Python Image Compression - python-3.x

Question

I am using the Pillow library of Python to read in image files. How can I compress and decompress using Huffman encoding? Here is an instruction:
You have been given a set of example images and your goal is to compress them as much as possible without losing any perceptible information –upon decompression they should appear identical to the original images. Images are essentially stored as a series of points of color, where each point is represented as a combination of red, green, and blue (rgb). Each component of the rgb value ranges between 0-255, so for example: (100, 0, 200) would represent a shade of purple. Using a fixed-length encoding, each component of the rgb value requires 8 bits to encode (28= 256) meaning that the entire rgb value requires 24 bits to encode. You could use a compression algorithm like Huffman encoding to reduce the number of bits needed for more common values and thereby reduce the total number of bits needed to encode your image.
# For my current code I just read the image, get all the rgb and build the tree
from PIL import Image
import sys, string
import copy
codes = {}
def sortFreq(freqs):
letters = freqs.keys()
tuples = []
for let in letters:
tuples.append (freqs[let],let)
tuples.sort()
return tuples
def buildTree(tuples):
while len (tuples) > 1:
leastTwo = tuple (tuples[0:2]) # get the 2 to combine
theRest = tuples[2:] # all the others
combFreq = leastTwo[0][0] + leastTwo[1][0] # the branch points freq
tuples = theRest + [(combFreq, leastTwo)] # add branch point to the end
tuples.sort() # sort it into place
return tuples[0] # Return the single tree inside the list
def trimTree(tree):
# Trim the freq counters off, leaving just the letters
p = tree[1] # ignore freq count in [0]
if type (p) == type (""):
return p # if just a leaf, return it
else:
return (trimTree (p[0]), trimTree (p[1]) # trim left then right and recombine
def assignCodes(node, pat=''):
global codes
if type (node) == type (""):
codes[node] = pat # A leaf. Set its code
else:
assignCodes(node[0], pat+"0") # Branch point. Do the left branch
assignCodes(node[1], pat+"1") # then do the right branch.
dictionary = {}
table = {}
image = Image.open('fall.bmp')
#image.show()
width, height = image.size
px = image.load()
totalpixel = width*height
print ("Total pixel: "+ str(totalpixel))
for x in range (width):
for y in range (height):
# print (px[x, y])
for i in range (3):
if dictionary.get(str(px[x, y][i])) is None:
dictionary[str(px[x, y][i])] = 1
else:
dictionary[str(px[x, y][i])] = dictionary[str(px[x, y][i])] +1
table = copy.deepcopy(dictionary)
#combination = len(dictionary)
#for value in table:
# table[value] = table[value] / (totalpixel * combination) * 100
#print(table)
print(dictionary)
sortdic = sortFreq(dictionary)
tree = buildTree(sortdic)
trim = trimTree(tree)
print(trim)
assignCodes(trim)
print(codes)

Answer 1

The class HuffmanCoding takes complete path of the text file to be compressed as parameter. (as its data members store data specific to the input file).
The compress() function returns the path of the output compressed file.
The function decompress() requires path of the file to be decompressed. (and decompress() is to be called from the same object created for compression, so as to get code mapping from its data members)
import heapq
import os
class HeapNode:
def __init__(self, char, freq):
self.char = char
self.freq = freq
self.left = None
self.right = None
def __cmp__(self, other):
if(other == None):
return -1
if(not isinstance(other, HeapNode)):
return -1
return self.freq > other.freq
class HuffmanCoding:
def __init__(self, path):
self.path = path
self.heap = []
self.codes = {}
self.reverse_mapping = {}
# functions for compression:
def make_frequency_dict(self, text):
frequency = {}
for character in text:
if not character in frequency:
frequency[character] = 0
frequency[character] += 1
return frequency
def make_heap(self, frequency):
for key in frequency:
node = HeapNode(key, frequency[key])
heapq.heappush(self.heap, node)
def merge_nodes(self):
while(len(self.heap)>1):
node1 = heapq.heappop(self.heap)
node2 = heapq.heappop(self.heap)
merged = HeapNode(None, node1.freq + node2.freq)
merged.left = node1
merged.right = node2
heapq.heappush(self.heap, merged)
def make_codes_helper(self, root, current_code):
if(root == None):
return
if(root.char != None):
self.codes[root.char] = current_code
self.reverse_mapping[current_code] = root.char
return
self.make_codes_helper(root.left, current_code + "0")
self.make_codes_helper(root.right, current_code + "1")
def make_codes(self):
root = heapq.heappop(self.heap)
current_code = ""
self.make_codes_helper(root, current_code)
def get_encoded_text(self, text):
encoded_text = ""
for character in text:
encoded_text += self.codes[character]
return encoded_text
def pad_encoded_text(self, encoded_text):
extra_padding = 8 - len(encoded_text) % 8
for i in range(extra_padding):
encoded_text += "0"
padded_info = "{0:08b}".format(extra_padding)
encoded_text = padded_info + encoded_text
return encoded_text
def get_byte_array(self, padded_encoded_text):
if(len(padded_encoded_text) % 8 != 0):
print("Encoded text not padded properly")
exit(0)
b = bytearray()
for i in range(0, len(padded_encoded_text), 8):
byte = padded_encoded_text[i:i+8]
b.append(int(byte, 2))
return b
def compress(self):
filename, file_extension = os.path.splitext(self.path)
output_path = filename + ".bin"
with open(self.path, 'r+') as file, open(output_path, 'wb') as output:
text = file.read()
text = text.rstrip()
frequency = self.make_frequency_dict(text)
self.make_heap(frequency)
self.merge_nodes()
self.make_codes()
encoded_text = self.get_encoded_text(text)
padded_encoded_text = self.pad_encoded_text(encoded_text)
b = self.get_byte_array(padded_encoded_text)
output.write(bytes(b))
print("Compressed")
return output_path
""" functions for decompression: """
def remove_padding(self, padded_encoded_text):
padded_info = padded_encoded_text[:8]
extra_padding = int(padded_info, 2)
padded_encoded_text = padded_encoded_text[8:]
encoded_text = padded_encoded_text[:-1*extra_padding]
return encoded_text
def decode_text(self, encoded_text):
current_code = ""
decoded_text = ""
for bit in encoded_text:
current_code += bit
if(current_code in self.reverse_mapping):
character = self.reverse_mapping[current_code]
decoded_text += character
current_code = ""
return decoded_text
def decompress(self, input_path):
filename, file_extension = os.path.splitext(self.path)
output_path = filename + "_decompressed" + ".txt"
with open(input_path, 'rb') as file, open(output_path, 'w') as output:
bit_string = ""
byte = file.read(1)
while(byte != ""):
byte = ord(byte)
bits = bin(byte)[2:].rjust(8, '0')
bit_string += bits
byte = file.read(1)
encoded_text = self.remove_padding(bit_string)
decompressed_text = self.decode_text(encoded_text)
output.write(decompressed_text)
print("Decompressed")
return output_path
Running the program:
Save the above code, in a file huffman.py.
Create a sample text file. Or download a sample file from sample.txt (right click, save as)
Save the code below, in the same directory as the above code, and Run this python code (edit the path variable below before running. initialize it to text file path)
UseHuffman.py
from huffman import HuffmanCoding
#input file path
path = "/home/ubuntu/Downloads/sample.txt"
h = HuffmanCoding(path)
output_path = h.compress()
h.decompress(output_path)
The compressed .bin file and the decompressed file are both saved in the same directory as of the input file.
Result
On running on the above linked sample text file:
Initial Size: 715.3 kB
Compressed file Size: 394.0 kB
Plus, the decompressed file comes out to be exactly the same as the original file, without any data loss.
And that is all for Huffman Coding implementation, with compression and decompression. This was fun to code.
The above program requires the decompression function to be run using the same object that created the compression file (because the code mapping is stored in its data members). We can also make the compression and decompression function run independently, if somehow, during compression we store the mapping info also in the compressed file (in the beginning). Then, during decompression, we will first read the mapping info from the file, then use that mapping info to decompress the rest file.