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>

inference/pipeline.py

@@ -4,7 +4,7 @@
 加载调度模型和所有专家模型 (ONNX 格式)，提供统一的 solve(image) 接口。
 
 推理流程:
-    输入图片 → 预处理 → 调度分类器 → 路由到专家模型 → CTC 解码 → 后处理 → 输出
+    输入图片 → 预处理 → 调度分类器 → 路由到专家模型 → CTC 解码 / 回归缩放 → 后处理 → 输出
 
 对算式类型，解码后还会调用 math_eval 计算结果。
 """
@@ -23,6 +23,7 @@ from config import (
     NORMAL_CHARS,
     MATH_CHARS,
     THREED_CHARS,
+    REGRESSION_RANGE,
 )
 from inference.math_eval import eval_captcha_math
 
@@ -59,19 +60,24 @@ class CaptchaPipeline:
         self.mean = INFERENCE_CONFIG["normalize_mean"]
         self.std = INFERENCE_CONFIG["normalize_std"]
 
-        # 字符集映射
+        # 字符集映射 (仅 CTC 模型需要)
         self._chars = {
             "normal": NORMAL_CHARS,
             "math": MATH_CHARS,
-            "3d": THREED_CHARS,
+            "3d_text": THREED_CHARS,
         }
 
+        # 回归模型类型
+        self._regression_types = {"3d_rotate", "3d_slider"}
+
         # 专家模型名 → ONNX 文件名
         self._model_files = {
             "classifier": "classifier.onnx",
             "normal": "normal.onnx",
             "math": "math.onnx",
-            "3d": "threed.onnx",
+            "3d_text": "threed_text.onnx",
+            "3d_rotate": "threed_rotate.onnx",
+            "3d_slider": "threed_slider.onnx",
         }
 
         # 加载所有可用模型
@@ -116,7 +122,7 @@ class CaptchaPipeline:
 
     def classify(self, image: Image.Image) -> str:
         """
-        调度分类，返回类型名: 'normal' / 'math' / '3d'。
+        调度分类，返回类型名。
 
         Raises:
             RuntimeError: 分类器模型未加载
@@ -141,7 +147,7 @@ class CaptchaPipeline:
 
         Args:
             image:        PIL.Image 或文件路径 (str/Path) 或 bytes
-            captcha_type: 指定类型可跳过分类 ('normal'/'math'/'3d')
+            captcha_type: 指定类型可跳过分类 ('normal'/'math'/'3d_text'/'3d_rotate'/'3d_slider')
 
         Returns:
             dict: {
@@ -166,24 +172,34 @@ class CaptchaPipeline:
                 f"专家模型 '{captcha_type}' 未加载，请先训练并导出对应 ONNX 模型"
             )
 
-        size_key = captcha_type  # "normal"/"math"/"3d"
+        size_key = captcha_type
         inp = self.preprocess(img, IMAGE_SIZE[size_key])
         session = self._sessions[captcha_type]
         input_name = session.get_inputs()[0].name
-        logits = session.run(None, {input_name: inp})[0]  # (T, 1, C)
 
-        # 4. CTC 贪心解码
-        chars = self._chars[captcha_type]
-        raw_text = self._ctc_greedy_decode(logits, chars)
-
-        # 5. 后处理
-        if captcha_type == "math":
-            try:
-                result = eval_captcha_math(raw_text)
-            except ValueError:
-                result = raw_text  # 解析失败则返回原始文本
+        # 4. 分支: CTC 解码 vs 回归
+        if captcha_type in self._regression_types:
+            # 回归模型: 输出 (batch, 1) sigmoid 值
+            output = session.run(None, {input_name: inp})[0]  # (1, 1)
+            sigmoid_val = float(output[0, 0])
+            lo, hi = REGRESSION_RANGE[captcha_type]
+            real_val = sigmoid_val * (hi - lo) + lo
+            raw_text = f"{real_val:.1f}"
+            result = str(int(round(real_val)))
         else:
-            result = raw_text
+            # CTC 模型
+            logits = session.run(None, {input_name: inp})[0]  # (T, 1, C)
+            chars = self._chars[captcha_type]
+            raw_text = self._ctc_greedy_decode(logits, chars)
+
+            # 后处理
+            if captcha_type == "math":
+                try:
+                    result = eval_captcha_math(raw_text)
+                except ValueError:
+                    result = raw_text
+            else:
+                result = raw_text
 
         elapsed = (time.perf_counter() - t0) * 1000