mindspore.dataset.vision.RandomVerticalFlipWithBBox

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class mindspore.dataset.vision.RandomVerticalFlipWithBBox(prob=0.5)[source]

Flip the input image vertically, randomly with a given probability and adjust bounding boxes accordingly.

Parameters

prob (float, optional) – Probability of the image being flipped, which must be in range of [0.0, 1.0]. Default: 0.5.

Raises

  • TypeError – If prob is not of type float.

  • ValueError – If prob is not in range [0.0, 1.0].

  • RuntimeError – If given tensor shape is not <H, W> or <H, W, C>.

Supported Platforms:

CPU

Examples

>>> import numpy as np
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>>
>>> # Use the transform in dataset pipeline mode
>>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.float32)
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"])
>>> func = lambda img: (data, np.array([[0, 0, data.shape[1], data.shape[0]]]).astype(np.float32))
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=[func],
...                                                 input_columns=["image"],
...                                                 output_columns=["image", "bbox"])
>>> transforms_list = [vision.RandomVerticalFlipWithBBox(0.20)]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list, input_columns=["image", "bbox"])
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["image"].shape, item["image"].dtype)
...     print(item["bbox"].shape, item["bbox"].dtype)
...     break
(100, 100, 3) float32
(1, 4) float32
>>>
>>> # Use the transform in eager mode
>>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.float32)
>>> func = lambda img: (data, np.array([[0, 0, data.shape[1], data.shape[0]]]).astype(data.dtype))
>>> func_data, func_bboxes = func(data)
>>> output = vision.RandomVerticalFlipWithBBox(1)(func_data, func_bboxes)
>>> print(output[0].shape, output[0].dtype)
(100, 100, 3) float32
>>> print(output[1].shape, output[1].dtype)
(1, 4) float32
Tutorial Examples: