View Source NxImage (NxImage v0.1.1)
Image processing in Nx.
All functions expect images to be tensors in either HWC or CHW order, with an arbitrary number of leading batch axes.
All transformations preserve the input type, rounding if necessary. For higher precision, cast the input to floating-point beforehand.
Link to this section Summary
Transformation
Crops an image at the center.
Normalizes an image according to the given per-channel mean and standard deviation.
Resizes an image.
Scales an image such that the short edge matches the given size.
Conversion
Converts values from continuous range into pixel values (0-255).
Converts pixel values (0-255) into a continuous range.
Link to this section Transformation
Crops an image at the center.
If the image is too small to be cropped to the desired size, it gets padded with zeros.
options
Options
:channels- channels location, either:firstor:last. Defaults to:last
examples
Examples
iex> image = Nx.iota({4, 4, 1}, type: :u8)
iex> NxImage.center_crop(image, {2, 2})
#Nx.Tensor<
u8[2][2][1]
[
[
[5],
[6]
],
[
[9],
[10]
]
]
>
iex> image = Nx.iota({2, 2, 1}, type: :u8)
iex> NxImage.center_crop(image, {1, 4})
#Nx.Tensor<
u8[1][4][1]
[
[
[0],
[0],
[1],
[0]
]
]
>Normalizes an image according to the given per-channel mean and standard deviation.
:channels- channels location, either:firstor:last. Defaults to:last
examples
Examples
iex> image = Nx.iota({2, 2, 3}, type: :f32)
iex> mean = Nx.tensor([0.485, 0.456, 0.406])
iex> std = Nx.tensor([0.229, 0.224, 0.225])
iex> NxImage.normalize(image, mean, std)
#Nx.Tensor<
f32[2][2][3]
[
[
[-2.1179039478302, 2.4285714626312256, 7.084444522857666],
[10.982532501220703, 15.821427345275879, 20.41777801513672]
],
[
[24.08296775817871, 29.214284896850586, 33.7511100769043],
[37.183406829833984, 42.607139587402344, 47.08444595336914]
]
]
>Resizes an image.
options
Options
:method- the resizing method to use, either of:nearest,:bilinear,:bicubic,:lanczos3,:lanczos5. Defaults to:bilinear:channels- channels location, either:firstor:last. Defaults to:last
examples
Examples
iex> image = Nx.iota({2, 2, 1}, type: :u8)
iex> NxImage.resize(image, {3, 3}, method: :nearest)
#Nx.Tensor<
u8[3][3][1]
[
[
[0],
[1],
[1]
],
[
[2],
[3],
[3]
],
[
[2],
[3],
[3]
]
]
>
iex> image = Nx.iota({2, 2, 1}, type: :f32)
iex> NxImage.resize(image, {3, 3}, method: :bilinear)
#Nx.Tensor<
f32[3][3][1]
[
[
[0.0],
[0.5],
[1.0]
],
[
[1.0],
[1.5],
[2.0]
],
[
[2.0],
[2.5],
[3.0]
]
]
>Scales an image such that the short edge matches the given size.
options
Options
:method- the resizing method to use, same asresize/2:channels- channels location, either:firstor:last. Defaults to:last
examples
Examples
iex> image = Nx.iota({2, 4, 1}, type: :u8)
iex> resized_image = NxImage.resize_short(image, 3, method: :nearest)
iex> Nx.shape(resized_image)
{3, 6, 1}
iex> image = Nx.iota({4, 2, 1}, type: :u8)
iex> resized_image = NxImage.resize_short(image, 3, method: :nearest)
iex> Nx.shape(resized_image)
{6, 3, 1}Link to this section Conversion
Converts values from continuous range into pixel values (0-255).
examples
Examples
iex> image = Nx.tensor([[[0.0], [0.5]], [[0.75], [1.0]]])
iex> NxImage.from_continuous(image, 0.0, 1.0)
#Nx.Tensor<
u8[2][2][1]
[
[
[0],
[128]
],
[
[191],
[255]
]
]
>
iex> image = Nx.tensor([[[-1.0], [0.0]], [[0.5], [1.0]]])
iex> NxImage.from_continuous(image, -1.0, 1.0)
#Nx.Tensor<
u8[2][2][1]
[
[
[0],
[128]
],
[
[191],
[255]
]
]
>Converts pixel values (0-255) into a continuous range.
examples
Examples
iex> image = Nx.tensor([[[0], [128]], [[191], [255]]])
iex> NxImage.to_continuous(image, 0.0, 1.0)
#Nx.Tensor<
f32[2][2][1]
[
[
[0.0],
[0.501960813999176]
],
[
[0.7490196228027344],
[1.0]
]
]
>
iex> image = Nx.tensor([[[0], [128]], [[191], [255]]])
iex> NxImage.to_continuous(image, -1.0, 1.0)
#Nx.Tensor<
f32[2][2][1]
[
[
[-1.0],
[0.003921627998352051]
],
[
[0.49803924560546875],
[1.0]
]
]
>