《封号码罗》python爬虫之猿人学第四题详解：雪碧图- CSS样式干扰（十七）

import requests
import json
import re
import hashlib
import base64

# 图片是数字，css偏移
# 每个数字对应的base64是静态的，可整理成对应的字典


img_dict = {
    'iVBORw0KGgoAAAANSUhEUgAAABQAAAAdCAYAAACqhkzFAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAMTSURBVEhLrZY/TBNRHMe/bY82LeGA0Dp4NTGYqO1gdKEMwmQcGh38M5CQlGDSwWiMAwwYg2iCJsZJQ9CJ0ETsQMJgwuagLIUFXIAFXNoYcqDloLRXrq3v7v3aXktbSPCTXO77fZRvf/feu9+r5dz5iwX8R+oESigMPka6/wb2vSJUBx+1QoVD3kTz7DScb+f4YBVHA6U+ZKLPsON1IE9DtbDLP9AxEIawRgOEle4cKYSD+ZeQq8IcKquMXTbyOllPL7aiEWgSDRCmQAnahyHsiGQZruUvkIKXcObyFeM62zcOd1ylvwJ5MYDkuxA5Tjlw8A2S12iyGM7YODrujsFqfqTFCJw9L+CWyTPS3QPIBMgwKFBC9n4AaW4AdQVtQxEy1czBObkIJznAi9TDO6SLgVIYqt9QBq5YFEKCTC2momg2VXngD5bmnAc+uIqUIXQUOL/X3hJl5mHfUEgzPJ04pMUxAnN+CYeG1UlAmCLZANtqAk2kARG5m1zxQI9paWUZAsmGsArLnxOh0ZSxwCBypjwo2zQPxzCTgJ2kTtbbZ9zZ/7pRKO8WOOWqrX9iePyJiqnNGgTTuhRhgT5o5kc+JaeosDYssHbpx+OtXEziSIWqx0fqOFpg7ns2Zd24s8B1WE0V5h2mJW/ELTc0kjo2Zcm4s8ClikCIlR+si59tZpL66yosc2U8clM8bhgDkfVFUzuqR569rqXOqG7CPsOlEWiJJSraUaa/i3Q9upC94CHNCtr4WXoN+aJMzcFlakd73eGG5wnuhZHykmZ1tnx9TboYqDfNWLxoWDvqhfK+h0w1Pcg87cUBOavMmu1HMoxShuXJNNpNi7N3ewLJTyEUzIdQIIT0wgQ7EcmzxWifZMcEOZ3KY3Qwgp3RQOnbOfqJx5XGtlSOS4aK1tlHEIcXyHNsrW0dY6SBFX0ufShc70S21OwE5AR+lb+ZVfb5OVpGviGTTkHeSmBfScJqtdX55SAFoY0OsMXxISM6SvvNrshwrS7A9WoENupyv+O/oGm831sslno/RU6OsvsHu3+3yQH/AOyW6SvqnweCAAAAAElFTkSuQmCC': 0,
    "iVBORw0KGgoAAAANSUhEUgAAABUAAAAcCAYAAACOGPReAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAD0SURBVEhLY5RVUPvPQGXABKUJAA2GvzP3Mry6f5PhzfI4qBhuQNjQqD6Gzxc3Mjxzk2H4CRUiBHAYKs3wP7Gd4evhSwzPWr0ZPvBBhYkEqIaaBzL8nrmS4f3FfQzP6oIY3smwM/yFSpECkAyNY/g2q4PhhZsBwxegy/5BRZlfP2HgIdbfUIAnTD8xCGyuZ5AyW8jATqmhzD9fMwjumsUgbWPKwJu3AipKGkAydC8DZ24sg5SGDQNPei8D01OoMBkAydCnDIyHTkHZlAE8YUo+GDWU+mAEGfq46RCUhQAUGypbZwdlIcBoRFEfjBpKfUADQxkYAKYHOb9g+7HMAAAAAElFTkSuQmCC": 1,
    "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": 2,
    "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": 3,
    "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": 4,
    "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": 5,
    "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": 6,
    "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": 7,
    "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": 8,
    "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": 9,
}

url = 'http://match.yuanrenxue.com/api/match/4'

response = requests.get(url=url)
response = json.loads(response.text)
# var j_key = '.' + hex_md5(btoa(data.key + data.value).replace(/=/g, '')); js用这个参数判断是否隐藏
j_key = hashlib.md5(base64.b64encode((response['key'] + response['value']).encode()).replace(b'=', b'')).hexdigest()
# print(j_key)    # 4a61fcfb44a06e90f0f8b37ed6662c25
img_list = re.findall("(?<=<td>).+?(?=</td>)", response["info"])
# print(len(img_list_str_list), img_list_str_list)

for img_str in img_list:
    imgs = re.findall("(?<=<img).+?(?=>)", img_str)
    # print(imgs)  每个td里面的img构成的列表
    out = ['', '', '', '', '']
    i = 0
    for img in imgs:
        # print(img)
        number = re.findall('(?<=data:image/png;base64,).+?(?=")', img)[0]
        # class="img_number cd1dfb27808d78d1f662b52a51f7f82a"
        # (?<=exp)  从exp开始向后匹配    (?=exp) 从前向后匹配到exp
        imgclass = re.findall('(?<=img_number ).+?(?=")', img)[0]
        imgstyle = int(float(re.findall('(?<=style="left:).+?(?=px")', img)[0]) / 11.5)
        # print(imgclass, "----", imgstyle)  # 34992842cb4f1c8a3471ecbd1ac498ed ---- -1
        if imgclass != j_key:
            # print(i, imgstyle)
            out[i + imgstyle] = str(img_dict[number])
            i += 1
    # print(out)
    out_num = int("".join(out))
    print(out_num)

# 主要思路是获取每一个td里面img对应的class
# 获取每个img的base64数据对应的真实数字
# 获取每个img的class
# 根据css偏移量做除法运算，进行定位插入
# 根据class和j_key进行比较，如果不相等就放到列表