Initial commit: digital reception monorepo (M1-M11 + demo extensions)

apps/face-service/tests/test_reid.py

@@ -0,0 +1,124 @@
+"""Тесты cross-camera re-id логики.
+
+Используем синтетические 512-мерные эмбеддинги (без InsightFace).
+Проверяем, что find_topk_in_window:
+  1. Возвращает соседей в правильном порядке по cos-дистанции.
+  2. Фильтрует по camera_id (исключает ту же камеру).
+  3. Фильтрует по временному окну.
+"""
+from datetime import datetime, timedelta
+
+import numpy as np
+import pytest
+
+from database import (
+    save_embedding_with_meta,
+    find_topk_in_window,
+    find_nearest_patient,
+    attach_track_to_patient,
+    delete_patient_embeddings,
+)
+
+
+def normed(vec: np.ndarray) -> np.ndarray:
+    return (vec / np.linalg.norm(vec)).astype(np.float32)
+
+
+def make_embedding(seed: int) -> np.ndarray:
+    rng = np.random.default_rng(seed)
+    return normed(rng.standard_normal(512))
+
+
+def test_topk_in_window_basic(seed_camera_and_track):
+    """Из 3 эмбеддингов на 3 разных камерах находим 2 ближайших к query (исключая саму камеру query)."""
+    cam_a, track_a = seed_camera_and_track("A")
+    cam_b, track_b = seed_camera_and_track("B")
+    cam_c, track_c = seed_camera_and_track("C")
+
+    base = make_embedding(seed=42)
+
+    # Соседи: tweak base слегка для cam_b, сильнее для cam_c.
+    near = normed(base + 0.05 * make_embedding(seed=43))
+    far = normed(base + 0.5 * make_embedding(seed=44))
+
+    save_embedding_with_meta(base, track_a, cam_a, quality=0.9, captured_at=datetime.utcnow())
+    save_embedding_with_meta(near, track_b, cam_b, quality=0.9, captured_at=datetime.utcnow())
+    save_embedding_with_meta(far, track_c, cam_c, quality=0.9, captured_at=datetime.utcnow())
+
+    # Запрос с cam_a — должен вернуть cam_b раньше cam_c, cam_a исключаем.
+    results = find_topk_in_window(
+        embedding=base.tolist(),
+        camera_id=cam_a,
+        window_minutes=5,
+        k=5,
+        exclude_same_camera=True,
+    )
+
+    cam_ids_in_results = [r["camera_id"] for r in results]
+    assert cam_a not in cam_ids_in_results
+    assert cam_b in cam_ids_in_results
+    assert cam_c in cam_ids_in_results
+    # Порядок: ближе → дальше
+    assert results[0]["camera_id"] == cam_b
+    assert results[0]["distance"] < results[-1]["distance"]
+
+
+def test_topk_filters_by_window(seed_camera_and_track):
+    """Старый эмбеддинг (вне окна) не должен попадать в результат."""
+    cam_a, track_a = seed_camera_and_track("A")
+    cam_b, track_b = seed_camera_and_track("B")
+
+    base = make_embedding(seed=7)
+
+    save_embedding_with_meta(
+        base, track_b, cam_b, quality=0.9,
+        captured_at=datetime.utcnow() - timedelta(hours=1),  # вне окна 5 мин
+    )
+
+    results = find_topk_in_window(
+        embedding=base.tolist(),
+        camera_id=cam_a,
+        window_minutes=5,
+        k=5,
+    )
+
+    cam_ids = [r["camera_id"] for r in results]
+    assert cam_b not in cam_ids
+
+
+def test_find_nearest_patient_only_consented(db_conn, seed_camera_and_track):
+    """find_nearest_patient ищет только среди эмбеддингов с patient_id IS NOT NULL."""
+    cam_a, track_a = seed_camera_and_track("A")
+    base = make_embedding(seed=100)
+
+    # Сохраняем эмбеддинг без patient_id.
+    save_embedding_with_meta(base, track_a, cam_a, quality=0.9)
+
+    # Ищем — никого не должно найти.
+    assert find_nearest_patient(base.tolist(), threshold=0.5) is None
+
+    # Создаём пациента и привязываем трек.
+    import uuid
+    patient_id = str(uuid.uuid4())
+    with db_conn.cursor() as cur:
+        cur.execute(
+            "INSERT INTO patients (id, full_name, updated_at) VALUES (%s, %s, NOW())",
+            (patient_id, "Тестовый Пациент"),
+        )
+    db_conn.commit()
+
+    affected = attach_track_to_patient(track_a, patient_id)
+    assert affected == 1
+
+    # Теперь должны найти.
+    result = find_nearest_patient(base.tolist(), threshold=0.5)
+    assert result is not None
+    assert result["patient_id"] == patient_id
+    assert result["distance"] < 0.01  # тот же эмбеддинг
+
+    # Очистка.
+    deleted = delete_patient_embeddings(patient_id)
+    assert deleted == 1
+    with db_conn.cursor() as cur:
+        cur.execute("DELETE FROM patients WHERE id = %s", (patient_id,))
+    db_conn.commit()