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anhir_method.py
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import time
import numpy as np
import matplotlib.pyplot as plt
import preprocessing as pp
import initial_alignment as ia
import fail_detector as fd
import nonrigid_registration as nr
import utils
def anhir_method(source, target, echo=True):
##### Step 0 - Parameters, Smoothing and Initial Resampling #####
b_time_total = time.time()
params = dict()
params["echo"] = echo
params["initial_alignment_size"] = 2048
params["centroid_rotation_size"] = 512
params["nonrigid_registration_size"] = 2048
params["gaussian_divider"] = 1.24
params['nr_method'] = "dm" # pd or dm
nr_params = dict()
nr_params['echo'] = echo
nr_params['global_min_size'] = 64
nr_params['global_max_size'] = 512
nr_params['local_min_size'] = 64
nr_params['local_max_size'] = 768
nr_params['global_iterations'] = 100
nr_params['inner_iterations'] = 15
nr_params['outer_iterations'] = 5
nr_params['L_smooth'] = 1e7
nr_params['L_sigma'] = 1
nr_params['R_sigma'] = 1
nr_params['M_sigma'] = 2
nr_params['x_box'] = 19
nr_params['y_box'] = 19
nr_params['spacing'] = (1.0, 1.0)
nr_params['update_mode'] = "composition"
nr_params['gradient_mode'] = "symmetric"
nr_params['diffusion_sigma'] = (2.0, 2.0)
nr_params['fluid_sigma'] = (0.5, 0.5)
nr_params['mind_sigma'] = (1.0, 1.0)
nr_params['mind_radius'] = (2, 2)
nr_params['early_stop'] = 10
return_dict = dict()
initial_resample_ratio = utils.calculate_resample_size(source, target, max(params["initial_alignment_size"], params["nonrigid_registration_size"]))
source = utils.gaussian_filter(source, initial_resample_ratio / params["gaussian_divider"])
target = utils.gaussian_filter(target, initial_resample_ratio / params["gaussian_divider"])
if echo:
print()
print("Registration start.")
print()
print("Source shape: ", source.shape)
print("Target shape: ", target.shape)
##### Step 1 - Preprocessing #####
if echo:
print()
print("Preprocessing start.")
print()
b_time_r = time.time()
p_source, p_target = utils.resample_both(source, target, initial_resample_ratio)
e_time_r = time.time()
tt_source = p_source.copy()
if echo:
print("Initially resampled source shape: ", p_source.shape)
print("Initially resampled target shape: ", p_target.shape)
print("Time for initial resampling: ", e_time_r - b_time_r, " seconds.")
b_time_p = time.time()
p_source, p_target, t_source, t_target, source_shift, target_shift = pp.preprocess(p_source, p_target, echo)
e_time_p = time.time()
return_dict["preprocessing_time"] = e_time_p - b_time_p
if echo:
print("Source shift: ", source_shift)
print("Target shift: ", target_shift)
print("Preprocessed source shape: ", p_source.shape)
print("Preprocessed target shape: ", p_target.shape)
print("Time for preprocessing: ", e_time_p - b_time_p, " seconds.")
print()
print("Preprocessing end.")
print()
##### Step 2 - Initial Alignment #####
b_ia_time = time.time()
if echo:
print("Initial alignment start.")
print()
ia_resample_ratio = params["nonrigid_registration_size"] / params["initial_alignment_size"]
to_cv_source, to_cv_target = utils.resample_both(p_source, p_target, ia_resample_ratio)
cv_failed = False
ct_failed = False
ia_failed = False
i_u_x, i_u_y, initial_transform, cv_failed = ia.cv_initial_alignment(to_cv_source, to_cv_target, echo)
if cv_failed:
if echo:
print("CV failed.")
print()
print("CT start.")
print()
ia_resample_ratio = params["nonrigid_registration_size"] / params["centroid_rotation_size"]
to_ct_source, to_ct_target = utils.resample_both(p_source, p_target, ia_resample_ratio)
i_u_x, i_u_y, initial_transform, ct_failed = ia.ct_initial_alignment(to_ct_source, to_ct_target, echo)
if ct_failed:
if echo:
print()
print("CT failed.")
print("Initial alignment failed.")
ia_failed = True
if ia_failed:
i_u_x, i_u_y = np.zeros(p_source.shape), np.zeros(p_target.shape)
else:
y_size, x_size = np.shape(p_source)
i_u_x, i_u_y = utils.resample_displacement_field(i_u_x, i_u_y, x_size, y_size)
e_ia_time = time.time()
return_dict["cv_failed"] = cv_failed
return_dict["ct_failed"] = ct_failed
return_dict["ia_failed"] = ia_failed
return_dict["initial_alignment_time"] = e_ia_time - b_ia_time
if echo:
print()
print("Elapsed time for initial alignment: ", e_ia_time - b_ia_time, " seconds.")
print("Initial alignment end.")
print()
ia_source = utils.warp_image(p_source, i_u_x, i_u_y)
u_x_g, u_y_g = nr.partial_data_registration_global(ia_source, p_target, nr_params)
u_x_g, u_y_g = utils.compose_vector_fields(i_u_x, i_u_y, u_x_g, u_y_g)
ng_source = utils.warp_image(p_source, u_x_g, u_y_g)
success = fd.detect_mind_failure(ia_source, p_target, ng_source, echo)
if not success:
u_x_g, u_y_g = i_u_x, i_u_y
ng_source = ia_source
return_dict["ng_failed"] = not success
##### Step 3 - Nonrigid Registration #####
b_nr_time = time.time()
if echo:
print("Nonrigid registration start.")
print()
if params['nr_method'] == "dm":
u_x_nr, u_y_nr = nr.dm(ng_source, p_target, nr_params)
u_x_nr, u_y_nr = utils.compose_vector_fields(u_x_g, u_y_g, u_x_nr, u_y_nr)
nr_source = utils.warp_image(p_source, u_x_nr, u_y_nr)
elif params['nr_method'] == "pd":
u_x_nr, u_y_nr = nr.partial_data_registration_local(ng_source, p_target, nr_params)
u_x_nr, u_y_nr = utils.compose_vector_fields(u_x_g, u_y_g, u_x_nr, u_y_nr)
nr_source = utils.warp_image(p_source, u_x_nr, u_y_nr)
e_nr_time = time.time()
return_dict["nonrigid_registration_time"] = e_nr_time - b_nr_time
if echo:
print()
print("Elapsed time for nonrigid registration: ", e_nr_time - b_nr_time, " seconds.")
print("Nonrigid registration end.")
print()
##### Step 4 - Warping function creation #####
def warp_original_landmarks(source_landmarks):
source_landmarks = source_landmarks / initial_resample_ratio
source_landmarks = utils.pad_landmarks(source_landmarks, target_shift[0], target_shift[2])
source_landmarks = utils.transform_landmarks(source_landmarks, u_x_nr, u_y_nr)
source_l_x, source_r_x, source_l_y, source_r_y = source_shift
target_l_x, target_r_x, target_l_y, target_r_y = target_shift
source_landmarks[:, 0] = source_landmarks[:, 0] - source_l_x
source_landmarks[:, 1] = source_landmarks[:, 1] - source_l_y
out_y_size, out_x_size = np.shape(source)
in_y_size, in_x_size = np.shape(tt_source)
source_landmarks[:, 0] = source_landmarks[:, 0] * out_x_size / in_x_size
source_landmarks[:, 1] = source_landmarks[:, 1] * out_y_size / in_y_size
return source_landmarks
def warp_resampled_landmarks(source_landmarks, target_landmarks):
source_landmarks = source_landmarks / initial_resample_ratio
target_landmarks = target_landmarks / initial_resample_ratio
source_landmarks = utils.pad_landmarks(source_landmarks, target_shift[0], target_shift[2])
target_landmarks = utils.pad_landmarks(target_landmarks, source_shift[0], source_shift[2])
transformed_source_landmarks = utils.transform_landmarks(source_landmarks, u_x_nr, u_y_nr)
return source_landmarks, transformed_source_landmarks, target_landmarks
e_time_total = time.time()
return_dict["total_time"] = e_time_total - b_time_total
if echo:
print("Total registration time: ", e_time_total - b_time_total, " seconds.")
print("End of registration.")
print()
return p_source, p_target, ia_source, ng_source, nr_source, i_u_x, i_u_y, u_x_nr, u_y_nr, warp_resampled_landmarks, warp_original_landmarks, return_dict