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'''
write by ygq
create on 2025-07-24
update kaggle data clean
依次解析train.csv以及test.csv文件，获取每个数据集基本信息；
根据解析的id查找对应的train/test目录下的影像并做规范处理，同时查找label的segment目录下的标签，提取不同部位的CT的标签位置保存到json文件中；
完成后保存json并退出
'''
import os
import glob
import pandas as pd
import SimpleITK as sitk
import argparse
import json
from tqdm import tqdm
from util import meta_data
import util
import numpy as np
# from bert_helper import *
# model_name = "bert-large-uncased"
# reduce_method = 'mean'
# max_words_num = 32 # max number of words in the caption > 2
# embeder, tokenizer = get_frozen_embeder(model_name)
# string1 = "modality: ct, gender: female, age: 51, roi: abdomen"
# embeder_output1 = str2emb(string1, max_words_num, embeder, tokenizer, reduce_method=reduce_method)
# string2 = "modality: ct, gender: female, age: 50, roi: head"
# embeder_output2 = str2emb(string2, max_words_num, embeder, tokenizer, reduce_method=reduce_method)
# input_size = embeder.config.vocab_size
# in_size = embeder.config.hidden_size
# print(embeder, input_size, in_size)
# print(tokenizer)
# print(embeder_output1)
# print(embeder_output1.shape) # torch.Size([1, 8, 768])
# print(embeder_output2)
# print(embeder_output2.shape) # torch.Size([1, 8, 768])
# error = torch.abs(embeder_output1 - embeder_output2)
# print(error)
# print("Embedding distance between the two sentences: ")
# print(f"String1: {string1}")
# print(f"String2: {string2}")
# print(torch.mean(error))
# exit()
meta_id_name='Patient'
meta_weeks_name='Weeks'
meta_fvc_name='FVC'
meta_percent_name='Percent'
meta_age_name='Age'
meta_sex_name='Sex'
meta_status_name='SmokingStatus'
TASK_VALUE="segmentation"
CLAMP_RANGE_CT = [-300,300]
CLAMP_RANGE_MRI = [-1,0] # MRI images threshold placeholder TBC...
# def find_metadata_files(path):
# # for Cancer Image Archive (TCIA) dataset
# search_pattern = os.path.join(path, '**', 'metadata.csv')
# return glob.glob(search_pattern, recursive=True)
def find_metadata_files(path):
# for Cancer Image Archive (TCIA) dataset
search_pattern = os.path.join(path, '*.csv')
return glob.glob(search_pattern, recursive=True)
##added by yanguoqing on 20250527
def find_image_dirs(path):
return os.listdir(path)
##modify by yanguoqing on 20250527
def load_dicom_images(folder_path):
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames(folder_path)
reader.SetFileNames(dicom_names)
image = reader.Execute()
return dicom_names,image
##added by yanguoqing on 20250527
def load_dicom_tag(imgs):
reader = sitk.ImageFileReader()
# dicom_names = reader.GetGDCMSeriesFileNames(folder_path)
reader.SetFileName(imgs)
reader.ReadImageInformation() # 仅读取元信息，不加载像素数据
# metadata_keys = reader.GetMetaDataKeys()
tag=reader.Execute()
return tag
def load_nrrd(fp):
return sitk.ReadImage(fp)
def save_nifti(image, output_path, folder_path):
# Set metadata in the NIfTI file's header
output_dirpath = os.path.dirname(output_path)
if not os.path.exists(output_dirpath):
print(f"Creating directory {output_dirpath}")
os.makedirs(output_dirpath)
# Set metadata in the NIfTI file's header
image.SetMetaData("FolderPath", folder_path)
sitk.WriteImage(image, output_path)
##modify by yanguoqing on 20250527
def convert_windows_to_linux_path(windows_path):
# Replace backslashes with forward slashes and remove the drive letter
# Some meta files have windows paths, but the data is stored on a linux server
linux_path = windows_path.replace('\\', '/')
if ':' in linux_path:
linux_path = linux_path.split(':', 1)[1]
return linux_path
def main(target_path, output_dir):
metadata_files = find_metadata_files(target_path)
pid_dirs=find_image_dirs(target_path)
failed_files = []
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
json_output_path = os.path.join(output_dir, 'nifti_mappings.json')
failed_files_path = os.path.join(output_dir, 'failed_files.json')
meta = meta_data()
# Initialize the JSON file
if not os.path.exists(json_output_path):
with open(json_output_path, 'w') as json_file:
json.dump({}, json_file)
if pid_dirs:
for pid_dir in tqdm(pid_dirs, desc="Processing pid dirs"):
if not os.path.isdir(os.path.join(target_path,pid_dir)):
continue
meta_file=os.path.join(target_path,'%s.csv'%pid_dir)
if os.path.isfile(meta_file):
mf_flag=True
df_meta=pd.read_csv(meta_file,sep=',')
else:
mf_flag=False
image_dirs=find_image_dirs(os.path.join(target_path,pid_dir))
for data_dir in tqdm(image_dirs, desc="Processing images files"):
location=data_dir
full_path=os.path.join(target_path,pid_dir,data_dir)
data_info_row=df_meta[df_meta[meta_id_name]==data_dir]
if data_info_row.shape[0]>0:
data_info_row=data_info_row.reset_index()
#print(data_info_row[meta_id_name])
meta_image_id=data_info_row[meta_id_name][0]
meta_weeks=data_info_row[meta_weeks_name][0]
meta_fvc=data_info_row[meta_fvc_name][0]
meta_percent=data_info_row[meta_percent_name][0]
meta_age=data_info_row[meta_age_name][0]
meta_sex=data_info_row[meta_sex_name][0]
meta_status=data_info_row[meta_status_name][0]
else:
meta_image_id=data_dir
meta_weeks=''
meta_fvc=''
meta_percent=''
meta_age=''
meta_sex=''
meta_status=''
# full_path = convert_windows_to_linux_path(full_path)
if not os.path.isdir(full_path):
continue
try:
print(full_path)
dicom_fp,dicom_image = load_dicom_images(full_path)
spacing_info = dicom_image.GetSpacing()
print('SPACING INFO:', spacing_info)
metadata_keys = dicom_image.GetMetaDataKeys()
dtag=load_dicom_tag(dicom_fp[0])
uid=dtag.GetMetaData('0020|000e') ##Series Instance UID
modality=dtag.GetMetaData('0008|0060')##Modality
study='OSIC_PFP'##Dataset_name
CIA_other_info = {
'Study_UID':uid,
'metadata_file':''
# 'Series_Description':serise_desc
}
CIA_other_info['split'] = pid_dir
if mf_flag:
CIA_other_info['metadata_file']=meta_file
size = list(dicom_image.GetSize())
resampler =util.get_unisize_resampler(dicom_image, interpolator='linear', spacing=spacing_info, size=size)
# resize the image
if resampler is not None:
proces_image = resampler.Execute(dicom_image)
print('SPACIE INFO AFTER', proces_image.GetSpacing())
CIA_other_info['Resample'] = True
else:
proces_image = dicom_image
CIA_other_info['Resample'] = False
##
CIA_other_info['Image_id']=meta_image_id
CIA_other_info['Weeks']=str(meta_weeks)
CIA_other_info['FVC']=str(meta_fvc)
CIA_other_info['Percent']=str(meta_percent)
CIA_other_info['Age']=str(meta_age)
CIA_other_info['Sex']=meta_sex
CIA_other_info['Smoke_Status']=meta_status
# threshold the image
if 'CT' in modality:
proces_image = util.clamp_image(proces_image, CLAMP_RANGE_CT)
else:
pass
output_path = os.path.join(output_dir,data_dir, f"{data_dir}.nii.gz")
# output_path=convert_windows_to_linux_path(output_path)
save_nifti(proces_image, output_path, full_path)
print(f"Saved NIfTI file to {output_path}")
##segment
label_path_dict = {}
label_flag=True
pare_path=os.path.dirname(target_path)
label_paths = os.path.join(pare_path, 'GT')
label_files=glob.glob("%s/*/*/%s_*.nrrd"%(label_paths,data_dir))
#print(label_paths,label_files)
if len(label_files)>0:
for lf in label_files:
lf_name=os.path.basename(lf)
lf_id=lf_name.split("_")[0]
lf_tissue=os.path.basename(os.path.dirname(lf)).split("_")[1]
label_image=load_nrrd(lf)
resampler =util.get_unisize_resampler(label_image, interpolator='nearest', spacing=spacing_info, size=size)
if resampler is not None:
proces_label = resampler.Execute(label_image)
else:
proces_label = label_image
label_output_path = os.path.join(output_dir, lf_id, TASK_VALUE, f"{lf_name}.nii.gz")
label_path_dict[lf_tissue] = label_output_path
util.save_nifti(proces_label, label_output_path, lf)
print(f"Saved Label Segment NIfTI file to {label_output_path}")
else:
label_flag=False
except RuntimeError:
failed_files.append(full_path)
print(f"Failed to load DICOM images from {full_path}")
continue
'''
meta.add_keyvalue('Image_id',meta_image_id)
meta.add_keyvalue('Weeks',meta_weeks)
meta.add_keyvalue('FVC',meta_fvc)
meta.add_keyvalue('Percent',meta_percent)
meta.add_keyvalue('Age',meta_age)
meta.add_keyvalue('Sex',meta_sex)
meta.add_keyvalue('Smoke_Status',meta_status)
'''
print(proces_image.GetSize(),proces_label.GetSize())
try:
assert proces_image.GetSize() == proces_label.GetSize()
except Exception as e:
failed_files.append(full_path)
continue
size_processed = list(proces_image.GetSize())
meta.add_keyvalue('Image_id',meta_image_id)
meta.add_keyvalue('Spacing_mm',min(spacing_info))
meta.add_keyvalue('OriImg_path',full_path)
meta.add_keyvalue('Size',size_processed) # 这里用处理后的size -- YH Jachin
meta.add_keyvalue('Modality',modality)
meta.add_keyvalue('Dataset_name',study)
meta.add_keyvalue('ROI','whole-body')
if label_flag:
print(label_path_dict.keys())
meta.add_keyvalue('Task',TASK_VALUE)
meta.add_keyvalue('Label_tissue',list(label_path_dict.keys()))
meta.add_keyvalue('Label_path',{TASK_VALUE:label_path_dict})
meta.add_extra_keyvalue('Metadata',CIA_other_info)
# Write the mapping to the JSON file on the fly
with open(json_output_path, 'r+') as json_file:
existing_mappings = json.load(json_file)
existing_mappings[output_path] = meta.get_meta_data()
json_file.seek(0)
json.dump(existing_mappings, json_file, indent=4)
json_file.truncate()
else:
print("No metadata.csv files found.")
with open(failed_files_path, "w") as json_file:
json.dump(failed_files, json_file)
print(f"The list has been written to {failed_files_path}")
print(f"Saved NIfTI mappings to {json_output_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Process DICOM files and save as NIfTI.")
parser.add_argument("--target_path", type=str, help="Path to the target directory containing metadata files.", default="/home/data/Github/data/data_gen_def/DATASETS/Kaggle/osic_pulmonary_fibrosis_progression_Segmentation/osic-pulmonary-fibrosis-progression")
parser.add_argument("--output_dir", type=str, help="Directory to save the NIfTI files.", default="/home/data/Github/data/data_gen_def/DATASETS_processed/Kaggle_osic_new/")
args = parser.parse_args()
print(args.target_path, args.output_dir)
main(args.target_path, args.output_dir)
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