def preprocess(key, src_name, sub_scan):
scan = sub_scan[key]
make_dir(TEMP_PATHS)
mri_path = scan['mri.nii']
pet_path = scan['pet.nii']
show_data("path", [mri_path, pet_path])
# Pipeline Configuration
Intensity_Normalization = True
Skull_Strip = False
Bias_Correction = True
Petpvc = True
if (not image_registration(mri_path, pet_path, f"{IMG_REG}/pet.nii")):
return False
if(not preprocess_mri(mri_path, Intensity_Normalization=Intensity_Normalization, Skull_Strip=Skull_Strip, Bias_Correction=Bias_Correction)):
return False
if(not preprocess_pet(f"{IMG_REG}/pet.nii", Skull_Strip=Skull_Strip, Petpvc=Petpvc)):
return False
make_dir([f"{PREPROCESSED}/{src_name}/{key}"])
copyfile(f"{TEMP_OUTPUT}/mri.nii",
f"{PREPROCESSED}/{src_name}/{key}/mri.nii")
copyfile(f"{TEMP_OUTPUT}/pet.nii",
f"{PREPROCESSED}/{src_name}/{key}/pet.nii")
def postprocess(key, src_name, sub_scan):
scan = sub_scan[key]
mri_path = scan['mri.nii']
pet_path = scan['pet.nii']
make_dir(POSTPROCESS_TEMP_PATHS)
show_data("path", [key, mri_path, pet_path])
# Pipeline Configuration
Dimension_Check = True
Feature_Selection = True
Structural_Similarity = True
if (not postprocess_file(key,
mri_path,
"mri",
Dimension_Check=Dimension_Check,
Feature_Selection=Feature_Selection,
Structural_Similarity=Structural_Similarity)):
return False
if (not postprocess_file(key,
pet_path,
"pet",
Dimension_Check=Dimension_Check,
Feature_Selection=Feature_Selection,
Structural_Similarity=Structural_Similarity)):
return False
make_dir([f"{POSTPROCESS}/{src_name}/{key}"])
make_dir([f"{POSTPROCESS}/{src_name}/{key}/img"])
print("COPYING FILES")
shutil.copyfile(mri_path, f"{POSTPROCESS}/{src_name}/{key}/mri.nii")
shutil.copyfile(pet_path, f"{POSTPROCESS}/{src_name}/{key}/pet.nii")
shutil.copyfile(f"{SSIM}/mri.jpg",
f"{POSTPROCESS}/{src_name}/{key}/img/mri.jpg")
shutil.copyfile(f"{SSIM}/pet.jpg",
f"{POSTPROCESS}/{src_name}/{key}/img/pet.jpg")
remove_dir(POSTPROCESS_TEMP_PATHS)
if (f"{dest_name}.zip" in os.listdir(POST_ZIPPED)):
continue
show_data("name", [src_name, dest_name])
extracted_paths = extract([file])
extracted_files = get_nii(extracted_paths)
print(f"\n{src_name.upper()} PREPROCESSING\n")
driver(extracted_files, dest_name)
global df
df.to_csv(f"{POSTPROCESS}/{dest_name}/postprocess.csv")
df = pd.DataFrame(
columns=['subject_id', 'type', 'mse', 'ssim', 'distance'])
print(f"\n{src_name.upper()} ZIPPING\n")
make_archive(f"{POSTPROCESS}/{dest_name}",
f"{POST_ZIPPED}/{dest_name}.zip")
# print("REMOVING")
# shutil.rmtree(f"{POSTPROCESS}")
if __name__ == "__main__":
make_dir(DATA_PATHS)
make_dir(SCRIPT_PATHS)
df = pd.DataFrame(
columns=['subject_id', 'type', 'mse', 'ssim', 'distance'])
post_preprocess()
def make_struct():
print("MAKING STRUCTRE")
files = get_nii(EXTRACT)
for file in files:
make_dir(file)
