def plot_gps_html(gps_list):
""" Plots gps coordinates on a map projection
InstallBasemap:
sudo apt-get install libgeos-dev
pip install git+https://github.com/matplotlib/basemap
http://matplotlib.org/basemap/users/examples.html
pip install gmplot
sudo apt-get install netcdf-bin
sudo apt-get install libnetcdf-dev
pip install netCDF4
Ignore:
pip install git+git://github.com/myuser/foo.git@v123
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.preproc.preproc_occurrence import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='testdb1')
>>> images = ibs.images()
>>> # Setup GPS points to draw
>>> print('Setup GPS points')
>>> gps_list_ = np.array(images.gps2)
>>> unixtime_list_ = np.array(images.unixtime2)
>>> has_gps = np.all(np.logical_not(np.isnan(gps_list_)), axis=1)
>>> has_unixtime = np.logical_not(np.isnan(unixtime_list_))
>>> isvalid = np.logical_and(has_gps, has_unixtime)
>>> gps_list = gps_list_.compress(isvalid, axis=0)
>>> unixtime_list = unixtime_list_.compress(isvalid) # NOQA
>>> plot_image_gps(gps_list)
"""
import plottool_ibeis as pt
import gmplot
import matplotlib as mpl
import vtool_ibeis as vt
pt.qt4ensure()
lat = gps_list.T[0]
lon = gps_list.T[1]
# Get extent of
bbox = vt.bbox_from_verts(gps_list)
centerx, centery = vt.bbox_center(bbox)
gmap = gmplot.GoogleMapPlotter(centerx, centery, 13)
color = mpl.colors.rgb2hex(pt.ORANGE)
gmap.scatter(lat, lon, color=color, size=100, marker=False)
gmap.draw("mymap.html")
ut.startfile('mymap.html')
def gridsearch_ratio_thresh(matches):
import sklearn
import sklearn.metrics
import vtool_ibeis as vt
# Param search for vsone
import plottool_ibeis as pt
pt.qt4ensure()
skf = sklearn.model_selection.StratifiedKFold(n_splits=10,
random_state=119372)
y = np.array([m.annot1['nid'] == m.annot2['nid'] for m in matches])
basis = {'ratio_thresh': np.linspace(.6, .7, 50).tolist()}
grid = ut.all_dict_combinations(basis)
xdata = np.array(ut.take_column(grid, 'ratio_thresh'))
def _ratio_thresh(y_true, match_list):
# Try and find optional ratio threshold
auc_list = []
for cfgdict in ut.ProgIter(grid, lbl='gridsearch'):
y_score = [
match.fs.compress(match.ratio_test_flags(cfgdict)).sum()
for match in match_list
]
auc = sklearn.metrics.roc_auc_score(y_true, y_score)
auc_list.append(auc)
auc_list = np.array(auc_list)
return auc_list
auc_list = _ratio_thresh(y, matches)
pt.plot(xdata, auc_list)
subx, suby = vt.argsubmaxima(auc_list, xdata)
best_ratio_thresh = subx[suby.argmax()]
skf_results = []
y_true = y
for train_idx, test_idx in skf.split(matches, y):
match_list_ = ut.take(matches, train_idx)
y_true = y.take(train_idx)
auc_list = _ratio_thresh(y_true, match_list_)
subx, suby = vt.argsubmaxima(auc_list, xdata, maxima_thresh=.8)
best_ratio_thresh = subx[suby.argmax()]
skf_results.append(best_ratio_thresh)
print('skf_results.append = %r' % (np.mean(skf_results),))
import utool
utool.embed()
def fix_splits_interaction(ibs):
"""
python -m ibeis fix_splits_interaction --show
Example:
>>> # DISABLE_DOCTEST GGR
>>> from ibeis.other.dbinfo import * # NOQA
>>> import ibeis
>>> dbdir = '/media/danger/GGR/GGR-IBEIS'
>>> dbdir = dbdir if ut.checkpath(dbdir) else ut.truepath('~/lev/media/danger/GGR/GGR-IBEIS')
>>> ibs = ibeis.opendb(dbdir=dbdir, allow_newdir=False)
>>> import guitool_ibeis as gt
>>> gt.ensure_qtapp()
>>> win = fix_splits_interaction(ibs)
>>> ut.quit_if_noshow()
>>> import plottool_ibeis as pt
>>> gt.qtapp_loop(qwin=win)
"""
split_props = {'splitcase', 'photobomb'}
all_annot_groups = ibs._annot_groups(ibs.group_annots_by_name(ibs.get_valid_aids())[0])
all_has_split = [len(split_props.intersection(ut.flatten(tags))) > 0 for tags in all_annot_groups.match_tags]
tosplit_annots = ut.compress(all_annot_groups.annots_list, all_has_split)
tosplit_annots = ut.take(tosplit_annots, ut.argsort(ut.lmap(len, tosplit_annots)))[::-1]
if ut.get_argflag('--reverse'):
tosplit_annots = tosplit_annots[::-1]
print('len(tosplit_annots) = %r' % (len(tosplit_annots),))
aids_list = [a.aids for a in tosplit_annots]
from ibeis.algo.graph import graph_iden
from ibeis.viz import viz_graph2
import guitool_ibeis as gt
import plottool_ibeis as pt
pt.qt4ensure()
gt.ensure_qtapp()
for aids in ut.InteractiveIter(aids_list):
infr = graph_iden.AnnotInference(ibs, aids)
infr.initialize_graph()
win = viz_graph2.AnnotGraphWidget(infr=infr, use_image=False,
init_mode='rereview')
win.populate_edge_model()
win.show()
return win
def show_data_image(data_uri_order, i, offset_list, all_kpts, all_vecs):
"""
i = 12
"""
import vtool_ibeis as vt
from os.path import join
imgdir = ut.truepath('/raid/work/Oxford/oxbuild_images')
gpath = join(imgdir, data_uri_order[i] + '.jpg')
image = vt.imread(gpath)
import plottool_ibeis as pt
pt.qt4ensure()
# pt.imshow(image)
l = offset_list[i]
r = offset_list[i + 1]
kpts = all_kpts[l:r]
vecs = all_vecs[l:r]
pt.interact_keypoints.ishow_keypoints(image,
kpts,
vecs,
ori=False,
ell_alpha=.4,
color='distinct')
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_ibeis 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 dans_splits(ibs):
"""
python -m ibeis dans_splits --show
Example:
>>> # DISABLE_DOCTEST GGR
>>> from ibeis.other.dbinfo import * # NOQA
>>> import ibeis
>>> dbdir = '/media/danger/GGR/GGR-IBEIS'
>>> dbdir = dbdir if ut.checkpath(dbdir) else ut.truepath('~/lev/media/danger/GGR/GGR-IBEIS')
>>> ibs = ibeis.opendb(dbdir=dbdir, allow_newdir=False)
>>> import guitool_ibeis as gt
>>> gt.ensure_qtapp()
>>> win = dans_splits(ibs)
>>> ut.quit_if_noshow()
>>> import plottool_ibeis as pt
>>> gt.qtapp_loop(qwin=win)
"""
#pair = 9262, 932
dans_aids = [26548, 2190, 9418, 29965, 14738, 26600, 3039, 2742, 8249,
20154, 8572, 4504, 34941, 4040, 7436, 31866, 28291,
16009, 7378, 14453, 2590, 2738, 22442, 26483, 21640, 19003,
13630, 25395, 20015, 14948, 21429, 19740, 7908, 23583, 14301,
26912, 30613, 19719, 21887, 8838, 16184, 9181, 8649, 8276,
14678, 21950, 4925, 13766, 12673, 8417, 2018, 22434, 21149,
14884, 5596, 8276, 14650, 1355, 21725, 21889, 26376, 2867,
6906, 4890, 21524, 6690, 14738, 1823, 35525, 9045, 31723,
2406, 5298, 15627, 31933, 19535, 9137, 21002, 2448,
32454, 12615, 31755, 20015, 24573, 32001, 23637, 3192, 3197,
8702, 1240, 5596, 33473, 23874, 9558, 9245, 23570, 33075,
23721, 24012, 33405, 23791, 19498, 33149, 9558, 4971,
34183, 24853, 9321, 23691, 9723, 9236, 9723, 21078,
32300, 8700, 15334, 6050, 23277, 31164, 14103,
21231, 8007, 10388, 33387, 4319, 26880, 8007, 31164,
32300, 32140]
is_hyrbid = [7123, 7166, 7157, 7158, ] # NOQA
needs_mask = [26836, 29742] # NOQA
justfine = [19862] # NOQA
annots = ibs.annots(dans_aids)
unique_nids = ut.unique(annots.nids)
grouped_aids = ibs.get_name_aids(unique_nids)
annot_groups = ibs._annot_groups(grouped_aids)
split_props = {'splitcase', 'photobomb'}
needs_tag = [len(split_props.intersection(ut.flatten(tags))) == 0 for tags in annot_groups.match_tags]
num_needs_tag = sum(needs_tag)
num_had_split = len(needs_tag) - num_needs_tag
print('num_had_split = %r' % (num_had_split,))
print('num_needs_tag = %r' % (num_needs_tag,))
#all_annot_groups = ibs._annot_groups(ibs.group_annots_by_name(ibs.get_valid_aids())[0])
#all_has_split = [len(split_props.intersection(ut.flatten(tags))) > 0 for tags in all_annot_groups.match_tags]
#num_nondan = sum(all_has_split) - num_had_split
#print('num_nondan = %r' % (num_nondan,))
from ibeis.algo.graph import graph_iden
from ibeis.viz import viz_graph2
import guitool_ibeis as gt
import plottool_ibeis as pt
pt.qt4ensure()
gt.ensure_qtapp()
aids_list = ut.compress(grouped_aids, needs_tag)
aids_list = [a for a in aids_list if len(a) > 1]
print('len(aids_list) = %r' % (len(aids_list),))
for aids in aids_list:
infr = graph_iden.AnnotInference(ibs, aids)
infr.initialize_graph()
win = viz_graph2.AnnotGraphWidget(infr=infr, use_image=False,
init_mode='rereview')
win.populate_edge_model()
win.show()
return win
assert False
def detect_sharks(ibs, gids):
#import ibeis
#ibs = ibeis.opendb('WS_ALL')
config = {
'algo' : 'yolo',
'sensitivity' : 0.2,
'config_filepath' : ut.truepath('~/work/WS_ALL/localizer_backup/detect.yolo.2.cfg'),
'weight_filepath' : ut.truepath('~/work/WS_ALL/localizer_backup/detect.yolo.2.39000.weights'),
'class_filepath' : ut.truepath('~/work/WS_ALL/localizer_backup/detect.yolo.2.cfg.classes'),
}
depc = ibs.depc_image
#imgsets = ibs.imagesets(text='Injured Sharks')
#images = ibs.images(imgsets.gids[0])
images = ibs.images(gids)
images = images.compress([ext not in ['.gif'] for ext in images.exts])
gid_list = images.gids
# result is a tuple:
# (score, bbox_list, theta_list, conf_list, class_list)
results_list = depc.get_property('localizations', gid_list, None, config=config)
results_list2 = []
multi_gids = []
failed_gids = []
#ibs.set_image_imagesettext(failed_gids, ['Fixme'] * len(failed_gids))
ibs.set_image_imagesettext(multi_gids, ['Fixme2'] * len(multi_gids))
failed_gids
for gid, res in zip(gid_list, results_list):
score, bbox_list, theta_list, conf_list, class_list = res
if len(bbox_list) == 0:
failed_gids.append(gid)
elif len(bbox_list) == 1:
results_list2.append((gid, bbox_list, theta_list))
elif len(bbox_list) > 1:
multi_gids.append(gid)
idx = conf_list.argmax()
res2 = (gid, bbox_list[idx:idx + 1], theta_list[idx:idx + 1])
results_list2.append(res2)
ut.dict_hist(([t[1].shape[0] for t in results_list]))
localized_imgs = ibs.images(ut.take_column(results_list2, 0))
assert all([len(a) == 1 for a in localized_imgs.aids])
old_annots = ibs.annots(ut.flatten(localized_imgs.aids))
#old_tags = old_annots.case_tags
# Override old bboxes
import numpy as np
bboxes = np.array(ut.take_column(results_list2, 1))[:, 0, :]
ibs.set_annot_bboxes(old_annots.aids, bboxes)
if False:
import plottool_ibeis as pt
pt.qt4ensure()
inter = pt.MultiImageInteraction(
ibs.get_image_paths(ut.take_column(results_list2, 0)),
bboxes_list=ut.take_column(results_list2, 1)
)
inter.dump_to_disk('shark_loc', num=50, prefix='shark_loc')
inter.start()
inter = pt.MultiImageInteraction(ibs.get_image_paths(failed_gids))
inter.start()
inter = pt.MultiImageInteraction(ibs.get_image_paths(multi_gids))
inter.start()
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()