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'''
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生成词云图片
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filePath词云所需的词语文本路径
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maskImgPath蒙版图片路径，如果无蒙版图片则置为''
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saveImgPath保存图片的路径
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width生成图片的横向尺寸
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height生成图片的纵向尺寸
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save是否保存图片
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'''
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import jieba
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import numpy as np
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import matplotlib.pyplot as plt
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from PIL import Image
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from wordcloud import WordCloud, ImageColorGenerator
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from sklearn.feature_extraction.text import TfidfVectorizer
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# 将弹幕文本分隔成易于处理的字词
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def ReadAndCutWords(filePath):
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with open(filePath, 'r', encoding='utf-8') as file:
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text = file.read()
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words = jieba.cut(text, cut_all=False)
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word_list = ' '.join(words)
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return word_list
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# 利用TF-IDF将字词按频率划分
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def ChangeToFreq(word_list):
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documents = [word_list]
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vectorizer = TfidfVectorizer()
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tfidf_matrix = vectorizer.fit_transform(documents)
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feature_names = vectorizer.get_feature_names_out()
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word_freq = dict(zip(feature_names, tfidf_matrix.toarray().sum(axis=0)))
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return word_freq
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# 根据字词频率来生成图云
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def CreateWordCloud(word_freq, width, height, maskImgPath, saveImgPath, save=False):
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if maskImgPath == '':
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mask = None
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else:
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mask = np.array(Image.open(maskImgPath))
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wordcloud = WordCloud(font_path='simhei.ttf',
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mask= mask,
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width=width,
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height=height,
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background_color='white').generate_from_frequencies(word_freq)
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if maskImgPath != '':
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image_colors = ImageColorGenerator(mask)
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wordcloud.recolor(color_func=image_colors)
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plt.imshow(wordcloud, interpolation='bilinear')
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plt.axis('off')
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plt.show()
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if save:
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wordcloud.to_file(saveImgPath)
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def main():
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filePath = './docs/allBarrage.txt'
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maskImgPath = './docs/maskImg.png'
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saveImgPath = './docs/wordCloud.png'
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word_list = ReadAndCutWords(filePath)
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word_freq = ChangeToFreq(word_list)
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width = 1920
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height = 1440
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isSave = True
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CreateWordCloud(word_freq, width, height, maskImgPath, saveImgPath, isSave)
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if __name__ == '__main__':
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main()
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