def main():
#logger = logging.getLogger(__name__)
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
#logger.debug('input params')
print 'version: ', qmisc.getVersionString()
###
# Configuration is has three sources:
# * Default function values
# * Config file 'organ_segmentation.config' in directory with application
# * Config file 'organ_segmentation.config' in output data directory
#
# It is read in this order so the last one has highest priority
###
## read confguraton from file, use default values from OrganSegmentation
cfgplus = {
'datapath': None,
'viewermax': 300,
'viewermin': -100,
'output_datapath': os.path.expanduser("~/lisa_data"),
'lisa_operator_identifier': "",
'experiment_caption': ''
}
cfg = config.get_default_function_config(OrganSegmentation.__init__)
cfg.update(cfgplus)
# now is in cfg default values
cfg = config.get_config("organ_segmentation.config", cfg)
# read user defined config in user data
cfg = config.get_config(
os.path.join(cfg['output_datapath'], "organ_segmentation.config"),
cfg
)
# 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', '--datapath',
default=cfg["datapath"],
help='path to data dir')
parser.add_argument('-d', '--debug', action='store_true',
help='run in debug mode')
parser.add_argument(
'-vs', '--working_voxelsize_mm',
default=cfg["working_voxelsize_mm"],
type=eval, # type=str,
help='Insert working voxelsize. It can be number or \
array of three numbers. It is possible use original \n \
resolution or half of original resolution. \n \
-vs 3 \n \
-vs [3,3,5] \n \
-vs orig \n \
-vs orig*2 \n \
-vs orig*4 \n \
'
)
parser.add_argument('-mroi', '--manualroi', action='store_true',
help='manual crop before data processing',
default=cfg["manualroi"])
parser.add_argument('-op', '--output_datapath',
default=cfg["output_datapath"],
help='path for output data')
parser.add_argument('-ol', '--output_label', default=1,
help='label for segmented data')
parser.add_argument(
'--slab',
default=cfg["slab"],
type=eval,
help='labels for segmentation,\
example -slab "{\'liver\':1, \'lesions\':6}"')
parser.add_argument(
'-acr', '--autocrop',
help='automatic crop after data processing',
default=cfg["autocrop"])
parser.add_argument(
'-iparams', '--iparams',
default=None,
help='filename of ipars file with stored interactivity')
parser.add_argument(
'-sp', '--segparams', type=eval,
default=cfg["segparams"],
help='params for segmentation,\
example -sp "{\'pairwise_alpha_per_mm2\':90}"')
parser.add_argument(
'-tx', '--texture_analysis', action='store_true',
help='run with texture analysis')
parser.add_argument('-exd', '--exampledata', action='store_true',
help='run unittest')
parser.add_argument('-ed', '--edit_data', action='store_true',
help='Run data editor')
parser.add_argument(
'-vmax', '--viewermax', type=eval, # type=int,
help='Maximum of viewer window, set None for automatic maximum.',
default=cfg["viewermax"])
parser.add_argument(
'-vmin', '--viewermin', type=eval, # type=int,
help='Minimum of viewer window, set None for automatic minimum.',
default=cfg["viewermin"])
parser.add_argument(
'-so', '--show_output', action='store_true',
help='Show output data in viewer')
parser.add_argument('-a', '--arg', nargs='+', type=float)
parser.add_argument(
'-ec', '--experiment_caption', type=str, # type=int,
help='Short caption of experiment. No special characters.',
default=cfg["experiment_caption"])
parser.add_argument(
'-oi', '--lisa_operator_identifier', type=str, # type=int,
help='Identifier of Lisa operator.',
default=cfg["experiment_caption"])
parser.add_argument(
'-ss',
'--segmentation_smoothing',
action='store_true',
help='Smoothing of output segmentation',
default=cfg["segmentation_smoothing"]
)
args_obj = parser.parse_args()
args = vars(args_obj)
#print args["arg"]
oseg_argspec_keys = config.get_function_keys(OrganSegmentation.__init__)
# voxelsize_mm can be number or array
#if args.voxelsize_mm != 'orig':
#TODO easy way to set half of resolution
# if isinstance(args["working_voxelsize_mm"], numbers.Number)
# if args["working_voxelsize_mm"] == -1:
# args["working_voxelsize_mm"] = 'orig'
## todo and so on
# if not args["working_voxelsize_mm"].startswith('orig'):
# #import pdb; pdb.set_trace()
# args["working_voxelsize_mm"] = eval(args["working_voxelsize_mm"])
#
#args["segparams"] = eval(args["segparams"])
#args["slab"] = eval(args["slab"])
#args["viewermin"] = eval(args["viewermin"])
#args["viewermax"] = eval(args["viewermax"])
if args["debug"]:
logger.setLevel(logging.DEBUG)
if args["exampledata"]:
args["datapath"] = '../sample_data/\
matlab/examples/sample_data/DICOM/digest_article/'
if args["iparams"] is not None:
iparams = misc.obj_from_file(args["iparams"], filetype='pickle')
oseg = OrganSegmentation(**iparams)
else:
#else:
#dcm_read_from_dir('/home/mjirik/data/medical/data_orig/46328096/')
#data3d, metadata = dcmreaddata.dcm_read_from_dir()
data3d, metadata, qt_app = readData3d(args["datapath"])
args['datapath'] = metadata['datadir']
oseg_params = config.subdict(args, oseg_argspec_keys)
oseg_params["data3d"] = data3d
oseg_params["metadata"] = metadata
oseg_params['qt_app'] = qt_app
oseg = OrganSegmentation(**oseg_params)
oseg.interactivity(args["viewermin"], args["viewermax"])
#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)
audiosupport.beep()
print(
"Volume " +
str(oseg.get_segmented_volume_size_mm3() / 1000000.0) + ' [l]')
#pyed = py3DSeedEditor.py3DSeedEditor(oseg.data3d, contour =
# oseg.segmentation)
#pyed.show()
print("Total time: " + str(oseg.processing_time))
if args["show_output"]:
oseg.show_output()
#print savestring
save_outputs(args, oseg, qt_app)
# import pdb; pdb.set_trace()
return
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 __init__(
self,
datapath=None,
working_voxelsize_mm=3,
viewermax=None,
viewermin=None,
series_number=None,
autocrop=True,
autocrop_margin_mm=[10, 10, 10],
manualroi=False,
texture_analysis=None,
segmentation_smoothing=False,
smoothing_mm=4,
volume_blowup=1.00,
data3d=None,
metadata=None,
seeds=None,
edit_data=False,
segparams={},
segmodelparams=default_segmodelparams,
roi=None,
output_label=1,
slab={},
output_datapath=None,
input_datapath_start='',
experiment_caption='',
lisa_operator_identifier='',
volume_unit='ml',
save_filetype='pklz',
# iparams=None,
):
""" Segmentation of objects from CT data.
datapath: path to directory with dicom files
manualroi: manual set of ROI before data processing, there is a
problem with correct coordinates
data3d, metadata: it can be used for data loading not from directory.
If both are setted, datapath is ignored
output_label: label for output segmented volume
slab: aditional label system for description segmented data
{'none':0, 'liver':1, 'lesions':6}
roi: region of interest. [[startx, stopx], [sty, spy], [stz, spz]]
seeds: ndimage array with size same as data3d
experiment_caption = this caption is used for naming of outputs
lisa_operator_identifier: used for logging
input_datapath_start: Path where user directory selection dialog
starts.
volume_blowup: Blow up volume is computed in smoothing so it is working
only if smoothing is turned on.
"""
self.iparams = {}
self.datapath = datapath
self.output_datapath = output_datapath
self.input_datapath_start = input_datapath_start
self.crinfo = [[0, -1], [0, -1], [0, -1]]
self.slab = slab
self.output_label = output_label
self.working_voxelsize_mm = None
self.input_wvx_size = working_voxelsize_mm
# print segparams
# @TODO each axis independent alpha
self.segparams = default_segparams
self.segparams.update(segparams)
self.segmodelparams = default_segmodelparams
self.segmodelparams.update(segmodelparams)
self.series_number = series_number
self.autocrop = autocrop
self.autocrop_margin_mm = np.array(autocrop_margin_mm)
self.texture_analysis = texture_analysis
self.segmentation_smoothing = segmentation_smoothing
self.smoothing_mm = smoothing_mm
self.volume_blowup = volume_blowup
self.edit_data = edit_data
self.roi = roi
self.data3d = data3d
self.seeds = seeds
self.segmentation = None
self.processing_time = None
self.experiment_caption = experiment_caption
self.lisa_operator_identifier = lisa_operator_identifier
self.version = qmisc.getVersionString()
self.viewermax = viewermax
self.viewermin = viewermin
self.volume_unit = volume_unit
self.organ_interactivity_counter = 0
self.dcmfilelist = None
self.save_filetype = save_filetype
#
oseg_input_params = locals()
oseg_input_params = self.__clean_oseg_input_params(oseg_input_params)
logger.debug("oseg_input_params")
logger.debug(str(oseg_input_params))
self.oseg_input_params = oseg_input_params
if data3d is None or metadata is None:
# if 'datapath' in self.iparams:
# datapath = self.iparams['datapath']
if datapath is not None:
reader = datareader.DataReader()
datap = reader.Get3DData(datapath, dataplus_format=True)
# self.iparams['series_number'] = metadata['series_number']
# self.iparams['datapath'] = datapath
self.import_dataplus(datap)
else:
# data will be selected from gui
pass
# logger.error('No input path or 3d data')
else:
# self.data3d = data3d
# default values are updated in next line
mindatap = {'series_number': -1,
'voxelsize_mm': 1,
'datapath': None,
'data3d': data3d
}
mindatap.update(metadata)
self.import_dataplus(mindatap)
def __init__(
self,
datapath=None,
working_voxelsize_mm=3,
viewermax=None,
viewermin=None,
series_number=None,
autocrop=True,
autocrop_margin_mm=[10, 10, 10],
manualroi=False,
texture_analysis=None,
segmentation_smoothing=False,
smoothing_mm=4,
volume_blowup=1.00,
data3d=None,
metadata=None,
seeds=None,
edit_data=False,
segparams={},
segmodelparams=default_segmodelparams,
roi=None,
output_label=1,
slab={},
output_datapath=None,
input_datapath_start='',
experiment_caption='',
lisa_operator_identifier='',
volume_unit='ml',
save_filetype='pklz',
debug_mode=False,
seg_postproc_pars={},
cache_filename='cache.yml',
seg_preproc_pars={},
# iparams=None,
):
""" Segmentation of objects from CT data.
:param datapath: path to directory with dicom files
:param manualroi: manual set of ROI before data processing, there is a
problem with correct coordinates
:param data3d, metadata: it can be used for data loading not from
directory. If both are setted, datapath is ignored
:param output_label: label for output segmented volume
:param slab: aditional label system for description segmented data
{'none':0, 'liver':1, 'lesions':6}
:param roi: region of interest.
[[startx, stopx], [sty, spy], [stz, spz]]
:param seeds: ndimage array with size same as data3d
:param experiment_caption = this caption is used for naming of outputs
:param lisa_operator_identifier: used for logging
:param input_datapath_start: Path where user directory selection dialog
starts.
:param volume_blowup: Blow up volume is computed in smoothing so it is
working only if smoothing is turned on.
:param seg_postproc_pars: Can be used for setting postprocessing
parameters. For example
"""
self.iparams = {}
self.datapath = datapath
self.output_datapath = output_datapath
self.input_datapath_start = input_datapath_start
self.crinfo = [[0, None], [0, None], [0, None]]
self.slab = data_plus.default_slab()
self.slab.update(slab)
self.output_label = output_label
self.working_voxelsize_mm = None
self.input_wvx_size = working_voxelsize_mm
# print segparams
# @TODO each axis independent alpha
self.segparams = default_segparams
self.segparams.update(segparams)
self.segmodelparams = default_segmodelparams
self.segmodelparams.update(segmodelparams)
self.series_number = series_number
self.autocrop = autocrop
self.autocrop_margin_mm = np.array(autocrop_margin_mm)
self.texture_analysis = texture_analysis
self.segmentation_smoothing = segmentation_smoothing
self.smoothing_mm = smoothing_mm
self.volume_blowup = volume_blowup
self.edit_data = edit_data
self.roi = roi
self.data3d = data3d
self.seeds = seeds
self.segmentation = None
self.processing_time = None
self.experiment_caption = experiment_caption
self.lisa_operator_identifier = lisa_operator_identifier
self.version = qmisc.getVersionString()
self.viewermax = viewermax
self.viewermin = viewermin
self.volume_unit = volume_unit
self.organ_interactivity_counter = 0
self.dcmfilelist = None
self.save_filetype = save_filetype
self.vessel_tree = {}
self.debug_mode = debug_mode
# SegPostprocPars = namedtuple(
# 'SegPostprocPars', [
# 'smoothing_mm',
# 'segmentation_smoothing',
# 'volume_blowup',
# 'snakes',
# 'snakes_method',
# 'snakes_params']
# )
self.cache = cachef.CacheFile(cache_filename)
self.seg_postproc_pars = {
'smoothing_mm': smoothing_mm,
'segmentation_smoothing': segmentation_smoothing,
'volume_blowup': volume_blowup,
'snakes': False,
'snakes_method': 'ACWE',
'snakes_params': {'smoothing': 1, 'lambda1': 100, 'lambda2': 1},
'snakes_niter': 20,
}
self.seg_postproc_pars.update(seg_postproc_pars)
self.seg_preproc_pars = {
'use_automatic_segmentation': True,
}
self.seg_preproc_pars.update(seg_preproc_pars)
# seg_postproc_pars.update(seg_postproc_pars)
# import ipdb; ipdb.set_trace() # noqa BREAKPOINT
# self.seg_postproc_pars = SegPostprocPars(**seg_postproc_pars_default)
#
oseg_input_params = locals()
oseg_input_params = self.__clean_oseg_input_params(oseg_input_params)
logger.debug("oseg_input_params")
logger.debug(str(oseg_input_params))
self.oseg_input_params = oseg_input_params
if data3d is None or metadata is None:
# if 'datapath' in self.iparams:
# datapath = self.iparams['datapath']
if datapath is not None:
reader = datareader.DataReader()
datap = reader.Get3DData(datapath, dataplus_format=True)
# self.iparams['series_number'] = metadata['series_number']
# self.iparams['datapath'] = datapath
self.import_dataplus(datap)
else:
# self.data3d = data3d
# default values are updated in next line
mindatap = {'series_number': -1,
'voxelsize_mm': 1,
'datapath': None,
'data3d': data3d
}
mindatap.update(metadata)
self.import_dataplus(mindatap)
def test_getVersionString(self):
"""
"""
verstr = qmisc.getVersionString()
self.assertTrue(type(verstr) == str)
