8314a61815
Task 1 — Presence: ping family phones, derive household state (EMPTY/KIDS_HOME/ADULTS_HOME/ALL_HOME), configurable departure delay, per-member roles, auto-arm actions via MQTT. Task 2 — Detection: MobileNet-SSD v2 via OpenCV DNN for person/vehicle classification. Vehicle color/size fingerprinting for known car matching. Zone-based filtering per camera. Model download script. Task 3 — Health: periodic disk/MQTT/subsystem checks, auto-prune oldest non-starred recordings on disk pressure, daily digest builder. 126 tests passing. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
"""Tests for person and vehicle detection."""
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import numpy as np
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from vigilar.detection.person import Detection, PersonDetector
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from vigilar.detection.vehicle import classify_dominant_color, classify_size
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from vigilar.detection.zones import filter_detections_by_zone
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class TestPersonDetector:
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def test_detector_initializes_without_model(self):
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detector = PersonDetector(model_path="nonexistent.pb", config_path="nonexistent.pbtxt")
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assert not detector.is_loaded
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def test_detect_returns_empty_when_not_loaded(self):
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detector = PersonDetector(model_path="nonexistent.pb", config_path="nonexistent.pbtxt")
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frame = np.zeros((480, 640, 3), dtype=np.uint8)
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detections = detector.detect(frame)
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assert detections == []
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def test_detection_dataclass(self):
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d = Detection(class_name="person", class_id=1, confidence=0.85, bbox=(10, 20, 100, 200))
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assert d.class_name == "person"
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assert d.confidence == 0.85
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class TestVehicleColor:
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def test_white_detection(self):
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white_region = np.full((100, 100, 3), 240, dtype=np.uint8)
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color = classify_dominant_color(white_region)
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assert color == "white"
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def test_black_detection(self):
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black_region = np.full((100, 100, 3), 15, dtype=np.uint8)
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color = classify_dominant_color(black_region)
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assert color == "black"
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def test_size_compact(self):
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assert classify_size(bbox_area=5000, zone_area=100000) == "compact"
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def test_size_midsize(self):
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assert classify_size(bbox_area=15000, zone_area=100000) == "midsize"
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def test_size_large(self):
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assert classify_size(bbox_area=30000, zone_area=100000) == "large"
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class TestZoneFiltering:
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def test_detection_inside_zone(self):
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detections = [Detection("person", 1, 0.9, (50, 50, 80, 80))]
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zone_region = (0, 0, 200, 200)
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filtered = filter_detections_by_zone(detections, zone_region, ["person"])
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assert len(filtered) == 1
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def test_detection_outside_zone(self):
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detections = [Detection("person", 1, 0.9, (300, 300, 50, 50))]
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zone_region = (0, 0, 200, 200)
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filtered = filter_detections_by_zone(detections, zone_region, ["person"])
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assert len(filtered) == 0
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def test_filter_by_class(self):
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detections = [
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Detection("person", 1, 0.9, (50, 50, 80, 80)),
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Detection("car", 3, 0.8, (50, 50, 80, 80)),
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]
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zone_region = (0, 0, 200, 200)
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filtered = filter_detections_by_zone(detections, zone_region, ["person"])
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assert len(filtered) == 1
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assert filtered[0].class_name == "person"
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def test_no_zone_returns_all(self):
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detections = [Detection("person", 1, 0.9, (50, 50, 80, 80))]
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filtered = filter_detections_by_zone(detections, None, ["person", "car"])
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assert len(filtered) == 1
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