无原图滑动验证码2

import xlwings as xw
import pandas as pd
import datetime
import re
import base64
import cv2
import pathlib
import zipfile
import shutil
def down_pic(data,data_type,path_dic):
    data = data.split(',')[1]
    image_data = base64.b64decode(data)
    file_path=rf"{path_dic['验证码存放路径']}\{data_type}.png"
    with open(file_path, 'wb') as f:
        f.write(image_data)
class SlideCrack(object):
    def __init__(self, gap, bg):
        """
        init code
        :param gap: 缺口图片
        :param bg: 背景图片
        """
        self.gap = gap
        self.bg = bg

    @staticmethod
    def clear_white(img):
        # 清除图片的空白区域，这里主要清除滑块的空白
        img = cv2.imread(img)
        rows, cols, channel = img.shape
        min_x = 255
        min_y = 255
        max_x = 0
        max_y = 0
        for x in range(1, rows):
            for y in range(1, cols):
                t = set(img[x, y])
                if len(t) >= 2:
                    if x <= min_x:
                        min_x = x
                    elif x >= max_x:
                        max_x = x

                    if y <= min_y:
                        min_y = y
                    elif y >= max_y:
                        max_y = y
        img1 = img[min_x:max_x, min_y: max_y]
        return img1

    def template_match(self, tpl, target):
        th, tw = tpl.shape[:2]
        result = cv2.matchTemplate(target, tpl, cv2.TM_CCOEFF_NORMED)
        # 寻找矩阵(一维数组当作向量,用Mat定义) 中最小值和最大值的位置
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
        tl = max_loc
        br = (tl[0] + tw, tl[1] + th)
        # 绘制矩形边框，将匹配区域标注出来
        # target：目标图像
        # tl：矩形定点
        # br：矩形的宽高
        # (0,0,255)：矩形边框颜色
        # 1：矩形边框大小
        cv2.rectangle(target, tl, br, (0, 0, 255), 2)
        # 暂不输出
        # cv2.imwrite(self.out, target)
        return tl[0]

    @staticmethod
    def image_edge_detection(img):
        edges = cv2.Canny(img, 100, 200)
        return edges

    def discern(self):
        img1 = self.clear_white(self.gap)
        img1 = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
        slide = self.image_edge_detection(img1)

        back = cv2.imread(self.bg, 0)
        back = self.image_edge_detection(back)

        slide_pic = cv2.cvtColor(slide, cv2.COLOR_GRAY2RGB)
        back_pic = cv2.cvtColor(back, cv2.COLOR_GRAY2RGB)
        x = self.template_match(slide_pic, back_pic)
        # 输出横坐标, 即 滑块在图片上的位置
        return x
# 获取滑动距离
def get_px(path_dic):
    # 滑块图片
    image1 = rf"{path_dic['验证码存放路径']}\small.png"
    # 背景图片
    image2 = rf"{path_dic['验证码存放路径']}\big.png"
    sc = SlideCrack(image1, image2)
    a = sc.discern()
    return  a
# 获取位移距离
def get_tracks(distance):
    #distance为上一步得出的总距离。20是等会要回退的像素
    distance+=20
    #初速度为0，s是已经走的路程，t是时间
    v0=2
    s=0
    t=0.4
   #mid是进行减速的路程
    mid=distance*3/5
   #存放走的距离
    forward_tracks=[]
    while s<distance:
        if s<mid:
            a=2
        else:
            a=-3
        #高中物理，匀加速路程的计算
        v=v0
        tance=v*t+0.5*a*(t**2)
        tance=round(tance)
        s+=tance
        v0=v+a*t
        forward_tracks.append(tance)
    #因为回退20像素，所以可以手动打出，只要和为20即可
    back_tracks = [-1, -1, -1, -2, -2, -2, -3, -3, -2, -2, -1]  # 20
    return {"forward_tracks": forward_tracks, 'back_tracks': back_tracks}
if __name__ == '__main__':
    # 只能是英文路径
    path_dic={}
    path_dic['验证码存放路径']=r"D:\tem"
    # 下载滑块图片.我的案例是base64的数据，获取数据之后，直接保存就行，要是url，用requests取一下
    down_pic(data, "small", path_dic)
    # 下载缺口图片
    down_pic(data, "big", path_dic)
    # 获取滑动距离(总距离)
    distance=get_px(path_dic)
    # 获取每次移动的距离和回退的距离，防反爬
    dic=get_tracks(distance)
    """根据实际测试，在distance-7px较为准确，可以根据个人电脑的分辨率来调整"""