图像分割标签+nii格式的预处理

# 将nii与对应的标签转化成npy格式 import os import glob import SimpleITK as sitk import numpy as np import torch from matplotlib import pyplot as plt import random # dataset_dir='/media/zongyouWorkSpace/test2' # # Xsum = [] # dir = dataset_dir + '/train/' # train dataset from "dir/train" # print("train dir is {}".format(dir)) # Label_train = os.listdir(dir) # # for l in range(len(Label_train)): # vname = os.listdir(os.path.join(dir,Label_train[l])) # for vn in vname: # Frame_paths = glob.glob(os.path.join(dir, Label_train[l], vn, '*.jpg')) # # print(Frame_paths[0]) # if len(Frame_paths) > 5 : # print('frame_path is {}'.format(len(Frame_paths))) Path = r'D:\Users\80080947\Desktop\yxLocalWork\AutoEF\czy\Segmentation\labeled\nifti' clsname = os.listdir(Path) newdata = [] for i in range(len(clsname)): img = sitk.ReadImage(os.path.join(Path,clsname[i],clsname[i]+'Image.nii'),sitk.sitkFloat32) arr_image = sitk.GetArrayFromImage(img) label = sitk.ReadImage(os.path.join(Path,clsname[i],'Untitled.nii.gz'),sitk.sitkFloat32) arr_label = sitk.GetArrayFromImage(label) for j in range(arr_image.shape[0]): if np.sum(arr_label[j,:,:])!=0: tensor_image = torch.from_numpy(arr_image[j,:,:]).unsqueeze(0) tensor_label = torch.from_numpy(arr_label[j,:,:]).unsqueeze(0) tensor_data = torch.nn.functional.interpolate(torch.stack([tensor_image, tensor_label], 0), [256, 256]) newdata.append(tensor_data.squeeze(1)) random.shuffle(newdata) newdata = torch.stack(newdata, 3) train = newdata[:,:,:,:int(0.9*newdata.shape[3])] test = newdata[:,:,:,int(0.9*newdata.shape[3]):] train = np.transpose(train, (0, 3, 1, 2)) test = np.transpose(test, (0, 3, 1, 2)) # plt.subplot(121) # plt.imshow(test[0,0,:,:]) # plt.subplot(122) # plt.imshow(test[1,0,:,:]) # plt.show() # # train = np.load(r'D:\Users\80080947\Desktop\yxLocalWork\AutoEF\A4C\train.npy') # # test = np.load(r'D:\Users\80080947\Desktop\yxLocalWork\AutoEF\A4C\test.npy') np.save(os.path.join(r'D:\Users\80080947\Desktop\yxLocalWork\AutoEF\A4C','train.npy'),train) np.save(os.path.join(r'D:\Users\80080947\Desktop\yxLocalWork\AutoEF\A4C','test.npy'),test)