plenoptic.to_numpy#

plenoptic.to_numpy(x, squeeze=False)[source]#

Cast tensor to numpy in the most conservative way possible.

In order this will:

  • Detach the tensor.

  • Move it to the CPU.

  • Convert it to numpy (calling .numpy()).

  • Convert to the corresponding datatype.

Parameters:

  • x (Tensor | ndarray) – Tensor to be converted to numpy.ndarray on CPU. If already an array, do nothing.

  • squeeze (bool (default: False)) – Whether to remove all dummy dimensions from input.

Return type:

ndarray

Returns:

array – The former tensor, now an array.

Examples

>>> import plenoptic as po
>>> import torch
>>> tensor = torch.rand((1, 1, 256, 256))
>>> array1 = po.to_numpy(tensor)
>>> print(f"Type: {type(array1)}, Shape: {array1.shape}")
Type: <class 'numpy.ndarray'>, Shape: (1, 1, 256, 256)
>>> array2 = po.to_numpy(tensor, squeeze=True)
>>> print(f"Type: {type(array2)}, Shape: {array2.shape}")
Type: <class 'numpy.ndarray'>, Shape: (256, 256)

This is handy for saving the synthesized metamer as an image to disk (here, we use plenoptic.convert_float_to_int to convert the image to an 8-bit integer for saving).

Note that we clip the image to [0, 1] before converting and saving; it contains some values just outside that range. In general, it’s up to the user to ensure this is reasonable (you could also use plenoptic.process.rescale to linearly rescale the image to that same range).

>>> import imageio
>>> import numpy as np
>>> # Load in an example metamer synthesis
>>> img = po.data.einstein()
>>> model = po.models.Gaussian(30).eval()
>>> po.remove_grad(model)
>>> met = po.Metamer(img, model)
>>> met.to(torch.float64)
>>> met.load(po.data.fetch_data("example_metamer_gaussian.pt"))
>>> met_squeezed = po.to_numpy(met.metamer, squeeze=True)
>>> met_squeezed = po.convert_float_to_int(np.clip(met_squeezed, 0, 1))
>>> imageio.v3.imwrite("example_metamer_gaussian.png", met_squeezed)