def test(path_data='', parameters=''):
if not parameters:
parameters = '-i t2/t2.nii.gz -c t2 -p auto'
# parameters
folder_data = 't2/'
file_data = [
't2.nii.gz', 't2_centerline_init.nii.gz',
't2_centerline_labels.nii.gz', 't2_seg_manual.nii.gz'
]
parser = sct_get_centerline.get_parser()
dict_param = parser.parse(parameters.split(), check_file_exist=False)
contrast = dict_param['-c']
dict_param_with_path = parser.add_path_to_file(dict_param,
path_data,
input_file=True)
param_with_path = parser.dictionary_to_string(dict_param_with_path)
# Check if input files exist
if not (os.path.isfile(dict_param_with_path['-i'])):
status = 200
output = 'ERROR: the file(s) provided to test function do not exist in folder: ' + path_data
return status, output, DataFrame(data={
'status': status,
'output': output,
'mse': float('nan'),
'dist_max': float('nan')
},
index=[path_data])
cmd = 'sct_get_centerline ' + param_with_path
status, output = sct.run(cmd, 0)
scad_centerline = Image(contrast + "_centerline.nii.gz")
manual_seg = Image(path_data + folder_data + contrast +
'_seg_manual.nii.gz')
max_distance = 0
standard_deviation = 0
average = 0
root_mean_square = 0
overall_distance = 0
max_distance = 0
overall_std = 0
rmse = 0
try:
if status == 0:
manual_seg.change_orientation()
scad_centerline.change_orientation()
from scipy.ndimage.measurements import center_of_mass
# find COM
iterator = range(manual_seg.data.shape[2])
com_x = [0 for ix in iterator]
com_y = [0 for iy in iterator]
for iz in iterator:
com_x[iz], com_y[iz] = center_of_mass(manual_seg.data[:, :,
iz])
max_distance = {}
distance = {}
for iz in range(1, scad_centerline.data.shape[2] - 1):
ind1 = np.argmax(scad_centerline.data[:, :, iz])
X, Y = ind2sub(scad_centerline.data[:, :, iz].shape, ind1)
com_phys = np.array(
manual_seg.transfo_pix2phys([[com_x[iz], com_y[iz], iz]]))
scad_phys = np.array(
scad_centerline.transfo_pix2phys([[X, Y, iz]]))
distance_magnitude = np.linalg.norm([
com_phys[0][0] - scad_phys[0][0],
com_phys[0][1] - scad_phys[0][1], 0
])
if math.isnan(distance_magnitude):
print "Value is nan"
else:
distance[iz] = distance_magnitude
max_distance = max(distance.values())
standard_deviation = np.std(np.array(distance.values()))
average = sum(distance.values()) / len(distance)
root_mean_square = np.sqrt(np.mean(np.square(distance.values())))
overall_distance = average
max_distance = max(distance.values())
overall_std = standard_deviation
rmse = root_mean_square
except Exception, e:
sct.printv("Exception found while testing scad integrity")
output = e.message
def validate_scad(folder_input, contrast):
"""
Expecting folder to have the following structure :
errsm_01:
- t2
-- errsm_01.nii.gz or t2.nii.gz
--
:param folder_input:
:return:
"""
from sct_get_centerline import ind2sub
import time
import math
import numpy
t0 = time.time()
current_folder = os.getcwd()
os.chdir(folder_input)
try:
patients = next(os.walk('.'))[1]
overall_distance = {}
max_distance = {}
standard_deviation = 0
overall_std = {}
rmse = {}
for i in patients:
directory = i + "/" + str(contrast)
try:
os.chdir(directory)
except Exception, e:
print str(i)+" : "+contrast+" directory not found"
try:
if os.path.isfile(i+"_"+contrast+".nii.gz"):
raw_image = Image(i+"_"+contrast+".nii.gz")
elif os.path.isfile(contrast+".nii.gz"):
raw_image = Image(contrast+".nii.gz")
else:
raise Exception("Patient scan not found")
if os.path.isfile(i+"_"+contrast+"_manual_segmentation.nii.gz"):
raw_orientation = raw_image.change_orientation()
scad = SCAD(raw_image, contrast=contrast, rm_tmp_file=1, verbose=1)
scad.execute()
manual_seg = Image(i+"_"+contrast+"_manual_segmentation.nii.gz")
manual_orientation = manual_seg.change_orientation()
from scipy.ndimage.measurements import center_of_mass
# find COM
iterator = range(manual_seg.data.shape[2])
com_x = [0 for ix in iterator]
com_y = [0 for iy in iterator]
for iz in iterator:
com_x[iz], com_y[iz] = center_of_mass(manual_seg.data[:, :, iz])
centerline_scad = Image(i+"_"+contrast+"_centerline.nii.gz")
# os.remove(i+"_"+contrast+"_centerline.nii.gz")
centerline_scad.change_orientation()
distance = {}
for iz in range(1, centerline_scad.data.shape[2]-1):
ind1 = np.argmax(centerline_scad.data[:, :, iz])
X,Y = ind2sub(centerline_scad.data[:, :, iz].shape,ind1)
com_phys = np.array(manual_seg.transfo_pix2phys([[com_x[iz], com_y[iz], iz]]))
scad_phys = np.array(centerline_scad.transfo_pix2phys([[X, Y, iz]]))
distance_magnitude = np.linalg.norm([com_phys[0][0]-scad_phys[0][0], com_phys[0][1]-scad_phys[0][1], 0])
if math.isnan(distance_magnitude):
print "Value is nan"
else:
distance[iz] = distance_magnitude
f = open(i+"_"+contrast+"_results.txt", 'w+')
f.write("Patient,Slice,Distance")
for key, value in distance.items():
f.write(i+","+str(key)+","+str(value))
standard_deviation = np.std(np.array(distance.values()))
average = sum(distance.values())/len(distance)
root_mean_square = np.sqrt(np.mean(np.square(distance.values())))
f.write("\nAverage : "+str(average))
f.write("\nStandard Deviation : "+str(standard_deviation))
f.close()
overall_distance[i] = average
max_distance[i] = max(distance.values())
overall_std[i] = standard_deviation
rmse[i] = root_mean_square
else:
printv("Cannot find the manual segmentation", type="warning")
except Exception, e:
print e.message
os.chdir(folder_input)
def test(path_data='', parameters=''):
if not parameters:
parameters = '-i t2/t2.nii.gz -c t2 -p auto'
# parameters
folder_data = 't2/'
file_data = ['t2.nii.gz', 't2_centerline_init.nii.gz', 't2_centerline_labels.nii.gz', 't2_seg_manual.nii.gz']
parser = sct_get_centerline.get_parser()
dict_param = parser.parse(parameters.split(), check_file_exist=False)
contrast = dict_param['-c']
dict_param_with_path = parser.add_path_to_file(dict_param, path_data, input_file=True)
param_with_path = parser.dictionary_to_string(dict_param_with_path)
# Check if input files exist
if not (os.path.isfile(dict_param_with_path['-i'])):
status = 200
output = 'ERROR: the file(s) provided to test function do not exist in folder: ' + path_data
return status, output, DataFrame(data={'status': status, 'output': output, 'mse': float('nan'), 'dist_max': float('nan')}, index=[path_data])
cmd = 'sct_get_centerline '+param_with_path
status, output = sct.run(cmd, 0)
scad_centerline = Image(contrast+"_centerline.nii.gz")
manual_seg = Image(path_data + folder_data + contrast +'_seg_manual.nii.gz')
max_distance = 0
standard_deviation = 0
average = 0
root_mean_square = 0
overall_distance = 0
max_distance = 0
overall_std = 0
rmse = 0
try:
if status == 0:
manual_seg.change_orientation()
scad_centerline.change_orientation()
from scipy.ndimage.measurements import center_of_mass
# find COM
iterator = range(manual_seg.data.shape[2])
com_x = [0 for ix in iterator]
com_y = [0 for iy in iterator]
for iz in iterator:
com_x[iz], com_y[iz] = center_of_mass(manual_seg.data[:, :, iz])
max_distance = {}
distance = {}
for iz in range(1, scad_centerline.data.shape[2]-1):
ind1 = np.argmax(scad_centerline.data[:, :, iz])
X,Y = ind2sub(scad_centerline.data[:, :, iz].shape,ind1)
com_phys = np.array(manual_seg.transfo_pix2phys([[com_x[iz], com_y[iz], iz]]))
scad_phys = np.array(scad_centerline.transfo_pix2phys([[X, Y, iz]]))
distance_magnitude = np.linalg.norm([com_phys[0][0]-scad_phys[0][0], com_phys[0][1]-scad_phys[0][1], 0])
if math.isnan(distance_magnitude):
print "Value is nan"
else:
distance[iz] = distance_magnitude
max_distance = max(distance.values())
standard_deviation = np.std(np.array(distance.values()))
average = sum(distance.values())/len(distance)
root_mean_square = np.sqrt(np.mean(np.square(distance.values())))
overall_distance = average
max_distance = max(distance.values())
overall_std = standard_deviation
rmse = root_mean_square
except Exception, e:
sct.printv("Exception found while testing scad integrity")
output = e.message
