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>

models/gap_detector.py

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+"""
+滑块缺口检测 CNN (GapDetectorCNN)
+
+用于检测滑块验证码中缺口的 x 坐标位置。
+输出 sigmoid 归一化到 [0,1]，推理时按图片宽度缩放回像素坐标。
+
+架构:
+    Conv(1→32) + BN + ReLU + Pool
+    Conv(32→64) + BN + ReLU + Pool
+    Conv(64→128) + BN + ReLU + Pool
+    Conv(128→128) + BN + ReLU + Pool
+    AdaptiveAvgPool2d(1) → FC(128→64) → ReLU → Dropout(0.2) → FC(64→1) → Sigmoid
+
+约 250K 参数，~1MB。
+"""
+
+import torch
+import torch.nn as nn
+
+
+class GapDetectorCNN(nn.Module):
+    """
+    滑块缺口检测 CNN，输出缺口 x 坐标的归一化百分比 [0,1]。
+
+    与 RegressionCNN 架构相同，但语义上专用于滑块缺口检测，
+    默认输入尺寸 1x128x256 (灰度)。
+    """
+
+    def __init__(self, img_h: int = 128, img_w: int = 256):
+        super().__init__()
+        self.img_h = img_h
+        self.img_w = img_w
+
+        self.features = nn.Sequential(
+            # block 1: 1 → 32, H/2, W/2
+            nn.Conv2d(1, 32, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(32),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(2, 2),
+
+            # block 2: 32 → 64, H/4, W/4
+            nn.Conv2d(32, 64, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(2, 2),
+
+            # block 3: 64 → 128, H/8, W/8
+            nn.Conv2d(64, 128, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(2, 2),
+
+            # block 4: 128 → 128, H/16, W/16
+            nn.Conv2d(128, 128, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(2, 2),
+        )
+
+        self.pool = nn.AdaptiveAvgPool2d(1)
+
+        self.regressor = nn.Sequential(
+            nn.Linear(128, 64),
+            nn.ReLU(inplace=True),
+            nn.Dropout(0.2),
+            nn.Linear(64, 1),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x: (batch, 1, H, W) 灰度图
+
+        Returns:
+            output: (batch, 1) sigmoid 输出 [0, 1]，表示缺口 x 坐标百分比
+        """
+        feat = self.features(x)
+        feat = self.pool(feat)          # (B, 128, 1, 1)
+        feat = feat.flatten(1)          # (B, 128)
+        out = self.regressor(feat)      # (B, 1)
+        return out