Dominant colors in images

Cluster Analysis in Python

Shaumik Daityari

Business Analyst

Dominant colors in images

All images consist of pixels
Each pixel has three values: Red, Green and Blue
Pixel color: combination of these RGB values
Perform k-means on standardized RGB values to find cluster centers
Uses: Identifying features in satellite images

Pixels in an image and their RGB values

Source

Feature identification in satellite images

Source

Tools to find dominant colors

Convert image to pixels: matplotlib.image.imread
Display colors of cluster centers: matplotlib.pyplot.imshow

Convert image to RGB matrix

import matplotlib.image as img
image = img.imread('sea.jpg')
image.shape

(475, 764, 3)

r = []
g = []
b = []

for row in image:
    for pixel in row:
        # A pixel contains RGB values
        temp_r, temp_g, temp_b = pixel
        r.append(temp_r)
        g.append(temp_g)
        b.append(temp_b)

DataFrame with RGB values

pixels = pd.DataFrame({'red': r,
                       'blue': b,
                       'green': g})
pixels.head()

red	blue	green
252	255	252
75	103	81
...	...	...

Create an elbow plot

distortions = []
num_clusters = range(1, 11)

# Create a list of distortions from the kmeans method
for i in num_clusters:
    cluster_centers, _ = kmeans(pixels[['scaled_red', 'scaled_blue', 
                                        'scaled_green']], i)
    distortions.append(distortion)

# Create a DataFrame with two lists - number of clusters and distortions
elbow_plot = pd.DataFrame({'num_clusters': num_clusters, 
                           'distortions': distortions})

# Creat a line plot of num_clusters and distortions
sns.lineplot(x='num_clusters', y='distortions', data = elbow_plot)
plt.xticks(num_clusters)
plt.show()

Elbow plot

Find dominant colors

cluster_centers, _ = kmeans(pixels[['scaled_red', 'scaled_blue', 
                                    'scaled_green']], 2)

colors = []

# Find Standard Deviations
r_std, g_std, b_std = pixels[['red', 'blue', 'green']].std()

# Scale actual RGB values in range of 0-1
for cluster_center in cluster_centers:
    scaled_r, scaled_g, scaled_b = cluster_center
    colors.append((
        scaled_r * r_std/255,
        scaled_g * g_std/255,
        scaled_b * b_std/255
    ))

Display dominant colors

#Dimensions: 2 x 3 (N X 3 matrix)
print(colors)

[(0.08192923122023911, 0.34205845943857993, 0.2824002984155429),
 (0.893281510956742, 0.899818770315129, 0.8979114272960784)]

#Dimensions: 1 x 2 x 3 (1 X N x 3 matrix)
plt.imshow([colors]) 
plt.show()

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Cluster Analysis in Python