Count number of unique colours in image

Question

I am trying to count the number of unique colours in an image. I have some code that I think should work however when I run it on an image its saying a I have 252 different colours out of a possible 16,777,216‬. That seems wrong given the image is BGR so shouldn't their be much more different colours (thousands not hundreds?)?

def count_colours(src):
    unique, counts = np.unique(src, return_counts=True)
    print(counts.size)
    return counts.size

src = cv2.imread('../../images/di8.jpg')
src = imutils.resize(src, height=300)
count_colours(src) # outputs 252 different colours!? only?

Is that value correct? And if not how can I fix my function count_colours()?

Source image:

Edit: is this correct?

def count_colours(src):
    unique, counts = np.unique(src.reshape(-1, src.shape[-1]), axis=0, return_counts=True)
    return counts.size

You might be only counting 1 layer of the RGB spectrum. You should convert the image into each of the three layers in numpy arrays and then find each of the unique combinations at index [i][j][k]. [More Reading Here](https://stackoverflow.com/questions/41500637/how-to-extract-r-g-b-values-with-numpy-into-seperate-arrays) and [here](https://docs.opencv.org/2.4/doc/tutorials/imgproc/histograms/histogram_calculation/histogram_calculation.html) — Edeki Okoh, Jun 15 '19 at 00:19

score 6 · Accepted Answer · edited Jun 04 '21 at 23:29

6

If you look at the uniques you are getting back, I'm pretty sure you'll find they are scalars. You need to use the axis keyword:

>>> import numpy as np
>>> from scipy.misc import face
>>> 
>>> img = face()
>>> np.unique(img.reshape(-1, img.shape[-1]), axis=0, return_counts=True)
(array([[  0,   0,   5],
       [  0,   0,   7],
       [  0,   0,   9],
       ...,
       [255, 248, 255],
       [255, 249, 255],
       [255, 252, 255]], dtype=uint8), array([1, 2, 2, ..., 1, 1, 1]))

edited Jun 04 '21 at 23:29

stateMachine

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answered Jun 15 '19 at 00:37

Paul Panzer

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thanks for your answer. I have editted my question with a solution. Do you think the math is adding up now? – sazr Jun 15 '19 at 00:49
3

@sazr Can't see anything wrong with it. Btw. if all you want is the number of distinct colors, then you do not need to `return_counts`. Just use `uniques = np.unique(src.reshape(-1, src.shape[-1]), axis=0)` and then `len(uniques)` or `uniques.shape[0]` – Paul Panzer Jun 15 '19 at 00:54
1

That is definitely a cleaner solution! I tested it and it returns the same value as my method. – Julian Jun 15 '19 at 01:03

score 4 · Answer 2 · answered Jun 15 '19 at 00:29

The comment by @ Edeki Okoh is correct. You need to find a way to take the color channels into account. There is probably a much cleaner solution but a hacky way to do this would be something like this. Each color channels has values from 0 to 255 so we add 1 in order to make sure that it gets multiplied. Blue will represent the last the digits, green the middle three ones and red the first three. Now every value is representing a unique color.

b,g,r = cv2.split(src)
shiftet_im = b + 1000 * (g + 1)  + 1000 * 1000 * (r + 1)

The resulting image should have one channel with each value representing a unique color combination.

score 1 · Answer 3 · answered Jun 15 '19 at 01:23

I think you only counted for a single channel e.g R-value out of full RGB channel. that's why you have only 252 discrete values.

In theory R G B each can have 256 discrete states.

256*256*256 =16777216

means in total you can have 16777216 possibilities of colors.

My suggestion is to convert RGB uchar CV_8UC3 into a single 32bit data structure like CV_32FC1

Let Given image as input

# my test small sie text image. which I can count the number of the state by hand

import cv2
import numpy as np
image=cv2.imread('/home/usr/naneDownloads/vuQ9y.png' )# change here
b,g,r = cv2.split(image)
out_in_32U_2D =  np.int32(b) << 16 + np.int32(g) << 8 + np.int32(r)  #bit wise shift 8 for each channel. 
out_in_32U_1D= out_in_32U_2D.reshape(-1) #convert to 1D
np.unique(out_in_32U_1D)
array([-2147483648, -2080374784, -1073741824, -1006632960,           0,
             14336,       22528,       30720,       58368,       91136,
            123904,      237568,      368640,      499712,      966656,
           1490944,     2015232,     3932160,     6029312,     8126464,
          15990784,    24379392,    32768000,    65011712,    67108864,
          98566144,   132120576,   264241152,   398458880,   532676608,
         536870912,   805306368,  1073741824,  1140850688,  1342177280,
        1610612736,  1879048192], dtype=int32)
len(np.unique(out_in_32U_1D))
37 # correct for my test wirting paper when compare when my manual counting

The code here should be able to provide you with what you needed

Count number of unique colours in image

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