def test_training(self):
sample_data_path = lisa.dataset.sample_data_path()
organ_localizator.train_liver_localizator_from_sliver_data(
"liver.ol.p",
sliver_reference_dir=sample_data_path,
orig_pattern="*orig*001.mhd",
ref_pattern="*seg*001.mhd",
)
ol = organ_localizator.OrganLocalizator()
ol.load("liver.ol.p")
dr = io3d.DataReader()
data3d, metadata = dr.Get3DData(
op.join(sample_data_path, 'liver-orig001.mhd'))
out = ol.predict(data3d, metadata['voxelsize_mm'])
seg, metadata = dr.Get3DData(
op.join(sample_data_path, 'liver-seg001.mhd'))
err = np.sum(np.abs(seg - out))
# import sed3
# sed3.show_slices(out-seg, out-seg, slice_step=20)
# self.assertEqual(True, False)
# less then 10% error expected
self.assertGreater(np.prod(seg.shape) * 0.1, err)
def test_trainin_kidney_from_pklz(self):
sample_data_path = lisa.dataset.join_sdp("kidney_training/")
test_data = lisa.dataset.join_sdp(
"kidney_training/liver-orig001.mhd.pklz")
label = "left kidney"
organ_model.train_organ_model_from_dir("left_kidney.ol.p",
reference_dir=sample_data_path,
orig_pattern="*orig*.pklz",
ref_pattern="*orig*.pklz",
label=label,
segmentation_key=True)
ol = organ_localizator.OrganLocalizator()
ol.load("left_kidney.ol.p")
dr = io3d.DataReader()
data3d, metadata = dr.Get3DData(test_data)
out = ol.predict(data3d, metadata['voxelsize_mm'])
seg = (metadata["segmentation"] == metadata['slab'][label]).astype(
np.int8)
err = np.sum(np.abs(seg - out))
import sed3
sed3.show_slices(out - seg, out - seg, slice_step=20)
# self.assertEqual(True, False)
# less then 10% error expected
self.assertGreater(np.prod(seg.shape) * 0.1, err)
def annotate2(number_of_scans): #annotation starting from scan 1
'''
Annotate the data form our medical dataset, starting from scan 1, up to scan passed in param.
Save the labels to an excel file 'tabulka2.xlsx'.
'''
df = pd.DataFrame(columns=[
'ID', 'Mark 1 slice id', 'Mark 2 slice id', 'Mark 3 slice id',
'Mark 4 slice id'
])
for i in range(number_of_scans):
print(f'Showing scan n. {str(i+1).zfill(2)}')
dr = io3d.DataReader()
datap = dr.Get3DData(
f'data/medical/orig/sliver07/training/liver-orig0{str(i+1).zfill(2)}.mhd',
dataplus_format=True)
ed = sed3.sed3(datap['data3d'][::-1, :, :], windowW=400, windowC=40)
ed.show()
nz = np.nonzero(ed.seeds)
ids = np.unique(nz[0])
df = df.append(
{
'ID': i + 1,
'Mark 1 slice id': ids[0],
'Mark 2 slice id': ids[1],
'Mark 3 slice id': ids[2],
'Mark 4 slice id': ids[3]
},
ignore_index=True)
df.to_excel('tabulka2.xlsx', sheet_name='List1', index=False)
def annotate(number_of_scans): #annotation starting from scan 1
'''
Annotate the data form 3DIrcad1 dataset, starting from scan 1, up to scan passed in param.
Save the labels to an excel file 'tabulka.xlsx'.
'''
df = pd.DataFrame(columns=[
'Ircad ID', 'Mark 1 slice id', 'Mark 2 slice id', 'Mark 3 slice id',
'Mark 4 slice id'
])
for i in range(number_of_scans):
dr = io3d.DataReader()
pth = io3d.datasets.join_path('medical/orig/3Dircadb1.{}/'.format(i +
1),
get_root=True)
datap = dr.Get3DData(pth + "PATIENT_DICOM/", dataplus_format=True)
datap_labelled = dr.Get3DData(pth + 'MASKS_DICOM/liver',
dataplus_format=True)
ed = sed3.sed3(datap['data3d'],
contour=datap_labelled['data3d'],
windowW=400,
windowC=40)
ed.show()
nz = np.nonzero(ed.seeds)
ids = np.unique(nz[0])
order = input("Did liver end before kidney started? (y/n)")
if order == "y":
df = df.append(
{
'Ircad ID': i + 1,
'Mark 1 slice id': ids[0],
'Mark 2 slice id': ids[1],
'Mark 3 slice id': ids[2],
'Mark 4 slice id': ids[3]
},
ignore_index=True)
elif order == "n":
df = df.append(
{
'Ircad ID': i + 1,
'Mark 1 slice id': ids[0],
'Mark 2 slice id': ids[2],
'Mark 3 slice id': ids[1],
'Mark 4 slice id': ids[3]
},
ignore_index=True)
else:
print("ERROR")
break
df.to_excel('tabulka.xlsx', sheet_name='List1', index=False)
def differencing(path, res, axis):
sez = list()
teacher = list()
dr = io3d.DataReader()
datap = dr.Get3DData(path, dataplus_format=True)
ed = sed3.sed3(datap['data3d'])
datap['data3d'] = misc.resize_to_shape(datap['data3d'], [datap['data3d'].shape[0],100,100])
ed.show()
try:
under = np.where(ed.seeds == 1)[0][0]
except:
under = None
try:
over = np.where(ed.seeds == 3)[0][0]
except:
over = None
number_of_slice = np.shape(ed.seeds)[0]
c = list()
if under is None and over is None:
return [],[]
elif under is None or over is None:
print u'byla zadana jenom jedna mez zadejte obe nebo zadnou'
w, y = differencing(path, res)
return w, y
if over > under:
c = [1 for i in range(under)]
b = [2 for i in range(over-under)]
c.extend(b)
b = [3 for i in range(number_of_slice-over)]
c.extend(b)
else:
c = [3 for i in range(over)]
b = [2 for i in range(under - over)]
c.extend(b)
b = [1 for i in range(number_of_slice - under)]
c.extend(b)
over, under = under, over
for i in range(number_of_slice):
a = datap['data3d'][i,:,:]
x = c[i]
teacher.append(x)
sez.append(a)
return sez, teacher, under, over
def test_automatickyTest(self):
''' nacte prvni dva soubory koncici .mhd z adresare sample_data
prvni povazuje za originalni a provede na nem segmentaci defaultni
metodou z liver_segmentation. Pote nacte druhy a povazuje jej za
rucni segmentaci, na vysledku a rucni provede srovnani a podle
vysledku vypise verdikt na konzoli'''
import io3d
logger.setLevel(
logging.DEBUG) #ZDE UPRAVIT POKUD NECHCETE VSECHNY VYPISY
# path_to_script = os.path.dirname(os.path.abspath(__file__))
# # print path_to_script
# b = path_to_script[0:-5]
# b = b + 'sample_data'
cesta = lisa.dataset.sample_data_path()
logger.info('probiha nacitani souboru z adresare sample_data')
seznamSouboru = liver_segmentation.vyhledejSoubory(cesta)
reader = io3d.DataReader()
vektorOriginal = liver_segmentation.nactiSoubor(
cesta, seznamSouboru, 0, reader)
originalPole = vektorOriginal[0]
originalVelikost = vektorOriginal[1]
logger.info('***zahajeni segmentace***')
vytvoreny = liver_segmentation.LiverSegmentation(
originalPole, originalVelikost)
#vytvoreny.setCisloMetody(2)
vytvoreny.run()
segmentovany = vytvoreny.segmentation
segmentovanyVelikost = vytvoreny.voxelSize
logger.info(
'segmentace dokoncena, nacitani rucni segmentace z adresare sample_data'
)
vektorOriginal = liver_segmentation.nactiSoubor(
cesta, seznamSouboru, 1, reader)
rucniPole = vektorOriginal[0]
rucniVelikost = vektorOriginal[1]
logger.info('zahajeni vyhodnoceni segmentace')
vysledky = liver_segmentation.vyhodnoceniSnimku(
rucniPole, rucniVelikost, segmentovany, segmentovanyVelikost)
logger.info(str(vysledky))
skore = vysledky[1]
pravda = True
import sed3
ed = sed3.sed3(vytvoreny.data3d, contour=vytvoreny.segmentation)
ed.show()
if (skore > 75):
logger.info('metoda funguje uspokojive')
pravda = False
if (pravda and (skore > 50)):
logger.info('metoda funguje relativne dobre')
pravda = False
if (pravda):
logger.info('metoda funguje spatne')
pravda = False
# self.assertGreater(skore, 5)
self.assertLess(vysledky[0]['voe'], 50)
return
default='mesh_geom.vtk',
help='temp file used in processing')
parser.add_argument(
'-d', '--degrad', type=int,
default=4,
help='data degradation, default 4')
parser.add_argument(
'-l', '--labels', type=int, metavar='N', nargs='+',
default=[1],
help='segmentation labels, default 1')
parser.add_argument(
'-s', '--show', action='store_true',
help='Show mode')
args = parser.parse_args()
dr = io3d.DataReader()
data = dr.Get3DData(args.inputfile, dataplus_format=True)
# args.label = np.array(eval(args.label))
# print args.label
# import pdb; pdb.set_trace()
ds = data['segmentation']
if args.show:
dsel = select_labels(ds, args.labels)
import sed3
ed = sed3.sed3(dsel.astype(np.double))
ed.show()
