kornia.utils

tensor_to_image(tensor: torch.Tensor) → numpy.array[source]

Converts 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 (torch.Tensor) – image of the form \((H, W)\),

  • math – (C, H, W), or math:(B, C, H, W).

Returns

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

Return type

numpy.ndarray

image_to_tensor(image: Union[numpy.ndarray, PIL.Image.Image], keepdim: bool = True) → torch.Tensor[source]

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

Parameters

  • image (numpy.ndarray or PIL Image) – image of the form math:(H, W, C), math: (H, W) or math:(B, H, W, C).

  • keepdim (bool) – If False unsqueeze the input image to match the shape

  • math – (B, H, W, C). Default: True

Returns

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

Return type

torch.Tensor

create_meshgrid(height: int, width: int, normalized_coordinates: Optional[bool] = True) → torch.Tensor[source]

Generates a coordinate grid for an image.

When the flag normalized_coordinates is set to True, the grid is normalized to be in the range [-1,1] to be consistent with the pytorch function grid_sample. http://pytorch.org/docs/master/nn.html#torch.nn.functional.grid_sample

Parameters

  • height (int) – the image height (rows).

  • width (int) – the image width (cols).

  • normalized_coordinates (Optional[bool]) – whether to normalize coordinates in the range [-1, 1] in order to be consistent with the PyTorch function grid_sample.

Returns

returns a grid tensor with shape \((1, H, W, 2)\).

Return type

torch.Tensor

save_pointcloud_ply(filename: str, pointcloud: torch.Tensor) → None[source]

Utility function to save to disk a pointcloud in PLY format.

Parameters

  • filename (str) – the path to save the pointcloud.

  • pointcloud (torch.Tensor) – tensor containing the pointcloud to save. The tensor must be in the shape of \((*, 3)\) where the last component is assumed to be a 3d point coordinate \((X, Y, Z)\).

load_pointcloud_ply(filename: str, header_size: Optional[int] = 8) → torch.Tensor[source]

Utility function to load from disk a pointcloud in PLY format.

Parameters

  • filename (str) – the path to the pointcloud.

  • header_size (Optional[int]) – the size of the ply file header that will be skipped during loading. Default is 8 lines.

Returns

a tensor containing the loaded point with shape

\((*, 3)\) where \(*\) represents the number of points.

Return type

torch.Tensor

one_hot(labels: torch.Tensor, num_classes: int, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, eps: Optional[float] = 1e-06) → torch.Tensor[source]

Converts an integer label 2D tensor to a one-hot 3D tensor.

Parameters

  • labels (torch.Tensor) – tensor with labels of shape \((N, H, W)\), where N is batch siz. Each value is an integer representing correct classification.

  • num_classes (int) – number of classes in labels.

  • device (Optional[torch.device]) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

  • dtype (Optional[torch.dpython:type]) – the desired data type of returned tensor. Default: if None, infers data type from values.

Returns

the labels in one hot tensor.

Return type

torch.Tensor

Examples::
>>> labels = torch.LongTensor([[[0, 1], [2, 0]]])
>>> kornia.losses.one_hot(labels, num_classes=3)
tensor([[[[1., 0.],
          [0., 1.]],
         [[0., 1.],
          [0., 0.]],
         [[0., 0.],
          [1., 0.]]]]