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>

solvers/rotate_solver.py

@@ -0,0 +1,80 @@
+"""
+旋转验证码求解器
+
+ONNX 推理 → (sin, cos) → atan2 → 角度
+"""
+
+import math
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+from config import ONNX_DIR, SOLVER_CONFIG
+from solvers.base import BaseSolver
+
+
+class RotateSolver(BaseSolver):
+    """旋转验证码求解器。"""
+
+    def __init__(self, onnx_path: str | Path | None = None):
+        self.cfg = SOLVER_CONFIG["rotate"]
+        self._onnx_session = None
+        self._onnx_path = Path(onnx_path) if onnx_path else ONNX_DIR / "rotation_regressor.onnx"
+
+    def _load_onnx(self):
+        """延迟加载 ONNX 模型。"""
+        if self._onnx_session is not None:
+            return
+        if not self._onnx_path.exists():
+            raise FileNotFoundError(f"ONNX 模型不存在: {self._onnx_path}")
+        import onnxruntime as ort
+        self._onnx_session = ort.InferenceSession(
+            str(self._onnx_path), providers=["CPUExecutionProvider"]
+        )
+
+    def solve(self, image: Image.Image | str | Path, **kwargs) -> dict:
+        """
+        求解旋转验证码。
+
+        Args:
+            image: 输入图片 (RGB)
+
+        Returns:
+            {"angle": float, "confidence": float}
+        """
+        if isinstance(image, (str, Path)):
+            image = Image.open(str(image)).convert("RGB")
+        else:
+            image = image.convert("RGB")
+
+        self._load_onnx()
+
+        h, w = self.cfg["input_size"]
+
+        # 预处理: RGB resize + normalize
+        img = image.resize((w, h))
+        arr = np.array(img, dtype=np.float32) / 255.0
+        # Normalize per channel: (x - 0.5) / 0.5
+        arr = (arr - 0.5) / 0.5
+        # HWC → CHW → NCHW
+        arr = arr.transpose(2, 0, 1)[np.newaxis, :, :, :]
+
+        outputs = self._onnx_session.run(None, {"input": arr})
+        sin_val = float(outputs[0][0][0])
+        cos_val = float(outputs[0][0][1])
+
+        # atan2 → 角度
+        angle_rad = math.atan2(sin_val, cos_val)
+        angle_deg = math.degrees(angle_rad)
+        if angle_deg < 0:
+            angle_deg += 360.0
+
+        # 置信度: sin^2 + cos^2 接近 1 表示预测稳定
+        magnitude = math.sqrt(sin_val ** 2 + cos_val ** 2)
+        confidence = min(magnitude, 1.0)
+
+        return {
+            "angle": round(angle_deg, 1),
+            "confidence": round(confidence, 3),
+        }