, inplace=False):
subarr = self.kparr.compress(flags, axis=0)
info = {key: ut.compress(val, flags) for key, val in self.info.items()}
return Keypoints(subarr, info)
img_fpath = ut.grab_test_imgpath(ut.get_argval('--fname', default='zebra.png'))
imgBGR = vt.imread(img_fpath)
imgGray = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2GRAY)
# http://docs.opencv.org/master/d3/d28/classcv_1_1MSER.html#gsc.tab=0
# http://stackoverflow.com/questions/17647500/exact-meaning-of-the-parameters-given-to-initialize-mser-in-opencv-2-4-x
factory = cv2.MSER_create
img_area = np.product(np.array(vt.get_size(imgGray)))
_max_area = (img_area // 10)
_delta = 8
_min_diversity = .5
extractor = factory(_delta=_delta, _max_area=_max_area, _min_diversity=_min_diversity)
# bboxes are x,y,w,h
regions, bboxes = extractor.detectRegions(imgGray)
# ellipse definition from [Fitzgibbon95]
# http://www.bmva.org/bmvc/1995/bmvc-95-050.pdf p518
# ell = [c_x, c_y, R_x, R_y, theta]
# (cx, cy) = conic center
# Rx and Ry = conic radii
# theta is the counterclockwise angle
fitz_ellipses = [cv2.fitEllipse(mser) for mser in regions]
# http://answers.opencv.org/question/19015/how-to-use-mser-in-python/
#hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]
#hull_ells = [cv2.fitEllipse(hull[:, 0]) for hull in hulls]
invVR_mats = []
for ell in fitz_ellipses:
((cx, cy), (dx, dy), degrees) = ell
theta = np.radians(degrees) # opencv lives in radians
# Convert diameter to radians
rx = dx / 2
ry = dy / 2
S = vt.scale_mat3x3(rx, ry)
T = vt.translation_mat3x3(cx, cy)
R = vt.rotation_mat3x3(theta)
invVR = T.dot(R.dot(S))
invVR_mats.append(invVR)
invVR_mats = np.array(invVR_mats)
#_oris = vt.get_invVR_mats_oris(invVR_mats)
kpts2_ = vt.flatten_invV_mats_to_kpts(invVR_mats)
self = Keypoints(kpts2_)
self.add_info('regions', regions)
flags = (self.eccentricity < .9)
#flags = self.scale < np.mean(self.scale)
#flags = self.scale < np.median(self.scale)
self = self.compress(flags)
import plottool as pt
#pt.interact_keypoints.ishow_keypoints(imgBGR, self.kparr, None, ell_alpha=.4, color='distinct', fnum=2)
#import plottool as pt
vis = imgBGR.copy()
for region in self.info['regions']:
vis[region.T[1], region.T[0], :] = 0
#regions, bbox = mser.detectRegions(gray)
#hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in self.info['regions']]
#cv2.polylines(vis, hulls, 1, (0, 255, 0))
#for region in self.info['regions']:
# ell = cv2.fitEllipse(region)
# cv2.ellipse(vis, ell, (255))
pt.interact_keypoints.ishow_keypoints(vis, self.kparr, None, ell_alpha=.4, color='distinct', fnum=2)
#pt.imshow(vis, fnum=2)
pt.update()
#extractor = extract_factory['DAISY']()
#desc_type_to_dtype = {
# cv2.CV_8U: np.uint8,
# cv2.CV_8s: np.uint,
#}
#def alloc_desc(extractor):
# desc_type = extractor.descriptorType()
# desc_size = extractor.descriptorSize()
# dtype = desc_type_to_dtype[desc_type]
# shape = (len(cv2_kpts), desc_size)
# desc = np.empty(shape, dtype=dtype)
# return desc
#ut.search_module(cv2, 'array', recursive=True)
#ut.search_module(cv2, 'freak', recursive=True)
#ut.search_module(cv2, 'out', recursive=True)
#cv2_kpts = cv2_kpts[0:2]
#for key, factory in just_desc_factory_.items():
# extractor = factory()
# desc = alloc_desc(extractor)
# desc = extractor.compute(imgGray, cv2_kpts)
# feats[key] = (desc,)
# #extractor.compute(imgGray, cv2_kpts, desc)
# pass
#kpts = np.array(list(map(from_cv2_kpts, cv2_kpts)))
#orb = cv2.ORB()
#kp1, des1 = orb.detectAndCompute(imgGray, None)
#blober = cv2.SimpleBlobDetector_create()
#haris_kpts = cv2.cornerHarris(imgGray, 2, 3, 0.04)
#[name for name in dir(cv2) if 'mat' in name.lower()]
#[name for name in dir(cv2.xfeatures2d) if 'desc' in name.lower()]
#[name for name in dir(cv2) if 'detect' in name.lower()]
#[name for name in dir(cv2) if 'extract' in name.lower()]
#[name for name in dir(cv2) if 'ellip' in name.lower()]
#sift = cv2.xfeatures2d.SIFT_create()
#cv2_kpts = sift.detect(imgGray)
#desc = sift.compute(imgGray, cv2_kpts)[1]
#freak = cv2.xfeatures2d.FREAK_create()
#cv2_kpts = freak.detect(imgGray)
#desc = freak.compute(imgGray, cv2_kpts)[1]
pass
def toggle_samefig(self):
self.same_fig = not self.same_fig
if self.mx is not None:
self.select_ith_match(self.mx)
pt.update()
