You can load in a image using OpenCV and then convert it to a usable format for use with pywt.

import cv2

import numpy as np

import pywt

image = cv2.imread('1.png')

image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Convert to float for more resolution for use with pywt

image = np.float32(image)

image /= 255

# ... # Do your processing # ...

# Convert back to uint8 OpenCV format

image *= 255

image = np.uint8(image)

cv2.imshow('image', image)

cv2.waitKey(0)

You can Also try the following.

import numpy as np

import matplotlib.pyplot as plt

import pywt

import pywt.data

# Load image

original = pywt.data.camera()

# Wavelet transform of image, and plot approximation and details

titles = ['Approximation', ' Horizontal detail', 'Vertical detail', 'Diagonal detail']

coeffs2 = pywt.dwt2(original, 'bior1.3')

LL, (LH, HL, HH) = coeffs2

fig = plt.figure(figsize=(12, 3))

for i, a in enumerate([LL, LH, HL, HH]):

ax = fig.add_subplot(1, 4, i + 1)

ax.imshow(a, interpolation="nearest", cmap=plt.cm.gray)

ax.set_title(titles[i], fontsize=10)

ax.set_xticks([])

ax.set_yticks([])

fig.tight_layout()

plt.show()

Thank you Mahesh Divakaran