Most dominant color in RGB image - OpenCV / NumPy / Python

Question

I have a python image processing function, that uses tries to get the dominant color of an image. I make use of a function I found here https://github.com/tarikd/python-kmea

Answer 1

Two approaches using np.unique and np.bincount to get the most dominant color could be suggested. Also, in the linked page, it talks about bincount as a faster alternative, so that could be the way to go.

Approach #1

def unique_count_app(a):
    colors, count = np.unique(a.reshape(-1,a.shape[-1]), axis=0, return_counts=True)
    return colors[count.argmax()]

Approach #2

def bincount_app(a):
    a2D = a.reshape(-1,a.shape[-1])
    col_range = (256, 256, 256) # generically : a2D.max(0)+1
    a1D = np.ravel_multi_index(a2D.T, col_range)
    return np.unravel_index(np.bincount(a1D).argmax(), col_range)

Verification and timings on 1000 x 1000 color image in a dense range [0,9) for reproducible results -

In [28]: np.random.seed(0)
    ...: a = np.random.randint(0,9,(1000,1000,3))
    ...: 
    ...: print unique_count_app(a)
    ...: print bincount_app(a)
[4 7 2]
(4, 7, 2)

In [29]: %timeit unique_count_app(a)
1 loop, best of 3: 820 ms per loop

In [30]: %timeit bincount_app(a)
100 loops, best of 3: 11.7 ms per loop

Further boost

Further boost upon leveraging multi-core with numexpr module for large data -

import numexpr as ne

def bincount_numexpr_app(a):
    a2D = a.reshape(-1,a.shape[-1])
    col_range = (256, 256, 256) # generically : a2D.max(0)+1
    eval_params = {'a0':a2D[:,0],'a1':a2D[:,1],'a2':a2D[:,2],
                   's0':col_range[0],'s1':col_range[1]}
    a1D = ne.evaluate('a0*s0*s1+a1*s0+a2',eval_params)
    return np.unravel_index(np.bincount(a1D).argmax(), col_range)

Timings -

In [90]: np.random.seed(0)
    ...: a = np.random.randint(0,9,(1000,1000,3))

In [91]: %timeit unique_count_app(a)
    ...: %timeit bincount_app(a)
    ...: %timeit bincount_numexpr_app(a)
1 loop, best of 3: 843 ms per loop
100 loops, best of 3: 12 ms per loop
100 loops, best of 3: 8.94 ms per loop