def experiment_parser(yparams, function, name):
all_params = yparams.get_params()
# Zero'th layer:
# --------------
zeroth = Rdict(all_params[name])
if 'default' in zeroth:
zeroth_defaults = zeroth.pop('default')
else:
zeroth_defaults = Rdict()
for exp_lbl, experiment in zeroth.iteritems():
# First layer
# -----------
# An experiment is now selected and performed
yparams.logging(
'\n\nPerforming experiment {}\n==============================',
exp_lbl)
final = zeroth_defaults.dcp()
final.merge(experiment)
function(final, yparams)
def compute_paths(yparams):
all_params = yparams.get_params()
# Zero'th layer:
# --------------
zeroth = Rdict(all_params['compute_paths'])
if 'default' in zeroth:
zeroth_defaults = zeroth.pop('default')
else:
zeroth_defaults = hp()
for exp_lbl, experiment in zeroth.iteritems():
# First layer
# -----------
# An experiment is now selected and performed
yparams.logging(
'Performing experiment {}\n==============================\n',
exp_lbl)
first = zeroth_defaults.dcp()
if experiment is not None:
first.merge(experiment)
if 'default' in first:
first_defaults = first.pop('default')
else:
first_defaults = hp()
statistics = Rdict()
for exp_class_lbl in ['truepaths', 'falsepaths']:
# Final layer
# -----------
# The true or false paths for the current experiment are here computed, respectively
yparams.logging(
'Computing {}...\n------------------------------\n',
exp_class_lbl)
final = first_defaults.dcp()
final.merge(first[exp_class_lbl])
exp_sources = final['sources']
exp_params = final['params']
exp_target = final['target']
# Load the necessary images
data = hp()
for datakey, content in exp_sources.iteritems():
data[datakey] = load_images(all_params[content[0]] +
all_params[content[1]],
skeys=content[2]['skeys'],
recursive_search=False,
logger=yparams)
yparams.logging('\nInitial datastructure: \n\n{}',
data.datastructure2string(maxdepth=4))
yparams.logging('experiment_params: \n{}', exp_params)
# Compute the paths
# -----------------
paths = hp()
for_class = False
if exp_class_lbl == 'truepaths':
for_class = True
paths[exp_lbl][exp_class_lbl], statistics[exp_lbl][
exp_class_lbl] = libhp.compute_paths_for_class(
data,
'segm',
'conts',
'dt',
'gt',
exp_params,
for_class=for_class,
ignore=[],
debug=all_params['debug'],
logger=yparams)
yparams.logging('\nPaths datastructure after running {}: \n\n{}',
exp_class_lbl, paths.datastructure2string())
def val(x):
return x
yparams.logging(
'\nStatistics after {}: \n\n{}', exp_class_lbl,
simplify_statistics(
statistics[exp_lbl]).datastructure2string(function=val))
# Save the result to disk
# -----------------------
targetfile = all_params[exp_target[0]] + all_params[exp_target[1]]
paths.write(filepath=targetfile)
def val(x):
return x
yparams.logging(
'\nStatistics after full experiment: \n\n{}',
simplify_statistics(
statistics[exp_lbl]).datastructure2string(function=val))
def random_forest(yparams, debug=False):
all_params = yparams.get_params()
# Zero'th layer:
# --------------
zeroth = Rdict(all_params['random_forest'])
if 'default' in zeroth:
zeroth_defaults = zeroth.pop('default')
else:
zeroth_defaults = hp()
# pathlist = ipl()
featlistfile = zeroth_defaults['targets', 'featlist']
featlistfile = all_params[featlistfile[0]] + all_params[featlistfile[1]]
classifier_file = zeroth_defaults['targets', 'classifier']
classifier_file = all_params[classifier_file[0]] + all_params[
classifier_file[1]]
# yparams.logging('\nDatastructure of pathlistin:\n\n{}', pathlistin.datastructure2string())
feature_space_lists = dict()
classifiers = dict()
for exp_lbl, experiment in zeroth.iteritems():
# First layer
# -----------
# An experiment is now selected and performed
yparams.logging(
'\n\nPerforming experiment {}\n==============================',
exp_lbl)
final = zeroth_defaults.dcp()
final.merge(experiment)
exp_sources = final['sources']
exp_params = final['params']
exp_targets = final['targets']
exp_source_kl = [exp_lbl]
if len(exp_sources['train']) == 4:
exp_source_kl = exp_sources['train'][3]
exp_predict_kl = ['predict']
if len(exp_sources['predict']) == 4:
exp_predict_kl = exp_sources['predict'][3]
if type(exp_source_kl) is str:
exp_source_kl = [exp_source_kl]
if type(exp_predict_kl) is str:
exp_predict_kl = [exp_predict_kl]
# Loading of the training pathlist(s)
# --------------------------
# Get the pathlist stored in features_of_paths
pathlist_source = exp_sources.pop('train_pl')
# Check for list or single file
if type(pathlist_source) is Rdict:
pathlistin_train = Rdict()
for key, val in pathlist_source.iteritems():
pathlistfile = all_params[val[0]] + all_params[val[1]]
with open(pathlistfile, 'r') as f:
pathlistin_train[key] = Rdict(pickle.load(f))
if 'skeys' in val[2]:
pathlistin_train[key] = pathlistin_train[key].subset(
*val[2]['skeys'])
else:
pathlistfile = all_params[pathlist_source[0]] \
+ all_params[pathlist_source[1]]
with open(pathlistfile, 'r') as f:
pathlistin_train = Rdict(pickle.load(f))
if 'skeys' in pathlist_source[2]:
pathlistin_train = pathlistin_train.subset(
*pathlist_source[2]['skeys'])
yparams.logging('pathlistin_train.datastructure: \n{}\n',
pathlistin_train.datastructure2string(maxdepth=4))
pathlistout = hp()
# Loading of the prediction pathlist
pathlist_source = exp_sources.pop('predict_pl')
pathlistfile = all_params[pathlist_source[0]] \
+ all_params[pathlist_source[1]]
with open(pathlistfile, 'r') as f:
pathlistin_predict = Rdict(pickle.load(f))
if 'skeys' in pathlist_source[2]:
pathlistin_predict = pathlistin_predict.subset(
*pathlist_source[2]['skeys'])
yparams.logging('pathlistin_predict.datastructure: \n{}\n',
pathlistin_predict.datastructure2string(maxdepth=4))
# Load training data
# ------------------
if 'train' in exp_sources.keys():
truesource = exp_sources['train']
falsesource = exp_sources['train']
else:
truesource = exp_sources['traintrue']
falsesource = exp_sources['trainfalse']
# Check for list or single file
if type(truesource) is Rdict:
truetrainfeats = hp()
for key, val in truesource.iteritems():
truetrainfeats[key] = load_data(all_params[val[0]] +
all_params[val[1]],
logger=yparams,
**val[2]).subset('truepaths',
search=True)
else:
truetrainfeats = load_data(all_params[truesource[0]] +
all_params[truesource[1]],
logger=yparams,
**truesource[2]).subset('truepaths',
search=True)
if type(falsesource) is Rdict:
falsetrainfeats = hp()
for key, val in falsesource.iteritems():
falsetrainfeats[key] = load_data(all_params[val[0]] +
all_params[val[1]],
logger=yparams,
**val[2]).subset('falsepaths',
search=True)
else:
falsetrainfeats = load_data(all_params[falsesource[0]] +
all_params[falsesource[1]],
logger=yparams,
**falsesource[2]).subset('falsepaths',
search=True)
# ------------------
yparams.logging('\ntruetrainfeats.datastructure: \n{}\n',
truetrainfeats.datastructure2string(maxdepth=4))
yparams.logging('\nfalsetrainfeats.datastructure: \n{}\n',
falsetrainfeats.datastructure2string(maxdepth=4))
# Load prediction data
predictsource = exp_sources['predict']
predictfeats = load_data(all_params[predictsource[0]] +
all_params[predictsource[1]],
logger=yparams,
**predictsource[2])
yparams.logging('\npredictfeats.datastructure: \n{}\n',
predictfeats.datastructure2string(maxdepth=4))
# # Load the data into memory
# truetrainfeats.populate()
# falsetrainfeats.populate()
# predictfeats.populate()
# Concatenate the different sources
# 1. Of training data
plo_true_train = hp()
plo_false_train = hp()
# truetrainfeats, plo_true['truepaths'] = libip.rf_combine_sources_new(
# truetrainfeats[exp_source_kl]['truepaths'].dcp(),
# pathlistin[exp_source_kl]['truepaths'].dcp()
# )
truetrainfeats, plo_true_train['train',
'truepaths'] = libhp.rf_combine_sources(
truetrainfeats,
pathlistin_train.subset(
'truepaths', search=True))
falsetrainfeats, plo_false_train[
'train', 'falsepaths'] = libhp.rf_combine_sources(
falsetrainfeats,
pathlistin_train.subset('falsepaths', search=True))
pathlistout[exp_source_kl] = plo_true_train + plo_false_train
# 2. Of prediction data
ipf_true = hp()
plo_true_predict = hp()
ipf_true['truepaths'], plo_true_predict[
'predict', 'truepaths'] = libhp.rf_combine_sources(
predictfeats.subset('truepaths', search=True),
pathlistin_predict.subset('truepaths', search=True))
ipf_false = hp()
plo_false_predict = hp()
ipf_false['falsepaths'], plo_false_predict[
'predict', 'falsepaths'] = libhp.rf_combine_sources(
predictfeats.subset('falsepaths', search=True),
pathlistin_predict.subset('falsepaths', search=True))
inpredictfeats = ipf_true + ipf_false
pathlistout[exp_source_kl,
'predict'] = plo_true_predict + plo_false_predict
# Note:
# Due to the feature input being a dictionary organized by the feature images where
# the feature values come from
#
# [source]
# 'truepaths'|'falsepaths'
# [featureims]
# 'Sum': [s1, ..., sN]
# 'Variance': [v1, ..., vN]
# ...
# [Pathlength]: [l1, ..., lN]
#
# the exact order in which items are iterated over by data_iterator() is not known.
#
# Solution:
# Iterate over it once and store the keylist in an array (which conserves the order)
# When accumulating the features for each of the four corresponding subsets, namely
# training and testing set with true and false paths each, i.e.
# ['0'|'1']['truefeats'|'falsefeats'],
# the the keylist is used, thus maintaining the correct order in every subset.
#
# And that is what is happening here:
# # 1. Get the keylist of a full feature list, e.g. one of true paths
# example_kl = None
# for d2, k2, v2, kl2 in truetrainfeats.data_iterator():
# if k2 == 'truepaths':
# example_kl = kl2
# break
# 2. Get the keylist order of the feature space
# TODO: Write this to file
feature_space_list = []
for d2, k2, v2, kl2 in truetrainfeats.data_iterator():
if type(v2) is not type(truetrainfeats):
feature_space_list.append(kl2)
feature_space_lists[exp_lbl] = feature_space_list
intrain = hp()
intrain['true'] = libhp.rf_make_feature_array_with_keylist(
truetrainfeats, feature_space_list)
yparams.logging(
"Computed feature array for train['true'] with shape {}",
intrain['true'].shape)
intrain['false'] = libhp.rf_make_feature_array_with_keylist(
falsetrainfeats, feature_space_list)
yparams.logging(
"Computed feature array for train['false'] with shape {}",
intrain['false'].shape)
inpredict = hp()
inpredict['true'] = libhp.rf_make_feature_array_with_keylist(
inpredictfeats['truepaths'], feature_space_list)
yparams.logging(
"Computed feature array for predict['true'] with shape {}",
inpredict['true'].shape)
inpredict['false'] = libhp.rf_make_feature_array_with_keylist(
inpredictfeats['falsepaths'], feature_space_list)
yparams.logging(
"Computed feature array for predict['false'] with shape {}",
inpredict['false'].shape)
# Classify
result = hp()
result[exp_lbl], classifiers[exp_lbl] = libhp.random_forest(
intrain,
inpredict,
debug=debug,
balance=exp_params['balance_classes'],
logger=yparams)
# Evaluate
new_eval = hp()
# print [x[0] for x in result[kl]]
# print [x[1] for x in result[kl]]
new_eval[exp_lbl] = libhp.new_eval([x[0] for x in result[exp_lbl]],
[x[1] for x in result[exp_lbl]])
yparams.logging('+++ RESULTS +++')
yparams.logging("[kl]")
# for i in result[kl]:
# yparams.logging('{}', i)
for key, value in new_eval[exp_lbl].iteritems():
yparams.logging('{} = {}', key, value)
with open(featlistfile, 'wb') as f:
pickle.dump(feature_space_lists, f)
# Store the classifiers
with open(classifier_file, 'wb') as f:
pickle.dump(classifiers, f)
def shortest_paths(penaltypower,
bounds,
lbl,
keylist_lblim,
gt,
disttransf,
pathends,
for_class=True,
correspondence={},
avoid_duplicates=True,
max_paths_per_object=[],
max_paths_per_object_seed=[],
yield_in_bounds=False,
return_pathim=True,
minimum_alternative_label_count=0,
logger=None):
"""
:param penaltypower:
:param bounds:
:param lbl:
:param keylist_lblim: Needed for correspondence table
:param disttransf:
:param pathends:
:param for_class:
True: paths are computed for when endpoints are in the same ground truth oject
False: paths are computed for when endpoints are in different ground truth objects
:param correspondence:
:param avoid_duplicates:
:param max_paths_per_object:
:param max_paths_per_object_seed:
:param yield_in_bounds:
:param return_pathim:
:param minimum_alternative_label_count: Paths of merges (for_class=False) are removed if
too little pixels of the merged object are found
:param logger:
:return:
"""
# Pick up some statistics along the way
stats_excluded_paths = 0
statistics = Rdict()
# Determine the endpoints of the current object
indices = np.where(pathends)
coords = zip(indices[0], indices[1], indices[2])
# Make pairwise list of coordinates serving as source and target
# First determine all pairings
all_pairs = []
for i in xrange(0, len(coords) - 1):
for j in xrange(i + 1, len(coords)):
all_pairs.append((coords[i], coords[j]))
# And only use those that satisfy certain criteria:
# a) Are in either the same gt object (for_class=True)
# or in different gt objects (for_class=False)
# b) Are not in the correspondence list
pairs = []
label_pairs = []
# if avoid_duplicates:
new_correspondence = {}
for pair in all_pairs:
# Determine whether the endpoints are in different gt objects
if (gt[pair[0]] == gt[pair[1]]) == for_class:
# Check correspondence list if pairings were already computed in different image
labelpair = tuple(sorted([gt[pair[0]], gt[pair[1]]]))
if avoid_duplicates:
if labelpair not in correspondence.keys():
pairs.append(pair)
label_pairs.append(labelpair)
# new_correspondence[labelpair] = [keylist_lblim, lbl]
if logger is not None:
logger.logging('Found pairing: {}', labelpair)
else:
if logger is not None:
logger.logging(
'Pairing already in correspondence table: {}',
labelpair)
else:
pairs.append(pair)
if logger is not None:
logger.logging('Found pairing: {}', labelpair)
# if avoid_duplicates:
# correspondence.update(new_correspondence)
# Select a certain number of pairs if number is too high
if max_paths_per_object:
if len(pairs) > max_paths_per_object:
if logger is not None:
logger.logging('Reducing number of pairs to {}',
max_paths_per_object)
if max_paths_per_object_seed:
random.seed(max_paths_per_object_seed)
else:
random.seed()
pairs = random.sample(pairs, max_paths_per_object)
if logger is not None:
logger.logging('Modified pairs list: {}', pairs)
# If pairs are found that satisfy all conditions
if pairs:
if logger is not None:
logger.logging('Found {} pairings which satisfy all criteria',
len(pairs))
else:
print 'Found {} pairings which satisfy all criteria'.format(
len(pairs))
# Pre-processing of the distance transform
# a) Invert: the lowest values (i.e. the lowest penalty for the shortest path
# detection) should be at the center of the current process
disttransf = lib.invert_image(disttransf)
#
# b) Set all values outside the process to infinity
disttransf = lib.filter_values(disttransf,
np.amax(disttransf),
type='eq',
setto=np.inf)
#
# c) Increase the value difference between pixels near the boundaries and pixels
# central within the processes. This increases the likelihood of the paths to
# follow the center of processes, thus avoiding short-cuts
disttransf = lib.power(disttransf, penaltypower)
# Compute the shortest paths according to the pairs list
ps_computed, ps_in_bounds = lib.shortest_paths(
disttransf,
pairs,
bounds=bounds,
logger=logger,
return_pathim=return_pathim,
yield_in_bounds=yield_in_bounds)
# Criteria for keeping paths which can only be computed after path computation
if for_class:
# A path without merge must not switch labels on the way!
ps = []
for i in xrange(0, len(ps_computed)):
if len(
np.unique(gt[ps_in_bounds[i][:, 0],
ps_in_bounds[i][:, 1],
ps_in_bounds[i][:, 2]])) == 1:
ps.append(ps_computed[i])
if logger is not None:
logger.logging('Path label = True')
# Add entry to correspondence table
if avoid_duplicates:
new_correspondence[label_pairs[i]] = [
keylist_lblim, lbl
]
else:
# The path switched objects multiple times on the way and is not added to the list\
if logger is not None:
logger.logging(
'Path starting and ending in label = {} had multiple labels and was excluded',
gt[tuple(ps_in_bounds[i][0])])
stats_excluded_paths += 1
else:
ps = []
for i in xrange(0, len(ps_computed)):
un, counts = np.unique(gt[ps_in_bounds[i][:, 0],
ps_in_bounds[i][:, 1],
ps_in_bounds[i][:, 2]],
return_counts=True)
# At least two of the entries in counts have to be larger than the threshold
c = 0
for count in counts:
if count >= minimum_alternative_label_count:
c += 1
if c > 1:
break
if c > 1:
ps.append(ps_computed[i])
# Add entry to correspondence table
if avoid_duplicates:
new_correspondence[label_pairs[i]] = [
keylist_lblim, lbl
]
else:
if logger is not None:
logger.logging(
'Path starting in label {} and ending in {} only crossed one of the labels for {} voxels',
gt[tuple(ps_in_bounds[i][0])],
gt[tuple(ps_in_bounds[i][-1])], np.min(counts))
statistics['excluded_paths'] = stats_excluded_paths
statistics['kept_paths'] = len(ps)
return ps, new_correspondence, statistics
else:
statistics['excluded_paths'] = 0
statistics['kept_paths'] = 0
return [], new_correspondence, statistics
def compute_paths_for_class(indata,
labelskey,
pathendkey,
disttransfkey,
gtkey,
params,
for_class=True,
ignore=[],
logger=None,
debug=False):
def shortest_paths(penaltypower,
bounds,
lbl,
keylist_lblim,
gt,
disttransf,
pathends,
for_class=True,
correspondence={},
avoid_duplicates=True,
max_paths_per_object=[],
max_paths_per_object_seed=[],
yield_in_bounds=False,
return_pathim=True,
minimum_alternative_label_count=0,
logger=None):
"""
:param penaltypower:
:param bounds:
:param lbl:
:param keylist_lblim: Needed for correspondence table
:param disttransf:
:param pathends:
:param for_class:
True: paths are computed for when endpoints are in the same ground truth oject
False: paths are computed for when endpoints are in different ground truth objects
:param correspondence:
:param avoid_duplicates:
:param max_paths_per_object:
:param max_paths_per_object_seed:
:param yield_in_bounds:
:param return_pathim:
:param minimum_alternative_label_count: Paths of merges (for_class=False) are removed if
too little pixels of the merged object are found
:param logger:
:return:
"""
# Pick up some statistics along the way
stats_excluded_paths = 0
statistics = Rdict()
# Determine the endpoints of the current object
indices = np.where(pathends)
coords = zip(indices[0], indices[1], indices[2])
# Make pairwise list of coordinates serving as source and target
# First determine all pairings
all_pairs = []
for i in xrange(0, len(coords) - 1):
for j in xrange(i + 1, len(coords)):
all_pairs.append((coords[i], coords[j]))
# And only use those that satisfy certain criteria:
# a) Are in either the same gt object (for_class=True)
# or in different gt objects (for_class=False)
# b) Are not in the correspondence list
pairs = []
label_pairs = []
# if avoid_duplicates:
new_correspondence = {}
for pair in all_pairs:
# Determine whether the endpoints are in different gt objects
if (gt[pair[0]] == gt[pair[1]]) == for_class:
# Check correspondence list if pairings were already computed in different image
labelpair = tuple(sorted([gt[pair[0]], gt[pair[1]]]))
if avoid_duplicates:
if labelpair not in correspondence.keys():
pairs.append(pair)
label_pairs.append(labelpair)
# new_correspondence[labelpair] = [keylist_lblim, lbl]
if logger is not None:
logger.logging('Found pairing: {}', labelpair)
else:
if logger is not None:
logger.logging(
'Pairing already in correspondence table: {}',
labelpair)
else:
pairs.append(pair)
if logger is not None:
logger.logging('Found pairing: {}', labelpair)
# if avoid_duplicates:
# correspondence.update(new_correspondence)
# Select a certain number of pairs if number is too high
if max_paths_per_object:
if len(pairs) > max_paths_per_object:
if logger is not None:
logger.logging('Reducing number of pairs to {}',
max_paths_per_object)
if max_paths_per_object_seed:
random.seed(max_paths_per_object_seed)
else:
random.seed()
pairs = random.sample(pairs, max_paths_per_object)
if logger is not None:
logger.logging('Modified pairs list: {}', pairs)
# If pairs are found that satisfy all conditions
if pairs:
if logger is not None:
logger.logging('Found {} pairings which satisfy all criteria',
len(pairs))
else:
print 'Found {} pairings which satisfy all criteria'.format(
len(pairs))
# Pre-processing of the distance transform
# a) Invert: the lowest values (i.e. the lowest penalty for the shortest path
# detection) should be at the center of the current process
disttransf = lib.invert_image(disttransf)
#
# b) Set all values outside the process to infinity
disttransf = lib.filter_values(disttransf,
np.amax(disttransf),
type='eq',
setto=np.inf)
#
# c) Increase the value difference between pixels near the boundaries and pixels
# central within the processes. This increases the likelihood of the paths to
# follow the center of processes, thus avoiding short-cuts
disttransf = lib.power(disttransf, penaltypower)
# Compute the shortest paths according to the pairs list
ps_computed, ps_in_bounds = lib.shortest_paths(
disttransf,
pairs,
bounds=bounds,
logger=logger,
return_pathim=return_pathim,
yield_in_bounds=yield_in_bounds)
# Criteria for keeping paths which can only be computed after path computation
if for_class:
# A path without merge must not switch labels on the way!
ps = []
for i in xrange(0, len(ps_computed)):
if len(
np.unique(gt[ps_in_bounds[i][:, 0],
ps_in_bounds[i][:, 1],
ps_in_bounds[i][:, 2]])) == 1:
ps.append(ps_computed[i])
if logger is not None:
logger.logging('Path label = True')
# Add entry to correspondence table
if avoid_duplicates:
new_correspondence[label_pairs[i]] = [
keylist_lblim, lbl
]
else:
# The path switched objects multiple times on the way and is not added to the list\
if logger is not None:
logger.logging(
'Path starting and ending in label = {} had multiple labels and was excluded',
gt[tuple(ps_in_bounds[i][0])])
stats_excluded_paths += 1
else:
ps = []
for i in xrange(0, len(ps_computed)):
un, counts = np.unique(gt[ps_in_bounds[i][:, 0],
ps_in_bounds[i][:, 1],
ps_in_bounds[i][:, 2]],
return_counts=True)
# At least two of the entries in counts have to be larger than the threshold
c = 0
for count in counts:
if count >= minimum_alternative_label_count:
c += 1
if c > 1:
break
if c > 1:
ps.append(ps_computed[i])
# Add entry to correspondence table
if avoid_duplicates:
new_correspondence[label_pairs[i]] = [
keylist_lblim, lbl
]
else:
if logger is not None:
logger.logging(
'Path starting in label {} and ending in {} only crossed one of the labels for {} voxels',
gt[tuple(ps_in_bounds[i][0])],
gt[tuple(ps_in_bounds[i][-1])], np.min(counts))
statistics['excluded_paths'] = stats_excluded_paths
statistics['kept_paths'] = len(ps)
return ps, new_correspondence, statistics
else:
statistics['excluded_paths'] = 0
statistics['kept_paths'] = 0
return [], new_correspondence, statistics
def shortest_paths_wrapper(labelim,
gt_im,
dt_im,
bc_im,
lbl,
kl,
k,
params,
for_class=True,
correspondence={},
logger=None):
print 'Wrapper called...'
# Create an image that contains only the one object
lblim = np.zeros(labelim.shape, dtype=np.uint16)
lblim[labelim == lbl] = lbl
# Get the region of the one object
bounds = lib.find_bounding_rect(lblim, s_=True)
# Crop the label image
lblim = lib.crop_bounding_rect(lblim, bounds)
# Crop the gt as well
gt_im = lib.crop_bounding_rect(gt_im, bounds=bounds)
# Crop and mask the distance transform
dt_im = lib.crop_bounding_rect(dt_im, bounds=bounds)
dt_im[lblim == 0] = 0
# Crop and mask border contacts
bc_im = lib.crop_bounding_rect(bc_im, bounds=bounds)
bc_im[lblim == 0] = 0
# Done: Check for correctness
# Compute all paths within this object which start and end in different
# gt-objects
# Supply the correspondence table to this function and only compute a path
# if the respective correspondence is not found
return shortest_paths(
params['penaltypower'],
bounds,
lbl,
kl + [k],
gt_im,
dt_im,
bc_im,
for_class=for_class,
correspondence=correspondence,
avoid_duplicates=params['avoid_duplicates'],
max_paths_per_object=params['max_paths_per_object'],
max_paths_per_object_seed=params['max_paths_per_object_seed'],
yield_in_bounds=True,
return_pathim=False,
minimum_alternative_label_count=params[
'minimum_alternative_label_count'],
logger=logger)
correspondence_table = {}
# correspondence_table (type=dict) should have the form:
# {tuple(labels_in_gt_i): [kl_labelsimage_i, label_i]
paths = hp()
statistics = Rdict()
if params['order_of_betas'] is not None:
key_lists = []
for i in params['order_of_betas']:
key_lists += indata[labelskey].find_key_lists(i)
else:
key_lists = []
for d, k, v, kl in indata[labelskey].data_iterator(leaves_only=True):
key_lists.append(kl)
# Iterate over segmentations
# for d, k, v, kl in indata[labelskey].data_iterator(leaves_only=True, yield_short_kl=True):
for i in key_lists:
k = i[-1]
kl = i[0:-1]
if logger is not None:
logger.logging('====================')
logger.logging('Working on image {}', k)
logger.logging('correspondence_table = {}', correspondence_table)
else:
print '===================='
print 'Working on image {}'.format(k)
print 'correspondence_table = {}'.format(correspondence_table)
# Load the current segmentation image
labelim = np.array(indata[labelskey][kl][k])
# indata[labelskey][kl].populate(k)
# TODO: Parallelize here
# Iterate over all labels of that image (including cropping for speed-up)
# Determine a list of present labels
label_list = np.unique(labelim)
label_list = filter(lambda x: x != 0, label_list)
if params['parallelize']:
logger.logging('Starting thread pool with {} threads',
params['max_threads'])
with futures.ThreadPoolExecutor(params['max_threads']) as do_stuff:
tasks = Rdict()
for lbl in label_list:
tasks[lbl] = do_stuff.submit(
shortest_paths_wrapper,
labelim,
np.array(
indata[gtkey][kl][indata[gtkey][kl].keys()[0]]),
np.array(indata[disttransfkey][kl][k]['disttransf',
'raw']),
np.array(indata[pathendkey][kl][k]['contacts']),
lbl,
kl,
k,
params,
for_class=for_class,
correspondence=correspondence_table,
logger=logger)
for lbl in label_list:
newpaths, new_correspondence_table, new_statistics = tasks[
lbl].result()
correspondence_table.update(new_correspondence_table)
statistics[kl + [k] + [lbl]] = new_statistics
# If new paths were detected
if newpaths:
# Store them
# paths.merge(newpaths)
pskeys = range(0, len(newpaths))
paths[kl + [k] +
[lbl]] = hp(data=dict(zip(pskeys, newpaths)))
if logger is not None:
logger.logging(
'Found {} paths in image {} at label {}',
len(newpaths), k, lbl)
logger.logging('-------------------')
else:
print 'Found {} paths in image {} at label {}'.format(
len(newpaths), k, lbl)
print '-------------------'
else:
# Iterate over these labels
for lbl in label_list:
newpaths, new_correspondence_table, new_statistics = shortest_paths_wrapper(
labelim,
np.array(indata[gtkey][kl][indata[gtkey][kl].keys()[0]]),
np.array(indata[disttransfkey][kl][k]['disttransf',
'raw']),
np.array(indata[pathendkey][kl][k]['contacts']),
lbl,
kl,
k,
params,
for_class=for_class,
correspondence=correspondence_table,
logger=logger)
correspondence_table.update(new_correspondence_table)
statistics[kl + [k] + [lbl]] = new_statistics
# If new paths were detected
if newpaths:
# Store them
# paths.merge(newpaths)
pskeys = range(0, len(newpaths))
paths[kl + [k] +
[lbl]] = hp(data=dict(zip(pskeys, newpaths)))
if logger is not None:
logger.logging(
'Found {} paths in image {} at label {}',
len(newpaths), k, lbl)
logger.logging('-------------------')
else:
print 'Found {} paths in image {} at label {}'.format(
len(newpaths), k, lbl)
print '-------------------'
# # Unload the current segmentation image
# indata[labelskey][kl].unpopulate()
return paths, statistics
def get_features(paths,
shp,
featureimages,
featurelist,
max_paths_per_label,
logger=None,
anisotropy=[1, 1, 1],
return_pathlist=False,
parallelized=False,
max_threads=5):
"""
:param paths:
:param featureimages:
:param featurelist:
:param max_paths_per_label:
:param ipl:
:param anisotropy:
:param return_pathlist: When True a list of the path keys is returned in the same order as
their features are stored -> Can be used for back-translation of the path classification
to the respective object the path is in.
It is basically a concatenation of the key list as yielded by the simultaneous iterator.
:return:
"""
newfeats = hp()
# The path lengths only have to be computed once without using the vigra region features
def compute_path_lengths(paths, anisotropy):
path_lengths = []
# for d, k, v, kl in paths.data_iterator():
# if type(v) is not type(paths):
for path in paths:
path_lengths.append(
lib.compute_path_length(np.array(path), anisotropy))
return np.array(path_lengths)
# And only do it when desired
pathlength = False
try:
featurelist.remove('Pathlength')
except ValueError:
# Means that 'Pathlength' was not in the list
pass
else:
# 'Pathlength' was in the list and is now successfully removed
pathlength = True
if max_paths_per_label is not None:
keylist = range(0, max_paths_per_label - 1)
keylist = [str(x) for x in keylist]
else:
keylist = None
if return_pathlist:
pathlist = []
# Iterate over all paths, yielding a list of one path per label object until no paths are left
for i, keys, vals in paths.simultaneous_iterator(
max_count_per_item=max_paths_per_label, keylist=keylist):
# i is the iteration number
# keys are respective labels and ids of the paths
# vals are the coordinates of the path positions
if return_pathlist:
pathlist += keys
if logger is not None:
logger.logging('Working in iteration = {}', i)
logger.logging('Keys: {}', keys)
if not keys:
continue
# Create a working image
image = np.zeros(shp, dtype=np.uint32)
# And fill it with one path per label object
c = 1
for curk, curv in (dict(zip(keys, vals))).iteritems():
curv = np.array(curv)
if pathlength:
if not newfeats.inkeys(['Pathlength']):
newfeats['Pathlength'] = np.array(
[lib.compute_path_length(curv, anisotropy)])
else:
newfeats['Pathlength'] = np.concatenate(
(newfeats['Pathlength'],
[lib.compute_path_length(curv, anisotropy)]))
curv = lib.swapaxes(curv, 0, 1)
lib.positions2value(image, curv, c)
c += 1
# TODO: If this loop iterated over the parameter list it would be more broadly applicable
if not parallelized:
for d, k, v, kl in featureimages.data_iterator():
if type(v) is not hp:
# Extract the region features of the working image
newnewfeats = hp(data=vigra.analysis.extractRegionFeatures(
np.array(v).astype(np.float32),
image,
ignoreLabel=0,
features=featurelist))
# Pick out the features that we asked for
newnewfeats = newnewfeats.subset(*featurelist)
# Done: Extract feature 'Count' manually due to anisotropy
# Append to the recently computed list of features
for nk, nv in newnewfeats.iteritems():
nv = nv[1:]
if newfeats.inkeys(kl + [nk]):
try:
newfeats[kl + [nk]] = np.concatenate(
(newfeats[kl + [nk]], nv))
except ValueError:
pass
else:
newfeats[kl + [nk]] = nv
elif parallelized:
def extract_region_features(feat, im, ignore_label, featlist):
return hp(
vigra.analysis.extractRegionFeatures(
feat, im, ignoreLabel=ignore_label, features=featlist))
logger.logging('Starting thread pool with a max of {} threads',
max_threads)
with futures.ThreadPoolExecutor(max_threads) as do_stuff:
keys = []
vals = []
tasks = Rdict()
for d, k, v, kl in featureimages.data_iterator(
leaves_only=True):
# tasks[kl] = do_stuff.submit(
# hp(vigra.analysis.extractRegionFeatures(
# np.array(v).astype(np.float32), image, ignoreLabel=0,
# features=featurelist
# ))
# )
tasks[kl] = do_stuff.submit(extract_region_features,
np.array(v).astype(np.float32),
image, 0, featurelist)
keys.append(kl)
for kl in keys:
newnewfeats = tasks[kl].result()
newnewfeats = newnewfeats.subset(*featurelist)
for nk, nv in newnewfeats.iteritems():
nv = nv[1:]
if newfeats.inkeys(kl + [nk]):
try:
newfeats[kl + [nk]] = np.concatenate(
(newfeats[kl + [nk]], nv))
except ValueError:
pass
else:
newfeats[kl + [nk]] = nv
if return_pathlist:
return newfeats, pathlist
else:
return newfeats
def features_of_paths(yparams):
all_params = yparams.get_params()
# Zero'th layer:
# --------------
zeroth = Rdict(all_params['features_of_paths'])
if 'default' in zeroth:
zeroth_defaults = zeroth.pop('default')
else:
zeroth_defaults = hp()
pathlist = hp()
pathlistfile = zeroth_defaults['targets', 'pathlist']
pathlistfile = all_params[pathlistfile[0]] + all_params[pathlistfile[1]]
for exp_lbl, experiment in zeroth.iteritems():
# First layer
# -----------
# An experiment is now selected and performed
yparams.logging(
'\n\nPerforming experiment {}\n==============================',
exp_lbl)
final = zeroth_defaults.dcp()
final.merge(experiment)
exp_sources = final['sources']
exp_params = final['params']
exp_targets = final['targets']
def val(x):
return x
yparams.logging('exp_sources = \n{}',
exp_sources.datastructure2string(function=val))
yparams.logging('exp_params = \n{}',
exp_sources.datastructure2string(function=val))
yparams.logging('exp_targets = \n{}',
exp_targets.datastructure2string(function=val))
# Load feature images
# -------------------
featureims = hp()
for k, v in exp_sources['featureims'].iteritems():
skeys = None
if 'skeys' in v[2]:
skeys = v[2]['skeys']
featureims[k] = load_images(all_params[v[0]] + all_params[v[1]],
skeys=skeys,
logger=yparams)
yparams.logging('\nFeatureims datastructure: \n\n{}',
featureims.datastructure2string(maxdepth=4))
for exp_class_lbl, exp_class_src in exp_sources['paths'].iteritems():
yparams.logging('\nWorking on {}\n------------------------------',
exp_class_lbl)
# Load paths
# ----------
skeys = None
if 'skeys' in exp_class_src[2]:
skeys = exp_class_src[2]['skeys']
paths = load_images(all_params[exp_class_src[0]] +
all_params[exp_class_src[1]],
skeys=skeys,
logger=yparams)
yparams.logging('\nPaths datastructure: \n\n{}',
paths.datastructure2string(maxdepth=4))
# Iterate over the paths
for d, k, v, kl in paths[exp_class_src[2]['skeys']
[0]].data_iterator(leaves_only=True,
yield_short_kl=True,
maxdepth=3):
yparams.logging(
'\nPath keylist: {}\n..............................',
kl + [k])
segm_kl = kl + [k]
imgs_kl = kl
yparams.logging('segm_kl = {}', segm_kl)
yparams.logging('imgs_kl = {}', imgs_kl)
# Bild an input featureims dict for the path computation
infeatims = hp()
sourcelist = exp_sources['featureims'].dcp()
if 'segmentation' in sourcelist:
infeatims['segmentation'] = featureims['segmentation'][
segm_kl]
sourcelist.pop('segmentation')
for source in sourcelist:
# TODO: This is not nice... Here I try to remove a redundant key
infeatims[source] = featureims[source][imgs_kl][
featureims[source][imgs_kl].keys()[0]]
# infeatims.populate()
# Bild an input dict for true paths
inpaths = v.dcp()
# inpaths.populate()
# Get the necessary image shape
for d2, k2, v2, kl2 in infeatims.data_iterator(
leaves_only=True):
im_shp = v2.shape
break
features = hp()
# import time
# start = time.time()
# print 'Starting get_features'
features[exp_lbl][[exp_class_lbl] + kl + [k]], pathlist[
exp_lbl][[exp_class_lbl] + kl + [k]] = libhp.get_features(
inpaths,
np.array(im_shp)[0:3],
infeatims,
list(exp_params['features']),
exp_params['max_paths_per_label'],
logger=yparams,
anisotropy=exp_params['anisotropy'],
return_pathlist=True,
parallelized=exp_params['parallelize'],
max_threads=exp_params['max_threads'])
# print 'Stopping get_features'
# stop = time.time()
# print stop-start
yparams.logging('\nFeatures datastructure: \n\n{}',
features.datastructure2string(maxdepth=4))
# Write the result to file
features.write(
filepath=all_params[exp_targets['features'][0]] +
all_params[exp_targets['features'][1]])
with open(pathlistfile, 'wb') as f:
pickle.dump(pathlist, f)