color-operations (fork of rio-color )
Apply basic color-oriented image operations.
Modified version of Mapbox's rio-color, removing rasterio dependency and with python>=3.10 compatibility.
Install
You can install color-operations using pip
python -m pip install -U pip
python -m pip install color-operations
or build from source
We recommand using uv as project manager for development.
See https://docs.astral.sh/uv/getting-started/installation/ for installation
git checkout https://github.com/vincentsarago/color-operations.git
cd color-operations
uv sync
Operations
Gamma adjustment adjusts RGB values according to a power law, effectively brightening or darkening the midtones. It can be very effective in satellite imagery for reducing atmospheric haze in the blue and green bands.
Sigmoidal contrast adjustment can alter the contrast and brightness of an image in a way that matches human's non-linear visual perception. It works well to increase contrast without blowing out the very dark shadows or already-bright parts of the image.
Saturation can be thought of as the "colorfulness" of a pixel. Highly saturated colors are intense and almost cartoon-like, low saturation is more muted, closer to black and white. You can adjust saturation independently of brightness and hue but the data must be transformed into a different color space.
Ref https://github.com/mapbox/rio-color/blob/master/README.md
Examples
Sigmoidal
Contrast
Bias
Gamma
Red
Green
Blue
Saturation
Combinations of operations
Ref https://github.com/mapbox/rio-color/blob/master/README.md
Python API
color_operations.operations
The following functions accept and return numpy ndarrays. The arrays are assumed to be scaled 0 to 1. In some cases, the input array is assumed to be in the RGB colorspace.
All arrays use rasterio ordering with the shape as (bands, columns, rows). Be aware that other image processing software may use the (columns, rows, bands) axis order.
sigmoidal(arr, contrast, bias)gamma(arr, g)saturation(rgb, proportion)simple_atmo(rgb, haze, contrast, bias)
The color_operations.operations.parse_operations function takes an operations string and
returns a list of python functions which can be applied to an array.
from color_operations import parse_operations
ops = "gamma b 1.85, gamma rg 1.95, sigmoidal rgb 35 0.13, saturation 1.15"
assert arr.shape[0] == 3
assert arr.min() >= 0
assert arr.max() <= 1
for func in parse_operations(ops):
arr = func(arr)
This provides a tiny domain specific language (DSL) to allow you
to compose ordered chains of image manipulations using the above operations.
For more information on operation strings, see the rio color command line help.
color_operations.colorspace
The colorspace module provides functions for converting scalars and numpy arrays between different colorspaces.
from color_operations.colorspace import ColorSpace as cs # enum defining available color spaces
from color_operations.colorspace import convert, convert_arr
convert_arr(array, src=cs.rgb, dst=cs.lch) # for arrays
...
convert(r, g, b, src=cs.rgb, dst=cs.lch) # for scalars
...
dict(cs.__members__) # can convert to/from any of these color spaces
{
'rgb': <ColorSpace.rgb: 0>,
'xyz': <ColorSpace.xyz: 1>,
'lab': <ColorSpace.lab: 2>,
'lch': <ColorSpace.lch: 3>,
'luv': <ColorSpace.luv: 4>
}
1 maintainer
Metadata
1 maintainer
Release files for color-operations 0.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| color_operations-0.4.0.tar.gz | 15.4 kB | Details |
Built distributions (wheels)
Total release size: 18.4 MB
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