Python基于有道翻译实现翻译功能

基于PyQT5、OCR、文字转语言等实现翻译功能(自动识别语言)，实现词频统计，生成词云等，读取txt文件内容

前言

提示：基于PyQT5、OCR、文字转语言等实现翻译功能(自动识别语言)

参考链接：https://blog.csdn.net/m0_46778548/article/details/121042773?ops_request_misc=&request_id=&biz_id=102&utm_term=%E6%9C%89%E9%81%93%E7%BF%BB%E8%AF%91&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-3-121042773.nonecase&spm=1018.2226.3001.4187https://blog.csdn.net/melo_1/article/details/105036899https://blog.csdn.net/melo_1/article/details/105036899https://blog.csdn.net/melo_1/article/details/105036899

用Python破解有道翻译反爬虫机制_南窗客斯黄的博客-CSDN博客_爬虫有道翻译https://blog.csdn.net/m0_46778548/article/details/121042773?ops_request_misc=&request_id=&biz_id=102&utm_term=%E6%9C%89%E9%81%93%E7%BF%BB%E8%AF%91&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-3-121042773.nonecase&spm=1018.2226.3001.4187

一、程序演示

二、核心代码

1.翻译

代码如下：

    def translatefun(self):import urllib.requestimport urllib.parseimport jsona = "123"def translate_action(content):# print(content)url = 'http://fanyi.youdao.com/translate?smartresult=dict&smartresult=rule'# 需要打开网页的链接，这个在Request URL栏head = {}head['User-Agent'] = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.122 Safari/537.36'# 模拟浏览器访问，简单的防止反爬机制。这个在Request Headers中的User-Agent栏。data = {}data['i'] = contentdata['from'] = 'AUTO'data['to'] = 'AUTO'data['smartresult'] = 'dict'data['client'] = 'fanyideskweb'data['salt'] = '15847864360501'data['sign'] = 'f762faa6901c6cf473fce719f8238ca8'data['ts'] = '1584786436050'data['bv'] = '0ed2e07b89acaa1301d499442c9fdf79'data['doctype'] = 'json'data['version'] = '2.1'data['keyfrom'] = 'fanyi.web'data['action'] = 'FY_BY_REALTlME'# 将Form Data数据存入data字典中。data = urllib.parse.urlencode(data).encode('utf-8')# 将data以utf-8的形式编码req = urllib.request.Request(url, data, head)# 以代码中的data与head形式访问代码中的url链接response = urllib.request.urlopen(req)# 打开url网页并传送data与headhtml = response.read().decode('utf-8')# 读取打开的网页并进行utf-8解码target = json.loads(html)# 识别jsonprint(target['translateResult'])result = ''for i in target['translateResult']:for j in range(len(i)):print(i[j]['tgt'])result += i[j]['tgt']result += '\n'print(result)return resulttry:lt=translate_action(self.leftT.toPlainText())self.rightT.setText(str(lt))except:print("translatefun 128 err")

2.词频统计

代码如下：

    def analyseText(self):txt = self.rightT.toPlainText()words = jieba.lcut(txt)# print(words)counts = {}# while i<len(words):for word in words:if len(word) == 1:continueelse:rword = wordcounts[rword] = counts.get(rword, 0) + 1  # 返回指定键的值，如果值不在字典中返回default值即0sed = sorted(counts.items(), key=lambda x: x[1], reverse=True)[:20]tk20=[]tv20=[]for (k, v) in sorted(counts.items(), key=lambda x: x[1], reverse=True)[:10]:print("{}\t\t\t\t{}".format(k, v))tk20.append(k)tv20.append(v)plt.rcParams['font.family'] = 'STsong'print(tk20,tv20)plt.bar(tk20, tv20, color='lightskyblue')plt.xlabel('出现词语')plt.ylabel('出现次数')plt.title('词频统计柱状图')plt.show()

3.词云图生成：

 def wordimg(self):import matplotlib.pyplot as pltfrom wordcloud import WordCloudimport jiebagetText=self.rightT.toPlainText()jtext = jieba.cut(getText, cut_all=True)jtextsplit = " ".join(jtext)font = r'C:\Windows\Fonts\simfang.ttf'  # 设置中文字体，否则词云图可能不显示中文my_wordcloud = WordCloud(font_path=font).generate(jtextsplit)plt.imshow(my_wordcloud)plt.axis("off")  # 消除坐标轴plt.show()

4.图片识别文字

 def extractText(self):from cnocr.utils import read_imgfrom cnocr import CnOcr# 图片转文字def exT(imgpath):ocr = CnOcr()img_fp = imgpathimg = read_img(img_fp)res = ocr.ocr(img)text = ''for i in res:# print("".join(i[0]))text += "".join(i[0])text += "\n"return textfname = QFileDialog.getOpenFileName()text=exT(fname[0])self.leftT.setText(text)

如需要整套源码请点击python有道翻译、ocr图片转文字、文字转语音、统计词频、生成词云-Python文档类资源-CSDN下载

总结

由于本人能力有限，以后的学习中将继续完善自己的编码能力