def buttonClicked(self):
print("button")
import sed3
import sed3.sed3
import numpy as np
img = np.zeros([10, 10, 15])
img[6:9, 2:7, 1:5] = 1
img[7:9, 3:9, 8:14] = 2
ed = sed3.sed3qt(img)
# ed.set_params(img)
if ed.exec_():
print("konec edu")
vals = ed.o
print(vals.seeds)
print(np.nonzero(ed.seeds))
# ed = sed3.sed3(img)
# ed.sed3_on_close = self.callback_close
# ed.show()
print("konec sed3")
def show(data3dw):
data3d = data3dw.datap["data3d"]
import sed3
ed = sed3.sed3qt(data3d)
# ed = sed3.se
# ed.show()
ed.exec_()
def compareSegmentationWithFile(self):
"""
Function make GUI for reading segmentaion file to compare it with
actual segmentation using Sliver methodics. It calls
organ_segmentation function to do the work.
"""
self.statusBar().showMessage('Reading segmentation from file ...')
QApplication.processEvents()
logger.debug("import segmentation from file to compare by sliver")
logger.debug(str(self.oseg.crinfo))
logger.debug(str(self.oseg.data3d.shape))
logger.debug(str(self.oseg.segmentation.shape))
if 'loadcomparedir' in self.oseg.cache.data.keys():
directory = self.oseg.cache.get('loadcomparedir')
else:
directory = self.oseg.input_datapath_start
#
seg_path = self.__get_datafile(
app=True,
directory=directory
)
if seg_path is None:
self.statusBar().showMessage('No data path specified!')
return
evaluation, segdiff = \
self.oseg.sliver_compare_with_other_volume_from_file(seg_path)
print 'Evaluation: ', evaluation
# print 'Score: ', score
text = self.__evaluation_to_text(evaluation)
segdiff[segdiff == -1] = 2
logger.debug('segdif unique ' + str(np.unique(segdiff)))
QApplication.processEvents()
try:
ed = sed3.sed3qt(
self.oseg.data3d,
seeds=segdiff,
# contour=(self.oseg.segmentation == self.oseg.slab['liver'])
contour=self.oseg.segmentation
)
ed.exec_()
except:
ed = sed3.sed3(
self.oseg.data3d,
seeds=segdiff,
contour=(self.oseg.segmentation == self.oseg.slab['liver'])
)
ed.show()
head, teil = os.path.split(seg_path)
self.oseg.cache.update('loadcompatext_seg_dataredir', head)
self.setLabelText(self.segBody.lblSegData, text)
self.statusBar().showMessage('Ready')
def compareSegmentationWithFile(self):
"""
Function make GUI for reading segmentaion file to compare it with
actual segmentation using Sliver methodics. It calls
organ_segmentation function to do the work.
"""
self.statusBar().showMessage('Reading segmentation from file ...')
QApplication.processEvents()
logger.debug("import segmentation from file to compare by sliver")
logger.debug(str(self.oseg.crinfo))
logger.debug(str(self.oseg.data3d.shape))
logger.debug(str(self.oseg.segmentation.shape))
if 'loadcomparedir' in self.oseg.cache.data.keys():
directory = self.oseg.cache.get('loadcomparedir')
else:
directory = self.oseg.input_datapath_start
#
seg_path = self.__get_datafile(
app=True,
directory=directory
)
if seg_path is None:
self.statusBar().showMessage('No data path specified!')
return
evaluation, segdiff = \
self.oseg.sliver_compare_with_other_volume_from_file(seg_path)
print 'Evaluation: ', evaluation
# print 'Score: ', score
text = self.__evaluation_to_text(evaluation)
segdiff[segdiff == -1] = 2
logger.debug('segdif unique ' + str(np.unique(segdiff)))
QApplication.processEvents()
try:
ed = sed3.sed3qt(
self.oseg.data3d,
seeds=segdiff,
# contour=(self.oseg.segmentation == self.oseg.slab['liver'])
contour=self.oseg.segmentation
)
ed.exec_()
except:
ed = sed3.sed3(
self.oseg.data3d,
seeds=segdiff,
contour=(self.oseg.segmentation == self.oseg.slab['liver'])
)
ed.show()
head, teil = os.path.split(seg_path)
self.oseg.cache.update('loadcomparedir', head)
# self.setLabelText(self.text_seg_data, text)
self.statusBar().showMessage('Ready')
def virtual_resection_visualization(data, segm, dist1, dist2, cut,
interactivity=True):
v1, v2 = liver_spit_volume_mm3(segm, data['voxelsize_mm'])
if interactivity:
print("Liver volume: %.4g l" % ((v1 + v2) * 1e-6))
print("volume1: %.4g l (%.3g %%)" % (
(v1) * 1e-6, 100 * v1 / (v1 + v2)))
print("volume2: %.4g l (%.3g %%)" % (
(v2) * 1e-6, 100 * v2 / (v1 + v2)))
# pyed = sed3.sed3(segm)
# pyed.show()
# import pdb; pdb.set_trace()
linie = (((data['segmentation'] != 0) *
(np.abs(dist1 - dist2) < 1))).astype(np.int8)
linie_vis = 2 * linie
linie_vis[cut == 1] = 1
linie_vis = linie_vis.astype(np.int8)
if interactivity:
pyed = sed3.sed3qt(
data['data3d'],
seeds=linie_vis,
contour=(data['segmentation'] != 0))
# pyed.show()
pyed.exec_()
# import pdb; pdb.set_trace()
# show3.show3(data['segmentation'])
slab = {
'liver': 1,
'porta': 2,
'resected_liver': 3,
'resected_porta': 4}
slab.update(data['slab'])
data['slab'] = slab
data['slab']['resected_liver'] = 3
data['slab']['resected_porta'] = 4
mask_resected_liver = (
(segm == 1) & (data['segmentation'] == data['slab']['liver']))
mask_resected_porta = (
(segm == 1) & (data['segmentation'] == data['slab']['porta']))
data['segmentation'][mask_resected_liver] = \
data['slab']['resected_liver']
data['segmentation'][mask_resected_porta] = \
data['slab']['resected_porta']
logger.debug('resection_old() end')
return data
def virtual_resection_visualization(data, segm, dist1, dist2, cut,
interactivity=True):
v1, v2 = liver_spit_volume_mm3(segm, data['voxelsize_mm'])
if interactivity:
print("Liver volume: %.4g l" % ((v1 + v2) * 1e-6))
print("volume1: %.4g l (%.3g %%)" % (
(v1) * 1e-6, 100 * v1 / (v1 + v2)))
print("volume2: %.4g l (%.3g %%)" % (
(v2) * 1e-6, 100 * v2 / (v1 + v2)))
# pyed = sed3.sed3(segm)
# pyed.show()
# import pdb; pdb.set_trace()
linie = (((data['segmentation'] != 0) *
(np.abs(dist1 - dist2) < 1))).astype(np.int8)
linie_vis = 2 * linie
linie_vis[cut == 1] = 1
linie_vis = linie_vis.astype(np.int8)
if interactivity:
pyed = sed3.sed3qt(
data['data3d'],
seeds=linie_vis,
contour=(data['segmentation'] != 0))
# pyed.show()
pyed.exec_()
# import pdb; pdb.set_trace()
# show3.show3(data['segmentation'])
slab = {
'liver': 1,
'porta': 2,
'resected_liver': 3,
'resected_porta': 4}
slab.update(data['slab'])
data['slab'] = slab
data['slab']['resected_liver'] = 3
data['slab']['resected_porta'] = 4
mask_resected_liver = (
(segm == 1) & (data['segmentation'] == data['slab']['liver']))
mask_resected_porta = (
(segm == 1) & (data['segmentation'] == data['slab']['porta']))
data['segmentation'][mask_resected_liver] = \
data['slab']['resected_liver']
data['segmentation'][mask_resected_porta] = \
data['slab']['resected_porta']
logger.debug('resection_old() end')
return data
def cut_editor_old(data, label=None):
logger.debug("editor input label: " + str(label))
if label is None:
contour=data['segmentation']
else:
if type(label) == str:
label = data['slab'][label]
contour=(data['segmentation'] == label).astype(np.int8)
pyed = sed3.sed3qt(data['data3d'], contour=contour)
pyed.exec_()
return pyed.seeds
def cut_editor_old(data, label=None):
logger.debug("editor input label: " + str(label))
if label is None:
contour = data['segmentation']
else:
if type(label) == str:
label = data['slab'][label]
contour = (data['segmentation'] == label).astype(np.int8)
pyed = sed3.sed3qt(data['data3d'], contour=contour)
pyed.exec_()
return pyed.seeds
def vesselSegmentation(data,
segmentation=-1,
threshold=None,
voxelsize_mm=[1, 1, 1],
inputSigma=-1,
aoi_dilation_iterations=0,
aoi_dilation_structure=None,
nObj=10,
biggestObjects=False,
useSeedsOfCompactObjects=False,
seeds=None,
interactivity=True,
binaryClosingIterations=2,
binaryOpeningIterations=0,
smartInitBinaryOperations=False,
returnThreshold=False,
binaryOutput=True,
returnUsedData=False,
qapp=None,
auto_method='',
aoi_label=1,
forbidden_label=None,
slab=None,
old_gui=False,
debug=False):
"""
Vessel segmentation z jater.
Input:
:param data: - CT (nebo MRI) 3D data
:param segmentation: - zakladni oblast pro segmentaci, oznacena struktura se
:param stejnymi: rozmery jako "data",
kde je oznaceni (label) jako:
1 jatra,
-1 zajimava tkan (kosti, ...)
0 jinde
:param threshold: - prah
:param voxelsize_mm: - (vektor o hodnote 3) rozmery jednoho voxelu
:param inputSigma: - pocatecni hodnota pro prahovani
:param aoi_dilation_iterations: - pocet operaci dilation nad zakladni oblasti pro
segmentaci ("segmantation")
:param aoi_dilation_structure: - struktura pro operaci dilation
:param nObj: - oznacuje, kolik nejvetsich objektu se ma vyhledat - pokud je
rovno 0 (nule), vraci cela data
:param biggestObjects: - moznost, zda se maji vracet nejvetsi objekty nebo ne
:param seeds: - moznost zadat pocatecni body segmentace na vstupu. Je to matice
o rozmerech jako data. Vsude nuly, tam kde je oznaceni jsou jednicky
It can be same shape like data, or it can be
indexes e.g. from np.nonzero(seeds)
:param interactivity: - nastavi, zda ma nebo nema byt pouzit interaktivni mod
upravy dat
:param binaryClosingIterations: - vstupni binary closing operations
:param binaryOpeningIterations: - vstupni binary opening operations
:param smartInitBinaryOperations: - logicka hodnota pro smart volbu pocatecnich
hodnot binarnich operaci (bin. uzavreni a bin. otevreni)
:param returnThreshold: - jako druhy parametr funkce vrati posledni hodnotu
prahu
:param binaryOutput: - zda ma byt vystup vracen binarne nebo ne (binarnim
vystupem se rozumi: cokoliv jineho nez hodnota 0 je hodnota 1)
:param returnUsedData: - vrati pouzita data
:param aoi_label: label of organ where is the target vessel
:param forbidden_label: int or list of ints. Labels of areas which are not used for segmentation.
Output:
filtrovana data
"""
# self.qapp = qapp
dim = numpy.ndim(data)
logger.debug('Dimenze vstupnich dat: ' + str(dim))
if (dim < 2) or (dim > 3):
logger.debug('Nepodporovana dimenze dat!')
logger.debug('Ukonceni funkce!')
return None
if seeds is None:
logger.debug('Funkce spustena bez prioritnich objektu!')
if biggestObjects:
logger.debug(
'Funkce spustena s vracenim nejvetsich objektu => nebude mozne\
vybrat prioritni objekty!')
if (nObj < 1):
nObj = 1
if biggestObjects:
logger.debug('Vybrano objektu k vraceni: ' + str(nObj))
logger.debug('Pripravuji data...')
voxel = numpy.array(voxelsize_mm)
# Kalkulace objemove jednotky (voxel) (V = a*b*c).
voxel1 = voxel[0] # [0]
voxel2 = voxel[1] # [0]
voxel3 = voxel[2] # [0]
voxelV = voxel1 * voxel2 * voxel3
# number je zaokrohleny 2x nasobek objemove jednotky na 2 desetinna mista.
# number stanovi doporucenou horni hranici parametru gauss. filtru.
number = (numpy.round((2 * voxelV**(1.0 / 3.0)), 2))
if aoi_label is None:
target_organ_segmentation = np.ones(segmentation.shape)
else:
target_organ_segmentation = image_manipulation.select_labels(
segmentation, aoi_label, slab)
# Operace dilatace (dilation) nad samotnymi jatry ("segmentation").
if (aoi_dilation_iterations > 0.0):
target_organ_segmentation = scipy.ndimage.binary_dilation(
input=target_organ_segmentation,
structure=aoi_dilation_structure,
iterations=aoi_dilation_iterations)
# remove forbidden areas from segmentation
if forbidden_label is not None:
forbidden_organ_segmentation = image_manipulation.select_labels(
segmentation, forbidden_label, slab)
target_organ_segmentation[forbidden_organ_segmentation] = 0
del (forbidden_organ_segmentation)
# Ziskani datove oblasti jater (bud pouze jater nebo i jejich okoli -
# zalezi, jakym zpusobem bylo nalozeno s operaci dilatace dat).
preparedData = (data * (target_organ_segmentation)) # .astype(numpy.float)
logger.debug('Typ vstupnich dat: ' + str(preparedData.dtype))
# if preparedData.dtype != numpy.uint8:
# print 'Data nejsou typu numpy.uint8 => muze dojit k errorum'
if not numpy.can_cast(preparedData.dtype, numpy.float):
logger.debug(
'ERROR: (debug message) Data nejsou takoveho typu, aby se daly \
prevest na typ "numpy.float" => muze dojit k errorum')
logger.debug('Ukoncuji funkci!')
raise ValueError("Cannot cast input data to numpy.float")
return None
if (preparedData == False).all():
logger.debug(
'ERROR: (debug message) Jsou spatna data nebo segmentacni matice: \
all is true == data is all false == bad segmentation matrix (if data matrix is \
ok)')
logger.debug('Ukoncuji funkci!')
raise ValueError(
"Wrong input data. All is true == data is all false == bad segmentation matrix (if data matrix is ok)"
)
return None
# del(data)
# del(segmentation)
# Nastaveni rozmazani a prahovani dat.
if (inputSigma == -1):
inputSigma = number
if (inputSigma > number):
inputSigma = number
# seeds = None
if biggestObjects == False and\
seeds is None and interactivity == True and threshold is None:
if old_gui:
logger.debug((
'Nyni si levym nebo pravym tlacitkem mysi (klepnutim nebo tazenim)\
oznacte specificke oblasti k vraceni.'))
import sed3
pyed = sed3.sed3qt(preparedData,
contour=segmentation,
windowW=400,
windowC=50)
# pyed.show()
pyed.exec_()
# from PyQt4.QtCore import pyqtRemoveInputHook
# pyqtRemoveInputHook()
# import ipdb; ipdb.set_trace() # noqa BREAKPOINT
seeds = pyed.seeds
# Zkontrolovat, jestli uzivatel neco vybral - nejaky item musi byt
# ruzny od nuly.
if (seeds != 0).any() == False:
seeds = None
logger.debug(
'Zadne seedy nezvoleny => nejsou prioritni objekty.')
else:
# seeds * (seeds != 0) ## seeds je n-tice poli indexu nenulovych
# prvku => item krychle je == krychle[ seeds[0][x], seeds[1][x],
# seeds[2][x] ]
seeds = seeds.nonzero()
logger.debug('Seedu bez nul: ' + str(len(seeds[0])))
closing = binaryClosingIterations
opening = binaryOpeningIterations
if (smartInitBinaryOperations and interactivity):
if (seeds == None): # noqa
closing = 5
opening = 1
else:
closing = 2
opening = 0
# Samotne filtrovani
if interactivity:
if old_gui:
uiT = uiThreshold.uiThresholdQt(
preparedData,
voxel=voxel,
threshold=threshold,
interactivity=interactivity,
number=number,
inputSigma=inputSigma,
nObj=nObj,
biggestObjects=biggestObjects,
useSeedsOfCompactObjects=useSeedsOfCompactObjects,
binaryClosingIterations=closing,
binaryOpeningIterations=opening,
seeds=seeds,
threshold_auto_method=auto_method,
)
output = uiT.run()
else:
# TODO use all parameters
import seededitorqt
se = seededitorqt.QTSeedEditor(preparedData, voxelSize=voxel)
import imtools.threshold_qsed_plugin
wg0 = imtools.threshold_qsed_plugin.QtSEdThresholdPlugin(
nObj=nObj, debug=debug)
se.addPlugin(wg0)
se.exec_()
output = se.getContours()
else:
uiT = uiThreshold.uiThreshold(
preparedData,
voxel=voxel,
threshold=threshold,
interactivity=interactivity,
number=number,
inputSigma=inputSigma,
nObj=nObj,
biggestObjects=biggestObjects,
useSeedsOfCompactObjects=useSeedsOfCompactObjects,
binaryClosingIterations=closing,
binaryOpeningIterations=opening,
seeds=seeds,
threshold_auto_method=auto_method,
)
output = uiT.run()
# Vypocet binarni matice.
if output is None: # noqa
logger.debug('Zadna data k vraceni! (output == None)')
elif binaryOutput:
output[output != 0] = 1
# Vraceni matice.
if returnThreshold:
if returnUsedData:
return preparedData, output, uiT.returnLastThreshold()
else:
return output, uiT.returnLastThreshold()
else:
if returnUsedData:
return preparedData, output
else:
return output
def cut_editor_old(data):
pyed = sed3.sed3qt(data['data3d'], contour=data['segmentation'])
pyed.exec_()
return pyed.seeds
def cut_editor_old(data):
pyed = sed3.sed3qt(data['data3d'], contour=data['segmentation'])
pyed.exec_()
return pyed.seeds
