ArithmeticEncodingPython/example_image_binary.py at main · ahmedfgad/ArithmeticEncodingPython · GitHub

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import scipy.misc

import pyae

import numpy

import matplotlib.pyplot

# Example for encoding an image using the PyAE module.

# This example returns the floating-point value in addition to its binary code that encodes the image.

# Change the precision to a bigger value

from decimal import getcontext

getcontext().prec = 444

# Read an image.

im = scipy.misc.face(gray=True)

# Just work on a small part to save time. The larger the image, the more time consumed.

im = im[:15, :15]

# Convert the image into a 1D vector.

msg = im.flatten()

# Create the frequency table based on its hitogram.

hist, bin_edges = numpy.histogram(a=im,

bins=range(0, 257))

frequency_table = {key: value for key, value in zip(bin_edges[0:256], hist)}

# Create an instance of the ArithmeticEncoding class.

AE = pyae.ArithmeticEncoding(frequency_table=frequency_table, save_stages=True)

# Encode the message

encoded_msg, encoder, interval_min_value, interval_max_value = AE.encode(msg=msg,

probability_table=AE.probability_table)

# Get the binary code that encodes the image

binary_code, encoder_binary = AE.encode_binary(float_interval_min=interval_min_value,

float_interval_max=interval_max_value)

print("The binary code is: {binary_code}".format(binary_code=binary_code))

# Decode the message

decoded_msg, decoder = AE.decode(encoded_msg=encoded_msg,

msg_length=len(msg),

probability_table=AE.probability_table)

# Reshape the image to its original shape.

decoded_msg = numpy.reshape(decoded_msg, im.shape)

# Show the original and decoded images.

fig, ax = matplotlib.pyplot.subplots(1, 2)

ax[0].imshow(im, cmap="gray")

ax[0].set_title("Original Image")

ax[0].set_xticks([])

ax[0].set_yticks([])

ax[1].imshow(decoded_msg, cmap="gray")

ax[1].set_title("Reconstructed Image")

ax[1].set_xticks([])

ax[1].set_yticks([])

ArithmeticEncodingPython/example_image_binary.py at main · ahmedfgad/ArithmeticEncodingPython · GitHub

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