def writeStatsToYAML(self, filename='hist_stats.yaml'):
logger.debug('writeStatsToYAML')
gr = self.stats['Graph']
for i in gr:
logger.info('Processing edge num '+str(i))
try:
gr[i]['lengthEstimationPoly'] = float(gr[i]['lengthEstimationPoly'])
except:
logger.warning('lengthEstimationPoly not a number')
gr[i]['lengthEstimationPoly'] = None
try:
gr[i]['lengthEstimationSpline'] = float(gr[i]['lengthEstimationSpline'])
except:
logger.warning('lengthEstimationSpline not a number')
gr[i]['lengthEstimationSpline'] = None
try:
self.stats['lengthEstimation'] = float(gr[i]['lengthEstimation'])
except:
logger.warning('lengthEstimation not a number')
gr[i]['lengthEstimation'] = None
try:
gr[i]['lengthEstimationPixel'] = float(gr[i]['lengthEstimationPixel'])
except:
logger.warning('lengthEstimationPixel not a number')
gr[i]['lengthEstimationPixel'] = None
self.stats['Graph'] = gr
misc.obj_to_file(self.stats, filename=filename, filetype='yaml')
def update_by_plan(filename="~/lisa_data/.update_plan.yaml", update_periode_days=10):
"""
:return: True or False
"""
filename = op.expanduser(filename)
retval = False
now = datetime.datetime.now()
try:
# this import is necessary here. This module requires scipy and it is
# not installed yet
import misc
data = misc.obj_from_file(filename)
string_date = data['update_datetime']
dt = datetime.datetime.strptime(string_date, "%Y-%m-%d %H:%M:%S.%f")
dt_delta = datetime.timedelta(days=update_periode_days)
if now > (dt + dt_delta):
retval = True
data['update_datetime'] = str(now)
misc.obj_to_file(data, filename)
# data['update_datetime'] =
except:
retval = True
data = {}
data['update_datetime'] = str(now)
import lisa_data
lisa_data.create_lisa_data_dir_tree()
misc.obj_to_file(data, filename)
return retval
def get_subdirs(dirpath, wildcard='*', outputfile='experiment_data.yaml'):
dirlist = []
if os.path.exists(dirpath):
logger.info('dirpath = ' + dirpath)
# print completedirpath
else:
logger.error('Wrong path: ' + dirpath)
raise Exception('Wrong path : ' + dirpath)
dirpath = os.path.abspath(dirpath)
# print 'copmpletedirpath = ', completedirpath
# import pdb; pdb.set_trace()
dirlist = {
o: {'abspath': os.path.abspath(os.path.join(dirpath, o))}
for o in os.listdir(dirpath) if os.path.isdir(
os.path.join(dirpath, o))
}
# import pdb; pdb.set_trace()
# print [o for o in os.listdir(dirpath) if
# os.path.isdir(os.path.abspath(o))]
# dirlist.append(infile)
# print "current file is: " + infile
misc.obj_to_file(dirlist, 'experiment_data.yaml', 'yaml')
return dirlist
def test_create_iparams(self):
"""
Interactivity is stored to file
"""
if self.verbose:
print "test_create_iparams"
import misc
dcmdir = os.path.join(path_to_script,'./../sample_data/jatra_5mm')
segparams = {'pairwiseAlpha':20, 'use_boundary_penalties':False,'boundary_penalties_sigma':200}
#oseg = organ_segmentation.OrganSegmentation(dcmdir, working_voxelsize_mm = 4)
oseg = organ_segmentation.OrganSegmentation(dcmdir, working_voxelsize_mm = 4, segparams=segparams, manualroi=False)
#oseg.add_seeds_mm([120,160],[150,120],[70], label=1, radius=20)
oseg.add_seeds_mm([120,160],[150,80],[85], label=1, radius=20)
oseg.add_seeds_mm([170,220,250,100],[250,300,200,350],[85], label=2, radius=20)
oseg.add_seeds_mm([170],[240],[70], label=2, radius=20)
#print "test_ipars"
#oseg.interactivity()
oseg.ninteractivity()
volume = oseg.get_segmented_volume_size_mm3()
#print 'vol %.3g ' %(volume)
misc.obj_to_file(oseg.get_iparams(),'iparams.pkl',filetype='pickle')
self.assertGreater(volume,1000000)
def sample_input_data():
inputdata = {'basedir': '/home/mjirik/data/medical/', # noqa
'data': [
{'sliverseg': 'data_orig/sliver07/training-part1/liver-seg001.mhd',
'ourseg': 'data_processed/organ_small-liver-orig001.mhd.pkl'}, # noqa
{'sliverseg': 'data_orig/sliver07/training-part1/liver-seg002.mhd',
'ourseg': 'data_processed/organ_small-liver-orig002.mhd.pkl'}, # noqa
{'sliverseg': 'data_orig/sliver07/training-part1/liver-seg003.mhd',
'ourseg': 'data_processed/organ_small-liver-orig003.mhd.pkl'}, # noqa
{'sliverseg': 'data_orig/sliver07/training-part1/liver-seg004.mhd',
'ourseg': 'data_processed/organ_small-liver-orig004.mhd.pkl'}, # noqa
{'sliverseg': 'data_orig/sliver07/training-part1/liver-seg005.mhd',
'ourseg': 'data_processed/organ_small-liver-orig005.mhd.pkl'}, # noqa
{'sliverseg': 'data_orig/sliver07/training-part2/liver-seg006.mhd',
'ourseg': 'data_processed/organ_small-liver-orig006.mhd.pkl'}, # noqa
{'sliverseg': 'data_orig/sliver07/training-part2/liver-seg007.mhd',
'ourseg': 'data_processed/organ_small-liver-orig007.mhd.pkl'}, # noqa
{'sliverseg': 'data_orig/sliver07/training-part2/liver-seg008.mhd',
'ourseg': 'data_processed/organ_small-liver-orig008.mhd.pkl'}, # noqa
{'sliverseg': 'data_orig/sliver07/training-part2/liver-seg009.mhd',
'ourseg': 'data_processed/organ_small-liver-orig009.mhd.pkl'}, # noqa
]
}
sample_data_file = os.path.join(path_to_script,
"20130812_liver_volumetry_sample.yaml")
# print sample_data_file, path_to_script
misc.obj_to_file(inputdata, sample_data_file, filetype='yaml')
def save_outputs(self, filepath=None):
# savestring_qt, ok = QInputDialog.getText(
# None,
# "Save",
# 'Save output data? Yes/No/All with input data (y/n/a):',
# text="a"
# )
# savestring = str(savestring_qt)
# if savestring in ['Y', 'y', 'a', 'A']:
data = self.export()
data['version'] = self.version # qmisc.getVersionString()
data['experiment_caption'] = self.experiment_caption
data['lisa_operator_identifier'] = self.lisa_operator_identifier
# data['organ_interactivity_counter'] = self.organ_interactivity_counter
# save renamed file too
# pth, filename = op.split(op.normpath(self.datapath))
# filename += "-" + self.experiment_caption
# # if savestring in ['a', 'A']:
# filepath = 'org-' + filename + '.' + self.save_filetype
# # rint filepath
# # rint 'op ', op
odp = self.output_datapath
if not op.exists(odp):
os.makedirs(odp)
if filepath is None:
filepath = self.get_standard_ouptut_filename()
misc.obj_to_file(data, filepath, filetype=self.save_filetype)
def main():
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
#logger.debug('input params')
# input parser
parser = argparse.ArgumentParser(
description='Module for segmentation of simple anatomical structures')
parser.add_argument('-i', '--inputfile',
default='organ.pkl',
help='path to data dir')
args = parser.parse_args()
data = misc.obj_from_file(args.inputfile, filetype = 'pickle')
data3d = data['data3d']
voxelsize_mm = data['voxelsize_mm']
ss = SimpleSegmentation()
simple_seg = ss.simple_segmentation(data3d, voxelsize_mm)
#visualization
pyed = sed3.sed3(data['data3d'], seeds=simple_seg)
pyed.show()
# save
savestring = raw_input('Save output data? (y/n): ')
if savestring in ['Y', 'y']:
misc.obj_to_file(data, "resection.pkl", filetype='pickle')
def save(self, outputfile, filetype='pklz'):
data = {
'data3d': self.data3d,
'voxelsize_mm': self.voxelsize_mm
}
misc.obj_to_file(data, outputfile, filetype=filetype)
print "saved"
def main():
#logger = logging.getLogger(__name__)
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
#logger.debug('input params')
parser = argparse.ArgumentParser(
description='Compute features on liver and other tissue.')
parser.add_argument('-si', '--sampleInput', action='store_true',
help='generate sample intput data', default=False)
parser.add_argument('-v', '--visualization', action='store_true',
help='Turn on visualization', default=False)
parser.add_argument('-fc', '--features_classifs', action='store_true',
help='Read features and classifs list from file',
default=False)
parser.add_argument('-i', '--input', help='input yaml file',
default="20130919_liver_statistics.yaml")
parser.add_argument('-o', '--output', help='output file',
default="20130919_liver_statistics_results.pkl")
parser.add_argument('-t', '--train', help='Training', default=False,
action='store_true'
)
args = parser.parse_args()
if args.sampleInput:
sample_input_data()
# input parser
#path_to_yaml = os.path.join(path_to_script, args.input)
path_to_yaml = args.input
#write_csv(fvall)
list_of_feature_fcn = [feat_hist]
from sklearn import svm
from sklearn.naive_bayes import GaussianNB
list_of_classifiers = [svm.SVC, GaussianNB]
tile_shape = [10, 100, 100]
if args.features_classifs:
import features_classifs
featrs_plus_classifs = features_classifs.fc
else:
featrs_plus_classifs = make_product_list(list_of_feature_fcn,
list_of_classifiers)
result = experiment(path_to_yaml, featrs_plus_classifs,
tile_shape=tile_shape,
visualization=args.visualization, train=args.train)
# Ukládání výsledku do souboru
output_file = os.path.join(path_to_script, args.output)
misc.obj_to_file(result, output_file, filetype='pickle')
def delete_config_records(filename, records_to_save=[]):
"""
All records of config file are deleted except records_to_save.
"""
if os.path.isfile(filename):
cfg = misc.obj_from_file(filename, filetype='yaml')
new_cfg = subdict(cfg, records_to_save)
print cfg
misc.obj_to_file(new_cfg, "test_" + filename , filetype='yaml')
def update_config_records(filename, new_cfg):
"""
All records of config file are updated exept records_to_save.
"""
if os.path.isfile(filename):
cfg = misc.obj_from_file(filename, filetype='yaml')
cfg.update(new_cfg)
print cfg
misc.obj_to_file(new_cfg, "test_" + filename , filetype='yaml')
def generate_input_yaml(sliver_dir, pklz_dir,
sliver_ext='*seg0*.mhd', pklz_ext='*0*.pklz',
yaml_filename=None,
return_dir_lists=False
):
"""
Function pair files from different directory by numer in format g0XX.
It is ok for seg001 and orig001 too.
If there is given some yaml_filename, it is created.
"""
import glob
import re
onlyfiles1 = glob.glob(os.path.join(sliver_dir, sliver_ext))
onlyfiles2 = glob.glob(os.path.join(pklz_dir, pklz_ext))
onlyfiles1.sort()
onlyfiles2.sort()
logger.debug('sliver files \n' + str(onlyfiles1))
logger.debug('pklz files \n' + str(onlyfiles2))
data = []
for flns in onlyfiles1:
base, flnsh = os.path.split(os.path.normpath(flns))
pattern = re.search('(g0[0-9]{2})', flnsh)
if pattern:
pattern = pattern.group(1)
logger.debug('pattern1 ' + pattern)
for flnp in onlyfiles2:
base, flnph = os.path.split(os.path.normpath(flnp))
pt = re.match('.*' + pattern + '.*', flnph)
if pt:
data.append({
'sliverseg': flns,
'ourseg': flnp
})
inputdata = {
'basedir': '',
'data': data
}
retval = []
if yaml_filename is None:
retval.append(inputdata)
else:
misc.obj_to_file(inputdata, yaml_filename, filetype='yaml')
if return_dir_lists:
retval.append(onlyfiles1)
retval.append(onlyfiles2)
if len(retval) > 1:
return tuple(retval)
elif len(retval) == 1:
return retval[0]
def test_obj_to_and_from_file_pickle(self):
testdata = np.random.random([4, 4, 3])
test_object = {'a': 1, 'data': testdata}
filename = 'test_obj_to_and_from_file.pkl'
misc.obj_to_file(test_object, filename, 'pickle')
saved_object = misc.obj_from_file(filename, 'pickle')
self.assertTrue(saved_object['a'] == 1)
self.assertTrue(saved_object['data'][1, 1, 1] == testdata[1, 1, 1])
os.remove(filename)
def skeleton_analysis(self, guiUpdateFunction = None):
"""
Glossary:
element: line structure of skeleton connected to node on both ends
node: connection point of elements. It is one or few voxelsize_mm
terminal: terminal node
"""
if not fast_debug:
if self.volume_data is not None:
skdst = self.__radius_analysis_init()
stats = {}
len_edg = np.max(self.sklabel)
#len_edg = 30
for edg_number in range(1,len_edg+1):
edgst = self.__connection_analysis(edg_number)
edgst.update(self.__edge_length(edg_number))
edgst.update(self.__edge_curve(edg_number, edgst, self.voxelsize_mm))
edgst.update(self.__edge_vectors(edg_number, edgst))
#edgst = edge_analysis(sklabel, i)
if self.volume_data is not None:
edgst['radius_mm'] = float(self.__radius_analysis(edg_number,skdst))
stats[edgst['id']] = edgst
if guiUpdateFunction is not None: # update gui progress
guiUpdateFunction(edg_number,len_edg,1)
#save data for faster debug
struct = {'sVD':self.volume_data, 'stats':stats, 'len_edg':len_edg}
misc.obj_to_file(struct, filename='tmp.pkl', filetype='pickle')
else:
struct = misc.obj_from_file(filename='tmp.pkl', filetype='pickle')
self.volume_data = struct['sVD']
stats = struct['stats']
len_edg = struct['len_edg']
#@TODO dokončit
for edg_number in range (1,len_edg+1):
edgst = stats[edg_number]
edgst.update(self.__connected_edge_angle(edg_number, stats))
if guiUpdateFunction is not None: # update gui progress
guiUpdateFunction(edg_number,len_edg,2)
return stats
def sample_input_data():
inputdata = {
'basedir': '/home/mjirik/data/medical/',
'data': [
{'sliverseg': 'data_orig/sliver07/training-part1/liver-seg001.mhd', 'sliverorig': 'data_orig/sliver07/training-part1/liver-orig001.mhd'},
{'sliverseg': 'data_orig/sliver07/training-part1/liver-seg002.mhd', 'sliverorig': 'data_orig/sliver07/training-part1/liver-orig002.mhd'},
]
}
sample_data_file = os.path.join(path_to_script,
"20130919_liver_statistics.yaml")
#print sample_data_file, path_to_script
misc.obj_to_file(inputdata, sample_data_file, filetype='yaml')
def test_obj_to_and_from_file_with_directories(self):
import shutil
testdata = np.random.random([4, 4, 3])
test_object = {'a': 1, 'data': testdata}
dirname = '__test_write_and_read'
filename = '__test_write_and_read/test_obj_to_and_from_file.pkl'
misc.obj_to_file(test_object, filename, 'pickle')
saved_object = misc.obj_from_file(filename, 'pickle')
self.assertTrue(saved_object['a'] == 1)
self.assertTrue(saved_object['data'][1, 1, 1] == testdata[1, 1, 1])
shutil.rmtree(dirname)
def evaluate_and_write_to_file(
inputYamlFile,
directoryPklz,
directorySliver,
outputfile,
visualization,
return_dir_lists=False,
special_evaluation_function=None,
return_all_data=False
):
"""
Function computes yaml file (if there are given input sliver and pklz
directories). Based on yaml file are compared sliver segmentations and
our pklz files.
"""
dirlists = None
if (directoryPklz is not None) and (directorySliver is not None):
dirlists = generate_input_yaml(
directorySliver,
directoryPklz,
yaml_filename=inputYamlFile,
return_dir_lists=return_dir_lists
)
# input parser
data_file = inputYamlFile
inputdata = misc.obj_from_file(data_file, filetype='yaml')
evaluation_all = eval_all_from_dataset_metadata(
inputdata, visualization,
special_evaluation_function=special_evaluation_function
)
logger.debug(str(evaluation_all))
logger.debug('eval all')
logger.debug(make_sum(evaluation_all))
write_csv(evaluation_all, filename=outputfile + '.csv')
misc.obj_to_file(evaluation_all, outputfile + '.pkl', filetype='pkl')
retval = []
if return_dir_lists:
retval.append(dirlists)
if return_all_data:
retval.append(evaluation_all)
if len(retval) > 0:
return retval
def get_config(filename, default_cfg):
"""
Looks config file and update default_cfg values.
If file does not exist it is created.
"""
if os.path.isfile(filename):
cfg = misc.obj_from_file(filename, filetype='yaml')
default_cfg.update(cfg)
cfg_out = default_cfg
else:
misc.obj_to_file(default_cfg, filename, filetype='yaml')
cfg_out = default_cfg
# default config
return cfg_out
def main():
#logger = logging.getLogger(__name__)
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
#logger.debug('input params')
# input parser
params = {'datadir':None, 'working_voxelsize_mm':3}
dirpath = os.path.join(path_to_script, "../../../data/medical/data_orig/volumetrie")
experiment_results = {'params':params,'dirpath':dirpath, 'volume_l':{}}
output_dirpath = os.path.join(path_to_script, '../../../data/medical')
dirlist = experiments.get_subdirs(dirpath)
for key in dirlist:
print key
# import pdb; pdb.set_trace()
try:
dirname = dirlist[key]['abspath']
params['datadir'] = dirname
oseg = organ_segmentation.OrganSegmentation(**params)
oseg.interactivity()
print (
"Volume " +
str(oseg.get_segmented_volume_size_mm3() / 1000000.0)
+ ' [l]')
volume_l = (oseg.get_segmented_volume_size_mm3() / 1000000.0)
experiment_results['volume_l'][key] = volume_l
head, teil = os.path.split(dirname)
filename_organ = os.path.join(output_dirpath, teil)
data = oseg.export()
misc.obj_to_file(data, filename_organ + "-organ.pkl", filetype='pickle')
#misc.obj_to_file(data, "organ.pkl", filetype='pickle')
misc.obj_to_file(oseg.get_iparams(), filename_organ + '-iparams.pkl', filetype='pickle')
misc.obj_to_file(experiment_results, filename_organ + "-info.yaml", filetype='yaml')
except:
print 'Selhani, pokracujeme dal'
print traceback.format_exc()
import pdb; pdb.set_trace()
misc.obj_to_file(experiment_results, "results.yaml", filetype='yaml')
def sample_input_data():
inputdata = {
"basedir": "/home/mjirik/data/medical/",
"data": [
{
"sliverseg": "data_orig/sliver07/training-part1/liver-seg001.mhd",
"sliverorig": "data_orig/sliver07/training-part1/liver-orig001.mhd",
},
{
"sliverseg": "data_orig/sliver07/training-part1/liver-seg002.mhd",
"sliverorig": "data_orig/sliver07/training-part1/liver-orig002.mhd",
},
],
}
sample_data_file = os.path.join(path_to_script, "20130919_liver_statistics.yaml")
# print sample_data_file, path_to_script
misc.obj_to_file(inputdata, sample_data_file, filetype="yaml")
def __vesselTree(self, binaryData3d, textLabel):
import skelet3d
import skeleton_analyser # histology_analyser as skan
data3d_thr = (binaryData3d > 0).astype(np.int8)
data3d_skel = skelet3d.skelet3d(data3d_thr)
skan = skeleton_analyser.SkeletonAnalyser(
data3d_skel,
volume_data=data3d_thr,
voxelsize_mm=self.voxelsize_mm)
stats = skan.skeleton_analysis(guiUpdateFunction=None)
if 'graph' not in self.vessel_tree.keys():
self.vessel_tree['voxelsize_mm'] = self.voxelsize_mm
self.vessel_tree['graph'] = {}
self.vessel_tree['graph'][textLabel] = stats
# print sa.stats
# save skeleton to special file
misc.obj_to_file(self.vessel_tree, 'vessel_tree.yaml', filetype='yaml')
def main():
# logger = logging.getLogger(__name__)
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
# logger.debug('input params')
parser = argparse.ArgumentParser(description="Compute features on liver and other tissue.")
parser.add_argument("-si", "--sampleInput", action="store_true", help="generate sample intput data", default=False)
parser.add_argument("-v", "--visualization", action="store_true", help="Turn on visualization", default=False)
parser.add_argument("-i", "--input", help="input yaml file", default="20130919_liver_statistics.yaml")
parser.add_argument("-o", "--output", help="output file", default="20130919_liver_statistics_results.pkl")
parser.add_argument("-t", "--train", help="Training", default=False, action="store_true")
args = parser.parse_args()
if args.sampleInput:
sample_input_data()
# input parser
# path_to_yaml = os.path.join(path_to_script, args.input)
path_to_yaml = args.input
# write_csv(fvall)
list_of_feature_fcn = [feat_hist]
from sklearn import svm
from sklearn.naive_bayes import GaussianNB
list_of_classifiers = [svm.SVC, GaussianNB]
tile_shape = [1, 100, 100]
featrs_plus_classifs = make_product_list(list_of_feature_fcn, list_of_classifiers)
result = experiment(
path_to_yaml, featrs_plus_classifs, tile_shape=tile_shape, visualization=args.visualization, train=args.train
)
# Ukládání výsledku do souboru
output_file = os.path.join(path_to_script, args.output)
misc.obj_to_file(result, output_file, filetype="pickle")
def check_config_version_and_remove_old_records(filename, version,
records_to_save):
"""
Check if config file version is ok. If it is not all records except
records_to_save are deleted and config_version in file is set to version.
It is used to update user configuration.
"""
if os.path.isfile(filename):
cfg = misc.obj_from_file(filename, filetype='yaml')
if ('config_version' in cfg and (cfg['config_version'] == version)):
# everything is ok, no need to panic
return
else:
# older version of config file
cfg = misc.obj_from_file(filename, filetype='yaml')
misc.obj_to_file(cfg, filename + '.old', filetype='yaml')
print 'cfg ', cfg
new_cfg = subdict(cfg, records_to_save)
new_cfg['config_version'] = version
print 'ncfg ', new_cfg
misc.obj_to_file(new_cfg, filename, filetype='yaml')
def sample_input_data():
inputdata = {
'basedir':
'/home/mjirik/data/medical/',
'data': [
{
'sliverseg':
'data_orig/sliver07/training-part1/liver-seg001.mhd',
'sliverorig':
'data_orig/sliver07/training-part1/liver-orig001.mhd'
}, # noqa
{
'sliverseg':
'data_orig/sliver07/training-part1/liver-seg002.mhd',
'sliverorig':
'data_orig/sliver07/training-part1/liver-orig002.mhd'
}, # noqa
]
}
sample_data_file = os.path.join(path_to_script,
"20130919_liver_statistics.yaml")
# print sample_data_file, path_to_script
misc.obj_to_file(inputdata, sample_data_file, filetype='yaml')
def save_outputs(self):
# savestring_qt, ok = QInputDialog.getText(
# None,
# "Save",
# 'Save output data? Yes/No/All with input data (y/n/a):',
# text="a"
# )
# savestring = str(savestring_qt)
# if savestring in ['Y', 'y', 'a', 'A']:
odp = self.output_datapath
if not op.exists(odp):
os.makedirs(odp)
data = self.export()
data['version'] = self.version # qmisc.getVersionString()
data['experiment_caption'] = self.experiment_caption
data['lisa_operator_identifier'] = self.lisa_operator_identifier
# data['organ_interactivity_counter'] = self.organ_interactivity_counter
pth, filename = op.split(op.normpath(self.datapath))
filename += "-" + self.experiment_caption
# if savestring in ['a', 'A']:
# save renamed file too
filepath = 'org-' + filename + '.' + self.save_filetype
# rint filepath
# rint 'op ', op
filepath = op.join(odp, filepath)
filepath = misc.suggest_filename(filepath)
misc.obj_to_file(data, filepath, filetype=self.save_filetype)
filepath = 'organ_last.' + self.save_filetype
filepath = op.join(odp, filepath)
# ilepath = misc.suggest_filename(filepath)
misc.obj_to_file(data, filepath, filetype=self.save_filetype)
# save to mat
# iparams = self.get_iparams()
# filepath = 'organ_iparams.pklz'
# filepath = op.join(odp, filepath)
# misc.obj_to_file(iparams, filepath, filetype='pklz')
# f savestring in ['a', 'A']:
if False:
# save renamed file too
data['data3d'] = None
filepath = 'organ_small-' + filename + '.pklz'
filepath = op.join(odp, filepath)
filepath = misc.suggest_filename(filepath)
misc.obj_to_file(data, filepath, filetype='pklz')
def main():
#logger = logging.getLogger(__name__)
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
#logger.debug('input params')
# input parser
parser = argparse.ArgumentParser(
description='Segmentation of bones, lungs and heart.')
parser.add_argument('-i',
'--datadir',
default=None,
help='path to data dir')
parser.add_argument('-o', '--output', default=None, help='output file')
parser.add_argument('-d',
'--debug',
action='store_true',
help='run in debug mode')
parser.add_argument('-ss',
'--segspine',
action='store_true',
help='run spine segmentaiton')
parser.add_argument('-sl',
'--seglungs',
action='store_true',
help='run lungs segmentation')
parser.add_argument('-sh',
'--segheart',
action='store_true',
help='run heart segmentation')
parser.add_argument('-sb',
'--segbones',
action='store_true',
help='run bones segmentation')
parser.add_argument('-exd',
'--exampledata',
action='store_true',
help='run unittest')
parser.add_argument('-so',
'--show_output',
action='store_true',
help='Show output data in viewer')
args = parser.parse_args()
if args.debug:
logger.setLevel(logging.DEBUG)
if args.exampledata:
args.dcmdir = '../sample_data/liver-orig001.raw'
# if dcmdir == None:
#else:
#dcm_read_from_dir('/home/mjirik/data/medical/data_orig/46328096/')
#data3d, metadata = dcmr.dcm_read_from_dir(args.dcmdir)
data3d, metadata = io3d.datareader.read(args.datadir)
sseg = SupportStructureSegmentation(
data3d=data3d,
voxelsize_mm=metadata['voxelsize_mm'],
)
#sseg.orientation()
if args.segbones:
sseg.bone_segmentation()
if args.segspine:
sseg.spine_segmentation()
if args.seglungs or args.segheart:
sseg.lungs_segmentation()
if args.segheart:
sseg.heart_segmentation()
sseg.resize_back_to_orig()
#print ("Data size: " + str(data3d.nbytes) + ', shape: ' + str(data3d.shape) )
#igc = pycut.ImageGraphCut(data3d, zoom = 0.5)
#igc.interactivity()
#igc.make_gc()
#igc.show_segmentation()
# volume
#volume_mm3 = np.sum(oseg.segmentation > 0) * np.prod(oseg.voxelsize_mm)
#pyed = sed3.sed3(oseg.data3d, contour = oseg.segmentation)
#pyed.show()
if args.show_output:
sseg.visualization()
#savestring = raw_input ('Save output data? (y/n): ')
#sn = int(snstring)
if args.output is not None: # savestring in ['Y','y']:
import misc
data = sseg.export()
misc.obj_to_file(data, args.output, filetype='pickle')
# seg = np.zeros([100,100,100])
# seg [50:80, 50:80, 60:75] = 1
# seg[58:60, 56:72, 66:68]=2
# dat = np.random.rand(100,100,100)
# dat [50:80, 50:80, 60:75] = dat [50:80, 50:80, 60:75] + 1
# dat [58:60, 56:72, 66:68] = dat [58:60, 56:72, 66:68] + 1
# slab = {'liver':1, 'porta':2, 'portaa':3, 'portab':4}
# data = {'segmentation':seg, 'data3d':dat, 'slab':slab}
name = 'porta'
# cut_editor(data,args.inputfile)
if args.use_old_editor:
resection(data, name, use_old_editor=args.use_old_editor)
else:
cut_editor(data, args.picklefile)
# print normal
# print coordinates
defaultoutputfile = "05-resection.pkl"
if args.defaultoutputfile:
args.outputfile = defaultoutputfile
if args.outputfile is None:
savestring = raw_input('Save output data? (y/n): ')
if savestring in ['Y', 'y']:
misc.obj_to_file(data, defaultoutputfile, filetype='pickle')
else:
misc.obj_to_file(data, args.outputfile, filetype='pickle')
def save_outputs(args, oseg, qt_app):
from PyQt4.QtGui import QInputDialog
savestring_qt, ok = QInputDialog.getText(
None,
"Save",
'Save output data? Yes/No/All+Dicom with input data (y/n/a/ad):',
text="a"
)
savestring = str(savestring_qt)
#import pdb; pdb.set_trace()
#savestring = raw_input(
# 'Save output data? Yes/No/All with input data (y/n/a): '
#)
if savestring in ['Y', 'y', 'a', 'A', 'ad']:
if not os.path.exists(args["output_datapath"]):
os.makedirs(args['output_datapath'])
op = args['output_datapath']
# rename
data = oseg.export()
data['version'] = qmisc.getVersionString()
data['experiment_caption'] = args['experiment_caption']
data['lisa_operator_identifier'] = args['lisa_operator_identifier']
#data['organ_segmentation_time'] = t1
iparams = oseg.get_iparams()
#import pdb; pdb.set_trace()
pth, filename = os.path.split(os.path.normpath(args['datapath']))
filename += "-" + args['experiment_caption']
if savestring in ['a', 'A', 'ad']:
# save renamed file too
filepath = 'ob-' + filename + '.pklz'
filepath = os.path.join(op, filepath)
filepath = misc.suggest_filename(filepath)
misc.obj_to_file(data, filepath, filetype='pklz')
filepath = 'organ.pklz'
filepath = os.path.join(op, filepath)
#filepath = misc.suggest_filename(filepath)
misc.obj_to_file(data, filepath, filetype='pklz')
filepath = 'organ_iparams.pklz'
filepath = os.path.join(op, filepath)
misc.obj_to_file(iparams, filepath, filetype='pklz')
data['data3d'] = None
filepath = 'organ_small-' + filename + '.pklz'
filepath = os.path.join(op, filepath)
filepath = misc.suggest_filename(filepath)
misc.obj_to_file(data, filepath, filetype='pklz')
#output = segmentation.vesselSegmentation(oseg.data3d,
# oseg.orig_segmentation)
# print "uf"
if savestring in ['ad']:
# save to DICOM
filepath = 'dicom-' + filename
filepath = os.path.join(op, filepath)
filepath = misc.suggest_filename(filepath)
output_dicom_dir = filepath
#import ipdb; ipdb.set_trace() # BREAKPOINT
overlays = {
3:
(data['segmentation'] == args['output_label']).astype(np.int8)
}
saveOverlayToDicomCopy(oseg.metadata['dcmfilelist'],
output_dicom_dir, overlays,
data['crinfo'], data['orig_shape'])
def save_config(cfg, filename):
cfg
misc.obj_to_file(cfg, filename, filetype="yaml")
def save_config(cfg, filename):
cfg
misc.obj_to_file(cfg, filename, filetype="yaml")
def generate_input_yaml(sliver_dir, pklz_dir,
sliver_ext='*seg0*.mhd', pklz_ext='*0*.pklz',
yaml_filename=None,
return_dir_lists=False
):
"""
Function pair files from different directory by numer in format g0XX.
It is ok for seg001 and orig001 too.
If there is given some yaml_filename, it is created.
"""
import glob
import re
if not os.path.exists(sliver_dir):
raise IOError("Directory with reference data (" + sliver_dir + ") does not exists")
if not os.path.exists(pklz_dir):
raise IOError("Directory with input pklz data (" + pklz_dir + ") does not exists")
onlyfiles1 = glob.glob(os.path.join(sliver_dir, sliver_ext))
onlyfiles2 = glob.glob(os.path.join(pklz_dir, pklz_ext))
onlyfiles1.sort()
onlyfiles2.sort()
if len(onlyfiles1) == 0:
raise IOError("Directory with reference data (%s) appears to be empty" % (sliver_dir))
if len(onlyfiles2) == 0:
raise IOError("Directory with pklz data (%s) appears to be empty" % (pklz_dir))
logger.debug('sliver files \n' + str(onlyfiles1))
logger.debug('pklz files \n' + str(onlyfiles2))
data = []
for flns in onlyfiles1:
base, flnsh = os.path.split(os.path.normpath(flns))
pattern = re.search('(g0[0-9]{2})', flnsh)
if pattern:
pattern = pattern.group(1)
logger.debug('pattern1 ' + pattern)
for flnp in onlyfiles2:
base, flnph = os.path.split(os.path.normpath(flnp))
pt = re.match('.*' + pattern + '.*', flnph)
if pt:
data.append({
'sliverseg': flns,
'ourseg': flnp
})
inputdata = {
'basedir': '',
'data': data
}
retval = []
if yaml_filename is None:
retval.append(inputdata)
else:
misc.obj_to_file(inputdata, yaml_filename, filetype='yaml')
if return_dir_lists:
retval.append(onlyfiles1)
retval.append(onlyfiles2)
if len(retval) > 1:
return tuple(retval)
elif len(retval) == 1:
return retval[0]
def main():
# # logger = logging.getLogger()
# logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
# logger.debug('input params')
parser = argparse.ArgumentParser(
description='Compute features on liver and other tissue.')
parser.add_argument('-tr',
'--training_yaml_path',
help='Input yaml file.' +
" You can check sample with -si parameter.",
default="20130919_liver_statistics.yaml")
parser.add_argument('-te',
'--testing_yaml_path',
help='Input yaml file.' +
" You can check sample with -si parameter.",
default=None)
parser.add_argument('-si',
'--sampleInput',
action='store_true',
help='generate sample intput data',
default=False)
parser.add_argument('-v',
'--visualization',
action='store_true',
help='Turn on visualization',
default=False)
parser.add_argument('-fc',
'--features_classifs',
action='store_true',
help='Read features and classifs list from file',
default=False)
parser.add_argument('-o',
'--output',
help='output file',
default="20130919_liver_statistics_results.pkl")
parser.add_argument('-t',
'--train',
help='Training',
default=False,
action='store_true')
parser.add_argument(
'-cl',
'--classifers',
help='classifer by string: "SVC", or "GaussianNB", ...',
nargs='+',
type=str,
default=['SVC'])
parser.add_argument('-fe',
'--features',
help='features by string: "hist", or "glcm", ...',
nargs='+',
type=str,
default=['hist'])
args = parser.parse_args()
if args.sampleInput:
sample_input_data()
# input parser
# path_to_yaml = os.path.join(path_to_script, args.input)
# training_yaml_path = args.training_yaml_path
# testing_yaml_path = args.testing_yaml_path
if args.testing_yaml_path is None:
print('testing is same as training')
args.testing_yaml_path = args.training_yaml_path
# write_csv(fvall)
gf = tfeat.GaborFeatures() # noqa
glcmf = tfeat.GlcmFeatures() # noqa
haralick = tfeat.HaralickFeatures() # noqa
list_of_feature_fcn = prepared_texture_features_by_string(args.features)
list_of_classifiers = prepared_classifiers_by_string(args.classifers)
tile_shape = [10, 50, 50]
if args.features_classifs:
import features_classifs
featrs_plus_classifs = features_classifs.fc
else:
featrs_plus_classifs = make_product_list(list_of_feature_fcn,
list_of_classifiers)
result = experiment(args.training_yaml_path,
args.testing_yaml_path,
featrs_plus_classifs,
tile_shape=tile_shape,
use_voxelsize_norm=True,
working_voxelsize_mm=[1, 1, 1],
visualization=args.visualization,
train=args.train)
# Ukládání výsledku do souboru
output_file = os.path.join(path_to_script, args.output)
misc.obj_to_file(result, output_file, filetype='pickle')
def save_labels(inputfile, data3d, segmentation, feature_fcn,
feature_fcn_params, classif_inst, classif_fcn_plus_params,
voxelsize, tile_shape, use_voxelsize_norm):
"""
classif_fcn_plus_params: used for directory name
"""
path_directory = 'lisa_data/'
subdirectory = 'experiments/'
# actual = os.getcwd()
path_directory = os.path.join(os.path.expanduser('~'), path_directory)
# Ukládání výsledku do souboru
if (os.path.exists(path_directory) is False):
os.makedirs(path_directory)
path_subdirectory = os.path.join(path_directory, subdirectory)
if (os.path.exists(path_subdirectory) is False):
os.makedirs(path_subdirectory)
# TODO : Main Saving Loop ...
dataplus = []
slab = {}
slab['liver'] = 1
slab['none'] = 0
# there are two possible ways for fature_fcn. One is function, other is object
# with "description" attribute and function 'features'
if hasattr(feature_fcn, 'description'):
feature_fcn_description = feature_fcn.description
else:
feature_fcn_description = str(feature_fcn)
dataplus = {
# 'segmentation': segmentation[:10, :10, :10].astype(np.int8),
# 'data3d': data3d[:10, :10, :10].astype(np.int16),
'segmentation': segmentation.astype(np.int8),
'data3d': data3d.astype(np.int16),
'processing_information': {
'feature_fcn': feature_fcn_description,
'feature_fcn_params': str(feature_fcn_params),
'classif_fcn_name': str(classif_inst.__class__.__name__),
'classif_fcn': str(classif_inst)
},
'voxelsize_mm': voxelsize,
'slab': slab
}
# inputfilename = path_leaf(inputfile)
str_feat_params = __struct_to_string_for_filename(feature_fcn_params)
str_clf_params = __struct_to_string_for_filename(str(classif_inst))
str_clf_params = __struct_to_string_for_filename(
str(classif_fcn_plus_params))
vsnorm_string = "vsnF_"
if use_voxelsize_norm:
vsnorm_string = "vsnT_"
# construct filename
experiment_dirname =\
feature_fcn_description + '_' + \
str_feat_params + '_' +\
vsnorm_string +\
classif_inst.__class__.__name__ + '_' + \
str_clf_params + '_' +\
str(tile_shape[0]) + '_' + str(tile_shape[1]) + '_' +\
str(tile_shape[2])
filename = __struct_to_string_for_filename(inputfile + '.pklz')
path_to_file = os.path.join(path_subdirectory, experiment_dirname,
filename)
misc.obj_to_file(dataplus, path_to_file, filetype='pklz')
# os.chdir(actual)
# save info to dir
info = {
'feature_fcn': feature_fcn_description,
'feature_fcn_params': str(feature_fcn_params),
'classif_fcn': str(classif_inst)
}
infofilepath = os.path.join(path_subdirectory, experiment_dirname,
'info.yaml')
misc.obj_to_file(info, infofilepath, 'yaml')
def main():
#logger = logging.getLogger(__name__)
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
#logger.debug('input params')
# input parser
parser = argparse.ArgumentParser(description=
'Segmentation of bones, lungs and heart.')
parser.add_argument('-i','--datadir',
default=None,
help='path to data dir')
parser.add_argument('-o','--output',
default=None,
help='output file')
parser.add_argument('-d', '--debug', action='store_true',
help='run in debug mode')
parser.add_argument('-ss', '--segspine', action='store_true',
help='run spine segmentaiton')
parser.add_argument('-sl', '--seglungs', action='store_true',
help='run lungs segmentation')
parser.add_argument('-sh', '--segheart', action='store_true',
help='run heart segmentation')
parser.add_argument('-sb', '--segbones', action='store_true',
help='run bones segmentation')
parser.add_argument('-exd', '--exampledata', action='store_true',
help='run unittest')
parser.add_argument('-so', '--show_output', action='store_true',
help='Show output data in viewer')
args = parser.parse_args()
if args.debug:
logger.setLevel(logging.DEBUG)
if args.exampledata:
args.dcmdir = '../sample_data/liver-orig001.raw'
# if dcmdir == None:
#else:
#dcm_read_from_dir('/home/mjirik/data/medical/data_orig/46328096/')
#data3d, metadata = dcmr.dcm_read_from_dir(args.dcmdir)
data3d , metadata = io3d.datareader.read(args.datadir)
sseg = SupportStructureSegmentation(data3d = data3d,
voxelsize_mm = metadata['voxelsize_mm'],
)
#sseg.orientation()
if args.segbones:
sseg.bone_segmentation()
if args.segspine:
sseg.spine_segmentation()
if args.seglungs or args.segheart:
sseg.lungs_segmentation()
if args.segheart:
sseg.heart_segmentation()
sseg.resize_back_to_orig()
#print ("Data size: " + str(data3d.nbytes) + ', shape: ' + str(data3d.shape) )
#igc = pycut.ImageGraphCut(data3d, zoom = 0.5)
#igc.interactivity()
#igc.make_gc()
#igc.show_segmentation()
# volume
#volume_mm3 = np.sum(oseg.segmentation > 0) * np.prod(oseg.voxelsize_mm)
#pyed = sed3.sed3(oseg.data3d, contour = oseg.segmentation)
#pyed.show()
if args.show_output:
sseg.visualization()
#savestring = raw_input ('Save output data? (y/n): ')
#sn = int(snstring)
if args.output is not None: # savestring in ['Y','y']:
import misc
data = sseg.export()
misc.obj_to_file(data, args.output, filetype = 'pickle')
def main():
#logger = logging.getLogger(__name__)
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
#logger.debug('input params')
# input parser
params = {'datadir': None, 'working_voxelsize_mm': 3}
dirpath = os.path.join(path_to_script,
"../../../data/medical/data_orig/volumetrie")
experiment_results = {'params': params, 'dirpath': dirpath, 'volume_l': {}}
output_dirpath = os.path.join(path_to_script, '../../../data/medical')
dirlist = experiments.get_subdirs(dirpath)
for key in dirlist:
print key
# import pdb; pdb.set_trace()
try:
dirname = dirlist[key]['abspath']
params['datadir'] = dirname
oseg = organ_segmentation.OrganSegmentation(**params)
oseg.interactivity()
print("Volume " +
str(oseg.get_segmented_volume_size_mm3() / 1000000.0) +
' [l]')
volume_l = (oseg.get_segmented_volume_size_mm3() / 1000000.0)
experiment_results['volume_l'][key] = volume_l
head, teil = os.path.split(dirname)
filename_organ = os.path.join(output_dirpath, teil)
data = oseg.export()
misc.obj_to_file(data,
filename_organ + "-organ.pkl",
filetype='pickle')
#misc.obj_to_file(data, "organ.pkl", filetype='pickle')
misc.obj_to_file(oseg.get_iparams(),
filename_organ + '-iparams.pkl',
filetype='pickle')
misc.obj_to_file(experiment_results,
filename_organ + "-info.yaml",
filetype='yaml')
except:
print 'Selhani, pokracujeme dal'
print traceback.format_exc()
import pdb
pdb.set_trace()
misc.obj_to_file(experiment_results, "results.yaml", filetype='yaml')
def main():
#logger = logging.getLogger(__name__)
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
#logger.debug('input params')
# input parser
parser = argparse.ArgumentParser(
description='Segment vessels from liver \n\
\npython organ_segmentation.py\n\
\npython organ_segmentation.py -mroi -vs 0.6')
parser.add_argument('-dd',
'--dcmdir',
default=None,
help='path to data dir')
parser.add_argument('-d',
'--debug',
action='store_true',
help='run in debug mode')
parser.add_argument('-vs',
'--voxelsize_mm',
default='3',
type=str,
help='Insert working voxelsize. It can be number or \
array of three numbers. \n \
-vs 3 \n \
-vs [3,3,5]')
parser.add_argument('-mroi',
'--manualroi',
action='store_true',
help='manual crop before data processing')
parser.add_argument(
'-iparams',
'--iparams',
default=None,
help='filename of ipars file with stored interactivity')
parser.add_argument('-t',
'--tests',
action='store_true',
help='run unittest')
parser.add_argument('-tx',
'--textureanalysis',
action='store_true',
help='run with texture analysis')
parser.add_argument('-exd',
'--exampledata',
action='store_true',
help='run unittest')
parser.add_argument('-ed',
'--editdata',
action='store_true',
help='Run data editor')
parser.add_argument('-so',
'--show_output',
action='store_true',
help='Show output data in viewer')
parser.add_argument('-ss',
'--segmentation_smoothing',
action='store_true',
help='Smoothing of output segmentation',
default=False)
args = parser.parse_args()
# voxelsize_mm can be number or array
args.voxelsize_mm = np.array(eval(args.voxelsize_mm))
if args.debug:
logger.setLevel(logging.DEBUG)
if args.tests:
# hack for use argparse and unittest in one module
sys.argv[1:] = []
unittest.main()
sys.exit()
if args.exampledata:
args.dcmdir = '../sample_data/\
matlab/examples/sample_data/DICOM/digest_article/'
if args.iparams is not None:
args.iparams = misc.obj_from_file(args.iparams)
#else:
#dcm_read_from_dir('/home/mjirik/data/medical/data_orig/46328096/')
#data3d, metadata = dcmreaddata.dcm_read_from_dir()
oseg = OrganSegmentation(args.dcmdir,
working_voxelsize_mm=args.voxelsize_mm,
manualroi=args.manualroi,
texture_analysis=args.textureanalysis,
edit_data=args.editdata,
smoothing=args.action_segmentation_smoothing,
iparams=args.iparams)
oseg.interactivity()
#igc = pycut.ImageGraphCut(data3d, zoom = 0.5)
#igc.interactivity()
#igc.make_gc()
#igc.show_segmentation()
# volume
#volume_mm3 = np.sum(oseg.segmentation > 0) * np.prod(oseg.voxelsize_mm)
print("Volume " + str(oseg.get_segmented_volume_size_mm3() / 1000000.0) +
' [l]')
#pyed = sed3.sed3(oseg.data3d, contour =
# oseg.segmentation)
#pyed.show()
if args.show_output:
oseg.show_output()
savestring = raw_input('Save output data? (y/n): ')
#sn = int(snstring)
if savestring in ['Y', 'y']:
data = oseg.export()
misc.obj_to_file(data, "organ.pkl", filetype='pickle')
misc.obj_to_file(oseg.get_ipars(), 'ipars.pkl', filetype='pickle')
#output = segmentation.vesselSegmentation(oseg.data3d,
# oseg.orig_segmentation)
def checkSegData(self):
if self.segmentation_data is None:
self.statusBar().showMessage('No SEG data!')
return
nzs = self.segmentation_data.nonzero()
nn = nzs[0].shape[0]
if nn > 0:
aux = ' voxels = %d, volume = %.2e mm3' % (nn,
nn * self.voxel_volume)
self.setLabelText(self.text_seg_data, aux)
self.setLabelText(self.text_mesh_in, 'segmentation data')
self.statusBar().showMessage('Ready')
else:
self.statusBar().showMessage('Zero SEG data!')
def autoSeg(self):
if self.dcm_3Ddata is None:
self.statusBar().showMessage('No DICOM data!')
return
igc = pycut.ImageGraphCut(self.dcm_3Ddata,
voxelsize=self.voxel_size_mm)
pyed = QTSeedEditor(self.dcm_3Ddata,
seeds=self.segmentation_seeds,
modeFun=igc.interactivity_loop,
voxelVolume=self.voxel_volume)
pyed.exec_()
self.segmentation_data = pyed.getContours()
self.segmentation_seeds = pyed.getSeeds()
self.checkSegData()
#print np.max(output)
#import pdb; pdb.set_trace()
#data = {}
#data['data3d'] = oseg.data3d
#data['crinfo'] = oseg.crinfo
#data['segmentation'] = oseg.segmentation
data['segmentation'][output] = data['slab']['porta']
#data['slab'] = slab
pyed = sed3.sed3(data['data3d'],
contour=data['segmentation'] == data['slab']['porta'])
pyed.show()
#pyed = sed3.sed3(data['segmentation'])
#pyed.show()
# Uvolneni pameti
garbage.collect()
if args.outputfile == None:
savestring = raw_input('Save output data? (y/n): ')
#sn = int(snstring)
if savestring in ['Y', 'y']:
pth, filename = os.path.split(os.path.normpath(args.inputfile))
misc.obj_to_file(data,
defaultoutputfile + '-' + filename,
filetype='pickle')
else:
misc.obj_to_file(data, args.outputfile, filetype='pickle')
def main():
#logger = logging.getLogger(__name__)
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
#logger.debug('input params')
# input parser
parser = argparse.ArgumentParser(description=
'Segmentation of bones, lungs and heart.')
parser.add_argument('-dd','--dcmdir',
default=None,
help='path to data dir')
parser.add_argument('-d', '--debug', action='store_true',
help='run in debug mode')
parser.add_argument('-exd', '--exampledata', action='store_true',
help='run unittest')
parser.add_argument('-so', '--show_output', action='store_true',
help='Show output data in viewer')
args = parser.parse_args()
if args.debug:
logger.setLevel(logging.DEBUG)
if args.exampledata:
args.dcmdir = '../sample_data/matlab/examples/sample_data/DICOM/digest_article/'
# if dcmdir == None:
#else:
#dcm_read_from_dir('/home/mjirik/data/medical/data_orig/46328096/')
#data3d, metadata = dcmr.dcm_read_from_dir(args.dcmdir)
reader = dcmr.DicomReader(args.datadir)
self.data3d = reader.get_3Ddata()
self.metadata = reader.get_metaData()
sseg = SupportStructureSegmentation(data3d = data3d,
voxelsize_mm = metadata['voxelsize_mm'],
)
sseg.bone_segmentation()
#print ("Data size: " + str(data3d.nbytes) + ', shape: ' + str(data3d.shape) )
#igc = pycut.ImageGraphCut(data3d, zoom = 0.5)
#igc.interactivity()
#igc.make_gc()
#igc.show_segmentation()
# volume
#volume_mm3 = np.sum(oseg.segmentation > 0) * np.prod(oseg.voxelsize_mm)
#pyed = py3DSeedEditor.py3DSeedEditor(oseg.data3d, contour = oseg.segmentation)
#pyed.show()
if args.show_output:
sseg.show_output()
savestring = raw_input ('Save output data? (y/n): ')
#sn = int(snstring)
if savestring in ['Y','y']:
import misc
data = sseg.export()
misc.obj_to_file(data, "organ.pickle", filetype = 'pickle')
def writeSkeletonToPickle(self, filename='skel.pkl'):
misc.obj_to_file(self.sklabel, filename=filename, filetype='pickle')
def main():
#logger = logging.getLogger(__name__)
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
#logger.debug('input params')
parser = argparse.ArgumentParser(
description='Compute features on liver and other tissue.')
parser.add_argument('-si',
'--sampleInput',
action='store_true',
help='generate sample intput data',
default=False)
parser.add_argument('-v',
'--visualization',
action='store_true',
help='Turn on visualization',
default=False)
parser.add_argument('-i',
'--input',
help='input yaml file',
default="20130919_liver_statistics.yaml")
parser.add_argument('-o',
'--output',
help='output file',
default="20130919_liver_statistics_results.pkl")
parser.add_argument('-t',
'--train',
help='Training',
default=False,
action='store_true')
args = parser.parse_args()
if args.sampleInput:
sample_input_data()
# input parser
#path_to_yaml = os.path.join(path_to_script, args.input)
path_to_yaml = args.input
#write_csv(fvall)
list_of_feature_fcn = [feat_hist]
from sklearn import svm
from sklearn.naive_bayes import GaussianNB
list_of_classifiers = [svm.SVC, GaussianNB]
tile_shape = [1, 100, 100]
featrs_plus_classifs = make_product_list(list_of_feature_fcn,
list_of_classifiers)
result = experiment(path_to_yaml,
featrs_plus_classifs,
tile_shape=tile_shape,
visualization=args.visualization,
train=args.train)
# Ukládání výsledku do souboru
output_file = os.path.join(path_to_script, args.output)
misc.obj_to_file(result, output_file, filetype='pickle')
