01-Kmeans-Color-Quantization

Driptanil DattaSoftware Developer

K Means Color Quantization

Imports

import numpy as np
 
import matplotlib.image as mpimg
import matplotlib.pyplot as plt

The Image

image_as_array = mpimg.imread('../DATA/palm_trees.jpg')

image_as_array # RGB CODES FOR EACH PIXEL

RESULT

array([[[ 25,  89, 127],
        [ 25,  89, 127],
        [ 25,  89, 127],
        ...,
        [ 23,  63,  98],

plt.figure(figsize=(6,6),dpi=200)
plt.imshow(image_as_array)

RESULT

<matplotlib.image.AxesImage at 0x16d48ff0eb0>

PLOT

Using Kmeans to Quantize Colors

Quantizing colors means we'll reduce the number of unique colors here to K unique colors. Let's try just 6 colors!

image_as_array.shape
# (h,w,3 color channels)

RESULT

(1401, 934, 3)

Convert from 3d to 2d

Kmeans is designed to train on 2D data (data rows and feature columns), so we can reshape the above strip by using (h,w,c) ---> (h * w,c)

(h,w,c) = image_as_array.shape

image_as_array2d = image_as_array.reshape(h*w,c)

from sklearn.cluster import KMeans

model = KMeans(n_clusters=6)

model

RESULT

KMeans(n_clusters=6)

labels = model.fit_predict(image_as_array2d)

labels

RESULT

array([3, 3, 3, ..., 0, 0, 0])

# THESE ARE THE 6 RGB COLOR CODES!
model.cluster_centers_

RESULT

array([[  2.78511649,   2.58768262,   3.70018933],
       [138.48117295, 144.26702702, 143.36832053],
       [193.42381114, 154.48097123, 107.32286981],
       [ 71.66897867, 109.55660624, 137.71325107],
       [219.38959087, 134.67545907,  45.42786667],

rgb_codes = model.cluster_centers_.round(0).astype(int)

rgb_codes

RESULT

array([[  3,   3,   4],
       [138, 144, 143],
       [193, 154, 107],
       [ 72, 110, 138],
       [219, 135,  45],

quantized_image = np.reshape(rgb_codes[labels], (h, w, c))

quantized_image

RESULT

array([[[ 72, 110, 138],
        [ 72, 110, 138],
        [ 72, 110, 138],
        ...,
        [ 68,  62,  62],

plt.figure(figsize=(6,6),dpi=200)
plt.imshow(quantized_image)

RESULT

<matplotlib.image.AxesImage at 0x16d5bd928b0>

PLOT

00 Kmeans Clustering 02 Kmeans Clustering Project Exercise