前陣子網上看到有人寫爬取妹子圖的派森代碼，因而乎我也想寫一個教程，確切地說我也想看妹子，哈哈哈，開個玩笑,不過不少教程都是調用的第三方模塊，今天就給你們開開葷，使用原生庫來爬，而且擴展實現了圖片鑑定，圖片去重等操做，通過了爬站驗證，穩如老狗，我已經爬了幾萬張了,只要你硬盤夠大。php
做者忠告：咱們是研究技術的，請勿沉迷，沉迷傷身，養分跟不上 O(∩_∩)O~html
今天就來個最簡單的吧，網站走起 https://www.meitulu.com/ 先分析頁面結構，圖片URL每次遞增，從第二個開始。前端
前端，被一個 img標籤包起來 <img src="https://mtl.gzhuibei.com/images/img/10431/5.jpg" alt= 直接正則匹配python
先來生成頁面連接，代碼以下es6
# 傳入參數,對頁面進行拼接並返回列表
def SplicingPage(page,start,end):
url = []
for each in range(start,end):
temporary = page.format(each)
url.append(temporary)
return url
接着使用內置庫爬行算法
# 經過內置庫,獲取到頁面的URL源代碼
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
最後正則匹配爬取，完事了。代碼本身研究一下就明白了，太簡單了，數據庫
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("[+] 原始名稱: {} 保存爲: {} 路徑: {}".format(img_name,save_name,each))
urllib.request.urlretrieve(each,save_name,None)
也能夠經過外部庫提取。多線程
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')
一些請求頭信息，用於繞過反爬蟲策略併發
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)}
# 隨機獲取一個請求頭
def get_user_agent():
return random.choice(USER_AGENTS)
運行結果，就是這樣，同窗們，都把褲子給我穿上！好好學習！app
接着咱們來擴展一個知識點，如何使用Python實現自動鑑別圖片，鑑別黃色圖片的思路是，講圖片中的每個位讀入內存而後將皮膚顏色填充爲白色，將衣服填充爲黑色，計算出整我的物的像素大小，而後計算身體顏色與衣服的比例，若是超出預約義的範圍則認爲是黃圖，這是基本的原理，實現起來須要各類算法的支持，Python有一個庫能夠實現 pip install Pillow porndetective 鑑別代碼以下。
>>> 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
鑑定結果以下，識別率不是很高，其實第一張並不算嚴格意義上的黃圖，你可使用爬蟲爬取全部妹子圖，而後經過調用這個庫對其進行檢測，若是是則保留，不是的直接刪除，只保留優質資源。
他這個庫使用的算法有一些問題，若是照這樣來分析，那肚皮舞之類的都會被鑑別爲黃圖，並且通常都會使用機器學習識別率更高，這種硬編碼的方式通常的還能夠，若是更加深刻的鑑別根本作不到，是否是黃圖，不能只從暴露皮膚方面判斷，還要綜合考量，姿式，暴露尺度，衣服類型，等各方面，不過這也夠用，若是想要在海量圖片中篩選出比較優質的資源,你能夠這樣來寫。
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] # 寬度
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("{} 圖片大讚,自動爲你保留.".format(each))
else:
print("----> {} 圖片正常,自動清除,節約空間,存着真的是浪費資源老鐵".format(each))
os.remove("./meizitu/"+str(each))
妹子圖去重，代碼以下，這個代碼我寫了好一陣子，一開始沒思路，後來纔想到的，其原理是利用CRC32算法，計算圖片hash值，比對hash值，並將目錄與hash關聯，最後定位到目錄，只刪除多餘的圖片，保留其中的一張，這裏給出思路代碼。
import zlib,os
def Find_Repeat_File(file_path,file_type):
Catalogue = os.listdir(file_path)
CatalogueDict = {} # 查詢字典,方便後期查詢鍵值對對應參數
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校驗: {}".format(path,str(crc32)))
CatalogueDict[each] = str(crc32)
CatalogueList = []
for value in CatalogueDict.values():
# 該過程實現提取字典中的crc32特徵組合成列表 CatalogueList
CatalogueList.append(value)
CountDict = {}
for each in CatalogueList:
# 該過程用於存儲文件特徵與特徵重複次數,放入 CountDict
CountDict[each] = CatalogueList.count(each)
RepeatFileFeatures = []
for key,value in CountDict.items():
# 循環查找字典中的數據,若是value大於1就存入 RepeatFileFeatures
if value > 1:
print("[-] 文件特徵: {} 重複次數: {}".format(key,value))
RepeatFileFeatures.append(key)
for key,value in CatalogueDict.items():
if value == "1926471896":
print("[*] 重複文件所在目錄: {}".format(file_path + key))
if __name__ == "__main__":
Find_Repeat_File("D://python/",".jpg")
來來來，小老弟，咱們去探討一下技術，學好技術，天天都開葷
蜘蛛爬蟲最終代碼：
import os,re,random,urllib,argparse
from urllib import request,parse
# 隨機獲取一個請求體
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
# 經過內置庫,獲取到頁面的URL源代碼
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根據頁面屬性而定
return html
# 傳入參數,對頁面進行拼接並返回列表
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("[+] 原始名稱: {} 保存爲: {} 路徑: {}".format(img_name,save_name,each))
#urllib.request.urlretrieve(each,save_name,None) # 無請求體的下載圖片方式
head = GetUserAgent(str(urls)) # 隨機彈出請求頭
ret = urllib.request.Request(each,headers=head) # each = 訪問圖片路徑
respons = urllib.request.urlopen(ret,timeout=10) # 打開圖片路徑
with open(save_name,"wb") as fp:
fp.write(respons.read())
except Exception:
# 刪除當前目錄下小於100kb的圖片
for each in os.listdir():
if each.split(".")[1] == "jpg":
if int(os.stat(each).st_size / 1024) < 100:
print("[-] 自動清除 {} 小於100kb文件.".format(each))
os.remove(each)
exit(1)
最後的效果，高併發下載（代碼分工明確：有負責清理重複的，有負責刪除小於150kb的，有負責爬行的，包工頭非你莫屬）今晚通宵
上方代碼還有許多須要優化的地方，例如咱們是隨機爬取，如今咱們只想爬取其中的一部分妹子圖，因此咱們須要改進一下,首先來獲取到須要的連接,找首先找全部A標籤，提取出頁面A標題。
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")
接着直接循環爬取就好，這裏並無多線程，爬行會有點慢的
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>
另外兩個網站的爬蟲程序公開: 那啥地址不易公開，通過了base64 加密 wuso 本身解密，我不說你懂得。
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
第二個爬蟲程序： 這個開一個多線程，用另一個程序開多進程，爬取速度很是快，CPU 100%利用率
import os,sys
import subprocess
# 每行一我的物名稱。
fp = open("lis.log","r")
aaa = fp.readlines()
for i in aaa:
nam = i.replace("\n","")
cmd = "python thread.py " + nam
os.popen(cmd)
多線程代碼。
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("隨機編號: {}".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("[+] 圖片解析: {} 保存爲: {}".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)
開20個進程，每一個進程裏面馱着100個線程，併發訪問每秒，1500次請求，由於有去重程序在不斷地掃描，全部圖片無重複，並保留質量最高的圖片，忽然發現，妹子圖多了以後，妹子都很差看了 ，哈哈哈
<br>
通過爬站，以後咱們獲得了幾萬張妹子圖，可是若是咱們想看其中的一個妹子的寫真，腫麼辦？ 接下來登場的是AI人臉識別軍團，經過簡單地機器學習，識別特定人臉，來篩選咱們想要看的妹子圖。
import cv2
import numpy as np
def Display_Face(img_path):
img = cv2.imread(img_path) # 讀取圖片
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 將圖片轉化成灰度
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") # 加載級聯分類器模型
face_cascade.load("haarcascade_frontalface_default.xml")
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
# 在原圖上畫出包圍框(藍色框,寬度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]
#載入圖像 讀取ORL人臉數據庫，準備訓練數據
def LoadImages(data):
images=[]
names=[]
labels=[]
label=0
#遍歷全部文件夾
for subdir in os.listdir(data):
subpath=os.path.join(data,subdir)
#print('path',subpath)
#判斷文件夾是否存在
if os.path.isdir(subpath):
#在每個文件夾中存放着一我的的許多照片
names.append(subdir)
#遍歷文件夾中的圖片文件
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識別器並開始訓練
face_recognizer.train(images, labels)
咳咳，快！派森扶我起來，我還能學技術,未完待續。。。