Python的Huffman编码文件压缩实现

Python的Huffman编码文件压缩实现

---

一、背景介绍：

Huffman编码是一种无损编码方式，采用Huffman树实现，具体可以见百度百科。

代码参考博客https://blog.csdn.net/EggWave/article/details/78697965。本篇将Huffman编码文件压缩脱离django框架，独立实现。

二、注意事项：

如果直接将Huffman编码写入文件，一股脑儿将0，1比特串怼进文件里，你会发现最后的压缩文件比没压缩之前的还大。因此，在输出文件之前，应该将8位比特串转化为一个字节，再逐个将字节写入文件中。

import six
#############################
#create time 2020.4.20
#定义一个huffman节点类
#############################
class Node:
    def __init__(self,freq):
        self.left = None
        self.right = None
        self.father = None
        self.freq = freq
 
    def is_left(self):
        return self.father.left == self

#############################
#create time 2020.4.20
#节点生成函数
#############################
def create_nodes(frequencies):
    return [Node(freq) for freq in frequencies]

#############################
#create time 2020.4.20
#huffman树生成函数
#############################
def create_huffman_tree(nodes):
    queue = nodes[:]
    while len(queue) > 1:
        queue.sort(key=lambda item: item.freq)
        node_left = queue.pop(0)
        node_right = queue.pop(0)
        node_father = Node(node_left.freq + node_right.freq)
        node_father.left = node_left
        node_father.right = node_right
        node_left.father = node_father
        node_right.father = node_father
        queue.append(node_father)
    queue[0].father = None
    return queue[0]

###############################
#create time 2020.4.20
#根据huffman树产生huffman编码
###############################
def huffman_encoding(nodes, root):
    codes = [''] * len(nodes)
    for i in range(len(nodes)):
        node_tmp = nodes[i]
        while node_tmp != root:
            if node_tmp.is_left():
                codes[i] = '0' + codes[i]
            else:
                codes[i] = '1' + codes[i]
            node_tmp = node_tmp.father
    return codes

#############################################
#create time 2020.4.20
# 获取字符出现的频数
#############################################
def count_frequency(input_string):
    # 用于存放字符
    char_store = []
    # 用于存放频数
    freq_store = []
 
    # 解析字符串
    for index in range(len(input_string)):
        if char_store.count(input_string[index]) > 0:
            temp = int(freq_store[char_store.index(input_string[index])])
            temp = temp + 1
            freq_store[char_store.index(input_string[index])] = temp
        else:
            char_store.append(input_string[index])
            freq_store.append(1)
    # 返回字符列表和频数列表
    return char_store, freq_store

############################
#create time 2020.4.20
# 获取字符、频数的列表
############################
    
def get_char_frequency(char_store=[], freq_store=[]):
    # 用于存放char_frequency
    char_frequency = []
    for item in zip(char_store, freq_store):
        temp = (item[0], item[1])
        char_frequency.append(temp)
    return char_frequency
################################
#create time 2020.4.20
#编码转换
################################
def write_file(code):
    f=open("huffman_encoding.txt","wb")
    out=0
    while len(code)>8:
        for x in range(8):
            out=out<<1
            if code[x]=="1":
                out=out|1
        code=code[8:]
        f.write(six.int2byte(out))
        out=0

    f.write(six.int2byte(len(code)))
    out=0
    for i in range(len(code)):
        out=out<<1
        if code[i]=="1":
            out=out|1
    
    for i in range(8-len(code)):
        out=out<<1
    f.write(six.int2byte(out))
    f.close()
    return True


###############################
#create time 2020.4.20
# 将字符转换成huffman编码
###############################
def get_huffman_file(input_string, char_frequency, codes):
    # 逐个字符替换
    file_content = ''
    for index in range(len(input_string)):
        for item in zip(char_frequency, codes):
            if input_string[index] == item[0][0]:
                file_content = file_content + item[1]
    
    return file_content

##################################
#create time 2020.4.20
#解码huffman编码文件
###################################
def decode_huffman(input_string,  char_store, freq_store):
    encode = ''
    decode = ''
    for index in range(len(input_string)):
        encode = encode + input_string[index]
        for item in zip(char_store, freq_store):
            if encode == item[1]:
                decode = decode + item[0]
                encode = ''
    return decode

fo=open("/home/ming/Desktop/图像处理/信息论大作业/text.txt","r+")#读取待压缩文件
input_string=fo.read()
fo.close()
char_store, freq_store=count_frequency(input_string)#字母出现次数统计
char_frequency=get_char_frequency(char_store,freq_store)#频段统计
nodes=create_nodes([i[1] for i in char_frequency])#节点生成
root= create_huffman_tree(nodes)#标记根结点
codes=huffman_encoding(nodes,root)#生成huffman树
save_file=get_huffman_file(input_string,char_frequency,codes)#根据生成的huffman树，生成huffman编码
write_file(save_file)#将01比特串按位写入文件

三、压缩结果：

待压缩文件大小为6.5kb，可以看如下图所示。

压缩后，压缩文件的大小为3.6kb，很明显，压缩效果显著。