feat(brain): add single-stream visual prototype (T-017)

Brain/.gitignore

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+__pycache__/
+*.py[cod]
+.venv/
+artifacts/
+*.local.json
+*.url

Brain/.gitkeep

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-

Brain/README.md

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+# YoVision Brain 单路工程原型
+
+T-017 提供一条可见、可重复的工程链路：单路 frame source → 匿名人员检测/fixture → track → 归一化多边形区域 → `zone_entry` 候选事实。它不是生产模型、不是 NVR，也没有 Brain→Bell transport。
+
+## 环境
+
+- Python `3.10.11`
+- NumPy `1.26.4`
+- OpenCV `4.9.0.80`
+
+本任务复用本机已验证版本；新环境显式安装：
+
+```powershell
+python -m pip install -r Brain/requirements-demo.txt
+```
+
+本机冻结的是 `opencv-python`；同一环境只能安装一种提供 `cv2` 命名空间的 OpenCV wheel。不要同时安装标准、headless 和 contrib 变体。后续生产容器若改用 headless，必须在独立任务核对 wheel、许可证与完整回归，不能在本版本号下静默替换包名。
+
+## 启动
+
+无需摄像头的确定性合成回放：
+
+```powershell
+python -m Brain.yovision_brain --source synthetic
+```
+
+打开 `http://127.0.0.1:8090/brain-demo`。合成人员框由 fixture 提供，页面和事件均显式标识，不得拿它证明模型效果。
+
+使用真实流时，把完整 RTSP URL 写入仓库外绝对路径文件。推荐指向 Sense 管理的 MediaMTX path，而不是绕过 Sense 固化摄像头地址：
+
+```powershell
+python -m Brain.yovision_brain --source stream --stream-url-file D:\private\brain-stream.url
+```
+
+URL 文件只允许一行、最多 4096 字节，服务不会在状态、页面和错误中返回其内容。演示服务只接受 `127.0.0.1`、`localhost` 或 `::1`；没有暴露到局域网的开关。
+
+真实流使用 OpenCV 内置 HOG/SVM 人员检测和轻量 centroid tracker，只验证可替换端口与区域链路。它不是 M3 生产模型，不能据此承诺召回率、误报率、GPU 容量或 16/128 路能力。
+
+## 验证
+
+```powershell
+python -m unittest discover -s Brain/tests -p "test_*.py" -v
+python -m compileall -q Brain
+```
+
+单文件 UI 原型位于 `docs/design/brain/index.html`，可直接打开；此时使用明确标识的离线原型数据。由本地服务打开时，同一文件消费回环 API，并用临时页面 token 保护区域写入。
+
+## 边界
+
+- `source_event_id` 由 Brain 产生，平台 `evt_` ULID 由 Bell 产生。
+- 事件只保留在最多 100 项的内存环中；重启即丢失是刻意边界。
+- T-018 冻结并实现身份映射、候选契约、持久 Outbox、HMAC、幂等和 Bell ingress。
+- `D:\OPC\silver_pose` 保持独立，不是本模块的源码目录、运行依赖或模型来源路径。

Brain/__init__.py

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+"""YoVision Brain package root."""

Brain/requirements-demo.txt

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+# T-017 engineering prototype only. Do not infer the production Brain stack.
+numpy==1.26.4
+opencv-python==4.9.0.80

Brain/tests/test_domain.py

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+from __future__ import annotations
+
+import unittest
+from datetime import datetime, timezone
+
+from Brain.yovision_brain.domain import (
+    Box,
+    Detection,
+    Point,
+    Zone,
+    ZoneEntryEvaluator,
+    point_in_polygon,
+    zone_from_payload,
+)
+
+
+class GeometryTests(unittest.TestCase):
+    def setUp(self) -> None:
+        self.zone = Zone(
+            "zone-1",
+            "危险区域",
+            1,
+            (Point(0.4, 0.2), Point(0.8, 0.2), Point(0.8, 0.8), Point(0.4, 0.8)),
+        )
+
+    def test_point_in_polygon_includes_boundary(self) -> None:
+        self.assertTrue(point_in_polygon(Point(0.6, 0.5), self.zone.points))
+        self.assertTrue(point_in_polygon(Point(0.4, 0.5), self.zone.points))
+        self.assertFalse(point_in_polygon(Point(0.2, 0.5), self.zone.points))
+
+    def test_zone_requires_normalized_three_to_thirty_two_points(self) -> None:
+        with self.assertRaisesRegex(ValueError, "3 to 32"):
+            Zone("zone-1", "bad", 1, (Point(0, 0), Point(1, 1)))
+        with self.assertRaisesRegex(ValueError, "within"):
+            Point(1.1, 0.5)
+        with self.assertRaisesRegex(ValueError, "within"):
+            Point(float("nan"), 0.5)
+        with self.assertRaisesRegex(ValueError, "within"):
+            Box(0.1, 0.1, float("inf"), 0.9)
+
+    def test_zone_payload_rejects_unknown_fields_and_increments_version(self) -> None:
+        updated = zone_from_payload(
+            {"name": "新区域", "points": [{"x": 0.1, "y": 0.1}, {"x": 0.9, "y": 0.1}, {"x": 0.5, "y": 0.9}]},
+            self.zone,
+        )
+        self.assertEqual(updated.version, 2)
+        self.assertEqual(updated.name, "新区域")
+        with self.assertRaisesRegex(ValueError, "unknown"):
+            zone_from_payload({"points": [], "tenant_id": "must-not-be-here"}, self.zone)
+
+
+class ZoneEntryTests(unittest.TestCase):
+    def setUp(self) -> None:
+        self.zone = Zone(
+            "zone-1",
+            "危险区域",
+            1,
+            (Point(0.5, 0.2), Point(0.9, 0.2), Point(0.9, 0.9), Point(0.5, 0.9)),
+        )
+        ids = iter(("BRN-0001", "BRN-0002", "BRN-0003"))
+        self.evaluator = ZoneEntryEvaluator("device-ref", True, track_ttl_frames=2, event_id_factory=lambda: next(ids))
+        self.now = datetime(2026, 8, 11, 1, 2, 3, tzinfo=timezone.utc)
+
+    @staticmethod
+    def detection(track: str, center_x: float) -> Detection:
+        return Detection(track, "person", Box(center_x - 0.05, 0.3, center_x + 0.05, 0.8))
+
+    def test_first_seen_inside_does_not_fake_an_entry(self) -> None:
+        events, states = self.evaluator.evaluate(1, self.now, self.zone, [self.detection("P-1", 0.7)])
+        self.assertEqual(events, [])
+        self.assertTrue(states["P-1"])
+
+    def test_entry_fires_once_until_track_exits_and_reenters(self) -> None:
+        self.evaluator.evaluate(1, self.now, self.zone, [self.detection("P-1", 0.3)])
+        events, _ = self.evaluator.evaluate(2, self.now, self.zone, [self.detection("P-1", 0.6)])
+        repeated, _ = self.evaluator.evaluate(3, self.now, self.zone, [self.detection("P-1", 0.7)])
+        self.evaluator.evaluate(4, self.now, self.zone, [self.detection("P-1", 0.3)])
+        reentered, _ = self.evaluator.evaluate(5, self.now, self.zone, [self.detection("P-1", 0.6)])
+
+        self.assertEqual([item.source_event_id for item in events], ["BRN-0001"])
+        self.assertEqual(repeated, [])
+        self.assertEqual([item.source_event_id for item in reentered], ["BRN-0002"])
+        payload = events[0].as_dict()
+        self.assertNotIn("id", payload)
+        self.assertEqual(payload["confidence"], None)
+        self.assertTrue(payload["fixture"])
+
+    def test_expired_track_reappearing_inside_is_not_an_entry(self) -> None:
+        self.evaluator.evaluate(1, self.now, self.zone, [self.detection("P-1", 0.3)])
+        self.evaluator.evaluate(4, self.now, self.zone, [])
+        events, _ = self.evaluator.evaluate(5, self.now, self.zone, [self.detection("P-1", 0.7)])
+        self.assertEqual(events, [])
+
+
+if __name__ == "__main__":
+    unittest.main()

Brain/tests/test_runtime.py

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+from __future__ import annotations
+
+import unittest
+
+from Brain.yovision_brain.runtime import DemoEngine
+from Brain.yovision_brain.source import SyntheticSource, cv2
+
+
+@unittest.skipIf(cv2 is None, "pinned OpenCV package is not installed")
+class RuntimeTests(unittest.TestCase):
+    def test_rejects_non_finite_fps(self) -> None:
+        with self.assertRaisesRegex(ValueError, "fps"):
+            DemoEngine(SyntheticSource(width=320, height=180), fps=float("nan"))
+
+    def test_synthetic_step_produces_safe_state_and_jpeg(self) -> None:
+        engine = DemoEngine(SyntheticSource(width=320, height=180), fps=2.0)
+        engine.step()
+        state = engine.state()
+        self.assertTrue(state["source"]["fixture"])
+        self.assertEqual(state["detector"]["name"], "scripted_fixture")
+        self.assertEqual(state["frame"]["width"], 320)
+        self.assertGreater(len(engine.frame_jpeg() or b""), 100)
+        self.assertNotIn("url", state["source"])
+
+    def test_zone_update_increments_version(self) -> None:
+        engine = DemoEngine(SyntheticSource(width=320, height=180))
+        updated = engine.update_zone({"name": "新区域", "points": [{"x": 0.1, "y": 0.1}, {"x": 0.9, "y": 0.1}, {"x": 0.5, "y": 0.9}]})
+        self.assertEqual(updated.version, 2)
+        self.assertEqual(engine.state()["zone"]["name"], "新区域")
+
+
+if __name__ == "__main__":
+    unittest.main()

Brain/tests/test_server.py

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+from __future__ import annotations
+
+import http.client
+import json
+import threading
+import unittest
+
+from Brain.yovision_brain.domain import Point, Zone, zone_from_payload
+from Brain.yovision_brain.server import DemoHTTPServer, DemoHandler, index_path, parse_bind
+
+
+class FakeEngine:
+    def __init__(self) -> None:
+        self.zone = Zone("zone-demo-01", "测试区域", 1, (Point(0.1, 0.1), Point(0.9, 0.1), Point(0.5, 0.9)))
+
+    def state(self) -> dict[str, object]:
+        return {"source": {"label": "safe", "connected": True}, "events": [], "zone": {"version": self.zone.version}}
+
+    def frame_jpeg(self) -> bytes:
+        return b"\xff\xd8safe-jpeg\xff\xd9"
+
+    def update_zone(self, payload: object) -> Zone:
+        self.zone = zone_from_payload(payload, self.zone)
+        return self.zone
+
+
+class BindTests(unittest.TestCase):
+    def test_only_explicit_loopback_is_allowed(self) -> None:
+        self.assertEqual(parse_bind("127.0.0.1:8090"), ("127.0.0.1", 8090))
+        self.assertEqual(parse_bind("localhost:8090"), ("localhost", 8090))
+        with self.assertRaisesRegex(ValueError, "loopback"):
+            parse_bind("0.0.0.0:8090")
+        with self.assertRaisesRegex(ValueError, "loopback"):
+            parse_bind("192.168.1.10:8090")
+
+
+class HTTPTests(unittest.TestCase):
+    def setUp(self) -> None:
+        html = index_path().read_text(encoding="utf-8")
+        self.server = DemoHTTPServer(("127.0.0.1", 0), FakeEngine(), "test-token", html)
+        self.thread = threading.Thread(target=self.server.serve_forever, daemon=True)
+        self.thread.start()
+        self.connection = http.client.HTTPConnection("127.0.0.1", self.server.server_port, timeout=2)
+
+    def tearDown(self) -> None:
+        self.connection.close()
+        self.server.shutdown()
+        self.server.server_close()
+        self.thread.join(timeout=2)
+
+    def test_index_substitutes_token_and_sets_security_headers(self) -> None:
+        self.connection.request("GET", "/brain-demo")
+        response = self.connection.getresponse()
+        body = response.read().decode("utf-8")
+        self.assertEqual(response.status, 200)
+        self.assertIn('content="test-token"', body)
+        self.assertNotIn("__BRAIN_DEMO_TOKEN__", body)
+        self.assertEqual(response.getheader("X-Frame-Options"), "DENY")
+        self.assertIn("default-src 'self'", response.getheader("Content-Security-Policy"))
+
+    def test_zone_write_requires_token_and_rejects_unknown_fields(self) -> None:
+        body = json.dumps({"name": "新区域", "points": [{"x": 0.1, "y": 0.1}, {"x": 0.9, "y": 0.1}, {"x": 0.5, "y": 0.9}]})
+        self.connection.request("PUT", "/api/v1/zones/active", body=body, headers={"Content-Type": "application/json"})
+        forbidden = self.connection.getresponse()
+        forbidden.read()
+        self.assertEqual(forbidden.status, 403)
+
+        self.connection.request("PUT", "/api/v1/zones/active", body=body, headers={"Content-Type": "application/json", "X-Brain-Demo-Token": "test-token"})
+        accepted = self.connection.getresponse()
+        payload = json.loads(accepted.read())
+        self.assertEqual(accepted.status, 200)
+        self.assertEqual(payload["version"], 2)
+
+        invalid = json.dumps({"name": "bad", "points": [], "tenant_id": "leak"})
+        self.connection.request("PUT", "/api/v1/zones/active", body=invalid, headers={"Content-Type": "application/json", "X-Brain-Demo-Token": "test-token"})
+        rejected = self.connection.getresponse()
+        self.assertEqual(rejected.status, 400)
+        self.assertEqual(json.loads(rejected.read())["code"], "invalid_zone")
+
+
+if __name__ == "__main__":
+    unittest.main()

Brain/tests/test_source.py

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+from __future__ import annotations
+
+import tempfile
+import unittest
+from pathlib import Path
+
+from Brain.yovision_brain.domain import Box
+from Brain.yovision_brain.source import CentroidTracker, read_stream_url
+
+
+class StreamURLTests(unittest.TestCase):
+    def test_requires_absolute_single_rtsp_url_file(self) -> None:
+        with tempfile.TemporaryDirectory() as directory:
+            path = Path(directory) / "stream.url"
+            path.write_text("rtsp://user:secret@127.0.0.1:8554/camera\n", encoding="utf-8")
+            self.assertEqual(read_stream_url(str(path)), "rtsp://user:secret@127.0.0.1:8554/camera")
+            path.write_text("rtsp://127.0.0.1/a\nrtsp://127.0.0.1/b\n", encoding="utf-8")
+            with self.assertRaisesRegex(ValueError, "exactly one") as caught:
+                read_stream_url(str(path))
+            self.assertNotIn("127.0.0.1", str(caught.exception))
+
+    def test_rejects_relative_path_without_echoing_input(self) -> None:
+        with self.assertRaisesRegex(ValueError, "absolute"):
+            read_stream_url("camera-secret.url")
+
+    def test_rejects_url_files_inside_repository(self) -> None:
+        repository_file = Path(__file__).resolve()
+        with self.assertRaisesRegex(ValueError, "outside the repository"):
+            read_stream_url(str(repository_file))
+
+
+class TrackerTests(unittest.TestCase):
+    def test_nearby_boxes_retain_track_and_distant_box_gets_new_track(self) -> None:
+        tracker = CentroidTracker(max_distance=0.2)
+        first = tracker.update([(Box(0.1, 0.1, 0.2, 0.4), 0.8)], 1)
+        nearby = tracker.update([(Box(0.12, 0.1, 0.22, 0.4), 0.7)], 2)
+        distant = tracker.update([(Box(0.7, 0.1, 0.8, 0.4), 0.9)], 3)
+        self.assertEqual(first[0].track_id, nearby[0].track_id)
+        self.assertNotEqual(nearby[0].track_id, distant[0].track_id)
+
+
+if __name__ == "__main__":
+    unittest.main()

Brain/tests/test_ui_contract.py

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+from __future__ import annotations
+
+import re
+import unittest
+from pathlib import Path
+
+
+HTML_PATH = Path(__file__).resolve().parents[2] / "docs" / "design" / "brain" / "index.html"
+
+
+class UIContractTests(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.html = HTML_PATH.read_text(encoding="utf-8")
+
+    def test_prototype_is_self_contained_and_labels_fixture_truthfully(self) -> None:
+        self.assertIn("合成回放不等于模型效果", self.html)
+        self.assertIn("离线 HTML 原型数据", self.html)
+        self.assertNotRegex(self.html, r'(?:src|href)=["\']https?://')
+        self.assertNotIn("@import url", self.html)
+
+    def test_accessibility_and_responsive_guards_are_present(self) -> None:
+        required = (
+            'name="viewport"',
+            'class="skip-link"',
+            'aria-live="polite"',
+            ':focus-visible',
+            'prefers-reduced-motion',
+            'min-height: 44px',
+            '@media (max-width: 420px)',
+            '.toolbar { display: grid; grid-template-columns: 1fr; }',
+            '键盘用户可使用右侧坐标表单',
+        )
+        for marker in required:
+            with self.subTest(marker=marker):
+                self.assertIn(marker, self.html)
+
+    def test_no_structural_emoji_or_unescaped_secret_placeholder_in_text(self) -> None:
+        visible_without_script = re.sub(r"<script[\s\S]*?</script>", "", self.html)
+        self.assertNotRegex(visible_without_script, r"[\U0001F300-\U0001FAFF]")
+        self.assertEqual(self.html.count("__BRAIN_DEMO_TOKEN__"), 1)
+
+
+if __name__ == "__main__":
+    unittest.main()

Brain/yovision_brain/__init__.py

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+"""Single-stream Brain engineering prototype.
+
+This package intentionally exposes no Brain-to-Bell transport. T-018 owns
+that contract and its delivery semantics.
+"""
+
+__version__ = "0.1.0"

Brain/yovision_brain/__main__.py

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+from __future__ import annotations
+
+import argparse
+import sys
+
+from .runtime import DemoEngine
+from .server import parse_bind, serve
+from .source import StreamSource, SyntheticSource, read_stream_url, require_opencv
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(description="YoVision Brain single-stream engineering prototype")
+    parser.add_argument("--source", choices=("synthetic", "stream"), default="synthetic")
+    parser.add_argument("--stream-url-file", help="absolute external file containing one RTSP URL")
+    parser.add_argument("--bind", default="127.0.0.1:8090", help="loopback bind address")
+    parser.add_argument("--fps", type=float, default=2.0, help="prototype processing FPS (0, 30]")
+    return parser
+
+
+def main(argv: list[str] | None = None) -> int:
+    args = build_parser().parse_args(argv)
+    try:
+        require_opencv()
+        parse_bind(args.bind)
+        if args.source == "stream":
+            if not args.stream_url_file:
+                raise ValueError("--stream-url-file is required for stream mode")
+            source = StreamSource(read_stream_url(args.stream_url_file))
+        else:
+            if args.stream_url_file:
+                raise ValueError("--stream-url-file is only valid for stream mode")
+            source = SyntheticSource()
+        engine = DemoEngine(source, fps=args.fps)
+    except (RuntimeError, ValueError) as exc:
+        print(f"Brain demo configuration error: {exc}", file=sys.stderr)
+        return 2
+    print(f"Brain demo listening on http://{args.bind}/brain-demo")
+    print("Engineering prototype only; synthetic replay and HOG output are not production model evidence.")
+    try:
+        serve(engine, args.bind)
+    except KeyboardInterrupt:
+        return 0
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

Brain/yovision_brain/domain.py

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+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from typing import Callable, Iterable, Sequence
+from uuid import uuid4
+
+
+@dataclass(frozen=True)
+class Point:
+    x: float
+    y: float
+
+    def __post_init__(self) -> None:
+        if not all(math.isfinite(value) for value in (self.x, self.y)) or not (
+            0.0 <= self.x <= 1.0 and 0.0 <= self.y <= 1.0
+        ):
+            raise ValueError("point coordinates must be within [0, 1]")
+
+
+@dataclass(frozen=True)
+class Box:
+    x1: float
+    y1: float
+    x2: float
+    y2: float
+
+    def __post_init__(self) -> None:
+        values = (self.x1, self.y1, self.x2, self.y2)
+        if not all(math.isfinite(value) for value in values) or any(value < 0.0 or value > 1.0 for value in values):
+            raise ValueError("box coordinates must be within [0, 1]")
+        if self.x1 >= self.x2 or self.y1 >= self.y2:
+            raise ValueError("box must have positive area")
+
+    @property
+    def center(self) -> Point:
+        return Point((self.x1 + self.x2) / 2.0, (self.y1 + self.y2) / 2.0)
+
+
+@dataclass(frozen=True)
+class Detection:
+    track_id: str
+    class_name: str
+    box: Box
+    detector_score: float | None = None
+
+    def __post_init__(self) -> None:
+        if not self.track_id or len(self.track_id) > 128:
+            raise ValueError("track_id must contain 1 to 128 characters")
+        if self.class_name != "person":
+            raise ValueError("T-017 only supports anonymous person detections")
+        if self.detector_score is not None and not math.isfinite(self.detector_score):
+            raise ValueError("detector_score must be finite")
+
+
+@dataclass(frozen=True)
+class Zone:
+    zone_id: str
+    name: str
+    version: int
+    points: tuple[Point, ...]
+
+    def __post_init__(self) -> None:
+        if not self.zone_id or len(self.zone_id) > 128:
+            raise ValueError("zone_id must contain 1 to 128 characters")
+        if not self.name.strip() or len(self.name) > 80:
+            raise ValueError("zone name must contain 1 to 80 characters")
+        if self.version < 1:
+            raise ValueError("zone version must be positive")
+        if not 3 <= len(self.points) <= 32:
+            raise ValueError("zone must contain 3 to 32 points")
+
+
+@dataclass(frozen=True)
+class EventCandidate:
+    source_event_id: str
+    kind: str
+    source_ref: str
+    track_id: str
+    zone_id: str
+    zone_version: int
+    occurred_at: str
+    confidence: None
+    fixture: bool
+
+    def as_dict(self) -> dict[str, object]:
+        # Bell owns the platform `id`; it is deliberately absent here.
+        return {
+            "candidate_version": "brain-demo-v1",
+            "source_event_id": self.source_event_id,
+            "kind": self.kind,
+            "source_ref": self.source_ref,
+            "track_id": self.track_id,
+            "zone_id": self.zone_id,
+            "zone_version": self.zone_version,
+            "occurred_at": self.occurred_at,
+            "confidence": self.confidence,
+            "fixture": self.fixture,
+        }
+
+
+def _on_segment(point: Point, start: Point, end: Point, epsilon: float = 1e-9) -> bool:
+    cross = (point.y - start.y) * (end.x - start.x) - (point.x - start.x) * (end.y - start.y)
+    if abs(cross) > epsilon:
+        return False
+    return (
+        min(start.x, end.x) - epsilon <= point.x <= max(start.x, end.x) + epsilon
+        and min(start.y, end.y) - epsilon <= point.y <= max(start.y, end.y) + epsilon
+    )
+
+
+def point_in_polygon(point: Point, polygon: Sequence[Point]) -> bool:
+    if len(polygon) < 3:
+        return False
+    inside = False
+    previous = polygon[-1]
+    for current in polygon:
+        if _on_segment(point, previous, current):
+            return True
+        crosses = (current.y > point.y) != (previous.y > point.y)
+        if crosses:
+            boundary_x = (previous.x - current.x) * (point.y - current.y) / (previous.y - current.y) + current.x
+            if point.x < boundary_x:
+                inside = not inside
+        previous = current
+    return inside
+
+
+class ZoneEntryEvaluator:
+    def __init__(
+        self,
+        source_ref: str,
+        fixture: bool,
+        track_ttl_frames: int = 8,
+        event_id_factory: Callable[[], str] | None = None,
+    ) -> None:
+        if track_ttl_frames < 1:
+            raise ValueError("track_ttl_frames must be positive")
+        self._source_ref = source_ref
+        self._fixture = fixture
+        self._track_ttl_frames = track_ttl_frames
+        self._event_id_factory = event_id_factory or (lambda: f"BRN-{uuid4().hex}")
+        self._inside: dict[str, bool] = {}
+        self._last_seen: dict[str, int] = {}
+
+    def reset(self) -> None:
+        self._inside.clear()
+        self._last_seen.clear()
+
+    def evaluate(
+        self,
+        sequence: int,
+        occurred_at: datetime,
+        zone: Zone,
+        detections: Iterable[Detection],
+    ) -> tuple[list[EventCandidate], dict[str, bool]]:
+        if sequence < 0:
+            raise ValueError("sequence cannot be negative")
+        if occurred_at.tzinfo is None:
+            raise ValueError("occurred_at must be timezone-aware")
+
+        expired = [
+            track_id
+            for track_id, last_seen in self._last_seen.items()
+            if sequence - last_seen > self._track_ttl_frames
+        ]
+        for track_id in expired:
+            self._inside.pop(track_id, None)
+            self._last_seen.pop(track_id, None)
+
+        events: list[EventCandidate] = []
+        states: dict[str, bool] = {}
+        for detection in detections:
+            inside = point_in_polygon(detection.box.center, zone.points)
+            previous = self._inside.get(detection.track_id)
+            if previous is False and inside:
+                events.append(
+                    EventCandidate(
+                        source_event_id=self._event_id_factory(),
+                        kind="zone_entry",
+                        source_ref=self._source_ref,
+                        track_id=detection.track_id,
+                        zone_id=zone.zone_id,
+                        zone_version=zone.version,
+                        occurred_at=occurred_at.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"),
+                        confidence=None,
+                        fixture=self._fixture,
+                    )
+                )
+            self._inside[detection.track_id] = inside
+            self._last_seen[detection.track_id] = sequence
+            states[detection.track_id] = inside
+        return events, states
+
+
+def zone_from_payload(payload: object, current: Zone) -> Zone:
+    if not isinstance(payload, dict):
+        raise ValueError("request body must be an object")
+    if set(payload) - {"name", "points"}:
+        raise ValueError("unknown zone fields are not allowed")
+    name = payload.get("name", current.name)
+    raw_points = payload.get("points")
+    if not isinstance(name, str):
+        raise ValueError("zone name must be a string")
+    if not isinstance(raw_points, list):
+        raise ValueError("zone points must be an array")
+    points: list[Point] = []
+    for raw_point in raw_points:
+        if not isinstance(raw_point, dict) or set(raw_point) != {"x", "y"}:
+            raise ValueError("each point must contain only x and y")
+        x = raw_point["x"]
+        y = raw_point["y"]
+        if isinstance(x, bool) or isinstance(y, bool) or not isinstance(x, (int, float)) or not isinstance(y, (int, float)):
+            raise ValueError("point coordinates must be numbers")
+        points.append(Point(float(x), float(y)))
+    return Zone(current.zone_id, name.strip(), current.version + 1, tuple(points))

Brain/yovision_brain/runtime.py

@@ -0,0 +1,164 @@
+from __future__ import annotations
+
+import math
+import threading
+import time
+from collections import deque
+from datetime import datetime, timezone
+from typing import Any
+
+from .domain import Detection, Point, Zone, ZoneEntryEvaluator, zone_from_payload
+from .source import CentroidTracker, HOGPersonDetector, require_opencv
+
+try:
+    import cv2  # type: ignore
+except ImportError:  # pragma: no cover
+    cv2 = None
+
+
+DEFAULT_ZONE = Zone(
+    "zone-demo-01",
+    "楼梯口危险区",
+    1,
+    (Point(0.55, 0.30), Point(0.90, 0.30), Point(0.90, 0.88), Point(0.55, 0.88)),
+)
+
+
+class DemoEngine:
+    def __init__(self, source: Any, fps: float = 2.0, event_limit: int = 100) -> None:
+        if not math.isfinite(fps) or fps <= 0.0 or fps > 30.0:
+            raise ValueError("fps must be within (0, 30]")
+        if not 1 <= event_limit <= 100:
+            raise ValueError("event_limit must be within [1, 100]")
+        require_opencv()
+        self._source = source
+        self._fps = fps
+        self._detector = None if source.fixture else HOGPersonDetector()
+        self._tracker = CentroidTracker()
+        self._evaluator = ZoneEntryEvaluator(source.source_ref, source.fixture)
+        self._zone = DEFAULT_ZONE
+        self._events: deque[dict[str, object]] = deque(maxlen=event_limit)
+        self._lock = threading.RLock()
+        self._stop = threading.Event()
+        self._thread: threading.Thread | None = None
+        self._sequence = 0
+        self._frame_jpeg: bytes | None = None
+        self._frame_width = 0
+        self._frame_height = 0
+        self._captured_at: str | None = None
+        self._detections: list[dict[str, object]] = []
+        self._latency_ms: float | None = None
+        self._connected = False
+        self._last_error_code: str | None = None
+
+    def start(self) -> None:
+        if self._thread is not None:
+            return
+        self._thread = threading.Thread(target=self._run, name="brain-demo", daemon=True)
+        self._thread.start()
+
+    def stop(self) -> None:
+        self._stop.set()
+        if self._thread is not None:
+            self._thread.join(timeout=3.0)
+            self._thread = None
+        self._source.close()
+
+    def _run(self) -> None:
+        interval = 1.0 / self._fps
+        while not self._stop.is_set():
+            started = time.perf_counter()
+            try:
+                self.step()
+            except RuntimeError:
+                with self._lock:
+                    self._connected = False
+                    self._last_error_code = "source_unavailable"
+            elapsed = time.perf_counter() - started
+            self._stop.wait(max(0.0, interval - elapsed))
+
+    def step(self) -> None:
+        started = time.perf_counter()
+        packet = self._source.read()
+        self._sequence += 1
+        if packet.scripted_detections is not None:
+            detections = list(packet.scripted_detections)
+        else:
+            raw_boxes = self._detector.detect(packet.frame) if self._detector is not None else []
+            detections = self._tracker.update(raw_boxes, self._sequence)
+        with self._lock:
+            zone = self._zone
+        new_events, inside_by_track = self._evaluator.evaluate(self._sequence, packet.captured_at, zone, detections)
+        ok, encoded = cv2.imencode(".jpg", packet.frame, [int(cv2.IMWRITE_JPEG_QUALITY), 82])
+        if not ok:
+            raise RuntimeError("frame encoding failed")
+        height, width = packet.frame.shape[:2]
+        serialized_detections = [self._serialize_detection(item, inside_by_track.get(item.track_id, False)) for item in detections]
+        with self._lock:
+            for event in new_events:
+                self._events.appendleft(event.as_dict())
+            self._frame_jpeg = encoded.tobytes()
+            self._frame_width = int(width)
+            self._frame_height = int(height)
+            self._captured_at = packet.captured_at.astimezone(timezone.utc).isoformat().replace("+00:00", "Z")
+            self._detections = serialized_detections
+            self._latency_ms = round((time.perf_counter() - started) * 1000.0, 1)
+            self._connected = True
+            self._last_error_code = None
+
+    @staticmethod
+    def _serialize_detection(detection: Detection, inside: bool) -> dict[str, object]:
+        return {
+            "track_id": detection.track_id,
+            "class": detection.class_name,
+            "bbox": [detection.box.x1, detection.box.y1, detection.box.x2, detection.box.y2],
+            "detector_score": detection.detector_score,
+            "inside_zone": inside,
+        }
+
+    def update_zone(self, payload: object) -> Zone:
+        with self._lock:
+            updated = zone_from_payload(payload, self._zone)
+            self._zone = updated
+            self._evaluator.reset()
+            return updated
+
+    def frame_jpeg(self) -> bytes | None:
+        with self._lock:
+            return self._frame_jpeg
+
+    def state(self) -> dict[str, object]:
+        with self._lock:
+            zone = self._zone
+            return {
+                "prototype": True,
+                "notice": "工程原型；合成回放不是模型输出，HOG 适配器不是生产检测模型。",
+                "source": {
+                    "mode": self._source.mode,
+                    "label": self._source.label,
+                    "fixture": self._source.fixture,
+                    "connected": self._connected,
+                    "ref": self._source.source_ref,
+                },
+                "detector": {
+                    "name": "scripted_fixture" if self._source.fixture else self._detector.name,
+                    "production_ready": False,
+                },
+                "frame": {
+                    "sequence": self._sequence,
+                    "width": self._frame_width,
+                    "height": self._frame_height,
+                    "captured_at": self._captured_at,
+                },
+                "inference": {"target_fps": self._fps, "latency_ms": self._latency_ms},
+                "zone": {
+                    "id": zone.zone_id,
+                    "name": zone.name,
+                    "version": zone.version,
+                    "points": [{"x": point.x, "y": point.y} for point in zone.points],
+                },
+                "detections": list(self._detections),
+                "events": list(self._events),
+                "last_error_code": self._last_error_code,
+                "generated_at": datetime.now(timezone.utc).isoformat().replace("+00:00", "Z"),
+            }

Brain/yovision_brain/server.py

@@ -0,0 +1,141 @@
+from __future__ import annotations
+
+import hmac
+import json
+import secrets
+from http import HTTPStatus
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
+from typing import Any
+from urllib.parse import urlsplit
+
+from .domain import Zone
+
+
+MAX_BODY_BYTES = 64 * 1024
+
+
+def parse_bind(value: str) -> tuple[str, int]:
+    parsed = urlsplit(f"//{value}")
+    host = parsed.hostname
+    try:
+        port = parsed.port
+    except ValueError as exc:
+        raise ValueError("invalid demo bind address") from exc
+    if host not in {"127.0.0.1", "localhost", "::1"} or port is None or not 1 <= port <= 65535:
+        raise ValueError("Brain demo must bind to an explicit loopback address and valid port")
+    return host, port
+
+
+def index_path() -> Path:
+    return Path(__file__).resolve().parents[2] / "docs" / "design" / "brain" / "index.html"
+
+
+class DemoHTTPServer(ThreadingHTTPServer):
+    daemon_threads = True
+
+    def __init__(self, address: tuple[str, int], engine: Any, token: str, html: str) -> None:
+        self.engine = engine
+        self.demo_token = token
+        self.html = html
+        super().__init__(address, DemoHandler)
+
+
+class DemoHandler(BaseHTTPRequestHandler):
+    server: DemoHTTPServer
+
+    def log_message(self, _format: str, *_args: object) -> None:
+        # Do not echo request paths or headers; URL credentials never reach HTTP paths.
+        return
+
+    def _security_headers(self) -> None:
+        self.send_header("X-Content-Type-Options", "nosniff")
+        self.send_header("X-Frame-Options", "DENY")
+        self.send_header("Referrer-Policy", "no-referrer")
+        self.send_header("Cache-Control", "no-store")
+        self.send_header(
+            "Content-Security-Policy",
+            "default-src 'self'; style-src 'unsafe-inline'; script-src 'unsafe-inline'; "
+            "img-src 'self' data: blob:; connect-src 'self'; base-uri 'none'; frame-ancestors 'none'",
+        )
+
+    def _send_bytes(self, status: int, content_type: str, body: bytes) -> None:
+        self.send_response(status)
+        self.send_header("Content-Type", content_type)
+        self.send_header("Content-Length", str(len(body)))
+        self._security_headers()
+        self.end_headers()
+        self.wfile.write(body)
+
+    def _send_json(self, status: int, payload: object) -> None:
+        body = json.dumps(payload, ensure_ascii=False, separators=(",", ":")).encode("utf-8")
+        self._send_bytes(status, "application/json; charset=utf-8", body)
+
+    def do_GET(self) -> None:  # noqa: N802
+        path = urlsplit(self.path).path
+        if path in {"/", "/brain-demo"}:
+            body = self.server.html.replace("__BRAIN_DEMO_TOKEN__", self.server.demo_token).encode("utf-8")
+            self._send_bytes(HTTPStatus.OK, "text/html; charset=utf-8", body)
+            return
+        if path == "/healthz":
+            self._send_json(HTTPStatus.OK, {"status": "ok"})
+            return
+        if path == "/api/v1/state":
+            self._send_json(HTTPStatus.OK, self.server.engine.state())
+            return
+        if path == "/api/v1/frame.jpg":
+            frame = self.server.engine.frame_jpeg()
+            if frame is None:
+                self._send_json(HTTPStatus.SERVICE_UNAVAILABLE, {"code": "frame_not_ready"})
+                return
+            self._send_bytes(HTTPStatus.OK, "image/jpeg", frame)
+            return
+        self._send_json(HTTPStatus.NOT_FOUND, {"code": "not_found"})
+
+    def do_PUT(self) -> None:  # noqa: N802
+        if urlsplit(self.path).path != "/api/v1/zones/active":
+            self._send_json(HTTPStatus.NOT_FOUND, {"code": "not_found"})
+            return
+        raw_length = self.headers.get("Content-Length")
+        try:
+            length = int(raw_length or "-1")
+        except ValueError:
+            length = -1
+        if length < 0 or length > MAX_BODY_BYTES:
+            self._send_json(HTTPStatus.REQUEST_ENTITY_TOO_LARGE, {"code": "body_too_large"})
+            return
+        body = self.rfile.read(length)
+        supplied = self.headers.get("X-Brain-Demo-Token", "")
+        if not hmac.compare_digest(supplied, self.server.demo_token):
+            self._send_json(HTTPStatus.FORBIDDEN, {"code": "forbidden"})
+            return
+        try:
+            payload = json.loads(body.decode("utf-8"))
+            zone: Zone = self.server.engine.update_zone(payload)
+        except (UnicodeError, json.JSONDecodeError, ValueError):
+            self._send_json(HTTPStatus.BAD_REQUEST, {"code": "invalid_zone"})
+            return
+        self._send_json(
+            HTTPStatus.OK,
+            {
+                "id": zone.zone_id,
+                "name": zone.name,
+                "version": zone.version,
+                "points": [{"x": point.x, "y": point.y} for point in zone.points],
+            },
+        )
+
+
+def build_server(engine: Any, bind: str = "127.0.0.1:8090", token: str | None = None) -> DemoHTTPServer:
+    html = index_path().read_text(encoding="utf-8")
+    return DemoHTTPServer(parse_bind(bind), engine, token or secrets.token_urlsafe(32), html)
+
+
+def serve(engine: Any, bind: str) -> None:
+    server = build_server(engine, bind)
+    engine.start()
+    try:
+        server.serve_forever(poll_interval=0.25)
+    finally:
+        server.server_close()
+        engine.stop()

Brain/yovision_brain/source.py

@@ -0,0 +1,201 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Iterable
+from urllib.parse import urlsplit
+
+from .domain import Box, Detection
+
+try:
+    import cv2  # type: ignore
+    import numpy as np  # type: ignore
+except ImportError:  # pragma: no cover - exercised by the startup failure path
+    cv2 = None
+    np = None
+
+
+@dataclass(frozen=True)
+class FramePacket:
+    frame: Any
+    captured_at: datetime
+    scripted_detections: tuple[Detection, ...] | None
+
+
+def require_opencv() -> None:
+    if cv2 is None or np is None:
+        raise RuntimeError("Brain demo requires the pinned NumPy and OpenCV packages")
+
+
+def read_stream_url(path_value: str) -> str:
+    path = Path(path_value)
+    if not path.is_absolute():
+        raise ValueError("stream URL file must be an absolute external path")
+    try:
+        resolved = path.resolve(strict=True)
+    except OSError as exc:
+        raise ValueError("stream URL file does not exist") from exc
+    if not resolved.is_file():
+        raise ValueError("stream URL file does not exist")
+    repository_root = Path(__file__).resolve().parents[2]
+    try:
+        resolved.relative_to(repository_root)
+    except ValueError:
+        pass
+    else:
+        raise ValueError("stream URL file must be outside the repository")
+    try:
+        with resolved.open("rb") as stream:
+            raw = stream.read(4097)
+    except OSError as exc:
+        raise ValueError("stream URL file cannot be read") from exc
+    if len(raw) > 4096:
+        raise ValueError("stream URL file exceeds 4096 bytes")
+    try:
+        lines = raw.decode("utf-8").splitlines()
+    except UnicodeError as exc:
+        raise ValueError("stream URL file must be UTF-8") from exc
+    values = [line.strip() for line in lines if line.strip()]
+    if len(values) != 1:
+        raise ValueError("stream URL file must contain exactly one non-empty line")
+    parsed = urlsplit(values[0])
+    if parsed.scheme not in {"rtsp", "rtsps"} or not parsed.hostname:
+        raise ValueError("stream URL must be an RTSP URL")
+    return values[0]
+
+
+class SyntheticSource:
+    mode = "synthetic"
+    label = "合成回放"
+    fixture = True
+    source_ref = "demo-camera-01"
+
+    def __init__(self, width: int = 960, height: int = 540) -> None:
+        require_opencv()
+        self.width = width
+        self.height = height
+        self._sequence = 0
+
+    def read(self) -> FramePacket:
+        self._sequence += 1
+        frame = np.zeros((self.height, self.width, 3), dtype=np.uint8)
+        frame[:] = (20, 28, 42)
+        cv2.rectangle(frame, (0, int(self.height * 0.72)), (self.width, self.height), (31, 42, 58), -1)
+        for x in range(0, self.width, 80):
+            cv2.line(frame, (x, int(self.height * 0.72)), (x + 80, self.height), (43, 57, 75), 1)
+        cv2.putText(frame, "SYNTHETIC FIXTURE - NOT MODEL OUTPUT", (24, 38), cv2.FONT_HERSHEY_SIMPLEX, 0.72, (82, 190, 245), 2)
+
+        phase = ((self._sequence - 1) % 160) / 159.0
+        center_x = -0.04 + phase * 1.08
+        x1 = max(0.0, center_x - 0.04)
+        x2 = min(1.0, center_x + 0.04)
+        detections: tuple[Detection, ...] = ()
+        if x2 - x1 > 0.01:
+            box = Box(x1, 0.34, x2, 0.82)
+            detections = (Detection("P-DEMO-001", "person", box, None),)
+            px = int(center_x * self.width)
+            head_y = int(self.height * 0.40)
+            cv2.circle(frame, (px, head_y), 17, (195, 210, 225), -1)
+            cv2.line(frame, (px, head_y + 18), (px, int(self.height * 0.65)), (195, 210, 225), 12)
+            cv2.line(frame, (px, int(self.height * 0.52)), (px - 30, int(self.height * 0.60)), (195, 210, 225), 8)
+            cv2.line(frame, (px, int(self.height * 0.52)), (px + 30, int(self.height * 0.60)), (195, 210, 225), 8)
+            cv2.line(frame, (px, int(self.height * 0.65)), (px - 24, int(self.height * 0.79)), (195, 210, 225), 9)
+            cv2.line(frame, (px, int(self.height * 0.65)), (px + 24, int(self.height * 0.79)), (195, 210, 225), 9)
+        return FramePacket(frame, datetime.now(timezone.utc), detections)
+
+    def close(self) -> None:
+        return
+
+
+class StreamSource:
+    mode = "stream"
+    label = "外部 MediaMTX / RTSP"
+    fixture = False
+    source_ref = "configured-video-source"
+
+    def __init__(self, stream_url: str) -> None:
+        require_opencv()
+        self._stream_url = stream_url
+        self._capture: Any = None
+
+    def _open(self) -> None:
+        if self._capture is not None:
+            self._capture.release()
+        self._capture = cv2.VideoCapture(self._stream_url)
+        self._capture.set(cv2.CAP_PROP_BUFFERSIZE, 1)
+
+    def read(self) -> FramePacket:
+        if self._capture is None or not self._capture.isOpened():
+            self._open()
+        ok, frame = self._capture.read()
+        if not ok or frame is None:
+            self._open()
+            raise RuntimeError("stream frame unavailable")
+        return FramePacket(frame, datetime.now(timezone.utc), None)
+
+    def close(self) -> None:
+        if self._capture is not None:
+            self._capture.release()
+            self._capture = None
+
+
+class HOGPersonDetector:
+    name = "opencv_hog_person_demo"
+    production_ready = False
+
+    def __init__(self) -> None:
+        require_opencv()
+        self._hog = cv2.HOGDescriptor()
+        self._hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+
+    def detect(self, frame: Any) -> list[tuple[Box, float]]:
+        height, width = frame.shape[:2]
+        scale = min(1.0, 960.0 / max(width, 1))
+        working = frame if scale == 1.0 else cv2.resize(frame, (int(width * scale), int(height * scale)))
+        boxes, weights = self._hog.detectMultiScale(working, winStride=(8, 8), padding=(8, 8), scale=1.05)
+        result: list[tuple[Box, float]] = []
+        work_height, work_width = working.shape[:2]
+        for raw_box, weight in zip(boxes, weights):
+            x, y, box_width, box_height = (int(value) for value in raw_box)
+            x1 = max(0.0, min(1.0, x / work_width))
+            y1 = max(0.0, min(1.0, y / work_height))
+            x2 = max(0.0, min(1.0, (x + box_width) / work_width))
+            y2 = max(0.0, min(1.0, (y + box_height) / work_height))
+            if x2 > x1 and y2 > y1:
+                result.append((Box(x1, y1, x2, y2), float(weight)))
+        return result
+
+
+class CentroidTracker:
+    def __init__(self, max_distance: float = 0.18, ttl_frames: int = 8) -> None:
+        self._max_distance = max_distance
+        self._ttl_frames = ttl_frames
+        self._next_id = 1
+        self._tracks: dict[str, tuple[Box, int]] = {}
+
+    def update(self, boxes: Iterable[tuple[Box, float]], sequence: int) -> list[Detection]:
+        incoming = list(boxes)
+        available = set(self._tracks)
+        detections: list[Detection] = []
+        for box, score in incoming:
+            center = box.center
+            selected: str | None = None
+            selected_distance = self._max_distance
+            for track_id in available:
+                old_center = self._tracks[track_id][0].center
+                distance = ((center.x - old_center.x) ** 2 + (center.y - old_center.y) ** 2) ** 0.5
+                if distance < selected_distance:
+                    selected = track_id
+                    selected_distance = distance
+            if selected is None:
+                selected = f"P-{self._next_id:04d}"
+                self._next_id += 1
+            else:
+                available.remove(selected)
+            self._tracks[selected] = (box, sequence)
+            detections.append(Detection(selected, "person", box, score))
+        for track_id, (_, last_seen) in list(self._tracks.items()):
+            if sequence - last_seen > self._ttl_frames:
+                del self._tracks[track_id]
+        return detections