def consolodate_duplicates(self):
fnames = map(basename, self.rel_fpath_list)
duplicate_map = ut.find_duplicate_items(fnames)
groups = []
for dupname, idxs in duplicate_map.items():
uuids = self.get_prop('uuids', idxs)
unique_uuids, groupxs = ut.group_indices(uuids)
groups.extend(ut.apply_grouping(idxs, groupxs))
multitons = [g for g in groups if len(g) > 1]
# singletons = [g for g in groups if len(g) <= 1]
ut.unflat_take(list(self.fpaths()), multitons)
def consolodate_duplicates(self):
fnames = map(basename, self.rel_fpath_list)
duplicate_map = ut.find_duplicate_items(fnames)
groups = []
for dupname, idxs in duplicate_map.items():
uuids = self.get_prop('uuids', idxs)
unique_uuids, groupxs = ut.group_indices(uuids)
groups.extend(ut.apply_grouping(idxs, groupxs))
multitons = [g for g in groups if len(g) > 1]
# singletons = [g for g in groups if len(g) <= 1]
ut.unflat_take(list(self.fpaths()), multitons)
def get_match_results(depc, qaid_list, daid_list, score_list, config):
""" converts table results into format for ipython notebook """
#qaid_list, daid_list = request.get_parent_rowids()
#score_list = request.score_list
#config = request.config
unique_qaids, groupxs = ut.group_indices(qaid_list)
#grouped_qaids_list = ut.apply_grouping(qaid_list, groupxs)
grouped_daids = ut.apply_grouping(daid_list, groupxs)
grouped_scores = ut.apply_grouping(score_list, groupxs)
ibs = depc.controller
unique_qnids = ibs.get_annot_nids(unique_qaids)
# FIXME: decision should not be part of the config for the one-vs-one
# scores
decision_func = getattr(np, config['decision'])
_iter = zip(unique_qaids, unique_qnids, grouped_daids, grouped_scores)
for qaid, qnid, daids, scores in _iter:
dnids = ibs.get_annot_nids(daids)
# Remove distance to self
annot_scores = np.array(scores)
daid_list_ = np.array(daids)
dnid_list_ = np.array(dnids)
is_valid = (daid_list_ != qaid)
daid_list_ = daid_list_.compress(is_valid)
dnid_list_ = dnid_list_.compress(is_valid)
annot_scores = annot_scores.compress(is_valid)
# Hacked in version of creating an annot match object
match_result = ibeis.AnnotMatch()
match_result.qaid = qaid
match_result.qnid = qnid
match_result.daid_list = daid_list_
match_result.dnid_list = dnid_list_
match_result._update_daid_index()
match_result._update_unique_nid_index()
grouped_annot_scores = vt.apply_grouping(annot_scores,
match_result.name_groupxs)
name_scores = np.array(
[decision_func(dists) for dists in grouped_annot_scores])
match_result.set_cannonical_name_score(annot_scores, name_scores)
yield match_result
def get_match_results(depc, qaid_list, daid_list, score_list, config):
""" converts table results into format for ipython notebook """
#qaid_list, daid_list = request.get_parent_rowids()
#score_list = request.score_list
#config = request.config
unique_qaids, groupxs = ut.group_indices(qaid_list)
#grouped_qaids_list = ut.apply_grouping(qaid_list, groupxs)
grouped_daids = ut.apply_grouping(daid_list, groupxs)
grouped_scores = ut.apply_grouping(score_list, groupxs)
ibs = depc.controller
unique_qnids = ibs.get_annot_nids(unique_qaids)
# FIXME: decision should not be part of the config for the one-vs-one
# scores
decision_func = getattr(np, config['decision'])
_iter = zip(unique_qaids, unique_qnids, grouped_daids, grouped_scores)
for qaid, qnid, daids, scores in _iter:
dnids = ibs.get_annot_nids(daids)
# Remove distance to self
annot_scores = np.array(scores)
daid_list_ = np.array(daids)
dnid_list_ = np.array(dnids)
is_valid = (daid_list_ != qaid)
daid_list_ = daid_list_.compress(is_valid)
dnid_list_ = dnid_list_.compress(is_valid)
annot_scores = annot_scores.compress(is_valid)
# Hacked in version of creating an annot match object
match_result = ibeis.AnnotMatch()
match_result.qaid = qaid
match_result.qnid = qnid
match_result.daid_list = daid_list_
match_result.dnid_list = dnid_list_
match_result._update_daid_index()
match_result._update_unique_nid_index()
grouped_annot_scores = vt.apply_grouping(annot_scores, match_result.name_groupxs)
name_scores = np.array([decision_func(dists) for dists in grouped_annot_scores])
match_result.set_cannonical_name_score(annot_scores, name_scores)
yield match_result
def as_parts(self):
if self.parts is not None:
return self.parts
text = self.as_text()
top, header, mid, bot = split_tabular(text)
colfmt = self._rectify_colfmt()
if colfmt is not None:
top = '\\begin{tabular}{%s}' % (colfmt, )
if self.theadify:
import textwrap
width = self.theadify
wrapper = textwrap.TextWrapper(width=width, break_long_words=False)
header_lines = header.split('\n')
new_lines = []
for line in header_lines:
line = line.rstrip('\\')
headers = [h.strip() for h in line.split('&')]
headers = ['\\\\'.join(wrapper.wrap(h)) for h in headers]
headers = [
h if h == '{}' else '\\thead{' + h + '}' for h in headers
]
line = ' & '.join(headers) + '\\\\'
new_lines.append(line)
new_header = '\n'.join(new_lines)
header = new_header
if True:
groupxs = self.groupxs
# Put midlines between multi index levels
if groupxs is None and isinstance(self._data, pd.DataFrame):
index = self._data.index
if len(index.names) == 2 and len(mid) == 1:
groupxs = ut.group_indices(index.labels[0])[1]
# part = '\n\multirow{%d}{*}{%s}\n' % (len(chunk), key,)
# part += '\n'.join(['& ' + c for c in chunk])
if groupxs is not None:
bodylines = mid[0].split('\n')
mid = ut.apply_grouping(bodylines, groupxs)
parts = (top, header, mid, bot)
return parts
def stratified_label_shuffle_split(y, labels, fractions, y_idx=None, rng=None):
"""
modified from sklearn to make n splits instaed of 2.
Also enforces that labels are not broken into separate groups.
Args:
y (ndarray): labels
labels (?):
fractions (?):
rng (RandomState): random number generator(default = None)
Returns:
?: index_sets
CommandLine:
python -m ibeis_cnn.dataset stratified_label_shuffle_split --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_cnn.dataset import * # NOQA
>>> y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
>>> labels = [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 4, 4, 5, 5, 6, 0, 7, 7, 7, 7]
>>> fractions = [.7, .3]
>>> rng = np.random.RandomState(0)
>>> index_sets = stratified_label_shuffle_split(y, labels, fractions, rng)
"""
rng = ut.ensure_rng(rng)
#orig_y = y
unique_labels, groupxs = ut.group_indices(labels)
grouped_ys = ut.apply_grouping(y, groupxs)
# Assign each group a probabilistic class
unique_ys = [ys[rng.randint(0, len(ys))] for ys in grouped_ys]
# TODO: should weight the following selection based on size of group
#class_weights = [ut.dict_hist(ys) for ys in grouped_ys]
unique_idxs = stratified_shuffle_split(unique_ys, fractions, rng)
index_sets = [np.array(ut.flatten(ut.take(groupxs, idxs))) for idxs in unique_idxs]
if y_idx is not None:
# These indicies subindex into parent set of indicies
index_sets = [np.take(y_idx, idxs, axis=0) for idxs in index_sets]
return index_sets
def _make_test_folds(self, X, y=None, groups=None):
"""
Args:
self (?):
X (ndarray): data
y (ndarray): labels(default = None)
groups (None): (default = None)
Returns:
?: test_folds
CommandLine:
python -m ibeis.algo.verif.sklearn_utils _make_test_folds
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.verif.sklearn_utils import * # NOQA
>>> import utool as ut
>>> rng = ut.ensure_rng(0)
>>> groups = [1, 1, 3, 4, 2, 2, 7, 8, 8]
>>> y = [1, 1, 1, 1, 2, 2, 2, 3, 3]
>>> X = np.empty((len(y), 0))
>>> self = StratifiedGroupKFold(random_state=rng)
>>> skf_list = list(self.split(X=X, y=y, groups=groups))
"""
# if self.shuffle:
# rng = check_random_state(self.random_state)
# else:
# rng = self.random_state
n_splits = self.n_splits
y = np.asarray(y)
n_samples = y.shape[0]
import utool as ut
# y_counts = bincount(y_inversed)
# min_classes_ = np.min(y_counts)
# if np.all(self.n_splits > y_counts):
# raise ValueError("All the n_groups for individual classes"
# " are less than n_splits=%d."
# % (self.n_splits))
# if self.n_splits > min_classes_:
# warnings.warn(("The least populated class in y has only %d"
# " members, which is too few. The minimum"
# " number of groups for any class cannot"
# " be less than n_splits=%d."
# % (min_classes_, self.n_splits)), Warning)
unique_y, y_inversed = np.unique(y, return_inverse=True)
n_classes = max(unique_y) + 1
unique_groups, group_idxs = ut.group_indices(groups)
# grouped_ids = list(grouping.keys())
grouped_y = ut.apply_grouping(y, group_idxs)
grouped_y_counts = np.array([
bincount(y_, minlength=n_classes) for y_ in grouped_y])
target_freq = grouped_y_counts.sum(axis=0)
target_ratio = target_freq / target_freq.sum()
# Greedilly choose the split assignment that minimizes the local
# * squared differences in target from actual frequencies
# * and best equalizes the number of items per fold
# Distribute groups with most members first
split_freq = np.zeros((n_splits, n_classes))
# split_ratios = split_freq / split_freq.sum(axis=1)
split_ratios = np.ones(split_freq.shape) / split_freq.shape[1]
split_diffs = ((split_freq - target_ratio) ** 2).sum(axis=1)
sortx = np.argsort(grouped_y_counts.sum(axis=1))[::-1]
grouped_splitx = []
for count, group_idx in enumerate(sortx):
# print('---------\n')
group_freq = grouped_y_counts[group_idx]
cand_freq = split_freq + group_freq
cand_ratio = cand_freq / cand_freq.sum(axis=1)[:, None]
cand_diffs = ((cand_ratio - target_ratio) ** 2).sum(axis=1)
# Compute loss
losses = []
# others = np.nan_to_num(split_diffs)
other_diffs = np.array([
sum(split_diffs[x + 1:]) + sum(split_diffs[:x])
for x in range(n_splits)
])
# penalize unbalanced splits
ratio_loss = other_diffs + cand_diffs
# penalize heavy splits
freq_loss = split_freq.sum(axis=1)
freq_loss = freq_loss / freq_loss.sum()
losses = ratio_loss + freq_loss
# print('group_freq = %r' % (group_freq,))
# print('freq_loss = %s' % (ut.repr2(freq_loss, precision=2),))
# print('ratio_loss = %s' % (ut.repr2(ratio_loss, precision=2),))
#-------
splitx = np.argmin(losses)
# print('losses = %r, splitx=%r' % (losses, splitx))
split_freq[splitx] = cand_freq[splitx]
split_ratios[splitx] = cand_ratio[splitx]
split_diffs[splitx] = cand_diffs[splitx]
grouped_splitx.append(splitx)
# if count > 4:
# break
# else:
# print('split_freq = \n' +
# ut.repr2(split_freq, precision=2, suppress_small=True))
# print('target_ratio = \n' +
# ut.repr2(target_ratio, precision=2, suppress_small=True))
# print('split_ratios = \n' +
# ut.repr2(split_ratios, precision=2, suppress_small=True))
# print(ut.dict_hist(grouped_splitx))
# final_ratio_loss = ((split_ratios - target_ratio) ** 2).sum(axis=1)
# print('split_freq = \n' +
# ut.repr2(split_freq, precision=3, suppress_small=True))
# print('target_ratio = \n' +
# ut.repr2(target_ratio, precision=3, suppress_small=True))
# print('split_ratios = \n' +
# ut.repr2(split_ratios, precision=3, suppress_small=True))
# print(ut.dict_hist(grouped_splitx))
test_folds = np.empty(n_samples, dtype=np.int)
for group_idx, splitx in zip(sortx, grouped_splitx):
idxs = group_idxs[group_idx]
test_folds[idxs] = splitx
return test_folds
def crftest():
"""
pip install pyqpbo
pip install pystruct
http://taku910.github.io/crfpp/#install
cd ~/tmp
#wget https://drive.google.com/folderview?id=0B4y35FiV1wh7fngteFhHQUN2Y1B5eUJBNHZUemJYQV9VWlBUb3JlX0xBdWVZTWtSbVBneU0&usp=drive_web#list
7z x CRF++-0.58.tar.gz
7z x CRF++-0.58.tar
cd CRF++-0.58
chmod +x configure
./configure
make
"""
import pystruct
import pystruct.models
inference_method_options = ['lp', 'max-product']
inference_method = inference_method_options[1]
# graph = pystruct.models.GraphCRF(
# n_states=None,
# n_features=None,
# inference_method=inference_method,
# class_weight=None,
# directed=False,
# )
num_annots = 5
num_names = num_annots
aids = np.arange(5)
rng = np.random.RandomState(0)
hidden_nids = rng.randint(0, num_names, num_annots)
unique_nids, groupxs = ut.group_indices(hidden_nids)
# Indicator vector indicating the name
node_features = np.zeros((num_annots, num_names))
node_features[(aids, hidden_nids)] = 1
toy_params = {True: {'mu': 1.0, 'sigma': 2.2}, False: {'mu': 7.0, 'sigma': 0.9}}
if False:
import vtool as vt
import wbia.plottool as pt
pt.ensureqt()
xdata = np.linspace(0, 100, 1000)
tp_pdf = vt.gauss_func1d(xdata, **toy_params[True])
fp_pdf = vt.gauss_func1d(xdata, **toy_params[False])
pt.plot_probabilities([tp_pdf, fp_pdf], ['TP', 'TF'], xdata=xdata)
def metric(aidx1, aidx2, hidden_nids=hidden_nids, toy_params=toy_params):
if aidx1 == aidx2:
return 0
rng = np.random.RandomState(int(aidx1 + aidx2))
same = hidden_nids[int(aidx1)] == hidden_nids[int(aidx2)]
mu, sigma = ut.dict_take(toy_params[same], ['mu', 'sigma'])
return np.clip(rng.normal(mu, sigma), 0, np.inf)
pairwise_aidxs = list(ut.iprod(range(num_annots), range(num_annots)))
pairwise_labels = np.array( # NOQA
[hidden_nids[a1] == hidden_nids[a2] for a1, a2 in pairwise_aidxs]
)
pairwise_scores = np.array([metric(*zz) for zz in pairwise_aidxs])
pairwise_scores_mat = pairwise_scores.reshape(num_annots, num_annots) # NOQA
graph = pystruct.models.EdgeFeatureGraphCRF( # NOQA
n_states=num_annots,
n_features=num_names,
n_edge_features=1,
inference_method=inference_method,
)
import opengm
numVar = 10
unaries = np.ones([numVar, 3], dtype=opengm.value_type)
gm = opengm.gm(np.ones(numVar, dtype=opengm.label_type) * 3)
unary_fids = gm.addFunctions(unaries)
gm.addFactors(unary_fids, np.arange(numVar))
infParam = opengm.InfParam(workflow=ut.ensure_ascii('(IC)(TTC-I,CC-I)'))
inf = opengm.inference.Multicut(gm, parameter=infParam)
visitor = inf.verboseVisitor(printNth=1, multiline=False)
inf.infer(visitor)
arg = inf.arg()
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
# regularizer = opengm.pottsFunction([3, 3], 0.0, beta)
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
unaries = np.random.rand(10, 10, 2)
potts = opengm.PottsFunction([2, 2], 0.0, 0.4)
gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts)
inf = opengm.inference.GraphCut(gm)
inf.infer()
arg = inf.arg() # NOQA
def group_indicies(self, labels):
unique_labels, groupxs = ut.group_indices(labels)
return unique_labels, groupxs
def collapse_labels(model, evidence, reduced_variables, reduced_row_idxs,
reduced_values):
import vtool as vt
#assert np.all(reduced_joint.values.ravel() == reduced_joint.values.flatten())
reduced_ttypes = [model.var2_cpd[var].ttype for var in reduced_variables]
evidence_vars = list(evidence.keys())
evidence_state_idxs = ut.dict_take(evidence, evidence_vars)
evidence_ttypes = [model.var2_cpd[var].ttype for var in evidence_vars]
ttype2_ev_indices = dict(zip(*ut.group_indices(evidence_ttypes)))
ttype2_re_indices = dict(zip(*ut.group_indices(reduced_ttypes)))
# ttype2_ev_indices = ut.group_items(range(len(evidence_vars)), evidence_ttypes)
# ttype2_re_indices = ut.group_items(range(len(reduced_variables)), reduced_ttypes)
# Allow specific types of labels to change
# everything is the same, only the names have changed.
# TODO: allow for multiple different label_ttypes
# for label_ttype in label_ttypes
if 'name' not in model.ttype2_template:
return reduced_row_idxs, reduced_values
label_ttypes = ['name']
for label_ttype in label_ttypes:
ev_colxs = ttype2_ev_indices[label_ttype]
re_colxs = ttype2_re_indices[label_ttype]
ev_state_idxs = ut.take(evidence_state_idxs, ev_colxs)
ev_state_idxs_tile = np.tile(ev_state_idxs, (len(reduced_values), 1)).astype(np.int)
num_ev_ = len(ev_colxs)
aug_colxs = list(range(num_ev_)) + (np.array(re_colxs) + num_ev_).tolist()
aug_state_idxs = np.hstack([ev_state_idxs_tile, reduced_row_idxs])
# Relabel rows based on the knowledge that
# everything is the same, only the names have changed.
num_cols = len(aug_state_idxs.T)
mask = vt.index_to_boolmask(aug_colxs, num_cols)
other_colxs, = np.where(~mask)
relbl_states = aug_state_idxs.compress(mask, axis=1)
other_states = aug_state_idxs.compress(~mask, axis=1)
tmp_relbl_states = np.array(list(map(make_temp_state, relbl_states)))
max_tmp_state = -1
min_tmp_state = tmp_relbl_states.min()
# rebuild original state structure with temp state idxs
tmp_state_cols = [None] * num_cols
for count, colx in enumerate(aug_colxs):
tmp_state_cols[colx] = tmp_relbl_states[:, count:count + 1]
for count, colx in enumerate(other_colxs):
tmp_state_cols[colx] = other_states[:, count:count + 1]
tmp_state_idxs = np.hstack(tmp_state_cols)
data_ids = np.array(
vt.compute_unique_data_ids_(list(map(tuple, tmp_state_idxs))))
unique_ids, groupxs = vt.group_indices(data_ids)
print('Collapsed %r states into %r states' % (
len(data_ids), len(unique_ids),))
# Sum the values in the cpd to marginalize the duplicate probs
new_values = np.array([
g.sum() for g in vt.apply_grouping(reduced_values, groupxs)
])
# Take only the unique rows under this induced labeling
unique_tmp_groupxs = np.array(ut.get_list_column(groupxs, 0))
new_aug_state_idxs = tmp_state_idxs.take(unique_tmp_groupxs, axis=0)
tmp_idx_set = set((-np.arange(-max_tmp_state,
(-min_tmp_state) + 1)).tolist())
true_idx_set = set(range(len(model.ttype2_template[label_ttype].basis)))
# Relabel the rows one more time to agree with initial constraints
for colx, true_idx in enumerate(ev_state_idxs):
tmp_idx = np.unique(new_aug_state_idxs.T[colx])
assert len(tmp_idx) == 1
tmp_idx_set -= {tmp_idx[0]}
true_idx_set -= {true_idx}
new_aug_state_idxs[new_aug_state_idxs == tmp_idx] = true_idx
# Relabel the remaining idxs
remain_tmp_idxs = sorted(list(tmp_idx_set))[::-1]
remain_true_idxs = sorted(list(true_idx_set))
for tmp_idx, true_idx in zip(remain_tmp_idxs, remain_true_idxs):
new_aug_state_idxs[new_aug_state_idxs == tmp_idx] = true_idx
# Remove evidence based augmented labels
new_state_idxs = new_aug_state_idxs.T[num_ev_:].T
return new_state_idxs, new_values
def stratified_kfold_label_split(y, labels, n_folds=2, y_idx=None, rng=None):
"""
Also enforces that labels are not broken into separate groups.
Args:
y (ndarray): labels
labels (?):
y_idx (array): indexes associated with y if it was already presampled
rng (RandomState): random number generator(default = None)
Returns:
?: index_sets
CommandLine:
python -m ibeis_cnn.dataset stratified_label_shuffle_split --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_cnn.dataset import * # NOQA
>>> y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
>>> labels = [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 4, 4, 5, 5, 6, 0, 7, 7, 7, 7]
>>> fractions = [.7, .3]
>>> rng = np.random.RandomState(0)
>>> index_sets = stratified_label_shuffle_split(y, labels, fractions, rng)
"""
rng = ut.ensure_rng(rng)
#orig_y = y
unique_labels, groupxs = ut.group_indices(labels)
grouped_ys = ut.apply_grouping(y, groupxs)
# Assign each group a probabilistic class
unique_ys = [ys[rng.randint(0, len(ys))] for ys in grouped_ys]
# TODO: should weight the following selection based on size of group
#class_weights = [ut.dict_hist(ys) for ys in grouped_ys]
import sklearn.cross_validation
xvalkw = dict(n_folds=n_folds, shuffle=True, random_state=rng)
skf = sklearn.cross_validation.StratifiedKFold(unique_ys, **xvalkw)
_iter = skf
folded_index_sets = []
for label_idx_set in _iter:
index_sets = [np.array(ut.flatten(ut.take(groupxs, idxs)))
for idxs in label_idx_set]
folded_index_sets.append(index_sets)
for train_idx, test_idx in folded_index_sets:
train_labels = set(ut.take(labels, train_idx))
test_labels = set(ut.take(labels, test_idx))
assert len(test_labels.intersection(train_labels)) == 0, 'same labels appeared in both train and test'
pass
if y_idx is not None:
# These indicies subindex into parent set of indicies
folded_index_sets2 = []
for index_sets in folded_index_sets:
index_sets = [np.take(y_idx, idxs, axis=0) for idxs in index_sets]
folded_index_sets2.append(index_sets)
folded_index_sets = folded_index_sets2
#import sklearn.model_selection
#skf = sklearn.model_selection.StratifiedKFold(**xvalkw)
#_iter = skf.split(X=np.empty(len(target)), y=target)
#unique_idxs = stratified_shuffle_split(unique_ys, fractions, rng)
#index_sets = [np.array(ut.flatten(ut.take(groupxs, idxs))) for idxs in unique_idxs]
#if idx is not None:
# # These indicies subindex into parent set of indicies
# index_sets = [np.take(idx, idxs, axis=0) for idxs in index_sets]
return folded_index_sets
def collapse_labels(model, evidence, reduced_variables, reduced_row_idxs,
reduced_values):
import vtool as vt
# assert np.all(reduced_joint.values.ravel() == reduced_joint.values.flatten())
reduced_ttypes = [model.var2_cpd[var].ttype for var in reduced_variables]
evidence_vars = list(evidence.keys())
evidence_state_idxs = ut.dict_take(evidence, evidence_vars)
evidence_ttypes = [model.var2_cpd[var].ttype for var in evidence_vars]
ttype2_ev_indices = dict(zip(*ut.group_indices(evidence_ttypes)))
ttype2_re_indices = dict(zip(*ut.group_indices(reduced_ttypes)))
# ttype2_ev_indices = ut.group_items(range(len(evidence_vars)), evidence_ttypes)
# ttype2_re_indices = ut.group_items(range(len(reduced_variables)), reduced_ttypes)
# Allow specific types of labels to change
# everything is the same, only the names have changed.
# TODO: allow for multiple different label_ttypes
# for label_ttype in label_ttypes
if NAME_TTYPE not in model.ttype2_template:
return reduced_row_idxs, reduced_values
label_ttypes = [NAME_TTYPE]
for label_ttype in label_ttypes:
ev_colxs = ttype2_ev_indices[label_ttype]
re_colxs = ttype2_re_indices[label_ttype]
ev_state_idxs = ut.take(evidence_state_idxs, ev_colxs)
ev_state_idxs_tile = np.tile(ev_state_idxs,
(len(reduced_values), 1)).astype(np.int)
num_ev_ = len(ev_colxs)
aug_colxs = list(
range(num_ev_)) + (np.array(re_colxs) + num_ev_).tolist()
aug_state_idxs = np.hstack([ev_state_idxs_tile, reduced_row_idxs])
# Relabel rows based on the knowledge that
# everything is the same, only the names have changed.
num_cols = len(aug_state_idxs.T)
mask = vt.index_to_boolmask(aug_colxs, num_cols)
(other_colxs, ) = np.where(~mask)
relbl_states = aug_state_idxs.compress(mask, axis=1)
other_states = aug_state_idxs.compress(~mask, axis=1)
tmp_relbl_states = np.array(list(map(make_temp_state, relbl_states)))
max_tmp_state = -1
min_tmp_state = tmp_relbl_states.min()
# rebuild original state structure with temp state idxs
tmp_state_cols = [None] * num_cols
for count, colx in enumerate(aug_colxs):
tmp_state_cols[colx] = tmp_relbl_states[:, count:count + 1]
for count, colx in enumerate(other_colxs):
tmp_state_cols[colx] = other_states[:, count:count + 1]
tmp_state_idxs = np.hstack(tmp_state_cols)
data_ids = np.array(
vt.compute_unique_data_ids_(list(map(tuple, tmp_state_idxs))))
unique_ids, groupxs = vt.group_indices(data_ids)
logger.info('Collapsed %r states into %r states' % (
len(data_ids),
len(unique_ids),
))
# Sum the values in the cpd to marginalize the duplicate probs
new_values = np.array(
[g.sum() for g in vt.apply_grouping(reduced_values, groupxs)])
# Take only the unique rows under this induced labeling
unique_tmp_groupxs = np.array(ut.get_list_column(groupxs, 0))
new_aug_state_idxs = tmp_state_idxs.take(unique_tmp_groupxs, axis=0)
tmp_idx_set = set((-np.arange(-max_tmp_state,
(-min_tmp_state) + 1)).tolist())
true_idx_set = set(range(len(
model.ttype2_template[label_ttype].basis)))
# Relabel the rows one more time to agree with initial constraints
for colx, true_idx in enumerate(ev_state_idxs):
tmp_idx = np.unique(new_aug_state_idxs.T[colx])
assert len(tmp_idx) == 1
tmp_idx_set -= {tmp_idx[0]}
true_idx_set -= {true_idx}
new_aug_state_idxs[new_aug_state_idxs == tmp_idx] = true_idx
# Relabel the remaining idxs
remain_tmp_idxs = sorted(list(tmp_idx_set))[::-1]
remain_true_idxs = sorted(list(true_idx_set))
for tmp_idx, true_idx in zip(remain_tmp_idxs, remain_true_idxs):
new_aug_state_idxs[new_aug_state_idxs == tmp_idx] = true_idx
# Remove evidence based augmented labels
new_state_idxs = new_aug_state_idxs.T[num_ev_:].T
return new_state_idxs, new_values
def crftest():
"""
pip install pyqpbo
pip install pystruct
http://taku910.github.io/crfpp/#install
cd ~/tmp
#wget https://drive.google.com/folderview?id=0B4y35FiV1wh7fngteFhHQUN2Y1B5eUJBNHZUemJYQV9VWlBUb3JlX0xBdWVZTWtSbVBneU0&usp=drive_web#list
7z x CRF++-0.58.tar.gz
7z x CRF++-0.58.tar
cd CRF++-0.58
chmod +x configure
./configure
make
"""
import pystruct
import pystruct.models
inference_method_options = ['lp', 'max-product']
inference_method = inference_method_options[1]
#graph = pystruct.models.GraphCRF(
# n_states=None,
# n_features=None,
# inference_method=inference_method,
# class_weight=None,
# directed=False,
#)
num_annots = 5
num_names = num_annots
aids = np.arange(5)
rng = np.random.RandomState(0)
hidden_nids = rng.randint(0, num_names, num_annots)
unique_nids, groupxs = ut.group_indices(hidden_nids)
# Indicator vector indicating the name
node_features = np.zeros((num_annots, num_names))
node_features[(aids, hidden_nids)] = 1
toy_params = {
True: {'mu': 1.0, 'sigma': 2.2},
False: {'mu': 7.0, 'sigma': .9}
}
if False:
import vtool as vt
import plottool as pt
pt.ensure_pylab_qt4()
xdata = np.linspace(0, 100, 1000)
tp_pdf = vt.gauss_func1d(xdata, **toy_params[True])
fp_pdf = vt.gauss_func1d(xdata, **toy_params[False])
pt.plot_probabilities([tp_pdf, fp_pdf], ['TP', 'TF'], xdata=xdata)
def metric(aidx1, aidx2, hidden_nids=hidden_nids, toy_params=toy_params):
if aidx1 == aidx2:
return 0
rng = np.random.RandomState(int(aidx1 + aidx2))
same = hidden_nids[int(aidx1)] == hidden_nids[int(aidx2)]
mu, sigma = ut.dict_take(toy_params[same], ['mu', 'sigma'])
return np.clip(rng.normal(mu, sigma), 0, np.inf)
pairwise_aidxs = list(ut.iprod(range(num_annots), range(num_annots)))
pairwise_labels = np.array([hidden_nids[a1] == hidden_nids[a2] for a1, a2 in pairwise_aidxs])
pairwise_scores = np.array([metric(*zz) for zz in pairwise_aidxs])
pairwise_scores_mat = pairwise_scores.reshape(num_annots, num_annots)
graph = pystruct.models.EdgeFeatureGraphCRF(
n_states=num_annots,
n_features=num_names,
n_edge_features=1,
inference_method=inference_method,
)
import opengm
numVar = 10
unaries = np.ones([numVar, 3], dtype=opengm.value_type)
gm = opengm.gm(np.ones(numVar, dtype=opengm.label_type) * 3)
unary_fids = gm.addFunctions(unaries)
gm.addFactors(unary_fids, np.arange(numVar))
infParam = opengm.InfParam(
workflow=ut.ensure_ascii('(IC)(TTC-I,CC-I)'),
)
inf = opengm.inference.Multicut(gm, parameter=infParam)
visitor = inf.verboseVisitor(printNth=1, multiline=False)
inf.infer(visitor)
arg = inf.arg()
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
# regularizer = opengm.pottsFunction([3, 3], 0.0, beta)
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
unaries = np.random.rand(10, 10, 2)
potts = opengm.PottsFunction([2, 2], 0.0, 0.4)
gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts)
inf = opengm.inference.GraphCut(gm)
inf.infer()
arg = inf.arg()
def merge_level_order(level_orders, topsort):
"""
Merge orders of individual subtrees into a total ordering for
computation.
>>> level_orders = {
>>> 'multi_chip_multitest': [['dummy_annot'], ['chip'], ['multitest'],
>>> ['multitest_score'], ],
>>> 'multi_fgweight_multitest': [ ['dummy_annot'], ['chip', 'probchip'],
>>> ['keypoint'], ['fgweight'], ['multitest'], ['multitest_score'], ],
>>> 'multi_keypoint_nnindexer': [ ['dummy_annot'], ['chip'], ['keypoint'],
>>> ['nnindexer'], ['multitest'], ['multitest_score'], ],
>>> 'normal': [ ['dummy_annot'], ['chip', 'probchip'], ['keypoint'],
>>> ['fgweight'], ['spam'], ['multitest'], ['multitest_score'], ],
>>> 'nwise_notch_multitest_1': [ ['dummy_annot'], ['notch'], ['multitest'],
>>> ['multitest_score'], ],
>>> 'nwise_notch_multitest_2': [ ['dummy_annot'], ['notch'], ['multitest'],
>>> ['multitest_score'], ],
>>> 'nwise_notch_notchpair_1': [ ['dummy_annot'], ['notch'], ['notchpair'],
>>> ['multitest'], ['multitest_score'], ],
>>> 'nwise_notch_notchpair_2': [ ['dummy_annot'], ['notch'], ['notchpair'],
>>> ['multitest'], ['multitest_score'], ],
>>> }
>>> topsort = [u'dummy_annot', u'notch', u'probchip', u'chip', u'keypoint',
>>> u'fgweight', u'nnindexer', u'spam', u'notchpair', u'multitest',
>>> u'multitest_score']
>>> print(ut.repr3(ut.merge_level_order(level_orders, topsort)))
EG2:
level_orders = {u'normal': [[u'dummy_annot'], [u'chip', u'probchip'], [u'keypoint'], [u'fgweight'], [u'spam']]}
topsort = [u'dummy_annot', u'probchip', u'chip', u'keypoint', u'fgweight', u'spam']
"""
import utool as ut
if False:
compute_order = []
level_orders = ut.map_dict_vals(ut.total_flatten, level_orders)
level_sets = ut.map_dict_vals(set, level_orders)
for tablekey in topsort:
compute_order.append((tablekey, [groupkey for groupkey, set_ in level_sets.items() if tablekey in set_]))
return compute_order
else:
# Do on common subgraph
import itertools
# Pointer to current level.: Start at the end and
# then work your way up.
main_ptr = len(topsort) - 1
stack = []
#from six.moves import zip_longest
keys = list(level_orders.keys())
type_to_ptr = {key: -1 for key in keys}
print('level_orders = %s' % (ut.repr3(level_orders),))
for count in itertools.count(0):
print('----')
print('count = %r' % (count,))
ptred_levels = []
for key in keys:
levels = level_orders[key]
ptr = type_to_ptr[key]
try:
level = tuple(levels[ptr])
except IndexError:
level = None
ptred_levels.append(level)
print('ptred_levels = %r' % (ptred_levels,))
print('main_ptr = %r' % (main_ptr,))
# groupkeys, groupxs = ut.group_indices(ptred_levels)
# Group keys are tablenames
# They point to the (type) of the input
# num_levelkeys = len(ut.total_flatten(ptred_levels))
groupkeys, groupxs = ut.group_indices(ptred_levels)
main_idx = None
while main_idx is None and main_ptr >= 0:
target = topsort[main_ptr]
print('main_ptr = %r' % (main_ptr,))
print('target = %r' % (target,))
# main_idx = ut.listfind(groupkeys, (target,))
# if main_idx is None:
possible_idxs = [idx for idx, keytup in enumerate(groupkeys) if keytup is not None and target in keytup]
if len(possible_idxs) == 1:
main_idx = possible_idxs[0]
else:
main_idx = None
if main_idx is None:
main_ptr -= 1
if main_idx is None:
print('break I')
break
found_groups = ut.apply_grouping(keys, groupxs)[main_idx]
print('found_groups = %r' % (found_groups,))
stack.append((target, found_groups))
for k in found_groups:
type_to_ptr[k] -= 1
if len(found_groups) == len(keys):
main_ptr -= 1
if main_ptr < 0:
print('break E')
break
print('stack = %s' % (ut.repr3(stack),))
print('have = %r' % (sorted(ut.take_column(stack, 0)),))
print('need = %s' % (sorted(ut.total_flatten(level_orders.values())),))
compute_order = stack[::-1]
return compute_order
def get_injured_sharks():
"""
>>> from wbia.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')
logger.info(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')
logger.info(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:
# logger.info('[%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
logger.info('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])
logger.info(ut.repr3(cat_to_keys))
logger.info(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)
# logger.info(ut.repr3(encdict))
# logger.info(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']
# logger.info(ut.repr3(info))
flags = ut.lmap(ut.fpath_has_imgext, clist['gpath'])
clist = clist.compress(flags)
import wbia
ibs = wbia.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'])
logger.info('# 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 wbia.plottool as pt
from wbia 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()
# logger.info(len(groupxs))
# if False:
# groupxs = ut.find_duplicate_items(ut.lmap(basename, suffix_list)).values()
# logger.info(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))
# logger.info('fpath = %r' % (fpath,))
# import wbia
# from wbia.dbio import ingest_dataset
# dbdir = wbia.sysres.lookup_dbdir('WS_ALL')
# self = ingest_dataset.Ingestable2(dbdir)
if False:
# Show overlap matrix
import wbia.plottool 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 wbia.plottool 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 wbia.plottool as pt
image2 = color.rgb2gray(data.astronaut()) # NOQA
fpath = './GOPR1120.JPG'
import vtool 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()
