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import os
import numpy as np
import multiprocessing
import argparse
from scipy import sparse
from sklearn.model_selection import train_test_split
import json
from monai.transforms import (
AddChanneld,
Compose,
LoadImaged,
Orientationd,
)
def set_parse():
# %% set up parser
parser = argparse.ArgumentParser()
parser.add_argument("-category", default=['liver', 'right kidney', 'spleen', 'pancreas', 'aorta', 'inferior vena cava', 'right adrenal gland', 'left adrenal gland', 'gallbladder', 'esophagus', 'stomach', 'duodenum', 'left kidney'], type=list)
parser.add_argument("-image_dir", type=str, required=True)
parser.add_argument("-label_dir", type=str, required=True)
parser.add_argument("-dataset_code", type=str, required=True)
parser.add_argument("-save_root", type=str, required=True)
parser.add_argument("-test_ratio", type=float, required=True)
args = parser.parse_args()
return args
args = set_parse()
# get ct> dir
image_list_all = [item for item in sorted(os.listdir(args.image_dir))]
label_list_all = [item for item in sorted(os.listdir(args.label_dir))]
assert len(image_list_all) == len(label_list_all)
print('dataset size ', len(image_list_all))
# build dataset
data_path_list_all = []
for idx in range(len(image_list_all)):
img_path = os.path.join(args.image_dir, image_list_all[idx])
label_path = os.path.join(args.label_dir, label_list_all[idx])
name = image_list_all[idx].split('.')[0]
info = (idx, name, img_path, label_path)
data_path_list_all.append(info)
img_loader = Compose(
[
LoadImaged(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
Orientationd(keys=['image', 'label'], axcodes="RAS"),
]
)
# save
save_path = os.path.join(args.save_root, args.dataset_code)
ct_save_path = os.path.join(save_path, 'ct')
gt_save_path = os.path.join(save_path, 'gt')
if not os.path.exists(ct_save_path):
os.makedirs(ct_save_path)
if not os.path.exists(gt_save_path):
os.makedirs(gt_save_path)
# exist file:
exist_file_list = os.listdir(ct_save_path)
print('exist_file_list ', exist_file_list)
def normalize(ct_narray):
ct_voxel_ndarray = ct_narray.copy()
ct_voxel_ndarray = ct_voxel_ndarray.flatten()
# for all data
thred = np.mean(ct_voxel_ndarray)
voxel_filtered = ct_voxel_ndarray[(ct_voxel_ndarray > thred)]
# for foreground data
upper_bound = np.percentile(voxel_filtered, 99.95)
lower_bound = np.percentile(voxel_filtered, 00.05)
mean = np.mean(voxel_filtered)
std = np.std(voxel_filtered)
### transform ###
ct_narray = np.clip(ct_narray, lower_bound, upper_bound)
ct_narray = (ct_narray - mean) / max(std, 1e-8)
return ct_narray
def run(info):
idx, file_name, case_path, label_path = info
item = {}
if file_name + '.npy' in exist_file_list:
print(file_name + '.npy exist, skip')
return
print('process ', idx, '---' ,file_name)
# generate ct_voxel_ndarray
item_load = {
'image' : case_path,
'label' : label_path,
}
item_load = img_loader(item_load)
ct_voxel_ndarray = item_load['image']
gt_voxel_ndarray = item_load['label']
ct_shape = ct_voxel_ndarray.shape
item['image'] = ct_voxel_ndarray
# generate gt_voxel_ndarray
gt_voxel_ndarray = np.array(gt_voxel_ndarray).squeeze()
present_categories = np.unique(gt_voxel_ndarray)
gt_masks = []
for cls_idx in range(len(args.category)):
cls = cls_idx + 1
if cls not in present_categories:
gt_voxel_ndarray_category = np.zeros(ct_shape)
gt_masks.append(gt_voxel_ndarray_category)
print('case {} ==> zero category '.format(idx) + args.category[cls_idx])
print(gt_voxel_ndarray_category.shape)
else:
gt_voxel_ndarray_category = gt_voxel_ndarray.copy()
gt_voxel_ndarray_category[gt_voxel_ndarray != cls] = 0
gt_voxel_ndarray_category[gt_voxel_ndarray == cls] = 1
gt_masks.append(gt_voxel_ndarray_category)
gt_voxel_ndarray = np.stack(gt_masks, axis=0)
assert gt_voxel_ndarray.shape[0] == len(args.category), str(gt_voxel_ndarray.shape[0])
assert gt_voxel_ndarray.shape[1:] == ct_voxel_ndarray.shape[1:]
item['label'] = gt_voxel_ndarray.astype(np.int32)
print(idx, ' load done!')
#############################
item['image'] = normalize(item['image'])
print(idx, ' transform done')
############################
print(file_name + ' ct gt <--> ', item['image'].shape, item['label'].shape)
np.save(os.path.join(ct_save_path, file_name + '.npy'), item['image'])
allmatrix_sp=sparse.csr_matrix(item['label'].reshape(item['label'].shape[0], -1))
sparse.save_npz(os.path.join(gt_save_path, file_name + '.' + str(item['label'].shape)), allmatrix_sp)
print(file_name + ' save done!')
def generate_dataset_json(root_dir, output_file, test_ratio=0.2):
ct_dir = os.path.join(root_dir, 'ct')
gt_dir = os.path.join(root_dir, 'gt')
ct_paths = sorted([os.path.join(ct_dir, f) for f in sorted(os.listdir(ct_dir))])
gt_paths = sorted([os.path.join(gt_dir, f) for f in sorted(os.listdir(gt_dir))])
data = list(zip(ct_paths, gt_paths))
train_data, val_data = train_test_split(data, test_size=test_ratio)
labels = {}
labels['0'] = 'background'
for idx in range(len(args.category)):
label_name = args.category[idx]
label_id = idx + 1
labels[str(label_id)] = label_name
dataset = {
'name': f'{args.dataset_code} Dataset',
'description': f'{args.dataset_code} Dataset',
'tensorImageSize': '4D',
'modality': {
'0': 'CT',
},
'labels': labels,
'numTraining': len(train_data),
'numTest': len(val_data),
'training': [{'image': ct_path, 'label': gt_path} for ct_path, gt_path in train_data],
'validation': [{'image': ct_path, 'label': gt_path} for ct_path, gt_path in val_data]
}
with open(output_file, 'w') as f:
print(f'{output_file} dump')
json.dump(dataset, f, indent=2)
if __name__ == "__main__":
with multiprocessing.Pool(processes=10) as pool:
pool.map(run, data_path_list_all)
print('Process Finished!')
generate_dataset_json(root_dir=save_path,
output_file=os.path.join(save_path, f'{args.dataset_code}.json'),
test_ratio=args.test_ratio)
print('Json Split Done!')
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