a5ddc53cf0
Add FaceRecognizer class that loads face encodings from the database, supports runtime add_encoding(), and matches new encodings by L2 distance. face_recognition import is deferred so the class works without dlib installed. FaceResult dataclass carries profile_id, name, confidence, crop, and bbox. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
49 lines
1.2 KiB
Python
49 lines
1.2 KiB
Python
import numpy as np
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import pytest
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from vigilar.detection.face import FaceRecognizer, FaceResult
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def test_face_recognizer_init():
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fr = FaceRecognizer(match_threshold=0.6)
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assert fr._threshold == 0.6
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assert fr.is_loaded is False
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def test_load_profiles_empty(test_db):
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fr = FaceRecognizer()
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fr.load_profiles(test_db)
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assert fr.is_loaded is True
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assert len(fr._known_encodings) == 0
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def test_add_encoding():
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fr = FaceRecognizer()
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fr.is_loaded = True
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enc = np.random.rand(128).astype(np.float32)
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fr.add_encoding(1, enc)
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assert len(fr._known_encodings) == 1
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def test_find_match():
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fr = FaceRecognizer(match_threshold=0.6)
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fr.is_loaded = True
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enc = np.random.rand(128).astype(np.float32)
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fr.add_encoding(1, enc)
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result = fr._find_match(enc)
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assert result is not None
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assert result[0] == 1
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def test_no_match():
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fr = FaceRecognizer(match_threshold=0.6)
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fr.is_loaded = True
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fr.add_encoding(1, np.ones(128, dtype=np.float32))
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result = fr._find_match(-np.ones(128, dtype=np.float32))
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assert result is None
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def test_face_result_dataclass():
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crop = np.zeros((100, 100, 3), dtype=np.uint8)
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r = FaceResult(1, "Bob", 0.85, crop, (10, 20, 50, 50))
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assert r.profile_id == 1
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