mindspore.dataset.vision.Decode

class mindspore.dataset.vision.Decode(to_pil=False)[source]

Decode the input image in RGB mode. Supported image formats: JPEG, BMP, PNG, TIFF, GIF(need to_pil=True ), WEBP(need to_pil=True ).

Parameters

to_pil (bool, optional) – Whether to decode the image to the PIL data type. If True, the image will be decoded to the PIL data type, otherwise it will be decoded to the NumPy data type. Default: False.

Raises

RuntimeError – If given tensor is not a 1D sequence.
RuntimeError – If the input is not raw image bytes.
RuntimeError – If the input image is already decoded.

Supported Platforms:: CPU Ascend

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>>
>>> # Eager usage
>>> import numpy as np
>>> raw_image = np.fromfile("/path/to/image/file", np.uint8)
>>> decoded_image = vision.Decode()(raw_image)
>>>
>>> # Pipeline usage
>>> image_folder_dataset = ds.ImageFolderDataset("/path/to/image_folder_dataset_directory")
>>> transforms_list = [vision.Decode(), vision.RandomHorizontalFlip()]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns=["image"])

Tutorial Examples:

Illustration of vision transforms

device(device_target='CPU')[source]

Set the device for the current operator execution.

Parameters

device_target (str, optional) – The operator will be executed on this device. Currently supports CPU . Default: CPU .

Raises

TypeError – If device_target is not of type str.
ValueError – If device_target is not CPU .

Supported Platforms:: CPU

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>> from mindspore.dataset.vision import Inter
>>>
>>> decode_op = vision.Decode().device("Ascend")
>>> resize_op = vision.Resize([100, 75], Inter.BICUBIC)
>>> transforms_list = [decode_op, resize_op]
>>> image_folder_dataset = ds.ImageFolderDataset("/path/to/image_folder_dataset_directory")
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns=["image"])

Tutorial Examples:

Illustration of vision transforms