mindspore.dataset.vision.RandomHorizontalFlipWithBBox
- class mindspore.dataset.vision.RandomHorizontalFlipWithBBox(prob=0.5)[source]
Randomly flip the input image and its bounding box horizontally with a given probability.
- 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.RandomHorizontalFlipWithBBox(0.70)] >>> 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.RandomHorizontalFlipWithBBox(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: