644 lines
24 KiB
Python
644 lines
24 KiB
Python
# -*- coding:utf-8 -*-
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from wxpy import *
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from platform import system
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from os.path import exists
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from os import makedirs
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from os import listdir
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from shutil import rmtree
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from queue import Queue
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from threading import Thread
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from time import sleep
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from pyecharts.charts import Pie
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from pyecharts.charts import Map
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from pyecharts.charts import WordCloud
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from pyecharts.charts import Bar
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from pyecharts import options as opts
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from requests import post
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import PIL.Image as Image
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import re
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import random
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import math
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from cv2 import CascadeClassifier
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from cv2 import imread
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from cv2 import cvtColor
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from cv2 import COLOR_BGR2GRAY
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# 引入打开文件所用的库
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# Window与Linux和Mac OSX有所不同
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# lambda用来定义一个匿名函数，可实现类似c语言的define定义
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if('Windows' in system()):
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# Windows
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from os import startfile
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open_html = lambda x : startfile(x)
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elif('Darwin' in system()):
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# MacOSX
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from subprocess import call
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open_html = lambda x : call(["open", x])
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else:
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# Linux
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from subprocess import call
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open_html = lambda x: call(["xdg-open", x])
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# 分析好友性别比例
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def sex_ratio():
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# 初始化
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male, female, other = 0, 0, 0
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# 遍历
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for user in friends:
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if(user.sex == 1):
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male += 1
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elif(user.sex == 2):
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female += 1
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else:
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other += 1
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name_list = ['男性', '女性', '未设置']
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num_list = [male, female, other]
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pie = Pie()
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pie.add("微信好友性别比例", [list(z) for z in zip(name_list, num_list)])
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pie.set_global_opts(title_opts=opts.TitleOpts(title="微信好友性别比例"))
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pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
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pie.render('data/好友性别比例.html')
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# 分析好友地区分布
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def region_distribution():
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# 使用一个字典统计好友地区分布数量
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province_dict = {'北京': 0, '上海': 0, '天津': 0, '重庆': 0,
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'河北': 0, '山西': 0, '吉林': 0, '辽宁': 0, '黑龙江': 0,
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'陕西': 0, '甘肃': 0, '青海': 0, '山东': 0, '福建': 0,
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'浙江': 0, '台湾': 0, '河南': 0, '湖北': 0, '湖南': 0,
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'江西': 0, '江苏': 0, '安徽': 0, '广东': 0, '海南': 0,
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'四川': 0, '贵州': 0, '云南': 0, '内蒙古': 0, '新疆': 0,
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'宁夏': 0, '广西': 0, '西藏': 0, '香港': 0, '澳门': 0}
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# 遍历
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for user in friends:
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# 判断省份是否存在，有可能是外国的，这种情况不考虑
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if (user.province in province_dict):
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key = user.province
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province_dict[key] += 1
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province = list(province_dict.keys())
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values = list(province_dict.values())
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# maptype='china' 只显示全国直辖市和省级，数据只能是省名和直辖市的名称
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map = Map()
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map.add("微信好友地区分布", [list(z) for z in zip(province, values)], "china")
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map.set_global_opts(
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title_opts=opts.TitleOpts(title="微信好友地区分布"),
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visualmap_opts=opts.VisualMapOpts(),
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)
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map.render(path="data/好友地区分布.html")
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# 对好友数最多的省份进行一进步分析
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max_count_province = ''
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for key, value in province_dict.items():
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if(value == max(province_dict.values())):
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max_count_province = key
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break
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# 使用一个字典统计好友地区分布数量
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city_dict = {}
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# 遍历
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for user in friends:
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if(user.province == max_count_province):
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# 更新键值对
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if(user.city in city_dict.keys()):
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city_dict[user.city] += 1
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else:
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city_dict[user.city] = 1
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bar = Bar()
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bar.add_xaxis([x for x in city_dict.keys()])
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bar.add_yaxis("地区分布", [x for x in city_dict.values()])
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bar.render('data/某省好友地区分布.html')
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# 统计认识的好友的比例
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def statistics_friends():
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# 初始化
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unknown, known_male, known_female, known_other = 0, 0, 0, 0
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# 遍历
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for user in friends:
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# 备注不为空
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if((user.remark_name).strip()):
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if(user.sex == 1):
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known_male += 1
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elif(user.sex == 2):
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known_female += 1
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else:
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known_other += 1
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else:
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unknown += 1
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name_list = ['未设置备注的好友', '设置备注的男性好友', '设置备注的女性好友', '设置备注的其他好友']
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num_list = [unknown, known_male, known_female, known_other]
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pie = Pie()
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pie.add("你认识的好友比例", [list(z) for z in zip(name_list, num_list)])
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pie.set_global_opts(title_opts=opts.TitleOpts(title="你认识的好友比例"))
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pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
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pie.render('data/你认识的好友比例.html')
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# 分析备注名称
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def analyze_remark_name():
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close_partner_dict = {'宝宝,猪,仙女,亲爱,老婆':0, '老公':0, '父亲,爸':0, '母亲,妈':0, '闺蜜,死党,基友':0}
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# 遍历好友数据
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for user in friends:
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for key in close_partner_dict.keys():
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# 判断该好友备注名是否包含close_partner_dict中的任意一个key
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name = key.split(',')
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for sub_name in name:
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if(sub_name in user.remark_name):
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close_partner_dict[key] += 1
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break
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name_list = ['最重要的她', '最重要的他', '爸爸', '妈妈', '死党']
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num_list = [x for x in close_partner_dict.values()]
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pie = Pie()
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pie.add("可能是你最亲密的人", [list(z) for z in zip(name_list, num_list)])
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pie.set_global_opts(title_opts=opts.TitleOpts(title="可能是你最亲密的人"))
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pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
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pie.render('data/你最亲密的人.html')
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# 分析个性签名
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def analyze_signature():
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# 个性签名列表
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data = []
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for user in friends:
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# 清除签名中的微信表情emoj，即<span class.*?</span>
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# 使用正则查找并替换方式，user.signature为源文本，将<span class.*?</span>替换成空
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new_signature = re.sub(re.compile(r"<span class.*?</span>", re.S), "", user.signature)
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# 只保留签名为1行的数据，过滤为多行的签名
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if(len(new_signature.split('\n')) == 1):
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data.append(new_signature)
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# 将个性签名列表转为string
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data = '\n'.join(data)
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# 进行分词处理，调用接口进行分词
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# 这里不使用jieba或snownlp的原因是无法打包成exe文件或者打包后文件非常大
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postData = {'data':data, 'type':'exportword', 'arg':'', 'beforeSend':'undefined'}
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response = post('http://life.chacuo.net/convertexportword',data=postData)
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data = response.text.replace('{"status":1,"info":"ok","data":["', '')
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# 解码
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data = data.encode('utf-8').decode('unicode_escape')
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# 将返回的分词结果json字符串转化为python对象，并做一些处理
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data = data.split("=====================================")[0]
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# 将分词结果转化为list，根据分词结果，可以知道以2个空格为分隔符
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data = data.split(' ')
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# 对分词结果数据进行去除一些无意义的词操作
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stop_words_list = [',', '，', '、', 'the', 'a', 'is', '…', '·', 'э', 'д', 'э', 'м', 'ж', 'и', 'л', 'т', 'ы', 'н', 'з', 'м', '…', '…', '…', '…', '…', '、', '.', '。', '!', '！', ':', '：', '~', '|', '▽', '`', 'ノ', '♪', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '\'', '‘', '’', '“', '”', '的', '了', '是', '你', '我', '他', '她','=', '\r', '\n', '\r\n', '\t', '以下关键词', '[', ']', '{', '}', '(', ')', '（', '）', 'span', '<', '>', 'class', 'html', '?', '就', '于', '下', '在', '吗', '嗯']
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tmp_data = []
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for word in data:
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if(word not in stop_words_list):
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tmp_data.append(word)
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data = tmp_data
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# 进行词频统计，结果存入字典signature_dict中
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signature_dict = {}
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for index, word in enumerate(data):
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print(u'正在统计好友签名数据，进度%d/%d，请耐心等待……' % (index + 1, len(data)))
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if(word in signature_dict.keys()):
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signature_dict[word] += 1
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else:
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signature_dict[word] = 1
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# 开始绘制词云
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name = [x for x in signature_dict.keys()]
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value = [x for x in signature_dict.values()]
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wordcloud = WordCloud()
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wordcloud.add('微信好友个性签名词云图', [list(z) for z in zip(name, value)], word_size_range=[1,100], shape='star')
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wordcloud.render('data/好友个性签名词云.html')
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# 下载好友头像，此步骤消耗时间比较长
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def download_head_image(thread_name):
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# 队列不为空的情况
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while(not queue_head_image.empty()):
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# 取出一个好友元素
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user = queue_head_image.get()
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# 下载该好友头像，并保存到指定位置，生成一个15位数的随机字符串
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random_file_name = ''.join([str(random.randint(0,9)) for x in range(15)])
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user.get_avatar(save_path='image/' + random_file_name + '.jpg')
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# 输出提示
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print(u'线程%d:正在下载微信好友头像数据，进度%d/%d，请耐心等待……' %(thread_name, len(friends)-queue_head_image.qsize(), len(friends)))
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# 生成一个html文件，并保存到文件file_name中
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def generate_html(file_name):
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with open(file_name, 'w', encoding='utf-8') as f:
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data = '''
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<meta http-equiv='Content-Type' content='text/html; charset=utf-8'>
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<meta charset="UTF-8">
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<title>一键生成微信个人专属数据报告(了解你的微信社交历史)</title>
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<meta name='keywords' content='微信个人数据'>
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<meta name='description' content=''>
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<iframe name="iframe1" marginwidth=0 marginheight=0 width=100% height=60% src="data/好友地区分布.html" frameborder=0></iframe>
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<iframe name="iframe2" marginwidth=0 marginheight=0 width=100% height=60% src="data/某省好友地区分布.html" frameborder=0></iframe>
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<iframe name="iframe3" marginwidth=0 marginheight=0 width=100% height=60% src="data/好友性别比例.html" frameborder=0></iframe>
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<iframe name="iframe4" marginwidth=0 marginheight=0 width=100% height=60% src="data/你认识的好友比例.html" frameborder=0></iframe>
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<iframe name="iframe5" marginwidth=0 marginheight=0 width=100% height=60% src="data/你最亲密的人.html" frameborder=0></iframe>
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<iframe name="iframe6" marginwidth=0 marginheight=0 width=100% height=60% src="data/特殊好友分析.html" frameborder=0></iframe>
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<iframe name="iframe7" marginwidth=0 marginheight=0 width=100% height=60% src="data/共同所在群聊分析.html" frameborder=0></iframe>
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<iframe name="iframe8" marginwidth=0 marginheight=0 width=100% height=60% src="data/好友个性签名词云.html" frameborder=0></iframe>
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<iframe name="iframe9" marginwidth=0 marginheight=0 width=100% height=60% src="data/微信好友头像拼接图.html" frameborder=0></iframe>
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<iframe name="iframe10" marginwidth=0 marginheight=0 width=100% height=60% src="data/使用人脸的微信好友头像拼接图.html" frameborder=0></iframe>
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'''
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f.write(data)
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# 初始化所需文件夹
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def init_folders():
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if(not (exists('image'))):
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makedirs('image')
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else:
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rmtree('image')
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makedirs('image')
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if(not (exists('data'))):
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makedirs('data')
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else:
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rmtree('data')
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makedirs('data')
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# 拼接所有微信好友头像
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def merge_head_image():
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# 拼接头像
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pics = listdir('image') # 得到user目录下的所有文件，即各个好友头像
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numPic = len(pics)
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eachsize = int(math.sqrt(float(640 * 640) / numPic)) # 先圈定每个正方形小头像的边长，如果嫌小可以加大
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numrow = int(640 / eachsize)
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numcol = int(numPic / numrow) # 向下取整
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toImage = Image.new('RGB', (eachsize * numrow, eachsize * numcol)) # 先生成头像集模板
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x = 0 # 小头像拼接时的左上角横坐标
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y = 0 # 小头像拼接时的左上角纵坐标
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for index, i in enumerate(pics):
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print(u'正在拼接微信好友头像数据，进度%d/%d，请耐心等待……' % (index + 1, len(pics)))
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try:
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# 打开图片
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img = Image.open('image/' + i)
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except IOError:
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print(u'Error: 没有找到文件或读取文件失败')
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else:
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# 缩小图片
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img = img.resize((eachsize, eachsize), Image.ANTIALIAS)
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# 拼接图片
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toImage.paste(img, (x * eachsize, y * eachsize))
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x += 1
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if x == numrow:
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x = 0
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y += 1
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toImage.save('data/拼接' + ".jpg")
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# 生成一个网页
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with open('data/微信好友头像拼接图.html', 'w', encoding='utf-8') as f:
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data = '''
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<!DOCTYPE html>
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<html xmlns="http://www.w3.org/1999/xhtml">
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<head>
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<meta http-equiv='Content-Type' content='text/html; charset=utf-8'>
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<meta charset="utf-8" />
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<title>微信好友头像拼接图</title>
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</head>
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<body>
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<p><font size=4px><strong>微信好友头像拼接图</strong></font></p>
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<img src="拼接.jpg" />
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</body>
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</html>
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'''
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f.write(data)
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# 检测使用真实人脸的好友个数
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def detect_human_face():
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# 得到user目录下的所有文件名称，即各个好友头像
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pics = listdir('image')
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# 使用人脸的头像个数
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count_face_image = 0
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# 存储使用人脸的头像的文件名
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list_name_face_image = []
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# 加载人脸识别模型
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face_cascade = CascadeClassifier('model/haarcascade_frontalface_default.xml')
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for index, file_name in enumerate(pics):
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print(u'正在进行人脸识别，进度%d/%d，请耐心等待……' % (index+1, len(pics)))
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# 读取图片
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img = imread('image/' + file_name)
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# 检测图片是否读取成功，失败则跳过
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if img is None:
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continue
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# 对图片进行灰度处理
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gray = cvtColor(img, COLOR_BGR2GRAY)
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# 进行实际的人脸检测，传递参数是scaleFactor和minNeighbor,分别表示人脸检测过程中每次迭代时图
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faces = face_cascade.detectMultiScale(gray, 1.3, 5)
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if (len(faces) > 0):
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count_face_image += 1
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list_name_face_image.append(file_name)
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print(u'使用人脸的头像%d/%d' %(count_face_image,len(pics)))
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# 开始拼接使用人脸的头像
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pics = list_name_face_image
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numPic = len(pics)
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eachsize = int(math.sqrt(float(640 * 640) / numPic)) # 先圈定每个正方形小头像的边长，如果嫌小可以加大
|
||
numrow = int(640 / eachsize)
|
||
numcol = int(numPic / numrow) # 向下取整
|
||
toImage = Image.new('RGB', (eachsize * numrow, eachsize * numcol)) # 先生成头像集模板
|
||
|
||
x = 0 # 小头像拼接时的左上角横坐标
|
||
y = 0 # 小头像拼接时的左上角纵坐标
|
||
|
||
for index, i in enumerate(pics):
|
||
|
||
print(u'正在拼接使用人脸的微信好友头像数据，进度%d/%d，请耐心等待……' %(index+1,len(pics)))
|
||
try:
|
||
# 打开图片
|
||
img = Image.open('image/' + i)
|
||
except IOError:
|
||
print(u'Error: 没有找到文件或读取文件失败')
|
||
else:
|
||
# 缩小图片
|
||
img = img.resize((eachsize, eachsize), Image.ANTIALIAS)
|
||
# 拼接图片
|
||
toImage.paste(img, (x * eachsize, y * eachsize))
|
||
x += 1
|
||
if x == numrow:
|
||
x = 0
|
||
y += 1
|
||
|
||
toImage.save('data/使用人脸的拼接' + ".jpg")
|
||
|
||
|
||
# 生成一个网页
|
||
with open('data/使用人脸的微信好友头像拼接图.html', 'w', encoding='utf-8') as f:
|
||
data = '''
|
||
<!DOCTYPE html>
|
||
<html xmlns="http://www.w3.org/1999/xhtml">
|
||
<head>
|
||
<meta http-equiv='Content-Type' content='text/html; charset=utf-8'>
|
||
<meta charset="utf-8" />
|
||
<title>使用人脸的微信好友头像拼接图</title>
|
||
</head>
|
||
<body>
|
||
<p><font size=4px><strong>描述内容</strong></font></p>
|
||
|
||
<img src="使用人脸的拼接.jpg" />
|
||
</body>
|
||
</html>
|
||
'''
|
||
|
||
data = data.replace('描述内容','在{}个好友中，有{}个好友使用真实的人脸作为头像'.format(len(friends), count_face_image))
|
||
f.write(data)
|
||
|
||
|
||
# 特殊好友分析
|
||
def analyze_special_friends():
|
||
|
||
# 星标好友(很重要的人), 不让他看我的朋友圈的好友, 不看他朋友圈的好友, 消息置顶好友, 陌生人
|
||
star_friends, hide_my_post_friends, hide_his_post_friends, sticky_on_top_friends, stranger_friends = 0, 0, 0, 0, 0
|
||
|
||
for user in friends:
|
||
|
||
|
||
# 星标好友为1,为0表示非星标,不存在星标选项的为陌生人
|
||
if('StarFriend' in (user.raw).keys()):
|
||
if((user.raw)['StarFriend'] == 1):
|
||
star_friends += 1
|
||
else:
|
||
stranger_friends += 1
|
||
|
||
# 好友类型及权限：1和3好友，259和33027不让他看我的朋友圈，65539和65537和66051不看他的朋友圈，65795两项设置全禁止, 73731陌生人
|
||
if((user.raw)['ContactFlag'] in [259, 33027, 65795]):
|
||
hide_my_post_friends += 1
|
||
if ((user.raw)['ContactFlag'] in [66051, 65537, 65539, 65795]):
|
||
hide_his_post_friends += 1
|
||
|
||
# 消息置顶好友为2051
|
||
if ((user.raw)['ContactFlag'] in [2051]):
|
||
sticky_on_top_friends += 1
|
||
|
||
# 陌生人
|
||
if ((user.raw)['ContactFlag'] in [73731]):
|
||
stranger_friends += 1
|
||
|
||
|
||
bar = Bar()
|
||
bar.add_xaxis(['星标', '不让他看我朋友圈', '不看他朋友圈', '消息置顶', '陌生人'])
|
||
bar.add_yaxis('特殊好友分析', [star_friends, hide_my_post_friends, hide_his_post_friends, sticky_on_top_friends, stranger_friends])
|
||
bar.render('data/特殊好友分析.html')
|
||
|
||
|
||
|
||
# 共同所在群聊成员分析
|
||
def group_common_in():
|
||
|
||
# 获取所有活跃的群聊
|
||
groups = bot.groups()
|
||
|
||
# 每个好友与你相同的群聊个数
|
||
dict_common_in = {}
|
||
|
||
# 遍历所有好友，第0个为你自己，所以去掉
|
||
for x in friends[1:]:
|
||
# 依次在每个群聊中搜索
|
||
for y in groups:
|
||
# x在y中
|
||
if(x in y):
|
||
# 获取微信名称
|
||
name = x.nick_name
|
||
# 判断是否有备注，有的话就使用备注
|
||
if(x.remark_name and x.remark_name != ''):
|
||
name = x.remark_name
|
||
|
||
# 增加计数
|
||
if(name in dict_common_in.keys()):
|
||
dict_common_in[name] += 1
|
||
else:
|
||
dict_common_in[name] = 1
|
||
|
||
# 从dict_common_in结果中取出前n大个数据
|
||
n = 0
|
||
|
||
if(len(dict_common_in) > 5):
|
||
n = 6
|
||
elif(len(dict_common_in) > 4):
|
||
n = 5
|
||
elif(len(dict_common_in) > 3):
|
||
n = 4
|
||
elif(len(dict_common_in) > 2):
|
||
n = 3
|
||
elif(len(dict_common_in) > 1):
|
||
n = 2
|
||
elif(len(dict_common_in) > 0):
|
||
n = 1
|
||
|
||
# 排序，并转化为list
|
||
sort_list = sorted(dict_common_in.items(), key=lambda item: item[1], reverse=True)
|
||
|
||
# 取出前n大的值
|
||
sort_list = sort_list[:n]
|
||
|
||
bar = Bar()
|
||
bar.add_xaxis([x[0] for x in sort_list])
|
||
bar.add_yaxis("共同所在群聊分析", [x[1] for x in sort_list])
|
||
bar.render('data/共同所在群聊分析.html')
|
||
|
||
|
||
# 运行前，请先确保安装了所需库文件
|
||
# 若没安装，请执行以下命令:pip install -r requirement.txt
|
||
if __name__ == '__main__':
|
||
|
||
# 初始化所需文件夹
|
||
init_folders()
|
||
|
||
|
||
# 启动微信机器人，自动根据操作系统执行不同的指令
|
||
if('Windows' in system()):
|
||
# Windows
|
||
bot = Bot()
|
||
elif('Darwin' in system()):
|
||
# MacOSX
|
||
bot = Bot(cache_path=True)
|
||
elif('Linux' in system()):
|
||
# Linux
|
||
bot = Bot(console_qr=2,cache_path=True)
|
||
else:
|
||
# 自行确定
|
||
print(u"无法识别你的操作系统类型，请自己设置")
|
||
exit()
|
||
|
||
|
||
# 获取好友数据
|
||
print(u'正在获取微信好友数据信息，请耐心等待……')
|
||
friends = bot.friends(update=False)
|
||
# i.nick_name, i.remark_name, i.sex, i.province, i.city, i.signature
|
||
print(u'微信好友数据信息获取完毕\n')
|
||
|
||
|
||
print(u'正在分析你的群聊，请耐心等待……')
|
||
group_common_in()
|
||
print(u'分析群聊完毕\n')
|
||
|
||
|
||
print(u'正在获取微信好友头像信息，请耐心等待……')
|
||
# 创建一个队列，用于多线程下载头像，提高下载速度
|
||
queue_head_image = Queue()
|
||
|
||
# 将每个好友元素存入队列中
|
||
# 如果为了方便调试，可以仅仅插入几个数据，friends[1:10]
|
||
for user in friends:
|
||
queue_head_image.put(user)
|
||
|
||
# 启动10个线程下载头像
|
||
for i in range(1, 10):
|
||
t = Thread(target=download_head_image,args=(i,))
|
||
t.start()
|
||
print(u'微信好友头像信息获取完毕\n')
|
||
|
||
|
||
print(u'正在分析好友性别比例，请耐心等待……')
|
||
sex_ratio()
|
||
print(u'分析好友性别比例完毕\n')
|
||
|
||
|
||
print(u'正在分析好友地区分布，请耐心等待……')
|
||
region_distribution()
|
||
print(u'分析好友地区分布完毕\n')
|
||
|
||
print(u'正在统计你认识的好友，请耐心等待……')
|
||
statistics_friends()
|
||
print(u'统计你认识的好友完毕\n')
|
||
|
||
print(u'正在分析你最亲密的人，请耐心等待……')
|
||
analyze_remark_name()
|
||
print(u'分析你最亲密的人完毕\n')
|
||
|
||
print(u'正在分析你的特殊好友，请耐心等待……')
|
||
analyze_special_friends()
|
||
print(u'分析你的特殊好友完毕\n')
|
||
|
||
print(u'正在分析你的好友的个性签名，请耐心等待……')
|
||
analyze_signature()
|
||
print(u'分析你的好友的个性签名完毕\n')
|
||
|
||
|
||
|
||
|
||
# 由于下载头像是多线程进行，并且存在可能下载时间比较久的情况
|
||
# 所以当我们完成所有其他功能以后，需要等待微信好友头像数据下载完毕后再进行操作
|
||
while(not queue_head_image.empty()):
|
||
sleep(1)
|
||
|
||
print(u'正在拼接所有微信好友头像数据，请耐心等待……')
|
||
merge_head_image()
|
||
print(u'拼接所有微信好友头像数据完毕\n')
|
||
|
||
print(u'正在检测使用人脸作为头像的好友数量，请耐心等待……')
|
||
detect_human_face()
|
||
print(u'检测使用人脸作为头像的好友数量完毕\n')
|
||
|
||
|
||
# 生成一份最终的html文件
|
||
print(u'所有数据获取完毕，正在生成微信个人数据报告，请耐心等待……')
|
||
generate_html('微信个人数据报告.html')
|
||
print(u'生成微信个人数据报告完毕，该文件为当前目录下的[微信个人数据报告.html]\n')
|
||
|
||
|
||
# 调用系统方式自动打开这个html文件
|
||
print(u'已为你自动打开 微信个人数据报告.html')
|
||
open_html('微信个人数据报告.html')
|