def check_tre(targets, fiducials):
# define number of trials, mean error and stdev
tre_total = []
tre_1 = []
tre_2 = []
tre_3 = []
tre_4 = []
original_fiducials = np.copy(fiducials)
#print('original_fiducials are', original_fiducials)
for i in range(N): # for each trial
# generate noise on fiducials
fiducials_new = []
# add biased errors
#print('loop', i)
if BIAS_ERROR_FLAG == 1:
center = np.array([-0.154, 0, 0])
if ORIENTATION == 1:
#print('fiducials before biased errors are', original_fiducials)
fiducials = random_points_3d.biased_3d_points_1(
original_fiducials, center, BIAS_MEAN, BIAS_STDEV)
#print('fiducials after biased errors are', fiducials)
if ORIENTATION == 2:
fiducials = random_points_3d.biased_3d_points_2(
original_fiducials, center, BIAS_MEAN, BIAS_STDEV)
if ORIENTATION == 3:
fiducials = random_points_3d.biased_3d_points_3(
original_fiducials, center, BIAS_MEAN, BIAS_STDEV)
for fiducial in fiducials:
# select element 0 from the random values
# add random errors
# the function random_3d_points only return a matrix, to select a vector, should specify the row number
fiducial_tem = fiducial + random_points_3d.random_3d_points(
1, RAN_MEAN, RAN_STDEV)[0]
fiducials_new.append(fiducial_tem)
# perform registration with perturbed fiducials
#print('fiducials_after random errors are', fiducials_new)
#print('fiducials original check are', original_fiducials)
#print('fiducial error vector is', np.linalg.norm(fiducials_new - original_fiducials, axis=1))
R, t = registration.point_set_registration(fiducials_new,
original_fiducials)
#print('R is', R)
#print('t is', np.linalg.norm(t))
# perform registration on targets
targets_new = []
for target in targets:
target_tem = np.matmul(R, target) + t
targets_new.append(target_tem)
tre = np.asarray(targets_new) - targets
#print('tre vector is', tre)
tre = np.linalg.norm(tre, axis=1)
#print('tre is', tre)
tre_total = np.append(tre_total, tre)
tre_1.append(tre[0])
tre_2.append(tre[1])
tre_3.append(tre[2])
tre_4.append(tre[3])
tre_sep = [tre_1, tre_2, tre_3, tre_4]
value_95 = []
for j in range(len(targets)):
value = sa.fit_models_np_plot(tre_sep[j])
value_95.append(value)
return value_95
def check_tre(targets, fiducials):
# define number of trials, mean error and stdev
tre_total = []
tre_ang_total = []
tre_trans_total = []
x_error = []
y_error = []
z_error = []
original_fiducials = np.copy(fiducials)
for i in range(N): # for each trial
# generate noise on fiducials
fiducials_new = []
# add biased errors
if BIAS_ERROR_FLAG == 1:
center = np.array([-0.154, 0, 0])
if ORIENTATION == 1:
fiducials = random_points_3d.biased_3d_points_1(
original_fiducials, center, BIAS_MEAN, BIAS_STDEV)
if ORIENTATION == 2:
fiducials = random_points_3d.biased_3d_points_2(
original_fiducials, center, BIAS_MEAN, BIAS_STDEV)
if ORIENTATION == 3:
fiducials = random_points_3d.biased_3d_points_3(
original_fiducials, center, BIAS_MEAN, BIAS_STDEV)
for fiducial in fiducials:
# select element 0 from the random values
# add random errors
# the function random_3d_points only return a matrix, to select a vector, should specify the row number
fiducial_tem = fiducial + random_points_3d.random_3d_points(
1, RAN_MEAN, RAN_STDEV)[0]
fiducials_new.append(fiducial_tem)
# perform registration with perturbed fiducials
R, t = registration.point_set_registration(fiducials_new,
original_fiducials)
# perform registration on targets
targets_new = []
if np.ndim(targets) == 1:
targets_new = np.matmul(R, targets) + t
tre_tem = np.asarray(targets_new) - targets
angle_err = np.matmul(R, [0, 0, 1])
print('angle_err is', angle_err)
# print('tre vector is', tre)
tre = np.linalg.norm(tre_tem)
diff_z = tre_tem[2]
diff_x = tre_tem[0]
diff_y = tre_tem[1]
# print('tre is', tre)
tre_total.append(tre)
x_error.append(diff_x)
y_error.append(diff_y)
z_error.append(diff_z)
else:
tre_sep = []
tre_ang_sep = []
tre_trans_sep = []
x_error_sep = []
y_error_sep = []
z_error_sep = []
for i in range(len(targets)):
target = targets[i, :]
target_tem = np.matmul(R, target) + t
angle_err = compare.comp_rot_misori(R, np.eye(3))
trans_err = np.linalg.norm(t)
tre_tem = target_tem - targets[i, :]
tre = np.linalg.norm(tre_tem)
diff_z = tre_tem[2]
diff_x = tre_tem[0]
diff_y = tre_tem[1]
x_error_sep.append(diff_x)
y_error_sep.append(diff_y)
z_error_sep.append(diff_z)
tre_sep.append(tre)
tre_ang_sep.append(angle_err)
tre_trans_sep.append(trans_err)
tre_total.append(tre_sep)
tre_ang_total.append(tre_ang_sep)
tre_trans_total.append(tre_trans_sep)
x_error.append(x_error_sep)
y_error.append(y_error_sep)
z_error.append(z_error_sep)
tre_total = np.asarray(tre_total)
tre_ang_total = np.asarray(tre_ang_total)
tre_trans_total = np.asarray(tre_trans_total)
x_error = np.array(x_error)
y_error = np.array(y_error)
z_error = np.array(z_error)
if np.ndim(targets) == 1:
value_95, mean, std = sa.fit_models_np_plot_mean_std(tre_total)
print('value 95 is', value_95)
print('mean is', mean)
print('std is', std)
else:
value_95 = []
value_95_ang = []
value_95_trans = []
value_95_x = []
value_95_y = []
value_95_z = []
for j in range(len(targets)):
value = sa.fit_models_np_plot(tre_total[:, j])
value_95.append(value)
value_ang = sa.fit_models_np_plot(tre_ang_total[:, j])
value_95_ang.append(value_ang)
value_trans = sa.fit_models_np_plot(tre_trans_total[:, j])
value_95_trans.append(value_trans)
value_x = sa.fit_models_np_plot_mean_std(x_error[:, j])
value_95_x.append(value_x[0])
value_y = sa.fit_models_np_plot_mean_std(y_error[:, j])
value_95_y.append(value_y[0])
value_z = sa.fit_models_np_plot_mean_std(z_error[:, j])
value_95_z.append(value_z[0])
print('value 95 is', value_95)
print('value 95 angle is', value_95_ang)
print('value 95 trans is', value_95_trans)
print('value 95 x is', value_95_x)
print('value 95 y is', value_95_y)
print('value 95 z is', value_95_z)
return value_95, value_95_x, value_95_y, value_95_z
mean = 0
stdev = 0.15
fiducial_pc = []
target_pc = []
tre_total = []
tre_1 = []
tre_2 = []
tre_3 = []
tre_4 = []
for i in range(n): # for each trial
# generate noise on fiducials
fiducials_new = []
for fiducial in fiducials_original:
# select element 0 from the random values
# the function random_3d_points only return a matrix, to select a vector, should specify the row number
fiducial_tem = fiducial + random_points_3d.random_3d_points(
1, mean, stdev)[0]
fiducials_new.append(fiducial_tem)
# perform registration with perturbed fiducials
R, t = registration.point_set_registration(fiducials_new,
fiducials_original)
# perform registration on targets
targets_new = []
for target in targets_original:
target_tem = np.matmul(R, target) + t
targets_new.append(target_tem)
tre = np.asarray(targets_new) - targets_original
tre = np.linalg.norm(tre, axis=1)
tre_total = np.append(tre_total, tre)
print('registration')
tre_total = []
tre_ang_total = []
tre_trans_total = []
x_error = []
y_error = []
z_error = []
for i in range(N):
# add random noise to all points
print('iteration ', i + 1)
turbulent_surface_points = []
rms = []
for point in original_surface_points:
# the function random_3d_points only return a matrix, to select a vector, should specify the row number
point_tem = point + random_points_3d.random_3d_points(
1, RAN_MEAN, RAN_STDEV)[0]
turbulent_surface_points.append(point_tem)
turbulent_surface_points = np.asarray(turbulent_surface_points)
# perform ICP surface matching
ICP_single = local_ICP.registration(VOXEL_SIZE_ICP, THRESHOLD_ICP,
original_surface_points,
turbulent_surface_points,
TRANS_INIT)
rms.append(ICP_single.inlier_rmse)
transformation = ICP_single.transformation
# check TRE
tre_sep = []
tre_ang_sep = []
tre_trans_sep = []
x_error_sep = []
