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import collections
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import matplotlib.pyplot as plt
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from wordcloud import WordCloud
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from PIL import Image
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import numpy as np
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# 读取弹幕文件
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def read_comments(file_path):
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with open(file_path, 'r', encoding='utf-8') as file:
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comments = file.readlines()
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# 移除每条弹幕的换行符
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cleaned_comments = [comment.strip() for comment in comments]
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return cleaned_comments
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# 统计词频
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def count_words(comments):
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word_counter = collections.Counter()
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for comment in comments:
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words = comment.split() # 简单分词，按空格切分词语
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word_counter.update(words)
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return word_counter
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# 生成词云图
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def generate_wordcloud(word_frequencies, output_file, mask_image=None):
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wordcloud = WordCloud(
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width=1600, # 提高图像分辨率
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height=800, # 提高图像分辨率
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background_color='white', # 设置背景为白色
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max_words=150, # 减少最大词数，避免文字重叠
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min_font_size=10, # 确保最小字体大小不会太小
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relative_scaling=0.5, # 调整字体大小比例
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colormap='cool', # 使用冷色调配色方案
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mask=mask_image, # 可选：使用自定义形状的mask
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contour_width=3, # 设置轮廓宽度
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contour_color='steelblue', # 轮廓颜色
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font_path='C:/Windows/Fonts/simhei.ttf' # Windows下的黑体字体
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).generate_from_frequencies(word_frequencies)
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# 显示词云图
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plt.figure(figsize=(12, 6)) # 设置展示窗口的尺寸
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plt.imshow(wordcloud, interpolation='bilinear')
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plt.axis('off') # 不显示坐标轴
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plt.show()
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# 保存词云图到文件
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wordcloud.to_file(output_file)
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print(f"词云图已保存至 {output_file}")
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# 主函数：读取文件、统计词频、生成词云
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file_path = '弹幕.txt' # 弹幕文件的路径
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output_wordcloud_file = 'bullet_wordcloud.png' # 输出的词云图文件名
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# 1. 读取所有弹幕
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comments = read_comments(file_path)
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# 2. 统计词频
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word_frequencies = count_words(comments)
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# 3. (可选) 使用自定义图片作为词云形状
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mask_image = None # 如果不使用自定义形状，设为 None
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# 4. 生成并展示词云图
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generate_wordcloud(word_frequencies, output_wordcloud_file, mask_image)
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