find dominant colour in an 8-bit RGB Image with libvips python

dominant-lab.py

#!/usr/bin/python

import sys
from gi.repository import Vips

N_BINS = 10
BIN_SIZE = 256 / N_BINS

im = Vips.Image.new_from_file(sys.argv[1], access = Vips.Access.SEQUENTIAL)

# turn to lab
im = im.colourspace("lab")

# turn to 8-bit unsigned so we can make a histogram
# use 0 - 255 to be -128 - +127 for a/b
# and 0 - 255 for 0 - 100 L
im += [0, 128, 128]
im *= [255.0 / 100, 1, 1]
im = im.cast("uchar")

# make a 3D histogram of the 8-bit LAB image 
hist = im.hist_find_ndim(bins = N_BINS)

# find the position of the maximum
v, x, y = hist.maxpos()

# get the pixel at (x, y)
pixel = hist(x, y)

# find the index of the max value in the pixel
band = pixel.index(v) 

# scale up for the number of bins
x = x * BIN_SIZE + BIN_SIZE / 2
y = y * BIN_SIZE + BIN_SIZE / 2
band = band * BIN_SIZE + BIN_SIZE / 2

# turn the index back into the LAB colour
L = x * (100.0 / 255)
a = y - 128
b = band - 128

print "dominant colour:"
print "   L = ", L
print "   a = ", a
print "   b = ", b

dominant.py

#!/usr/bin/python

import sys
from gi.repository import Vips

im = Vips.Image.new_from_file(sys.argv[1], access = Vips.Access.SEQUENTIAL)

N_BINS = 10
BIN_SIZE = 256 / N_BINS

# make a 3D histogram of the RGB image ... 10 bins in each axis
hist = im.hist_find_ndim(bins = N_BINS)

# find the position of the maximum
v, x, y = hist.maxpos()

# get the pixel at (x, y)
pixel = hist(x, y)

# find the index of the max value in the pixel
band = pixel.index(v)

print "dominant colour:"
print "   R = ", x * BIN_SIZE + BIN_SIZE / 2
print "   G = ", y * BIN_SIZE + BIN_SIZE / 2
print "   B = ", band * BIN_SIZE + BIN_SIZE / 2

You need to ask for the output array, eg.:

john@banana ~/pics $ python3
Python 3.10.7 (main, Nov 24 2022, 19:45:47) [GCC 12.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pyvips
>>> x = pyvips.Image.new_from_file("k2.jpg")
>>> x.max(size=12)
255.0
>>> x.max(size=12, out_array=True)
[255.0, {'out_array': [255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0]}]

You can get the positions of the maxima like this:

>>> x.max(size=12, out_array=True, x_array=True, y_array=True)
[255.0, {'out_array': [255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0], 'x_array': [376, 377, 378, 378, 379, 385, 386, 387, 390, 391, 391, 392], 'y_array': [2032, 2032, 2032, 2032, 2032, 2032, 2032, 2032, 2032, 2032, 2032, 2032]}]

@jcupitt, you helped a lot, I really appreciate it! Maybe we’ll continue in the discussion?