Expand 3D captcha into three subtypes: 3d_text, 3d_rotate, 3d_slider

Split the single "3d" captcha type into three independent expert models:
- 3d_text: 3D perspective text OCR (renamed from old "3d", CTC-based ThreeDCNN)
- 3d_rotate: rotation angle regression (new RegressionCNN, circular loss)
- 3d_slider: slider offset regression (new RegressionCNN, SmoothL1 loss)

CAPTCHA_TYPES expanded from 3 to 5 classes. Classifier samples updated
to 50000 (10000 per class). New generators, model, dataset, training
utilities, and full pipeline/export/CLI support for all subtypes.

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

training/dataset.py

@@ -1,16 +1,19 @@
 """
 通用 Dataset 类
 
-提供两种数据集：
-- CaptchaDataset:  用于分类器训练 (图片 → 类别标签)
-- CRNNDataset:     用于 CRNN/CTC 识别训练 (图片 → 字符序列编码)
+提供三种数据集：
+- CaptchaDataset:    用于分类器训练 (图片 → 类别标签)
+- CRNNDataset:       用于 CRNN/CTC 识别训练 (图片 → 字符序列编码)
+- RegressionDataset: 用于回归模型训练 (图片 → 数值标签 [0,1])
 
 文件名格式约定: {label}_{任意}.png
   - 分类器: label 可为任意字符，所在子目录名即为类别
   - 识别器: label 即标注内容 (如 "A3B8" 或 "3+8")
+  - 回归器: label 为数值 (如 "135" 或 "87")
 """
 
 import os
+import warnings
 from pathlib import Path
 
 from PIL import Image
@@ -98,7 +101,15 @@ class CRNNDataset(Dataset):
             img = self.transform(img)
 
         # 编码标签为整数序列
-        target = [self.char_to_idx[c] for c in label if c in self.char_to_idx]
+        target = []
+        for c in label:
+            if c in self.char_to_idx:
+                target.append(self.char_to_idx[c])
+            else:
+                warnings.warn(
+                    f"标签 '{label}' 含字符集外字符 '{c}'，已跳过 (文件: {path})",
+                    stacklevel=2,
+                )
         return img, target, label
 
     @staticmethod
@@ -119,7 +130,7 @@ class CaptchaDataset(Dataset):
     """
     分类器训练数据集。
 
-    每个子目录名为类别名 (如 "normal", "math", "3d")，
+    每个子目录名为类别名 (如 "normal", "math", "3d_text")，
     目录内所有 .png 文件属于该类。
     """
 
@@ -157,3 +168,59 @@ class CaptchaDataset(Dataset):
         if self.transform:
             img = self.transform(img)
         return img, label
+
+
+# ============================================================
+# 回归模型用数据集
+# ============================================================
+class RegressionDataset(Dataset):
+    """
+    回归模型数据集 (3d_rotate / 3d_slider)。
+
+    从目录中读取 {value}_{xxx}.png 文件，
+    将 value 解析为浮点数并归一化到 [0, 1]。
+    """
+
+    def __init__(
+        self,
+        dirs: list[str | Path],
+        label_range: tuple[float, float],
+        transform: transforms.Compose | None = None,
+    ):
+        """
+        Args:
+            dirs:        数据目录列表
+            label_range: (min_val, max_val) 标签原始范围
+            transform:   图片预处理/增强
+        """
+        self.label_range = label_range
+        self.lo, self.hi = label_range
+        self.transform = transform
+
+        self.samples: list[tuple[str, float]] = []  # (文件路径, 归一化标签)
+        for d in dirs:
+            d = Path(d)
+            if not d.exists():
+                continue
+            for f in sorted(d.glob("*.png")):
+                raw_label = f.stem.rsplit("_", 1)[0]
+                try:
+                    value = float(raw_label)
+                except ValueError:
+                    continue
+                # 归一化到 [0, 1]
+                norm = (value - self.lo) / max(self.hi - self.lo, 1e-6)
+                norm = max(0.0, min(1.0, norm))
+                self.samples.append((str(f), norm))
+
+    def __len__(self) -> int:
+        return len(self.samples)
+
+    def __getitem__(self, idx: int):
+        import torch
+        path, label = self.samples[idx]
+        img = Image.open(path).convert("RGB")
+        if self.transform:
+            img = self.transform(img)
+        return img, torch.tensor([label], dtype=torch.float32)
+