def show_many_chips(ibs,
aid_list,
config2_=None,
fnum=None,
pnum=None,
vert=True):
r"""
CommandLine:
python -m ibeis.viz.viz_chip --test-show_many_chips
python -m ibeis.viz.viz_chip --test-show_many_chips --show
python -m ibeis.viz.viz_chip --test-show_many_chips --show --db NNP_Master3 --aids=13276,14047,14489,14906,10194,10201,12656,10150,11002,15315,7191,13127,15591,12838,13970,14123,14167 --no-annote --dpath figures --save ~/latex/crall-candidacy-2015/figures/challengechips.jpg '--caption=challenging images'
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.viz.viz_chip import * # NOQA
>>> import numpy as np
>>> in_image = False
>>> ibs, aid_list, kwargs, config2_ = testdata_showchip()
>>> # execute function
>>> show_many_chips(ibs, aid_list, config2_)
>>> ut.show_if_requested()
"""
if ut.VERBOSE:
print('[viz] show_many_chips')
in_image = False
chip_list = vh.get_chips(ibs,
aid_list,
in_image=in_image,
config2_=config2_)
import vtool_ibeis as vt
stacked_chips = vt.stack_image_recurse(chip_list,
modifysize=True,
vert=vert)
pt.imshow(stacked_chips, fnum=None, pnum=None)
def chip_montage(ibs, qaids, config=None):
r"""
CommandLine:
python -m ibeis.viz.viz_other chip_montage --show --db PZ_MTEST
python -m ibeis.viz.viz_other chip_montage --show --db GZ_ALL
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_other import * # NOQA
>>> defaltdb = 'seaturtles'
>>> import ibeis
>>> a = ['default']
>>> ibs = ibeis.opendb(defaultdb=defaltdb)
>>> ibs, qaids, daids = ibeis.testdata_expanded_aids(ibs=ibs, a=a)
>>> config = None
>>> chip_montage(ibs, qaids, config)
>>> ut.quit_if_noshow()
>>> import plottool_ibeis as pt
>>> ut.show_if_requested()
"""
import vtool_ibeis as vt
chip_list = ibs.get_annot_chips(qaids, config2_=config)
height = 2000
dsize = (int(height * ut.PHI), height)
dst = vt.montage(chip_list, dsize)
pt.imshow(dst)
def show_single_coverage_mask(qreq_,
cm,
weight_mask_m,
weight_mask,
daids,
fnum=None):
"""
DEPRICATE
"""
import plottool_ibeis 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 update_ui(co_wgt):
if not co_wgt.hack:
if co_wgt.current_gindex == 0:
co_wgt.button_list[0].setEnabled(False)
else:
co_wgt.button_list[0].setEnabled(True)
if co_wgt.current_gindex == len(co_wgt.gid_list) - 1:
co_wgt.button_list[1].setEnabled(False)
else:
co_wgt.button_list[1].setEnabled(True)
def extract_tuple(li, idx):
return list(zip(*li)[idx])
# Update option setting, assume datetime has been updated
co_wgt.combo_list[1].setCurrentIndex(extract_tuple(co_wgt.opt_list['year'], 1).index(co_wgt.dtime.year))
co_wgt.combo_list[3].setCurrentIndex(extract_tuple(co_wgt.opt_list['month'], 1).index(co_wgt.dtime.month))
co_wgt.combo_list[5].setCurrentIndex(extract_tuple(co_wgt.opt_list['day'], 1).index(co_wgt.dtime.day))
co_wgt.combo_list[7].setCurrentIndex(extract_tuple(co_wgt.opt_list['hour'], 1).index(co_wgt.dtime.hour))
co_wgt.combo_list[9].setCurrentIndex(extract_tuple(co_wgt.opt_list['minute'], 1).index(co_wgt.dtime.minute))
co_wgt.combo_list[11].setCurrentIndex(extract_tuple(co_wgt.opt_list['second'], 1).index(co_wgt.dtime.second))
# Redraw image
if not co_wgt.hack:
if co_wgt.imfig is not None:
pt.close_figure(co_wgt.imfig)
image = co_wgt.ibs.get_images(co_wgt.gid_list[co_wgt.current_gindex])
figtitle = "Time Synchronization Picture"
co_wgt.imfig, co_wgt.imax = pt.imshow(image, fnum=co_wgt.fnum, title=figtitle)
co_wgt.imfig.show()
def HARDCODE_SHOW_PB_PAIR():
"""
python -m ibeis.viz.viz_chip HARDCODE_SHOW_PB_PAIR --show
Example:
>>> # SCRIPT
>>> from ibeis.viz.viz_chip import * # NOQA
>>> import plottool_ibeis as pt
>>> HARDCODE_SHOW_PB_PAIR()
>>> pt.show_if_requested()
"""
# TODO: generalize into testdata_annotmatches which filters ams propertly
# Then a function to show these ams
import ibeis
import ibeis.viz
has_any = ut.get_argval('--has_any', default=['photobomb'])
index = ut.get_argval('--index', default=0)
ibs = ibeis.opendb(defaultdb='PZ_Master1')
ams = ibs._get_all_annotmatch_rowids()
tags = ibs.get_annotmatch_case_tags(ams)
flags = ut.filterflags_general_tags(tags, has_any=has_any)
selected_ams = ut.compress(ams, flags)
aid_pairs = ibs.get_annotmatch_aids(selected_ams)
aid1, aid2 = aid_pairs[index]
import plottool_ibeis as pt
fnum = 1
if ut.get_argflag('--match'):
request = ibs.depc_annot.new_request('vsone', [aid1], [aid2])
res_list2 = request.execute()
match = res_list2[0]
match.show_single_annotmatch(qreq_=request,
vert=False,
colorbar_=False,
notitle=True,
draw_lbl=False,
draw_border=False)
else:
chip1, chip2 = ibs.get_annot_chips([aid1, aid2])
pt.imshow(chip1, pnum=(1, 2, 1), fnum=fnum)
pt.imshow(chip2, pnum=(1, 2, 2), fnum=fnum)
def show_chip(ibs,
aid,
in_image=False,
annote=True,
title_suffix='',
weight_label=None,
weights=None,
config2_=None,
**kwargs):
r""" Driver function to show chips
Args:
ibs (ibeis.IBEISController):
aid (int): annotation rowid
in_image (bool): displays annotation with the context of its source image
annote (bool): enables overlay annoations
title_suffix (str):
weight_label (None): (default = None)
weights (None): (default = None)
config2_ (dict): (default = None)
Kwargs:
enable_chip_title_prefix, nokpts, kpts_subset, kpts, text_color,
notitle, draw_lbls, show_aidstr, show_gname, show_name, show_nid,
show_exemplar, show_num_gt, show_quality_text, show_viewcode, fnum,
title, figtitle, pnum, interpolation, cmap, heatmap, data_colorbar,
darken, update, xlabel, redraw_image, ax, alpha, docla, doclf,
projection, pts, ell
color (3/4-tuple, ndarray, or str): colors for keypoints
CommandLine:
python -m ibeis.viz.viz_chip show_chip --show --ecc
python -c "import utool as ut; ut.print_auto_docstr('ibeis.viz.viz_chip', 'show_chip')"
python -m ibeis.viz.viz_chip show_chip --show --db NNP_Master3 --aids 14047 --no-annote
python -m ibeis.viz.viz_chip show_chip --show --db NNP_Master3 --aids 14047 --no-annote
python -m ibeis.viz.viz_chip show_chip --show --db PZ_MTEST --aid 1 --bgmethod=cnn
python -m ibeis.viz.viz_chip show_chip --show --db PZ_MTEST --aid 1 --bgmethod=cnn --scale_max=30
python -m ibeis.viz.viz_chip show_chip --show --db PZ_MTEST --aid 1 --ecc --draw_lbls=False --notitle --save=~/slides/lnbnn_query.jpg --dpi=300
Example:
>>> # VIZ_TEST
>>> from ibeis.viz.viz_chip import * # NOQA
>>> import numpy as np
>>> import vtool_ibeis as vt
>>> in_image = False
>>> ibs, aid_list, kwargs, config2_ = testdata_showchip()
>>> aid = aid_list[0]
>>> if True:
>>> import matplotlib as mpl
>>> from ibeis.scripts.thesis import TMP_RC
>>> mpl.rcParams.update(TMP_RC)
>>> if ut.get_argflag('--ecc'):
>>> kpts = ibs.get_annot_kpts(aid, config2_=config2_)
>>> weights = ibs.get_annot_fgweights([aid], ensure=True, config2_=config2_)[0]
>>> kpts = ut.random_sample(kpts[weights > .9], 200, seed=0)
>>> ecc = vt.get_kpts_eccentricity(kpts)
>>> scale = 1 / vt.get_scales(kpts)
>>> #s = ecc if config2_.affine_invariance else scale
>>> s = scale
>>> colors = pt.scores_to_color(s, cmap_='jet')
>>> kwargs['color'] = colors
>>> kwargs['kpts'] = kpts
>>> kwargs['ell_linewidth'] = 3
>>> kwargs['ell_alpha'] = .7
>>> show_chip(ibs, aid, in_image=in_image, config2_=config2_, **kwargs)
>>> pt.show_if_requested()
"""
if ut.VERBOSE:
print('[viz] show_chip(aid=%r)' % (aid, ))
#ibs.assert_valid_aids((aid,))
# Get chip
#print('in_image = %r' % (in_image,))
chip = vh.get_chips(ibs, aid, in_image=in_image, config2_=config2_)
# Create chip title
chip_text = vh.get_annot_texts(ibs, [aid], **kwargs)[0]
if kwargs.get('enable_chip_title_prefix', True):
chip_title_text = chip_text + title_suffix
else:
chip_title_text = title_suffix
chip_title_text = chip_title_text.strip('\n')
# Draw chip
fig, ax = pt.imshow(chip, **kwargs)
# Populate axis user data
vh.set_ibsdat(ax, 'viztype', 'chip')
vh.set_ibsdat(ax, 'aid', aid)
if annote and not kwargs.get('nokpts', False):
# Get and draw keypoints
if 'color' not in kwargs:
if weight_label == 'fg_weights':
if weights is None and ibs.has_species_detector(
ibs.get_annot_species_texts(aid)):
weight_label = 'fg_weights'
weights = ibs.get_annot_fgweights([aid],
ensure=True,
config2_=config2_)[0]
if weights is not None:
cmap_ = 'hot'
#if weight_label == 'dstncvs':
# cmap_ = 'rainbow'
color = pt.scores_to_color(weights,
cmap_=cmap_,
reverse_cmap=False)
kwargs['color'] = color
kwargs['ell_color'] = color
kwargs['pts_color'] = color
kpts_ = vh.get_kpts(ibs,
aid,
in_image,
config2_=config2_,
kpts_subset=kwargs.get('kpts_subset', None),
kpts=kwargs.pop('kpts', None))
pt.viz_keypoints._annotate_kpts(kpts_, **kwargs)
if kwargs.get('draw_lbls', True):
pt.upperleft_text(chip_text, color=kwargs.get('text_color', None))
use_title = not kwargs.get('notitle', False)
if use_title:
pt.set_title(chip_title_text)
if in_image:
gid = ibs.get_annot_gids(aid)
aid_list = ibs.get_image_aids(gid)
annotekw = viz_image.get_annot_annotations(ibs,
aid_list,
sel_aids=[aid],
draw_lbls=kwargs.get(
'draw_lbls', True))
# Put annotation centers in the axis
ph.set_plotdat(ax, 'annotation_bbox_list', annotekw['bbox_list'])
ph.set_plotdat(ax, 'aid_list', aid_list)
pt.viz_image2.draw_image_overlay(ax, **annotekw)
zoom_ = ut.get_argval('--zoom', type_=float, default=None)
if zoom_ is not None:
import vtool_ibeis as vt
# Zoom into the chip for some image context
rotated_verts = ibs.get_annot_rotated_verts(aid)
bbox = ibs.get_annot_bboxes(aid)
#print(bbox)
#print(rotated_verts)
rotated_bbox = vt.bbox_from_verts(rotated_verts)
imgw, imgh = ibs.get_image_sizes(gid)
pad_factor = zoom_
pad_length = min(bbox[2], bbox[3]) * pad_factor
minx = max(rotated_bbox[0] - pad_length, 0)
miny = max(rotated_bbox[1] - pad_length, 0)
maxx = min((rotated_bbox[0] + rotated_bbox[2]) + pad_length, imgw)
maxy = min((rotated_bbox[1] + rotated_bbox[3]) + pad_length, imgh)
#maxy = imgh - maxy
#miny = imgh - miny
ax = pt.gca()
ax.set_xlim(minx, maxx)
ax.set_ylim(miny, maxy)
ax.invert_yaxis()
else:
ph.set_plotdat(ax, 'chipshape', chip.shape)
#if 'featweights' in vars() and 'color' in kwargs:
if weights is not None and weight_label is not None:
## HACK HACK HACK
if len(weights) > 0:
cb = pt.colorbar(weights, kwargs['color'])
cb.set_label(weight_label)
return fig, ax
def get_injured_sharks():
"""
>>> from ibeis.scripts.getshark import * # NOQA
"""
import requests
url = 'http://www.whaleshark.org/getKeywordImages.jsp'
resp = requests.get(url)
assert resp.status_code == 200
keywords = resp.json()['keywords']
key_list = ut.take_column(keywords, 'indexName')
key_to_nice = {k['indexName']: k['readableName'] for k in keywords}
injury_patterns = [
'injury', 'net', 'hook', 'trunc', 'damage', 'scar', 'nicks', 'bite',
]
injury_keys = [key for key in key_list if any([pat in key for pat in injury_patterns])]
noninjury_keys = ut.setdiff(key_list, injury_keys)
injury_nice = ut.lmap(lambda k: key_to_nice[k], injury_keys) # NOQA
noninjury_nice = ut.lmap(lambda k: key_to_nice[k], noninjury_keys) # NOQA
key_list = injury_keys
keyed_images = {}
for key in ut.ProgIter(key_list, lbl='reading index', bs=True):
key_url = url + '?indexName={indexName}'.format(indexName=key)
key_resp = requests.get(key_url)
assert key_resp.status_code == 200
key_imgs = key_resp.json()['images']
keyed_images[key] = key_imgs
key_hist = {key: len(imgs) for key, imgs in keyed_images.items()}
key_hist = ut.sort_dict(key_hist, 'vals')
print(ut.repr3(key_hist))
nice_key_hist = ut.map_dict_keys(lambda k: key_to_nice[k], key_hist)
nice_key_hist = ut.sort_dict(nice_key_hist, 'vals')
print(ut.repr3(nice_key_hist))
key_to_urls = {key: ut.take_column(vals, 'url') for key, vals in keyed_images.items()}
overlaps = {}
import itertools
overlap_img_list = []
for k1, k2 in itertools.combinations(key_to_urls.keys(), 2):
overlap_imgs = ut.isect(key_to_urls[k1], key_to_urls[k2])
num_overlap = len(overlap_imgs)
overlaps[(k1, k2)] = num_overlap
overlaps[(k1, k1)] = len(key_to_urls[k1])
if num_overlap > 0:
#print('[%s][%s], overlap=%r' % (k1, k2, num_overlap))
overlap_img_list.extend(overlap_imgs)
all_img_urls = list(set(ut.flatten(key_to_urls.values())))
num_all = len(all_img_urls) # NOQA
print('num_all = %r' % (num_all,))
# Determine super-categories
categories = ['nicks', 'scar', 'trunc']
# Force these keys into these categories
key_to_cat = {'scarbite': 'other_injury'}
cat_to_keys = ut.ddict(list)
for key in key_to_urls.keys():
flag = 1
if key in key_to_cat:
cat = key_to_cat[key]
cat_to_keys[cat].append(key)
continue
for cat in categories:
if cat in key:
cat_to_keys[cat].append(key)
flag = 0
if flag:
cat = 'other_injury'
cat_to_keys[cat].append(key)
cat_urls = ut.ddict(list)
for cat, keys in cat_to_keys.items():
for key in keys:
cat_urls[cat].extend(key_to_urls[key])
cat_hist = {}
for cat in list(cat_urls.keys()):
cat_urls[cat] = list(set(cat_urls[cat]))
cat_hist[cat] = len(cat_urls[cat])
print(ut.repr3(cat_to_keys))
print(ut.repr3(cat_hist))
key_to_cat = dict([(val, key) for key, vals in cat_to_keys.items() for val in vals])
#ingestset = {
# '__class__': 'ImageSet',
# 'images': ut.ddict(dict)
#}
#for key, key_imgs in keyed_images.items():
# for imgdict in key_imgs:
# url = imgdict['url']
# encid = imgdict['correspondingEncounterNumber']
# # Make structure
# encdict = encounters[encid]
# encdict['__class__'] = 'Encounter'
# imgdict = ut.delete_keys(imgdict.copy(), ['correspondingEncounterNumber'])
# imgdict['__class__'] = 'Image'
# cat = key_to_cat[key]
# annotdict = {'relative_bbox': [.01, .01, .98, .98], 'tags': [cat, key]}
# annotdict['__class__'] = 'Annotation'
# # Ensure structures exist
# encdict['images'] = encdict.get('images', [])
# imgdict['annots'] = imgdict.get('annots', [])
# # Add an image to this encounter
# encdict['images'].append(imgdict)
# # Add an annotation to this image
# imgdict['annots'].append(annotdict)
##http://springbreak.wildbook.org/rest/org.ecocean.Encounter/1111
#get_enc_url = 'http://www.whaleshark.org/rest/org.ecocean.Encounter/%s' % (encid,)
#resp = requests.get(get_enc_url)
#print(ut.repr3(encdict))
#print(ut.repr3(encounters))
# Download the files to the local disk
#fpath_list =
all_urls = ut.unique(ut.take_column(
ut.flatten(
ut.dict_subset(keyed_images, ut.flatten(cat_to_keys.values())).values()
), 'url'))
dldir = ut.truepath('~/tmpsharks')
from os.path import commonprefix, basename # NOQA
prefix = commonprefix(all_urls)
suffix_list = [url_[len(prefix):] for url_ in all_urls]
fname_list = [suffix.replace('/', '--') for suffix in suffix_list]
fpath_list = []
for url, fname in ut.ProgIter(zip(all_urls, fname_list), lbl='downloading imgs', freq=1):
fpath = ut.grab_file_url(url, download_dir=dldir, fname=fname, verbose=False)
fpath_list.append(fpath)
# Make sure we keep orig info
#url_to_keys = ut.ddict(list)
url_to_info = ut.ddict(dict)
for key, imgdict_list in keyed_images.items():
for imgdict in imgdict_list:
url = imgdict['url']
info = url_to_info[url]
for k, v in imgdict.items():
info[k] = info.get(k, [])
info[k].append(v)
info['keys'] = info.get('keys', [])
info['keys'].append(key)
#url_to_keys[url].append(key)
info_list = ut.take(url_to_info, all_urls)
for info in info_list:
if len(set(info['correspondingEncounterNumber'])) > 1:
assert False, 'url with two different encounter nums'
# Combine duplicate tags
hashid_list = [ut.get_file_uuid(fpath_, stride=8) for fpath_ in ut.ProgIter(fpath_list, bs=True)]
groupxs = ut.group_indices(hashid_list)[1]
# Group properties by duplicate images
#groupxs = [g for g in groupxs if len(g) > 1]
fpath_list_ = ut.take_column(ut.apply_grouping(fpath_list, groupxs), 0)
url_list_ = ut.take_column(ut.apply_grouping(all_urls, groupxs), 0)
info_list_ = [ut.map_dict_vals(ut.flatten, ut.dict_accum(*info_))
for info_ in ut.apply_grouping(info_list, groupxs)]
encid_list_ = [ut.unique(info_['correspondingEncounterNumber'])[0]
for info_ in info_list_]
keys_list_ = [ut.unique(info_['keys']) for info_ in info_list_]
cats_list_ = [ut.unique(ut.take(key_to_cat, keys)) for keys in keys_list_]
clist = ut.ColumnLists({
'gpath': fpath_list_,
'url': url_list_,
'encid': encid_list_,
'key': keys_list_,
'cat': cats_list_,
})
#for info_ in ut.apply_grouping(info_list, groupxs):
# info = ut.dict_accum(*info_)
# info = ut.map_dict_vals(ut.flatten, info)
# x = ut.unique(ut.flatten(ut.dict_accum(*info_)['correspondingEncounterNumber']))
# if len(x) > 1:
# info = info.copy()
# del info['keys']
# print(ut.repr3(info))
flags = ut.lmap(ut.fpath_has_imgext, clist['gpath'])
clist = clist.compress(flags)
import ibeis
ibs = ibeis.opendb('WS_Injury', allow_newdir=True)
gid_list = ibs.add_images(clist['gpath'])
clist['gid'] = gid_list
failed_flags = ut.flag_None_items(clist['gid'])
print('# failed %s' % (sum(failed_flags)),)
passed_flags = ut.not_list(failed_flags)
clist = clist.compress(passed_flags)
ut.assert_all_not_None(clist['gid'])
#ibs.get_image_uris_original(clist['gid'])
ibs.set_image_uris_original(clist['gid'], clist['url'], overwrite=True)
#ut.zipflat(clist['cat'], clist['key'])
if False:
# Can run detection instead
clist['tags'] = ut.zipflat(clist['cat'])
aid_list = ibs.use_images_as_annotations(clist['gid'], adjust_percent=0.01,
tags_list=clist['tags'])
aid_list
import plottool_ibeis as pt
from ibeis import core_annots
pt.qt4ensure()
#annots = ibs.annots()
#aids = [1, 2]
#ibs.depc_annot.get('hog', aids , 'hog')
#ibs.depc_annot.get('chip', aids, 'img')
for aid in ut.InteractiveIter(ibs.get_valid_aids()):
hogs = ibs.depc_annot.d.get_hog_hog([aid])
chips = ibs.depc_annot.d.get_chips_img([aid])
chip = chips[0]
hogimg = core_annots.make_hog_block_image(hogs[0])
pt.clf()
pt.imshow(hogimg, pnum=(1, 2, 1))
pt.imshow(chip, pnum=(1, 2, 2))
fig = pt.gcf()
fig.show()
fig.canvas.draw()
#print(len(groupxs))
#if False:
#groupxs = ut.find_duplicate_items(ut.lmap(basename, suffix_list)).values()
#print(ut.repr3(ut.apply_grouping(all_urls, groupxs)))
# # FIX
# for fpath, fname in zip(fpath_list, fname_list):
# if ut.checkpath(fpath):
# ut.move(fpath, join(dirname(fpath), fname))
# print('fpath = %r' % (fpath,))
#import ibeis
#from ibeis.dbio import ingest_dataset
#dbdir = ibeis.sysres.lookup_dbdir('WS_ALL')
#self = ingest_dataset.Ingestable2(dbdir)
if False:
# Show overlap matrix
import plottool_ibeis as pt
import pandas as pd
import numpy as np
dict_ = overlaps
s = pd.Series(dict_, index=pd.MultiIndex.from_tuples(overlaps))
df = s.unstack()
lhs, rhs = df.align(df.T)
df = lhs.add(rhs, fill_value=0).fillna(0)
label_texts = df.columns.values
def label_ticks(label_texts):
import plottool_ibeis as pt
truncated_labels = [repr(lbl[0:100]) for lbl in label_texts]
ax = pt.gca()
ax.set_xticks(list(range(len(label_texts))))
ax.set_xticklabels(truncated_labels)
[lbl.set_rotation(-55) for lbl in ax.get_xticklabels()]
[lbl.set_horizontalalignment('left') for lbl in ax.get_xticklabels()]
#xgrid, ygrid = np.meshgrid(range(len(label_texts)), range(len(label_texts)))
#pt.plot_surface3d(xgrid, ygrid, disjoint_mat)
ax.set_yticks(list(range(len(label_texts))))
ax.set_yticklabels(truncated_labels)
[lbl.set_horizontalalignment('right') for lbl in ax.get_yticklabels()]
[lbl.set_verticalalignment('center') for lbl in ax.get_yticklabels()]
#[lbl.set_rotation(20) for lbl in ax.get_yticklabels()]
#df = df.sort(axis=0)
#df = df.sort(axis=1)
sortx = np.argsort(df.sum(axis=1).values)[::-1]
df = df.take(sortx, axis=0)
df = df.take(sortx, axis=1)
fig = pt.figure(fnum=1)
fig.clf()
mat = df.values.astype(np.int32)
mat[np.diag_indices(len(mat))] = 0
vmax = mat[(1 - np.eye(len(mat))).astype(np.bool)].max()
import matplotlib.colors
norm = matplotlib.colors.Normalize(vmin=0, vmax=vmax, clip=True)
pt.plt.imshow(mat, cmap='hot', norm=norm, interpolation='none')
pt.plt.colorbar()
pt.plt.grid('off')
label_ticks(label_texts)
fig.tight_layout()
#overlap_df = pd.DataFrame.from_dict(overlap_img_list)
class TmpImage(ut.NiceRepr):
pass
from skimage.feature import hog
from skimage import data, color, exposure
import plottool_ibeis as pt
image2 = color.rgb2gray(data.astronaut()) # NOQA
fpath = './GOPR1120.JPG'
import vtool_ibeis as vt
for fpath in [fpath]:
"""
http://scikit-image.org/docs/dev/auto_examples/plot_hog.html
"""
image = vt.imread(fpath, grayscale=True)
image = pt.color_funcs.to_base01(image)
fig = pt.figure(fnum=2)
fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualise=True)
fig, (ax1, ax2) = pt.plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)
ax1.axis('off')
ax1.imshow(image, cmap=pt.plt.cm.gray)
ax1.set_title('Input image')
ax1.set_adjustable('box-forced')
# Rescale histogram for better display
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))
ax2.axis('off')
ax2.imshow(hog_image_rescaled, cmap=pt.plt.cm.gray)
ax2.set_title('Histogram of Oriented Gradients')
ax1.set_adjustable('box-forced')
pt.plt.show()
def show_multichip_match(rchip1,
rchip2_list,
kpts1,
kpts2_list,
fm_list,
fs_list,
featflag_list,
fnum=None,
pnum=None,
**kwargs):
"""
move to df2
rchip = rchip1
H = H1 = None
target_wh = None
"""
import vtool_ibeis.image as gtool
import plottool_ibeis as pt
import numpy as np
import vtool_ibeis as vt
kwargs = kwargs.copy()
colorbar_ = kwargs.pop('colorbar_', True)
stack_larger = kwargs.pop('stack_larger', False)
stack_side = kwargs.pop('stack_side', False)
# mode for features disabled by name scoring
NONVOTE_MODE = kwargs.get('nonvote_mode', 'filter')
def preprocess_chips(rchip, H, target_wh):
rchip_ = rchip if H is None else gtool.warpHomog(rchip, H, target_wh)
return rchip_
if fnum is None:
fnum = pt.next_fnum()
target_wh1 = None
H1 = None
rchip1_ = preprocess_chips(rchip1, H1, target_wh1)
# Hack to visually identify the query
rchip1_ = vt.draw_border(rchip1_,
out=rchip1_,
thickness=15,
color=(pt.UNKNOWN_PURP[0:3] * 255).astype(
np.uint8).tolist())
wh1 = gtool.get_size(rchip1_)
rchip2_list_ = [
preprocess_chips(rchip2, None, wh1) for rchip2 in rchip2_list
]
wh2_list = [gtool.get_size(rchip2) for rchip2 in rchip2_list_]
num = 0 if len(rchip2_list) < 3 else 1
#vert = True if len(rchip2_list) > 1 else False
vert = True if len(rchip2_list) > 1 else None
#num = 0
if False and kwargs.get('fastmode', False):
# This doesn't actually help the speed very much
stackkw = dict(
# Hack draw results faster Q
#initial_sf=.4,
#initial_sf=.9,
use_larger=stack_larger,
#use_larger=True,
)
else:
stackkw = dict()
#use_larger = True
#vert = kwargs.get('fastmode', False)
if stack_side:
# hack to stack all database images vertically
num = 0
# TODO: heatmask
match_img, offset_list, sf_list = vt.stack_image_list_special(rchip1_,
rchip2_list_,
num=num,
vert=vert,
**stackkw)
wh_list = np.array(ut.flatten([[wh1], wh2_list])) * sf_list
offset1 = offset_list[0]
wh1 = wh_list[0]
sf1 = sf_list[0]
fig, ax = pt.imshow(match_img, fnum=fnum, pnum=pnum)
if kwargs.get('show_matches', True):
ut.flatten(fs_list)
#ut.embed()
flat_fs, cumlen_list = ut.invertible_flatten2(fs_list)
flat_colors = pt.scores_to_color(np.array(flat_fs), 'hot')
colors_list = ut.unflatten2(flat_colors, cumlen_list)
for _tup in zip(offset_list[1:], wh_list[1:], sf_list[1:], kpts2_list,
fm_list, fs_list, featflag_list, colors_list):
offset2, wh2, sf2, kpts2, fm2_, fs2_, featflags, colors = _tup
xywh1 = (offset1[0], offset1[1], wh1[0], wh1[1])
xywh2 = (offset2[0], offset2[1], wh2[0], wh2[1])
#colors = pt.scores_to_color(fs2)
if kpts1 is not None and kpts2 is not None:
if NONVOTE_MODE == 'filter':
fm2 = fm2_.compress(featflags, axis=0)
fs2 = fs2_.compress(featflags, axis=0)
elif NONVOTE_MODE == 'only':
fm2 = fm2_.compress(np.logical_not(featflags), axis=0)
fs2 = fs2_.compress(np.logical_not(featflags), axis=0)
else:
# TODO: optional coloring of nonvotes instead
fm2 = fm2_
fs2 = fs2_
pt.plot_fmatch(xywh1,
xywh2,
kpts1,
kpts2,
fm2,
fs2,
fm_norm=None,
H1=None,
H2=None,
scale_factor1=sf1,
scale_factor2=sf2,
colorbar_=False,
colors=colors,
**kwargs)
if colorbar_:
pt.colorbar(flat_fs, flat_colors)
bbox_list = [(x, y, w, h) for (x, y), (w, h) in zip(offset_list, wh_list)]
return offset_list, sf_list, bbox_list