Add the features analytics collector

frigate/analytics/collectors/features.py

@@ -0,0 +1,145 @@
+"""Features section: enrichments, GenAI, integrations, and users."""
+
+from collections import Counter
+
+from frigate.analytics.collectors.common import closed, histogram
+from frigate.analytics.context import ReportContext
+from frigate.analytics.schema import (
+    BirdseyeUsage,
+    ClassificationUsage,
+    EnrichmentDevice,
+    EnrichmentUsage,
+    FeaturesSection,
+    GenAIUsage,
+    SemanticSearchModel,
+    SemanticSearchUsage,
+    TranscriptionModel,
+    TranscriptionUsage,
+    UserRole,
+)
+from frigate.config.camera.genai import GenAIProviderEnum, GenAIRoleEnum
+from frigate.const import REPLAY_CAMERA_PREFIX
+from frigate.models import User
+
+RUNTIME_DEVICES = {
+    "cpu": EnrichmentDevice.cpu,
+    "cuda": EnrichmentDevice.cuda,
+    "tensorrt": EnrichmentDevice.tensorrt,
+    "migraphx": EnrichmentDevice.migraphx,
+}
+OPENVINO_DEVICES = {
+    "cpu": EnrichmentDevice.openvino_cpu,
+    "gpu": EnrichmentDevice.openvino_gpu,
+    "npu": EnrichmentDevice.openvino_npu,
+}
+
+
+def enrichment_device(label: object) -> EnrichmentDevice | None:
+    """Map a runner's device label, like "CUDA" or "OpenVINO GPU.0,CPU"."""
+    if not isinstance(label, str) or not label:
+        return None
+
+    runtime, _, target = label.partition(" ")
+
+    if runtime == "OpenVINO":
+        first = target.split(",")[0].split(".")[0].strip().lower()
+        return OPENVINO_DEVICES.get(first, EnrichmentDevice.other)
+
+    return RUNTIME_DEVICES.get(label.lower(), EnrichmentDevice.other)
+
+
+def model_name(model: object) -> str | None:
+    if model is None:
+        return None
+
+    return str(getattr(model, "value", model))
+
+
+def semantic_model(model: object) -> SemanticSearchModel | None:
+    # any string that isn't a built-in model names a GenAI provider
+    name = model_name(model)
+
+    if name is None:
+        return None
+
+    if name in ("jinav1", "jinav2"):
+        return SemanticSearchModel(name)
+
+    return SemanticSearchModel.genai
+
+
+def transcription_model(model: object) -> TranscriptionModel | None:
+    name = model_name(model)
+
+    if name is None:
+        return None
+
+    return TranscriptionModel.whisper if name == "whisper" else TranscriptionModel.genai
+
+
+def users() -> dict[UserRole, int]:
+    roles: Counter[UserRole] = Counter(
+        closed(UserRole, user.role, UserRole.custom) for user in User.select(User.role)
+    )
+    return histogram(roles)
+
+
+def collect(ctx: ReportContext) -> FeaturesSection:
+    config = ctx.config
+    devices = ctx.stats.get("embeddings", {}).get("devices", {})
+    cameras = [
+        camera
+        for name, camera in config.cameras.items()
+        if not name.startswith(REPLAY_CAMERA_PREFIX)
+    ]
+    providers: Counter[GenAIProviderEnum] = Counter(
+        genai.provider for genai in config.genai.values()
+    )
+    roles: Counter[GenAIRoleEnum] = Counter(
+        role for genai in config.genai.values() for role in genai.roles
+    )
+    custom = list(config.classification.custom.values())
+
+    return FeaturesSection(
+        face_recognition=EnrichmentUsage(
+            enabled=config.face_recognition.enabled,
+            model_size=config.face_recognition.model_size,
+            device=enrichment_device(devices.get("face_recognition")),
+        ),
+        lpr=EnrichmentUsage(
+            enabled=config.lpr.enabled,
+            model_size=config.lpr.model_size,
+            device=enrichment_device(devices.get("lpr")),
+        ),
+        semantic_search=SemanticSearchUsage(
+            enabled=config.semantic_search.enabled,
+            model=semantic_model(config.semantic_search.model),
+            model_size=config.semantic_search.model_size,
+            device=enrichment_device(devices.get("semantic_search")),
+            triggers=sum(len(camera.semantic_search.triggers) for camera in cameras),
+        ),
+        audio_transcription=TranscriptionUsage(
+            enabled=config.audio_transcription.enabled,
+            model=transcription_model(config.audio_transcription.model),
+            model_size=config.audio_transcription.model_size,
+        ),
+        genai=GenAIUsage(providers=histogram(providers), roles=histogram(roles)),
+        classification_models=ClassificationUsage(
+            state=sum(1 for model in custom if model.state_config is not None),
+            object=sum(1 for model in custom if model.object_config is not None),
+        ),
+        birdseye=BirdseyeUsage(
+            enabled=config.birdseye.enabled,
+            modes=list(config.birdseye.modes),
+            restream=config.birdseye.restream,
+        ),
+        mqtt=config.mqtt.enabled,
+        notifications=config.notifications.enabled,
+        auth=config.auth.enabled,
+        proxy_auth=config.proxy.header_map.user is not None,
+        tls=config.tls.enabled,
+        users=users(),
+        camera_groups=len(config.camera_groups),
+        profiles=len(config.profiles),
+        plus_api_key=config.plus_api.is_active(),
+    )

frigate/test/test_analytics_collectors_features.py

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+"""Tests for the features collector."""
+
+import logging
+import os
+import unittest
+
+from peewee_migrate import Router
+from playhouse.sqlite_ext import SqliteExtDatabase
+from playhouse.sqliteq import SqliteQueueDatabase
+
+from frigate.analytics.collectors import features
+from frigate.analytics.schema import EnrichmentDevice, SemanticSearchModel
+from frigate.models import User
+from frigate.test.analytics_helpers import FRONT_CAMERA, make_config, make_context
+from frigate.test.const import TEST_DB, TEST_DB_CLEANUPS
+
+CONFIG = {
+    "mqtt": {"host": "mqtt", "enabled": True},
+    "genai": {
+        "local": {
+            "provider": "ollama",
+            "model": "llava",
+            "base_url": "http://ollama:11434",
+            "roles": ["descriptions", "embeddings"],
+        }
+    },
+    "semantic_search": {"enabled": True, "model": "local"},
+    "face_recognition": {"enabled": True, "model_size": "large"},
+    "birdseye": {"enabled": True, "modes": ["motion", "alerts"], "restream": True},
+    "proxy": {"header_map": {"user": "x-forwarded-user"}},
+    "camera_groups": {"outside": {"cameras": ["front"], "icon": "LuCar", "order": 0}},
+    "cameras": {
+        "front": {
+            **FRONT_CAMERA,
+            "semantic_search": {
+                "triggers": {
+                    "cat": {
+                        "type": "description",
+                        "data": "a cat",
+                        "threshold": 0.8,
+                        "actions": ["notification"],
+                    }
+                }
+            },
+        }
+    },
+}
+
+
+class TestFeaturesCollector(unittest.TestCase):
+    def setUp(self):
+        migrate_db = SqliteExtDatabase(TEST_DB)
+        del logging.getLogger("peewee_migrate").handlers[:]
+        Router(migrate_db).run()
+        migrate_db.close()
+        self.db = SqliteQueueDatabase(TEST_DB)
+        self.db.bind([User])
+
+    def tearDown(self):
+        # close() leaves the queue's writer thread running against the deleted file
+        self.db.stop()
+
+        if not self.db.is_closed():
+            self.db.close()
+
+        for file in TEST_DB_CLEANUPS:
+            try:
+                os.remove(file)
+            except OSError:
+                pass
+
+    def test_collects_enrichments_genai_integrations_and_users(self):
+        for username, role in (("a", "admin"), ("v", "viewer"), ("o", "operator")):
+            User.create(
+                username=username, role=role, password_hash="x", notification_tokens=[]
+            )
+
+        stats = {
+            "embeddings": {
+                "devices": {
+                    "face_recognition": "OpenVINO GPU.0",
+                    "semantic_search": "CUDA",
+                }
+            }
+        }
+
+        section = features.collect(make_context(make_config(CONFIG), stats))
+
+        self.assertEqual(section.face_recognition.device, EnrichmentDevice.openvino_gpu)
+        self.assertEqual(section.face_recognition.model_size, "large")
+        self.assertIsNone(section.lpr.device)
+        self.assertEqual(section.semantic_search.model, SemanticSearchModel.genai)
+        self.assertEqual(section.semantic_search.device, EnrichmentDevice.cuda)
+        self.assertEqual(section.semantic_search.triggers, 1)
+        self.assertEqual(section.genai.providers, {"ollama": 1})
+        self.assertEqual(section.genai.roles, {"descriptions": 1, "embeddings": 1})
+        self.assertEqual(section.birdseye.modes, ["motion", "alerts"])
+        self.assertTrue(section.mqtt)
+        self.assertTrue(section.proxy_auth)
+        self.assertEqual(section.users, {"admin": 1, "viewer": 1, "custom": 1})
+        self.assertEqual((section.camera_groups, section.profiles), (1, 0))
+        self.assertFalse(section.plus_api_key)
+
+
+class TestFeatureMappings(unittest.TestCase):
+    def test_enrichment_device_labels(self):
+        cases = {
+            "CPU": EnrichmentDevice.cpu,
+            "MIGraphX": EnrichmentDevice.migraphx,
+            "OpenVINO NPU": EnrichmentDevice.openvino_npu,
+            "OpenVINO GPU.0,CPU": EnrichmentDevice.openvino_gpu,
+            "OpenVINO": EnrichmentDevice.other,
+            "CoreML": EnrichmentDevice.other,
+        }
+
+        for label, device in cases.items():
+            with self.subTest(label=label):
+                self.assertEqual(features.enrichment_device(label), device)
+
+        self.assertIsNone(features.enrichment_device(None))
+
+    def test_models_named_after_a_provider_are_genai(self):
+        self.assertEqual(features.semantic_model("jinav2"), SemanticSearchModel.jinav2)
+        self.assertEqual(
+            features.semantic_model("my-ollama"), SemanticSearchModel.genai
+        )
+        self.assertIsNone(features.semantic_model(None))
+        self.assertEqual(features.transcription_model("openai"), "genai")