Removing background color(s) from when analysing dominant colors in an image (python, OpenCV)

Question

I'm working with the excellent code (this isn't mine!) from https://www.pyimagesearch.com/2014/05/26/opencv-python-k-means-color-clustering/ .

What I'm trying to work out is, when plotting the colors I want to remove certain ones - in this case I want to strip out / not measure the white background of the toy when plotting the histogram. In other cases when working with transparent backgrounds, it's pulling the grey/white checked background (eg https://i.stack.imgur.com/WLjsK.png ) so I'd want to move both those "colors".

Can anyone nudge me towards the right way of solving this please?

Many thanks!!

import cv2
import numpy as np
from sklearn import metrics
import matplotlib.pyplot as plt

!wget "https://www.beatrix-potter-shop.co.uk/app/uploads/2017/07/PO1424_Benjamin.jpg"

# Load the image
image = cv2.imread("PO1424_Benjamin.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) 

# Resize it for efficiency (color ratios are the same)
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));

# Step 1: Clustering colours with K-Means
from sklearn.cluster import KMeans

# Reshape the image to be a list of pixels
image_array = image.reshape((image.shape[0] * image.shape[1], 3))

# Clusters the pixels
clt = KMeans(n_clusters = 7)
clt.fit(image_array)

# By Adrian Rosebrock
def centroid_histogram(clt):
 # grab the number of different clusters and create a histogram
 # based on the number of pixels assigned to each cluster
 numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)
 (hist, _) = np.histogram(clt.labels_, bins = numLabels)

 # normalize the histogram, such that it sums to one
 hist = hist.astype("float")
 hist /= hist.sum()

 # return the histogram
 return hist

# Finds how many pixels are in each cluster
hist = centroid_histogram(clt)

# Sort the clusters according to how many pixel they have
zipped = list(zip(hist, clt.cluster_centers_))
zipped.sort(reverse=True, key=lambda x : x[0])
hist, clt.cluster_centers = zip(*zipped)

def plot_colors(hist, centroids):
    # initialize the bar chart representing the relative frequency
    # of each of the colors
    bar = np.zeros((50, 300, 3), dtype = "uint8")
    startX = 0

    # loop over the percentage of each cluster and the color of
    # each cluster
    for (percent, color) in zip(hist, centroids):
        # plot the relative percentage of each cluster
        endX = startX + (percent * 300)
        cv2.rectangle(bar, (int(startX), 0), (int(endX), 50),
            color.astype("uint8").tolist(), -1)
        startX = endX

    # return the bar chart
    return bar

# build a histogram of clusters and then create a figure
# representing the number of pixels labeled to each color
hist = centroid_histogram(clt)
bar = plot_colors(hist, clt.cluster_centers_)

# show our color bart
plt.figure()
plt.axis("on")
plt.imshow(bar)
plt.show()