Apply basic color-oriented image operations.

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  • License: MIT License (MIT)
  • Author: Vincent Sarago
  • Requires: Python >=3.8
  • Provides-Extra: dev, test
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Project description

color-operations

Apply basic color-oriented image operations.

Test Coverage Package version license

Lightweight version of rio-color but removing rasterio dependency.

Install

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 .

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

sigmoidal_contrast

Bias

sigmoidal_bias

Gamma

Red

gamma_red

Green

gamma_green

Blue

gamma_blue

Saturation

saturation

Combinations of operations

combos

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>
}

Project details

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  • License: MIT License (MIT)
  • Author: Vincent Sarago
  • Requires: Python >=3.8
  • Provides-Extra: dev, test
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