前阵子网上看到有人写爬取妹子图的派森代码，因而乎我也想写一个教程，确切地说我也想看妹子，哈哈哈，开个玩笑,不过不少教程都是调用的第三方模块，今天就给你们开开荤，使用原生库来爬，而且扩展实现了图片鉴定，图片去重等操做，通过了爬站验证，稳如老狗，我已经爬了几万张了,只要你硬盘够大。php
做者忠告：咱们是研究技术的，请勿沉迷，沉迷伤身，养分跟不上 O(∩_∩)O~html
今天就来个最简单的吧，网站走起 https://www.meitulu.com/ 先分析页面结构，图片URL每次递增，从第二个开始。前端
前端，被一个 img标签包起来 <img src="https://mtl.gzhuibei.com/images/img/10431/5.jpg" alt= 直接正则匹配python
先来生成页面连接，代码以下es6
# 传入参数,对页面进行拼接并返回列表
def SplicingPage(page,start,end):
url = []
for each in range(start,end):
temporary = page.format(each)
url.append(temporary)
return url
接着使用内置库爬行算法
# 经过内置库,获取到页面的URL源代码
def GetPageURL(page):
head = GetUserAgent(page)
req = request.Request(url=page,headers=head,method="GET")
respon = request.urlopen(req,timeout=3)
if respon.status == 200:
html = respon.read().decode("utf-8")
return html
最后正则匹配爬取，完事了。代码本身研究一下就明白了，太简单了，数据库
page_list = SplicingPage(str(args.url),2,100)
for item in page_list:
respon = GetPageURL(str(item))
subject = re.findall('<img src="([^"]+\.jpg)"',respon,re.S)
for each in subject:
img_name = each.split("/")[-1]
img_type = each.split("/")[-1].split(".")[1]
save_name = str(random.randint(1111111,99999999)) + "." + img_type
print("[+] 原始名称: {} 保存为: {} 路径: {}".format(img_name,save_name,each))
urllib.request.urlretrieve(each,save_name,None)
也能够经过外部库提取。多线程
from lxml import etree
html = etree.HTML(response.content.decode())
src_list = html.xpath('//ul[@id="pins"]/li/a/img/@data-original')
alt_list = html.xpath('//ul[@id="pins"]/li/a/img/@alt')
一些请求头信息，用于绕过反爬虫策略并发
user_agent = [
"Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6_8; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50",
"Mozilla/5.0 (Windows; U; Windows NT 6.1; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50",
"Mozilla/5.0 (Windows NT 10.0; WOW64; rv:38.0) Gecko/20100101 Firefox/38.0",
"Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; .NET4.0C; .NET4.0E; .NET CLR 2.0.50727; .NET CLR 3.0.30729; .NET CLR 3.5.30729; InfoPath.3; rv:11.0) like Gecko",
"Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0)",
"Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0)",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0)",
"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1)",
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10.6; rv:2.0.1) Gecko/20100101 Firefox/4.0.1",
"Mozilla/5.0 (Windows NT 6.1; rv:2.0.1) Gecko/20100101 Firefox/4.0.1",
"Opera/9.80 (Macintosh; Intel Mac OS X 10.6.8; U; en) Presto/2.8.131 Version/11.11",
"Opera/9.80 (Windows NT 6.1; U; en) Presto/2.8.131 Version/11.11",
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_0) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Maxthon 2.0)",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; TencentTraveler 4.0)",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; The World)",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Trident/4.0; SE 2.X MetaSr 1.0; SE 2.X MetaSr 1.0; .NET CLR 2.0.50727; SE 2.X MetaSr 1.0)",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; 360SE)",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Avant Browser)",
"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)",
"Mozilla/5.0 (iPhone; U; CPU iPhone OS 4_3_3 like Mac OS X; en-us) AppleWebKit/533.17.9 (KHTML, like Gecko) Version/5.0.2 Mobile/8J2 Safari/6533.18.5",
"Mozilla/5.0 (iPod; U; CPU iPhone OS 4_3_3 like Mac OS X; en-us) AppleWebKit/533.17.9 (KHTML, like Gecko) Version/5.0.2 Mobile/8J2 Safari/6533.18.5",
"Mozilla/5.0 (iPad; U; CPU OS 4_3_3 like Mac OS X; en-us) AppleWebKit/533.17.9 (KHTML, like Gecko) Version/5.0.2 Mobile/8J2 Safari/6533.18.5",
"Mozilla/5.0 (Linux; U; Android 2.3.7; en-us; Nexus One Build/FRF91) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1",
"MQQBrowser/26 Mozilla/5.0 (Linux; U; Android 2.3.7; zh-cn; MB200 Build/GRJ22; CyanogenMod-7) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1",
"Opera/9.80 (Android 2.3.4; Linux; Opera Mobi/build-1107180945; U; en-GB) Presto/2.8.149 Version/11.10",
"Mozilla/5.0 (Linux; U; Android 3.0; en-us; Xoom Build/HRI39) AppleWebKit/534.13 (KHTML, like Gecko) Version/4.0 Safari/534.13",
"Mozilla/5.0 (BlackBerry; U; BlackBerry 9800; en) AppleWebKit/534.1+ (KHTML, like Gecko) Version/6.0.0.337 Mobile Safari/534.1+",
"Mozilla/5.0 (hp-tablet; Linux; hpwOS/3.0.0; U; en-US) AppleWebKit/534.6 (KHTML, like Gecko) wOSBrowser/233.70 Safari/534.6 TouchPad/1.0",
"Mozilla/5.0 (SymbianOS/9.4; Series60/5.0 NokiaN97-1/20.0.019; Profile/MIDP-2.1 Configuration/CLDC-1.1) AppleWebKit/525 (KHTML, like Gecko) BrowserNG/7.1.18124",
"Mozilla/5.0 (compatible; MSIE 9.0; Windows Phone OS 7.5; Trident/5.0; IEMobile/9.0; HTC; Titan)",
"UCWEB7.0.2.37/28/999",
"NOKIA5700/ UCWEB7.0.2.37/28/999",
"Openwave/ UCWEB7.0.2.37/28/999",
"Mozilla/4.0 (compatible; MSIE 6.0; ) Opera/UCWEB7.0.2.37/28/999",
# iPhone 6：
"Mozilla/6.0 (iPhone; CPU iPhone OS 8_0 like Mac OS X) AppleWebKit/536.26 (KHTML, like Gecko) Version/8.0 Mobile/10A5376e Safari/8536.25",
]
headers = {'User-Agent': random.choice(user_agent)}
# 随机获取一个请求头
def get_user_agent():
return random.choice(USER_AGENTS)
运行结果，就是这样，同窗们，都把裤子给我穿上！好好学习！app
接着咱们来扩展一个知识点，如何使用Python实现自动鉴别图片，鉴别黄色图片的思路是，讲图片中的每个位读入内存而后将皮肤颜色填充为白色，将衣服填充为黑色，计算出整我的物的像素大小，而后计算身体颜色与衣服的比例，若是超出预约义的范围则认为是黄图，这是基本的原理，实现起来须要各类算法的支持，Python有一个库能够实现 pip install Pillow porndetective 鉴别代码以下。
>>> from porndetective import PornDetective
>>> test=PornDetective("c://1.jpg")
>>> test.parse()
c://1.jpg JPEG 1600×2400: result=True message='Porn Pic!!'
<porndetective.PornDetective object at 0x0000021ACBA0EFD0>
>>>
>>> test=PornDetective("c://2.jpg")
>>> test.parse()
c://2.jpg JPEG 1620×2430: result=False message='Total skin percentage lower than 15 (12.51)'
<porndetective.PornDetective object at 0x0000021ACBA5F5E0>
>>> test.result
False
鉴定结果以下，识别率不是很高，其实第一张并不算严格意义上的黄图，你可使用爬虫爬取全部妹子图，而后经过调用这个库对其进行检测，若是是则保留，不是的直接删除，只保留优质资源。
他这个库使用的算法有一些问题，若是照这样来分析，那肚皮舞之类的都会被鉴别为黄图，并且通常都会使用机器学习识别率更高，这种硬编码的方式通常的还能够，若是更加深刻的鉴别根本作不到，是否是黄图，不能只从暴露皮肤方面判断，还要综合考量，姿式，暴露尺度，衣服类型，等各方面，不过这也够用，若是想要在海量图片中筛选出比较优质的资源,你能够这样来写。
from PIL import Image
import os
from porndetective import PornDetective
if __name__ == "__main__":
img_dic = os.listdir("./meizitu/")
for each in img_dic:
img = Image.open("./meizitu/{}".format(each))
width = img.size[0] # 宽度
height = img.size[1] # 高度
img = img.resize((int(width*0.3), int(height*0.3)), Image.ANTIALIAS)
img.save("image.jpg")
test = PornDetective("./image.jpg")
test.parse()
if test.result == True:
print("{} 图片大赞,自动为你保留.".format(each))
else:
print("----> {} 图片正常,自动清除,节约空间,存着真的是浪费资源老铁".format(each))
os.remove("./meizitu/"+str(each))
妹子图去重，代码以下，这个代码我写了好一阵子，一开始没思路，后来才想到的，其原理是利用CRC32算法，计算图片hash值，比对hash值，并将目录与hash关联，最后定位到目录，只删除多余的图片，保留其中的一张，这里给出思路代码。
import zlib,os
def Find_Repeat_File(file_path,file_type):
Catalogue = os.listdir(file_path)
CatalogueDict = {} # 查询字典,方便后期查询键值对对应参数
for each in Catalogue:
path = (file_path + each)
if os.path.splitext(path)[1] == file_type:
with open(path,"rb") as fp:
crc32 = zlib.crc32(fp.read())
# print("[*] 文件名: {} CRC32校验: {}".format(path,str(crc32)))
CatalogueDict[each] = str(crc32)
CatalogueList = []
for value in CatalogueDict.values():
# 该过程实现提取字典中的crc32特征组合成列表 CatalogueList
CatalogueList.append(value)
CountDict = {}
for each in CatalogueList:
# 该过程用于存储文件特征与特征重复次数,放入 CountDict
CountDict[each] = CatalogueList.count(each)
RepeatFileFeatures = []
for key,value in CountDict.items():
# 循环查找字典中的数据,若是value大于1就存入 RepeatFileFeatures
if value > 1:
print("[-] 文件特征: {} 重复次数: {}".format(key,value))
RepeatFileFeatures.append(key)
for key,value in CatalogueDict.items():
if value == "1926471896":
print("[*] 重复文件所在目录: {}".format(file_path + key))
if __name__ == "__main__":
Find_Repeat_File("D://python/",".jpg")
来来来，小老弟，咱们去探讨一下技术，学好技术，天天都开荤
蜘蛛爬虫最终代码：
import os,re,random,urllib,argparse
from urllib import request,parse
# 随机获取一个请求体
def GetUserAgent(url):
UsrHead = ["Windows; U; Windows NT 6.1; en-us","Windows NT 5.1; x86_64","Ubuntu U; NT 18.04; x86_64",
"Windows NT 10.0; WOW64","X11; Ubuntu i686;","X11; Centos x86_64;","compatible; MSIE 9.0; Windows NT 8.1;",
"X11; Linux i686","Macintosh; U; Intel Mac OS X 10_6_8; en-us","compatible; MSIE 7.0; Windows Server 6.1",
"Macintosh; Intel Mac OS X 10.6.8; U; en","compatible; MSIE 7.0; Windows NT 5.1","iPad; CPU OS 4_3_3;"]
UsrFox = ["Chrome/60.0.3100.0","Auburn Browser","Safari/522.13","Chrome/80.0.1211.0","Firefox/74.0",
"Gecko/20100101 Firefox/4.0.1","Presto/2.8.131 Version/11.11","Mobile/8J2 Safari/6533.18.5",
"Version/4.0 Safari/534.13","wOSBrowser/233.70 Baidu Browser/534.6 TouchPad/1.0","BrowserNG/7.1.18124",
"rident/4.0; SE 2.X MetaSr 1.0;","360SE/80.1","wOSBrowser/233.70","UCWEB7.0.2.37/28/999","Opera/UCWEB7.0.2.37"]
UsrAgent = "Mozilla/5.0 (" + str(random.sample(UsrHead,1)[0]) + ") AppleWebKit/" + str(random.randint(100,1000)) \
+ ".36 (KHTML, like Gecko) " + str(random.sample(UsrFox,1)[0])
UsrRefer = str(url + "/" + "".join(random.sample("abcdef23457sdadw",10)))
UserAgent = {"User-Agent": UsrAgent,"Referer":UsrRefer}
return UserAgent
# 经过内置库,获取到页面的URL源代码
def GetPageURL(page):
head = GetUserAgent(page)
req = request.Request(url=page,headers=head,method="GET")
respon = request.urlopen(req,timeout=3)
if respon.status == 200:
html = respon.read().decode("utf-8") # 或是gbk根据页面属性而定
return html
# 传入参数,对页面进行拼接并返回列表
def SplicingPage(page,start,end):
url = []
for each in range(start,end):
temporary = page.format(each)
url.append(temporary)
return url
if __name__ == "__main__":
urls = "https://www.meitulu.com/item/{}_{}.html".format(str(random.randint(1000,20000)),"{}")
page_list = SplicingPage(urls,2,100)
for item in page_list:
try:
respon = GetPageURL(str(item))
subject = re.findall('<img src="([^"]+\.jpg)"',respon,re.S)
for each in subject:
img_name = each.split("/")[-1]
img_type = each.split("/")[-1].split(".")[1]
save_name = str(random.randint(11111111,999999999)) + "." + img_type
print("[+] 原始名称: {} 保存为: {} 路径: {}".format(img_name,save_name,each))
#urllib.request.urlretrieve(each,save_name,None) # 无请求体的下载图片方式
head = GetUserAgent(str(urls)) # 随机弹出请求头
ret = urllib.request.Request(each,headers=head) # each = 访问图片路径
respons = urllib.request.urlopen(ret,timeout=10) # 打开图片路径
with open(save_name,"wb") as fp:
fp.write(respons.read())
except Exception:
# 删除当前目录下小于100kb的图片
for each in os.listdir():
if each.split(".")[1] == "jpg":
if int(os.stat(each).st_size / 1024) < 100:
print("[-] 自动清除 {} 小于100kb文件.".format(each))
os.remove(each)
exit(1)
最后的效果，高并发下载（代码分工明确：有负责清理重复的，有负责删除小于150kb的，有负责爬行的，包工头非你莫属）今晚通宵
上方代码还有许多须要优化的地方，例如咱们是随机爬取，如今咱们只想爬取其中的一部分妹子图，因此咱们须要改进一下,首先来获取到须要的连接,找首先找全部A标签，提取出页面A标题。
from bs4 import BeautifulSoup
import requests
if __name__ == "__main__":
get_url = []
urls = requests.get("https://www.meitulu.com/t/youhuo/")
soup = BeautifulSoup(urls.text,"html.parser")
soup_ret = soup.select('div[class="boxs"] ul[class="img"] a')
for each in soup_ret:
if str(each["href"]).endswith("html"):
get_url.append(each["href"])
for item in get_url:
for each in range(2,30):
url = item.replace(".html","_{}.html".format(each))
with open("url.log","a+") as fp:
fp.write(url + "\n")
接着直接循环爬取就好，这里并无多线程，爬行会有点慢的
from bs4 import BeautifulSoup
import requests,random
def GetUserAgent(url):
UsrHead = ["Windows; U; Windows NT 6.1; en-us","Windows NT 5.1; x86_64","Ubuntu U; NT 18.04; x86_64",
"Windows NT 10.0; WOW64","X11; Ubuntu i686;","X11; Centos x86_64;","compatible; MSIE 9.0; Windows NT 8.1;",
"X11; Linux i686","Macintosh; U; Intel Mac OS X 10_6_8; en-us","compatible; MSIE 7.0; Windows Server 6.1",
"Macintosh; Intel Mac OS X 10.6.8; U; en","compatible; MSIE 7.0; Windows NT 5.1","iPad; CPU OS 4_3_3;"]
UsrFox = ["Chrome/60.0.3100.0","Auburn Browser","Safari/522.13","Chrome/80.0.1211.0","Firefox/74.0",
"Gecko/20100101 Firefox/4.0.1","Presto/2.8.131 Version/11.11","Mobile/8J2 Safari/6533.18.5",
"Version/4.0 Safari/534.13","wOSBrowser/233.70 Baidu Browser/534.6 TouchPad/1.0","BrowserNG/7.1.18124",
"rident/4.0; SE 2.X MetaSr 1.0;","360SE/80.1","wOSBrowser/233.70","UCWEB7.0.2.37/28/999","Opera/UCWEB7.0.2.37"]
UsrAgent = "Mozilla/5.0 (" + str(random.sample(UsrHead,1)[0]) + ") AppleWebKit/" + str(random.randint(100,1000)) \
+ ".36 (KHTML, like Gecko) " + str(random.sample(UsrFox,1)[0])
UsrRefer = str(url + "/" + "".join(random.sample("abcdef23457sdadw",10)))
UserAgent = {"User-Agent": UsrAgent,"Referer":UsrRefer}
return UserAgent
url = []
with open("url.log","r") as fp:
files = fp.readlines()
for i in files:
url.append(i.replace("\n",""))
for i in range(0,9999):
aget = GetUserAgent(url[i])
try:
ret = requests.get(url[i],timeout=10,headers=aget)
if ret.status_code == 200:
soup = BeautifulSoup(ret.text,"html.parser")
soup_ret = soup.select('div[class="content"] img')
for x in soup_ret:
try:
down = x["src"]
save_name = str(random.randint(11111111,999999999)) + ".jpg"
print("xiazai -> {}".format(save_name))
img_download = requests.get(url=down, headers=aget, stream=True)
with open(save_name,"wb") as fp:
for chunk in img_download.iter_content(chunk_size=1024):
fp.write(chunk)
except Exception:
pass
except Exception:
pass
<br>
另外两个网站的爬虫程序公开: 那啥地址不易公开，通过了base64 加密 wuso 本身解密，我不说你懂得。
import os,urllib,random,argparse,sys
from urllib import request,parse
from bs4 import BeautifulSoup
def GetUserAgent(url):
UsrHead = ["Windows; U; Windows NT 6.1; en-us","Windows NT 5.1; x86_64","Ubuntu U; NT 18.04; x86_64",
"Windows NT 10.0; WOW64","X11; Ubuntu i686;","X11; Centos x86_64;","compatible; MSIE 9.0; Windows NT 8.1;",
"X11; Linux i686","Macintosh; U; Intel Mac OS X 10_6_8; en-us","compatible; MSIE 7.0; Windows Server 6.1",
"Macintosh; Intel Mac OS X 10.6.8; U; en","compatible; MSIE 7.0; Windows NT 5.1","iPad; CPU OS 4_3_3;"]
UsrFox = ["Chrome/60.0.3100.0","Auburn Browser","Safari/522.13","Chrome/80.0.1211.0","Firefox/74.0",
"Gecko/20100101 Firefox/4.0.1","Presto/2.8.131 Version/11.11","Mobile/8J2 Safari/6533.18.5",
"Version/4.0 Safari/534.13","wOSBrowser/233.70 Baidu Browser/534.6 TouchPad/1.0","BrowserNG/7.1.18124",
"rident/4.0; SE 2.X MetaSr 1.0;","360SE/80.1","wOSBrowser/233.70","UCWEB7.0.2.37/28/999","Opera/UCWEB7.0.2.37"]
UsrAgent = "Mozilla/5.0 (" + str(random.sample(UsrHead,1)[0]) + ") AppleWebKit/" + str(random.randint(100,1000)) \
+ ".36 (KHTML, like Gecko) " + str(random.sample(UsrFox,1)[0])
UsrRefer = url + str("/" + "".join(random.sample("abcdefghi123457sdadw",10)))
UserAgent = {"User-Agent": UsrAgent,"Referer":UsrRefer}
return UserAgent
def GetPageURL(page):
head = GetUserAgent(page)
req = request.Request(url=page,headers=head,method="GET")
respon = request.urlopen(req,timeout=30)
if respon.status == 200:
html = respon.read().decode("utf-8")
return html
if __name__ == "__main__":
runt = []
waibu = GetPageURL("https://xxx.me/forum.php?mod=forumdisplay&fid=48&typeid=114&filter=typeid&typeid=114")
soup1 = BeautifulSoup(waibu,"html.parser")
ret1 = soup1.select("div[id='threadlist'] ul[id='waterfall'] a")
for x in ret1:
runt.append(x.attrs["href"])
for ss in runt:
print("[+] 爬行: {}".format(ss))
try:
resp = []
respon = GetPageURL(str(ss))
soup = BeautifulSoup(respon,"html.parser")
ret = soup.select("div[class='pct'] div[class='pcb'] td[class='t_f'] img")
try:
for i in ret:
url = "https://xxx.me/" + str(i.attrs["file"])
print(url)
resp.append(url)
except Exception:
pass
for each in resp:
try:
img_name = each.split("/")[-1]
print("down: {}".format(img_name))
head=GetUserAgent("https://wuso.me")
ret = urllib.request.Request(each,headers=head)
respons = urllib.request.urlopen(ret,timeout=60)
with open(img_name,"wb") as fp:
fp.write(respons.read())
fp.close()
except Exception:
pass
except Exception:
pass
第二个爬虫程序： 这个开一个多线程，用另一个程序开多进程，爬取速度很是快，CPU 100%利用率
import os,sys
import subprocess
# 每行一我的物名称。
fp = open("lis.log","r")
aaa = fp.readlines()
for i in aaa:
nam = i.replace("\n","")
cmd = "python thread.py " + nam
os.popen(cmd)
多线程代码。
import requests,random
from bs4 import BeautifulSoup
import os,re,random,urllib,argparse
from urllib import request,parse
import threading,sys
def GetUserAgent(url):
head = ["Windows; U; Windows NT 6.1; en-us","Windows NT 6.3; x86_64","Windows U; NT 6.2; x86_64",
"Windows NT 6.1; WOW64","X11; Linux i686;","X11; Linux x86_64;","compatible; MSIE 9.0; Windows NT 6.1;",
"X11; Linux i686","Macintosh; U; Intel Mac OS X 10_6_8; en-us","compatible; MSIE 7.0; Windows NT 6.0",
"Macintosh; Intel Mac OS X 10.6.8; U; en","compatible; MSIE 7.0; Windows NT 5.1","iPad; CPU OS 4_3_3;",]
fox = ["Chrome/60.0.3100.0","Chrome/59.0.2100.0","Safari/522.13","Chrome/80.0.1211.0","Firefox/74.0",
"Gecko/20100101 Firefox/4.0.1","Presto/2.8.131 Version/11.11","Mobile/8J2 Safari/6533.18.5",
"Version/4.0 Safari/534.13","wOSBrowser/233.70 Safari/534.6 TouchPad/1.0","BrowserNG/7.1.18124"]
agent = "Mozilla/5.0 (" + str(random.sample(head,1)[0]) + ") AppleWebKit/" + str(random.randint(100,1000)) \
+ ".36 (KHTML, like Gecko) " + str(random.sample(fox,1)[0])
refer = url
UserAgent = {"User-Agent": agent,"Referer":refer}
return UserAgent
def run(user):
head = GetUserAgent("https://aHR0cHM6Ly93d3cuYW1ldGFydC5jb20v")
ret = requests.get("https://aHR0cHM6Ly93d3cuYW1ldGFydC5jb20vbW9kZWxzL3t9Lw==".format(user),headers=head,timeout=3)
scan_url = []
if ret.status_code == 200:
soup = BeautifulSoup(ret.text,"html.parser")
a = soup.select("div[class='thumbs'] a")
for each in a:
url = "https://aHR0cHM6Ly93d3cuYW1ldGFydC5jb20v" + str(each["href"])
scan_url.append(url)
rando = random.choice(scan_url)
print("随机编号: {}".format(rando))
try:
ret = requests.get(url=str(rando),headers=head,timeout=10)
if ret.status_code == 200:
soup = BeautifulSoup(ret.text,"html.parser")
img = soup.select("div[class='container'] div div a")
try:
for each in img:
head = GetUserAgent(str(each["href"]))
down = requests.get(url=str(each["href"]),headers=head)
img_name = str(random.randint(100000000,9999999999)) + ".jpg"
print("[+] 图片解析: {} 保存为: {}".format(each["href"],img_name))
with open(img_name,"wb") as fp:
fp.write(down.content)
except Exception:
pass
except Exception:
exit(1)
if __name__ == "__main__":
args = sys.argv
user = str(args[1])
try:
os.mkdir(user)
os.chdir("D://python/ametart/" + user)
for item in range(100):
t = threading.Thread(target=run,args=(user,))
t.start()
except FileExistsError:
exit(0)
开20个进程，每一个进程里面驮着100个线程，并发访问每秒，1500次请求，由于有去重程序在不断地扫描，全部图片无重复，并保留质量最高的图片，忽然发现，妹子图多了以后，妹子都很差看了 ，哈哈哈
<br>
通过爬站，以后咱们获得了几万张妹子图，可是若是咱们想看其中的一个妹子的写真，肿么办？ 接下来登场的是AI人脸识别军团，经过简单地机器学习，识别特定人脸，来筛选咱们想要看的妹子图。
import cv2
import numpy as np
def Display_Face(img_path):
img = cv2.imread(img_path) # 读取图片
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 将图片转化成灰度
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") # 加载级联分类器模型
face_cascade.load("haarcascade_frontalface_default.xml")
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
# 在原图上画出包围框(蓝色框,宽度3)
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 3)
cv2.namedWindow("img",0);
cv2.resizeWindow("img", 300, 400);
cv2.imshow('img', img)
cv2.waitKey()
def Return_Face(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5)
if (len(faces) == 0):
return None,None
(x, y, w, h) = faces[0]
return gray[y:y + w, x:x + h], faces[0]
ret = Return_Face("./meizi/172909315.jpg")
print(ret)
Display_Face("./meizi/172909315.jpg")
import cv2,os
import numpy as np
def Return_Face(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5)
if (len(faces) == 0):
return None,None
(x, y, w, h) = faces[0]
return gray[y:y + w, x:x + h], faces[0]
#载入图像 读取ORL人脸数据库，准备训练数据
def LoadImages(data):
images=[]
names=[]
labels=[]
label=0
#遍历全部文件夹
for subdir in os.listdir(data):
subpath=os.path.join(data,subdir)
#print('path',subpath)
#判断文件夹是否存在
if os.path.isdir(subpath):
#在每个文件夹中存放着一我的的许多照片
names.append(subdir)
#遍历文件夹中的图片文件
for filename in os.listdir(subpath):
imgpath=os.path.join(subpath,filename)
img=cv2.imread(imgpath,cv2.IMREAD_COLOR)
gray_img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#cv2.imshow('1',img)
#cv2.waitKey(0)
images.append(gray_img)
labels.append(label)
label+=1
images=np.asarray(images)
#names=np.asarray(names)
labels=np.asarray(labels)
return images,labels,names
images,labels,names = LoadImages("./")
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
# 建立LBPH识别器并开始训练
face_recognizer.train(images, labels)
咳咳，快！派森扶我起来，我还能学技术,未完待续。。。