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color-operations
Readme
Apply basic color-oriented image operations.
Lightweight version of rio-color but removing rasterio dependency.
You can install color-operations using pip
pip install -U pip
pip install color-operations
Build from source
git checkout https://github.com/vincentsarago/color-operations.git
cd color-operations
pip install -U pip
pip install -e .
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
Contrast
Bias
Red
Green
Blue
Ref https://github.com/mapbox/rio-color/blob/master/README.md
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>
}
FAQs
Apply basic color-oriented image operations.
We found that color-operations demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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