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csv_creator.py
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csv_creator.py
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import os
import pathlib
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import paths as p
import utils as u
"""
Scripts used to create .csv files from the original dataset structure, as well as after the preprocessing, registration, and augmentation
"""
def create_task_1_training_csv(data_folder : pathlib.Path, output_csv_path : pathlib.Path):
complete_skull_path = pathlib.PurePath(data_folder, "complete_skull")
defective_skull_path = pathlib.PurePath(data_folder, "defective_skull")
cases = os.listdir(complete_skull_path / "bilateral")
cases = [item for item in cases if ".nrrd" in item]
defect_types = os.listdir(defective_skull_path)
data = list()
for defect_type in defect_types:
for case in cases:
complete_skull_path = pathlib.Path("complete_skull", defect_type, case)
defective_skull_path = pathlib.Path("defective_skull", defect_type, case)
implant_path = pathlib.Path("implant", defect_type, case)
data.append([complete_skull_path, defective_skull_path, implant_path])
dataframe = pd.DataFrame(data, columns=['Complete Skull Path', "Defective Skull Path", "Implant Path"])
dataframe.to_csv(output_csv_path, index=False)
def create_task_1_testing_csv(data_folder : pathlib.Path, output_csv_path : pathlib.Path):
defective_skull_path = pathlib.PurePath(data_folder, "defective_skull")
defect_types = os.listdir(defective_skull_path)
data = list()
for defect_type in defect_types:
cases = os.listdir(defective_skull_path / defect_type)
cases = [item for item in cases if ".nrrd" in item]
for case in cases:
skull_path = pathlib.Path("defective_skull", defect_type, case)
data.append([skull_path])
dataframe = pd.DataFrame(data, columns=["Defective Skull Path"])
dataframe.to_csv(output_csv_path, index=False)
def create_task_2_testing_csv(data_folder : pathlib.Path, output_csv_path : pathlib.Path):
cases = os.listdir(data_folder)
cases = [item for item in cases if ".nrrd" in item]
data = list()
for case in cases:
skull_path = pathlib.Path(case)
data.append([skull_path])
dataframe = pd.DataFrame(data, columns=["Defective Skull Path"])
dataframe.to_csv(output_csv_path, index=False)
def create_task_3_training_csv(data_folder : pathlib.Path, output_csv_path : pathlib.Path):
complete_skull_path = pathlib.PurePath(data_folder, "complete_skull")
cases = os.listdir(complete_skull_path)
cases = [item for item in cases if ".nrrd" in item]
data = list()
for case in cases:
complete_skull_path = pathlib.Path("complete_skull", case)
defective_skull_path = pathlib.Path("defective_skull", case)
implant_path = pathlib.Path("implant", case)
data.append([complete_skull_path, defective_skull_path, implant_path])
dataframe = pd.DataFrame(data, columns=['Complete Skull Path', "Defective Skull Path", "Implant Path"])
dataframe.to_csv(output_csv_path, index=False)
def create_task_3_testing_csv(data_folder : pathlib.Path, output_csv_path : pathlib.Path):
cases = os.listdir(data_folder)
cases = [item for item in cases if ".nrrd" in item]
data = list()
for case in cases:
skull_path = pathlib.Path(case)
data.append([skull_path])
dataframe = pd.DataFrame(data, columns=["Defective Skull Path"])
dataframe.to_csv(output_csv_path, index=False)
def split_training_validation(input_csv_path : pathlib.Path, output_training_csv_path : pathlib.Path, output_validation_csv_path : pathlib.Path, split_ratio : float):
dataframe = pd.read_csv(input_csv_path)
np.random.seed(12345)
training_indices = np.random.rand(len(dataframe)) < split_ratio
validation_indices = np.logical_not(training_indices)
training_dataframe = dataframe[training_indices]
validation_dataframe = dataframe[validation_indices]
training_dataframe.to_csv(output_training_csv_path, index=False)
validation_dataframe.to_csv(output_validation_csv_path, index=False)
def training_set_summary(data_folder : pathlib.Path, csv_path : pathlib.Path, dataset_name : str = "", show : bool=False):
dataframe = pd.read_csv(csv_path)
print("Summary of dataset:", dataset_name)
print("Dataset size: ", len(dataframe))
for current_id, case in dataframe.iterrows():
complete_skull_path = data_folder / case['Complete Skull Path']
defective_skull_path = data_folder / case['Defective Skull Path']
implant_path = data_folder / case['Implant Path']
complete_skull, defective_skull, implant, spacing = u.load_training_case(complete_skull_path, defective_skull_path, implant_path)
print("Current ID: ", current_id, "Shape: ", complete_skull.shape, "Spacing: ", spacing)
if defective_skull.shape != complete_skull.shape or defective_skull.shape != implant.shape:
raise ValueError("Images do not have the same shape.")
if show:
u.show_training_case(complete_skull, defective_skull, implant, spacing)
plt.close()
def testing_set_summary(data_folder : pathlib.Path, csv_path : pathlib.Path, dataset_name : str = "", show : bool=False):
dataframe = pd.read_csv(csv_path)
print("Summary of dataset:", dataset_name)
print("Dataset size: ", len(dataframe))
for current_id, case in dataframe.iterrows():
defective_skull_path = data_folder / case['Defective Skull Path']
defective_skull, spacing = u.load_testing_case(defective_skull_path)
print("Current ID: ", current_id, "Shape: ", defective_skull.shape, "Spacing: ", spacing)
if show:
u.show_training_case(None, defective_skull, None, spacing)
plt.close()
def run():
# create_task_1_training_csv(p.task_1_training_path, p.task_1_dataset_csv_path)
# split_training_validation(p.task_1_dataset_csv_path, p.task_1_training_csv_path, p.task_1_validation_csv_path, 0.9)
# create_task_1_testing_csv(p.task_1_testing_path, p.task_1_testing_csv_path)
# create_task_2_testing_csv(p.task_2_testing_path, p.task_2_testing_csv_path)
# create_task_3_training_csv(p.task_3_training_path, p.task_3_dataset_csv_path)
# split_training_validation(p.task_3_dataset_csv_path, p.task_3_training_csv_path, p.task_3_validation_csv_path, 0.9)
# create_task_3_testing_csv(p.task_3_testing_path, p.task_3_testing_csv_path)
# training_set_summary(p.task_1_training_path, p.task_1_training_csv_path, "Task 1 Training Set")
# training_set_summary(p.task_1_training_path, p.task_1_validation_csv_path, "Task 1 Validation Set")
# training_set_summary(p.task_3_training_path, p.task_3_training_csv_path, "Task 3 Training Set")
# training_set_summary(p.task_3_training_path, p.task_3_validation_csv_path, "Task 3 Validation Set")
# testing_set_summary(p.task_1_testing_path, p.task_1_testing_csv_path, "Task 1 Testing Set")
# testing_set_summary(p.task_2_testing_path, p.task_2_testing_csv_path, "Task 2 Testing Set")
# testing_set_summary(p.task_3_testing_path, p.task_3_testing_csv_path, "Task 3 Testing Set")
# training_set_summary(p.task_1_training_preprocessed_path, p.task_1_training_csv_path, "Task 1 Preprocessed Training Set")
# training_set_summary(p.task_1_training_preprocessed_path, p.task_1_validation_csv_path, "Task 1 Preprocessed Validation Set")
# training_set_summary(p.task_3_training_preprocessed_path, p.task_3_training_csv_path, "Task 3 Preprocessed Training Set")
# training_set_summary(p.task_3_training_preprocessed_path, p.task_3_validation_csv_path, "Task 3 Preprocessed Validation Set")
# testing_set_summary(p.task_1_testing_preprocessed_path, p.task_1_testing_csv_path, "Task 1 Preprocessed Testing Set")
# testing_set_summary(p.task_2_testing_preprocessed_path, p.task_2_testing_csv_path, "Task 2 Preprocessed Testing Set")
# testing_set_summary(p.task_3_testing_preprocessed_path, p.task_3_testing_csv_path, "Task 3 Preprocessed Testing Set")
pass
if __name__ == "__main__":
run()