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2ndDerivativeFilter(Laplacian).py
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2ndDerivativeFilter(Laplacian).py
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# -*- coding: utf-8 -*-
"""
Created on Sun Apr 3 21:17:44 2022
@author: Anil Pudasaini 076MSICE003
"""
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
def secondDerivative_filter(image,filter_size):
image = image.resize((400,400), Image.LANCZOS)
numpy_image = np.array(image)
#new array for manipulation
new_array = np.zeros((400+(filter_size-1),(400+(filter_size-1))))
"""
How i got the formulae --> say filter size 3 then array should start from 1
if filter size 3 then it should start from 2 and so on. then
so (required_index * 2 + 1 = filter_size)
that gives required_index = (filter_size-1)/2
"""
#padding 0s to image
start = int((filter_size-1)/2)
end = int(400+((filter_size-1)/2))
new_array[start:end,start:end] = numpy_image
#initializing FILTER
# This is for 3x3 filter, this can also be done for other filter sizes
# by chaging filter size and filter matrix.
#Laplacian filter
#Filter = [[0,1,0],[1,-4,1],[0,1,0]]
#Laplacian Image Enhancement (Original - Laplacian Filtered Img = Sharpened Image)
Filter = [[0,-1,0],[-1,5,-1],[0,-1,0]]
#Variants of laplacian
#Filter = [[1,1,1],[1,-8,1],[1,1,1]]
#Filter = [[-1,-1,-1],[-1,9,-1],[-1,-1,-1]]
filter_mapped_values = []
for i in range(400):
for j in range(400):
#extracting part of array equal to the filter size
temp = new_array[i:(filter_size+i),j:(filter_size+j)]
multiplied_array = np.multiply(Filter,temp)
sum_array = np.sum(multiplied_array)
filter_mapped_values.append(sum_array)
# creating array of defined size from the calculated list
result = np.resize(filter_mapped_values,(400,400))
result_image = Image.fromarray(result)
return result_image
filter_size = 3 #show values for different filter size
loaded_img = Image.open('gray.jpg').convert('L')
image = loaded_img.resize((400,400), Image.LANCZOS)
final_img = secondDerivative_filter(loaded_img,filter_size)
'''
saving the image
if final_img.mode != 'RGB':
final_img = final_img.convert('RGB')
final_img.save("saved.jpg")
'''
#displaying the images
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 7), dpi=90)
ax[0].imshow(final_img, cmap='gray')
#ax[0].set_title('Laplacina Filtered image using filter of size ' +str(filter_size) +' x ' + str(filter_size))
ax[0].set_title('Laplacian Enhanced Image using filter of size ' +str(filter_size) +' x ' + str(filter_size))
ax[1].imshow(image, cmap='gray')
ax[1].set_title('Original image')