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 test_import():
# io3d.read()
data3d_orig = io3d.read_dataset("3Dircadb1",
"data3d",
1,
orientation_axcodes="SPL")
bp = bpo.BodyPosition(data3d_orig['data3d'], data3d_orig['voxelsize_mm'])
sed3.show_slices(np.asarray(data3d_orig['data3d'][0:-1]),
slice_step=10,
axis=0)
sdf = bp.dist_to_lungs()
assert np.max(sdf) > 0
assert np.min(sdf) < 0
im_volume_px = np.prod(data3d_orig['data3d'].shape)
sed3.show_slices(np.asarray(data3d_orig['data3d'][0:-1]),
np.asarray(sdf[0:-1] > 0),
slice_step=10,
axis=0)
organ_volume_px = np.sum(sdf >= 0)
logger.debug(im_volume_px)
logger.debug(organ_volume_px)
assert organ_volume_px > (im_volume_px * 0.03)
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 visualize(seg, data3d_orig):
"""Show segmentation on original data using sed3 visualizer.
Args:
seg: data after segmentation
data3d_orig: orig data
"""
sed3.show_slices(data3d_orig["data3d"], seg, slice_step=10, axis=0)
plt.show()
def test_first_slice_offset(self):
import sed3
img, seeds, segmentation = self.create_data()
sed3.show_slices(img,
seeds=seeds,
contour=segmentation,
slice_step=5,
first_slice_offset_to_see_seed_with_label=1,
show=False)
def print_it_all_front(self, ss, data3dr_tmp, seg, pattern):
""" Vykresli jednotlive rezy v danem smeru (axis)
do sed3.show_slices() vstupuji:
- obrazek pro danou instanci Bona (surovy obrazek), ktery
zpracovavame
- obrazek seg - vysledek segmentace, tedy vystup metody get_ribs(lungs, ...))
- dalsi parametry jako jsou krok po rezech, osa, atd."""
fig = plt.figure(figsize=(25, 25))
sed3.show_slices(
data3dr_tmp, # vychozi obrazek
seg.astype(np.int8),
slice_step=10,
axis=1,
flipV=True,
flipH=False)
def print_it_all_front(self, ss, data3dr_tmp, seg, pattern):
""" Vykresli jednotlive rezy v danem smeru (axis)
do sed3.show_slices() vstupuji:
- obrazek pro danou instanci Bona (surovy obrazek), ktery
zpracovavame
- obrazek seg - vysledek segmentace, tedy vystup metody get_ribs(lungs, ...))
- dalsi parametry jako jsou krok po rezech, osa, atd."""
import sed3
fig = plt.figure(figsize = (25,25))
sed3.show_slices(
data3dr_tmp, # vychozi obrazek
seg.astype(np.int8),
slice_step=10,
axis=1,
flipV=True,
flipH=False
)
def test_first_slice_offset_interactive(self):
"""
set offset to see seed with defined label
:return:
"""
import sed3
img, seeds, segmentation = self.create_data()
seeds[6:9, 10, 1:5] = 3
seeds[7:9, 11, 8:14] = 4
ed = sed3.show_slices(img, seeds=seeds, contour=segmentation, slice_step=5, first_slice_offset_to_see_seed_with_label=1)
def test_first_slice_offset_interactive(self):
"""
set offset to see seed with defined label
:return:
"""
import sed3
img, seeds, segmentation = self.create_data()
seeds[6:9, 10, 1:5] = 3
seeds[7:9, 11, 8:14] = 4
ed = sed3.show_slices(img,
seeds=seeds,
contour=segmentation,
slice_step=5,
first_slice_offset_to_see_seed_with_label=1)
def test_intensity_training(self):
# TODO use ircad
sliver_reference_dir = op.expanduser("~/data/medical/orig/sliver07/training/")
# Train
import imtools.trainer3d
import imtools.datasets
ol = imtools.trainer3d.Trainer3D()
# ol.feature_function = localization_fv
for one in imtools.datasets.sliver_reader("*[0].mhd", read_seg=True):
numeric_label, vs_mm, oname, orig_data, rname, ref_data = one
ol.add_train_data(orig_data, ref_data, voxelsize_mm=vs_mm)
ol.fit()
# Testing
one = list(imtools.datasets.sliver_reader("*018.mhd", read_seg=True))[0]
numeric_label, vs_mm, oname, orig_data, rname, ref_data = one
fit = ol.predict(orig_data, voxelsize_mm=vs_mm)
# visualization
plt.figure(figsize=(15,10))
sed3.show_slices(orig_data, fit, slice_step=20, axis=1, flipV=True)
def model_score_from_sliver_data(
# output_file="~/lisa_data/liver_intensity.Model.p",
sliver_reference_dir='~/data/medical/orig/sliver07/training/',
orig_pattern="*orig*[1-9].mhd",
ref_pattern="*seg*[1-9].mhd",
modelparams={},
likelihood_ratio=0.5,
savefig=False,
savefig_fn_prefix='../graphics/bn-symmetry-',
show=False,
label='',
):
"""
:param label: text label added to all records in output table
:param sliver_reference_dir:
:param orig_pattern:
:param ref_pattern:
:param modelparams:
:param likelihood_ratio: float number between 0 and 1, scalar or list. Set the segmentation threshodl
:param savefig:
:param savefig_fn_prefix:
:param show: show images
:return:
"""
import pandas as pd
from imcut import pycut
import sed3
import matplotlib.pyplot as plt
import volumetry_evaluation
sliver_reference_dir = op.expanduser(sliver_reference_dir)
orig_fnames = glob.glob(sliver_reference_dir + orig_pattern)
ref_fnames = glob.glob(sliver_reference_dir + ref_pattern)
orig_fnames.sort()
ref_fnames.sort()
evaluation_all = []
for oname, rname in zip(orig_fnames, ref_fnames):
print(oname)
data3d_orig, metadata = io3d.datareader.read(oname,
dataplus_format=False)
vs_mm1 = metadata['voxelsize_mm']
data3d_seg, metadata = io3d.datareader.read(rname,
dataplus_format=False)
vs_mm = metadata['voxelsize_mm']
mdl = pycut.Model(modelparams=modelparams)
# m0 = mdl.mdl[2]
# len(m0.means_)
vs_mmr = [1.5, 1.5, 1.5]
data3dr = io3d.misc.resize_to_mm(data3d_orig, vs_mm1, vs_mmr)
lik1 = mdl.likelihood_from_image(data3dr, vs_mmr, 0)
lik2 = mdl.likelihood_from_image(data3dr, vs_mmr, 1)
if np.isscalar(likelihood_ratio):
likelihood_ratio = [likelihood_ratio]
for likelihood_ratio_i in likelihood_ratio:
if (likelihood_ratio_i <= 0) or (likelihood_ratio_i >= 1.0):
logger.error("likelihood ratio should be between 0 and 1")
seg = ((likelihood_ratio_i * lik1) >
((1.0 - likelihood_ratio_i) * lik2)).astype(np.uint8)
# seg = (lik1).astype(np.uint8)
seg_orig = io3d.misc.resize_to_shape(seg, data3d_orig.shape)
# seg_orig = io3d.misc.resize_to_shape(seg, data3d_orig.shape)
if show:
plt.figure(figsize=(15, 15))
sed3.show_slices(data3d_orig,
seg_orig,
show=False,
slice_step=20)
# likres = io3d.misc.resize_to_shape(lik1, data3d_orig.shape)
# sed3.show_slices(likres , seg_orig, show=False, slice_step=20)
import re
numeric_label = re.search(".*g(\d+)", oname).group(1)
# plt.figure(figsize = (5,5))
if savefig:
plt.axis('off')
# plt.imshow(symmetry_img)
filename = savefig_fn_prefix + numeric_label + '-lr-' + str(
likelihood_ratio_i) + ".png"
# if np.isscalar(likelihood_ratio):
# filename = filename + ''+str
plt.savefig(filename, bbox_inches='tight')
evaluation = volumetry_evaluation.compare_volumes_sliver(
seg_orig, data3d_seg, vs_mm)
evaluation['likelihood_ratio'] = likelihood_ratio_i
evaluation['numeric_label'] = numeric_label
evaluation['label'] = label
evaluation_all.append(evaluation)
# print evaluation
ev = pd.DataFrame(evaluation_all)
return ev
def test(
imshape=256,
# sdf_type='diaphragm_axial',
# sdf_type='coronal',
# sdf_type='sagittal',
sdf_type='surface',
# sdf_type='bones',
# sdf_type='fatless',
# sdf_type='liver',
# sdf_type='spleen',
# sdf_type='lungs',
filename_prefix='final_',
test_ids=[20, 40],
):
"""Evaluates a U-net NN with test data loaded from a .h5 file.
Args:
imshape (int, optional): Shape of dataset's images. Defaults to 256.
sdf_type (str, optional): Type of sdf method we train. Can be: sagittal, coronal, surface, liver, spleen, lungs, bones, fatless or diaphragm_axial.
filename_prefix (str, optional): Option to add a prefix to create new files when testing. Defaults to ''.
test_ids (list, optional): Test data scans' indexes in the dataset. Defaults to [20, 40].
"""
model = load_model(f"{filename_prefix}sdf_unet_{sdf_type}.h5")
# import visualkeras
# visualkeras.graph_view(model, to_file='output.png')
for i in range(len(test_ids)):
X_test = []
Y_test = []
#Loading test data
with h5py.File(f'sdf_{sdf_type}{imshape}.h5', 'r') as h5f:
logger.info('Loading...')
X_test.extend(np.asarray(h5f[f'scan_{i}']))
Y_test.extend(np.asarray(h5f[f'label_{i}']))
logger.info(F'Scan {test_ids[i]} loaded')
# Reshaping
X_test = np.asarray(X_test).reshape(
np.asarray(X_test).shape[0], 256, 256, 1)
Y_test = np.asarray(Y_test).reshape(
np.asarray(Y_test).shape[0], 256, 256, 1)
# Predictions
predictions = model.predict(X_test)
# Visualization
X_test = np.asarray(X_test).reshape(
np.asarray(X_test).shape[0], 256, 256)
predictions = np.asarray(predictions).reshape(
np.asarray(predictions).shape[0], 256, 256)
Y_test = np.asarray(Y_test).reshape(
np.asarray(Y_test).shape[0], 256, 256)
plt.plot(Y_test[20])
plt.contour(Y_test[20] > 0)
plt.colorbar()
plt.show()
plt.imshow(predictions[20])
plt.contour(predictions[20] > 0)
plt.show()
sed3.show_slices(np.asarray(X_test[0:-1]),
np.asarray(Y_test[0:-1]),
slice_step=10,
axis=0)
sed3.show_slices(np.asarray(X_test[0:-1]),
np.asarray(predictions[0:-1]),
slice_step=10,
axis=0)
organ_label = "rightkidney"
show = False
# %%
dtstr = datetime.datetime.now().strftime("%Y%m%d_%H%M")
rdir = f"report_{experiment_label}_{dtstr}"
report = exsu.Report(outputdir=rdir,
show=show,
additional_spreadsheet_fn="data.xlsx")
reporti = 0
# %%
datap1 = io3d.datasets.read_dataset("3Dircadb1", "data3d", 1)
# datap1 = io3d.datasets.read_dataset("sliver07", "data3d", 1)
data3d = datap1["data3d"]
sed3.show_slices(data3d, shape=[2, 3], show=show)
report.savefig(f"{reporti:03d}.png")
reporti += 1
# %%
datap_mask = io3d.datasets.read_dataset("3Dircadb1", organ_label, 1)
data3d_mask = datap_mask["data3d"]
sed3.show_slices(data3d_mask, shape=[2, 3], show=show)
report.savefig(f"{reporti:03d}.png")
reporti += 1
# plt.figure()
# %% md
## windowing
def model_score_from_sliver_data(
# output_file="~/lisa_data/liver_intensity.Model.p",
sliver_reference_dir='~/data/medical/orig/sliver07/training/',
orig_pattern="*orig*[1-9].mhd",
ref_pattern="*seg*[1-9].mhd",
modelparams={},
likelihood_ratio=0.5,
savefig=False,
savefig_fn_prefix='../graphics/bn-symmetry-',
show=False,
label='',
):
"""
:param label: text label added to all records in output table
:param sliver_reference_dir:
:param orig_pattern:
:param ref_pattern:
:param modelparams:
:param likelihood_ratio: float number between 0 and 1, scalar or list. Set the segmentation threshodl
:param savefig:
:param savefig_fn_prefix:
:param show: show images
:return:
"""
import pandas as pd
from imcut import pycut
import sed3
import matplotlib.pyplot as plt
import volumetry_evaluation
sliver_reference_dir = op.expanduser(sliver_reference_dir)
orig_fnames = glob.glob(sliver_reference_dir + orig_pattern)
ref_fnames = glob.glob(sliver_reference_dir + ref_pattern)
orig_fnames.sort()
ref_fnames.sort()
evaluation_all = []
for oname, rname in zip(orig_fnames, ref_fnames):
print(oname)
data3d_orig, metadata = io3d.datareader.read(oname, dataplus_format=False)
vs_mm1 = metadata['voxelsize_mm']
data3d_seg, metadata = io3d.datareader.read(rname, dataplus_format=False)
vs_mm = metadata['voxelsize_mm']
mdl = pycut.Model(modelparams=modelparams)
# m0 = mdl.mdl[2]
# len(m0.means_)
vs_mmr = [1.5, 1.5, 1.5]
data3dr = io3d.misc.resize_to_mm(data3d_orig, vs_mm1, vs_mmr)
lik1 = mdl.likelihood_from_image(data3dr, vs_mmr, 0)
lik2 = mdl.likelihood_from_image(data3dr, vs_mmr, 1)
if np.isscalar(likelihood_ratio):
likelihood_ratio = [likelihood_ratio]
for likelihood_ratio_i in likelihood_ratio:
if (likelihood_ratio_i <= 0) or (likelihood_ratio_i >= 1.0):
logger.error("likelihood ratio should be between 0 and 1")
seg = ((likelihood_ratio_i * lik1) > ((1.0 - likelihood_ratio_i) * lik2)).astype(np.uint8)
# seg = (lik1).astype(np.uint8)
seg_orig = io3d.misc.resize_to_shape(seg, data3d_orig.shape)
# seg_orig = io3d.misc.resize_to_shape(seg, data3d_orig.shape)
if show:
plt.figure(figsize = (15,15))
sed3.show_slices(data3d_orig , seg_orig, show=False, slice_step=20)
# likres = io3d.misc.resize_to_shape(lik1, data3d_orig.shape)
# sed3.show_slices(likres , seg_orig, show=False, slice_step=20)
import re
numeric_label = re.search(".*g(\d+)", oname).group(1)
# plt.figure(figsize = (5,5))
if savefig:
plt.axis('off')
# plt.imshow(symmetry_img)
filename = savefig_fn_prefix + numeric_label + '-lr-' + str(likelihood_ratio_i) + ".png"
# if np.isscalar(likelihood_ratio):
# filename = filename + ''+str
plt.savefig(filename, bbox_inches='tight')
evaluation = volumetry_evaluation.compare_volumes_sliver(seg_orig, data3d_seg, vs_mm)
evaluation['likelihood_ratio'] = likelihood_ratio_i
evaluation['numeric_label'] = numeric_label
evaluation['label'] = label
evaluation_all.append(evaluation)
# print evaluation
ev = pd.DataFrame(evaluation_all)
return ev
def compare(
imshape=256,
# sdf_type='coronal',
# sdf_type='sagittal',
# sdf_type='surface',
# sdf_type='bones',
# sdf_type='fatless',
sdf_type='liver',
# sdf_type='spleen',
# sdf_type='lungs',
dataset = "3Dircadb1",
scannum = 20,
):
"""Compare bodyposition and bodynavigation.
Args:
imshape (int, optional): Shape of dataset's images. Defaults to 256.
sdf_type (str, optional): Type of sdf method. Can be: sagittal, coronal, surface, liver, spleen, lungs, bones, fatless or diaphragm_axial.
dataset (string): "3Dircadb1" or "sliver07"
scannum (int): Number of the scan this loads
"""
import bodynavigation
from bodynavigation.organ_detection import OrganDetection
import bodyposition
pth = Path(__file__).parent.parent
outputdir = pth / "experiments/"
commonsheet = outputdir / "experiments.xlsx"
report = exsu.report.Report(outputdir=outputdir, additional_spreadsheet_fn=commonsheet, check_version_of=["numpy", "scipy"])
organ_detection_on = False
if sdf_type == "liver" or sdf_type == "spleen" or sdf_type == "lungs" or sdf_type == "fatless" or sdf_type == "bones":
organ_detection_on = True
data3d_orig = io3d.read_dataset(dataset, "data3d", scannum, orientation_axcodes='SPL')
voxelsize = data3d_orig["voxelsize_mm"]
# BODYNAVIGATION
start_time = time.time()
if not organ_detection_on:
ss = bodynavigation.body_navigation.BodyNavigation(data3d_orig["data3d"], data3d_orig["voxelsize_mm"])
sdf_bodynavigation = eval(f"ss.dist_to_{sdf_type}()")
else:
od = OrganDetection(data3d_orig["data3d"], voxelsize)
if sdf_type == "liver":
st = "Liver"
if sdf_type == "spleen":
st = "Spleen"
if sdf_type == "lungs":
st = "Lungs"
if sdf_type == "fatless":
st = "FatlessBody"
if sdf_type == "bones":
st = "Bones"
sdf_bodynavigation = eval(f"od.get{st}()")
time_bodynavigation = time.time() - start_time
sed3.show_slices(np.asarray(data3d_orig["data3d"][0:-1]), np.asarray(sdf_bodynavigation[0:-1]), axis=0)
# BODYPOSITION
start_time = time.time()
bpo = bodyposition.BodyPosition(data3d_orig["data3d"], data3d_orig['voxelsize_mm'])
sdf_bodyposition = eval(f"bpo.dist_to_{sdf_type}()")
time_bodyposition = time.time() - start_time
sed3.show_slices(np.asarray(data3d_orig["data3d"][0:-1]), np.asarray(sdf_bodyposition[0:-1]), axis=0)
# EVALUATION AND REPORT SAVING
evaluation = compare_volumes(sdf_bodynavigation, sdf_bodyposition, voxelsize_mm=voxelsize)
# logger.info(evaluation)
report.add_cols_to_actual_row({
"sdf type": sdf_type,
"dataset": dataset,
"scannum": scannum,
"datetime": time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime(time.time())),
"time_bodynavigation": time_bodynavigation,
"time_bodyposition": time_bodyposition,
})
report.add_cols_to_actual_row(evaluation)
report.finish_actual_row()
report.dump()
logger.info(f"Report finished - scan {scannum} (from {dataset}), {sdf_type} segmentation - BN {round(time_bodynavigation)} s, BP {round(time_bodyposition)} s")
def main():
#logger = logging.getLogger(__name__)
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
ch = logging.StreamHandler()
logger.addHandler(ch)
#logger.debug('input params')
# input parser
parser = argparse.ArgumentParser(description=
'Segmentation of bones, lungs and heart.')
parser.add_argument('-i','--datadir',
default=None,
help='path to data dir')
parser.add_argument('-o','--output',
default=None,
help='output file')
parser.add_argument('-d', '--debug', action='store_true',
help='run in debug mode')
parser.add_argument('-ss', '--segspine', action='store_true',
help='run spine segmentaiton')
parser.add_argument('-sb', '--segbody', action='store_true',
help='run body segmentaiton')
parser.add_argument('-exd', '--exampledata', action='store_true',
help='run unittest')
parser.add_argument('-so', '--show_output', action='store_true',
help='Show output data in viewer')
args = parser.parse_args()
if args.debug:
logger.setLevel(logging.DEBUG)
if args.exampledata:
args.dcmdir = '../sample_data/liver-orig001.raw'
# if dcmdir == None:
#else:
#dcm_read_from_dir('/home/mjirik/data/medical/data_orig/46328096/')
#data3d, metadata = dcmr.dcm_read_from_dir(args.dcmdir)
import io3d
data3d , metadata = io3d.datareader.read(args.datadir)
bn = BodyNavigation(data3d = data3d,
voxelsize_mm = metadata['voxelsize_mm'],
)
seg = np.zeros(data3d.shape, dtype=np.int8)
#sseg.orientation()
if args.segspine:
seg += bn.get_spine()
if args.segspine:
seg += bn.get_body()
#print ("Data size: " + str(data3d.nbytes) + ', shape: ' + str(data3d.shape) )
#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)
#pyed = sed3.sed3(oseg.data3d, contour = oseg.segmentation)
#pyed.show()
if args.show_output:
import sed3
sed3.show_slices(data3d, contour=seg)
#savestring = raw_input ('Save output data? (y/n): ')
#sn = int(snstring)
if args.output is not None: # savestring in ['Y','y']:
md = {
'voxelsize_mm': metadata['voxelsize_mm'],
'segmentation': seg, }
io3d.write(data3d, args.output, metadata=md)
def test_show_slices(self):
import sed3
img, seeds, segmentation = self.create_data()
sed3.show_slices(img, seeds=seeds, contour=segmentation, show=False)
def test_show_slices(self):
import sed3
img, seeds, segmentation = self.create_data()
sed3.show_slices(img, seeds=seeds, contour=segmentation, show=False)
def test_first_slice_offset(self):
import sed3
img, seeds, segmentation = self.create_data()
sed3.show_slices(img, seeds=seeds, contour=segmentation, slice_step=5,
first_slice_offset_to_see_seed_with_label=1, show=False)
import numpy as np
import io3d
import sed3
import bodyposition as bpo
print(bpo.__file__)
data3d_orig = io3d.read_dataset("3Dircadb1", "data3d", 1)
bp = bpo.BodyPosition(data3d_orig['data3d'], data3d_orig['voxelsize_mm'])
sdf_surface = bp.dist_to_surface()
sed3.show_slices(np.asarray(data3d_orig['data3d'][0:-1]), np.asarray(sdf_surface[0:-1]), slice_step=10, axis=0)
def make_image_processing(data,
voxelsize_mm,
seeds=None,
sigma_mm=1,
min_threshold=None,
max_threshold=None,
closeNum=0,
openNum=0,
min_threshold_auto_method="",
fill_holes=True,
get_priority_objects=True,
nObj=1,
debug=False):
if (sys.version_info[0] < 3):
import copy
data_copy = copy.copy(data)
else:
data_copy = data.copy()
if sigma_mm > 0:
sigmaNew = thresholding_functions.calculateSigma(
voxelsize_mm, sigma_mm)
data_copy = thresholding_functions.gaussFilter(data_copy, sigmaNew)
del (sigmaNew)
if debug:
import sed3
# ed = sed3.sed3qt(data_copy)
sed3.show_slices(data_copy, shape=[6, 9])
# ed.show()
if min_threshold is None:
min_threshold = prepare_threshold_from_seeds(
data=data_copy,
seeds=seeds,
min_threshold_auto_method=min_threshold_auto_method)
data_thr = thresholding_functions.thresholding(
data_copy,
min_threshold,
max_threshold,
use_min_threshold=True,
use_max_threshold=max_threshold is not None)
if debug:
logger.debug("np min median max input data " + str(np.min(data_copy)) +
" " + str(np.median(data_copy)) + " " +
str(np.max(data_copy)) + " ")
if debug:
logger.debug("np unique sum binar " + str(np.unique(data_thr)) + " " +
str(np.sum(data_thr)) + " ")
data_thr = thresholding_functions.binaryClosingOpening(
data_thr, closeNum, openNum, True)
# Fill holes
if fill_holes:
data_thr = thresholding_functions.fillHoles(data_thr)
# use a wall for label 2
if type(seeds) is np.ndarray:
data_thr[seeds == 2] = 0
# Zjisteni nejvetsich objektu.
if get_priority_objects:
if seeds is not None:
selected_seeds = seeds == 1
else:
selected_seeds = seeds
data_thr = thresholding_functions.get_priority_objects(
data_thr, nObj, selected_seeds)
if debug:
logger.debug("np unique sum binar hist end " +
str(np.unique(data_thr)) + " " + str(np.sum(data_thr)) +
" "
# + str(np.histogram(data_thr, bins="auto")) + " "
)
return data_thr, min_threshold
