#arch_ct_in_ios_fxt = fe.Fixture(MISSING_TOOTH_NUMBER, 'CT', 'IOS')
# arch after curvilinear correction - original ios arch
arch_ios_curvilinear_correction = fe.FullArch(MISSING_TOOTH_NUMBER, 'IOS', 'IOS')
#arch_ios_fxt_curvilinear_correction = fe.Fixture(MISSING_TOOTH_NUMBER, 'IOS', 'IOS')
# arch after curvilinear correction - virtual ct features
arch_ct_in_ios_curvilinear_correction = fe.FullArch(MISSING_TOOTH_NUMBER, 'CT', 'IOS')
#arch_ct_in_ios_fxt_curvilinear_correction = fe.Fixture(MISSING_TOOTH_NUMBER, 'CT', 'IOS')
# Read CT and IOS full arch scan
for i in TOOTH_NUMBER:
print('reading CT tooth', i)
new_dicom_path = target_dicom_base + dicom_file_name + np.str(i) + '.csv'
dicom_tem = Yomiread.read_csv(new_dicom_path, 3, -1)
tooth_dicom_tem = fe.ToothFeature(dicom_tem, i, 'CT', 'CT')
arch_ct.add_tooth(i, tooth_dicom_tem)
#arch_ct_original.add_tooth(i, tooth_dicom_tem)
print('reading IOS tooth', i)
if i in MISSING_TOOTH_NUMBER:
print('Tooth '+np.str(i) + ' is covered by splint')
if i in TARGET_TOOTH:
print('Tooth ' + np.str(i) + ' is covered by target')
else:
new_stl_path = source_stl_base + stl_file_name + np.str(i) + '.csv'
stl_tem = Yomiread.read_csv(new_stl_path, 3, -1)
tooth_stl_tem = fe.ToothFeature(stl_tem, i, 'IOS', 'IOS')
arch_ios.add_tooth(i, tooth_stl_tem)
if 'stl_tem' in locals():
del stl_tem
# generate virtual landmarks using ct features with local ICP
arch_ct_in_ios = fe.FullArch(MISSING_TOOTH_NUMBER, 'CT', 'IOS')
# arch after curvilinear correction - original ios arch
arch_ios_curvilinear_correction = fe.FullArch(MISSING_TOOTH_NUMBER, 'IOS', 'IOS')
# arch after curvilinear correction - virtual ct features
arch_ct_in_ios_curvilinear_correction = fe.FullArch(MISSING_TOOTH_NUMBER, 'CT', 'IOS')
for i in TOOTH_NUMBER:
print('reading tooth ', i)
new_dicom_path = target_dicom_base + dicom_file_name + np.str(i) + '.csv'
new_stl_path = source_stl_base + stl_file_name + np.str(i) + '.csv'
stl_tem = Yomiread.read_csv(new_stl_path, 3, -1)
dicom_tem = Yomiread.read_csv(new_dicom_path, 3, -1)
tooth_stl_tem = fe.ToothFeature(stl_tem, i, 'IOS', 'IOS')
arch_ios.add_tooth(i, tooth_stl_tem)
tooth_dicom_tem = fe.ToothFeature(dicom_tem, i, 'CT', 'CT')
arch_ct.add_tooth(i, tooth_dicom_tem)
del stl_tem, dicom_tem
# ---- Perform Local ICP
local_ICP.do_local_registration(TRANS_INIT,THRESHOLD_ICP,RMS_LOCAL_REGISTRATION, arch_ios, arch_ct)
# check local registration quality (rms of local ICP of each tooth)
for i in arch_ios.tooth_list: # used for algorithm verification (count missing tooth as well)
#for i in arch_ios.existing_tooth_list: # used for real 3 images processing
print('tooth ' + np.str(i) + ' ICP rms is ' + np.str(arch_ios.get_tooth(i).ICP.inlier_rmse))
# Update spline points
arch_ios.update_spline()
# arch after curvilinear correction - original ios arch
arch_ios_curvilinear_correction = fe.FullArch(MISSING_TOOTH_NUMBER, 'IOS', 'IOS')
# arch after curvilinear correction - virtual ct features
arch_ct_in_ios_curvilinear_correction = fe.FullArch(MISSING_TOOTH_NUMBER, 'CT', 'IOS')
# Read CT and IOS full arch scan
for i in TOOTH_NUMBER:
print('reading CT tooth', i)
if i in MISSING_TOOTH_NUMBER:
print('missing tooth', i)
else:
new_dicom_path = target_dicom_base + dicom_file_name + np.str(i) + '.csv'
dicom_tem = Yomiread.read_csv(new_dicom_path, 3, -1)
tooth_dicom_tem = fe.ToothFeature(dicom_tem, i, 'CT', 'CT')
arch_ct.add_tooth(i, tooth_dicom_tem)
print('reading IOS tooth', i)
if i in MISSING_TOOTH_NUMBER:
print('Tooth '+np.str(i) + ' is covered by splint')
if i in TARGET_TOOTH:
print('Tooth ' + np.str(i) + ' is covered by target')
else:
new_stl_path = source_stl_base + stl_file_name + np.str(i) + '.csv'
stl_tem = Yomiread.read_csv(new_stl_path, 3, -1)
tooth_stl_tem = fe.ToothFeature(stl_tem, i, 'IOS', 'IOS')
arch_ios.add_tooth(i, tooth_stl_tem)
if 'stl_tem' in locals():
del stl_tem
if 'dicom_tem' in locals():
# arch after curvilinear correction - original ios arch
arch_ios_curvilinear_correction = fe.FullArch(MISSING_TOOTH_NUMBER, 'IOS',
'IOS')
# arch after curvilinear correction - virtual ct features
arch_ct_in_ios_curvilinear_correction = fe.FullArch(
MISSING_TOOTH_NUMBER, 'CT', 'IOS')
# Read CT and IOS full arch scan
for i in TOOTH_NUMBER:
print('reading CT tooth', i)
new_dicom_path = target_dicom_base + dicom_file_name + np.str(
i) + '.csv'
dicom_tem = Yomiread.read_csv(new_dicom_path, 3, -1)
tooth_dicom_tem = fe.ToothFeature(dicom_tem, i, 'CT', 'CT')
arch_ct.add_tooth(i, tooth_dicom_tem)
print('reading IOS tooth', i)
if i in MISSING_TOOTH_NUMBER:
print('Tooth ' + np.str(i) + ' is covered by splint')
if i in TARGET_TOOTH:
print('Tooth ' + np.str(i) + ' is covered by target')
else:
new_stl_path = source_stl_base + stl_file_name + np.str(i) + '.csv'
stl_tem = Yomiread.read_csv(new_stl_path, 3, -1)
tooth_stl_tem = fe.ToothFeature(stl_tem, i, 'IOS', 'IOS')
arch_ios.add_tooth(i, tooth_stl_tem)
if 'stl_tem' in locals():
del stl_tem
if 'dicom_tem' in locals():
# arch after curvilinear correction - virtual ct features
arch_ct_in_ios_curvilinear_correction = fe.FullArch(
MISSING_TOOTH_NUMBER, 'CT', 'IOS')
arch_ct_in_ios_fxt_curvilinear_correction = fe.Fixture(
MISSING_TOOTH_NUMBER, 'CT', 'IOS')
for i in TOOTH_NUMBER:
print('reading tooth ', i)
new_dicom_path = target_dicom_base + dicom_file_name + np.str(
i) + '.csv'
new_stl_path = source_stl_base + stl_file_name + np.str(i) + '.csv'
stl_tem = Yomiread.read_csv(new_stl_path, 3, -1)
dicom_tem = Yomiread.read_csv(new_dicom_path, 3, -1)
tooth_stl_tem = fe.ToothFeature(stl_tem, i, 'IOS', 'IOS')
arch_ios.add_tooth(i, tooth_stl_tem)
tooth_dicom_tem = fe.ToothFeature(dicom_tem, i, 'CT', 'CT')
arch_ct.add_tooth(i, tooth_dicom_tem)
arch_ct_original.add_tooth(i, tooth_dicom_tem)
del stl_tem, dicom_tem
# ---- Perform Local ICP
local_ICP.do_local_registration(TRANS_INIT, THRESHOLD_ICP,
RMS_LOCAL_REGISTRATION, arch_ios, arch_ct)
# check local registration quality (rms of local ICP of each tooth)
for i in arch_ios.tooth_list: # used for algorithm verification (count missing tooth as well)
# for i in arch_ios.existing_tooth_list: # used for real 3 images processing
print('tooth ' + np.str(i) + ' ICP rms is ' +
np.str(arch_ios.get_tooth(i).ICP.inlier_rmse))