Image conversion#

kornia.image.tensor_to_image(tensor, keepdim=False, force_contiguous=False)[source]#

Convert a PyTorch tensor image to a numpy image.

In case the tensor is in the GPU, it will be copied back to CPU.

Parameters:

tensor (Tensor) – image of the form \((H, W)\), \((C, H, W)\) or \((B, C, H, W)\).
keepdim (bool, optional) – If False squeeze the input image to match the shape \((H, W, C)\) or \((H, W)\). Default: False
force_contiguous (bool, optional) – If True call contiguous to the tensor before Default: False

Return type:

Any

Returns:

image of the form \((H, W)\), \((H, W, C)\) or \((B, H, W, C)\).

Example

>>> img = torch.ones(1, 3, 3)
>>> tensor_to_image(img).shape
(3, 3)

>>> img = torch.ones(3, 4, 4)
>>> tensor_to_image(img).shape
(4, 4, 3)

kornia.image.image_to_tensor(image, keepdim=True)[source]#

Convert a numpy image to a PyTorch 4d tensor image.

Parameters:

image (Any) – image of the form \((H, W, C)\), \((H, W)\) or \((B, H, W, C)\).
keepdim (bool, optional) – If False unsqueeze the input image to match the shape \((B, H, W, C)\). Default: True

Return type:

Tensor

Returns:

tensor of the form \((B, C, H, W)\) if keepdim is False,: \((C, H, W)\) otherwise.

Example

>>> img = np.ones((3, 3))
>>> image_to_tensor(img).shape
torch.Size([1, 3, 3])

>>> img = np.ones((4, 4, 1))
>>> image_to_tensor(img).shape
torch.Size([1, 4, 4])

>>> img = np.ones((4, 4, 3))
>>> image_to_tensor(img, keepdim=False).shape
torch.Size([1, 3, 4, 4])

kornia.image.image_list_to_tensor(images)[source]#

Convert a list of numpy images to a PyTorch 4d tensor image.

Parameters:

Return type:

Tensor

Returns:

tensor of the form \((B, C, H, W)\).

Example

>>> imgs = [np.ones((4, 4, 1)), np.zeros((4, 4, 1))]
>>> image_list_to_tensor(imgs).shape
torch.Size([2, 1, 4, 4])

class kornia.image.ImageToTensor(keepdim=False)[source]#

Converts a numpy image to a PyTorch 4d tensor image.

Parameters:: keepdim (bool, optional) – If False unsqueeze the input image to match the shape \((B, H, W, C)\). Default: False