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/__init__.py

@@ -0,0 +1,15 @@
+# -*- coding: utf-8 -*-
+"""Tencent Captcha Package"""
+
+from .client import TencentCaptchaClient
+from .crypto import extract_eks, generate_collect
+from .slicer import slice_prompt_image
+from .solver import CaptchaSolver
+
+__all__ = [
+    "TencentCaptchaClient",
+    "CaptchaSolver",
+    "extract_eks",
+    "generate_collect",
+    "slice_prompt_image",
+]

tcaptcha/server.py

@@ -0,0 +1,149 @@
+# -*- coding: utf-8 -*-
+"""
+腾讯天御点选验证码纯协议 HTTP 微服务 (Tencent Turing Captcha HTTP Microservice)
+用于在独立机器/宿主机/GPU服务器上运行，供青龙容器或其他轻量级客户端通过 HTTP API 跨机调用。
+基于 Python 原生 ThreadingHTTPServer 实现，零额外依赖 (无需额外安装 FastAPI/Flask)。
+
+使用方式:
+  uv run python -m tcaptcha.server --port 8000 --host 0.0.0.0
+调用接口:
+  POST http://<IP>:8000/solve
+  请求体 (可选): {"aid": "2039519451"}
+  返回 JSON:
+  {
+      "code": 200,
+      "success": true,
+      "ticket": "tr03...",
+      "randstr": "@...",
+      "elapsed_ms": 820
+  }
+"""
+
+import argparse
+import json
+import logging
+import sys
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from tcaptcha.client import TencentCaptchaClient
+from tcaptcha.solver import CaptchaSolver
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    datefmt="%H:%M:%S",
+)
+logger = logging.getLogger("tcaptcha_server")
+
+GLOBAL_SOLVER = None
+
+
+def get_solver() -> CaptchaSolver:
+    global GLOBAL_SOLVER
+    if GLOBAL_SOLVER is None:
+        logger.info("正在初始化并预加载 AI 求解推理引擎 (YOLOv8 + ResNet18)...")
+        GLOBAL_SOLVER = CaptchaSolver()
+        logger.info("✓ AI 推理引擎预加载完成！")
+    return GLOBAL_SOLVER
+
+
+class CaptchaHttpHandler(BaseHTTPRequestHandler):
+    def _send_json(self, code: int, data: dict):
+        body = json.dumps(data, ensure_ascii=False).encode("utf-8")
+        self.send_response(code)
+        self.send_header("Content-Type", "application/json; charset=utf-8")
+        self.send_header("Content-Length", str(len(body)))
+        self.send_header("Access-Control-Allow-Origin", "*")
+        self.send_header("Access-Control-Allow-Headers", "*")
+        self.send_header("Access-Control-Allow-Methods", "GET, POST, OPTIONS")
+        self.end_headers()
+        self.wfile.write(body)
+
+    def do_OPTIONS(self):
+        self._send_json(200, {"status": "ok"})
+
+    def do_GET(self):
+        if self.path in ["/health", "/"]:
+            self._send_json(200, {
+                "status": "online",
+                "service": "Tencent Captcha Solver Microservice",
+                "endpoints": {
+                    "solve": "POST /solve (body: {'aid': '2039519451'})",
+                    "health": "GET /health"
+                }
+            })
+        elif self.path.startswith("/solve"):
+            # 允许通过 GET 快速调试
+            self._handle_solve({"aid": "2039519451"})
+        else:
+            self._send_json(404, {"error": "Not Found"})
+
+    def do_POST(self):
+        if self.path.startswith("/solve"):
+            content_length = int(self.headers.get("Content-Length", 0))
+            body = self.rfile.read(content_length) if content_length > 0 else b"{}"
+            try:
+                params = json.loads(body.decode("utf-8")) if body else {}
+            except Exception:
+                params = {}
+            self._handle_solve(params)
+        else:
+            self._send_json(404, {"error": "Not Found"})
+
+    def _handle_solve(self, params: dict):
+        aid = params.get("aid") or "2039519451"
+        logger.info(f"收到求解请求 (AppID: {aid})...")
+        solver = get_solver()
+        client = TencentCaptchaClient(aid=aid, solver=solver)
+        result = client.solve()
+
+        if result.get("success"):
+            self._send_json(200, {
+                "code": 200,
+                "success": True,
+                "ticket": result.get("ticket"),
+                "randstr": result.get("randstr"),
+                "elapsed_ms": result.get("elapsed_ms"),
+                "points": result.get("points"),
+            })
+        else:
+            self._send_json(500, {
+                "code": 500,
+                "success": False,
+                "error": result.get("error"),
+                "elapsed_ms": result.get("elapsed_ms"),
+            })
+
+    def log_message(self, format, *args):
+        logger.info("%s - %s", self.address_string(), format % args)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Tencent Captcha Microservice")
+    parser.add_argument("--host", type=str, default="0.0.0.0", help="Listen host (default: 0.0.0.0)")
+    parser.add_argument("--port", type=int, default=8000, help="Listen port (default: 8000)")
+    args = parser.parse_args()
+
+    # 预加载推理引擎
+    get_solver()
+
+    server_address = (args.host, args.port)
+    httpd = ThreadingHTTPServer(server_address, CaptchaHttpHandler)
+    logger.info(f"🚀 验证码微服务已启动: http://{args.host}:{args.port}")
+    logger.info("可用于青龙面板配置环境变量: TCAPTCHA_API_URL=http://<IP>:%d/solve", args.port)
+
+    try:
+        httpd.serve_forever()
+    except KeyboardInterrupt:
+        logger.info("服务已停止")
+    finally:
+        httpd.server_close()
+
+
+if __name__ == "__main__":
+    main()

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)"""