import numpy as np
import matplotlib.pyplot as plt

from skimage import data
from skimage.transform import pyramid_gaussian

import cv2
image = cv2.imread("Berkeley.jpg")

rows, cols, dim = image.shape
pyramid = tuple(pyramid_gaussian(image, downscale=2))

composite_image = np.zeros((rows, cols + cols // 2, 3), dtype=np.double)

composite_image[:rows, :cols, :] = pyramid[0]
i_row = 0
x=0
for p in pyramid[1:]:
    x+=1
    if x >4:
        break
    n_rows, n_cols = p.shape[:2]
    composite_image[i_row:i_row + n_rows, cols:cols + n_cols] = p
    i_row += n_rows

fig, ax = plt.subplots()
ax.imshow(composite_image)
plt.show()