Performance of saturation

Saturation operator needs to be faster. It’s really slow compared to imagemagick’s modulate.

$ rio info $orig | jq .shape
[
  8192,
  8192
]
$ time rio color $orig f416_rio_lch.tif "saturation 130"

real    1m10.490s
$ time convert $orig -modulate 100,130 f416_magick.tif

real    0m6.954s

Investigating…

[perrygeo]

Instead of going through the CLI, a direct comparison might be more illustrative of the performance difference:

with rasterio.open(srcpath) as src:
    arr = src.read().astype('float64') / 255.0
    assert arr.shape == (3, 4096, 4096)

    start = time.time()
    res = skimage_saturation(arr, 120)
    print("Scikit-image based saturation: {} seconds".format(time.time() - start))

    start = time.time()
    res2 = cython_saturation(arr, 120)
    print("Cython saturation: {} seconds".format(time.time() - start))

Saturation on a 4096x4096 array gives us

Scikit-image based saturation: 20.527370929718018 seconds
Cython saturation: 12.189836978912354 seconds

So the Cython is about 68% faster for a large array. Modest but significant.

[perrygeo]

See https://github.com/mapbox/rio-color/pull/22 for cython implementation

Benchmarks

Tested on 4096x4096 tiled, uncompressed, rgba GeoTIFF. New method is significantly faster than old. Not as fast a imagemagick but it works better, retaining the profile of the input data.

method	time	notes
convert -modulate	1.5s	HSV colorspace is way faster
convert -set option:modulate:colorspace lch -modulate	6.8s	loses geotiff tags, tiling, compression, trouble with alpha bands
old skimage-based rio color "saturation ..."	12.5s
new cython-based rio color "saturation ..."	8.8s

Bottom line is performance is 1/3 better than the skimage method but still way slower than our HSV-based saturation with convert.

Visuals

The results are not floating-point-exact with the skimage method but, when cast to 8bit rgb there is no perceptual difference.

Here’s a demo of the saturation. I chose to use a stock photo instead of satellite image since it more clearly demonstrates the effect of saturation. This was created by the following rio_color script

from rio_color.colorspace import saturate_rgb
import rasterio

with rasterio.open("test.png") as src:
    arr = src.read().astype('float64') / 255.0
    arr = arr[0:3,]
    prof = src.profile
    prof['count'] = 3
    for sat in range(0, 250, 5):
        with rasterio.open("test_{:0>3}.png".format(sat), 'w', **prof) as dst:
            rgb = saturate_rgb(arr, sat/100.0)
            rgb8 = (rgb * 255).astype('uint8')
            dst.write(rgb8)

saturation ranging from 0 (B&W) to 250% (garishly over-saturated). Sorry about the giffiness of the image - you get the idea though.