Python语言实现哈夫曼编码

汉语版：使用python实现huffman编码是一个能够很快地实现。所以我们选择使用python来实现我们这个程序。 l

E-version: we will use python to realize this program called huffman encoding and decoding. why we use python, because in python we can finish this program faster then other codes. this program are not the final implementation. actually, this is the first version i commit to git. i changed a lot in the least version . so if you run those codes on your environment. some problems may be exist; don`t worry, the first four drafts are right, you  can build everything based on them. so good lucky to you.

I：实现节点类

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

II：为每一个节点赋权值

def create_nodes(frequencies):    return [Node(freq) for freq in frequencies]

III：创建哈夫曼树

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]

III：遍历叶节点

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

IV：获取字符出现的频数

# 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

V：获取字符、频数的列表

# 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

VI：将字符转换成哈夫曼编码

# 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]    file_name = 'huffman_' + str(uuid.uuid1())+'.txt'    with open(file_name, 'w+') as destination:        destination.write(file_content)    return file_name

VII：解压缩哈夫曼文件

# 解压缩huffman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

VIII：计算压缩比(写错了，可以自行改写)

# 计算压缩比def get_encode_ration(codes):    # 计算所需要的二进制个数    h_length = 0    for item in codes:        h_length = h_length + len(item)    t_length = bin_middle(len(codes))*len(codes)    ratio = t_length/h_length    return str(ratio)[0:3]# 计算所在的二进制空间def bin_middle(number):    n, i = 1, 0    while n < number:        n = n * 2        i = i + 1    return i

最后:Django文件接收，并返回

def upload(request):    ctx = {}    if request.method == "POST":        file_name = str(request.FILES['file'])        if not file_name.endswith('txt'):            ctx['fail'] = 'file format exist wrong!'        else:            file = request.FILES['file']            ctx['success'] = 'Successful'            input_string = tool.read_file(tool.save_file(file))            char_store, freq_store = tool.count_frequency(input_string)            char_frequency = tool.get_char_frequency(char_store, freq_store)            nodes = huf.create_nodes([item[1] for item in char_frequency])            root = huf.create_huffman_tree(nodes)            codes = huf.huffman_encoding(nodes, root)            save_file_name = tool.get_huffman_file(input_string, char_frequency, codes)            for item in zip(char_frequency, codes):                print('Character:%s freq:%-2d   encoding: %s', item[0][0], item[0][1], item[1])            ctx['node'] = char_frequency            def file_iterator(files, chunk_size=512):                with open(files) as f:                    while True:                        c = f.read(chunk_size)                        if c:                            yield c                        else:                            break            the_file_name = tool.get_encode_ration(codes)+'_'+str(uuid.uuid1())+'.txt'            response = StreamingHttpResponse(file_iterator(save_file_name))            response['Content-Type'] = 'application/octet-stream'            response['Content-Disposition'] = 'attachment;filename="{0}"'.format(the_file_name)            return response