K-means color clustering - omit background pixels with masked numpy arrays

Question

I'm trying to find the 3 dominant colours of an several images using K-means clustering. The problem I'm facing is that K-means also clusters the background of the image. I am using Python 2.7 and OpenCV 3

All images have the same grey background of the following RGB colour: 150,150,150. To avoid that K-means also clusters the background color, I created a masked array which masks all '150' pixel values from the original image array, theoretically leaving only the non-background pixels in the array for K-Means to work with. However, when I run my script, it still returns the grey as one of the dominant colours.

My question: is a masked array the way to go (and did I do something wrong) or are there better alternatives to somehow exclude pixels from K-means clustering?

Please find my code below:

from sklearn.cluster import KMeans
from sklearn import metrics
import cv2
import numpy as np

def centroid_histogram(clt):
    numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)
    (hist, _) = np.histogram(clt.labels_, bins=numLabels)
    hist = hist.astype("float")
    hist /= hist.sum()
    return hist

image = cv2.imread("test1.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

h, w, _ = image.shape
w_new = int(100 * w / max(w, h))
h_new = int(100 * h / max(w, h))
image = cv2.resize(image, (w_new, h_new))

image_array = image.reshape((image.shape[0] * image.shape[1], 3))
image_array = np.ma.masked_values(image_array,150)

clt = KMeans(n_clusters=3)
clt.fit(image_array)

hist = centroid_histogram(clt)
zipped = zip(hist, clt.cluster_centers_)
zipped.sort(reverse=True, key=lambda x: x[0])

hist, clt.cluster_centers = zip(*zipped)
print(clt.cluster_centers_)

Answer 1

If you want to extract the values of pixels other than your background, you can use numpy indexation :

img2=image_array[image_array!=[150,150,150]]
img2=img2.reshape((len(img2)/3,3))

This will yield the list of pixels which are not [150,150,150].
However, it does not preserve the structure of the image, just gives you the list of pixels values. I can't really remember, but maybe for K-means you need to give the whole image, i.e. you also need to feed it the position of the pixels ? But in that case, no masking will ever help because masking is just replacing values of certain pixels by another, not getting rid of pixels all together.