yml2213
74a863c8b3
- 真因：dt 分布与真机严重不符（旧 50% 落 0~16ms，真机 79% 落 13~20ms）+ 点数 随机 57~65（真机 6/6 恒 61）。按 6 条真机轨迹重拟合 _sample_dt，新增 _fit_points 钉死 61 点（补齐用 [0,0,dt] 驻留点，不能复制位移点——净位移会被放大）。 - A/B：1ms 图标 0×4→0×7（前 7 连 0）；残余偶发 12 为连发频控形态，低频使用即可。 - errorCode 语义标定：50=答案被否（容差实测 25~40px，我们点偏仅 2~3px）， 12=包级拒答；判点位只看 50/0。 - 顺带修 build_plain：轨迹落末槽（k=37）时 chunk2 恒补前导逗号 → 非法 JSON。 - 新增 scripts/verify/send_campaign.py（批量真发+落图+统计）与 judge_icons.py （提示目标 vs 点击点并排判读）；账本 #64~#74 + 专文 ICON2408.md。 复验：selftest 全绿；判读图 24/24 点位全对；mliev 图标 9/10=0。
740 lines
35 KiB
Python
740 lines
35 KiB
Python
"""
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腾讯天御点选验证码纯协议客户端 (Tencent Turing Captcha Protocol Client)
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覆盖腾讯天御 `cap_union_new` 协议族的完整纯协议闭环:
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1. GET /cap_union_prehandle 获取会话 sess / PoW 题目 / 题型 / 首道题目
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2. 本地求解 PoW (MD5 哈希碰撞)
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3. GET <tdc_path> 拉取当前会话动态混淆的 tdc.js, 提取会话密钥 eks
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4. GET /cap_union_new_getcapbysig 下载 672x480 背景图 (img_index=1);
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图标题型额外下载 img_index=0 提示图
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5. 视觉求解:
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* 文字点选 (subcapclass 2404) -> tcaptcha.glyph_solver.TextClickSolver
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* 图标点选 (subcapclass 2408) -> tcaptcha.solver.CaptchaSolver (YOLO+ResNet18)
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6. 生成行为遥测密文 collect (纯 Python 还原的自定义 XTEA + 贝塞尔轨迹)
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7. POST /cap_union_new_verify 提交 ans/collect/eks/sess/pow_answer
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8. 失败 → POST /cap_union_new_getsig 在同一会话内刷新下一道题并重试
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题型识别不依赖 subcapclass 数字 (getsig 不返回该字段), 而是根据
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instruction + bg_elem_cfg.click_cfg 推断, 详见 classify_challenge()。
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"""
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import base64
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import hashlib
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import json
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import logging
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import random
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import re
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import sys
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import tempfile
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import time
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Dict, List, Optional, Sequence, Tuple
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import httpx
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from urllib.parse import urlsplit
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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from tcaptcha.telemetry.crypto import extract_eks, generate_telemetry
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from tcaptcha.telemetry.values import drag_trajectory_points
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from tcaptcha.solvers.slider import SliderChallenge, SliderSolver, piece_from_sprite
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from tcaptcha.solvers.text_click import TextClickSolver, parse_instruction
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
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logger = logging.getLogger("tcaptcha_client")
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DEFAULT_UA = ("Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 "
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"(KHTML, like Gecko) Chrome/155.0.0.0 Safari/537.36")
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# 题型
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KIND_TEXT_CLICK = "text_click" # 2404 文字点选
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KIND_ICON_CLICK = "icon_click" # 2408 图标点选 (需要提示图 img_index=0)
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KIND_SLIDER = "slider" # 2402 滑块拼图 (本客户端未实现)
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KIND_UNKNOWN = "unknown"
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@dataclass
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class Challenge:
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"""一道待求解的验证码题目"""
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sess: str
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instruction: str = ""
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img_url: str = ""
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kind: str = KIND_UNKNOWN
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subcapclass: str = ""
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click_cfg: Optional[dict] = None
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raw: dict = field(default_factory=dict)
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@property
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def targets(self) -> List[str]:
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return parse_instruction(self.instruction)
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def classify_challenge(dyn_show_info: dict, subcapclass: str = "") -> str:
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"""根据题目配置推断题型 (getsig 不返回 subcapclass, 只能靠结构判断)"""
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instruction = (dyn_show_info or {}).get("instruction", "") or ""
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click_cfg = ((dyn_show_info or {}).get("bg_elem_cfg") or {}).get("click_cfg") or {}
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data_types = click_cfg.get("data_type") or []
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if "DynAnswerType_POS" in data_types or "DynAnswerType_POINT" in data_types:
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# 有点选坐标配置: 有目标文字 => 文字点选; 无目标文字 => 图标点选(提示走提示图)
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return KIND_TEXT_CLICK if parse_instruction(instruction) else KIND_ICON_CLICK
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if "滑块" in instruction or "拖动" in instruction or subcapclass == "2402":
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return KIND_SLIDER
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if subcapclass == "2404":
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return KIND_TEXT_CLICK
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if subcapclass == "2408":
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return KIND_ICON_CLICK
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return KIND_UNKNOWN
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def _site_origin(url: str) -> str:
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"""取 `scheme://host[:port]/` —— 页内 <script>/<img> 资源的 Referer 是 site-only。"""
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try:
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parts = urlsplit(url)
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if parts.scheme and parts.netloc:
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return f"{parts.scheme}://{parts.netloc}/"
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except Exception: # pragma: no cover
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pass
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return url
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def _build_transport(transport: str, *, base_url: str, headers: Dict[str, str], timeout: float,
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proxy: Optional[str], impersonate: Optional[str]):
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"""
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构造 HTTP 传输层。
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* transport="httpx" (默认): 原行为 (HTTP/1.1 + OpenSSL)
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* transport="curl" : curl_cffi 复刻 Chrome 网络指纹 (JA3/JA4 + HTTP/2 + 头序)
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背景: 同一 aid / 同一 cookie / 同一参数下, 真实浏览器从不吃 errorCode=12, 而 httpx 会 ——
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12 的自变量落在"传输指纹"上 (见 har/1ms_dianxuan/ANALYSIS.md §7.6)。
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"""
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transport = (transport or "httpx").lower()
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if transport == "httpx":
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return httpx.Client(
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base_url=base_url,
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trust_env=False, # 屏蔽环境代理干扰
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proxy=proxy,
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headers=headers,
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timeout=timeout,
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follow_redirects=True,
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)
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if transport == "curl":
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from tcaptcha.protocol.transport import CurlCffiClient
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return CurlCffiClient(base_url=base_url, headers=headers, timeout=timeout,
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proxy=proxy, impersonate=impersonate or "chrome")
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raise ValueError(f"未知 transport: {transport!r} (支持 'httpx' / 'curl')")
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class TencentCaptchaClient:
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BASE_URL = "https://turing.captcha.qcloud.com"
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DEFAULT_AID = "2039519451"
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def __init__(
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self,
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aid: str = DEFAULT_AID,
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solver=None,
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text_solver: Optional[TextClickSolver] = None,
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timeout: float = 15.0,
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user_agent: Optional[str] = None,
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entry_url: str = "https://app.rainyun.com/account/reward/earn#",
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referer: Optional[str] = None,
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wb: str = "2",
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js: str = "/tcaptcha-frame.ba387dd1.js",
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accept_kinds: Sequence[str] = (KIND_TEXT_CLICK, KIND_ICON_CLICK, KIND_SLIDER),
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min_posterior: float = 0.95,
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pow_timeout: int = 4000000,
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inter_attempt_delay: float = 0.25,
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telemetry_mode: str = "python_values",
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proxy: Optional[str] = None,
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cookies: Optional[dict] = None,
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transport: str = "httpx",
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impersonate: Optional[str] = None,
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):
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"""
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telemetry_mode 决定 collect/eks 怎么来 (见 tcaptcha.telemetry.crypto.generate_telemetry):
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* "python_values" (默认, 2026-10-12 起): **纯离线**按**本次会话的** tdc.js 生成整颗
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collect —— 37 个语义名 -> 38 槽取值 -> 4 段明文 -> 4 段加密，
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零 Node、零真机明文。"必须用本次会话的构建"这一点是实测出来的
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分水岭: 同一 aid 下它 3/3 拿 0, 而模板回填 2/2 恒 12
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(har/1ms_dianxuan/PROGRESS.md 已知 #58/#59)。
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* "python_keyed": 明文来自仓库里的**成功样本模板回填**
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(generate_collect_success): 槽序来自**另一份构建**、
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取值来自**另一台设备** -> 1ms aid 上实测恒 12。
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* "python": 完全不调用 Node, 用内置 DEFAULT_KEY + 同样的模板回填
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(仅对不校验遥测的宽松 aid 有效)。
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关于 errorCode=12 (**2026-10-12 已收口, 旧结论已推翻**):
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真因不是"环境/IP/请求方", 而是 **collect 没有用本次会话的那份构建生成**。
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同 aid / 同客户端 / 同求解器、答案都正确, 只换 collect 构造方式:
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python_values -> 0 (3/3), python_keyed (旧模板回填) -> 12 (2/2)。
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见 har/1ms_dianxuan/PROGRESS.md 已知 #58/#59。
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附带一条方法学: **答错一律 50/9**, 所以任何用"答错"做的对照都没有区分度。
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cookies: 可选, 逐条写入 captcha 域 (turing.captcha.qcloud.com) 的 Cookie 头。
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成功样本 (har/签到成功-1.har) 的 captcha 域请求带着腾讯云登录 cookie
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(uin / skey / qcmainCSRFToken / intl), 而本客户端此前完全不带 ——
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这是账本 §7.5 的 H3 单变量实验入口 (默认 None, 不改变原有行为)。
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"""
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self.aid = str(aid)
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self.timeout = timeout
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self.user_agent = user_agent or DEFAULT_UA
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self.entry_url = entry_url
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self.referer = referer or entry_url
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self.wb = str(wb)
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self.js = js
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self.accept_kinds = tuple(accept_kinds)
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self.min_posterior = min_posterior
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self.pow_timeout = pow_timeout
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# 抽题/投票之间的最小间隔 (降低接口压力; 与 errorCode=12 无因果关系)
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self.inter_attempt_delay = inter_attempt_delay
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# 遥测生成方式: 只走纯算 (见 tcaptcha.telemetry.crypto.generate_telemetry)
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self.telemetry_mode = telemetry_mode
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# 出口代理 (可选)。注意: 实测换出口 IP 并不能解决 errorCode=12
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self.proxy = proxy
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# 图标题型求解器 (惰性加载, 避免只跑文字题型时白加载 56MB 权重)
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self._icon_solver = solver
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self._text_solver = text_solver
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self._slider_solver: Optional[SliderSolver] = None
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self.subsid = 1
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# 可选: 透传 captcha 域的腾讯云登录 cookie (账本 §7.5 的 H3 单变量实验入口)。
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# 注意: **不做成全局头** —— 真实浏览器只在 prehandle / tdc.js / getcapbysig
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# (页面资源 GET) 上带它; getsig / verify (跨站 POST, SameSite=Lax) 上**不带**
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# (har/签到成功-1.har 与 点选.har 实测)。这里保存, 由各调用点按需附加。
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self._cookie_header = ("; ".join(f"{k}={v}" for k, v in cookies.items())
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if cookies else None)
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self.site_origin = _site_origin(self.referer)
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self._gtimg_referer = "https://turing.captcha.gtimg.com/"
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headers = {
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"User-Agent": self.user_agent,
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"Referer": self.referer,
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"Accept-Language": "zh-CN,zh;q=0.9,fr;q=0.8,de;q=0.7,en;q=0.6",
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"sec-ch-ua": '"Google Chrome";v="155", "Chromium";v="155", "Not(A:Brand";v="24"',
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"sec-ch-ua-mobile": "?0",
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"sec-ch-ua-platform": '"macOS"',
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"Sec-Fetch-Dest": "empty",
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"Sec-Fetch-Mode": "cors",
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"Sec-Fetch-Site": "cross-site",
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}
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self.http_client = (
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_build_transport(transport, base_url=self.BASE_URL, headers=headers,
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timeout=self.timeout, proxy=self.proxy, impersonate=impersonate)
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)
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# --------------------------------------------------------------- helpers
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def _resource_headers(self, dest: str, referer: str) -> Dict[str, str]:
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"""
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页面资源 GET (prehandle / tdc.js / getcapbysig) 的**真实浏览器**请求头:
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`Sec-Fetch-*` = cross-site + no-cors + Storage-Access: active, 且带 captcha 域 cookie。
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(机器人常用 XHR 语义的 empty/cors 去拉这些资源 —— 这是可测的指纹差异。)
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"""
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h = {
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"Referer": referer,
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"Accept": "*/*",
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"Sec-Fetch-Dest": dest,
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"Sec-Fetch-Mode": "no-cors",
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"Sec-Fetch-Site": "cross-site",
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"Sec-Fetch-Storage-Access": "active",
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}
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if self._cookie_header:
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h["Cookie"] = self._cookie_header
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return h
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@property
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def text_solver(self) -> TextClickSolver:
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if self._text_solver is None:
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logger.info("初始化文字点选求解器 (ddddocr + PP-OCRv4 集成)...")
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self._text_solver = TextClickSolver()
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return self._text_solver
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@property
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def icon_solver(self):
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if self._icon_solver is None:
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from tcaptcha.solvers.icon_click import CaptchaSolver
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logger.info("初始化图标点选求解器 (YOLOv8 + ResNet18)...")
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self._icon_solver = CaptchaSolver()
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return self._icon_solver
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@property
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def slider_solver(self) -> SliderSolver:
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if self._slider_solver is None:
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self._slider_solver = SliderSolver()
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return self._slider_solver
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def _solve_pow(self, prefix: str, target_md5: str) -> Tuple[str, int]:
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"""求解 Proof of Work 哈希碰撞"""
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t0 = time.time()
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for i in range(self.pow_timeout):
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candidate = f"{prefix}{i}"
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if hashlib.md5(candidate.encode("utf-8")).hexdigest() == target_md5:
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calc_time = max(1, round((time.time() - t0) * 1000))
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return candidate, calc_time
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raise RuntimeError(f"PoW MD5 collision search failed for prefix {prefix}")
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# ------------------------------------------------------------- 1. prehandle
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def prehandle(self, tkid: str) -> Tuple[Challenge, dict, str]:
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"""GET /cap_union_prehandle —— 返回 (题目, comm_captcha_cfg, sid)"""
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cb_name = f"_aq_{random.randint(100000, 999999)}"
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ua_b64 = base64.b64encode(self.user_agent.encode()).decode()
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self.subsid += 2
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params = {
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"aid": self.aid,
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"protocol": "https",
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"accver": "1",
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"showtype": "popup",
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"ua": ua_b64,
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"noheader": "1",
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"fb": "1",
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"aged": "0",
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"enableAged": "0",
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"enableDarkMode": "0",
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"grayscale": "1",
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"clientype": "2",
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"cap_cd": "",
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"uid": "",
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"lang": "zh-cn",
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"entry_url": self.entry_url,
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"elder_captcha": "0",
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"js": self.js,
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"login_appid": "",
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"wb": self.wb,
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"tkid": tkid,
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"subsid": str(self.subsid),
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"callback": cb_name,
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"sess": "",
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}
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resp = self.http_client.get("/cap_union_prehandle", params=params,
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headers=self._resource_headers("script", self.site_origin))
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match = re.search(rf"{re.escape(cb_name)}\((.*)\)\s*$", resp.text, re.S)
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if not match:
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raise RuntimeError(f"prehandle 回调解析失败: {resp.text[:200]}")
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data = json.loads(match.group(1))
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dyn = (data.get("data") or {}).get("dyn_show_info") or {}
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sub = str(data.get("subcapclass", ""))
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challenge = Challenge(
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sess=data.get("sess", ""),
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instruction=dyn.get("instruction", ""),
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img_url=((dyn.get("bg_elem_cfg") or {}).get("img_url") or ""),
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kind=classify_challenge(dyn, sub),
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subcapclass=sub,
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click_cfg=(dyn.get("bg_elem_cfg") or {}).get("click_cfg"),
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raw=dyn,
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)
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return challenge, (data.get("data") or {}).get("comm_captcha_cfg", {}) or {}, data.get("sid", "")
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# --------------------------------------------------------------- 2. getsig
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def getsig(self, sess: str, tkid: str) -> Tuple[Challenge, str]:
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"""POST /cap_union_new_getsig —— 在同一会话内刷新题目"""
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resp = self.http_client.post(
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"/cap_union_new_getsig",
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data={"sess": sess, "tkid": tkid},
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headers={"Content-Type": "application/x-www-form-urlencoded",
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"Origin": "https://turing.captcha.gtimg.com",
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"Referer": "https://turing.captcha.gtimg.com/"},
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)
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payload = resp.json()
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if str(payload.get("ret")) != "0":
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raise RuntimeError(f"getsig 返回异常: {payload}")
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dyn = payload.get("data") or {}
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new_sess = payload.get("sess") or sess
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challenge = Challenge(
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sess=new_sess,
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instruction=dyn.get("instruction", ""),
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img_url=((dyn.get("bg_elem_cfg") or {}).get("img_url") or ""),
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kind=classify_challenge(dyn),
|
|
click_cfg=(dyn.get("bg_elem_cfg") or {}).get("click_cfg"),
|
|
raw=dyn,
|
|
)
|
|
return challenge, new_sess
|
|
|
|
# ------------------------------------------------------------ 3. download
|
|
def fetch_tdc(self, tdc_path: str) -> str:
|
|
resp = self.http_client.get(
|
|
tdc_path, headers=self._resource_headers("script", self._gtimg_referer))
|
|
return resp.text
|
|
|
|
def fetch_image(self, img_url: str, img_index: Optional[int] = None) -> bytes:
|
|
if img_index is not None:
|
|
img_url = re.sub(r"img_index=\d+", f"img_index={img_index}", img_url)
|
|
resp = self.http_client.get(
|
|
img_url,
|
|
headers={**self._resource_headers("image", self._gtimg_referer),
|
|
"Accept": "image/jxl,image/avif,image/webp,image/apng,image/svg+xml,image/*,*/*;q=0.8"},
|
|
)
|
|
return resp.content
|
|
|
|
# ------------------------------------------------------------------ solve
|
|
def solve(self, max_retries: int = 3, max_refreshes: int = 6) -> Dict:
|
|
"""
|
|
端到端解题并提交验证。
|
|
|
|
返回: {success, ticket, randstr, elapsed_ms, points, kind, instruction,
|
|
raw_response, error}
|
|
"""
|
|
start_time = time.time()
|
|
tkid = str(random.randint(100000000, 999999999))
|
|
sess: Optional[str] = None
|
|
eks: Optional[str] = None
|
|
tdc_code: Optional[str] = None
|
|
pow_answer: Optional[str] = None
|
|
pow_calc_time: Optional[int] = None
|
|
comm_cfg: Dict = {}
|
|
challenge: Optional[Challenge] = None
|
|
last_raw: Dict = {}
|
|
last_error = "未完成任何一次验证提交"
|
|
|
|
attempts = 0
|
|
refreshes = 0
|
|
prehandles = 0
|
|
while attempts < max_retries and refreshes < max_refreshes:
|
|
try:
|
|
# ---- 拿题: 首选 prehandle, 之后用 getsig 在同会话内刷新
|
|
round_start_time = time.time()
|
|
if challenge is None:
|
|
if sess:
|
|
challenge, sess = self.getsig(sess, tkid)
|
|
logger.info("getsig 刷新题目 -> kind=%s instruction=%r",
|
|
challenge.kind, challenge.instruction)
|
|
else:
|
|
challenge, comm_cfg, _sid = self.prehandle(tkid)
|
|
prehandles += 1
|
|
logger.info("prehandle -> subcapclass=%s kind=%s instruction=%r",
|
|
challenge.subcapclass, challenge.kind, challenge.instruction)
|
|
pow_cfg = comm_cfg.get("pow_cfg") or {}
|
|
if pow_cfg.get("prefix") and pow_cfg.get("md5"):
|
|
pow_answer, pow_calc_time = self._solve_pow(pow_cfg["prefix"],
|
|
pow_cfg["md5"])
|
|
logger.info("PoW 命中 %s (%dms)", pow_answer, pow_calc_time)
|
|
tdc_code = self.fetch_tdc(comm_cfg.get("tdc_path", ""))
|
|
eks = extract_eks(tdc_code)
|
|
if not eks:
|
|
logger.warning("未从 tdc.js 中提取到 eks, 继续尝试")
|
|
|
|
# 无论是预期的还是非预期题型，第一时间下载原图落盘到系统临时目录，辅助用户肉眼判断。
|
|
# 注意: 别再写回仓库内的 scratch/ —— 该目录已清理且不入库（.gitignore）。
|
|
bg_bytes: Optional[bytes] = None
|
|
debug_dir = Path(tempfile.gettempdir()) / "captcha_debug"
|
|
debug_dir.mkdir(parents=True, exist_ok=True)
|
|
ts = time.strftime("%Y%m%d_%H%M%S")
|
|
safe_inst = re.sub(r"[^\w\u4e00-\u9fff]+", "_", challenge.instruction).strip("_") or "no_inst"
|
|
|
|
if challenge.img_url:
|
|
try:
|
|
bg_bytes = self.fetch_image(challenge.img_url, img_index=1)
|
|
raw_path = debug_dir / f"{ts}_{challenge.kind}_{safe_inst}.jpg"
|
|
raw_path.write_bytes(bg_bytes)
|
|
logger.info("📸 [本地图片落盘] %s (题型: %s, 指令: %r)", raw_path, challenge.kind, challenge.instruction)
|
|
except Exception as e:
|
|
logger.warning("下载题目原图失败: %s", e)
|
|
|
|
if challenge.kind not in self.accept_kinds:
|
|
# 题型在会话内是固定的 (getsig 不会换题型), 必须重新 prehandle 抽题
|
|
# 严格模拟人类审题、发现不是目标题型、移动鼠标并点击换题的交互耗时 (2.8 ~ 4.2 秒，拒绝秒换)
|
|
wait_switch = random.uniform(2.8, 4.2)
|
|
logger.info("⏳ 题型 %s 不在支持范围 %s, 模拟人类思考与换题等待 %.2fs (已抽 %d 次)...",
|
|
challenge.kind, list(self.accept_kinds), wait_switch, prehandles)
|
|
refreshes += 1
|
|
challenge = None
|
|
sess = None
|
|
eks = None
|
|
pow_answer = None
|
|
time.sleep(wait_switch)
|
|
continue
|
|
|
|
# ---- 视觉求解
|
|
points: List[Tuple[int, int]] = []
|
|
slider_ch: Optional[SliderChallenge] = None
|
|
drag_traj: Optional[List[List[int]]] = None
|
|
if challenge.kind == KIND_TEXT_CLICK:
|
|
bg = bg_bytes if bg_bytes else self.fetch_image(challenge.img_url, img_index=1)
|
|
|
|
result = self.text_solver.solve_ex(bg, challenge.instruction)
|
|
logger.info("文字点选求解: %s", result.describe())
|
|
|
|
# 如果识别出坐标，生成带红圈和序号 (1, 2, 3) 的标注图便于肉眼核验
|
|
if result.points:
|
|
try:
|
|
import cv2
|
|
import numpy as np
|
|
|
|
bgr = cv2.imdecode(np.frombuffer(bg, np.uint8), cv2.IMREAD_COLOR)
|
|
for idx, pt in enumerate(result.points):
|
|
cv2.circle(bgr, (int(pt[0]), int(pt[1])), 18, (0, 0, 255), 2)
|
|
cv2.putText(
|
|
bgr,
|
|
str(idx + 1),
|
|
(int(pt[0]) - 6, int(pt[1]) + 7),
|
|
cv2.FONT_HERSHEY_SIMPLEX,
|
|
0.7,
|
|
(0, 255, 0),
|
|
2,
|
|
)
|
|
anno_path = debug_dir / f"{ts}_{challenge.kind}_{safe_inst}_annotated.jpg"
|
|
cv2.imwrite(str(anno_path), bgr)
|
|
logger.info("📸 [标注图已落盘] %s", anno_path)
|
|
except Exception as e:
|
|
logger.warning("生成标注图失败: %s", e)
|
|
|
|
if not result.ok:
|
|
wait_refresh = random.uniform(2.5, 3.8)
|
|
logger.warning("文字点选求解失败(未识别全目标字), 模拟人类找字无果换题等待 %.2fs", wait_refresh)
|
|
refreshes += 1
|
|
challenge = None
|
|
time.sleep(wait_refresh)
|
|
continue
|
|
if result.posterior < self.min_posterior or result.min_target_score < 0.2:
|
|
wait_refresh = random.uniform(2.5, 3.8)
|
|
logger.warning("求解置信度过低 (posterior=%.3f, min_score=%.3f), 模拟犹豫换题等待 %.2fs",
|
|
result.posterior, result.min_target_score, wait_refresh)
|
|
refreshes += 1
|
|
challenge = None
|
|
time.sleep(wait_refresh)
|
|
continue
|
|
points = result.points
|
|
elif challenge.kind == KIND_ICON_CLICK:
|
|
bg = bg_bytes if bg_bytes else self.fetch_image(challenge.img_url, img_index=1)
|
|
prompt = self.fetch_image(challenge.img_url, img_index=0)
|
|
if prompt:
|
|
prompt_path = debug_dir / f"{ts}_icon_prompt_{safe_inst}.jpg"
|
|
prompt_path.write_bytes(prompt)
|
|
logger.info("📸 [提示图落盘] %s", prompt_path)
|
|
else:
|
|
wait_refresh = random.uniform(2.5, 3.8)
|
|
logger.warning("图标题型未取到提示图, 等待 %.2fs 后刷新题目", wait_refresh)
|
|
refreshes += 1
|
|
time.sleep(wait_refresh)
|
|
continue
|
|
points = self.icon_solver.solve(bg, prompt)
|
|
if len(points) < 3:
|
|
wait_refresh = random.uniform(2.5, 3.8)
|
|
logger.warning("图标点选求解失败, 等待 %.2fs 后刷新题目", wait_refresh)
|
|
refreshes += 1
|
|
time.sleep(wait_refresh)
|
|
continue
|
|
else: # KIND_SLIDER (2401/2402)
|
|
import cv2
|
|
import numpy as np
|
|
|
|
try:
|
|
slider_ch = SliderChallenge.from_challenge(challenge.raw or {})
|
|
except ValueError as exc:
|
|
wait_refresh = random.uniform(2.5, 3.8)
|
|
logger.warning("滑块课题配置解析失败 (%s), 等待 %.2fs 后刷新", exc, wait_refresh)
|
|
refreshes += 1
|
|
challenge = None
|
|
time.sleep(wait_refresh)
|
|
continue
|
|
|
|
bg = bg_bytes if bg_bytes else self.fetch_image(slider_ch.bg_url, img_index=1)
|
|
sprite = cv2.imdecode(
|
|
np.frombuffer(self.fetch_image(slider_ch.sprite_url, img_index=0), np.uint8),
|
|
cv2.IMREAD_UNCHANGED)
|
|
if bg is None or sprite is None:
|
|
wait_refresh = random.uniform(2.5, 3.8)
|
|
logger.warning("滑块图片拉取失败, 等待 %.2fs 后刷新题目", wait_refresh)
|
|
refreshes += 1
|
|
challenge = None
|
|
time.sleep(wait_refresh)
|
|
continue
|
|
|
|
# 落盘一张底图便于人工核对缺口定位
|
|
(debug_dir / f"{ts}_slider_bg.jpg").write_bytes(bg)
|
|
crop = piece_from_sprite(sprite, slider_ch.sprite_rect)
|
|
(debug_dir / f"{ts}_slider_piece.png").write_bytes(
|
|
cv2.imencode(".png", crop)[1].tobytes())
|
|
sres = self.slider_solver.solve(bg, crop,
|
|
piece_x_image=slider_ch.init_pos[0],
|
|
piece_y_image=slider_ch.init_pos[1],
|
|
scale=1.0)
|
|
logger.info("滑块求解: %s", sres.describe())
|
|
if not sres.ok:
|
|
wait_refresh = random.uniform(2.5, 3.8)
|
|
logger.warning("滑块缺口定位置信度不足 (score=%.3f margin=%.3f), 等待 %.2fs 后刷新题目",
|
|
sres.score, sres.margin, wait_refresh)
|
|
refreshes += 1
|
|
challenge = None
|
|
time.sleep(wait_refresh)
|
|
continue
|
|
|
|
target_x = slider_ch.clamp_x(sres.target_x_image)
|
|
points = [(int(round(target_x)), int(sres.y_image))]
|
|
drag_traj = drag_trajectory_points(slider_ch.init_pos, points[0],
|
|
img_w=slider_ch.bg_size[0] or 672)
|
|
logger.info("滑块答案: elem_id=%d target=(%d,%d) dx=%d 轨迹点=%d",
|
|
slider_ch.elem_id, points[0][0], points[0][1],
|
|
points[0][0] - int(slider_ch.init_pos[0]), len(drag_traj))
|
|
|
|
# ---- 模拟真实人类阅读审题、定位字形与逐个点击耗时 (5.5 ~ 7.5 秒，对齐抓包实测 7.41s)
|
|
elapsed = time.time() - round_start_time
|
|
target_duration = random.uniform(5.5, 7.5)
|
|
if elapsed < target_duration:
|
|
sleep_secs = target_duration - elapsed
|
|
logger.info("模拟人类阅读审题与点击耗时: 等待 %.2fs (目标交互时长 %.2fs)", sleep_secs, target_duration)
|
|
time.sleep(sleep_secs)
|
|
|
|
# ---- 组包并提交
|
|
attempts += 1
|
|
if slider_ch is not None:
|
|
# 滑块: 单个元素, elem_id / data_type 都取自课题配置的 move_cfg
|
|
ans_list = slider_ch.answer(points[0][0], points[0][1])
|
|
else:
|
|
ans_list = [{"elem_id": i + 1, "type": "DynAnswerType_POS",
|
|
"data": f"{p[0]},{p[1]}"} for i, p in enumerate(points)]
|
|
ans_str = json.dumps(ans_list, separators=(",", ":"))
|
|
telemetry = generate_telemetry(
|
|
tdc_code or "", points, aid=self.aid, user_agent=self.user_agent,
|
|
referrer=self.referer, mode=self.telemetry_mode,
|
|
trajectory=drag_traj,
|
|
)
|
|
logger.info("遥测密文来源=%s collect_len=%s eks_len=%s",
|
|
telemetry.get("source"), len(telemetry["collect"]),
|
|
len(telemetry.get("eks", "")))
|
|
verify_body = {
|
|
"ans": ans_str,
|
|
"collect": telemetry["collect"],
|
|
"tlg": telemetry["tlg"],
|
|
"eks": telemetry.get("eks") or eks or "",
|
|
"sess": challenge.sess,
|
|
"tkid": tkid,
|
|
"pow_answer": pow_answer or "",
|
|
"pow_calc_time": str(pow_calc_time or 1),
|
|
}
|
|
verify_resp = self.http_client.post(
|
|
"/cap_union_new_verify",
|
|
data=verify_body,
|
|
headers={
|
|
"Content-Type": "application/x-www-form-urlencoded; charset=UTF-8",
|
|
"Accept": "application/json, text/javascript, */*; q=0.01",
|
|
"Origin": "https://turing.captcha.gtimg.com",
|
|
"Referer": "https://turing.captcha.gtimg.com/",
|
|
},
|
|
)
|
|
v_json = verify_resp.json()
|
|
last_raw = v_json
|
|
elapsed = int((time.time() - start_time) * 1000)
|
|
|
|
if str(v_json.get("errorCode")) == "0":
|
|
logger.info("✅ 验证通过! randstr=%s", v_json.get("randstr"))
|
|
return {
|
|
"success": True,
|
|
"ticket": v_json.get("ticket", ""),
|
|
"randstr": v_json.get("randstr", ""),
|
|
"elapsed_ms": elapsed,
|
|
"points": points,
|
|
"kind": challenge.kind,
|
|
"instruction": challenge.instruction,
|
|
"raw_response": v_json,
|
|
"error": "",
|
|
}
|
|
|
|
err_code = str(v_json.get("errorCode"))
|
|
if err_code == "12":
|
|
last_error = ("服务端返回 errorCode=12 (腾讯天御风控评估未通过)。"
|
|
"实测与遥测后端 / 出口 IP / 静默时长均无关 "
|
|
"(见 har/1ms_dianxuan/ANALYSIS.md §7), 不要再尝试换环境或换 IP")
|
|
logger.error(last_error)
|
|
break
|
|
if err_code in ("50", "9"):
|
|
last_error = f"坐标未命中 (errorCode: {err_code})"
|
|
else:
|
|
last_error = (f"验证失败 (errorCode: {err_code}, "
|
|
f"msg: {v_json.get('errMessage', '')})")
|
|
logger.warning(last_error)
|
|
|
|
# 服务端返回新 sess 时沿用, 否则回到 prehandle
|
|
new_sess = v_json.get("sess")
|
|
if new_sess:
|
|
sess = new_sess
|
|
challenge = None
|
|
else:
|
|
challenge = None
|
|
sess = None
|
|
eks = None
|
|
pow_answer = None
|
|
except Exception as exc: # 网络/解析异常: 丢弃当前会话重来
|
|
last_error = f"请求异常: {exc}"
|
|
logger.error("第 %d 次尝试异常: %s", attempts + 1, exc)
|
|
challenge = None
|
|
sess = None
|
|
eks = None
|
|
pow_answer = None
|
|
|
|
elapsed = int((time.time() - start_time) * 1000)
|
|
return {
|
|
"success": False,
|
|
"ticket": "",
|
|
"randstr": "",
|
|
"elapsed_ms": elapsed,
|
|
"points": [],
|
|
"kind": challenge.kind if challenge else "",
|
|
"instruction": challenge.instruction if challenge else "",
|
|
"raw_response": last_raw,
|
|
"error": last_error,
|
|
}
|
|
|
|
def close(self):
|
|
self.http_client.close()
|
|
|
|
|
|
def main():
|
|
import argparse
|
|
|
|
parser = argparse.ArgumentParser(description="Tencent Captcha Pure Protocol Solver Client")
|
|
parser.add_argument("--aid", default="195672344", help="验证码 AppID (默认毫秒 1ms.run)")
|
|
parser.add_argument("--entry-url",
|
|
default="https://1ms.run/console/personal/benefits/check-in")
|
|
parser.add_argument("--wb", default="1")
|
|
parser.add_argument("--retries", type=int, default=3)
|
|
parser.add_argument("--telemetry", default="python_values",
|
|
choices=["python_values"],
|
|
help="遥测方式: 只有 python_values（纯离线；模板回填/Node 两条老路已删除）")
|
|
parser.add_argument("--proxy", default=None, help="出口代理, 如 http://1.2.3.4:8080")
|
|
parser.add_argument("--proxy-api", default=None,
|
|
help="代理提取 API (可选; 注意换 IP 并不能解决 errorCode=12)")
|
|
args = parser.parse_args()
|
|
|
|
proxy = args.proxy
|
|
if not proxy and args.proxy_api:
|
|
from tcaptcha.protocol.proxy import fetch_proxy
|
|
|
|
proxy = fetch_proxy(args.proxy_api)
|
|
print(f"[*] 从代理 API 提取到: {proxy}")
|
|
|
|
client = TencentCaptchaClient(aid=args.aid, entry_url=args.entry_url,
|
|
referer="https://1ms.run/", wb=args.wb,
|
|
proxy=proxy, telemetry_mode=args.telemetry)
|
|
print(f"[*] 正在发起腾讯天御验证码纯协议识别 (AppID: {args.aid}, proxy={proxy})...")
|
|
result = client.solve(max_retries=args.retries)
|
|
|
|
# 注意: errorCode=12 实测与出口 IP 无关 (ANALYSIS.md §7), 这里不再自动换 IP。
|
|
# 如需指定出口 IP, 请在启动时用 --proxy / --proxy-api。
|
|
|
|
if result["success"]:
|
|
print("\n" + "=" * 50)
|
|
print(" 🎉 验证码纯协议求解成功！")
|
|
print("=" * 50)
|
|
print(f"耗时: {result['elapsed_ms']} ms")
|
|
print(f"题型: {result['kind']} 题目: {result['instruction']!r}")
|
|
print(f"点击坐标: {result['points']}")
|
|
print(f"randstr: {result['randstr']}")
|
|
print(f"ticket: {result['ticket']}")
|
|
print("=" * 50)
|
|
else:
|
|
print(f"\n❌ 验证失败: {result['error']}")
|
|
sys.exit(1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|