How do you log transform an image in python?

c = 255/log(1+Maximum pixel value from the input image).

#----- Example Python program for logarithmic transformation of a Digital Image -----

# import Pillow modules

from PIL import Image

from PIL import ImageFilter

import math

# Compute log

def logTransform(c, f):

    g = c * math.log(float(1 + f),10);

    return g;

# Apply logarithmic transformation for an image  

def logTransformImage(image, outputMax = 255, inputMax=255):

    c = outputMax/math.log(inputMax+1,10);

    # Read pixels and apply logarithmic transformation

    for i in range(0, img.size[0]-1):

        for j in range(0, img.size[1]-1):

            # Get pixel value at (x,y) position of the image

            f = img.getpixel((i,j));

            # Do log transformation of the pixel

            redPixel    = round(logTransform(c, f[0]));

            greenPixel  = round(logTransform(c, f[1]));

            bluePixel   = round(logTransform(c, f[2]));

            # Modify the image with the transformed pixel values

            img.putpixel((i,j),(redPixel, greenPixel, bluePixel));

    return image;

# Display the original image

imageFileName = "forest.jpg";

img = Image.open(imageFileName);

img.show();

# Display the image after applying the logarithmic transformation

logTransformedImage = logTransformImage(img);

logTransformedImage.show();

In the original image the trees are darker and a small region of a stream or a path is not visible. Many trees as well are not clearly visibile.

The logarithmically transformed image has the most of the trees distinctly visible. However the background snow corresponding to the brightest pixels(not the foreground snow) is mostly the same in both the images. 

Logarithm value of a number is a number that raises power to a base number which gives the same number as in input. Simply, the logarithm is the inversion of exponential value of a number.

log(exp(x)) = x

How log value of a number is calculated?

Let’s see an example,

By applying logarithm in both sides,

log(2^3) = log(8)
3 * log(2) = log(8)
3 = log(8) / log(2)
Log(8) = 3 (base is 2)

We know, value of a number with power 0 is equal to 1. So,

log1 = 0 

and

log0 = infinity

We can find the log value of a number using Python as follow:

import numpy as np

a = int(input())

print("Natural log value of the input number is", 

      np.log(a))

print("Log value of the number with base 2 is", 

      np.log2(a))

print("Log value of the number with base 10 is",

      np.log10(a))

Examples:

Input : 8
Output : 
Natural log value of the input number is 2.0794415416798357
Log value of the number with base 2 is 3.0
Log value of the number with base 10 is 0.9030899869919435

Input : 255
Output : 
Natural log value of the input number is 5.541263545158426
Log value of the number with base 2 is 7.994353436858858
Log value of the number with base 10 is 2.406540180433955

Note: You can see log function in Python by visiting here.

Log transformation

Logarithmic transformation of an image is one of the gray level image transformations. Log transformation of an image means replacing all pixel values, present in the image, with its logarithmic values. Log transformation is used for image enhancement as it expands dark pixels of the image as compared to higher pixel values.

The formula for applying log transformation in an image is,

S = c * log (1 + r)

where,
R = input pixel value,
C = scaling constant and
S = output pixel value

The value of ‘c’ is chosen such that we get the maximum output value corresponding to the bit size used. So, the formula for calculating ‘c’ is as follows:

c = 255 / (log (1 + max_input_pixel_value))

When we apply log transformation in an image and any pixel value is ‘0’ then its log value will become infinite. That’s why we are adding ‘1’ to each pixel value at the time of log transformation so that if any pixel value is ‘0’, it will become ‘1’ and its log value will be ‘0’.

Let’s apply log transformation in an image using Python.

Input File –

import cv2

import numpy as np

import matplotlib.pyplot as plt

image = cv2.imread('GFG.png')

c = 255 / np.log(1 + np.max(image))

log_image = c * (np.log(image + 1))

log_image = np.array(log_image, dtype = np.uint8)

plt.imshow(image)

plt.show()

plt.imshow(log_image)

plt.show()

Output :

Log transformation of gives actual information by enhancing the image. If we apply this method in an image having higher pixel values then it will enhance the image more and actual information of the image will be lost. So, this method can’t be applied everywhere. It can be applied in images where low pixel values are more than higher ones.

What does log transformation do to an image?

How do you do log transformation in Python?

How do you transform an image in Python?

How log transformation function can be used for changing intensity values of pixels?