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https://github.com/blakeblackshear/frigate.git
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Add the features analytics collector
This commit is contained in:
Josh Hawkins
@@ -0,0 +1,145 @@
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"""Features section: enrichments, GenAI, integrations, and users."""
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from collections import Counter
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from frigate.analytics.collectors.common import closed, histogram
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from frigate.analytics.context import ReportContext
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from frigate.analytics.schema import (
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BirdseyeUsage,
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ClassificationUsage,
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EnrichmentDevice,
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EnrichmentUsage,
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FeaturesSection,
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GenAIUsage,
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SemanticSearchModel,
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SemanticSearchUsage,
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TranscriptionModel,
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TranscriptionUsage,
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UserRole,
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)
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from frigate.config.camera.genai import GenAIProviderEnum, GenAIRoleEnum
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from frigate.const import REPLAY_CAMERA_PREFIX
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from frigate.models import User
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RUNTIME_DEVICES = {
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"cpu": EnrichmentDevice.cpu,
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"cuda": EnrichmentDevice.cuda,
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"tensorrt": EnrichmentDevice.tensorrt,
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"migraphx": EnrichmentDevice.migraphx,
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}
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OPENVINO_DEVICES = {
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"cpu": EnrichmentDevice.openvino_cpu,
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"gpu": EnrichmentDevice.openvino_gpu,
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"npu": EnrichmentDevice.openvino_npu,
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}
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def enrichment_device(label: object) -> EnrichmentDevice | None:
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"""Map a runner's device label, like "CUDA" or "OpenVINO GPU.0,CPU"."""
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if not isinstance(label, str) or not label:
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return None
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runtime, _, target = label.partition(" ")
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if runtime == "OpenVINO":
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first = target.split(",")[0].split(".")[0].strip().lower()
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return OPENVINO_DEVICES.get(first, EnrichmentDevice.other)
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return RUNTIME_DEVICES.get(label.lower(), EnrichmentDevice.other)
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def model_name(model: object) -> str | None:
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if model is None:
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return None
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return str(getattr(model, "value", model))
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def semantic_model(model: object) -> SemanticSearchModel | None:
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# any string that isn't a built-in model names a GenAI provider
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name = model_name(model)
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if name is None:
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return None
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if name in ("jinav1", "jinav2"):
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return SemanticSearchModel(name)
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return SemanticSearchModel.genai
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def transcription_model(model: object) -> TranscriptionModel | None:
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name = model_name(model)
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if name is None:
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return None
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return TranscriptionModel.whisper if name == "whisper" else TranscriptionModel.genai
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def users() -> dict[UserRole, int]:
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roles: Counter[UserRole] = Counter(
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closed(UserRole, user.role, UserRole.custom) for user in User.select(User.role)
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)
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return histogram(roles)
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def collect(ctx: ReportContext) -> FeaturesSection:
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config = ctx.config
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devices = ctx.stats.get("embeddings", {}).get("devices", {})
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cameras = [
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camera
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for name, camera in config.cameras.items()
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if not name.startswith(REPLAY_CAMERA_PREFIX)
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]
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providers: Counter[GenAIProviderEnum] = Counter(
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genai.provider for genai in config.genai.values()
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)
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roles: Counter[GenAIRoleEnum] = Counter(
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role for genai in config.genai.values() for role in genai.roles
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)
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custom = list(config.classification.custom.values())
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return FeaturesSection(
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face_recognition=EnrichmentUsage(
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enabled=config.face_recognition.enabled,
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model_size=config.face_recognition.model_size,
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device=enrichment_device(devices.get("face_recognition")),
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),
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lpr=EnrichmentUsage(
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enabled=config.lpr.enabled,
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model_size=config.lpr.model_size,
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device=enrichment_device(devices.get("lpr")),
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),
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semantic_search=SemanticSearchUsage(
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enabled=config.semantic_search.enabled,
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model=semantic_model(config.semantic_search.model),
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model_size=config.semantic_search.model_size,
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device=enrichment_device(devices.get("semantic_search")),
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triggers=sum(len(camera.semantic_search.triggers) for camera in cameras),
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),
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audio_transcription=TranscriptionUsage(
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enabled=config.audio_transcription.enabled,
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model=transcription_model(config.audio_transcription.model),
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model_size=config.audio_transcription.model_size,
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),
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genai=GenAIUsage(providers=histogram(providers), roles=histogram(roles)),
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classification_models=ClassificationUsage(
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state=sum(1 for model in custom if model.state_config is not None),
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object=sum(1 for model in custom if model.object_config is not None),
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),
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birdseye=BirdseyeUsage(
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enabled=config.birdseye.enabled,
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modes=list(config.birdseye.modes),
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restream=config.birdseye.restream,
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),
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mqtt=config.mqtt.enabled,
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notifications=config.notifications.enabled,
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auth=config.auth.enabled,
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proxy_auth=config.proxy.header_map.user is not None,
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tls=config.tls.enabled,
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users=users(),
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camera_groups=len(config.camera_groups),
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profiles=len(config.profiles),
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plus_api_key=config.plus_api.is_active(),
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)
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@@ -0,0 +1,128 @@
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"""Tests for the features collector."""
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import logging
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import os
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import unittest
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from peewee_migrate import Router
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from playhouse.sqlite_ext import SqliteExtDatabase
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from playhouse.sqliteq import SqliteQueueDatabase
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from frigate.analytics.collectors import features
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from frigate.analytics.schema import EnrichmentDevice, SemanticSearchModel
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from frigate.models import User
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from frigate.test.analytics_helpers import FRONT_CAMERA, make_config, make_context
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from frigate.test.const import TEST_DB, TEST_DB_CLEANUPS
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CONFIG = {
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"mqtt": {"host": "mqtt", "enabled": True},
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"genai": {
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"local": {
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"provider": "ollama",
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"model": "llava",
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"base_url": "http://ollama:11434",
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"roles": ["descriptions", "embeddings"],
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}
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},
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"semantic_search": {"enabled": True, "model": "local"},
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"face_recognition": {"enabled": True, "model_size": "large"},
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"birdseye": {"enabled": True, "modes": ["motion", "alerts"], "restream": True},
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"proxy": {"header_map": {"user": "x-forwarded-user"}},
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"camera_groups": {"outside": {"cameras": ["front"], "icon": "LuCar", "order": 0}},
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"cameras": {
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"front": {
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**FRONT_CAMERA,
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"semantic_search": {
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"triggers": {
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"cat": {
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"type": "description",
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"data": "a cat",
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"threshold": 0.8,
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"actions": ["notification"],
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}
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}
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},
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}
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},
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}
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class TestFeaturesCollector(unittest.TestCase):
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def setUp(self):
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migrate_db = SqliteExtDatabase(TEST_DB)
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del logging.getLogger("peewee_migrate").handlers[:]
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Router(migrate_db).run()
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migrate_db.close()
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self.db = SqliteQueueDatabase(TEST_DB)
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self.db.bind([User])
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def tearDown(self):
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# close() leaves the queue's writer thread running against the deleted file
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self.db.stop()
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if not self.db.is_closed():
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self.db.close()
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for file in TEST_DB_CLEANUPS:
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try:
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os.remove(file)
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except OSError:
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pass
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def test_collects_enrichments_genai_integrations_and_users(self):
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for username, role in (("a", "admin"), ("v", "viewer"), ("o", "operator")):
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User.create(
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username=username, role=role, password_hash="x", notification_tokens=[]
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)
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stats = {
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"embeddings": {
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"devices": {
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"face_recognition": "OpenVINO GPU.0",
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"semantic_search": "CUDA",
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}
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}
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}
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section = features.collect(make_context(make_config(CONFIG), stats))
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self.assertEqual(section.face_recognition.device, EnrichmentDevice.openvino_gpu)
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self.assertEqual(section.face_recognition.model_size, "large")
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self.assertIsNone(section.lpr.device)
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self.assertEqual(section.semantic_search.model, SemanticSearchModel.genai)
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self.assertEqual(section.semantic_search.device, EnrichmentDevice.cuda)
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self.assertEqual(section.semantic_search.triggers, 1)
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self.assertEqual(section.genai.providers, {"ollama": 1})
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self.assertEqual(section.genai.roles, {"descriptions": 1, "embeddings": 1})
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self.assertEqual(section.birdseye.modes, ["motion", "alerts"])
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self.assertTrue(section.mqtt)
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self.assertTrue(section.proxy_auth)
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self.assertEqual(section.users, {"admin": 1, "viewer": 1, "custom": 1})
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self.assertEqual((section.camera_groups, section.profiles), (1, 0))
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self.assertFalse(section.plus_api_key)
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class TestFeatureMappings(unittest.TestCase):
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def test_enrichment_device_labels(self):
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cases = {
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"CPU": EnrichmentDevice.cpu,
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"MIGraphX": EnrichmentDevice.migraphx,
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"OpenVINO NPU": EnrichmentDevice.openvino_npu,
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"OpenVINO GPU.0,CPU": EnrichmentDevice.openvino_gpu,
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"OpenVINO": EnrichmentDevice.other,
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"CoreML": EnrichmentDevice.other,
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}
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for label, device in cases.items():
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with self.subTest(label=label):
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self.assertEqual(features.enrichment_device(label), device)
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self.assertIsNone(features.enrichment_device(None))
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def test_models_named_after_a_provider_are_genai(self):
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self.assertEqual(features.semantic_model("jinav2"), SemanticSearchModel.jinav2)
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self.assertEqual(
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features.semantic_model("my-ollama"), SemanticSearchModel.genai
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)
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self.assertIsNone(features.semantic_model(None))
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self.assertEqual(features.transcription_model("openai"), "genai")
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