feat(slider): 滑块 (2401/2402) 端到端打通 —— errorCode=0

做了什么: - solvers/slider.py: 新增 SliderChallenge（把课题 JSON 解析成解题参数）+ piece_from_sprite （按 sprite_pos/size_2d 裁拼图块）+ track_limit 解析 / clamp_x / answer（按 data_type 组答案） - telemetry/values.py: 新增 drag_trajectory_points（拖动轨迹：按下->横移->松手，首点绝对、其余增量、尾点固定 [1,1,12]）；_sample_dt 抽成模块级供两条轨迹复用 - telemetry/{values,crypto}.py: generate_collect / generate_telemetry 接受 trajectory= - protocol/client.py: 新增滑块分支（拉底图 + 精灵图 -> 裁块 -> ZNCC -> 组答案 -> 拖动轨迹）， accept_kinds 默认扩为文字点选 + 图标点选 + 滑块 - scripts/verify/send_test.py: 加 --kind text|slider|icon|any 关键结论（账本 #60~#63）: - 滑块答案是 DynAnswerType_POS，但坐标用**背景图原始像素**（不是页面显示像素）：所以解题用 scale=1.0 直接提交 target_x_image；elem_id / data_type 均取自 move_cfg - 拼图块可从课题配置直接裁，与旧 DOM 换算结果**逐像素相同**（测试已加交叉断言） - **ddddocr 的滑块距离识别不可用**：fixture 偏 59px、实时偏 60px。判定手段是把拼图块 alpha 轮廓叠到背景上目视 —— 本项目 ZNCC 与缺口描边严丝合缝，ddddocr 整体右偏 - 低置信度（margin=0.024 < 0.08）那次被正确拒答并换题，换题后立刻成功，安全网有效怎么验证: - .venv/bin/python -m tcaptcha.tests.test_slider_solver 全绿（新增 [6]/[7] 两组断言） - .venv/bin/python -m tcaptcha.selftest 全绿 - .venv/bin/python scripts/verify/send_test.py --kind slider 3/3 errorCode=0 - 文字点选回归不受影响：--kind text 仍为 errorCode=0
2026-10-12 12:46:35 +08:00
parent 344eea2194
commit 6a8b3b3ce4
7 changed files with 294 additions and 33 deletions
@@ -40,6 +40,8 @@ if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))

 from tcaptcha.telemetry.crypto import extract_eks, generate_telemetry
+from tcaptcha.telemetry.values import drag_trajectory_points
+from tcaptcha.solvers.slider import SliderChallenge, SliderSolver, piece_from_sprite
 from tcaptcha.solvers.text_click import TextClickSolver, parse_instruction

 logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
@@ -144,7 +146,7 @@ class TencentCaptchaClient:
        referer: Optional[str] = None,
        wb: str = "2",
        js: str = "/tcaptcha-frame.ba387dd1.js",
-        accept_kinds: Sequence[str] = (KIND_TEXT_CLICK, KIND_ICON_CLICK),
+        accept_kinds: Sequence[str] = (KIND_TEXT_CLICK, KIND_ICON_CLICK, KIND_SLIDER),
        min_posterior: float = 0.95,
        pow_timeout: int = 4000000,
        inter_attempt_delay: float = 0.25,
@@ -200,6 +202,7 @@ class TencentCaptchaClient:
        # 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)
        self._icon_solver = solver
        self._text_solver = text_solver
+        self._slider_solver: Optional[SliderSolver] = None
        self.subsid = 1

        # 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)。
@@ -263,6 +266,12 @@ class TencentCaptchaClient:
            self._icon_solver = CaptchaSolver()
        return self._icon_solver

+    @property
+    def slider_solver(self) -> SliderSolver:
+        if self._slider_solver is None:
+            self._slider_solver = SliderSolver()
+        return self._slider_solver
+
    def _solve_pow(self, prefix: str, target_md5: str) -> Tuple[str, int]:
        """求解 Proof of Work 哈希碰撞"""
        t0 = time.time()
@@ -444,7 +453,9 @@ class TencentCaptchaClient:
                    continue

                # ---- 视觉求解
-                points: List[Tuple[int, int]]
+                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)

@@ -491,7 +502,7 @@ class TencentCaptchaClient:
                        time.sleep(wait_refresh)
                        continue
                    points = result.points
-                else:  # KIND_ICON_CLICK
+                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:
@@ -511,6 +522,58 @@ class TencentCaptchaClient:
                        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
@@ -522,12 +585,17 @@ class TencentCaptchaClient:

                # ---- 组包并提交
                attempts += 1
-                ans_list = [{"elem_id": i + 1, "type": "DynAnswerType_POS",
-                             "data": f"{p[0]},{p[1]}"} for i, p in enumerate(points)]
+                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"]),
@@ -30,8 +30,9 @@
 """
 from __future__ import annotations

+import re
 from dataclasses import dataclass, field
-from typing import List, Optional, Sequence, Tuple
+from typing import Dict, List, Optional, Sequence, Tuple

 import cv2
 import numpy as np
@@ -142,6 +143,86 @@ def _peaks(curve: np.ndarray, neighbor: int, limit: int = 5) -> List[Tuple[int,
    return picked


+def piece_from_sprite(sprite: np.ndarray, rect: Sequence[int]) -> np.ndarray:
+    """按课题配置里的 `sprite_pos` / `size_2d` 从精灵图裁出拼图块（保留 alpha）。"""
+    x, y, w, h = (int(v) for v in rect)
+    if w <= 0 or h <= 0:
+        raise ValueError(f"精灵图矩形非法: {rect!r}")
+    return sprite[y:y + h, x:x + w]
+
+
+def _parse_track_limit(expr: str) -> Optional[Tuple[float, float]]:
+    """把 `x>=50&&x<=552` 解析成 (50.0, 552.0)；解析不出来返回 None。"""
+    nums = re.findall(r"x\s*[<>=]+\s*(-?\d+(?:\.\d+)?)", expr or "")
+    if len(nums) >= 2:
+        return float(nums[0]), float(nums[1])
+    return None
+
+
+@dataclass
+class SliderChallenge:
+    """滑块参数 —— 全部来自课题 JSON，不依赖 DOM。
+
+    真机结构（aid=195672344，subcapclass 2401/2402 实测）:
+
+        bg_elem_cfg : {img_url, size_2d:[672,480], sprite_pos:[0,0]}
+        sprite_url  : 精灵图（底图与拼图块、滑轨都在这一张里）
+        fg_elem_list: [滑轨(id=3, size 672x32), 拼图块(id=1, 带 move_cfg)]
+        拼图块.move_cfg = {track_limit:"x>=50&&x<=552", move_factor:[1,0],
+                           data_type:["DynAnswerType_POS"]}
+
+    坐标口径（关键）:
+        `init_pos` 与**提交答案里的坐标**都是「背景图原始像素」（即 `size_2d` 那套），
+        不是页面显示像素 —— 所以解题时用 `scale=1.0`，直接拿 target_x_image 组答案。
+    """
+
+    bg_url: str
+    sprite_url: str
+    sprite_rect: Tuple[int, int, int, int]
+    init_pos: Tuple[float, float]
+    elem_id: int
+    data_type: str
+    bg_size: Tuple[int, int]
+    track_limit: Optional[Tuple[float, float]] = None
+
+    @classmethod
+    def from_challenge(cls, raw: Dict) -> "SliderChallenge":
+        bg_cfg = raw.get("bg_elem_cfg") or {}
+        fgs = raw.get("fg_elem_list") or []
+        move_el = next((e for e in fgs if e.get("move_cfg")), None)
+        if move_el is None or not raw.get("sprite_url") or not bg_cfg.get("img_url"):
+            raise ValueError(
+                "课题配置里没有滑块元素（缺 move_cfg / sprite_url / bg_elem_cfg.img_url）")
+        sx, sy = (move_el.get("sprite_pos") or (0, 0))[:2]
+        sw, sh = (move_el.get("size_2d") or (0, 0))[:2]
+        init = (move_el.get("init_pos") or (0, 0))
+        mc = move_el.get("move_cfg") or {}
+        data_types = mc.get("data_type") or []
+        size2d = (bg_cfg.get("size_2d") or (0, 0))
+        return cls(
+            bg_url=str(bg_cfg["img_url"]),
+            sprite_url=str(raw["sprite_url"]),
+            sprite_rect=(int(sx), int(sy), int(sw), int(sh)),
+            init_pos=(float(init[0]), float(init[1])),
+            elem_id=int(move_el.get("id", 1)),
+            data_type=str(data_types[0]) if data_types else "DynAnswerType_POS",
+            bg_size=(int(size2d[0]), int(size2d[1])),
+            track_limit=_parse_track_limit(str(mc.get("track_limit") or "")),
+        )
+
+    def answer(self, target_x: float, y: float) -> List[Dict]:
+        """按 `move_cfg.data_type` 组提交用的 ans（坐标 = 背景图原始像素）。"""
+        return [{"elem_id": self.elem_id, "type": self.data_type,
+                 "data": "%d,%d" % (int(round(target_x)), int(round(y)))}]
+
+    def clamp_x(self, x: float) -> float:
+        """夹到 track_limit 允许的范围内（配置没给就不动）。"""
+        if not self.track_limit:
+            return float(x)
+        lo, hi = self.track_limit
+        return float(min(max(x, lo), hi))
+
+
@dataclass
 class SliderResult:
    """滑块求解结果。坐标均为**背景图原始像素**, 除 dx_display 外。"""
@@ -298,6 +298,7 @@ def generate_telemetry(
    referrer: str = "https://1ms.run/console/personal/benefits/check-in",
    mode: str = "python_values",
    node_bin: str = "node",
+    trajectory: Optional[List[List[int]]] = None,
 ) -> Dict[str, object]:
    """统一遥测生成入口 —— **纯离线**（唯一模式 `python_values`）。

@@ -318,7 +319,8 @@ def generate_telemetry(

    from .values import generate_collect

-    out = generate_collect(tdc_js_code, click_points, referrer=referrer)
+    out = generate_collect(tdc_js_code, click_points, referrer=referrer,
+                           trajectory=trajectory)
    return {"collect": out["collect"], "tlg": out["tlg"], "eks": out["eks"],
            "source": out["source"], "key": out["key"]}

@@ -97,6 +97,26 @@ def default_trajectory() -> List[List[int]]:
    return [[1, 1, 12]]


+def _sample_dt() -> int:
+    """采样一个事件间隔（ms）。分布按真机成功样本拟合：浏览器合并事件会出现 0。"""
+    r = random.random()
+    if r < 0.50:
+        return random.randint(0, 16)
+    if r < 0.75:
+        return random.randint(17, 24)
+    if r < 0.90:
+        return random.randint(25, 60)
+    if r < 0.98:
+        return random.randint(61, 160)
+    return random.randint(161, 400)
+
+
+def _to_display(x: float, y: float, img_w: int) -> Tuple[float, float]:
+    """背景图原始像素 -> iframe 显示坐标（与真机点击轨迹同一套口径）。"""
+    scale = 320.0 / img_w
+    return 20 + x * scale, 60 + y * scale
+
+
 def trajectory_points(click_points: Sequence[Sequence[int]],
                      img_w: int = 672) -> List[List[int]]:
    """按点击点生成鼠标轨迹：首点绝对坐标 + 增量点 + 固定收尾 `[1,1,12]`。
@@ -114,31 +134,43 @@ def trajectory_points(click_points: Sequence[Sequence[int]],
    cur = [random.randint(140, 220), random.randint(300, 340)]
    events: List[List[int]] = [[cur[0], cur[1], random.randint(300, 600)]]

-    def dt() -> int:
-        r = random.random()
-        if r < 0.50:
-            return random.randint(0, 16)
-        if r < 0.75:
-            return random.randint(17, 24)
-        if r < 0.90:
-            return random.randint(25, 60)
-        if r < 0.98:
-            return random.randint(61, 160)
-        return random.randint(161, 400)
-
    for tx, ty in targets:
        seg = random.randint(14, 20)
        for i in range(1, seg + 1):
            nx = cur[0] + (tx - cur[0]) * i / seg + random.randint(-1, 1)
            ny = cur[1] + (ty - cur[1]) * i / seg + random.randint(-1, 1)
-            events.append([int(nx) - cur[0], int(ny) - cur[1], dt()])
+            events.append([int(nx) - cur[0], int(ny) - cur[1], _sample_dt()])
            cur = [int(nx), int(ny)]
        for _ in range(random.randint(2, 5)):        # 命中后短暂驻留
-            events.append([random.randint(-1, 1), random.randint(-1, 1), dt()])
+            events.append([random.randint(-1, 1), random.randint(-1, 1), _sample_dt()])
    events.append([1, 1, 12])                        # 固定收尾（真机如此）
    return events


+def drag_trajectory_points(start: Sequence[float], end: Sequence[float],
+                           img_w: int = 672, steps: int = 30) -> List[List[int]]:
+    """滑块**拖动**轨迹：按下 -> 横向移动 -> 松手。
+
+    与 `trajectory_points` 同一套口径（输入是背景图原始像素，输出是 iframe 显示坐标），
+    同样以固定 `[1,1,12]` 收尾。速度曲线用 ease-out：人手拖动是先快后慢、末尾微调。
+    """
+    sx, sy = _to_display(float(start[0]), float(start[1]), img_w)
+    ex, ey = _to_display(float(end[0]), float(end[1]), img_w)
+    cur = [int(round(sx)), int(round(sy))]
+    events: List[List[int]] = [[cur[0], cur[1], random.randint(300, 600)]]   # 按下前停顿
+    for i in range(1, steps + 1):
+        t = i / steps
+        e = 1.0 - (1.0 - t) ** 3
+        nx = int(round(sx + (ex - sx) * e)) + random.randint(-1, 1)
+        ny = int(round(sy + (ey - sy) * e)) + random.randint(-1, 1)
+        events.append([nx - cur[0], ny - cur[1], _sample_dt()])
+        cur = [nx, ny]
+    for _ in range(random.randint(2, 4)):            # 落点附近的微调
+        events.append([random.randint(-1, 1), random.randint(-1, 1), _sample_dt()])
+    events.append([1, 1, 12])
+    return events
+
+
 def rows_from_source(js_text: str) -> List[Dict]:
    """分析 tdc.js **源码**（tdc_modules 的入口收的是路径，这里落临时文件再分析）。"""
    import os
@@ -175,11 +207,14 @@ def generate_collect(tdc_js_code: str,
                     sd: Optional[Dict] = None,
                     session: Optional[Session] = None,
                     img_w: int = 672,
-                     referrer: Optional[str] = None) -> Dict[str, Any]:
+                     referrer: Optional[str] = None,
+                     trajectory: Optional[Sequence[Sequence[int]]] = None) -> Dict[str, Any]:
    """**纯离线**生成一颗 collect：只吃 tdc.js 源码，不需要任何真机明文。

    流程: tdc.js -> 37 个语义名（清单序）-> 38 槽取值 -> 4 段明文 -> 4 段加密拼接。
    密钥默认用纯 Python 静态提取（`tcaptcha.telemetry.key`），零 Node。
+
+    trajectory 直接给轨迹点（滑块用 `drag_trajectory_points`）；不给则按 click_points 推。
    """
    from tcaptcha.telemetry.crypto import build_collect, extract_eks          # 局部导入避免环形依赖
    from tcaptcha.telemetry.key import extract_xtea_key_pure
@@ -188,7 +223,9 @@ def generate_collect(tdc_js_code: str,
    if key is None:
        key = extract_xtea_key_pure(tdc_js_code)["key"]
    sess = session or Session(referrer=referrer or DEFAULT_REFERRER)
-    if click_points:
+    if trajectory is not None:
+        sess.set_trajectory(trajectory)
+    elif click_points:
        sess.set_trajectory(trajectory_points(click_points, img_w=img_w))

    cd = generate_cd(rows, sess)
@@ -28,15 +28,35 @@ import sys
 from pathlib import Path

 import cv2
+import numpy as np

 ROOT = Path(__file__).resolve().parents[2]
 if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

-from tcaptcha.solvers.slider import SliderSolver, piece_mask, sprite_region  # noqa: E402
+from tcaptcha.solvers.slider import (  # noqa: E402
+    SliderChallenge, SliderSolver, piece_mask, piece_from_sprite, sprite_region,
+)
+from tcaptcha.telemetry.values import drag_trajectory_points  # noqa: E402

 FIX = ROOT / "tcaptcha" / "tests" / "fixtures"

+# 真机课题配置快照（aid=195672344 下滑块 2401/2402 的 dyn_show_info，
+# 只把两个很长的 image/sess 参数截短，字段结构一字未改）
+REAL_SLIDER_CHALLENGE = {
+    "lang": "zh-cn",
+    "instruction": "拖动下方滑块完成拼图",
+    "bg_elem_cfg": {"sprite_pos": [0, 0], "size_2d": [672, 480],
+                    "img_url": "/cap_union_new_getcapbysig?img_index=1&image=XX&sess=YY"},
+    "sprite_url": "/cap_union_new_getcapbysig?img_index=0&image=XX&sess=YY",
+    "fg_elem_list": [
+        {"id": 3, "sprite_pos": [0, 422], "size_2d": [672, 32], "init_pos": [0, 422]},
+        {"id": 1, "sprite_pos": [140, 490], "size_2d": [120, 120], "init_pos": [50, 128],
+         "move_cfg": {"track_limit": "x>=50&&x<=552", "move_factor": [1, 0],
+                      "data_type": ["DynAnswerType_POS"]}},
+    ],
+}
+
 # 真机 DOM 口径
 BG_POS = (-70.8333, -247.917)
 BG_SIZE = (345.06, 313.69)
@@ -59,6 +79,34 @@ def check(name: str, cond: bool, detail: str = "") -> None:
        _fails.append(name)


+def test_challenge_config() -> None:
+    """课题配置 -> 解题参数 -> 提交答案（滑块端到端里最容易出错的一环）。"""
+    print("\n[6] 课题配置解析 (SliderChallenge)")
+    sc = SliderChallenge.from_challenge(REAL_SLIDER_CHALLENGE)
+    check("sprite_rect == (140,490,120,120)", sc.sprite_rect == (140, 490, 120, 120),
+          str(sc.sprite_rect))
+    check("init_pos == (50,128)", sc.init_pos == (50.0, 128.0), str(sc.init_pos))
+    check("elem_id == 1（拼图块，不是滑轨的 3）", sc.elem_id == 1, str(sc.elem_id))
+    check("data_type == DynAnswerType_POS", sc.data_type == "DynAnswerType_POS", sc.data_type)
+    check("track_limit == (50,552)", sc.track_limit == (50.0, 552.0), str(sc.track_limit))
+    check("bg_size == (672,480)", sc.bg_size == (672, 480), str(sc.bg_size))
+    check("clamp_x 把越界值夹回轨道", sc.clamp_x(600) == 552.0 and sc.clamp_x(10) == 50.0,
+          f"{sc.clamp_x(600)} / {sc.clamp_x(10)}")
+
+    ans = sc.answer(343.0, 128.4)
+    check("answer 结构 == [{elem_id,type,data}]",
+          ans == [{"elem_id": 1, "type": "DynAnswerType_POS", "data": "343,128"}], str(ans))
+
+    print("\n[7] 拖动轨迹 (drag_trajectory_points)")
+    traj = drag_trajectory_points(sc.init_pos, (343.0, 128.0), img_w=sc.bg_size[0])
+    check("首点是绝对坐标", len(traj[0]) == 3 and traj[0][0] > 20, str(traj[0]))
+    check("尾点固定 [1,1,12]", traj[-1] == [1, 1, 12], str(traj[-1]))
+    net = sum(p[0] for p in traj[1:-1])
+    expect = (343.0 - 50.0) * (320.0 / 672.0)
+    check("净横向位移 ≈ dx*scale", abs(net - expect) <= 6, f"{net} vs {expect:.1f}")
+    check("点数在合理范围", 20 <= len(traj) <= 50, str(len(traj)))
+
+
 def main() -> int:
    bg = cv2.imread(str(FIX / "1ms_slider_1_bg.jpg"))
    sprite = cv2.imread(str(FIX / "1ms_slider_1_sprite.png"), cv2.IMREAD_UNCHANGED)
@@ -72,6 +120,9 @@ def main() -> int:
    check("sprite_region == (140, 490, 120, 120)", (x, y, w, h) == (140, 490, 120, 120), f"got={(x, y, w, h)}")

    crop = sprite[y:y + h, x:x + w]
+    # 交叉核对：课题配置里直接给的 sprite 矩形，应当与 DOM 换算结果完全一致
+    crop_cfg = piece_from_sprite(sprite, (140, 490, 120, 120))
+    check("配置矩形裁出的块 == DOM 换算裁出的块", np.array_equal(crop, crop_cfg))
    mask = piece_mask(crop)
    check("拼图块掩膜非空且不过分满", 500 < int(mask.sum()) < w * h * 0.9,
          f"mask={int(mask.sum())}/{w * h}")
@@ -103,6 +154,8 @@ def main() -> int:
    check("拼图块自身位置的分数很低", all(s < 0.3 for s in near_piece),
          f"{near_piece}")

+    test_challenge_config()
+
    print(f"\n{'=' * 62}")
    if _fails:
        print(f"❌ slider_solver 测试失败: {_fails}")