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

tcaptcha/tests/test_slider_solver.py

@@ -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}")