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from light_training.dataloading.dataset import get_train_val_test_loader_from_train
from monai.utils import set_determinism
import torch
import os
import numpy as np
import SimpleITK as sitk
from medpy import metric
import argparse
from tqdm import tqdm
import numpy as np
set_determinism(123)
parser = argparse.ArgumentParser()
parser.add_argument("--pred_name", required=True, type=str)
results_root = "prediction_results"
args = parser.parse_args()
pred_name = args.pred_name
def cal_metric(gt, pred, voxel_spacing):
if pred.sum() > 0 and gt.sum() > 0:
dice = metric.binary.dc(pred, gt)
hd95 = metric.binary.hd95(pred, gt, voxelspacing=voxel_spacing)
return np.array([dice, hd95])
else:
return np.array([0.0, 50])
def each_cases_metric(gt, pred, voxel_spacing):
classes_num = 3
class_wise_metric = np.zeros((classes_num, 2))
for cls in range(0, classes_num):
class_wise_metric[cls, ...] = cal_metric(pred[cls], gt[cls], voxel_spacing)
print(class_wise_metric)
return class_wise_metric
def convert_labels(labels):
## TC, WT and ET
labels = labels.unsqueeze(dim=0)
result = [(labels == 1) | (labels == 3), (labels == 1) | (labels == 3) | (labels == 2), labels == 3]
return torch.cat(result, dim=0).float()
if __name__ == "__main__":
data_dir = "./data/fullres/train"
raw_data_dir = "./data/raw_data/BraTS2023/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData/"
train_ds, val_ds, test_ds = get_train_val_test_loader_from_train(data_dir)
print(len(test_ds))
all_results = np.zeros((250,3,2))
ind = 0
for batch in tqdm(test_ds, total=len(test_ds)):
properties = batch["properties"]
case_name = properties["name"]
gt_itk = os.path.join(raw_data_dir, case_name, f"seg.nii.gz")
voxel_spacing = [1, 1, 1]
gt_itk = sitk.ReadImage(gt_itk)
gt_array = sitk.GetArrayFromImage(gt_itk).astype(np.int32)
gt_array = torch.from_numpy(gt_array)
gt_array = convert_labels(gt_array).numpy()
pred_itk = sitk.ReadImage(f"./{results_root}/{pred_name}/{case_name}.nii.gz")
pred_array = sitk.GetArrayFromImage(pred_itk)
m = each_cases_metric(gt_array, pred_array, voxel_spacing)
all_results[ind, ...] = m
ind += 1
os.makedirs(f"./{results_root}/result_metrics/", exist_ok=True)
np.save(f"./{results_root}/result_metrics/{pred_name}.npy", all_results)
result = np.load(f"./{results_root}/result_metrics/{pred_name}.npy")
print(result.shape)
print(result.mean(axis=0))
print(result.std(axis=0))
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