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import matplotlib.pyplot as pyplot
import numpy as np

n = 1
for i in range (30):
    img_path = './321/2/%d.npy' % n
    image = np.load(img_path)
    n = n+1
    pyplot.imshow(image[0, :, :], cmap=pyplot.cm.bone)
    pyplot.show()

import matplotlib.pyplot as pyplot
import SimpleITK as sitk
import numpy as np

n = 1
for i in range (30):
    img_path = './321/2/%d.dcm' % n
    im_A = sitk.ReadImage(img_path)
    im_A_npy = np.array(sitk.GetArrayFromImage(im_A))
    np.save('./321/3/%d.npy' % n, im_A_npy)
    n = n+1
    pyplot.imshow(im_A_npy[0, :, :], cmap=pyplot.cm.bone)
    pyplot.show()

 读取.mat

import scipy.io as scio
import numpy as np #导入矩阵处理库
from PIL import Image

import SimpleITK as sitk
import matplotlib.pyplot as pyplot


def nii_maskto2D():

    image_train = []
    data = scio.loadmat('./ktrans_pj_5-P015216.mat')
    image_array = np.array(data['x'])
    image_array = np.flip(image_array, axis=2)
    print(len(image_array.shape))
    print(image_array.shape)
    w,h,c = image_array.shape


    total_slices = c  # 总切片数
    slice_counter = 0  # 从第几个切片开始
    n = 0
    # iterate through slices
    for current_slice in range(slice_counter, total_slices):
        # alternate slices
        if (slice_counter % 1) == 0:
            data = image_array[:, :, current_slice]  # 保存该切片，可以选择不同方向。
            data = np.fliplr(data)
            # data = np.rot90(data)
            pyplot.imshow(data, cmap=pyplot.cm.bone)
            pyplot.show()
            image_train.append(data)

            np.save('./321/%d.npy' % int(n+1), image_train[n:n+1])
            n = n + 1

    print('Finished converting images')
print(nii_maskto2D())