def test_synthetic_data_segmentation(self):
"""
Function uses organ_segmentation for synthetic box object
segmentation.
"""
data3d, segm, voxelsize_mm, slab = self.synthetic_data()
# @TODO je tam bug, prohlížeč neumí korektně pracovat s doubly
# app = QApplication(sys.argv)
# #pyed = QTSeedEditor(noise )
# pyed = QTSeedEditor(data3d)
# pyed.exec_()
# #img3d = np.zeros([256,256,80], dtype=np.int16)
# pyed = sed3.sed3(data3d)
# pyed.show()
outputTmp = segmentation.vesselSegmentation(
data3d, # .astype(np.uint8),
segmentation=(segm == slab['liver']), # .astype(np.uint8),
# segmentation = oseg.orig_scale_segmentation,
voxelsize_mm=voxelsize_mm,
threshold=180,
inputSigma=0.15,
dilationIterations=2,
nObj=1,
interactivity=False,
biggestObjects=True,
binaryClosingIterations=5,
binaryOpeningIterations=1)
# ověření výsledku
# pyed = sed3.sed3(outputTmp, contour=segm==slab['porta'])
# pyed.show()
# @TODO opravit chybu v vesselSegmentation
outputTmp = (outputTmp == 2)
errim = np.abs(
outputTmp.astype(np.int) - (segm == slab['porta']).astype(np.int)
)
# ověření výsledku
# pyed = sed3.sed3(errim, contour=segm==slab['porta'])
# pyed.show()
# evaluation
sum_of_wrong_voxels = np.sum(errim)
sum_of_voxels = np.prod(segm.shape)
# print "wrong ", sum_of_wrong_voxels
# print "voxels", sum_of_voxels
errorrate = sum_of_wrong_voxels/sum_of_voxels
# import pdb; pdb.set_trace()
self.assertLess(errorrate, 0.1)
def test_synthetic_data_segmentation(self):
"""
Function uses organ_segmentation for synthetic box object
segmentation.
"""
data3d, segm, voxelsize_mm, slab = self.synthetic_data()
# @TODO je tam bug, prohlížeč neumí korektně pracovat s doubly
# app = QApplication(sys.argv)
# #pyed = QTSeedEditor(noise )
# pyed = QTSeedEditor(data3d)
# pyed.exec_()
# #img3d = np.zeros([256,256,80], dtype=np.int16)
# pyed = sed3.sed3(data3d)
# pyed.show()
outputTmp = segmentation.vesselSegmentation(
data3d, # .astype(np.uint8),
segmentation=(segm == slab['liver']), # .astype(np.uint8),
# segmentation = oseg.orig_scale_segmentation,
voxelsize_mm=voxelsize_mm,
threshold=180,
inputSigma=0.15,
dilationIterations=2,
nObj=1,
interactivity=False,
biggestObjects=True,
binaryClosingIterations=5,
binaryOpeningIterations=1)
# ověření výsledku
# pyed = sed3.sed3(outputTmp, contour=segm==slab['porta'])
# pyed.show()
# @TODO opravit chybu v vesselSegmentation
outputTmp = (outputTmp == 2)
errim = np.abs(
outputTmp.astype(np.int) - (segm == slab['porta']).astype(np.int)
)
# ověření výsledku
# pyed = sed3.sed3(errim, contour=segm==slab['porta'])
# pyed.show()
# evaluation
sum_of_wrong_voxels = np.sum(errim)
sum_of_voxels = np.prod(segm.shape)
# print "wrong ", sum_of_wrong_voxels
# print "voxels", sum_of_voxels
errorrate = sum_of_wrong_voxels/sum_of_voxels
# import pdb; pdb.set_trace()
self.assertLess(errorrate, 0.1)
def test_ui_threshold_same_params_as_portal_segmentation(self):
params = {
'threshold': -1,
'inputSigma': 0.15,
'dilationIterations': 10,
'nObj': 1,
'biggestObjects': False,
'useSeedsOfCompactObjects': True,
'interactivity': True,
'binaryClosingIterations': 2,
'binaryOpeningIterations': 0
}
import imtools
import imtools.sample_data
datap = imtools.sample_data.generate()
app = QApplication(sys.argv)
outputTmp = segmentation.vesselSegmentation(
datap['data3d'],
datap['segmentation'],
# datap['voxelsize_mm'],
**params)
def test_ui_threshold_same_params_as_portal_segmentation(self):
params = {
'threshold': -1,
'inputSigma': 0.15,
'dilationIterations': 10,
'nObj': 1,
'biggestObjects': False,
'useSeedsOfCompactObjects': True,
'interactivity': True,
'binaryClosingIterations': 2,
'binaryOpeningIterations': 0
}
import imtools
import imtools.sample_data
datap = imtools.sample_data.generate()
app = QApplication(sys.argv)
outputTmp = segmentation.vesselSegmentation(
datap['data3d'],
datap['segmentation'],
# datap['voxelsize_mm'],
**params)
def do_segmentation_with_connected_threshold(self,
image: np.ndarray,
resolution: np.ndarray,
time_resolution: float,
qapp=None):
from imtools import segmentation as imsegmentation
params = {
# 'threshold': threshold,
'inputSigma': 0.15,
'aoi_dilation_iterations': 0,
'nObj': 1,
'biggestObjects': False,
'useSeedsOfCompactObjects': True,
'interactivity': True,
'binaryClosingIterations': 2,
'binaryOpeningIterations': 0,
# 'seeds': seeds,
}
# params.update(inparams)
# logger.debug("ogran_label ", organ_label)
# target_segmentation = (self.segmentation == self.nlabels(organ_label)).astype(np.int8)
import imma.image_manipulation as ima
# target_segmentation = ima.select_labels(
# self.segmentation, organ_label, self.slab
# )
vxsz_mm = np.array([1.0, resolution[0] * 1000, resolution[1] * 1000])
outputSegmentation = imsegmentation.vesselSegmentation(
image,
voxelsize_mm=vxsz_mm,
# target_segmentation,
segmentation=np.ones(image.shape, dtype=np.uint8),
aoi_label=1,
# aoi_label=organ_label,
# forbidden_label=forbidden_label,
# slab=self.slab,
# debug=self.debug_mode,
**params)