points_tem_corrected = transpose_pc(points_tem_corrected_transformed, modify_matrix2)
tooth_feature_splint_correction = fe.ToothFeature(points_tem_corrected, i, 'IOS', 'IOS')
arch_ios_splint_correction.add_tooth(i, tooth_feature_splint_correction)
#target_points_transfromed = transpose_pc(arch_ios.target_points, modify_matrix)
target_points_transfromed = transpose_pc(splint_fiducial_ios[20:, :], modify_matrix)
target_points_transfromed_cylindrical = coordinates.convert_cylindrical(target_points_transfromed, splint_ground.spline_points_fine_cylindrical_mid_points)
target_points_corrected_transformed = sc.displacement(target_points_transfromed, target_points_transfromed_cylindrical, splint_ground.spline_points_fine_cylindrical_mid_points, displacement_splint)
#target_points_corrected = transpose_pc(target_points_corrected_transformed, np.linalg.inv(modify_matrix))
target_points_corrected = transpose_pc(target_points_corrected_transformed, modify_matrix2)
arch_ios_splint_correction.add_target(target_points_corrected)
print('original target points are', splint_fiducial_ios[20:, :])
print('target_points corrected are', target_points_corrected)
print('target_points corrected check are', arch_ios_splint_correction.target_points)
local_ICP.do_local_registration(TRANS_INIT, THRESHOLD_ICP, RMS_LOCAL_REGISTRATION, arch_ios_splint_correction, arch_ct, DEBUG=0)
# check local registration quality (rms of local ICP of each tooth)
for i in arch_ios_splint_correction.existing_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_splint_correction.get_tooth(i).ICP.inlier_rmse))
# ---- Step 2 Arch correction
# Transform ct features to ios space using ICP transformation
for i in arch_ct_in_ios.existing_tooth_list:
print('transform tooth ', i)
points_ICP_tem = transpose_pc(arch_ct.get_tooth(i).points, arch_ct.get_tooth(i).local_ICP_transformation)
tooth_feature_ICP_tem = fe.ToothFeature(points_ICP_tem, i, 'CT', 'IOS')
arch_ct_in_ios.add_tooth(i, tooth_feature_ICP_tem)
# generate virtual landmarks
ctl_target = []
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_ct.update_spline()
arch_ct_original.update_spline()
# ---- Perform initial alignment
trans_init = arch_ct.get_tooth(NEIGHBOUR_TOOTH).local_ICP_transformation
# Transform ct features to ios space
for i in arch_ct.tooth_list:
print('transform tooth ', i)