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organ_segmentation.py
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organ_segmentation.py
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# /usr/bin/env python
# -*- coding: utf-8 -*-
"""
LISA - organ segmentation tool.
Liver Surgery Analyser
python organ_segmentation.py
python organ_segmentation.py -mroi -vs 0.6
Author: Miroslav Jirik
Email: miroslav.jirik@gmail.com
"""
import logging
logger = logging.getLogger(__name__)
import sys
import os
import os.path as op
# from collections import namedtuple
import exceptionProcessing
# from scipy.io import loadmat, savemat
import scipy
import scipy.ndimage
import numpy as np
import datetime
import argparse
import copy
# tady uz je logger
# import dcmreaddata as dcmreader
# from pysegbase import pycut
# try:
# import pysegbase # noqa
# from pysegbase import pycut
# except:
# path_to_script = os.path.dirname(os.path.abspath(__file__))
# sys.path.append(os.path.join(path_to_script, "../extern/pyseg_base/src"))
# logger.warning("Deprecated of pyseg_base as submodule")
# import traceback
# traceback.print_exc()
# import pycut
path_to_script = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(path_to_script, "../../pysegbase/"))
import volumetry_evaluation
# from seg2fem import gen_mesh_from_voxels, gen_mesh_from_voxels_mc
# from viewer import QVTKViewer
import qmisc
import misc
import config
from io3d import datareader
from io3d import datawriter
import data_plus
import support_structure_segmentation as sss
import cachefile as cachef
import config_default
import organ_seeds
import lisa_data
# import audiosupport
# import skimage
# import skimage.transform
scaling_modes = {
'original': (None, None, None),
'double': (None, 'x2', 'x2'),
'3mm': (None, '3', '3')
}
# Defaultparameters for segmentation
# version comparison
from pkg_resources import parse_version
import sklearn
if parse_version(sklearn.__version__) > parse_version('0.10'):
# new versions
cvtype_name = 'covariance_type'
else:
cvtype_name = 'cvtype'
default_segmodelparams = {
'type': 'gmmsame',
'params': {cvtype_name: 'full', 'n_components': 3}
}
config_version = [1, 0, 0]
def import_gui():
# from lisaWindow import OrganSegmentationWindow
# from PyQt4.QtGui import QApplication, QMainWindow, QWidget,\
# QGridLayout, QLabel, QPushButton, QFrame, \
# QFont, QPixmap
# from PyQt4.Qt import QString
pass
def printTotals(transferred, toBeTransferred):
print "Transferred: {0}\tOut of: {1}".format(transferred, toBeTransferred)
class OrganSegmentation():
"""
Main object of Lisa user interface.
"""
def set_params(self, *args, **kwargs):
"""
Function set parameters in same way as constructor does
:param args:
:param kwargs:
:return:
"""
self.__init__(*args, **kwargs)
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={},
after_load_processing={},
segmentation_alternative_params={},
sftp_username='lisa_default',
sftp_password=''
# 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
:param segmentation_alternative_params: dict of alternative params f,e.
{'vs5: {'voxelsize_mm':[5,5,5]}, 'vs3: {'voxelsize_mm':[3,3,3]}}
"""
from pysegbase import pycut
default_segparams = {
'method': pycut.methods[0],
'pairwise_alpha_per_mm2': 40,
'use_boundary_penalties': False,
'boundary_penalties_sigma': 50}
self.iparams = {}
self.datapath = datapath
self.set_output_datapath(output_datapath)
self.sftp_username=sftp_username
self.sftp_password=sftp_password
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()
# if self.version is None:
self.version = "1.9.0"
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
self.segmentation_alternative_params = segmentation_alternative_params
# 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,
# 'postproc_working_voxelsize': [1.0, 1.0, 1.0],
'postproc_working_voxelsize': 'orig',
}
self.seg_postproc_pars.update(seg_postproc_pars)
self.seg_preproc_pars = {
'use_automatic_segmentation': True,
}
self.seg_preproc_pars.update(seg_preproc_pars)
self.after_load_processing={
'run_automatic_liver_seeds': False,
}
self.after_load_processing.update(after_load_processing)
self.apriori = None
# 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)
# self.iparams['series_number'] = self.metadata['series_number']
# self.iparams['datapath'] = self.metadata['datapath']
# self.import_dataplus()
# def importDataPlus(self, datap):
# """
# Function for input data
# """
# self.data3d = datap['data3d']
# self.crinfo = datap['crinfo']
# self.segmentation = datap['segmentation']
# self.slab = datap['slab']
# self.voxelsize_mm = datap['voxelsize_mm']
# self.orig_shape = datap['orig_shape']
# self.seeds = datap[
# 'processing_information']['organ_segmentation']['seeds']
def set_output_datapath(self, output_datapath):
if output_datapath is None:
output_datapath = '~/lisa_data'
self.output_datapath = os.path.expanduser(output_datapath)
def update(self):
import update_stable
update_stable.make_update()
# import subprocess
# print subprocess.call(['conda', 'update', '-y', '-c', 'mjirik', '-c', 'SimpleITK', 'lisa']) #, shell=True)
def update_parameters_based_on_label(self, label):
self.update_parameters(self.segmentation_alternative_params[label])
def update_parameters(self, params):
if 'segmodelparams' in params.keys():
self.segmodelparams = params['segmodelparams']
logger.debug('segmodelparams updated')
if 'output_label' in params.keys():
self.output_label = params['output_label']
logger.debug('output_label updated')
if 'working_voxelsize_mm' in params.keys():
self.input_wvx_size = copy.copy(params['working_voxelsize_mm'])
self.working_voxelsize_mm = params['working_voxelsize_mm']
vx_size = self.working_voxelsize_mm
if np.isscalar(vx_size):
vx_size = ([vx_size] * 3)
vx_size = np.array(vx_size).astype(float)
self.working_voxelsize_mm = vx_size
logger.debug('working_voxelsize_mm updated')
if 'smoothing_mm' in params.keys():
self.smoothing_mm = params['smoothing_mm']
logger.debug('smoothing_mm updated')
if 'seg_postproc_pars' in params.keys():
self.seg_postproc_pars = params['seg_postproc_pars']
logger.debug('seg_postproc_pars updated')
if 'clean_seeds_after_update_parameters' in params.keys():
if self.seeds is not None:
self.seeds[...] = 0
logger.debug('clean_seeds_after_update_parameters')
def run_sss(self):
sseg = sss.SupportStructureSegmentation(
data3d=self.data3d,
voxelsize_mm=self.voxelsize_mm,
)
sseg.run()
# sseg.bone_segmentation()
# sseg.lungs_segmentation()
# sseg.heart_segmentation()
# TODO remove hack - force remove number 1 from segmentation
# this sould be fixed in sss
sseg.segmentation[sseg.segmentation == 1] = 0
self.segmentation = sseg.segmentation
self.slab = sseg.slab
def __clean_oseg_input_params(self, oseg_params):
"""
Used for storing input params of organ segmentation. Big data are not
stored due to big memory usage.
"""
oseg_params['data3d'] = None
oseg_params['segmentation'] = None
oseg_params.pop('self')
oseg_params.pop('pycut')
return oseg_params
def process_wvx_size_mm(self, metadata):
"""This function does something.
Args:
name (str): The name to use.
Kwargs:
state (bool): Current state to be in.
"""
# vx_size = self.working_voxelsize_mm
vx_size = self.input_wvx_size
if vx_size == 'orig':
vx_size = metadata['voxelsize_mm']
elif vx_size == 'orig*2':
vx_size = np.array(metadata['voxelsize_mm']) * 2
elif vx_size == 'orig*4':
vx_size = np.array(metadata['voxelsize_mm']) * 4
if np.isscalar(vx_size):
vx_size = ([vx_size] * 3)
vx_size = np.array(vx_size).astype(float)
# if np.isscalar(vx_sizey):
# vx_size = (np.ones([3]) *vx_size).astype(float)
# self.iparams['working_voxelsize_mm'] = vx_size
self.working_voxelsize_mm = vx_size
# return vx_size
def __volume_blowup_criterial_function(self, threshold, wanted_volume,
segmentation_smooth
):
segm = (1.0 * segmentation_smooth > threshold).astype(np.int8)
vol2 = np.sum(segm)
criterium = (wanted_volume - vol2) ** 2
return criterium
def sliver_compare_with_other_volume_from_file(self, filepath):
reader = datareader.DataReader()
segmentation_datap = reader.Get3DData(filepath, dataplus_format=True)
evaluation = self.sliver_compare_with_other_volume(segmentation_datap)
return evaluation
def sliver_compare_with_other_volume(self, segmentation_datap):
"""
Compares actual Lisa data with other which are given by
segmentation_datap. That means
segmentation_datap = {
'segmentation': 3d np.array,
'crinfo': information about crop (optional)
}
"""
# if there is no segmentation, data can be stored in data3d. It is the
# way how are data stored in sliver.
if 'segmentation' in segmentation_datap.keys():
segm_key = 'segmentation'
else:
segm_key = 'data3d'
if 'crinfo' in segmentation_datap.keys():
data3d_segmentation = qmisc.uncrop(
segmentation_datap[segm_key],
segmentation_datap['crinfo'],
self.orig_shape)
else:
data3d_segmentation = segmentation_datap[segm_key]
pass
# now we can uncrop actual Lisa data
data3d_segmentation_actual = qmisc.uncrop(
self.segmentation,
self.crinfo,
self.orig_shape)
label1 = 1
label2 = 1
# TODO make GUI in Qt
from PyQt4.QtCore import pyqtRemoveInputHook
pyqtRemoveInputHook()
print 'unique data1 ', np.unique(data3d_segmentation_actual)
print 'unique data2 ', np.unique(data3d_segmentation)
print "set label1 and label2"
print "then press 'c' and 'Enter'"
import ipdb; ipdb.set_trace() # noqa BREAKPOINT
evaluation = volumetry_evaluation.compare_volumes_sliver(
data3d_segmentation_actual == label1,
data3d_segmentation == label2,
self.voxelsize_mm
)
# score = volumetry_evaluation.sliver_score_one_couple(evaluation)
segdiff = qmisc.crop(
((data3d_segmentation) - data3d_segmentation_actual),
self.crinfo)
return evaluation, segdiff
def segm_smoothing(self, sigma_mm):
"""
Shape of output segmentation is smoothed with gaussian filter.
Sigma is computed in mm
"""
# import scipy.ndimage
sigma = float(sigma_mm) / np.array(self.voxelsize_mm)
# print sigma
# from PyQt4.QtCore import pyqtRemoveInputHook
# pyqtRemoveInputHook()
vol1 = np.sum(self.segmentation)
wvol = vol1 * self.volume_blowup
logger.debug('unique segm ' + str(np.unique(self.segmentation)))
segsmooth = scipy.ndimage.filters.gaussian_filter(
self.segmentation.astype(np.float32), sigma)
logger.debug('unique segsmooth ' + str(np.unique(segsmooth)))
# import ipdb; ipdb.set_trace()
# import pdb; pdb.set_trace()
# pyed = sed3.sed3(self.orig_scale_segmentation)
# pyed.show()
logger.debug('wanted volume ' + str(wvol))
logger.debug('sigma ' + str(sigma))
critf = lambda x: self.__volume_blowup_criterial_function(
x, wvol, segsmooth)
thr = scipy.optimize.fmin(critf, x0=0.5, disp=False)[0]
logger.debug('optimal threshold ' + str(thr))
logger.debug('segsmooth ' + str(np.nonzero(segsmooth)))
self.segmentation = (1.0 *
(segsmooth > thr) # self.volume_blowup)
).astype(np.int8)
vol2 = np.sum(self.segmentation)
logger.debug("volume ratio " + str(vol2 / float(vol1)))
# import ipdb; ipdb.set_trace()
def import_segmentation_from_file(self, filepath):
"""
Loads data from file. Expected are uncropped data.
"""
# logger.debug("import segmentation from file")
# logger.debug(str(self.crinfo))
reader = datareader.DataReader()
datap = reader.Get3DData(filepath, dataplus_format=True)
segmentation = datap['data3d']
segmentation = qmisc.crop(segmentation, self.crinfo)
logger.debug(str(segmentation.shape))
self.segmentation = segmentation
def import_dataplus(self, dataplus):
datap = {
'dcmfilelist': None,
}
datap.update(dataplus)
dpkeys = datap.keys()
self.data3d = datap['data3d']
if self.roi is not None:
self.crop(self.roi)
self.voxelsize_mm = np.array(datap['voxelsize_mm'])
self.process_wvx_size_mm(datap)
self.autocrop_margin = self.autocrop_margin_mm / self.voxelsize_mm
if 'orig_shape' in dpkeys:
self.orig_shape = datap['orig_shape']
else:
self.orig_shape = self.data3d.shape
if 'crinfo' in dpkeys:
self.crinfo = datap['crinfo']
if 'slab' in dpkeys:
self.slab = datap['slab']
if ('segmentation' in dpkeys) and datap['segmentation'] is not None:
self.segmentation = datap['segmentation']
else:
self.segmentation = np.zeros(self.data3d.shape, dtype=np.int8)
if 'vessel_tree' in dpkeys:
self.vessel_tree = datap['vessel_tree']
if ('apriori' in dpkeys) and datap['apriori'] is not None:
self.apriori= datap['apriori']
else:
self.apriori = None
self.dcmfilelist = datap['dcmfilelist']
self.segparams['pairwise_alpha'] = \
self.segparams['pairwise_alpha_per_mm2'] / \
np.mean(self.working_voxelsize_mm)
self.__import_dataplus_seeds(datap)
# chci, abych nepřepisoval uložené seedy
if self.after_load_processing['run_automatic_liver_seeds']:
if self.seeds is None or (self.seeds == 0).all():
self.automatic_liver_seeds()
# try read prev information about time processing
try:
time_prev = datap['processing_information']['processing_time']
self.processing_time = time_prev
self.time_start = datetime.datetime.now() - time_prev
except:
self.time_start = datetime.datetime.now()
def __import_dataplus_seeds(self, datap):
"""
:type self: seeds are changed
"""
try:
self.seeds = datap['processing_information'][
'organ_segmentation']['seeds']
except:
logger.info('seeds not found in dataplus')
# if dicomdir is readed after something with seeds, seeds needs to be reseted
# self.seeds = None
# for each mm on boundary there will be sum of penalty equal 10
if self.seeds is None:
logger.debug("Seeds are generated")
self.seeds = np.zeros(self.data3d.shape, dtype=np.int8)
logger.debug("unique seeds labels " + str(np.unique(self.seeds)))
logger.info('dir ' + str(self.datapath) + ", series_number" +
str(datap['series_number']) + 'voxelsize_mm' +
str(self.voxelsize_mm))
def crop(self, tmpcrinfo):
"""
Function makes crop of 3d data and seeds and stores it in crinfo.
tmpcrinfo: temporary crop information
"""
# print ('sedds ', str(self.seeds.shape), ' se ',
# str(self.segmentation.shape), ' d3d ', str(self.data3d.shape))
self.data3d = qmisc.crop(self.data3d, tmpcrinfo)
# No, size of seeds should be same as data3d
if self.seeds is not None:
self.seeds = qmisc.crop(self.seeds, tmpcrinfo)
if self.segmentation is not None:
self.segmentation = qmisc.crop(self.segmentation, tmpcrinfo)
self.crinfo = qmisc.combinecrinfo(self.crinfo, tmpcrinfo)
logger.debug("crinfo " + str(self.crinfo))
# print '----sedds ', self.seeds.shape, ' se ',
# self.segmentation.shape,\
# ' d3d ', self.data3d.shape
def _interactivity_begin(self):
from pysegbase import pycut
logger.debug('_interactivity_begin()')
# TODO make copy and work with it
# TODO really make the copy and work with it
data3d_tmp = self.data3d
if self.seg_preproc_pars['use_automatic_segmentation']:
data3d_tmp = self.data3d.copy()
data3d_tmp[(self.segmentation > 0) & (self.segmentation != self.output_label)] = -1000
# print 'zoom ', self.zoom
# print 'svs_mm ', self.working_voxelsize_mm
self.zoom = self.voxelsize_mm / (1.0 * self.working_voxelsize_mm)
data3d_res = scipy.ndimage.zoom(
self.data3d,
self.zoom,
mode='nearest',
order=1
).astype(np.int16)
# data3d_res = data3d_res.astype(np.int16)
logger.debug('pycut segparams ' + str(self.segparams) +
'\nmodelparams ' + str(self.segmodelparams)
)
# insert feature function instead of string description
import organ_model
self.segmodelparams = organ_model.add_fv_extern_into_modelparams(self.segmodelparams)
if 'method' not in self.segparams.keys() or\
self.segparams['method'] in pycut.methods:
igc = pycut.ImageGraphCut(
# self.data3d,
data3d_res,
segparams=self.segparams,
voxelsize=self.working_voxelsize_mm,
modelparams=self.segmodelparams,
volume_unit='ml'
# oxelsize=self.voxelsize_mm
)
# elif self.segparams['method'] == '':
else:
import liver_segmentation
igc = liver_segmentation.LiverSegmentation(
data3d_res,
voxelsize_mm=self.working_voxelsize_mm,
segparams=self.segparams
)
if self.apriori is not None:
apriori_res = misc.resize_to_shape(
# seeds_res = scipy.ndimage.zoom(
self.apriori,
data3d_res.shape,
)
igc.apriori = apriori_res
# igc.modelparams = self.segmodelparams
# @TODO uncomment this for kernel model
# igc.modelparams = {
# 'type': 'kernel',
# 'params': {}
# }
# if self.iparams['seeds'] is not None:
if self.seeds is not None:
seeds_res = misc.resize_to_shape(
# seeds_res = scipy.ndimage.zoom(
self.seeds,
data3d_res.shape,
mode='nearest',
order=0
)
seeds_res = seeds_res.astype(np.int8)
igc.set_seeds(seeds_res)
# tohle je tu pro to, aby bylo možné přidávat nově objevené segmentace k těm starým
# jinak jsou stará data přepsána
if self.segmentation is not None:
self.segmentation_prev = copy.copy(self.segmentation)
else:
self.segmentation_prev = None
return igc
def sync_lisa_data(self, username, password, host="147.228.47.162", callback=printTotals):
self.sftp_username = username
self.create_lisa_data_dir_tree()
import sftpsync
import paramiko
paramiko_log = os.path.join(self.output_datapath, 'paramiko.log')
paramiko.util.log_to_file(paramiko_log)
sftp = sftpsync.Sftp(host=host, username=username, password=password)
localfrom = self._output_datapath_from_server.replace(os.sep, '/')
localto = self._output_datapath_to_server.replace(os.sep, '/')
# this makes sure that all paths ends with slash
if not localfrom.endswith('/'):
localfrom += '/'
if not localto.endswith('/'):
localto += '/'
remotefrom = "from_server/"
remoteto = "to_server/"
exclude = []
logger.info("Download started\nremote from {}\nlocal from {}".format(remotefrom, localfrom))
logger.info("from")
sftp.sync(remotefrom, localfrom, download=True, exclude=exclude, delete=False, callback=callback)
logger.info("Download finished")
logger.info("Upload started\nremote to {}\nlocal to {}".format(remoteto, localto))
sftp.sync(localto, remoteto, download=False, exclude=exclude, delete=False, callback=callback)
logger.info("Upload finished")
def __resize_to_orig(self, igc_seeds):
# @TODO remove old code in except part
self.segmentation = misc.resize_to_shape(
self.segmentation,
self.data3d.shape,
self.zoom
)
self.seeds = misc.resize_to_shape(
igc_seeds,
self.data3d.shape,
self.zoom
).astype(np.uint8)
# try:
# # rint 'pred vyjimkou'
# # aise Exception ('test without skimage')
# # rint 'za vyjimkou'
# import skimage
# import skimage.transform
# # Now we need reshape seeds and segmentation to original size
#
# segm_orig_scale = skimage.transform.resize(
# self.segmentation, self.data3d.shape, order=0,
# preserve_range=True
# )
#
# seeds = skimage.transform.resize(
# igc_seeds, self.data3d.shape, order=0,
# preserve_range=True
# )
#
# # self.segmentation = segm_orig_scale
# self.seeds = seeds
# logger.debug('resize to orig with skimage')
# except:
#
# segm_orig_scale = scipy.ndimage.zoom(
# self.segmentation,
# 1.0 / self.zoom,
# mode='nearest',
# order=0
# ).astype(np.int8)
# seeds = scipy.ndimage.zoom(
# igc_seeds,
# 1.0 / self.zoom,
# mode='nearest',
# order=0
# )
# logger.debug('resize to orig with scipy.ndimage')
#
# # @TODO odstranit hack pro oříznutí na stejnou velikost
# # v podstatě je to vyřešeno, ale nechalo by se to dělat elegantněji v zoom
# # tam je bohužel patrně bug
# # rint 'd3d ', self.data3d.shape
# # rint 's orig scale shape ', segm_orig_scale.shape
# shp = [
# np.min([segm_orig_scale.shape[0], self.data3d.shape[0]]),
# np.min([segm_orig_scale.shape[1], self.data3d.shape[1]]),
# np.min([segm_orig_scale.shape[2], self.data3d.shape[2]]),
# ]
# # elf.data3d = self.data3d[0:shp[0], 0:shp[1], 0:shp[2]]
# # mport ipdb; ipdb.set_trace() # BREAKPOINT
#
# self.segmentation = np.zeros(self.data3d.shape, dtype=np.int8)
# self.segmentation[
# 0:shp[0],
# 0:shp[1],
# 0:shp[2]] = segm_orig_scale[0:shp[0], 0:shp[1], 0:shp[2]]
#
# del segm_orig_scale
#
# self.seeds[
# 0:shp[0],
# 0:shp[1],
# 0:shp[2]] = seeds[0:shp[0], 0:shp[1], 0:shp[2]]
def _interactivity_end(self, igc):
"""
This is called after processing step. All data are rescaled to original
resolution.
"""
logger.debug('_interactivity_end()')
self.__resize_to_orig(igc.seeds)
self.organ_interactivity_counter = igc.interactivity_counter
logger.debug("org inter counter " +
str(self.organ_interactivity_counter))
logger.debug('nonzero segm ' + str(np.nonzero(self.segmentation)))
# if False:
if False:
# TODO dodělat postprocessing PV
import segmentation
outputSegmentation = segmentation.vesselSegmentation( # noqa
self.data3d,
self.segmentation,
threshold=-1,
inputSigma=0.15,
dilationIterations=10,
nObj=1,
biggestObjects=False,
seeds=(self.segmentation > 0).astype(np.int8),
useSeedsOfCompactObjects=True,
interactivity=True,
binaryClosingIterations=2,
binaryOpeningIterations=0)
self._segmentation_postprocessing()
# rint 'autocrop', self.autocrop
if self.autocrop is True:
# rint
# mport pdb; pdb.set_trace()
tmpcrinfo = qmisc.crinfo_from_specific_data(
self.segmentation,
self.autocrop_margin)
self.crop(tmpcrinfo)
if self.texture_analysis not in (None, False):
import texture_analysis
# doplnit nějaký kód, parametry atd
# elf.orig_scale_segmentation =
# texture_analysis.segmentation(self.data3d,
# self.orig_scale_segmentation, params = self.texture_analysis)
self.segmentation = texture_analysis.segmentation(
self.data3d,
self.segmentation,
self.voxelsize_mm
)
# set label number
# !! pomaly!!!
# @TODO make faster
# spojení staré a nové segmentace
if self.segmentation_prev is None:
# pokud neznáme žádnou předchozí segmentaci, tak se chováme jako dříve
self.segmentation[self.segmentation == 1] = self.output_label
else:
# remove old pixels for this label
self.segmentation_prev[self.segmentation_prev == self.output_label] = 0
# set new labels
self.segmentation_prev[np.where(self.segmentation == 1)] = self.output_label
# clean up
self.segmentation = self.segmentation_prev
self.segmentation_prev = None
#
logger.debug('self.slab')
logger.debug(str(self.slab))
self.processing_time = (
datetime.datetime.now() - self.time_start).total_seconds()
logger.debug('processing_time = ' + str(self.processing_time))
def _segmentation_postprocessing(self):
"""
:segmentation_smoothing:
"""
logger.debug(str(self.seg_postproc_pars))
if self.seg_postproc_pars['segmentation_smoothing']:
# if self.segmentation_smoothing:
self.segm_smoothing(self.seg_postproc_pars['smoothing_mm'])
if self.seg_postproc_pars['snakes']:
import morphsnakes as ms
logger.debug('Making snakes')
if self.seg_postproc_pars['snakes_method'] is 'ACWE':
method = ms.MorphACWE
elif self.seg_postproc_pars['snakes_method'] is 'GAC':
method = ms.MorphGAC
else:
logger.error('Unknown snake method')
return
sp = self.seg_postproc_pars['snakes_params']
if 'seeds' in sp.keys() and sp['seeds'] is True:
sp['seeds'] = self.seeds
logger.debug('snakes')
d3d = qmisc.resize_to_mm(
self.data3d,
self.voxelsize_mm,
self.seg_postproc_pars['postproc_working_voxelsize'])
segw = qmisc.resize_to_mm(
self.segmentation,
self.voxelsize_mm,
self.seg_postproc_pars['postproc_working_voxelsize'])
macwe = method(
d3d,
# self.data3d,
**self.seg_postproc_pars['snakes_params']
)
macwe.levelset = (
# self.segmentation == self.slab['liver']
segw == self.slab['liver']
).astype(np.uint8)
macwe.run(self.seg_postproc_pars['snakes_niter'])
seg = qmisc.resize_to_shape(macwe.levelset, self.data3d.shape)
# for debug visualization preprocessing use fallowing line
# self.segmentation[seg == 1] += 1
self.segmentation[seg == 1] = self.slab['liver']
logger.debug('postprocessing with snakes finished')
# def interactivity(self, min_val=800, max_val=1300):
# @TODO generovat QApplication
def interactivity(self, min_val=None, max_val=None):
try:
from pysegbase.seed_editor_qt import QTSeedEditor
except:
logger.warning("Deprecated of pyseg_base as submodule")
from seed_editor_qt import QTSeedEditor
import_gui()
logger.debug('interactivity')
# if self.edit_data:
# self.data3d = self.data_editor(self.data3d)
igc = self._interactivity_begin()
# from PyQt4.QtCore import pyqtRemoveInputHook
# pyqtRemoveInputHook()
# import ipdb; ipdb.set_trace() # noqa BREAKPOINT
pyed = QTSeedEditor(igc.img,
seeds=igc.seeds,