Image pyramid

pyramid, or pyramid representation, is a type of multi-scale signal representation in which a signal or image is subject to repeated smoothing and subsampling.

Types of pyramid:

1.       Gaussian pyramid

2.       Laplacian pyramid

-A lavel in laplacian pyramid is formed by the difference between that level in  Gaussian pyramid and expanded version of its upper level in Gaussian pyramid.

Uses of laplacian/Gaussian pyramid: help us to bland the images and reconstruction of the images.

import cv2
import numpy as np

img = cv2.imread("lena.jpg")
#lower resolution
 #lr1 = cv2.pyrDown(img)
 #lr2 = cv2.pyrDown(lr1)
#higher resolution
 #hr2 = cv2.pyrUp(lr2)

 #cv2.imshow("pyrDown 1 image", lr1)
 #cv2.imshow("pyrDown 2 image", lr2)
 #cv2.imshow("pyrUp 1 image", hr2)
 #in gaussian two methods available
  #-pyrDown and pyrUp
#but in laplacian pyramid no methods available
#A level in laplacian pyramid is formed by the difference between that lavel
#in gaussian pyramid and expanded version of its upper level in gaussian pyramid

layer = img.copy()
gp=[layer]

for i in range(6):
    layer = cv2.pyrDown(layer)
    gp.append(layer)
   # cv2.imshow(str(i), layer)

layer = gp[5]
cv2.imshow('upper level gaussian pyramid', layer)
lp = [layer]

for i in range(5, 0, -1):
   # print(i)
    gaussian_extended = cv2.pyrUp(gp[i])
    laplacian = cv2.subtract(gp[i-1], gaussian_extended)
    cv2.imshow(str(i), laplacian)

cv2.imshow("Original image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()