How to plot pie chart of KMeans of image colors

Question

I am a newbie in computer vision, and I am trying to do a plot to summarize color identification on an image. I have used KMeans to find the pixel values I needed to detect. But I would like to improve the code with visualization on a palette or a pie chart. I think I should count the pixel's values falling on each cluster. However, I just got stuck, and I do not know how to do it.

from sklearn.cluster import KMeans 
import matplotlib.pyplot as plt
import numpy as np
from collections import Counter
from skimage import io, color

def kmeans_segementation(image, n_clusters, random_state=0):
    rows, cols, channels = image.shape                                #get the image shape
    X = image.reshape(rows*cols, channels)
    kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(X)
    labels = kmeans.labels_.reshape(rows, cols)    
    labels = kmeans.cluster_centers_                                  # get group of colors   
    ordered_colors = [labels[key] for key,value in enumerate(labels)]
    print(ordered_colors)
    

img2_rgb = io.imread('frame41.png')

n_clusters = 15

kmeans_segementation(img2_rgb, n_clusters, random_state=0)

user_12046409 · Answer

The following code shows how one could create a pie chart, a histogram and a resampled image from the K-Means algorithm (image from wikipedia):

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np

# image_name = 'astronaut.png' # or 'coffee.png' or 'motorcycle_left.png'
# image = plt.imread('https://raw.githubusercontent.com/scikit-image/scikit-image/main/skimage/data/' + image_name)
image = plt.imread('wikipedia_rainbow_lorikeet.png')

n_clusters = 9
rows, cols, channels = image.shape  # get the image shape
X = image.reshape(rows * cols, channels)
kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(X)
labels = kmeans.labels_.reshape(rows, cols)
centers = kmeans.cluster_centers_.clip(0, 1)  # get group of colors, make sure they are in the interval 0-1

unique_labels, counts = np.unique(labels, return_counts=True)
label_order = np.argsort(counts)[::-1]  # descending order
unique_labels = unique_labels[label_order]
counts = counts[label_order]
percentages = counts / counts.sum() * 100

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2, figsize=(12, 9))

ax1.imshow(image)
ax1.axis('off')

color_labels = [f'{label}:
{perc:.1f} %' for label, perc in zip(unique_labels, percentages)]
ax2.pie(counts, labels=color_labels, colors=centers[unique_labels])

ax3.imshow(centers[labels])
ax3.axis('off')

bars = ax4.bar(unique_labels.astype(str), counts, color=centers[unique_labels], edgecolor='black')
ax4.bar_label(bars, [f'{perc:.1f} %' for perc in percentages])
for spine in ['top', 'right']:
    ax4.spines[spine].set_visible(False)

plt.tight_layout()
plt.show()

An example using an astronaut image:

An example with the 7 colors of the South African flag.

How to plot pie chart of KMeans of image colors

1 Answers