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example_all.py
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example_all.py
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import os
import shutil
import time
import numpy as np
import SimpleITK as sitk
import pandas as pd
import anhir_method as am
import utils
import json
def run():
ids_to_run = list(range(0, 481))
data_path = None # Path to data folder
csv_file_path = None # Path to the CSV with registration pairs
results_path = None # Path to the result folder
output_csv_path = None # Path to the output CSV
if not os.path.isfile(output_csv_path):
df = pd.read_csv(csv_file_path)
del df['Unnamed: 0']
i_tre = np.empty(len(df))
i_tre[:] = np.nan
f_tre = np.empty(len(df))
f_tre[:] = np.nan
df['Initial TRE Median'] = i_tre
df['Final TRE Median'] = f_tre
df.to_csv(output_csv_path)
for current_id in ids_to_run:
dataframe = pd.read_csv(output_csv_path)
del dataframe['Unnamed: 0']
print("Current ID: ", current_id)
source_path = dataframe['Source image'][current_id]
target_path = dataframe['Target image'][current_id]
source_landmarks_path = dataframe['Source landmarks'][current_id]
target_landmarks_path = dataframe['Target landmarks'][current_id]
status = dataframe['status'][current_id]
# if status != "training":
# continue
sizes = dataframe['Image size [pixels]'][current_id]
sizes = sizes[:].split(", ")
y_size = int(sizes[0][1:])
x_size = int(sizes[1][:-1])
params = dict()
params['y_size'] = y_size
params['x_size'] = x_size
params['status'] = status
params['output_path'] = results_path
params['id'] = str(current_id)
params['source_path'] = os.path.join(data_path, source_path)
params['target_path'] = os.path.join(data_path, target_path)
params['source_landmarks_path'] = os.path.join(data_path, source_landmarks_path)
params['target_landmarks_path'] = os.path.join(data_path, target_landmarks_path)
b_time = time.time()
results = run_single(params)
e_time = time.time()
elapsed_time = (e_time - b_time) / 60
print("Elapsed time: ", elapsed_time, " minutes.")
transformed_source_landmarks_path = results['transformed_source_landmarks_path']
if status == "training":
i_tre = results['initial_tre']
f_tre = results['resulting_tre']
dataframe['Initial TRE Median'][current_id] = i_tre
dataframe['Final TRE Median'][current_id] = f_tre
dataframe['Execution time [minutes]'][current_id] = str(elapsed_time)
dataframe['Warped source landmarks'][current_id] = transformed_source_landmarks_path
dataframe.to_csv(output_csv_path)
def run_single(params):
source_path = params['source_path']
target_path = params['target_path']
source_landmarks_path = params['source_landmarks_path']
target_landmarks_path = params['target_landmarks_path']
status = params['status']
y_size = params['y_size']
x_size = params['x_size']
results_path = params['output_path']
current_id = params['id']
if not os.path.isdir(os.path.join(results_path, str(current_id))):
os.mkdir(os.path.join(results_path, str(current_id)))
source = utils.load_image(source_path)
target = utils.load_image(target_path)
source_landmarks = utils.load_landmarks(source_landmarks_path)
if status == "training":
print()
print("Training case.")
target_landmarks = utils.load_landmarks(target_landmarks_path)
else:
print()
print("Evaluation case.")
p_target, p_source, ia_target, ng_target, nr_target, i_u_x, i_u_y, u_x_nr, u_y_nr, warp_resampled_landmarks, warp_original_landmarks, return_dict = am.anhir_method(target, source)
transformed_landmarks = warp_original_landmarks(source_landmarks)
p_target = utils.normalize(p_target)
p_source = utils.normalize(p_source)
ia_target = utils.normalize(ia_target)
ng_target = utils.normalize(ng_target)
nr_target = utils.normalize(nr_target)
p_target_i = utils.to_image(p_target)
p_source_i = utils.to_image(p_source)
ia_target_i = utils.to_image(ia_target)
ng_target_i = utils.to_image(ng_target)
nr_target_i = utils.to_image(nr_target)
json_return_dict = json.dumps(return_dict)
with open(os.path.join(results_path, str(current_id), "info.json"), "w") as f:
f.write(json_return_dict)
if status == "training":
try:
o_median = np.median(utils.rtre(source_landmarks, target_landmarks, x_size, y_size))
r_median = np.median(utils.rtre(transformed_landmarks, target_landmarks, x_size, y_size))
print("Initial rTRE: ", o_median)
print("Resulting rTRE: ", r_median)
string_to_save = "Initial TRE: " + str(o_median) + "\n" + "Resulting TRE: " + str(r_median)
txt_path = os.path.join(results_path, str(current_id), "tre.txt")
with open(txt_path, "w") as file:
file.write(string_to_save)
except:
string_to_save = "Landmarks ERROR"
txt_path = os.path.join(results_path, str(current_id), "tre_error.txt")
with open(txt_path, "w") as file:
file.write(string_to_save)
source_save_path = os.path.join(results_path, str(current_id), "source.png")
target_save_path = os.path.join(results_path, str(current_id), "target.png")
transformed_target_g_save_path = os.path.join(results_path, str(current_id), "target_ng.png")
transformed_target_save_path = os.path.join(results_path, str(current_id), "target_nr.png")
ia_target_save_path = os.path.join(results_path, str(current_id), "target_ia.png")
sitk.WriteImage(p_source_i, source_save_path)
sitk.WriteImage(p_target_i, target_save_path)
sitk.WriteImage(ng_target_i, transformed_target_g_save_path)
sitk.WriteImage(nr_target_i, transformed_target_save_path)
sitk.WriteImage(ia_target_i, ia_target_save_path)
transformed_source_landmarks_path = os.path.join(results_path, str(current_id), "transformed_source_landmarks.csv")
utils.save_landmarks(transformed_source_landmarks_path, transformed_landmarks)
return_dict = dict()
return_dict['transformed_source_landmarks_path'] = os.path.join(str(current_id), "transformed_source_landmarks.csv")
if status == "training":
try:
return_dict['initial_tre'] = o_median
return_dict['resulting_tre'] = r_median
except:
return_dict['initial_tre'] = 0
return_dict['resulting_tre'] = 0
return return_dict
if __name__ == "__main__":
run()