The values of ‘R’, ‘G’, and ‘B’ are changed and applied to the original image to get the required tint.
Below is a Python program that uses scikit-learn to implement the same. Scikit-learn, commonly known as sklearn is a library in Python that is used for the purpose of implementing machine learning algorithms −
import matplotlib.pyplot as plt from skimage import data from skimage import color path = "path to puppy_1.jpg" orig_img = io.imread(path) grayscale_img = rgb2gray(orig_img) image = color.gray2rgb(grayscale_img) red_multiplier = [0.7, 0, 0] yellow_multiplier = [1, 0.9, 0] fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True, sharey=True) ax1.imshow(red_multiplier * image) ax1.set_title('Original image') ax2.imshow(yellow_multiplier * image) ax2.set_title('Tinted image')
The required packages are imported into the environment.
The path where the image is stored is defined.
The ‘imread’ function is used to visit the path and read the image.
The ‘imshow’ function is used to display the image on the console.
The function ‘rgb2gray’ is used to convert the image from RGB color space to grayscale color space.
The function ‘gray2rgb’ is used to convert the image from grayscale to RGB color space.
The matplotlib library is used to plot this data on console.
The R, G, B values for the multipliers are defined and applied on the image.
Output is displayed on the console.