Wire detection pipeline: throttle YOLO, save crops, insert sightings, route person/vehicle

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

vigilar/camera/worker.py

@@ -21,6 +21,7 @@ from vigilar.camera.recorder import AdaptiveRecorder
 from vigilar.camera.ring_buffer import RingBuffer
 from vigilar.config import CameraConfig, MQTTConfig, PetsConfig, RemoteConfig
 from vigilar.constants import Topics
+from vigilar.detection.crop_manager import CropManager
 from vigilar.detection.pet_id import PetIDClassifier
 from vigilar.detection.wildlife import classify_wildlife_threat
 from vigilar.detection.yolo import YOLODetector
@@ -113,6 +114,7 @@ def run_camera_worker(
     # Object detection (YOLOv8 unified detector)
     yolo_detector = None
     pet_classifier = None
+    crop_manager = None
     if pets_cfg and pets_cfg.enabled:
         yolo_detector = YOLODetector(
             model_path=pets_cfg.model_path,
@@ -124,9 +126,14 @@ def run_camera_worker(
                 high_threshold=pets_cfg.pet_id_threshold,
                 low_threshold=pets_cfg.pet_id_low_confidence,
             )
+        crop_manager = CropManager(
+            staging_dir=pets_cfg.crop_staging_dir,
+            training_dir=pets_cfg.training_dir,
+        )
 
     state = CameraState()
     shutdown = False
+    last_detection_time: float = 0
 
     def handle_signal(signum, frame):
         nonlocal shutdown
@@ -261,15 +268,24 @@ def run_camera_worker(
                 if state.frame_count % idle_skip_factor == 0:
                     recorder.write_frame(frame)
 
-        # Run object detection on motion frames
+        # Run object detection on motion frames — throttled to 1 inference/second
-        if state.motion_active and yolo_detector and yolo_detector.is_loaded:
+        if (state.motion_active and yolo_detector and yolo_detector.is_loaded
+                and now - last_detection_time >= 1.0):
+            last_detection_time = now
             detections = yolo_detector.detect(frame)
             for det in detections:
                 category = YOLODetector.classify(det)
                 if category == "domestic_animal":
-                    # Crop for pet ID
+                    # Crop for pet ID and staging
                     x, y, w, h = det.bbox
                     crop = frame[max(0, y):y + h, max(0, x):x + w]
+
+                    crop_path = None
+                    if crop_manager and crop.size > 0:
+                        crop_path = crop_manager.save_staging_crop(
+                            crop, species=det.class_name, camera_id=camera_id
+                        )
+
                     pet_result = None
                     if pet_classifier and pet_classifier.is_loaded and crop.size > 0:
                         pet_result = pet_classifier.identify(crop, species=det.class_name)
@@ -278,6 +294,7 @@ def run_camera_worker(
                         "species": det.class_name,
                         "confidence": round(det.confidence, 3),
                         "camera_location": camera_cfg.location,
+                        "crop_path": crop_path,
                     }
                     if pet_result and pet_result.is_identified:
                         payload["pet_id"] = pet_result.pet_id
@@ -302,6 +319,21 @@ def run_camera_worker(
                         threat_level=threat_level,
                         confidence=round(det.confidence, 3),
                         camera_location=camera_cfg.location,
+                        crop_path=None,
+                    )
+
+                elif category == "person":
+                    bus.publish_event(
+                        Topics.camera_motion_start(camera_id),
+                        detection="person",
+                        confidence=round(det.confidence, 3),
+                    )
+
+                elif category == "vehicle":
+                    bus.publish_event(
+                        Topics.camera_motion_start(camera_id),
+                        detection="vehicle",
+                        confidence=round(det.confidence, 3),
                     )
 
         # Heartbeat every 10 seconds

vigilar/events/processor.py

@@ -101,6 +101,34 @@ class EventProcessor:
                 payload=payload,
             )
 
+            # Insert pet/wildlife sightings
+            if event_type in (
+                EventType.PET_DETECTED, EventType.PET_ESCAPE, EventType.UNKNOWN_ANIMAL
+            ):
+                from vigilar.storage.queries import insert_pet_sighting
+                insert_pet_sighting(
+                    engine,
+                    pet_id=payload.get("pet_id"),
+                    species=payload.get("species", "unknown"),
+                    camera_id=source_id or "",
+                    confidence=payload.get("confidence", 0.0),
+                    crop_path=payload.get("crop_path"),
+                    event_id=event_id,
+                )
+            elif event_type in (
+                EventType.WILDLIFE_PREDATOR, EventType.WILDLIFE_NUISANCE, EventType.WILDLIFE_PASSIVE
+            ):
+                from vigilar.storage.queries import insert_wildlife_sighting
+                insert_wildlife_sighting(
+                    engine,
+                    species=payload.get("species", "unknown"),
+                    threat_level=payload.get("threat_level", "PASSIVE"),
+                    camera_id=source_id or "",
+                    confidence=payload.get("confidence", 0.0),
+                    crop_path=payload.get("crop_path"),
+                    event_id=event_id,
+                )
+
             # Evaluate rules
             actions = rule_engine.evaluate(topic, payload, fsm.state)