Log transformation of an image using Python and OpenCV
log(exp(x)) = x
How log value of a number is calculated?
log(2^3) = log(8) 3 * log(2) = log(8) 3 = log(8) / log(2) Log(8) = 3 (base is 2)
log1 = 0
log0 = infinity
import numpy as np # input a number as integer a = int(input()) print("Natural log value of the input number is", np.log(a)) # If you want base of log to be set to 2 print("Log value of the number with base 2 is", np.log2(a)) # If you want base of log to be set to 10 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:
Log transformation
S = c * log (1 + r) where, R = input pixel value, C = scaling constant and S = output pixel value
c = 255 / (log (1 + max_input_pixel_value))
Input File –
import cv2 import numpy as np import matplotlib.pyplot as plt # Read an image image = cv2.imread('GFG.png') # Apply log transformation method c = 255 / np.log(1 + np.max(image)) log_image = c * (np.log(image + 1)) # Specify the data type so that # float value will be converted to int log_image = np.array(log_image, dtype = np.uint8) # Display both images plt.imshow(image) plt.show() plt.imshow(log_image) plt.show() |
Output :
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