diff --git a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md
index b3fe180a5b809b1681c39df352d68a6f6fc1ebda..3a861abe2aa70f8958338d5e1eae328f2afbb7d8 100644
--- a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md
+++ b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md
@@ -276,9 +276,9 @@ Data augmentation requires the `map` function. For details about how to use the
 
 1. Import the module to the code.
     ```python
+    from mindspore.dataset.transforms.vision import Inter
     import mindspore.dataset.transforms.vision.c_transforms as transforms
     import matplotlib.pyplot as plt
-    import matplotlib.image as mpimg
     ```
 2. Define data augmentation operators. The following uses `Resize` as an example:
     ```python
@@ -305,7 +305,6 @@ Figure 2: Image after its size is reset
     ```python
     import mindspore.dataset.transforms.vision.py_transforms as transforms
     import matplotlib.pyplot as plt
-    import matplotlib.image as mpimg
     ```
 2. Define data augmentation operators and use the `ComposeOp` API to combine multiple data augmentation operations. The following uses `RandomCrop` as an example:
     ```python
diff --git a/tutorials/source_en/use/data_preparation/loading_the_datasets.md b/tutorials/source_en/use/data_preparation/loading_the_datasets.md
index 4ffaf9de19e9d997bfb30ac93723f44a4026af01..955183815b638e55e11961f83fe57370ab926e88 100644
--- a/tutorials/source_en/use/data_preparation/loading_the_datasets.md
+++ b/tutorials/source_en/use/data_preparation/loading_the_datasets.md
@@ -61,6 +61,7 @@ To read a dataset using the `MindDataset` object, perform the following steps:
 
 1. Create `MindDataset` for reading data.
     ```python
+    import os
     CV_FILE_NAME = os.path.join(MODULE_PATH, "./imagenet.mindrecord")
     data_set = ds.MindDataset(dataset_file=CV_FILE_NAME)
     ```
diff --git a/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md b/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md
index fad8413cfd7a1c91e33b9e07bdd5d1ab9f3f7bf9..277da870e0a11c091e294d1b61ccc7da0cbb9c5d 100644
--- a/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md
+++ b/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md
@@ -276,9 +276,9 @@ MindSpore提供`c_transforms`模块以及`py_transforms`模块函数供用户进
 
 1. 将该模块引入进代码。
     ```python
+    from mindspore.dataset.transforms.vision import Inter
     import mindspore.dataset.transforms.vision.c_transforms as transforms
     import matplotlib.pyplot as plt
-    import matplotlib.image as mpimg
     ```
 2. 定义数据增强算子，以`Resize`为例：
     ```python
@@ -305,7 +305,6 @@ MindSpore提供`c_transforms`模块以及`py_transforms`模块函数供用户进
     ```python
     import mindspore.dataset.transforms.vision.py_transforms as transforms
     import matplotlib.pyplot as plt
-    import matplotlib.image as mpimg
     ```
 2. 定义数据增强算子，通过`ComposeOp`接口将多个数据增强组合使用, 以`RandomCrop`为例：
     ```python
diff --git a/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md b/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md
index 26ddc142db4beb0018256f866b200ed3c5c4493f..2f1a0ae827790b8650eb37ae065fb9a5c48dc06c 100644
--- a/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md
+++ b/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md
@@ -61,6 +61,7 @@ MindSpore天然支持读取MindSpore数据格式——`MindRecord`存储的数
 
 1. 创建`MindDataset`，用于读取数据。
     ```python
+    import os
     CV_FILE_NAME = os.path.join(MODULE_PATH, "./imagenet.mindrecord")
     data_set = ds.MindDataset(dataset_file=CV_FILE_NAME)
     ```