convolution.py

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
import time as t
in_img = input("Enter the exact location path of the input image on which transformation is to be performed:")
img_1 = cv.imread(in_img)
img = cv.cvtColor(img_1, cv.COLOR_BGR2RGB)
while True:
	print("-----------------------------------------------------------------------------------------------------------------------------------------------------------------------")
	print("Convolution techniques\n")
	t.sleep(0.2)
	print("Select any one transformation to be applied on the given image:")
	print("1.Blurring")
	print("2.Noise reduction")
	print("3.edge detection and sharpness")
	print("4.exit");
	ch = int(input("choose any number corresponding to  the above listed transformations: "))	
	if ch == 1:
		o_dir = input("please enter a valid directory location to save:")
		plt.imshow(img)
		plt.title("input image")
		plt.savefig(o_dir +'blur_input.png')
		plt.show()
		avg_blur = cv.blur(img, (10,10))
		plt.imshow(avg_blur)
		plt.title("Blurring of image using averaging technique")
		plt.savefig(o_dir +'avg_blur.png')
		plt.show()
		gaus_blur = cv.GaussianBlur(img,(5,5),0)
		plt.imshow(gaus_blur)
		plt.title("Image after gaussian bluring")
		plt.savefig(o_dir + 'gaus_blur.png')
		plt.show()
		medBlur = cv.medianBlur(img,7)
		plt.imshow(medBlur)
		plt.title("Image after median blurring")
		plt.savefig(o_dir + 'med_blur.png')
		plt.show()
		bilateral = cv.bilateralFilter(img,5,6,6)
		plt.imshow(bilateral)
		plt.title("Image after bilateral filtering")
		plt.savefig(o_dir + 'out_bilateral.png')
		plt.show()
		im_mixed1 = np.hstack((avg_blur,gaus_blur))
		im_mixed2 = np.hstack((medBlur, bilateral))
		im_mixed  = np.vstack((im_mixed1, im_mixed2))
		plt.imshow(im_mixed)
		plt.title("comparision between different filters average, gaussian, median, bilateral")
		plt.savefig(o_dir + 'all_filters.png')
		plt.show()
		t.sleep(1)
	elif ch == 2:
		o_dir = input("please enter a valid directory location to save:")
		img_new = cv.medianBlur(img,5)
		plt.imshow(img)
		plt.title("input image")
		plt.savefig(o_dir +'noise_input.png')
		plt.show()
		plt.imshow(img_new)
		plt.title("Image after noise reduction")
		plt.savefig(o_dir+'without_noise.png')
		plt.show()
		denoise_median = np.hstack((img, img_new))
		plt.imshow(denoise_median)
		plt.title("Image before and after noise reduction using median filter")
		plt.savefig(o_dir + 'denoise_median.png')
		plt.show()
		t.sleep(1)
	elif ch == 3:
		o_dir = input("please enter a valid directory location to save:")
		gaus_blur = cv.GaussianBlur(img, (5,5), 0)
		res_img = cv.addWeighted(img, 1.5,gaus_blur ,-0.5, 0)
		plt.imshow(img)
		plt.title("image before sharpening")
		plt.show()
		plt.imshow(res_img)
		plt.title("Image after sharpening")
		plt.savefig(o_dir + 'img_sharpened.png')
		plt.show()
		img_sharpened = np.hstack((img,res_img))
		plt.imshow(img_sharpened)
		plt.title("Image before and after sharpening")
		plt.savefig(o_dir + 'sharpened_unsharpened.png')
		plt.show()
		t.sleep(1)
	elif ch == 4:
		t.sleep(0.2)
		print("see you again!!!")
		t.sleep(0.2)
		quit()
	else:
		print("bad response... Try again\n")