View Source NxImage (NxImage v0.1.2)
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.
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.
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
:channels- channels location, either:firstor:last. Defaults to:last
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
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
:method- the resizing method to use, either of:nearest,:bilinear,:bicubic,:lanczos3,:lanczos5. Defaults to:bilinear:antialias- whether an anti-aliasing filter should be used when downsampling. This has no effect with upsampling. Defaults totrue:channels- channels location, either:firstor:last. Defaults to:last
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
:method- the resizing method to use, same asresize/2:antialias- whether an anti-aliasing filter should be used when downsampling. This has no effect with upsampling. Defaults totrue:channels- channels location, either:firstor:last. Defaults to:last
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}Conversion
Converts values from continuous range into pixel values (0-255).
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
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]
]
]
>