某税Tpass滑块验证逆向「已过时」

相关接口代码已失效且过时，为便于研究与参考，本文仅公开图形计算部分的代码。

使用方法

python3 example.py

完整代码

import cv2
import numpy as np
import base64


class ImageUtils:
    """图像处理工具类"""
    
    @staticmethod
    def fix_base64_padding(b64str: str) -> str:
        """修复base64填充"""
        if "," in b64str:
            b64str = b64str.split(",")[1]
        return b64str + '=' * (-len(b64str) % 4)
    
    @staticmethod
    def save_base64_image(base64_str: str, filename: str) -> None:
        """保存base64图片到文件"""
        if "," in base64_str:
            base64_str = base64_str.split(",")[1]
        
        image_data = base64.b64decode(base64_str)
        with open(filename, "wb") as f:
            f.write(image_data)
    
    @staticmethod
    def decode_base64_image(data_url: str) -> np.ndarray:
        """解码base64图片为numpy数组"""
        if ',' in data_url:
            _, base64_data = data_url.split(',', 1)
        else:
            base64_data = data_url
        
        img_bytes = base64.b64decode(base64_data)
        img_array = np.frombuffer(img_bytes, dtype=np.uint8)
        return cv2.imdecode(img_array, cv2.IMREAD_COLOR)
    
    @staticmethod
    def identify_gap(bg_bytes: bytes, tp_bytes: bytes) -> int:
        """识别滑块缺口位置"""
        # 读取图片
        bg_img = cv2.imdecode(np.frombuffer(bg_bytes, np.uint8), cv2.IMREAD_GRAYSCALE)
        tp_img = cv2.imdecode(np.frombuffer(tp_bytes, np.uint8), cv2.IMREAD_GRAYSCALE)
        
        # 裁剪滑块图片（去除透明边缘）
        yy, xx = [], []
        for y in range(tp_img.shape[0]):
            for x in range(tp_img.shape[1]):
                if tp_img[y, x] < 200:
                    yy.append(y)
                    xx.append(x)
        
        if yy and xx:
            tp_img = tp_img[min(yy):max(yy), min(xx):max(xx)]
        
        # 边缘检测
        bg_edge = cv2.Canny(bg_img, 100, 200)
        tp_edge = cv2.Canny(tp_img, 100, 200)
        
        # 转换为RGB
        bg_pic = cv2.cvtColor(bg_edge, cv2.COLOR_GRAY2RGB)
        tp_pic = cv2.cvtColor(tp_edge, cv2.COLOR_GRAY2RGB)
        
        # 模板匹配
        res = cv2.matchTemplate(bg_pic, tp_pic, cv2.TM_CCOEFF_NORMED)
        _, _, _, max_loc = cv2.minMaxLoc(res)
        
        # 绘制结果并保存
        th, tw = tp_pic.shape[:2]
        tl = max_loc
        br = (tl[0] + tw, tl[1] + th)
        cv2.rectangle(bg_img, tl, br, (0, 0, 255), 2)
        cv2.imwrite('distinguish.jpg', bg_img)
        
        return max_loc[0]

class CaptchaSolver:
    """验证码求解器"""
    
    def calculate_slide_distance(self, canvas_base64: str, block_base64: str) -> int:
        """计算滑动距离"""
        print("🧮 正在计算滑动距离...")
        
        try:
            bg_bytes = base64.b64decode(ImageUtils.fix_base64_padding(canvas_base64))
            tp_bytes = base64.b64decode(ImageUtils.fix_base64_padding(block_base64))
            
            offset = ImageUtils.identify_gap(bg_bytes, tp_bytes)
            print(f"🎯 计算得出滑动距离: {offset} px")
            
            return offset
            
        except Exception as ex:
            print(f"❌ 计算滑动距离失败: {ex}")
            return 0