Add YOLOv8 unified detector with class classification

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

tests/unit/test_yolo_detector.py

@@ -0,0 +1,52 @@
+"""Tests for YOLOv8 unified detector."""
+
+import numpy as np
+import pytest
+
+from vigilar.detection.person import Detection
+from vigilar.detection.yolo import YOLODetector, ANIMAL_CLASSES, WILDLIFE_CLASSES
+
+
+class TestYOLOConstants:
+    def test_animal_classes(self):
+        assert "cat" in ANIMAL_CLASSES
+        assert "dog" in ANIMAL_CLASSES
+
+    def test_wildlife_classes(self):
+        assert "bear" in WILDLIFE_CLASSES
+        assert "bird" in WILDLIFE_CLASSES
+
+    def test_no_overlap_animal_wildlife(self):
+        assert not ANIMAL_CLASSES.intersection(WILDLIFE_CLASSES)
+
+
+class TestYOLODetector:
+    def test_initializes_without_model(self):
+        detector = YOLODetector(model_path="nonexistent.pt", confidence_threshold=0.5)
+        assert not detector.is_loaded
+
+    def test_detect_returns_empty_when_not_loaded(self):
+        detector = YOLODetector(model_path="nonexistent.pt")
+        frame = np.zeros((480, 640, 3), dtype=np.uint8)
+        detections = detector.detect(frame)
+        assert detections == []
+
+    def test_classify_detection_person(self):
+        d = Detection(class_name="person", class_id=0, confidence=0.9, bbox=(10, 20, 100, 200))
+        assert YOLODetector.classify(d) == "person"
+
+    def test_classify_detection_vehicle(self):
+        d = Detection(class_name="car", class_id=2, confidence=0.85, bbox=(10, 20, 100, 200))
+        assert YOLODetector.classify(d) == "vehicle"
+
+    def test_classify_detection_domestic_animal(self):
+        d = Detection(class_name="cat", class_id=15, confidence=0.9, bbox=(10, 20, 100, 200))
+        assert YOLODetector.classify(d) == "domestic_animal"
+
+    def test_classify_detection_wildlife(self):
+        d = Detection(class_name="bear", class_id=21, confidence=0.8, bbox=(10, 20, 100, 200))
+        assert YOLODetector.classify(d) == "wildlife"
+
+    def test_classify_detection_other(self):
+        d = Detection(class_name="chair", class_id=56, confidence=0.7, bbox=(10, 20, 100, 200))
+        assert YOLODetector.classify(d) == "other"