def draw_scatterplot(figdir, ibs, datax, datay, xlabel, ylabel, color, fnum=None):
from plottool import df2
datac = [color for _ in range(len(datax))]
assert len(datax) == len(datay), '%r %r' % (len(datax), len(datay))
df2.figure(fnum=fnum, doclf=True, docla=True)
df2.plt.scatter(datax, datay, c=datac, s=20, marker='o', alpha=.9)
ax = df2.gca()
title = '%s vs %s.\nnWords=%r. db=%r' % (xlabel, ylabel, len(datax), ibs.get_dbname())
df2.set_xlabel(xlabel)
df2.set_ylabel(ylabel)
ax.set_ylim(min(datay) - 1, max(datay) + 1)
ax.set_xlim(min(datax) - 1, max(datax) + 1)
df2.dark_background()
df2.set_figtitle(title)
figpath = join(figdir, title)
df2.save_figure(fnum, figpath)
def draw_scatterplot(figdir,
ibs,
datax,
datay,
xlabel,
ylabel,
color,
fnum=None):
from plottool import df2
datac = [color for _ in range(len(datax))]
assert len(datax) == len(datay), '%r %r' % (len(datax), len(datay))
df2.figure(fnum=fnum, doclf=True, docla=True)
df2.plt.scatter(datax, datay, c=datac, s=20, marker='o', alpha=.9)
ax = df2.gca()
title = '%s vs %s.\nnWords=%r. db=%r' % (xlabel, ylabel, len(datax),
ibs.get_dbname())
df2.set_xlabel(xlabel)
df2.set_ylabel(ylabel)
ax.set_ylim(min(datay) - 1, max(datay) + 1)
ax.set_xlim(min(datax) - 1, max(datax) + 1)
df2.dark_background()
df2.set_figtitle(title)
figpath = join(figdir, title)
df2.save_figure(fnum, figpath)
def plot_chip_metric(ibs,
aid,
metric=None,
fnum=1,
lbl='',
figtitle='',
colortype='score',
darken=.5,
cmap_='hot',
reverse_cmap=False,
**kwargs):
"""
Plots one annotation with one metric.
The word metric is used liberally.
Example:
>>> from ibeis.algo.hots.smk.smk_plots import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> from ibeis.algo.hots.smk import smk_plots
>>> from ibeis.algo.hots.smk import smk_repr
>>> #tup = smk_debug.testdata_raw_internals0(db='GZ_ALL', nWords=64000)
>>> #tup = smk_debug.testdata_raw_internals0(db='GZ_ALL', nWords=8000)
>>> #tup = smk_debug.testdata_raw_internals0(db='PZ_Master0', nWords=64000)
>>> tup = smk_debug.testdata_raw_internals0(db='PZ_Mothers', nWords=8000)
>>> ibs, annots_df, daids, qaids, invindex, qreq_ = tup
>>> smk_repr.compute_data_internals_(invindex, qreq_.qparams, delete_rawvecs=False)
>>> invindex.idx2_wxs = np.array(invindex.idx2_wxs)
>>> metric = None
>>> aid = 1
>>> fnum = 0
>>> lbl='test'
>>> colortype='score'
>>> kwargs = {'annote': False}
#>>> df2.rrr()
>>> smk_plots.plot_chip_metric(ibs, aid, metric, fnum, lbl, colortype, **kwargs)
>>> df2.present()
"""
import plottool.draw_func2 as df2
from ibeis.viz import viz_chip
df2.figure(fnum=fnum, doclf=True, docla=True)
if metric is not None:
if colortype == 'score':
colors = df2.scores_to_color(metric,
cmap_=cmap_,
reverse_cmap=reverse_cmap)
elif colortype == 'label':
colors = df2.label_to_colors(metric)
elif colortype == 'custom':
# Give ranks of -1 and -2 special meaning
val2_customcolor = {
-1: df2.UNKNOWN_PURP,
-2: df2.LIGHT_BLUE,
}
# Inconsistent but visable colors
scale_max = .7
#consistent colors (needs to know highest K)
#maxval = np.array(metric).max()
#scale_max = .7 * (float(maxval) / 20.0)
colors = df2.scores_to_color(metric,
cmap_=cmap_,
reverse_cmap=reverse_cmap,
scale_max=scale_max,
val2_customcolor=val2_customcolor)
else:
raise ValueError('no known colortype = %r' % (colortype, ))
else:
colors = 'distinct'
viz_chip.show_chip(
ibs,
aid,
color=colors,
darken=darken,
ell_alpha=.8,
#ell_linewidth=4,
ell_linewidth=2,
**kwargs)
df2.set_figtitle(figtitle)
if metric is not None:
cb = df2.colorbar(metric, colors, custom=(colortype == 'custom'))
cb.set_label(lbl)
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 plot_chip_metric(ibs, aid, metric=None, fnum=1, lbl='', figtitle='', colortype='score',
darken=.5, cmap_='hot', reverse_cmap=False, **kwargs):
"""
Plots one annotation with one metric.
The word metric is used liberally.
Example:
>>> from ibeis.algo.hots.smk.smk_plots import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> from ibeis.algo.hots.smk import smk_plots
>>> from ibeis.algo.hots.smk import smk_repr
>>> #tup = smk_debug.testdata_raw_internals0(db='GZ_ALL', nWords=64000)
>>> #tup = smk_debug.testdata_raw_internals0(db='GZ_ALL', nWords=8000)
>>> #tup = smk_debug.testdata_raw_internals0(db='PZ_Master0', nWords=64000)
>>> tup = smk_debug.testdata_raw_internals0(db='PZ_Mothers', nWords=8000)
>>> ibs, annots_df, daids, qaids, invindex, qreq_ = tup
>>> smk_repr.compute_data_internals_(invindex, qreq_.qparams, delete_rawvecs=False)
>>> invindex.idx2_wxs = np.array(invindex.idx2_wxs)
>>> metric = None
>>> aid = 1
>>> fnum = 0
>>> lbl='test'
>>> colortype='score'
>>> kwargs = {'annote': False}
#>>> df2.rrr()
>>> smk_plots.plot_chip_metric(ibs, aid, metric, fnum, lbl, colortype, **kwargs)
>>> df2.present()
"""
import plottool.draw_func2 as df2
from ibeis.viz import viz_chip
df2.figure(fnum=fnum, doclf=True, docla=True)
if metric is not None:
if colortype == 'score':
colors = df2.scores_to_color(metric, cmap_=cmap_, reverse_cmap=reverse_cmap)
elif colortype == 'label':
colors = df2.label_to_colors(metric)
elif colortype == 'custom':
# Give ranks of -1 and -2 special meaning
val2_customcolor = {
-1: df2.UNKNOWN_PURP,
-2: df2.LIGHT_BLUE,
}
# Inconsistent but visable colors
scale_max = .7
#consistent colors (needs to know highest K)
#maxval = np.array(metric).max()
#scale_max = .7 * (float(maxval) / 20.0)
colors = df2.scores_to_color(metric, cmap_=cmap_,
reverse_cmap=reverse_cmap,
scale_max=scale_max,
val2_customcolor=val2_customcolor)
else:
raise ValueError('no known colortype = %r' % (colortype,))
else:
colors = 'distinct'
viz_chip.show_chip(ibs, aid, color=colors, darken=darken,
ell_alpha=.8,
#ell_linewidth=4,
ell_linewidth=2,
**kwargs)
df2.set_figtitle(figtitle)
if metric is not None:
cb = df2.colorbar(metric, colors, custom=(colortype == 'custom'))
cb.set_label(lbl)
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)
