mindspore.dataset.vision.LinearTransformation

class mindspore.dataset.vision.LinearTransformation(transformation_matrix, mean_vector)[source]

Linearly transform the input numpy.ndarray image with a square transformation matrix and a mean vector.

It will first flatten the input image and subtract the mean vector from it, then compute the dot product with the transformation matrix, finally reshape it back to its original shape.

Parameters

transformation_matrix (numpy.ndarray) – A square transformation matrix in shape of (D, D), where \(D = C \times H \times W\) .
mean_vector (numpy.ndarray) – A mean vector in shape of (D,), where \(D = C \times H \times W\) .

Raises

TypeError – If transformation_matrix is not of type numpy.ndarray .
TypeError – If mean_vector is not of type numpy.ndarray .

Supported Platforms:: CPU

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>> import numpy as np
>>> from mindspore.dataset.transforms import Compose
>>>
>>> height, width = 32, 32
>>> dim = 3 * height * width
>>> transformation_matrix = np.ones([dim, dim])
>>> mean_vector = np.zeros(dim)
>>> transforms_list = Compose([vision.Decode(to_pil=True),
...                            vision.Resize((height,width)),
...                            vision.ToTensor(),
...                            vision.LinearTransformation(transformation_matrix, mean_vector)])
>>> # apply the transform to dataset through map function
>>> 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