import pandas as pd
import numpy as np
import wordcloud
from matplotlib.image import imread
import jieba
import jieba.analyse as analyse
import re


# 定义蓝色调色板
def blue_color_func(word, font_size, position, orientation, random_state=None, **kwargs):
    return "hsl(210, 100%%, %d%%)" % np.random.randint(50, 90)


# 归一化“哈哈哈”
def normalize_hahaha(text):
    return re.sub(r'哈{3,}', '哈哈哈', text)


# 将数据导入
dm = pd.read_excel('All_Danmu.xlsx', sheet_name='Sheet1')

# 扩展停用词列表
my_stopwords = set(['我', '你', '他', '这', '个', '是', '的', '了', '啊', '吗', '吧', '就', '都', '不是', '也', '哈哈哈',
                    '吧', '呀', '哦', '呢',  '哇', '么', '嘛', '呵呵', '呵', '嘿嘿', '哎呀', '哎', '哼', '呃'])


# 词云图生成
def wordcloud_generation(dm):
    dm_list = dm['danmu'].dropna().astype(str).tolist()

    # 归一化处理
    dm_list = [normalize_hahaha(text) for text in dm_list]
    dm_string = ' '.join(dm_list)  # 弹幕字符串

    # 使用TF-IDF提取关键词
    keywords = analyse.extract_tags(dm_string, topK=100, withWeight=False, allowPOS=())

    # 去掉停用词后的关键词
    keywords = [word for word in keywords if word not in my_stopwords]

    # 将关键词拼接为一个字符串
    dmreal_string = ' '.join(keywords)

    img = imread("OIP.jpg")
    # 词云生成
    wc = wordcloud.WordCloud(
        stopwords=my_stopwords,
        width=1920,
        height=1200,
        background_color='white',
        font_path='msyhl.ttc',
        mask=img,
        max_words=100,
        color_func=blue_color_func,
    ).generate(dmreal_string)
    wc.to_file('alldanmu_dwordcloud.png')


# 调用词云生成
wordcloud_generation(dm)