Add slide and rotate interactive captcha solvers

New solver subsystem with independent models:
- GapDetectorCNN (1x128x256 grayscale → sigmoid) for slide gap detection
- RotationRegressor (3x128x128 RGB → sin/cos via tanh) for rotation angle prediction
- SlideSolver with 3-tier strategy: template match → edge detect → CNN fallback
- RotateSolver with ONNX sin/cos → atan2 inference
- Generators, training scripts, CLI commands, and slide track utility

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

utils/__init__.py

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+"""
+工具函数包
+"""

utils/slide_utils.py

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+"""
+滑块轨迹生成工具
+
+生成模拟人类操作的滑块拖拽轨迹。
+"""
+
+import math
+import random
+
+
+def generate_slide_track(
+    distance: int,
+    duration: float = 1.0,
+    seed: int | None = None,
+) -> list[dict]:
+    """
+    生成滑块拖拽轨迹。
+
+    使用贝塞尔曲线 ease-out 加速减速，末尾带微小过冲回退。
+    y 轴 ±1~3px 随机抖动，时间间隔不均匀。
+
+    Args:
+        distance: 滑动距离 (像素)
+        duration: 总时长 (秒)
+        seed:     随机种子
+
+    Returns:
+        [{"x": float, "y": float, "t": float}, ...]
+    """
+    rng = random.Random(seed)
+
+    if distance <= 0:
+        return [{"x": 0.0, "y": 0.0, "t": 0.0}]
+
+    track = []
+    # 采样点数
+    num_points = rng.randint(30, 60)
+    total_ms = duration * 1000
+
+    # 生成不均匀时间点
+    raw_times = sorted([rng.random() for _ in range(num_points - 2)])
+    times = [0.0] + raw_times + [1.0]
+
+    # 过冲距离
+    overshoot = rng.uniform(2, 6)
+    overshoot_start = 0.85  # 85% 时到达目标 + 过冲
+
+    for t_norm in times:
+        t_ms = round(t_norm * total_ms, 1)
+
+        if t_norm <= overshoot_start:
+            # ease-out: 快速启动，缓慢减速
+            progress = t_norm / overshoot_start
+            eased = 1 - (1 - progress) ** 3  # cubic ease-out
+            x = eased * (distance + overshoot)
+        else:
+            # 过冲回退段
+            retract_progress = (t_norm - overshoot_start) / (1 - overshoot_start)
+            eased_retract = retract_progress ** 2  # ease-in 回退
+            x = (distance + overshoot) - overshoot * eased_retract
+
+        # y 轴随机抖动
+        y_jitter = rng.uniform(-3, 3) if t_norm > 0.05 else 0.0
+
+        track.append({
+            "x": round(x, 1),
+            "y": round(y_jitter, 1),
+            "t": t_ms,
+        })
+
+    # 确保最后一个点精确到达目标
+    track[-1]["x"] = float(distance)
+    track[-1]["y"] = round(rng.uniform(-0.5, 0.5), 1)
+
+    return track