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# 从爬虫生成的Excel表格中读取数据并生成词云图
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import configparser
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import os
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import sys
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from collections import Counter
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from multiprocessing import Pool
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import PIL
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import jieba
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import matplotlib.pyplot as plt
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import numpy as np
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import openpyxl
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import pandas as pd
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import wordcloud
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# 定义一些参数，参数的详细介绍见GitHub上的readme.md
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config_file = 'config/config.ini'
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config_Section_Name = 'GC_DEFAULT' # 要读取的配置页名
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stop_Word = ['!', '！', ':', '*', '，', ',', '？', '《', '》',
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'。', ' ', '的', '了', '是', '啊', '吗', '吧', '这', '你', '我', '他', '就'] # 停用词表
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def read_Danmu(workbook_Name, sheet_Name): # 从Excel表中读取数据
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try:
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workbook = openpyxl.load_workbook(workbook_Name)
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worksheet = workbook[sheet_Name] # 当然也可以通过索引读sheet,为了可读性选择用名称
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data = worksheet.iter_rows(values_only=1)
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return data
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# 若报错，则返回空迭代器
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except openpyxl.utils.exceptions.InvalidFileException:
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print(f"输入文件的路径或格式错误，请打开{config_file}文件重新配置路径\n")
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return iter(())
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except KeyError:
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print(f"工作表页名错误，请检查Sheet的名字和{config_file}中是否一致\n")
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return iter(())
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except:
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exc_type, exc_value, exc_traceback = sys.exc_info()
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print(f"发生错误: {exc_type} - {exc_value}")
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return iter(())
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def cut_words(row):
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try:
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# 每行第一列是弹幕，第二列是出现次数
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sentence = row[0]
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count = row[1]
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# 运用jieba 进行分词，将结果储存在Counter中，再将其中词语的出现次数翻count倍
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words = jieba.lcut(sentence)
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# 去除停用词表中的词
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cut_Words = pd.Series(words)
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cut_Words = cut_Words[~cut_Words.isin(stop_Word)]
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# 将分词存入计数器中
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new_Counter = Counter(cut_Words.tolist())
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for item in new_Counter:
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new_Counter[item] *= count # 弹幕中词语出现数 = 弹幕出现次数*弹幕中词语出现次数
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return new_Counter
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except TypeError:
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return Counter() # 遇见异常输入的情况，返回空计数器。
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def generate_Word_Cloud(counter): # 生成词云图
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try:
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if not counter: # 如果计数器对象为空，则给出提示并退出函数
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return "输入的词频为空！"
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img = PIL.Image.open(pic_Path).convert('RGBA') # 解决灰度图像ERROR
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pic = np.array(img)
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image_colors = wordcloud.ImageColorGenerator(pic)
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word_Cloud = wordcloud.WordCloud(
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font_path=font_Path, mask=pic, width=WC_Width, height=WC_Height, mode="RGBA", background_color='white')
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word_Cloud.generate_from_frequencies(counter)
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plt.imshow(word_Cloud.recolor(color_func=image_colors),
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interpolation='bilinear')
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word_Cloud.to_file(output_Path)
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plt.axis('off')
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plt.show()
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return f"词云图生成完成，请前往{output_Path}查看"
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except FileNotFoundError: # pic_Path 或 font_Path错误的情况
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return f"图片或字体路径错误，请前往{config_file}核查。"
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except TypeError or ValueError: # WC_Width 或WC_Height类型或数组错误的情况
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return f"图片的Height与Width设置有误，请前往{config_file}核查。"
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except PIL.UnidentifiedImageError:
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return f"不支持该类型的图片，请修改图片路径。"
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except Exception as e:
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return f"生成词云图时发生错误：{e}"
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def main():
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rows = read_Danmu(workbook_Name, sheet_Name)
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word_counts = Counter()
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# 利用线程池优化分词速度，在生成所有弹幕的词云图是能节省时间
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with Pool() as pool:
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cut_words_results = pool.map(cut_words, rows)
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for result in cut_words_results:
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word_counts.update(result)
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print(generate_Word_Cloud(word_counts))
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if __name__ == "__main__":
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# 读取参数的配置
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config = configparser.ConfigParser()
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if not os.path.exists(config_file):
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print(f"配置文件 {config_file} 不存在！")
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exit(1)
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with open(config_file, encoding='utf-8') as f:
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config.read_file(f)
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workbook_Name = config.get(config_Section_Name, 'workbook_name',
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fallback='output/Top8_danmu.xlsx') # 要读取的Excel表的名称，默认为crawler.py生成的文件
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# 要读取的Excel表的页的名称，可从['Top 8', '所有弹幕']中选择
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sheet_Name = config.get(config_Section_Name, 'sheet_Name', fallback='所有弹幕')
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WC_Width = config.getint(
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config_Section_Name, 'WC_Width', fallback=1200) # 词云图的宽度
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WC_Height = config.getint(
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config_Section_Name, 'WC_Height', fallback=1200) # 词云图的高度
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font_Path = config.get(config_Section_Name, 'font_Path',
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fallback="config/msyh.ttc") # 字体存储路径
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pic_Path = config.get(config_Section_Name, 'pic_Path',
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fallback="config/m.png") # 词云背景图路径
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output_Path = config.get(
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config_Section_Name, 'output_Path', fallback="output/word_could.png")
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main()
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