diff --git a/CTAI_model/data_set/make.py b/CTAI_model/data_set/make.py
new file mode 100644
index 0000000..63dc55a
--- /dev/null
+++ b/CTAI_model/data_set/make.py
@@ -0,0 +1,178 @@
+import os
+
+import SimpleITK as sitk
+import cv2 as cv
+import numpy as np
+import torch
+from torch.utils import data
+from torch.utils.data import random_split
+
+train_data_path = 'E:/projects/python projects/ct_data/'
+test_data_path = '../data/test/'
+
+
+def get_person_files(data_path):
+    # 数据结构
+    # [[person_id,image,mask],[person_id,image,mask],..,]
+
+    all = []
+    dir_list = [data_path + i for i in os.listdir(data_path)]
+    for dir in dir_list:
+        person_id = dir.split('/')[-1]
+        filename_list = []
+        image_list, mask_list, = [], []
+        # 所有数据跑
+        temp = os.listdir(dir + '/arterial phase')
+        filename_list.extend([dir + '/arterial phase/' + name for name in temp])
+        for i in filename_list:
+            if '.dcm' in i:
+                image_list.append(i)
+            if '_mask' in i:
+                mask_list.append(i)
+
+        all.append([person_id, image_list, mask_list])
+
+    return all
+
+
+def get_train_files(data_path, all, get_dice=False):
+    image_list, mask_list, finish_list, id_list = [], [], [], []
+    # id_list  先是病人id再是图片
+    dir_list = [data_path + i for i in os.listdir(data_path)]
+    filename_list = []
+    for dir in dir_list:
+        # 所有数据跑
+        if all:
+            temp = os.listdir(dir + '/arterial phase')
+            filename_list.extend([dir + '/arterial phase/' + name for name in temp])
+        if not all:
+            filename_list.append(dir)
+            # temp = os.listdir(dir)
+            # filename_list.extend([dir + '/' + name for name in temp])
+
+    for i in filename_list:
+        if '.dcm' in i:
+            image_list.append((i, i.split('/')[-3], i.split('/')[-1].replace('.dcm', '')))
+        if '_mask' in i:
+            mask_list.append(i)
+        if 'finish' in i:
+            finish_list.append(i)
+    if get_dice:
+        return image_list, mask_list, id_list
+    else:
+        return image_list
+
+
+def data_in_one(inputdata):
+    if not inputdata.any():
+        return inputdata
+    inputdata = (inputdata - inputdata.min()) / (inputdata.max() - inputdata.min())
+    return inputdata
+
+
+def get_dataset(data_path, have):
+    global test_image, test_mask
+    image_list, mask_list, image_data, mask_data = [], [], [], []
+
+    image_list = get_train_files(data_path, all=True)
+    for i in image_list:
+        image = sitk.ReadImage(i[0])
+        image_array = sitk.GetArrayFromImage(image)
+        mask = i[0].replace('.dcm', '_mask.png')
+        mask_array = cv.imread(mask, cv.IMREAD_GRAYSCALE)
+
+        if have:
+            if not mask_array.any():
+                continue
+
+        mask_array = data_in_one(mask_array)
+        mask_tensor = torch.from_numpy(mask_array).float()
+        j = i[0].split('/')[-1].replace('_mask.png', '')
+        mask_data.append((j, mask_tensor))
+
+        ROI_mask = np.zeros(shape=image_array.shape)
+        ROI_mask_mini = np.zeros(shape=(1, 160, 100))
+        ROI_mask_mini[0] = image_array[0][270:430, 200:300]
+        ROI_mask_mini = data_in_one(ROI_mask_mini)
+        ROI_mask[0][270:430, 200:300] = ROI_mask_mini[0]
+        test_image = ROI_mask
+        image_tensor = torch.from_numpy(ROI_mask).float()
+        image_data.append((image_tensor, i[1], i[2]))
+
+    return image_data, mask_data
+
+
+def get_onlytest(data_path, have):
+    global test_image, test_mask
+    image_list, mask_list, image_data, mask_data = [], [], [], []
+
+    image_list = get_train_files(data_path, all=True)
+    for i in image_list:
+        image = sitk.ReadImage(i[0])
+        image_array = sitk.GetArrayFromImage(image)
+
+        ROI_mask = np.zeros(shape=image_array.shape)
+        ROI_mask_mini = np.zeros(shape=(1, 160, 100))
+        ROI_mask_mini[0] = image_array[0][270:430, 200:300]
+        ROI_mask_mini = data_in_one(ROI_mask_mini)
+        ROI_mask[0][270:430, 200:300] = ROI_mask_mini[0]
+        test_image = ROI_mask
+        image_tensor = torch.from_numpy(ROI_mask).float()
+        # print(image_tensor.shape)
+        image_data.append((image_tensor, i[1], i[2]))
+
+    return image_data
+
+
+class Dataset(data.Dataset):
+    def __init__(self, path, have=True, transform=None):
+        imgs = get_dataset(data_path=path, have=have)
+        self.imgs = imgs
+        # self.transform = transform
+        # self.target_transform = target_transform
+
+    def __getitem__(self, index):
+        image = self.imgs[0][index]
+        mask = self.imgs[1][index]
+
+        return image, mask
+
+    def __len__(self):
+        return len(self.imgs[0])
+
+
+class testDataset(data.Dataset):
+    def __init__(self, path, have=True, transform=None):
+        imgs = get_onlytest(data_path=path, have=have)
+        self.imgs = imgs
+        # self.transform = transform
+        # self.target_transform = target_transform
+
+    def __getitem__(self, index):
+        image = self.imgs[index]
+        return image
+
+    def __len__(self):
+        return len(self.imgs)
+
+
+def get_d1(path):
+    bag = Dataset(path, have=True)
+    train_size = int(0.9 * len(bag))
+    test_size = len(bag) - train_size
+    train_dataset, test_dataset = random_split(bag, [train_size, test_size])
+    return train_dataset, test_dataset
+
+
+def get_d1_local(path):
+    bag = testDataset(path, have=False)
+    # train_size = int(0.9 * len(bag))
+    # test_size = len(bag) - train_size
+    # train_dataset, test_dataset = random_split(bag, [train_size, test_size])
+    return bag
+
+
+if __name__ == '__main__':
+    # get_train_files(train_data_path)
+    # get_dataset(train_data_path,have=True)
+    bag = get_d1_local()