Python图像压缩

Question

我正在使用Python的枕头库来读取图像文件。如何使用Huffman编码压缩和解压缩？以下是一项指示：

给您提供了一组示例图像，您的目标是在不丢失任何可感知信息的情况下尽可能压缩它们，-upon解压缩它们应该与原始图像相同。图像本质上存储为一系列颜色点，其中每个点被表示为红色、绿色和蓝色(rgb)的组合。rgb值的每个组件都在0-255之间，因此例如：(100，0,200)将代表紫色。使用固定长度编码，rgb值的每个组件都需要8位编码(28= 256)，这意味着整个rgb值需要24位编码。您可以使用像Huffman编码这样的压缩算法来减少更多公共值所需的位数，从而减少编码图像所需的总比特数。

# 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

类HuffmanCoding采用要压缩的文本文件的完整路径作为参数。(因为它的数据成员存储特定于输入文件的数据)。

函数返回输出压缩文件的路径。

函数decompress()需要解压缩文件的路径。(并从为压缩而创建的同一个对象中调用decompress()，以便从其数据成员获得代码映射)

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

运行程序:将上述代码保存在huffman.py文件中。

创建一个示例文本文件。或者从sample.txt下载一个示例文件(右击，另存为)

将下面的代码保存在与上述代码相同的目录中，并运行以下python代码(在运行之前编辑下面的路径变量)。将其初始化为文本文件路径)

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.

在上面链接的示例文本文件上运行的结果：

初始大小: 715.3 kB压缩文件大小: 394.0 kB加号，解压缩后的文件与原始文件完全相同，没有任何数据丢失。

这就是霍夫曼编码实现的全部，包括压缩和解压缩。这是很有趣的代码。

上述程序要求使用创建压缩文件的相同对象运行解压缩函数(因为代码映射存储在其数据成员中)。我们还可以使压缩和解压缩功能独立运行，如果在压缩过程中我们也将映射信息存储在压缩文件中(在开始时)。然后，在解压缩过程中，我们将首先从文件中读取映射信息，然后使用映射信息来解压缩rest文件。