kornia.image

Module to provide a high level API to process images.

class kornia.image.ImageSize(height, width)

Data class to represent image shape.

Parameters:

• height (int | Tensor) – image height.

• width (int | Tensor) – image width.

Example

>>> size = ImageSize(3, 4)
>>> size.height
3
>>> size.width
4

height: int | Tensor

width: int | Tensor

class kornia.image.PixelFormat(color_space, bit_depth)

Data class to represent the pixel format of an image.

Parameters:

• color_space (ColorSpace) – color space.

• bit_depth (int) – the number of bits per channel.

Example

>>> pixel_format = PixelFormat(ColorSpace.RGB, 8)
>>> pixel_format.color_space
<ColorSpace.RGB: 2>
>>> pixel_format.bit_depth
8

bit_depth: int

color_space: ColorSpace

class kornia.image.ChannelsOrder(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

Enum that represents the channels order of an image.

CHANNELS_FIRST = 0

CHANNELS_LAST = 1

class kornia.image.ImageLayout(image_size, channels, channels_order)

Data class to represent the layout of an image.

Parameters:

• image_size (ImageSize) – image size.

• channels (int) – number of channels.

• channels_order (ChannelsOrder) – channels order.

Example

>>> layout = ImageLayout(ImageSize(3, 4), 3, ChannelsOrder.CHANNELS_LAST)
>>> layout.image_size
ImageSize(height=3, width=4)
>>> layout.channels
3
>>> layout.channels_order
<ChannelsOrder.CHANNELS_LAST: 1>

channels: int

channels_order: ChannelsOrder

image_size: ImageSize

class kornia.image.Image(data, pixel_format, layout)

Class that holds an Image Tensor representation.

Note

Disclaimer: This class provides the minimum functionality for image manipulation. However, as soon as you start to experiment with advanced tensor manipulation, you might expect fancy polymorphic behaviours.

Warning

This API is experimental and might suffer changes in the future.

Parameters:

• data (Tensor) – a torch tensor containing the image data.

• layout (ImageLayout) – a dataclass containing the image layout information.

Examples

>>> # from a torch.tensor
>>> data = torch.randint(0, 255, (3, 4, 5), dtype=torch.uint8)  # CxHxW
>>> pixel_format = PixelFormat(
...     color_space=ColorSpace.RGB,
...     bit_depth=8,
... )
>>> layout = ImageLayout(
...     image_size=ImageSize(4, 5),
...     channels=3,
...     channels_order=ChannelsOrder.CHANNELS_FIRST,
... )
>>> img = Image(data, pixel_format, layout)
>>> assert img.channels == 3

>>> # from a numpy array (like opencv)
>>> data = np.ones((4, 5, 3), dtype=np.uint8)  # HxWxC
>>> img = Image.from_numpy(data, color_space=ColorSpace.RGB)
>>> assert img.channels == 3
>>> assert img.width == 5
>>> assert img.height == 4

property channels: int

Return the number channels of the image.

property channels_order: ChannelsOrder

Return the channels order.

clone()

Return a copy of the image.

Return type:

Image

property data: Tensor

Return the underlying tensor data.

property device: device

Return the image device.

property dtype: dtype

Return the image data type.

float()

Return the image as float.

Return type:

Image

classmethod from_dlpack(data)

Construct an image tensor from a DLPack capsule.

Parameters:

data (Any) – a DLPack capsule from numpy, tvm or jax.

Return type:

Image

Example

>>> x = np.ones((4, 5, 3))
>>> img = Image.from_dlpack(x.__dlpack__())

classmethod from_file(file_path)

Construct an image tensor from a file.

Parameters:

file_path (str | Path) – the path to the file to read the image from.

Return type:

Image

classmethod from_numpy(data, color_space=ColorSpace.RGB, channels_order=ChannelsOrder.CHANNELS_LAST)

Construct an image tensor from a numpy array.

Parameters:

• data (Any) – a numpy array containing the image data.

• color_space (ColorSpace, optional) – the color space of the image. Default: ColorSpace.RGB

• pixel_format – the pixel format of the image.

Return type:

Image

Example

>>> data = np.ones((4, 5, 3), dtype=np.uint8)  # HxWxC
>>> img = Image.from_numpy(data, color_space=ColorSpace.RGB)
>>> assert img.channels == 3
>>> assert img.width == 5
>>> assert img.height == 4

property height: int

Return the image height (columns).

property image_size: ImageSize

Return the image size.

property layout: ImageLayout

Return the image layout.

property pixel_format: PixelFormat

Return the pixel format.

print(max_width=256)

Print the image tensor to the console.

Parameters:

max_width (int, optional) – the maximum width of the image to print. Default: 256

Return type:

None

img = Image.from_file("panda.png")
img.print()

property shape: tuple[int, ...]

Return the image shape.

to(device=None, dtype=None)

Move the image to the given device and dtype.

Parameters:

• device (Union[str, device, None], optional) – the device to move the image to. Default: None

• dtype (Optional[dtype], optional) – the data type to cast the image to. Default: None

Returns:

the image moved to the given device and dtype.

Return type:

Image

to_color_space(color_space)

Convert the image to a different color space.

Return type:

Image

to_dlpack()

Return a DLPack capsule from the image tensor.

Return type:

Any

to_numpy()

Return a numpy array in cpu from the image tensor.

Return type:

Any

property width: int

Return the image width (rows).

write(file_path)

Write the image to a file.

For now, only support writing to JPEG format.

Parameters:

file_path (str | Path) – the path to the file to write the image to.

Return type:

None

Example

>>> data = np.ones((4, 5, 3), dtype=np.uint8)  # HxWxC
>>> img = Image.from_numpy(data)
>>> img.write("test.jpg")