腾讯滑块验证码

https://github.com/godtoy/python-tecent-slider-crack

#!/usr/bin/python3
# -*- coding:utf-8 -*-
# __author__ = '__zx-coder__'
import os
import time
import uuid

import cv2
import cv2 as cv
import pyautogui


def myscreen(region):
    time.sleep(0.5)
    file = os.path.join(os.path.dirname(__file__), "tmp", str(uuid.uuid4()) + "_captcha.jpeg")
    return pyautogui.screenshot(file, region=region)


def get_pos(image):
    """
    缺口轮廓检测
    对付腾讯滑块够用
    该方法识别率 50% 左右，使用背景简单的图
    """
    blurred = cv.GaussianBlur(image, (5, 5), 0)
    canny = cv.Canny(blurred, 84, 400)
    # cv.imshow('canny', canny)
    # cv.waitKey()
    contours, hierarchy = cv.findContours(canny, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)

    # 画轮廓
    # areas = []
    # for contour in contours:
    #     areas.append(cv2.contourArea(contour))
    # areas = np.asarray(areas)
    # index = areas.argsort()
    # print(index[-1])
    # img = cv2.drawContours(image, contours, index[-1], (0, 255, 0), 2)
    # cv.imshow('cnts', img)
    # cv2.waitKey()
    # cv2.destroyAllWindows()

    for i, contour in enumerate(contours):
        # for contour in contours:
        m = cv.moments(contour)
        if m['m00'] == 0:
            cx = cy = 0
        else:
            cx, cy = m['m10'] / m['m00'], m['m01'] / m['m00']

        print('周长：', cv.arcLength(contour, True))
        print('面积：', cv.contourArea(contour))
        # print(contour)
        print('重心：', cx, cy)
        print(i)
        print("-" * 200)

        if 1500 < cv.contourArea(contour) < 3000 and 200 < cv.arcLength(contour, True) < 300:
            if cx < 200:
                continue
            x, y, w, h = cv.boundingRect(contour)  # 外接矩形
            cv.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
            cv.imshow('image', image)
            cv.waitKey()
            # print(x)
            # real_show(image)
            return x
    return 0
# img= cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  #转化为灰度图
def onmouse(event, x, y, flags, param):  # 标准鼠标交互函数
    #    if event==cv2.EVENT_LBUTTONDBLCLK :      #当鼠标点击时
    #        print("y=",y), print("x=",x), print(img[y,x],"\n")           #显示鼠标所在像素的数值，注意像素表示方法和坐标位置的不同
    if event == cv2.EVENT_MOUSEMOVE:  # 当鼠标移动时
        print("y=", y, "x=", x, img[y, x], "\n")  # 显示鼠标所在像素的数值，注意像素表示方法和坐标位置的不同


def real_show(img):
    '''实时显示坐标'''
    cv2.namedWindow("img")  # 构建窗口
    cv2.setMouseCallback("img", onmouse)  # 回调绑定窗口
    while True:  # 无限循环
        cv2.imshow("img", img)  # 显示图像
        if cv2.waitKey() == 27:
            cv2.destroyAllWindows()  # 关闭窗口
            break


if __name__ == '__main__':
    """
    这里是滑块缺口识别
    识别到后
    1。可以通过自动化工具取拖动滑块
    2。可以通过参数解析的形式生成参数提交通过验证
    """
    from pathlib import Path

    BASE_DIR = Path(__file__).resolve().parent / 'tmp'
    for path in BASE_DIR.iterdir():
        print(path)
        img = cv2.imread(str(path))
        get_pos(img)

    # img = cv2.imread('tmp/1cef093f-0950-46e6-bff6-70fd2e88fb7d_captcha.jpeg')
    # print(get_pos(img))