feat: 串联 Brain 独立纵切与内部事件 (#16)

2026-08-28 23:25:23 +08:00
parent 6ffdbcce84
commit adbd1c6aba
11 changed files with 426 additions and 0 deletions
@@ -0,0 +1,5 @@
+"""Brain independent vertical-slice application."""
+
+from .runner import RunSummary, run_pipeline
+
+__all__ = ["RunSummary", "run_pipeline"]
@@ -0,0 +1,63 @@
+"""CLI for the isolated Brain local-event vertical slice."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+from yovision_brain.config import ConfigError
+from yovision_brain.decode import DecoderError
+from yovision_brain.events import JsonLinesSink
+from yovision_brain.input import InputError
+from yovision_brain.rules import RuleConfigError
+
+from .runner import run_pipeline
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(description="Run the isolated Brain internal-event pipeline")
+    parser.add_argument("--config", required=True, help="explicit Brain-internal JSON configuration")
+    parser.add_argument("--output", default="-", help="JSON Lines output file, or - for stdout")
+    return parser
+
+
+def main(argv: list[str] | None = None) -> int:
+    args = build_parser().parse_args(argv)
+    config_path = Path(args.config)
+    try:
+        raw = json.loads(config_path.read_text(encoding="utf-8"))
+    except (OSError, UnicodeError, json.JSONDecodeError):
+        print(json.dumps({"status": "error", "message": "Brain internal config cannot be read"}), file=sys.stderr)
+        return 2
+    if not isinstance(raw, dict):
+        print(json.dumps({"status": "error", "message": "Brain internal config must be an object"}), file=sys.stderr)
+        return 2
+
+    stream = sys.stdout
+    owned_stream = None
+    try:
+        if args.output != "-":
+            try:
+                owned_stream = Path(args.output).open("w", encoding="utf-8", newline="\n")
+            except OSError:
+                print(json.dumps({"status": "error", "message": "event output cannot be opened"}), file=sys.stderr)
+                return 2
+            stream = owned_stream
+        summary = run_pipeline(raw, JsonLinesSink(stream), base_dir=config_path.parent)
+    except (ConfigError, DecoderError, InputError, RuleConfigError, RuntimeError, ValueError) as exc:
+        print(json.dumps({"status": "error", "message": str(exc)}, ensure_ascii=False), file=sys.stderr)
+        return 3
+    except KeyboardInterrupt:
+        print(json.dumps({"status": "cancelled"}), file=sys.stderr)
+        return 130
+    finally:
+        if owned_stream is not None:
+            owned_stream.close()
+    print(json.dumps({"status": summary.status, "frames": summary.frames, "detections": summary.detections, "events": summary.events}, sort_keys=True), file=sys.stderr)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
@@ -0,0 +1,110 @@
+"""Compose input, decode, anonymous vision, rules and local event output."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Mapping
+
+from yovision_brain.config import BrainInputConfig, parse_input_config
+from yovision_brain.decode import decode_packets
+from yovision_brain.events import EventSink, candidate_from_decision
+from yovision_brain.input import CancellationToken, build_input_source
+from yovision_brain.rules import (
+    AreaDefinition,
+    DirectionalLineDefinition,
+    NormalizedPoint,
+    RuleEngine,
+    RuleSet,
+)
+from yovision_brain.vision import LumaBlobDetector, SingleStreamTracker, TorchLumaBlobDetector
+
+
+@dataclass(frozen=True, slots=True)
+class RunSummary:
+    status: str
+    frames: int
+    detections: int
+    events: int
+
+
+def _rules(config: BrainInputConfig, version: str) -> RuleSet:
+    return RuleSet(
+        version=version,
+        profile_id=config.profile.profile_id,
+        width=config.profile.width,
+        height=config.profile.height,
+        areas=tuple(
+            AreaDefinition(area.rule_id, tuple(NormalizedPoint(point.x, point.y) for point in area.points))
+            for area in config.areas
+        ),
+        directional_lines=tuple(
+            DirectionalLineDefinition(
+                line.rule_id,
+                NormalizedPoint(line.start.x, line.start.y),
+                NormalizedPoint(line.end.x, line.end.y),
+                line.trigger_direction,
+            )
+            for line in config.directional_lines
+        ),
+    )
+
+
+def run_pipeline(
+    raw: Mapping[str, Any],
+    sink: EventSink,
+    *,
+    base_dir: Path | None = None,
+    cancellation: CancellationToken | None = None,
+) -> RunSummary:
+    input_raw = raw.get("input")
+    if not isinstance(input_raw, Mapping):
+        raise ValueError("input must be an object")
+    config = parse_input_config(input_raw, base_dir=base_dir)
+    version = raw.get("rules_version")
+    if not isinstance(version, str) or not version:
+        raise ValueError("rules_version must be a non-empty string")
+    detector_raw = raw.get("detector", {})
+    if not isinstance(detector_raw, Mapping):
+        raise ValueError("detector must be an object")
+    backend = detector_raw.get("backend", "python")
+    threshold = detector_raw.get("threshold", 200)
+    minimum_area = detector_raw.get("minimum_area", 1)
+    if not isinstance(threshold, int) or not isinstance(minimum_area, int):
+        raise ValueError("detector threshold and minimum_area must be integers")
+    if backend == "python":
+        detector = LumaBlobDetector(threshold=threshold, minimum_area=minimum_area)
+    elif backend == "torch_cpu":
+        detector = TorchLumaBlobDetector(threshold=threshold, minimum_area=minimum_area, device="cpu")
+    else:
+        raise ValueError("detector.backend must be python or torch_cpu")
+
+    source = build_input_source(config)
+    tracker = SingleStreamTracker()
+    engine = RuleEngine(_rules(config, version))
+    frame_count = detection_count = event_count = 0
+    for frame in decode_packets(source.packets(cancellation), cancellation):
+        frame_count += 1
+        detections = detector.detect(frame)
+        detection_count += len(detections)
+        tracks = tracker.update(detections, frame_sequence=frame.sequence, timestamp_ns=frame.timestamp_ns)
+        decisions = engine.evaluate(
+            tracks,
+            profile_id=frame.profile_id,
+            width=frame.width,
+            height=frame.height,
+        )
+        tracks_by_id = {track.track_id: track for track in tracks}
+        for decision in decisions:
+            if not decision.triggered:
+                continue
+            sink.write(candidate_from_decision(
+                decision,
+                tracks_by_id[decision.track_id],
+                logical_input_id=config.logical_device_id,
+                detector=detector.metadata,
+            ))
+            event_count += 1
+    tracker.finish()
+    status = "cancelled" if cancellation is not None and cancellation.cancelled else "completed"
+    return RunSummary(status, frame_count, detection_count, event_count)
@@ -0,0 +1,13 @@
+"""Brain-internal event candidates and local sinks."""
+
+from .mapper import candidate_from_decision
+from .models import INTERNAL_EVENT_SCHEMA, InternalEventCandidate
+from .sink import EventSink, JsonLinesSink
+
+__all__ = [
+    "EventSink",
+    "INTERNAL_EVENT_SCHEMA",
+    "InternalEventCandidate",
+    "JsonLinesSink",
+    "candidate_from_decision",
+]
@@ -0,0 +1,53 @@
+"""Stable mapping from an internal rule hit to an internal event candidate."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+
+from yovision_brain.rules import RuleDecision
+from yovision_brain.vision import DetectorMetadata, TrackedObject
+
+from .models import INTERNAL_EVENT_SCHEMA, InternalEventCandidate
+
+
+def candidate_from_decision(
+    decision: RuleDecision,
+    track: TrackedObject,
+    *,
+    logical_input_id: str,
+    detector: DetectorMetadata,
+) -> InternalEventCandidate:
+    if not decision.triggered or decision.track_id != track.track_id:
+        raise ValueError("only a triggered decision for the same anonymous track can become an event")
+    event_type = "danger_area_entered" if decision.rule_type == "danger_area" else "directional_line_crossed"
+    observation: dict[str, object] = {
+        "category": track.category,
+        "confidence": track.confidence,
+        "box": {
+            "left": track.box.left,
+            "top": track.box.top,
+            "right": track.box.right,
+            "bottom": track.box.bottom,
+        },
+        "anchor": {"x": decision.anchor.x, "y": decision.anchor.y},
+    }
+    fact = {
+        "schema": INTERNAL_EVENT_SCHEMA,
+        "logical_input_id": logical_input_id,
+        "event_type": event_type,
+        "occurred_at_ns": decision.timestamp_ns,
+        "rule_id": decision.rule_id,
+        "rule_version": decision.config_version,
+        "model_name": detector.name,
+        "model_version": detector.version,
+        "profile_id": decision.profile_id,
+        "frame_width": decision.width,
+        "frame_height": decision.height,
+        "track_id": track.track_id,
+        "observation": observation,
+        "reason": decision.reason,
+    }
+    canonical = json.dumps(fact, ensure_ascii=False, sort_keys=True, separators=(",", ":"))
+    event_id = "brain-local-" + hashlib.sha256(canonical.encode("utf-8")).hexdigest()
+    return InternalEventCandidate(event_id=event_id, **fact)
@@ -0,0 +1,29 @@
+"""Explicitly internal event candidate model; not a shared contract."""
+
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass
+
+INTERNAL_EVENT_SCHEMA = "brain.internal.event-candidate/v1"
+
+
+@dataclass(frozen=True, slots=True)
+class InternalEventCandidate:
+    schema: str
+    event_id: str
+    logical_input_id: str
+    event_type: str
+    occurred_at_ns: int
+    rule_id: str
+    rule_version: str
+    model_name: str
+    model_version: str
+    profile_id: str
+    frame_width: int
+    frame_height: int
+    track_id: str
+    observation: dict[str, object]
+    reason: str
+
+    def to_dict(self) -> dict[str, object]:
+        return asdict(self)
@@ -0,0 +1,21 @@
+"""Replaceable local event sinks."""
+
+from __future__ import annotations
+
+import json
+from typing import Protocol, TextIO
+
+from .models import InternalEventCandidate
+
+
+class EventSink(Protocol):
+    def write(self, candidate: InternalEventCandidate) -> None: ...
+
+
+class JsonLinesSink:
+    def __init__(self, stream: TextIO) -> None:
+        self._stream = stream
+
+    def write(self, candidate: InternalEventCandidate) -> None:
+        self._stream.write(json.dumps(candidate.to_dict(), ensure_ascii=False, sort_keys=True) + "\n")
+        self._stream.flush()