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prepare_submission.py
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prepare_submission.py
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import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import SimpleITK as sitk
import paths
import utils
original_data_path = paths.original_data_path
csv_path = paths.csv_path
results_path = None # Path to the results (from main.py)
submission_path = None # Path where submission should be saved
def prepare_submission():
if not os.path.isdir(submission_path):
os.makedirs(submission_path)
output_csv_path = os.path.join(submission_path, "registration-results.csv")
prepare_output_csv(csv_path, output_csv_path)
ids = range(0, 481)
for current_id in ids:
current_id = str(current_id)
case_path = os.path.join(submission_path, current_id)
if not os.path.isdir(case_path):
os.makedirs(case_path)
source_landmarks_path = os.path.join(results_path, current_id, "source_landmarks.csv")
transformed_source_landmarks_path = os.path.join(results_path, current_id, "transformed_source_landmarks.csv")
target_landmarks_path = os.path.join(results_path, current_id, "target_landmarks.csv")
time_path = os.path.join(results_path, current_id, "time.txt")
source_landmarks = utils.load_landmarks(source_landmarks_path)
transformed_source_landmarks = utils.load_landmarks(transformed_source_landmarks_path)
try:
target_landmarks = utils.load_landmarks(target_landmarks_path)
if target_landmarks.shape == transformed_source_landmarks.shape:
status = "training"
else:
status = "evaluation"
except:
status = "evaluation"
with open(time_path, "r") as file:
execution_time = file.read()
dataframe = pd.read_csv(output_csv_path)
del dataframe['Unnamed: 0']
print()
print("Current ID: ", current_id)
print("Execution time: ", execution_time)
current_id = int(current_id)
org_source_path = dataframe['Source image'][current_id]
org_target_path = dataframe['Target image'][current_id]
sizes = dataframe['Image size [pixels]'][current_id]
sizes = sizes[:].split(", ")
y_size = int(sizes[0][1:])
x_size = int(sizes[1][:-1])
diagonal = np.sqrt(y_size**2 + x_size**2)
current_id = str(current_id)
if ".jpg" in org_source_path:
org_source_path = org_source_path.replace(".jpg", ".mha")
org_target_path = org_target_path.replace(".jpg", ".mha")
elif ".png" in org_source_path:
org_source_path = org_source_path.replace(".png", ".mha")
org_target_path = org_target_path.replace(".png", ".mha")
source_path = os.path.join(results_path, current_id, "source.mha")
original_source_path = os.path.join(original_data_path, org_source_path)
original_target_path = os.path.join(original_data_path, org_target_path)
def get_size(path):
reader = sitk.ImageFileReader()
reader.SetFileName(path)
reader.LoadPrivateTagsOn()
reader.ReadImageInformation()
size = reader.GetSize()
return size
source_size = get_size(source_path)
original_source_size = get_size(original_source_path)
original_target_size = get_size(original_target_path)
print("Source path: ", source_path)
print("Original source path: ", original_source_path)
print("Original target path: ", original_target_path)
print("Resampled size: ", source_size)
print("Original source size: ", original_source_size)
print("Original target size: ", original_target_size)
if status == "training":
print("Resampled median initial rTRE: ", np.median(utils.calculate_rtre(source_landmarks, target_landmarks, np.sqrt(source_size[0]**2 + source_size[1]**2))))
print("Resampled median final rTRE: ", np.median(utils.calculate_rtre(transformed_source_landmarks, target_landmarks, np.sqrt(source_size[0]**2 + source_size[1]**2))))
_, _, _, _, new_shape = calculate_pad_size(original_source_size, original_target_size)
resample_ratio = max(new_shape) / max(source_size)
org_source_landmarks = source_landmarks.copy()
org_source_landmarks[:, 0] = org_source_landmarks[:, 0] * resample_ratio - (int(np.floor((new_shape[0] - original_source_size[0])/2)))
org_source_landmarks[:, 1] = org_source_landmarks[:, 1] * resample_ratio - (int(np.floor((new_shape[1] - original_source_size[1])/2)))
org_transformed_source_landmarks = transformed_source_landmarks.copy()
org_transformed_source_landmarks[:, 0] = org_transformed_source_landmarks[:, 0] * resample_ratio - (int(np.floor((new_shape[0] - original_target_size[0])/2)))
org_transformed_source_landmarks[:, 1] = org_transformed_source_landmarks[:, 1] * resample_ratio - (int(np.floor((new_shape[1] - original_target_size[1])/2)))
if status == "training":
org_target_landmarks = target_landmarks.copy()
org_target_landmarks[:, 0] = org_target_landmarks[:, 0] * resample_ratio - (int(np.floor((new_shape[0] - original_target_size[0])/2)))
org_target_landmarks[:, 1] = org_target_landmarks[:, 1] * resample_ratio - (int(np.floor((new_shape[1] - original_target_size[1])/2)))
i_tre = np.median(utils.calculate_rtre(org_source_landmarks, org_target_landmarks, diagonal))
f_tre = np.median(utils.calculate_rtre(org_transformed_source_landmarks, org_target_landmarks, diagonal))
print("Original median initial rTRE: ", i_tre)
print("Original median final rTRE: ", f_tre)
dataframe['Initial TRE Median'][int(current_id)] = i_tre
dataframe['Final TRE Median'][int(current_id)] = f_tre
try:
string_to_save = "Initial TRE: " + str(i_tre) + "\n" + "Resulting TRE: " + str(f_tre)
txt_path = os.path.join(submission_path, str(current_id), "tre.txt")
with open(txt_path, "w") as file:
file.write(string_to_save)
except:
pass
transformed_path = os.path.join(current_id, "transformed_source_landmarks.csv")
org_src_path = os.path.join(current_id, "org_source_landmarks.csv")
utils.save_landmarks_submission(org_transformed_source_landmarks, os.path.join(submission_path, transformed_path))
utils.save_landmarks_submission(org_source_landmarks, os.path.join(submission_path, org_src_path))
if status == "training":
org_trg_path = os.path.join(current_id, "org_target_landmarks.csv")
utils.save_landmarks_submission(org_target_landmarks, os.path.join(submission_path, org_trg_path))
dataframe['Execution time [minutes]'][int(current_id)] = str(float(execution_time) / 60)
dataframe['Warped source landmarks'][int(current_id)] = transformed_path
dataframe.to_csv(output_csv_path)
def calculate_pad_size(source_size, target_size):
y_size_source, x_size_source = source_size
y_size_target, x_size_target = target_size
new_y_size = max(y_size_source, y_size_target)
new_x_size = max(x_size_source, x_size_target)
new_shape = (new_y_size, new_x_size)
source_y_pad = ((int(np.floor((new_shape[0] - y_size_source)/2))), int(np.ceil((new_shape[0] - y_size_source)/2)))
source_x_pad = ((int(np.floor((new_shape[1] - x_size_source)/2))), int(np.ceil((new_shape[1] - x_size_source)/2)))
target_y_pad = ((int(np.floor((new_shape[0] - y_size_target)/2))), int(np.ceil((new_shape[0] - y_size_target)/2)))
target_x_pad = ((int(np.floor((new_shape[1] - x_size_target)/2))), int(np.ceil((new_shape[1] - x_size_target)/2)))
return source_x_pad, source_y_pad, target_x_pad, target_y_pad, new_shape
def prepare_output_csv(input_csv_path, output_csv_path):
df = pd.read_csv(input_csv_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)
if __name__ == "__main__":
prepare_submission()