mindspore.dataset.vision.ToPIL

class mindspore.dataset.vision.ToPIL[source]

Convert the input decoded numpy.ndarray image to PIL Image.

Raises: TypeError – If the input image is not of type numpy.ndarray or PIL.Image.Image .

Supported Platforms:: CPU

Examples

>>> import numpy as np
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>> from mindspore.dataset.transforms import Compose
>>>
>>> # Use the transform in dataset pipeline mode
>>> data = np.random.randint(0, 255, size=(1, 100, 100, 3)).astype(np.uint8)
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"])
>>> # data is already decoded, but not in PIL Image format
>>> transforms_list = Compose([vision.ToPIL(),
...                            vision.RandomHorizontalFlip(0.5),
...                            vision.ToTensor()])
>>> # apply the transform to dataset through map function
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list, input_columns="image")
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["image"].shape, item["image"].dtype)
...     break
(3, 100, 100) float32
>>>
>>> # Use the transform in eager mode
>>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8)
>>> output = vision.ToPIL()(data)
>>> print(type(output), np.array(output).shape, np.array(output).dtype)
<class 'PIL.Image.Image'> (100, 100, 3) uint8

Tutorial Examples:

Illustration of vision transforms