A rasterio plugin for applying basic color-oriented image operations to geospatial rasters.
- No heavy dependencies: rio-color is purposefully limited in scope to remain lightweight
- Use the image structure: By iterating over the internal blocks of the input image, we keep memory usage low and predictable while gaining the ability to
- Use multiple cores: thanks to rio-mucho
- Retain all the GeoTIFF info and TIFF structure: nothing is lost. A GeoTIFF input → GeoTIFF output with the same georeferencing, internal tiling, compression, nodata values, etc.
- Efficient colorspace conversions: the intensive math is written in highly optimized C functions and for use with scalars and numpy arrays.
- CLI and Python module: accessing the functionality as a python module that can act on in-memory numpy arrays opens up new opportunities for composing this with other array operations without using intermediate files.
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.
Contrast
Bias
Red
Green
Blue
We highly recommend installing in a virtualenv. Once activated,
pip install -U pip
pip install rio-color
Or if you want to install from source
git checkout https://github.com/mapbox/rio-color.git
cd rio-color
pip install -U pip
pip install -e .
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 rio_color.operations.parse_operations function takes an operations string and
returns a list of python functions which can be applied to an array.
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.
The colorspace module provides functions for converting scalars and numpy arrays between different colorspaces.
>>> from rio_color.colorspace import ColorSpace as cs # enum defining available color spaces
>>> from rio_color.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>
}Rio color provides two command line interfaces:
A general-purpose color correction tool to perform gamma, contrast and saturation adjustments.
The advantages over Imagemagick convert: rio color is
geo-aware, retains the profile of the source image, iterates efficiently over interal tiles
and can use multiple cores.
Usage: rio color [OPTIONS] SRC_PATH DST_PATH OPERATIONS...
Color correction
Operations will be applied to the src image in the specified order.
Available OPERATIONS include:
"gamma BANDS VALUE"
Applies a gamma curve, brightening or darkening midtones.
VALUE > 1 brightens the image.
"sigmoidal BANDS CONTRAST BIAS"
Adjusts the contrast and brightness of midtones.
BIAS > 0.5 darkens the image.
"saturation PROPORTION"
Controls the saturation in LCH color space.
PROPORTION = 0 results in a grayscale image
PROPORTION = 1 results in an identical image
PROPORTION = 2 is likely way too saturated
BANDS are specified as a single arg, no delimiters
`123` or `RGB` or `rgb` are all equivalent
Example:
rio color -d uint8 -j 4 input.tif output.tif \
gamma 3 0.95, sigmoidal rgb 35 0.13
Options:
-j, --jobs INTEGER Number of jobs to run simultaneously, Use -1
for all cores, default: 1
-d, --out-dtype [uint8|uint16] Integer data type for output data, default:
same as input
--co NAME=VALUE Driver specific creation options.See the
documentation for the selected output driver
for more information.
--help Show this message and exit.
Example:
$ rio color -d uint8 -j 4 rgb.tif test.tif \
gamma G 1.85 gamma B 1.95 sigmoidal RGB 35 0.13 saturation 1.15
Provides a higher-level tool for general atmospheric correction of satellite imagery using a proven set of operations to adjust for haze.
Usage: rio atmos [OPTIONS] SRC_PATH DST_PATH
Atmospheric correction
Options:
-a, --atmo FLOAT How much to dampen cool colors, thus cutting
through haze. 0..1 (0 is none), default:
0.03.
-c, --contrast FLOAT Contrast factor to apply to the scene.
-infinity..infinity(0 is none), default: 10.
-b, --bias FLOAT Skew (brighten/darken) the output. Lower
values make it brighter. 0..1 (0.5 is none),
default: 0.15
-d, --out-dtype [uint8|uint16] Integer data type for output data, default:
same as input
--as-color Prints the equivalent rio color command to
stdout.Does NOT run either command, SRC_PATH
will not be created
-j, --jobs INTEGER Number of jobs to run simultaneously, Use -1
for all cores, default: 1
--co NAME=VALUE Driver specific creation options.See the
documentation for the selected output driver
for more information.
--help Show this message and exit.
To enable multiprocessing, rio-mucho is required. You can install it by doing python -m pip install rio-color["mucho"]