feat(rainyun): 完成雨云纯协议自动登录签到脚本及青龙单目录部署包

- 逆向分析雨云登录与签到接口，打通腾讯天御点选验证码全协议过码闭环
- 支持 RAINYUN_ACCOUNT='账号#密码' 极简配置 (多账号支持 & 与换行分割)
- 增加积分提现金额换算展示 (2000积分 = 1元现金) 及青龙系统通知推送
- 新增 dist/rainyun_qinglong 单层平铺部署目录与专属 README
- 增强 solver.py 在模板缺失时的 ResNet18 分类器双模降级能力

tcaptcha/solver.py

@@ -79,19 +79,30 @@ class CaptchaSolver:
     def parse_prompt_targets(self, prompt_img: str | Path | bytes | Image.Image) -> List[int]:
         """
         自动识别并解析提示图中 3 个目标图标的真实类别 ID (0~11)
+        支持干净模板匹配与 ResNet18 分类器自动双模降级
         """
         crops = slice_prompt_image(prompt_img)
-        target_classes = []
-        for crop_img, _ in crops[:3]:
-            arr = np.array(crop_img.convert("L"))
-            bg = np.median(arr)
-            bin_c = cv2.resize(((arr < bg - 20).astype(np.uint8) * 255), (48, 48), interpolation=cv2.INTER_AREA)
+        if not crops:
+            return []
 
-            scores = [cv2.matchTemplate(bin_c, t[1], cv2.TM_CCOEFF_NORMED)[0, 0] for t in self.templates]
-            best_cid = int(np.argmax(scores))
-            target_classes.append(best_cid)
+        if self.templates:
+            target_classes = []
+            for crop_img, _ in crops[:3]:
+                arr = np.array(crop_img.convert("L"))
+                bg = np.median(arr)
+                bin_c = cv2.resize(((arr < bg - 20).astype(np.uint8) * 255), (48, 48), interpolation=cv2.INTER_AREA)
 
-        return target_classes
+                scores = [cv2.matchTemplate(bin_c, t[1], cv2.TM_CCOEFF_NORMED)[0, 0] for t in self.templates]
+                best_cid = int(np.argmax(scores))
+                target_classes.append(best_cid)
+            return target_classes
+
+        # 模板缺失时自动使用 ResNet18 分类器直接预测提示小图类别
+        patch_tensors = torch.stack([self.transform(c[0].convert("RGB")) for c in crops[:3]]).to(self.device)
+        with torch.no_grad():
+            logits = self.classifier(patch_tensors)
+            probs = torch.softmax(logits, dim=1).cpu().numpy()
+        return [int(np.argmax(p[:12])) for p in probs]
 
     def _extract_candidates(self, bg_img: str | Path | bytes | Image.Image) -> List[Tuple[int, int, Image.Image]]:
         """从背景图中提取所有去重后的候选图标 (cx, cy, patch)"""