def get_global_species_scorenorm_cachedir(ibs, species_text, ensure=True):
"""
Args:
species_text (str):
ensure (bool):
Returns:
str: species_cachedir
CommandLine:
python -m ibeis.control.IBEISControl --test-get_global_species_scorenorm_cachedir
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.control.IBEISControl import * # NOQA
>>> import ibeis # NOQA
>>> ibs = ibeis.opendb('testdb1')
>>> species_text = ibeis.const.TEST_SPECIES.ZEB_GREVY
>>> ensure = True
>>> species_cachedir = ibs.get_global_species_scorenorm_cachedir(species_text, ensure)
>>> resourcedir = ibs.get_ibeis_resource_dir()
>>> result = ut.relpath_unix(species_cachedir, resourcedir)
>>> print(result)
scorenorm/zebra_grevys
"""
scorenorm_cachedir = join(ibs.get_ibeis_resource_dir(),
const.PATH_NAMES.scorenormdir)
species_cachedir = join(scorenorm_cachedir, species_text)
if ensure:
ut.ensurepath(scorenorm_cachedir)
ut.ensuredir(species_cachedir)
return species_cachedir
def get_global_species_scorenorm_cachedir(ibs, species_text, ensure=True):
"""
Args:
species_text (str):
ensure (bool):
Returns:
str: species_cachedir
CommandLine:
python -m ibeis.control.IBEISControl --test-get_global_species_scorenorm_cachedir
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.control.IBEISControl import * # NOQA
>>> import ibeis # NOQA
>>> ibs = ibeis.opendb('testdb1')
>>> species_text = ibeis.const.TEST_SPECIES.ZEB_GREVY
>>> ensure = True
>>> species_cachedir = ibs.get_global_species_scorenorm_cachedir(species_text, ensure)
>>> resourcedir = ibs.get_ibeis_resource_dir()
>>> result = ut.relpath_unix(species_cachedir, resourcedir)
>>> print(result)
scorenorm/zebra_grevys
"""
scorenorm_cachedir = join(ibs.get_ibeis_resource_dir(),
const.PATH_NAMES.scorenormdir)
species_cachedir = join(scorenorm_cachedir, species_text)
if ensure:
ut.ensurepath(scorenorm_cachedir)
ut.ensuredir(species_cachedir)
return species_cachedir
def save_if_requested(ibs, subdir):
if not ibs.args.save_figures:
return
#print('[viz] Dumping Image')
fpath = ibs.dirs.result_dir
if not subdir is None:
subdir = utool.sanatize_fname2(subdir)
fpath = join(fpath, subdir)
utool.ensurepath(fpath)
df2.save_figure(fpath=fpath, usetitle=True)
df2.reset()
def save_if_requested(ibs, subdir):
if not ibs.args.save_figures:
return
#print('[viz] Dumping Image')
fpath = ibs.dirs.result_dir
if not subdir is None:
subdir = utool.sanitize_fname2(subdir)
fpath = join(fpath, subdir)
utool.ensurepath(fpath)
df2.save_figure(fpath=fpath, usetitle=True)
df2.reset()
def __dump_text_report(allres, report_type):
if not 'report_type' in vars():
report_type = 'rankres_str'
print('[rr2] Dumping textfile: ' + report_type)
report_str = allres.__dict__[report_type]
# Get directories
result_dir = allres.ibs.dirs.result_dir
timestamp_dir = join(result_dir, 'timestamped_results')
utool.ensurepath(timestamp_dir)
utool.ensurepath(result_dir)
# Write to timestamp and result dir
timestamp = utool.get_timestamp()
csv_timestamp_fname = report_type + allres.title_suffix + timestamp + '.csv'
csv_timestamp_fpath = join(timestamp_dir, csv_timestamp_fname)
csv_fname = report_type + allres.title_suffix + '.csv'
csv_fpath = join(result_dir, csv_fname)
utool.write_to(csv_fpath, report_str)
utool.write_to(csv_timestamp_fpath, report_str)
def __dump_text_report(allres, report_type):
if not 'report_type' in vars():
report_type = 'rankres_str'
print('[rr2] Dumping textfile: ' + report_type)
report_str = allres.__dict__[report_type]
# Get directories
result_dir = allres.ibs.dirs.result_dir
timestamp_dir = join(result_dir, 'timestamped_results')
utool.ensurepath(timestamp_dir)
utool.ensurepath(result_dir)
# Write to timestamp and result dir
timestamp = utool.get_timestamp()
csv_timestamp_fname = report_type + allres.title_suffix + timestamp + '.csv'
csv_timestamp_fpath = join(timestamp_dir, csv_timestamp_fname)
csv_fname = report_type + allres.title_suffix + '.csv'
csv_fpath = join(result_dir, csv_fname)
utool.write_to(csv_fpath, report_str)
utool.write_to(csv_timestamp_fpath, report_str)
def testdata_ensure_unconverted_hsdb():
r"""
Makes an unconverted test datapath
CommandLine:
python -m ibeis.dbio.ingest_hsdb --test-testdata_ensure_unconverted_hsdb
Example:
>>> # SCRIPT
>>> from ibeis.dbio.ingest_hsdb import * # NOQA
>>> result = testdata_ensure_unconverted_hsdb()
>>> print(result)
"""
import utool as ut
assert ut.is_developer(), 'dev function only'
# Make an unconverted test database
ut.ensurepath('/raid/tests/tmp')
ut.delete('/raid/tests/tmp/Frogs')
ut.copy('/raid/tests/Frogs', '/raid/tests/tmp/Frogs')
hsdb_dir = '/raid/tests/tmp/Frogs'
return hsdb_dir
def testdata_ensure_unconverted_hsdb():
r"""
Makes an unconverted test datapath
CommandLine:
python -m ibeis.dbio.ingest_hsdb --test-testdata_ensure_unconverted_hsdb
Example:
>>> # SCRIPT
>>> from ibeis.dbio.ingest_hsdb import * # NOQA
>>> result = testdata_ensure_unconverted_hsdb()
>>> print(result)
"""
import utool as ut
assert ut.is_developer(), 'dev function only'
# Make an unconverted test database
ut.ensurepath('/raid/tests/tmp')
ut.delete('/raid/tests/tmp/Frogs')
ut.copy('/raid/tests/Frogs', '/raid/tests/tmp/Frogs')
hsdb_dir = '/raid/tests/tmp/Frogs'
return hsdb_dir
def dump_to_disk(self, dpath, num=None, prefix='temp_img'):
import numpy as np
import wbia.plottool as pt
dpath = ut.ensurepath(dpath)
num_zeros = np.ceil(np.log10(len(self.gpath_list)))
total = len(self.gpath_list)
if num is None:
num = total
fmtstr = prefix + '_%0' + str(num_zeros) + 'd.jpg'
fig = pt.figure(fnum=self.fnum)
for index in ut.ProgIter(range(num), lbl='dumping images to disk'):
fig = pt.figure(fnum=self.fnum)
fig.clf()
ax = self._plot_index(index, {'fnum': self.fnum})
fig = ax.figure
axes_extents = pt.extract_axes_extents(fig)
assert len(axes_extents) == 1, 'more than one axes'
extent = axes_extents[0]
fpath = ut.unixjoin(dpath, fmtstr % (index))
fig.savefig(fpath, bbox_inches=extent)
pt.plt.close(fig)
def make_wordfigures(ibs, metrics, invindex, figdir, wx_sample, wx2_dpath):
"""
Builds mosaics of patches assigned to words in sample
ouptuts them to disk
"""
from plottool import draw_func2 as df2
import vtool as vt
import parse
vocabdir = join(figdir, 'vocab_patches2')
ut.ensuredir(vocabdir)
dump_word_patches(ibs, vocabdir, invindex, wx_sample, metrics)
# COLLECTING PART --- collects patches in word folders
#vocabdir
seldpath = vocabdir + '_selected'
ut.ensurepath(seldpath)
# stack for show
for wx, dpath in ut.progiter(six.iteritems(wx2_dpath),
lbl='Dumping Word Images:',
num=len(wx2_dpath),
freq=1,
backspace=False):
#df2.rrr()
fpath_list = ut.ls(dpath)
fname_list = [basename(fpath_) for fpath_ in fpath_list]
patch_list = [vt.imread(fpath_) for fpath_ in fpath_list]
# color each patch by nid
nid_list = [
int(parse.parse('{}_nid={nid}_{}', fname)['nid'])
for fname in fname_list
]
nid_set = set(nid_list)
nid_list = np.array(nid_list)
if len(nid_list) == len(nid_set):
# no duplicate names
newpatch_list = patch_list
else:
# duplicate names. do coloring
sortx = nid_list.argsort()
patch_list = np.array(patch_list, dtype=object)[sortx]
fname_list = np.array(fname_list, dtype=object)[sortx]
nid_list = nid_list[sortx]
colors = (255 *
np.array(df2.distinct_colors(len(nid_set)))).astype(
np.int32)
color_dict = dict(zip(nid_set, colors))
wpad, hpad = 3, 3
newshape_list = [
tuple(
(np.array(patch.shape) + (wpad * 2, hpad * 2, 0)).tolist())
for patch in patch_list
]
color_list = [color_dict[nid_] for nid_ in nid_list]
newpatch_list = [
np.zeros(shape) + color[None, None]
for shape, color in zip(newshape_list, color_list)
]
for patch, newpatch in zip(patch_list, newpatch_list):
newpatch[wpad:-wpad, hpad:-hpad, :] = patch
#img_list = patch_list
#bigpatch = vt.stack_image_recurse(patch_list)
#bigpatch = vt.stack_image_list(patch_list, vert=False)
bigpatch = vt.stack_square_images(newpatch_list)
bigpatch_fpath = join(seldpath, basename(dpath) + '_patches.png')
#
def _dictstr(dict_):
str_ = ut.dict_str(dict_, newlines=False)
str_ = str_.replace('\'', '').replace(': ', '=').strip('{},')
return str_
figtitle = '\n'.join([
'wx=%r' % wx,
'stat(pdist): %s' % _dictstr(metrics.wx2_pdist_stats[wx]),
'stat(wdist): %s' % _dictstr(metrics.wx2_wdist_stats[wx]),
])
metrics.wx2_nMembers[wx]
df2.figure(fnum=1, doclf=True, docla=True)
fig, ax = df2.imshow(bigpatch, figtitle=figtitle)
#fig.show()
df2.set_figtitle(figtitle)
df2.adjust_subplots(top=.878, bottom=0)
df2.save_figure(1, bigpatch_fpath)
def make_wordfigures(ibs, metrics, invindex, figdir, wx_sample, wx2_dpath):
"""
Builds mosaics of patches assigned to words in sample
ouptuts them to disk
"""
from plottool import draw_func2 as df2
import vtool as vt
import parse
vocabdir = join(figdir, 'vocab_patches2')
ut.ensuredir(vocabdir)
dump_word_patches(ibs, vocabdir, invindex, wx_sample, metrics)
# COLLECTING PART --- collects patches in word folders
#vocabdir
seldpath = vocabdir + '_selected'
ut.ensurepath(seldpath)
# stack for show
for wx, dpath in ut.progiter(six.iteritems(wx2_dpath), lbl='Dumping Word Images:', num=len(wx2_dpath), freq=1, backspace=False):
#df2.rrr()
fpath_list = ut.ls(dpath)
fname_list = [basename(fpath_) for fpath_ in fpath_list]
patch_list = [gtool.imread(fpath_) for fpath_ in fpath_list]
# color each patch by nid
nid_list = [int(parse.parse('{}_nid={nid}_{}', fname)['nid']) for fname in fname_list]
nid_set = set(nid_list)
nid_list = np.array(nid_list)
if len(nid_list) == len(nid_set):
# no duplicate names
newpatch_list = patch_list
else:
# duplicate names. do coloring
sortx = nid_list.argsort()
patch_list = np.array(patch_list, dtype=object)[sortx]
fname_list = np.array(fname_list, dtype=object)[sortx]
nid_list = nid_list[sortx]
colors = (255 * np.array(df2.distinct_colors(len(nid_set)))).astype(np.int32)
color_dict = dict(zip(nid_set, colors))
wpad, hpad = 3, 3
newshape_list = [tuple((np.array(patch.shape) + (wpad * 2, hpad * 2, 0)).tolist()) for patch in patch_list]
color_list = [color_dict[nid_] for nid_ in nid_list]
newpatch_list = [np.zeros(shape) + color[None, None] for shape, color in zip(newshape_list, color_list)]
for patch, newpatch in zip(patch_list, newpatch_list):
newpatch[wpad:-wpad, hpad:-hpad, :] = patch
#img_list = patch_list
#bigpatch = vt.stack_image_recurse(patch_list)
#bigpatch = vt.stack_image_list(patch_list, vert=False)
bigpatch = vt.stack_square_images(newpatch_list)
bigpatch_fpath = join(seldpath, basename(dpath) + '_patches.png')
#
def _dictstr(dict_):
str_ = ut.dict_str(dict_, newlines=False)
str_ = str_.replace('\'', '').replace(': ', '=').strip('{},')
return str_
figtitle = '\n'.join([
'wx=%r' % wx,
'stat(pdist): %s' % _dictstr(metrics.wx2_pdist_stats[wx]),
'stat(wdist): %s' % _dictstr(metrics.wx2_wdist_stats[wx]),
])
metrics.wx2_nMembers[wx]
df2.figure(fnum=1, doclf=True, docla=True)
fig, ax = df2.imshow(bigpatch, figtitle=figtitle)
#fig.show()
df2.set_figtitle(figtitle)
df2.adjust_subplots(top=.878, bottom=0)
df2.save_figure(1, bigpatch_fpath)
import os
import utool as ut
from os.path import basename, join, split # NOQA
app_dpath = ut.truepath('~/fsclean_indexes')
ut.ensurepath(app_dpath)
root = '/media/joncrall/media'
class Path(ut.NiceRepr):
@property
@ut.memoize
def abspath(self):
return join(self.r, self.n)
def __nice__(self):
return (self.r, self.n)
def __eq__(self, other):
return self.abspath == other.abspath
def __hash__(self):
return hash(self.abspath)
@property
@ut.memoize
def depth(self):
return self.abspath.count(os.path.sep)
import os
import utool as ut
from os.path import basename, join, split # NOQA
app_dpath = ut.truepath('~/fsclean_indexes')
ut.ensurepath(app_dpath)
root = '/media/joncrall/media'
class Path(ut.NiceRepr):
@property
@ut.memoize
def abspath(self):
return join(self.r, self.n)
def __nice__(self):
return (self.r, self.n)
def __eq__(self, other):
return self.abspath == other.abspath
def __hash__(self):
return hash(self.abspath)
@property
@ut.memoize
def depth(self):
return self.abspath.count(os.path.sep)
