264 lines
12 KiB
Python
264 lines
12 KiB
Python
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"""独立于布控分析流水线的车间实时定位进程。"""
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import multiprocessing as mp
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import queue
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import threading
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import time
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from .calibration import foot_point_world
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from .fusion import GlobalFusionTracker
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class _CameraReader(threading.Thread):
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def __init__(self, config, target_fps):
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super().__init__(name="workshop-camera-%s" % config["camera_id"], daemon=True)
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self.config = config
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self.target_fps = target_fps
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self._lock = threading.Lock()
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self._running = True
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self._frame = None
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self._timestamp = 0.0
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self._sequence = 0
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self._health = {"stream_health": "connecting", "stalled_sec": 0.0}
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def run(self):
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from app.analysis.frames import FrameSource
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source = FrameSource(self.config["rtsp_url"], target_fps=max(2, int(self.target_fps * 2)))
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try:
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while self._running:
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ok, frame = source.read()
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with self._lock:
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self._health = source.health_snapshot()
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if ok and frame is not None:
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self._frame = frame
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self._timestamp = time.time()
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self._sequence += 1
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if not ok:
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time.sleep(0.1)
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finally:
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source.close()
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def latest(self):
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with self._lock:
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return self._sequence, self._timestamp, self._frame, dict(self._health)
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def close(self):
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self._running = False
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def _build_engine(spec):
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from app.analysis.engines.factory import EngineFactory
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engine = EngineFactory.create(
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spec["inference_engine"], model_file=spec["model_file"], labels=spec.get("labels") or [],
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input_size=tuple(spec.get("input_size") or (640, 640)),
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conf_threshold=float(spec.get("conf_threshold", 0.4)),
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iou_threshold=float(spec.get("iou_threshold", 0.5)),
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algorithm_type=spec.get("algorithm_type", "yolo"),
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task_type=spec.get("task_type", "detect"), device=spec.get("device", "cpu"),
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target_labels=spec.get("target_labels") or [],
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)
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if not engine.load():
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raise RuntimeError("模型加载失败: %s" % spec.get("name", spec.get("model_file", "")))
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return engine
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def workshop_worker_main(config, state_queue, command_queue):
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readers = []
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try:
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detector = _build_engine(config["detector"])
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reid = _build_engine(config["reid_model"]) if config.get("reid_model") else None
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tracker_cls = __import__("app.analysis.tracker", fromlist=["IoUTracker"]).IoUTracker
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trackers = {int(c["camera_id"]): tracker_cls() for c in config["cameras"]}
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readers = [_CameraReader(c, config["analysis_fps"]) for c in config["cameras"]]
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for reader in readers:
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reader.start()
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fusion = GlobalFusionTracker(
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config["fusion_radius_m"], config["observation_window_sec"],
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config["max_speed_mps"], config["lost_ttl_sec"], config["trail_sec"],
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)
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last_seq = {int(c["camera_id"]): 0 for c in config["cameras"]}
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last_process = {int(c["camera_id"]): 0.0 for c in config["cameras"]}
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latest_observations = {int(c["camera_id"]): [] for c in config["cameras"]}
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frame_index = {int(c["camera_id"]): 0 for c in config["cameras"]}
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state_sequence = 0
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period = 1.0 / max(0.1, float(config["analysis_fps"]))
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running = True
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while running:
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try:
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while True:
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command = command_queue.get_nowait()
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if command.get("cmd") == "stop":
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running = False
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except queue.Empty:
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pass
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if not running:
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break
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changed = False
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camera_states = []
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now = time.time()
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for camera, reader in zip(config["cameras"], readers):
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cid = int(camera["camera_id"])
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sequence, captured_at, frame, health = reader.latest()
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if frame is not None and sequence != last_seq[cid] and now - last_process[cid] >= period:
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last_seq[cid] = sequence
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last_process[cid] = now
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frame_index[cid] += 1
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try:
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detections = [d for d in detector.detect(frame)
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if d.get("label") in config["target_labels"]]
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active, _ended, _new, _idx = trackers[cid].update(
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detections, frame_index[cid], timestamp=captured_at)
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confirmed = [t for t in active if t.get("confirmed") and t.get("observed")]
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embeddings = {}
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if reid and confirmed:
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valid, values = reid.extract_embeddings(frame, [t["box"] for t in confirmed])
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for output_index, track_index in enumerate(valid):
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embeddings[confirmed[track_index]["track_id"]] = values[output_index]
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h, w = frame.shape[:2]
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observations = []
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calibration = camera["calibration"]
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for track in confirmed:
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world = foot_point_world(track["box"], calibration["homography"],
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config["width_m"], config["height_m"])
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if world is None:
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continue
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box = [float(track["box"][0]) / w, float(track["box"][1]) / h,
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float(track["box"][2]) / w, float(track["box"][3]) / h]
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observations.append({
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"camera_id": cid, "stream_id": camera["stream_id"],
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"local_track_id": int(track["track_id"]), "class": track.get("label", "person"),
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"score": float(track.get("score", 0)), "bbox": box,
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"x": world[0], "y": world[1], "timestamp": captured_at,
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"calibration_error_m": calibration.get("validation_mean_m") or 1.0,
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"embedding": embeddings.get(track["track_id"]),
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})
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latest_observations[cid] = observations
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health["analysis_health"] = "running"
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health["analysis_error"] = ""
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changed = True
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except Exception as exc:
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health["analysis_health"] = "error"
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health["analysis_error"] = str(exc)
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fresh = [o for o in latest_observations[cid]
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if now - float(o.get("timestamp", 0)) <= config["observation_window_sec"]]
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camera_states.append({
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"camera_id": cid, "stream_id": camera["stream_id"], "slot": camera["slot"],
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"display_name": camera["display_name"], **health, "observations": fresh,
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})
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if changed or state_sequence == 0:
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all_observations = [o for rows in latest_observations.values() for o in rows]
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targets = fusion.update(all_observations, now)
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for obs in all_observations:
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choices = [t for t in targets if t["class"] == obs["class"] and
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obs["camera_id"] in t["source_camera_ids"]]
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if choices:
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obs["global_id"] = min(choices, key=lambda t: (t["x"] - obs["x"]) ** 2 +
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(t["y"] - obs["y"]) ** 2)["global_id"]
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obs.pop("embedding", None)
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state_sequence += 1
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payload = {"kind": "state", "sequence": state_sequence, "timestamp": now,
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"running": True, "cameras": camera_states, "targets": targets}
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try:
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while True:
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state_queue.get_nowait()
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except queue.Empty:
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pass
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state_queue.put(payload)
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time.sleep(0.02)
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except Exception as exc:
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state_queue.put({"kind": "error", "running": False, "error": str(exc), "timestamp": time.time()})
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finally:
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for reader in readers:
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reader.close()
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for reader in readers:
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reader.join(timeout=2.0)
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state_queue.put({"kind": "stopped", "running": False, "timestamp": time.time()})
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class WorkshopRuntimeManager:
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_instance = None
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_instance_lock = threading.Lock()
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def __new__(cls):
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if cls._instance is None:
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with cls._instance_lock:
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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cls._instance._initialized = False
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return cls._instance
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def __init__(self):
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if self._initialized:
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return
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self._initialized = True
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self._lock = threading.RLock()
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self._process = None
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self._state_queue = None
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self._command_queue = None
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self._listener = None
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self._state = {"running": False, "sequence": 0, "cameras": [], "targets": []}
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def _listen(self):
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while self._process is not None:
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try:
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message = self._state_queue.get(timeout=0.5)
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with self._lock:
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self._state = message
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except queue.Empty:
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if self._process is not None and not self._process.is_alive():
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with self._lock:
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self._state = {**self._state, "running": False}
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if self._state.get("kind") != "stopped" and not self._state.get("error"):
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self._state["error"] = "车间定位进程已退出"
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break
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def start(self, config):
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with self._lock:
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if self._process is not None and self._process.is_alive():
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return True, "already running"
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context = mp.get_context("spawn")
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self._state_queue = context.Queue(maxsize=4)
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self._command_queue = context.Queue(maxsize=8)
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self._process = context.Process(
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target=workshop_worker_main, args=(config, self._state_queue, self._command_queue),
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name="workshop-monitor", daemon=True,
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)
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self._state = {"running": True, "sequence": 0, "cameras": [], "targets": [],
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"timestamp": time.time()}
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self._process.start()
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self._listener = threading.Thread(target=self._listen, name="workshop-state-listener", daemon=True)
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self._listener.start()
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return True, "started"
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def stop(self):
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with self._lock:
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process = self._process
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if process is None or not process.is_alive():
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self._state = {**self._state, "running": False}
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return True, "already stopped"
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try:
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self._command_queue.put({"cmd": "stop"}, timeout=1.0)
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except Exception:
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pass
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process.join(timeout=8.0)
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if process.is_alive():
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process.terminate()
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process.join(timeout=3.0)
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with self._lock:
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self._process = None
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self._state = {**self._state, "running": False}
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return True, "stopped"
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def snapshot(self, since=None):
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with self._lock:
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state = dict(self._state)
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sequence = int(state.get("sequence") or 0)
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if since is not None and sequence <= int(since or 0):
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return {"changed": False, "sequence": sequence, "running": bool(state.get("running")),
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"timestamp": state.get("timestamp"), "error": state.get("error", "")}
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state["changed"] = True
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return state
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def get_runtime_manager():
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return WorkshopRuntimeManager()
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