1. Color inversion, logarithmic transformation, gamma transformation source code - miniopencv from zero

From scratch MiniOpenCV——1. Color reversal , Logarithmic transformation , Gamma transform source code

Build your own from scratch mini edition opencv, Know the principle of various image transformations from the bottom , Have a deeper understanding of digital image processing .
The environment is python3.6

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider,Button,RadioButtons

def set_chinese():
    """ To make sure to show Chinese in plot """
    import matplotlib
    matplotlib.rcParams['font.sans-serif']=['SimHei']
    matplotlib.rcParams['axes.unicode_minus']=False

def image_inverse(input):
    """ The function of invert color transformation, The input is an array of numpy. """
    value_max = np.max(input)
    output = value_max - input
    return output

def image_log(input):
    """ Name:log transform, Params:input is an array of numpy. Function: Compress the dynamic range to show more image detail. """
    return np.log(1+input)
	# For more information, please follow wechat GongZhonghao“ The story of endless stories ”
def gamma_trans(input, gamma=2, eps=0.5):
    """ Name:gamma transform, params: input is an array of numpy; eps is a supplement. Function: Extend the dynamic range, increase image contrast and show more images' detail. Useage:CRT correction. """
    return 255. * (((input + eps)/255.) ** gamma)
    
def update_gamma(val):
    gamma = slider1.val
    output = gamma_trans(img, gamma=gamma, eps=0.5)
    print("--------\n", output)
    ax1.set_title(" After gamma transformation ,gamma = " + str(gamma))
    ax1.imshow(output, cmap='gray', vmin=0, vmax=255)

if __name__ == "__main__":
    """ x1 and x2 The random matrix of , Whether its dynamic range is greater than 256 Still less than 256,  The values in the matrix , Map equally to 0-255 It shows that , This is a matplotlib In the library , imshow(),save() The default operation , The same is true of other drawing libraries . """ 
    set_chinese()
    img = np.asarray(Image.open("img.jpg").convert("L"))

    fig = plt.figure()
    ax0 = fig.add_subplot(121)
    ax0.set_title(" Input matrix ")
    ax0.imshow(img, cmap='gray', vmin=0,vmax=255)

    ax1 = fig.add_subplot(122)

    plt.subplots_adjust(bottom=0.3)
    s1 = plt.axes([0.25,0.1,0.55,0.03], facecolor='lightgoldenrodyellow') # Parameters of slider 
    slider1 = Slider(s1, ' Parameters gamma',0.0,2.0,valfmt="%.f",valinit=1.0,valstep=0.1)
    slider1.on_changed(update_gamma)# Monitoring methods 
    slider1.reset()
    slider1.set_val(1)

    plt.show()

The author just follows others' online tutorials , Some improvements have been made .
Follow up on this column .
The key points have been commented , Please feel free to let me know if you have any questions .