yml2213
99565f65f0
- tcaptcha/web/：FastAPI + SSE + 零构建单页。把同步阻塞的 solve() 包成可观测 后台任务（工作线程 + 全局串行，因 onnxruntime session 非线程安全），用 logging.Handler 零侵入把 client 日志变成事件流；按靶子实测 min_interval 强制节流。 - tcaptcha/render.py：标注图（点击顺序编号 + 质心偏差）与点位量尺，CLI 与 web 共用。 - tcaptcha/targets.py：靶子表提为单一来源（含实测频控间隔 1ms=60s / mliev=3s）。 - fastapi/uvicorn 走 optional extra `web`，核心包零 web 依赖（带回归断言）。 - 修两个真 bug：hook 求解器时 args[0] 是实例自身（真入参从 args[1] 起）； client 默认每次新建 solver，预热实例未被复用（每发重载 56MB ONNX）。 - 复验：icon/text/slider 三题型各拿到 errorCode=0；selftest 全绿；依赖隔离通过。 用法：uv run --extra web python -m tcaptcha.web.app
269 lines
12 KiB
Python
269 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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"""send_campaign.py —— 真实发送**批量实验**（从仓库里跑，不再用 /tmp 临时脚本）。
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和 `send_test.py` 的区别：这个是"打 N 发 + 落图 + 统计学"的实验脚手架，用来判定
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`errorCode` 到底跟踪什么（答案对不对？点位准不准？），并把每次点击的**顺序**画在图上。
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用法:
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# 基线：可复现靶子上连续跑 6 次图标题
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.venv/bin/python scripts/verify/send_campaign.py --target mliev --kind icon --n 6
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# 容差标定：把答案整体平移一个固定偏移（判定服务端命中半径有多紧）
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.venv/bin/python scripts/verify/send_campaign.py --target mliev --kind icon --n 2 --offset 30,30
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.venv/bin/python scripts/verify/send_campaign.py --target mliev --kind icon --n 2 --offset 0,60
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# 负对照：故意交换前两个点（必错 -> 期望 50）
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.venv/bin/python scripts/verify/send_campaign.py --target mliev --kind icon --n 2 --swap
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产物（默认 /tmp/icon_campaign/<时间戳>/）:
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run{i}/bg.jpg 底图
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run{i}/prompt.jpg 提示图（仅图标/文字点选题）
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run{i}/annotated.jpg 底图 + **按点击顺序编号**的红圈（+ 估计的图标质心十字）
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run{i}/points.json 点位 / 每点离图标质心的距离 / errorCode
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summary.json 汇总（含每种 errorCode 计数）
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"""
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from __future__ import annotations
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import argparse
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import base64
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import json
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import sys
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import time
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from pathlib import Path
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from typing import Dict, List, Optional
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import numpy as np
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ROOT = Path(__file__).resolve().parents[2]
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if str(ROOT) not in sys.path:
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sys.path.insert(0, str(ROOT))
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import tcaptcha.protocol.client as client_mod # noqa: E402
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import tcaptcha.solvers.icon_click as icon_mod # noqa: E402
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from tcaptcha.protocol.client import (KIND_ICON_CLICK, KIND_SLIDER, # noqa: E402
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KIND_TEXT_CLICK, TencentCaptchaClient)
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from tcaptcha.render import annotate_points, icon_centroid_offset # noqa: E402
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from tcaptcha.targets import TARGETS, get_target # noqa: E402
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KIND_MAP = {
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"text": (KIND_TEXT_CLICK,),
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"icon": (KIND_ICON_CLICK,),
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"slider": (KIND_SLIDER,),
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"any": (KIND_TEXT_CLICK, KIND_ICON_CLICK, KIND_SLIDER),
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}
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# 点位的"量尺"（点击点 vs 图标暗色笔画质心）与标注图渲染统一在 tcaptcha.render，
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# 与 Web 界面共用同一份实现 —— 别在这里再抄一遍。
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def main() -> int:
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ap = argparse.ArgumentParser(description="真实发送批量实验（打 N 发 + 落图 + 统计）")
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ap.add_argument("--target", default="mliev", choices=list(TARGETS))
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ap.add_argument("--kind", default="icon", choices=list(KIND_MAP))
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ap.add_argument("--n", type=int, default=6)
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ap.add_argument("--offset", default="0,0",
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help="给答案整体平移 DX,DY 像素（容差标定用）")
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ap.add_argument("--swap", action="store_true", help="故意交换前两个点（负对照，必错）")
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ap.add_argument("--max-refreshes", type=int, default=40)
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ap.add_argument("--interval", type=float, default=1.0,
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help="两次求解之间的间隔秒数（1ms 连打会触发频控，建议 3~5）")
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ap.add_argument("--outdir", default=None)
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ap.add_argument("--max-retries", type=int, default=1)
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args = ap.parse_args()
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tgt = get_target(args.target)
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aid, entry, referer, wb = tgt["aid"], tgt["entry_url"], tgt["referer"], tgt["wb"]
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dx, dy = (int(v) for v in args.offset.split(","))
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tag = "%s_%s_n%d_off%s%s" % (args.target, args.kind, args.n, args.offset,
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"_swap" if args.swap else "")
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# 默认落在**项目内**（har/ 只跟踪 .md，所以图片不会进 git，但会留在项目树里便于复核）
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outdir = Path(args.outdir or (ROOT / "har" / "1ms_dianxuan" / "icon_campaign" / tag))
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outdir.mkdir(parents=True, exist_ok=True)
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# ---- hook：抓每次求解的图 + 点位，并施加扰动
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state: Dict[str, object] = {}
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real_icon_solve = icon_mod.CaptchaSolver.solve
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def _as_pil(img):
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from PIL import Image
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import io as _io
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if isinstance(img, Image.Image):
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return img.convert("RGB")
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if isinstance(img, bytes):
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return Image.open(_io.BytesIO(img)).convert("RGB")
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return Image.open(img).convert("RGB")
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def _diag(self, bg, prompt) -> Dict[str, object]:
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"""记录求解器看到的东西：提示目标类 + 每个候选的 top3 类概率。
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用来判"是不是把某个图标认成了别的类/挑错了同类的另一个实例"。
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"""
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try:
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bgr, pim = _as_pil(bg), _as_pil(prompt)
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targets = self.parse_prompt_targets(pim)
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cands = self._extract_candidates(bgr)
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blobs = np.stack([self._preprocess_patch(c[2]) for c in cands],
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axis=0).astype(np.float32)
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logits = self.cls_sess.run(None, {"input": blobs})[0]
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exp = np.exp(logits - np.max(logits, axis=1, keepdims=True))
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probs = exp / np.sum(exp, axis=1, keepdims=True)
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out = []
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for (cx, cy, _p), pr in zip(cands, probs):
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order = np.argsort(pr[:12])[::-1][:3]
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out.append({"xy": [int(cx), int(cy)],
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"top": [[int(i), round(float(pr[i]), 3)] for i in order]})
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return {"targets": targets, "candidates": out}
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except Exception as exc: # noqa: BLE001
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return {"diag_err": str(exc)[:120]}
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def spy(self, bg, prompt):
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pts = list(real_icon_solve(self, bg, prompt))
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state["bg_raw"], state["prompt_raw"] = bg, prompt
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state["diag"] = _diag(self, bg, prompt)
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if pts and (dx or dy):
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pts = [(x + dx, y + dy) for x, y in pts]
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if pts and args.swap and len(pts) >= 2:
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pts = [pts[1], pts[0]] + pts[2:]
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state["points"] = pts
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return pts
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icon_mod.CaptchaSolver.solve = spy
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real_gen = client_mod.generate_telemetry
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last_gen: Dict[str, object] = {}
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def hooked(tdc_code, points, **kw):
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out = real_gen(tdc_code, points, **kw)
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info: Dict[str, object] = {"source": out.get("source"),
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"collect_len": len(out.get("collect") or "")}
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try:
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from tcaptcha.telemetry.crypto import split_collect_segments, xtea_decrypt_bytes
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plain = xtea_decrypt_bytes(base64.b64decode(out["collect"]),
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out["key"]).decode("latin1").rstrip("\x00")
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info["segments"] = [len(s) for s in split_collect_segments(plain)]
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except Exception as exc: # noqa: BLE001
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info["segments_err"] = str(exc)[:160]
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try:
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Path("/tmp/icon_campaign_last_fail.txt").write_bytes(plain.encode("latin1"))
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except Exception: # noqa: BLE001
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pass
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last_gen.clear()
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last_gen.update(info)
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return out
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client_mod.generate_telemetry = hooked
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c = TencentCaptchaClient(aid=aid, entry_url=entry, referer=referer, wb=wb,
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accept_kinds=KIND_MAP[args.kind], telemetry_mode="python_values",
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proxy=None)
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# 记录「这一次求解向服务端发了多少个 prehandle」—— 用来判 errorCode 是否与请求量相关
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counters = {"prehandle": 0}
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real_prehandle = c.prehandle
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def counting_prehandle(tkid):
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counters["prehandle"] += 1
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return real_prehandle(tkid)
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c.prehandle = counting_prehandle
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rows: List[Dict[str, object]] = []
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try:
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for n in range(1, args.n + 1):
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state.clear()
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counters["prehandle"] = 0
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t0 = time.time()
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try:
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res = c.solve(max_retries=args.max_retries, max_refreshes=args.max_refreshes)
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except Exception as exc: # noqa: BLE001
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rows.append({"run": n, "errorCode": "EXC", "err": str(exc)[:80]})
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print("run%-2d EXC %s" % (n, exc))
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continue
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ec = (res.get("raw_response") or {}).get("errorCode")
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pts = list(state.get("points") or [])
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bg_raw, prompt_raw = state.get("bg_raw"), state.get("prompt_raw")
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d = outdir / ("run%d" % n)
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d.mkdir(parents=True, exist_ok=True)
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offsets: List[Optional[float]] = []
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centroids: List[Optional[List[float]]] = []
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if bg_raw is not None:
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from PIL import Image
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import io as _io
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bg_pil = Image.open(_io.BytesIO(bg_raw)).convert("RGB")
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for (x, y) in pts:
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est = icon_centroid_offset(bg_pil, int(x), int(y))
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offsets.append(None if est is None else round(est[2], 1))
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centroids.append(None if est is None else [round(est[0], 1), round(est[1], 1)])
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(d / "bg.jpg").write_bytes(bg_raw)
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(d / "annotated.jpg").write_bytes(
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annotate_points(bg_raw, pts, prompt=prompt_raw, offsets=offsets))
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if prompt_raw is not None:
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(d / "prompt.jpg").write_bytes(prompt_raw)
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(d / "points.json").write_text(json.dumps({
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"run": n, "errorCode": ec, "points": [list(p) for p in pts],
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"centroid_offsets_px": offsets, "centroids": centroids,
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"offset_applied": [dx, dy], "swapped": args.swap,
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"segments": last_gen.get("segments") or last_gen.get("segments_err"),
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"solver_diag": state.get("diag"),
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"prehandles": counters["prehandle"],
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}, ensure_ascii=False, indent=1), encoding="utf-8")
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row = {"run": n, "errorCode": ec, "kind": res.get("kind"),
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"instruction": res.get("instruction"),
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"points": [list(p) for p in pts],
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"collect_len": last_gen.get("collect_len"),
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"off_px": offsets, "secs": round(time.time() - t0, 1),
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"prehandles": counters["prehandle"]}
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rows.append(row)
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print("run%-2d ec=%-4s kind=%-10s 偏离质心px=%-20s prehandle=%-3d points=%s %.0fs" % (
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n, ec, res.get("kind") or "?", str(offsets), counters["prehandle"], pts,
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time.time() - t0))
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time.sleep(max(0.0, args.interval))
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finally:
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c.close()
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icon_mod.CaptchaSolver.solve = real_icon_solve
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client_mod.generate_telemetry = real_gen
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counts: Dict[str, int] = {}
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by_kind: Dict[str, Dict[str, int]] = {}
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for r in rows:
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ec_s = str(r.get("errorCode"))
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counts[ec_s] = counts.get(ec_s, 0) + 1
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k = str(r.get("kind") or "?")
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by_kind.setdefault(k, {})
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by_kind[k][ec_s] = by_kind[k].get(ec_s, 0) + 1
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all_off = [v for r in rows for v in (r.get("off_px") or []) if isinstance(v, (int, float))]
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summary = {
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"target": args.target, "kind": args.kind, "n": args.n,
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"offset_applied": [dx, dy], "swapped": args.swap,
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"errorCode_counts": counts,
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"by_kind": by_kind,
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"centroid_offset_px": {
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"count": len(all_off),
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"min": min(all_off) if all_off else None,
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"max": max(all_off) if all_off else None,
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"mean": round(float(np.mean(all_off)), 1) if all_off else None,
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},
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"rows": rows,
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}
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(outdir / "summary.json").write_text(json.dumps(summary, ensure_ascii=False, indent=1),
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encoding="utf-8")
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print("\n" + "=" * 74)
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print("靶子=%s 题型=%s 次数=%d 答案平移=%s 交换=%s" % (args.target, args.kind, args.n,
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args.offset, args.swap))
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print("errorCode 计数: %s" % json.dumps(counts, ensure_ascii=False))
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if len(by_kind) > 1 or "?" not in by_kind:
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for k in sorted(by_kind):
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print(" 按题型 %-12s %s" % (k, json.dumps(by_kind[k], ensure_ascii=False)))
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if all_off:
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print("点击点偏离图标笔画质心: 均值 %.1fpx / 最小 %.1f / 最大 %.1f（%d 个点）"
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% (np.mean(all_off), min(all_off), max(all_off), len(all_off)))
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print("产物: %s" % outdir)
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print("=" * 74)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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