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2
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Josh Hawkins
|
b5a360be39 | ||
|
Josh Hawkins
|
54a7c5015e |
@@ -8,7 +8,6 @@ amdgpu
|
||||
analyzeduration
|
||||
Annke
|
||||
apexcharts
|
||||
Aqara
|
||||
arange
|
||||
argmax
|
||||
argmin
|
||||
@@ -65,7 +64,6 @@ dsize
|
||||
dtype
|
||||
ECONNRESET
|
||||
edgetpu
|
||||
Eufy
|
||||
facenet
|
||||
fastapi
|
||||
faststart
|
||||
@@ -84,7 +82,6 @@ frontdoor
|
||||
fstype
|
||||
fullchain
|
||||
fullscreen
|
||||
gatekeep
|
||||
genai
|
||||
generativeai
|
||||
genpts
|
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@@ -165,7 +162,6 @@ mpegts
|
||||
mqtt
|
||||
mse
|
||||
msenc
|
||||
muxing
|
||||
namedtuples
|
||||
nbytes
|
||||
nchw
|
||||
@@ -201,8 +197,6 @@ OWASP
|
||||
paddleocr
|
||||
paho
|
||||
passwordless
|
||||
PCMA
|
||||
PCMU
|
||||
popleft
|
||||
posthog
|
||||
postprocess
|
||||
@@ -228,9 +222,7 @@ radeontop
|
||||
rawvideo
|
||||
rcond
|
||||
RDONLY
|
||||
realmonitor
|
||||
rebranded
|
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recvonly
|
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referer
|
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reindex
|
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Reolink
|
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@@ -247,11 +239,8 @@ rocminfo
|
||||
rootfs
|
||||
rtmp
|
||||
RTSP
|
||||
rtsps
|
||||
rtspx
|
||||
ruamel
|
||||
scroller
|
||||
sendonly
|
||||
setproctitle
|
||||
setpts
|
||||
shms
|
||||
@@ -262,7 +251,6 @@ SNDMORE
|
||||
socs
|
||||
sqliteq
|
||||
sqlitevecq
|
||||
Srtp
|
||||
ssdlite
|
||||
statm
|
||||
stimeout
|
||||
|
||||
@@ -10,11 +10,7 @@ body:
|
||||
|
||||
Before submitting, read the [beta documentation][docs].
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[docs]: https://docs-dev.frigate.video/
|
||||
[discussions]: https://github.com/blakeblackshear/frigate/discussions
|
||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
|
||||
[docs]: https://deploy-preview-19787--frigate-docs.netlify.app/
|
||||
- type: textarea
|
||||
id: description
|
||||
attributes:
|
||||
@@ -26,8 +22,8 @@ body:
|
||||
id: version
|
||||
attributes:
|
||||
label: Beta Version
|
||||
description: Visible on the System Metrics page in the Web UI. Please include the full version including the build identifier (eg. 0.18.0-beta1, 0.18.0-8b72c7a, etc.)
|
||||
placeholder: "0.18.0-beta1"
|
||||
description: Visible on the System page in the Web UI. Please include the full version including the build identifier (eg. 0.17.0-beta1)
|
||||
placeholder: "0.17.0-beta1"
|
||||
validations:
|
||||
required: true
|
||||
- type: dropdown
|
||||
@@ -75,12 +71,11 @@ body:
|
||||
attributes:
|
||||
label: Install method
|
||||
options:
|
||||
- Home Assistant App
|
||||
- Home Assistant Add-on
|
||||
- Docker Compose
|
||||
- Docker CLI
|
||||
- Proxmox via Docker
|
||||
- Proxmox via installation script
|
||||
- Proxomox via VM
|
||||
- Proxmox via TTeck Script
|
||||
- Windows WSL2
|
||||
validations:
|
||||
required: true
|
||||
|
||||
@@ -8,12 +8,9 @@ body:
|
||||
|
||||
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
|
||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
|
||||
- type: textarea
|
||||
id: description
|
||||
attributes:
|
||||
@@ -90,12 +87,11 @@ body:
|
||||
attributes:
|
||||
label: Install method
|
||||
options:
|
||||
- Home Assistant App
|
||||
- Home Assistant Add-on
|
||||
- Docker Compose
|
||||
- Docker CLI
|
||||
- Proxmox via Docker
|
||||
- Proxmox via installation script
|
||||
- Proxomox via VM
|
||||
- Proxmox via TTeck Script
|
||||
- Windows WSL2
|
||||
validations:
|
||||
required: true
|
||||
|
||||
@@ -8,12 +8,9 @@ body:
|
||||
|
||||
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
|
||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
|
||||
- type: textarea
|
||||
id: description
|
||||
attributes:
|
||||
@@ -76,12 +73,11 @@ body:
|
||||
attributes:
|
||||
label: Install method
|
||||
options:
|
||||
- Home Assistant App
|
||||
- Home Assistant Add-on
|
||||
- Docker Compose
|
||||
- Docker CLI
|
||||
- Proxmox via Docker
|
||||
- Proxmox via installation script
|
||||
- Proxomox via VM
|
||||
- Proxmox via TTeck Script
|
||||
- Windows WSL2
|
||||
validations:
|
||||
required: true
|
||||
|
||||
@@ -8,12 +8,9 @@ body:
|
||||
|
||||
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
|
||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
|
||||
- type: textarea
|
||||
id: description
|
||||
attributes:
|
||||
@@ -56,12 +53,11 @@ body:
|
||||
attributes:
|
||||
label: Install method
|
||||
options:
|
||||
- Home Assistant App
|
||||
- Home Assistant Add-on
|
||||
- Docker Compose
|
||||
- Docker CLI
|
||||
- Proxmox via Docker
|
||||
- Proxmox via installation script
|
||||
- Proxomox via VM
|
||||
- Proxmox via TTeck Script
|
||||
- Windows WSL2
|
||||
validations:
|
||||
required: true
|
||||
|
||||
@@ -8,12 +8,9 @@ body:
|
||||
|
||||
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
|
||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
|
||||
- type: textarea
|
||||
id: description
|
||||
attributes:
|
||||
@@ -76,12 +73,11 @@ body:
|
||||
attributes:
|
||||
label: Install method
|
||||
options:
|
||||
- Home Assistant App
|
||||
- Home Assistant Add-on
|
||||
- Docker Compose
|
||||
- Docker CLI
|
||||
- Proxmox via Docker
|
||||
- Proxmox via installation script
|
||||
- Proxmox via VM
|
||||
- Proxmox via TTeck Script
|
||||
- Windows WSL2
|
||||
validations:
|
||||
required: true
|
||||
|
||||
@@ -8,12 +8,9 @@ body:
|
||||
|
||||
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
|
||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
|
||||
- type: textarea
|
||||
id: description
|
||||
attributes:
|
||||
@@ -72,12 +69,11 @@ body:
|
||||
attributes:
|
||||
label: Install method
|
||||
options:
|
||||
- Home Assistant App
|
||||
- Home Assistant Add-on
|
||||
- Docker Compose
|
||||
- Docker CLI
|
||||
- Proxmox via Docker
|
||||
- Proxmox via installation script
|
||||
- Proxomox via VM
|
||||
- Proxmox via TTeck Script
|
||||
- Windows WSL2
|
||||
validations:
|
||||
required: true
|
||||
|
||||
@@ -10,12 +10,9 @@ body:
|
||||
|
||||
**If you are looking for support, start a new discussion and use a support category.**
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
|
||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
|
||||
- type: textarea
|
||||
id: description
|
||||
attributes:
|
||||
|
||||
@@ -6,20 +6,17 @@ body:
|
||||
value: |
|
||||
Use this form to submit a reproducible bug in Frigate or Frigate's UI.
|
||||
|
||||
**⚠️ If you are running a beta version (0.18.0-beta or similar), please use the [Beta Support template](https://github.com/blakeblackshear/frigate/discussions/new?category=beta-support) instead.**
|
||||
**⚠️ If you are running a beta version (0.17.0-beta or similar), please use the [Beta Support template](https://github.com/blakeblackshear/frigate/discussions/new?category=beta-support) instead.**
|
||||
|
||||
Before submitting your bug report, please ask the AI with the "Ask AI" button on the [official documentation site][ai] about your issue, [search the discussions][discussions], look at recent open and closed [pull requests][prs], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your bug has already been fixed by the developers or reported by the community.
|
||||
|
||||
**If you are unsure if your issue is actually a bug or not, please submit a support request first.**
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[prs]: https://www.github.com/blakeblackshear/frigate/pulls
|
||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
|
||||
[ai]: https://docs.frigate.video
|
||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
|
||||
- type: checkboxes
|
||||
attributes:
|
||||
label: Checklist
|
||||
@@ -119,13 +116,9 @@ body:
|
||||
attributes:
|
||||
label: Install method
|
||||
options:
|
||||
- Home Assistant App
|
||||
- Home Assistant Add-on
|
||||
- Docker Compose
|
||||
- Docker CLI
|
||||
- Proxmox via Docker
|
||||
- Proxmox via installation script
|
||||
- Proxomox via VM
|
||||
- Windows WSL2
|
||||
validations:
|
||||
required: true
|
||||
- type: dropdown
|
||||
|
||||
@@ -7,13 +7,6 @@ assignees: ''
|
||||
|
||||
---
|
||||
|
||||
<!--
|
||||
By posting here you agree to follow our AI policy:
|
||||
https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
|
||||
|
||||
Requests that appear to be written by an AI on your behalf may be closed without a response.
|
||||
-->
|
||||
|
||||
**Describe what you are trying to accomplish and why in non technical terms**
|
||||
I want to be able to ... so that I can ...
|
||||
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
AGENTS.md
|
||||
@@ -0,0 +1,401 @@
|
||||
# GitHub Copilot Instructions for Frigate NVR
|
||||
|
||||
This document provides coding guidelines and best practices for contributing to Frigate NVR, a complete and local NVR designed for Home Assistant with AI object detection.
|
||||
|
||||
## Project Overview
|
||||
|
||||
Frigate NVR is a realtime object detection system for IP cameras that uses:
|
||||
|
||||
- **Backend**: Python 3.13+ with FastAPI, OpenCV, TensorFlow/ONNX
|
||||
- **Frontend**: React with TypeScript, Vite, TailwindCSS
|
||||
- **Architecture**: Multiprocessing design with ZMQ and MQTT communication
|
||||
- **Focus**: Minimal resource usage with maximum performance
|
||||
|
||||
## Code Review Guidelines
|
||||
|
||||
When reviewing code, do NOT comment on:
|
||||
|
||||
- Missing imports - Static analysis tooling catches these
|
||||
- Code formatting - Ruff (Python) and Prettier (TypeScript/React) handle formatting
|
||||
- Minor style inconsistencies already enforced by linters
|
||||
|
||||
## Python Backend Standards
|
||||
|
||||
### Python Requirements
|
||||
|
||||
- **Compatibility**: Python 3.13+
|
||||
- **Language Features**: Use modern Python features:
|
||||
- Pattern matching
|
||||
- Type hints (comprehensive typing preferred)
|
||||
- f-strings (preferred over `%` or `.format()`)
|
||||
- Dataclasses
|
||||
- Async/await patterns
|
||||
|
||||
### Code Quality Standards
|
||||
|
||||
- **Formatting**: Ruff (configured in `pyproject.toml`)
|
||||
- **Linting**: Ruff with rules defined in project config
|
||||
- **Type Checking**: Use type hints consistently
|
||||
- **Testing**: unittest framework - use `python3 -u -m unittest` to run tests
|
||||
- **Language**: American English for all code, comments, and documentation
|
||||
|
||||
### Logging Standards
|
||||
|
||||
- **Logger Pattern**: Use module-level logger
|
||||
|
||||
```python
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
```
|
||||
|
||||
- **Format Guidelines**:
|
||||
- No periods at end of log messages
|
||||
- No sensitive data (keys, tokens, passwords)
|
||||
- Use lazy logging: `logger.debug("Message with %s", variable)`
|
||||
- **Log Levels**:
|
||||
- `debug`: Development and troubleshooting information
|
||||
- `info`: Important runtime events (startup, shutdown, state changes)
|
||||
- `warning`: Recoverable issues that should be addressed
|
||||
- `error`: Errors that affect functionality but don't crash the app
|
||||
- `exception`: Use in except blocks to include traceback
|
||||
|
||||
### Error Handling
|
||||
|
||||
- **Exception Types**: Choose most specific exception available
|
||||
- **Try/Catch Best Practices**:
|
||||
- Only wrap code that can throw exceptions
|
||||
- Keep try blocks minimal - process data after the try/except
|
||||
- Avoid bare exceptions except in background tasks
|
||||
|
||||
Bad pattern:
|
||||
|
||||
```python
|
||||
try:
|
||||
data = await device.get_data() # Can throw
|
||||
# ❌ Don't process data inside try block
|
||||
processed = data.get("value", 0) * 100
|
||||
result = processed
|
||||
except DeviceError:
|
||||
logger.error("Failed to get data")
|
||||
```
|
||||
|
||||
Good pattern:
|
||||
|
||||
```python
|
||||
try:
|
||||
data = await device.get_data() # Can throw
|
||||
except DeviceError:
|
||||
logger.error("Failed to get data")
|
||||
return
|
||||
|
||||
# ✅ Process data outside try block
|
||||
processed = data.get("value", 0) * 100
|
||||
result = processed
|
||||
```
|
||||
|
||||
### Async Programming
|
||||
|
||||
- **External I/O**: All external I/O operations must be async
|
||||
- **Best Practices**:
|
||||
- Avoid sleeping in loops - use `asyncio.sleep()` not `time.sleep()`
|
||||
- Avoid awaiting in loops - use `asyncio.gather()` instead
|
||||
- No blocking calls in async functions
|
||||
- Use `asyncio.create_task()` for background operations
|
||||
- **Thread Safety**: Use proper synchronization for shared state
|
||||
|
||||
### Documentation Standards
|
||||
|
||||
- **Module Docstrings**: Concise descriptions at top of files
|
||||
```python
|
||||
"""Utilities for motion detection and analysis."""
|
||||
```
|
||||
- **Function Docstrings**: Required for public functions and methods
|
||||
|
||||
```python
|
||||
async def process_frame(frame: ndarray, config: Config) -> Detection:
|
||||
"""Process a video frame for object detection.
|
||||
|
||||
Args:
|
||||
frame: The video frame as numpy array
|
||||
config: Detection configuration
|
||||
|
||||
Returns:
|
||||
Detection results with bounding boxes
|
||||
"""
|
||||
```
|
||||
|
||||
- **Comment Style**:
|
||||
- Explain the "why" not just the "what"
|
||||
- Keep lines under 88 characters when possible
|
||||
- Use clear, descriptive comments
|
||||
|
||||
### File Organization
|
||||
|
||||
- **API Endpoints**: `frigate/api/` - FastAPI route handlers
|
||||
- **Configuration**: `frigate/config/` - Configuration parsing and validation
|
||||
- **Detectors**: `frigate/detectors/` - Object detection backends
|
||||
- **Events**: `frigate/events/` - Event management and storage
|
||||
- **Utilities**: `frigate/util/` - Shared utility functions
|
||||
|
||||
## Frontend (React/TypeScript) Standards
|
||||
|
||||
### Internationalization (i18n)
|
||||
|
||||
- **CRITICAL**: Never write user-facing strings directly in components
|
||||
- **Always use react-i18next**: Import and use the `t()` function
|
||||
|
||||
```tsx
|
||||
import { useTranslation } from "react-i18next";
|
||||
|
||||
function MyComponent() {
|
||||
const { t } = useTranslation(["views/live"]);
|
||||
return <div>{t("camera_not_found")}</div>;
|
||||
}
|
||||
```
|
||||
|
||||
- **Translation Files**: Add English strings to the appropriate json files in `web/public/locales/en`
|
||||
- **Namespaces**: Organize translations by feature/view (e.g., `views/live`, `common`, `views/system`)
|
||||
|
||||
### Code Quality
|
||||
|
||||
- **Linting**: ESLint (see `web/.eslintrc.cjs`)
|
||||
- **Formatting**: Prettier with Tailwind CSS plugin
|
||||
- **Type Safety**: TypeScript strict mode enabled
|
||||
- **Testing**: Vitest for unit tests
|
||||
|
||||
### Component Patterns
|
||||
|
||||
- **UI Components**: Use Radix UI primitives (in `web/src/components/ui/`)
|
||||
- **Styling**: TailwindCSS with `cn()` utility for class merging
|
||||
- **State Management**: React hooks (useState, useEffect, useCallback, useMemo)
|
||||
- **Data Fetching**: Custom hooks with proper loading and error states
|
||||
|
||||
### ESLint Rules
|
||||
|
||||
Key rules enforced:
|
||||
|
||||
- `react-hooks/rules-of-hooks`: error
|
||||
- `react-hooks/exhaustive-deps`: error
|
||||
- `no-console`: error (use proper logging or remove)
|
||||
- `@typescript-eslint/no-explicit-any`: warn (always use proper types instead of `any`)
|
||||
- Unused variables must be prefixed with `_`
|
||||
- Comma dangles required for multiline objects/arrays
|
||||
|
||||
### File Organization
|
||||
|
||||
- **Pages**: `web/src/pages/` - Route components
|
||||
- **Views**: `web/src/views/` - Complex view components
|
||||
- **Components**: `web/src/components/` - Reusable components
|
||||
- **Hooks**: `web/src/hooks/` - Custom React hooks
|
||||
- **API**: `web/src/api/` - API client functions
|
||||
- **Types**: `web/src/types/` - TypeScript type definitions
|
||||
|
||||
## Testing Requirements
|
||||
|
||||
### Backend Testing
|
||||
|
||||
- **Framework**: Python unittest
|
||||
- **Run Command**: `python3 -u -m unittest`
|
||||
- **Location**: `frigate/test/`
|
||||
- **Coverage**: Aim for comprehensive test coverage of core functionality
|
||||
- **Pattern**: Use `TestCase` classes with descriptive test method names
|
||||
```python
|
||||
class TestMotionDetection(unittest.TestCase):
|
||||
def test_detects_motion_above_threshold(self):
|
||||
# Test implementation
|
||||
```
|
||||
|
||||
### Test Best Practices
|
||||
|
||||
- Always have a way to test your work and confirm your changes
|
||||
- Write tests for bug fixes to prevent regressions
|
||||
- Test edge cases and error conditions
|
||||
- Mock external dependencies (cameras, APIs, hardware)
|
||||
- Use fixtures for test data
|
||||
|
||||
## Development Commands
|
||||
|
||||
### Python Backend
|
||||
|
||||
```bash
|
||||
# Run all tests
|
||||
python3 -u -m unittest
|
||||
|
||||
# Run specific test file
|
||||
python3 -u -m unittest frigate.test.test_ffmpeg_presets
|
||||
|
||||
# Check formatting (Ruff)
|
||||
ruff format --check frigate/
|
||||
|
||||
# Apply formatting
|
||||
ruff format frigate/
|
||||
|
||||
# Run linter
|
||||
ruff check frigate/
|
||||
```
|
||||
|
||||
### Frontend (from web/ directory)
|
||||
|
||||
```bash
|
||||
# Start dev server (AI agents should never run this directly unless asked)
|
||||
npm run dev
|
||||
|
||||
# Build for production
|
||||
npm run build
|
||||
|
||||
# Run linter
|
||||
npm run lint
|
||||
|
||||
# Fix linting issues
|
||||
npm run lint:fix
|
||||
|
||||
# Format code
|
||||
npm run prettier:write
|
||||
```
|
||||
|
||||
### Docker Development
|
||||
|
||||
AI agents should never run these commands directly unless instructed.
|
||||
|
||||
```bash
|
||||
# Build local image
|
||||
make local
|
||||
|
||||
# Build debug image
|
||||
make debug
|
||||
```
|
||||
|
||||
## Common Patterns
|
||||
|
||||
### API Endpoint Pattern
|
||||
|
||||
```python
|
||||
from fastapi import APIRouter, Request
|
||||
from frigate.api.defs.tags import Tags
|
||||
|
||||
router = APIRouter(tags=[Tags.Events])
|
||||
|
||||
@router.get("/events")
|
||||
async def get_events(request: Request, limit: int = 100):
|
||||
"""Retrieve events from the database."""
|
||||
# Implementation
|
||||
```
|
||||
|
||||
### Configuration Access
|
||||
|
||||
```python
|
||||
# Access Frigate configuration
|
||||
config: FrigateConfig = request.app.frigate_config
|
||||
camera_config = config.cameras["front_door"]
|
||||
```
|
||||
|
||||
### Database Queries
|
||||
|
||||
```python
|
||||
from frigate.models import Event
|
||||
|
||||
# Use Peewee ORM for database access
|
||||
events = (
|
||||
Event.select()
|
||||
.where(Event.camera == camera_name)
|
||||
.order_by(Event.start_time.desc())
|
||||
.limit(limit)
|
||||
)
|
||||
```
|
||||
|
||||
## Common Anti-Patterns to Avoid
|
||||
|
||||
### ❌ Avoid These
|
||||
|
||||
```python
|
||||
# Blocking operations in async functions
|
||||
data = requests.get(url) # ❌ Use async HTTP client
|
||||
time.sleep(5) # ❌ Use asyncio.sleep()
|
||||
|
||||
# Hardcoded strings in React components
|
||||
<div>Camera not found</div> # ❌ Use t("camera_not_found")
|
||||
|
||||
# Missing error handling
|
||||
data = await api.get_data() # ❌ No exception handling
|
||||
|
||||
# Bare exceptions in regular code
|
||||
try:
|
||||
value = await sensor.read()
|
||||
except Exception: # ❌ Too broad
|
||||
logger.error("Failed")
|
||||
|
||||
# Returning exceptions in JSON responses
|
||||
except ValueError as e:
|
||||
return JSONResponse(
|
||||
content={"success": False, "message": str(e)},
|
||||
)
|
||||
```
|
||||
|
||||
### ✅ Use These Instead
|
||||
|
||||
```python
|
||||
# Async operations
|
||||
import aiohttp
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.get(url) as response:
|
||||
data = await response.json()
|
||||
|
||||
await asyncio.sleep(5) # ✅ Non-blocking
|
||||
|
||||
# Translatable strings in React
|
||||
const { t } = useTranslation();
|
||||
<div>{t("camera_not_found")}</div> # ✅ Translatable
|
||||
|
||||
# Proper error handling
|
||||
try:
|
||||
data = await api.get_data()
|
||||
except ApiException as err:
|
||||
logger.error("API error: %s", err)
|
||||
raise
|
||||
|
||||
# Specific exceptions
|
||||
try:
|
||||
value = await sensor.read()
|
||||
except SensorException as err: # ✅ Specific
|
||||
logger.exception("Failed to read sensor")
|
||||
|
||||
# Safe error responses
|
||||
except ValueError:
|
||||
logger.exception("Invalid parameters for API request")
|
||||
return JSONResponse(
|
||||
content={
|
||||
"success": False,
|
||||
"message": "Invalid request parameters",
|
||||
},
|
||||
)
|
||||
```
|
||||
|
||||
## Project-Specific Conventions
|
||||
|
||||
### Configuration Files
|
||||
|
||||
- Main config: `config/config.yml`
|
||||
|
||||
### Directory Structure
|
||||
|
||||
- Backend code: `frigate/`
|
||||
- Frontend code: `web/`
|
||||
- Docker files: `docker/`
|
||||
- Documentation: `docs/`
|
||||
- Database migrations: `migrations/`
|
||||
|
||||
### Code Style Conformance
|
||||
|
||||
Always conform new and refactored code to the existing coding style in the project:
|
||||
|
||||
- Follow established patterns in similar files
|
||||
- Match indentation and formatting of surrounding code
|
||||
- Use consistent naming conventions (snake_case for Python, camelCase for TypeScript)
|
||||
- Maintain the same level of verbosity in comments and docstrings
|
||||
|
||||
## Additional Resources
|
||||
|
||||
- Documentation: https://docs.frigate.video
|
||||
- Main Repository: https://github.com/blakeblackshear/frigate
|
||||
- Home Assistant Integration: https://github.com/blakeblackshear/frigate-hass-integration
|
||||
@@ -1,4 +1,4 @@
|
||||
_Please read the [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) and the [AI policy](https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md) before submitting a PR. Every PR must be read and submitted by a person, and PRs that appear to be unreviewed AI output will be closed without review._
|
||||
_Please read the [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) before submitting a PR._
|
||||
|
||||
## Proposed change
|
||||
|
||||
@@ -26,7 +26,7 @@ _Please read the [contributing guidelines](https://github.com/blakeblackshear/fr
|
||||
|
||||
- This PR fixes or closes issue: fixes #
|
||||
- This PR is related to issue:
|
||||
- Link to discussion with maintainers (**required** for any large or "planned" features):
|
||||
- Link to discussion with maintainers (**required** for large/pinned features):
|
||||
|
||||
## For new features
|
||||
|
||||
|
||||
@@ -42,89 +42,6 @@ jobs:
|
||||
tags: ${{ steps.setup.outputs.image-name }}-amd64
|
||||
cache-from: type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
|
||||
cache-to: type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64,mode=max
|
||||
smoke_test:
|
||||
runs-on: ubuntu-22.04
|
||||
name: AMD64 Smoke Test
|
||||
needs:
|
||||
- amd64_build
|
||||
steps:
|
||||
- name: Check out code
|
||||
uses: actions/checkout@v6
|
||||
with:
|
||||
persist-credentials: false
|
||||
- name: Set up QEMU and Buildx
|
||||
id: setup
|
||||
uses: ./.github/actions/setup
|
||||
with:
|
||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
- name: Start container
|
||||
run: |
|
||||
mkdir -p /tmp/frigate-config
|
||||
printf 'mqtt:\n enabled: false\ncameras: {}\n' > /tmp/frigate-config/config.yml
|
||||
docker run -d --name frigate --shm-size 256m \
|
||||
-v /tmp/frigate-config:/config \
|
||||
-p 5000:5000 -p 8971:8971 \
|
||||
${{ steps.setup.outputs.image-name }}-amd64
|
||||
- name: Wait for API
|
||||
run: |
|
||||
for i in $(seq 1 60); do
|
||||
curl -fs http://127.0.0.1:5000/api/version && exit 0
|
||||
sleep 5
|
||||
done
|
||||
echo "API never came up"; docker logs frigate; exit 1
|
||||
- name: Assert security headers and permissions
|
||||
run: |
|
||||
headers=$(curl -ksI https://127.0.0.1:8971/)
|
||||
echo "$headers"
|
||||
echo "$headers" | grep -qi "x-content-type-options: nosniff"
|
||||
echo "$headers" | grep -qi "referrer-policy: strict-origin-when-cross-origin"
|
||||
# server_tokens off: Server header must not include a version.
|
||||
# written as an if rather than "! grep", because bash exempts a
|
||||
# negated command from set -e and the assertion would never fail
|
||||
if echo "$headers" | grep -qiE "^server: nginx/[0-9]"; then
|
||||
echo "Server header leaks the nginx version; server_tokens is not off"
|
||||
exit 1
|
||||
fi
|
||||
# Frigate never ships frame-ancestors: HA's Webpage card and iframe
|
||||
# panels frame it cross-origin and it would break them silently
|
||||
if echo "$headers" | grep -qi "frame-ancestors"; then
|
||||
echo "response carries frame-ancestors, which breaks cross-origin iframe embedding"
|
||||
exit 1
|
||||
fi
|
||||
docker exec frigate /usr/local/nginx/sbin/nginx -t
|
||||
docker exec frigate stat -c %a /etc/letsencrypt/live/frigate/privkey.pem | grep -qx 600
|
||||
docker exec frigate stat -c %a /dev/shm/go2rtc.yaml | grep -qx 640
|
||||
- name: Assert PUID/PGID remapping
|
||||
run: |
|
||||
mkdir -p /tmp/frigate-config-puid
|
||||
printf 'mqtt:\n enabled: false\ncameras: {}\n' > /tmp/frigate-config-puid/config.yml
|
||||
docker run -d --name frigate-puid --shm-size 256m \
|
||||
-e PUID=1500 -e PGID=1500 \
|
||||
-v /tmp/frigate-config-puid:/config \
|
||||
${{ steps.setup.outputs.image-name }}-amd64
|
||||
up=0
|
||||
for i in $(seq 1 60); do
|
||||
docker exec frigate-puid curl -fs http://127.0.0.1:5000/api/version && up=1 && break
|
||||
sleep 5
|
||||
done
|
||||
if [ "$up" -ne 1 ]; then echo "PUID container never became healthy"; docker logs frigate-puid; exit 1; fi
|
||||
docker exec frigate-puid id -u frigate | grep -qx 1500
|
||||
docker exec frigate-puid id -g frigate | grep -qx 1500
|
||||
docker exec frigate-puid cat /config/.permissions_version | grep -qx "1:1500:1500"
|
||||
# second boot must skip the sweep (sentinel hit). Poll rather than
|
||||
# sleep: the string can only come from the second boot (the first
|
||||
# had no sentinel), so grepping the full log is unambiguous.
|
||||
docker restart frigate-puid
|
||||
ok=0
|
||||
for i in $(seq 1 30); do
|
||||
docker logs frigate-puid 2>&1 | grep -q "already applied" && ok=1 && break
|
||||
sleep 2
|
||||
done
|
||||
if [ "$ok" -ne 1 ]; then echo "sentinel skip never logged"; docker logs frigate-puid; exit 1; fi
|
||||
docker rm -f frigate-puid
|
||||
- name: Teardown
|
||||
if: always()
|
||||
run: docker rm -f frigate || true
|
||||
arm64_build:
|
||||
runs-on: ubuntu-22.04-arm
|
||||
name: ARM Build
|
||||
|
||||
@@ -13,7 +13,7 @@ jobs:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Check PR description against template
|
||||
uses: actions/github-script@v9
|
||||
uses: actions/github-script@v7
|
||||
with:
|
||||
script: |
|
||||
const maintainers = ['blakeblackshear', 'NickM-27', 'hawkeye217', 'dependabot[bot]', 'weblate'];
|
||||
|
||||
@@ -72,7 +72,7 @@ jobs:
|
||||
run: npm run e2e
|
||||
working-directory: ./web
|
||||
- name: Upload test artifacts
|
||||
uses: actions/upload-artifact@v7
|
||||
uses: actions/upload-artifact@v4
|
||||
if: failure()
|
||||
with:
|
||||
name: playwright-report
|
||||
@@ -125,7 +125,5 @@ jobs:
|
||||
run: devcontainer up --workspace-folder .
|
||||
- name: Run mypy in devcontainer
|
||||
run: devcontainer exec --workspace-folder . bash -lc "python3 -u -m mypy --config-file frigate/mypy.ini frigate"
|
||||
- name: Check API spec is up to date
|
||||
run: devcontainer exec --workspace-folder . bash -lc "python3 generate_api_auth_spec.py --check"
|
||||
- name: Run unit tests in devcontainer
|
||||
run: devcontainer exec --workspace-folder . bash -lc "python3 -u -m unittest"
|
||||
|
||||
@@ -18,9 +18,9 @@ jobs:
|
||||
close-issue-message: ""
|
||||
days-before-stale: 30
|
||||
days-before-close: 3
|
||||
exempt-draft-pr: false
|
||||
exempt-issue-labels: "planned,security"
|
||||
exempt-pr-labels: "planned,security,dependencies"
|
||||
exempt-draft-pr: true
|
||||
exempt-issue-labels: "pinned,security"
|
||||
exempt-pr-labels: "pinned,security,dependencies"
|
||||
operations-per-run: 120
|
||||
- name: Print outputs
|
||||
env:
|
||||
|
||||
@@ -12,7 +12,6 @@ config/*
|
||||
models
|
||||
*.mp4
|
||||
*.db
|
||||
*.db-*
|
||||
*.csv
|
||||
frigate/version.py
|
||||
web/build
|
||||
@@ -23,8 +22,3 @@ core
|
||||
!/web/**/*.ts
|
||||
.idea/*
|
||||
.ipynb_checkpoints
|
||||
|
||||
# Auto-generated Docker Compose Generator config files
|
||||
docs/src/components/DockerComposeGenerator/config/devices.ts
|
||||
docs/src/components/DockerComposeGenerator/config/hardware.ts
|
||||
docs/src/components/DockerComposeGenerator/config/ports.ts
|
||||
|
||||
@@ -1,450 +0,0 @@
|
||||
# Agent Instructions for Frigate NVR
|
||||
|
||||
This document provides coding guidelines and best practices for contributing to Frigate NVR, a complete and local NVR designed for Home Assistant with AI object detection.
|
||||
|
||||
## Project Overview
|
||||
|
||||
Frigate NVR is a realtime object detection system for IP cameras that uses:
|
||||
|
||||
- **Backend**: Python 3.13+ with FastAPI, OpenCV, TensorFlow/ONNX
|
||||
- **Frontend**: React with TypeScript, Vite, TailwindCSS
|
||||
- **Architecture**: Multiprocessing design with ZMQ and MQTT communication
|
||||
- **Focus**: Minimal resource usage with maximum performance
|
||||
|
||||
## Code Review Guidelines
|
||||
|
||||
When reviewing code, do NOT comment on:
|
||||
|
||||
- Missing imports - Static analysis tooling catches these
|
||||
- Code formatting - Ruff (Python) and Prettier (TypeScript/React) handle formatting
|
||||
- Minor style inconsistencies already enforced by linters
|
||||
|
||||
## Python Backend Standards
|
||||
|
||||
### Python Requirements
|
||||
|
||||
- **Compatibility**: Python 3.13+
|
||||
- **Language Features**: Use modern Python features:
|
||||
- Pattern matching
|
||||
- Type hints (comprehensive typing preferred)
|
||||
- f-strings (preferred over `%` or `.format()`)
|
||||
- Dataclasses
|
||||
- Async/await patterns
|
||||
|
||||
### Code Quality Standards
|
||||
|
||||
- **Formatting**: Ruff (configured in `pyproject.toml`)
|
||||
- **Linting**: Ruff with rules defined in project config
|
||||
- **Type Checking**: Use type hints consistently
|
||||
- **Testing**: unittest framework - use `python3 -u -m unittest` to run tests
|
||||
- **Language**: American English for all code, comments, and documentation
|
||||
- **Punctuation**: Do not use em dashes in documentation, comments, or strings; reword with standard punctuation (commas, colons, parentheses, or separate sentences)
|
||||
|
||||
### Logging Standards
|
||||
|
||||
- **Logger Pattern**: Use module-level logger
|
||||
|
||||
```python
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
```
|
||||
|
||||
- **Format Guidelines**:
|
||||
- No periods at end of log messages
|
||||
- No sensitive data (keys, tokens, passwords)
|
||||
- Use lazy logging: `logger.debug("Message with %s", variable)`
|
||||
- **Log Levels**:
|
||||
- `debug`: Development and troubleshooting information
|
||||
- `info`: Important runtime events (startup, shutdown, state changes)
|
||||
- `warning`: Recoverable issues that should be addressed
|
||||
- `error`: Errors that affect functionality but don't crash the app
|
||||
- `exception`: Use in except blocks to include traceback
|
||||
|
||||
### Error Handling
|
||||
|
||||
- **Exception Types**: Choose most specific exception available
|
||||
- **Try/Catch Best Practices**:
|
||||
- Only wrap code that can throw exceptions
|
||||
- Keep try blocks minimal - process data after the try/except
|
||||
- Avoid bare exceptions except in background tasks
|
||||
|
||||
Bad pattern:
|
||||
|
||||
```python
|
||||
try:
|
||||
data = await device.get_data() # Can throw
|
||||
# ❌ Don't process data inside try block
|
||||
processed = data.get("value", 0) * 100
|
||||
result = processed
|
||||
except DeviceError:
|
||||
logger.error("Failed to get data")
|
||||
```
|
||||
|
||||
Good pattern:
|
||||
|
||||
```python
|
||||
try:
|
||||
data = await device.get_data() # Can throw
|
||||
except DeviceError:
|
||||
logger.error("Failed to get data")
|
||||
return
|
||||
|
||||
# ✅ Process data outside try block
|
||||
processed = data.get("value", 0) * 100
|
||||
result = processed
|
||||
```
|
||||
|
||||
### Async Programming
|
||||
|
||||
- **External I/O**: All external I/O operations must be async
|
||||
- **Best Practices**:
|
||||
- Avoid sleeping in loops - use `asyncio.sleep()` not `time.sleep()`
|
||||
- Avoid awaiting in loops - use `asyncio.gather()` instead
|
||||
- No blocking calls in async functions
|
||||
- Use `asyncio.create_task()` for background operations
|
||||
- **Thread Safety**: Use proper synchronization for shared state
|
||||
|
||||
### Documentation Standards
|
||||
|
||||
- **Module Docstrings**: Concise descriptions at top of files
|
||||
```python
|
||||
"""Utilities for motion detection and analysis."""
|
||||
```
|
||||
- **Function Docstrings**: Required for public functions and methods
|
||||
|
||||
```python
|
||||
async def process_frame(frame: ndarray, config: Config) -> Detection:
|
||||
"""Process a video frame for object detection.
|
||||
|
||||
Args:
|
||||
frame: The video frame as numpy array
|
||||
config: Detection configuration
|
||||
|
||||
Returns:
|
||||
Detection results with bounding boxes
|
||||
"""
|
||||
```
|
||||
|
||||
- **Comment Style**:
|
||||
- Explain the "why" not just the "what"
|
||||
- Keep lines under 88 characters when possible
|
||||
- Use clear, descriptive comments
|
||||
|
||||
### File Organization
|
||||
|
||||
- **API Endpoints**: `frigate/api/` - FastAPI route handlers
|
||||
- **Configuration**: `frigate/config/` - Configuration parsing and validation
|
||||
- **Detectors**: `frigate/detectors/` - Object detection backends
|
||||
- **Events**: `frigate/events/` - Event management and storage
|
||||
- **Utilities**: `frigate/util/` - Shared utility functions
|
||||
|
||||
## Frontend (React/TypeScript) Standards
|
||||
|
||||
### Internationalization (i18n)
|
||||
|
||||
- **CRITICAL**: Never write user-facing strings directly in components
|
||||
- **Always use react-i18next**: Import and use the `t()` function
|
||||
|
||||
```tsx
|
||||
import { useTranslation } from "react-i18next";
|
||||
|
||||
function MyComponent() {
|
||||
const { t } = useTranslation(["views/live"]);
|
||||
return <div>{t("camera_not_found")}</div>;
|
||||
}
|
||||
```
|
||||
|
||||
- **Translation Files**: Add English strings to the appropriate json files in `web/public/locales/en`
|
||||
- **Namespaces**: Organize translations by feature/view (e.g., `views/live`, `common`, `views/system`)
|
||||
|
||||
### Code Quality
|
||||
|
||||
- **Linting**: ESLint (see `web/.eslintrc.cjs`)
|
||||
- **Formatting**: Prettier with Tailwind CSS plugin
|
||||
- **Type Safety**: TypeScript strict mode enabled
|
||||
|
||||
### Component Patterns
|
||||
|
||||
- **UI Components**: Use Radix UI primitives (in `web/src/components/ui/`)
|
||||
- **Styling**: TailwindCSS with `cn()` utility for class merging
|
||||
- **State Management**: React hooks (useState, useEffect, useCallback, useMemo)
|
||||
- **Data Fetching**: Custom hooks with proper loading and error states
|
||||
|
||||
### ESLint Rules
|
||||
|
||||
Key rules enforced:
|
||||
|
||||
- `react-hooks/rules-of-hooks`: error
|
||||
- `react-hooks/exhaustive-deps`: error
|
||||
- `no-console`: error (use proper logging or remove)
|
||||
- `@typescript-eslint/no-explicit-any`: warn (always use proper types instead of `any`)
|
||||
- Unused variables must be prefixed with `_`
|
||||
- Comma dangles required for multiline objects/arrays
|
||||
|
||||
### File Organization
|
||||
|
||||
- **Pages**: `web/src/pages/` - Route components
|
||||
- **Views**: `web/src/views/` - Complex view components
|
||||
- **Components**: `web/src/components/` - Reusable components
|
||||
- **Hooks**: `web/src/hooks/` - Custom React hooks
|
||||
- **API**: `web/src/api/` - API client functions
|
||||
- **Types**: `web/src/types/` - TypeScript type definitions
|
||||
|
||||
## Testing Requirements
|
||||
|
||||
### Backend Testing
|
||||
|
||||
- **Framework**: Python unittest
|
||||
- **Run Command**: `python3 -u -m unittest`
|
||||
- **Location**: `frigate/test/`
|
||||
- **Coverage**: Aim for comprehensive test coverage of core functionality
|
||||
- **Pattern**: Use `TestCase` classes with descriptive test method names
|
||||
```python
|
||||
class TestMotionDetection(unittest.TestCase):
|
||||
def test_detects_motion_above_threshold(self):
|
||||
# Test implementation
|
||||
```
|
||||
|
||||
### Test Best Practices
|
||||
|
||||
- Always have a way to test your work and confirm your changes
|
||||
- Write tests for bug fixes to prevent regressions
|
||||
- Test edge cases and error conditions
|
||||
- Mock external dependencies (cameras, APIs, hardware)
|
||||
- Use fixtures for test data
|
||||
|
||||
## Development Commands
|
||||
|
||||
### Python Backend
|
||||
|
||||
```bash
|
||||
# Run all tests
|
||||
python3 -u -m unittest
|
||||
|
||||
# Run specific test file
|
||||
python3 -u -m unittest frigate.test.test_ffmpeg_presets
|
||||
|
||||
# Check formatting (Ruff)
|
||||
ruff format --check frigate/
|
||||
|
||||
# Apply formatting
|
||||
ruff format frigate/
|
||||
|
||||
# Run linter
|
||||
ruff check frigate/
|
||||
|
||||
# Type check
|
||||
python3 -u -m mypy --config-file frigate/mypy.ini frigate
|
||||
|
||||
# Regenerate the OpenAPI spec after adding, changing, or removing an API
|
||||
# endpoint or its auth dependency — outputs docs/static/frigate-api.yaml,
|
||||
# annotated with each endpoint's auth requirement (admin / any / camera /
|
||||
# public). NEVER edit that file by hand. CI runs the --check variant and fails
|
||||
# if it is out of date. (from repo root)
|
||||
python3 generate_api_auth_spec.py
|
||||
python3 generate_api_auth_spec.py --check
|
||||
```
|
||||
|
||||
### Frontend (from web/ directory)
|
||||
|
||||
```bash
|
||||
# Start dev server (AI agents should never run this directly unless asked)
|
||||
npm run dev
|
||||
|
||||
# Build for production
|
||||
npm run build
|
||||
|
||||
# Run linter
|
||||
npm run lint
|
||||
|
||||
# Fix linting issues
|
||||
npm run lint:fix
|
||||
|
||||
# Format code
|
||||
npm run prettier:write
|
||||
|
||||
# E2E: first-time setup
|
||||
npm install
|
||||
npx playwright install chromium
|
||||
|
||||
# E2E: build the app and run all tests
|
||||
npm run e2e:build && npm run e2e
|
||||
|
||||
# E2E: interactive UI for debugging
|
||||
npm run e2e:ui
|
||||
|
||||
# E2E: run a specific spec
|
||||
npx playwright test --config e2e/playwright.config.ts e2e/specs/live.spec.ts
|
||||
|
||||
# E2E: filter by name, or run only desktop/mobile
|
||||
npx playwright test --config e2e/playwright.config.ts --grep="severity tab"
|
||||
npx playwright test --config e2e/playwright.config.ts --project=desktop
|
||||
|
||||
# E2E: regenerate mock data after backend model changes (from repo root)
|
||||
PYTHONPATH=. python3 web/e2e/fixtures/mock-data/generate-mock-data.py
|
||||
|
||||
# Regenerate config translations from Pydantic models — outputs to
|
||||
# web/public/locales/en/config/{global,cameras}.json. NEVER edit those
|
||||
# JSON files by hand; change the Pydantic field title/description and
|
||||
# re-run this script. (from repo root)
|
||||
python3 generate_config_translations.py
|
||||
|
||||
# Extract i18n keys from source into the locale files after adding
|
||||
# new t() calls. Use the :ci variant to verify the locale files are
|
||||
# in sync with source (fails if extraction would change anything).
|
||||
npm run i18n:extract
|
||||
npm run i18n:extract:ci
|
||||
```
|
||||
|
||||
### Docker Development
|
||||
|
||||
AI agents should never run these commands directly unless instructed.
|
||||
|
||||
```bash
|
||||
# Build local image
|
||||
make local
|
||||
|
||||
# Build debug image
|
||||
make debug
|
||||
```
|
||||
|
||||
## Common Patterns
|
||||
|
||||
### API Endpoint Pattern
|
||||
|
||||
```python
|
||||
from fastapi import APIRouter, Request
|
||||
from frigate.api.defs.tags import Tags
|
||||
|
||||
router = APIRouter(tags=[Tags.Events])
|
||||
|
||||
@router.get("/events")
|
||||
async def get_events(request: Request, limit: int = 100):
|
||||
"""Retrieve events from the database."""
|
||||
# Implementation
|
||||
```
|
||||
|
||||
After adding, changing, or removing an endpoint (or its auth dependency), regenerate the OpenAPI spec with `python3 generate_api_auth_spec.py` so `docs/static/frigate-api.yaml` stays in sync and the endpoint's auth requirement is documented. CI enforces this via the `--check` variant; never edit that file by hand.
|
||||
|
||||
### Configuration Access
|
||||
|
||||
```python
|
||||
# Access Frigate configuration
|
||||
config: FrigateConfig = request.app.frigate_config
|
||||
camera_config = config.cameras["front_door"]
|
||||
```
|
||||
|
||||
### Database Queries
|
||||
|
||||
```python
|
||||
from frigate.models import Event
|
||||
|
||||
# Use Peewee ORM for database access
|
||||
events = (
|
||||
Event.select()
|
||||
.where(Event.camera == camera_name)
|
||||
.order_by(Event.start_time.desc())
|
||||
.limit(limit)
|
||||
)
|
||||
```
|
||||
|
||||
## Common Anti-Patterns to Avoid
|
||||
|
||||
### ❌ Avoid These
|
||||
|
||||
```python
|
||||
# Blocking operations in async functions
|
||||
data = requests.get(url) # ❌ Use async HTTP client
|
||||
time.sleep(5) # ❌ Use asyncio.sleep()
|
||||
|
||||
# Hardcoded strings in React components
|
||||
<div>Camera not found</div> # ❌ Use t("camera_not_found")
|
||||
|
||||
# Missing error handling
|
||||
data = await api.get_data() # ❌ No exception handling
|
||||
|
||||
# Bare exceptions in regular code
|
||||
try:
|
||||
value = await sensor.read()
|
||||
except Exception: # ❌ Too broad
|
||||
logger.error("Failed")
|
||||
|
||||
# Returning exceptions in JSON responses
|
||||
except ValueError as e:
|
||||
return JSONResponse(
|
||||
content={"success": False, "message": str(e)},
|
||||
)
|
||||
```
|
||||
|
||||
### ✅ Use These Instead
|
||||
|
||||
```python
|
||||
# Async operations
|
||||
import aiohttp
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.get(url) as response:
|
||||
data = await response.json()
|
||||
|
||||
await asyncio.sleep(5) # ✅ Non-blocking
|
||||
|
||||
# Translatable strings in React
|
||||
const { t } = useTranslation();
|
||||
<div>{t("camera_not_found")}</div> # ✅ Translatable
|
||||
|
||||
# Proper error handling
|
||||
try:
|
||||
data = await api.get_data()
|
||||
except ApiException as err:
|
||||
logger.error("API error: %s", err)
|
||||
raise
|
||||
|
||||
# Specific exceptions
|
||||
try:
|
||||
value = await sensor.read()
|
||||
except SensorException as err: # ✅ Specific
|
||||
logger.exception("Failed to read sensor")
|
||||
|
||||
# Safe error responses
|
||||
except ValueError:
|
||||
logger.exception("Invalid parameters for API request")
|
||||
return JSONResponse(
|
||||
content={
|
||||
"success": False,
|
||||
"message": "Invalid request parameters",
|
||||
},
|
||||
)
|
||||
```
|
||||
|
||||
## WebSocket Broadcasts
|
||||
|
||||
Outbound WebSocket broadcasts go through a per-recipient classifier in `frigate/comms/ws.py` that enforces camera-level access. **The classifier is fail-closed: any topic it doesn't recognize is dropped for every client.** New outbound topics must be classified there or they'll silently disappear.
|
||||
|
||||
## Project-Specific Conventions
|
||||
|
||||
### Configuration Files
|
||||
|
||||
- Main config: `config/config.yml`
|
||||
|
||||
### Directory Structure
|
||||
|
||||
- Backend code: `frigate/`
|
||||
- Frontend code: `web/`
|
||||
- Docker files: `docker/`
|
||||
- Documentation: `docs/`
|
||||
- Database migrations: `migrations/`
|
||||
|
||||
### Code Style Conformance
|
||||
|
||||
Always conform new and refactored code to the existing coding style in the project:
|
||||
|
||||
- Follow established patterns in similar files
|
||||
- Match indentation and formatting of surrounding code
|
||||
- Use consistent naming conventions (snake_case for Python, camelCase for TypeScript)
|
||||
- Maintain the same level of verbosity in comments and docstrings
|
||||
|
||||
## Additional Resources
|
||||
|
||||
- Documentation: https://docs.frigate.video
|
||||
- Main Repository: https://github.com/blakeblackshear/frigate
|
||||
- Home Assistant Integration: https://github.com/blakeblackshear/frigate-hass-integration
|
||||
-126
@@ -1,126 +0,0 @@
|
||||
# Frigate AI Policy
|
||||
|
||||
## TL;DR
|
||||
|
||||
- **Use AI tools if they help you.** We do too. This is about what you post, not which tools you use to write it.
|
||||
- **A person has to read it and send it.** Don't wire a bot or an agent up to post on your behalf.
|
||||
- **Write your posts yourself.** Your own words, the template filled in, and you answering maintainers rather than your assistant.
|
||||
- **Don't paste an AI's guess at the cause as though it were a diagnosis.** Tell us what you actually observed.
|
||||
- **Read your code before you submit it.** Disclose that AI was used, and be ready to explain every line.
|
||||
- **If we misjudge something you wrote, just say so.** We'll take you at your word.
|
||||
|
||||
The rest of this document explains each of these, and why.
|
||||
|
||||
## Scope
|
||||
|
||||
AI tools are a reality of modern development and we're not opposed to their use. You are responsible for anything you submit, however it was produced, and we are responsible for anything we merge and release. We hold a high bar for both.
|
||||
|
||||
This policy applies everywhere this project is discussed: issues, discussions, pull requests, code reviews, and commit comments.
|
||||
|
||||
## Why this exists
|
||||
|
||||
Frigate is built and supported by a small group of maintainers and a community of volunteers who read every post and review every pull request. Nobody here is paid to do it, and time spent reading a post is time not spent fixing bugs or building features.
|
||||
|
||||
We're not opposed to AI tools. We use them too. But content generated by an AI and submitted without review costs a real person real time, and usually gives them less to work with than a few honest sentences would have. That is the problem this policy addresses.
|
||||
|
||||
## A person has to be in the loop
|
||||
|
||||
Every issue, discussion, comment, and pull request here must be read and submitted by a person. Using an AI tool to help you write is fine. Wiring one up to post on your behalf is not.
|
||||
|
||||
Specifically, do not:
|
||||
|
||||
- Connect a bot or agent to GitHub that opens issues, discussions, or pull requests without you reading them first
|
||||
- Post output from a tool you have not read
|
||||
- Use tooling to file bulk or drive-by contributions across the repository
|
||||
|
||||
We will close anything we believe was posted without a person reading it, and we may mark it as spam. Posts that skip the templates are the most common sign of this.
|
||||
|
||||
## Issues, discussions, and comments
|
||||
|
||||
We do not mind if you use AI tools to help you write. Do not have tools post unreviewed content on your behalf. We may hide any comment we believe to be unreviewed AI output.
|
||||
|
||||
Keep posts to what is needed to communicate your point. A long, confidently written, AI-padded post is harder to help with than a short direct one, not easier, and it is usually obvious.
|
||||
|
||||
**Describe your actual problem in your own words.** Tell us what you did, what you expected, and what actually happened. That is the information we need, and only you have it.
|
||||
|
||||
**Do not paste an AI's guess at the cause as though it were a diagnosis.** It is frequently wrong in ways that send everyone down the wrong path, and it buries the details that would have led to the real answer. We would rather see what you observed than what a model inferred.
|
||||
|
||||
**Fill in the template completely.** The templates ask for logs, config, version, and hardware because those are the things needed to help you. An AI cannot supply them for you, and a post missing them cannot be acted on.
|
||||
|
||||
**Answer maintainers yourself.** If we ask you a question, we are asking _you_, not your AI assistant. These are the spaces where we build trust and understanding with the community, and that only works if we're talking to each other. Using AI to fix your grammar or clarity is fine, but the substance has to be yours.
|
||||
|
||||
This applies to pull request descriptions and review replies as much as it does to bug reports and discussions.
|
||||
|
||||
### Quoting AI output
|
||||
|
||||
If you want to include something an AI told you, it must be:
|
||||
|
||||
- In a quote block, using `>`
|
||||
- Disclosed as AI output, saying which tool it came from
|
||||
- Accompanied by your own comment explaining why you think it is relevant
|
||||
|
||||
Keep the excerpt short. Do not paste long transcripts.
|
||||
|
||||
### Non-native English speakers
|
||||
|
||||
AI is genuinely useful for participating in a project that operates in English, and we would rather hear from you through a translation tool than not hear from you at all. Using AI to improve the grammar or clarity of something you wrote yourself is fine.
|
||||
|
||||
If you are translating your posts, make sure the translation says what you meant. Including your original text in a `<details>` block helps us verify the translation if something reads oddly, and keeps the thread readable.
|
||||
|
||||
## Code contributions
|
||||
|
||||
We need to understand your relationship with the code you're submitting. The more AI was involved, the more important it is that you've genuinely reviewed, tested, and understood what it produced.
|
||||
|
||||
Because of the long-term maintenance burden every merged change creates, we require a human in the loop who understands the work the AI produced. Pull requests that appear to be unreviewed AI output will be closed without review.
|
||||
|
||||
### Requirements when AI is used
|
||||
|
||||
If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
|
||||
|
||||
1. **Explicitly disclose the manner in which AI was employed.** The PR template asks for this. Be honest, this won't automatically disqualify your PR. We'd rather have an honest disclosure than find out later. Trust matters more than method.
|
||||
2. **Perform a comprehensive manual review prior to submitting the pull request.** Don't submit code you haven't read carefully and tested locally.
|
||||
3. **Be prepared to explain every line of code you submitted when asked about it by a maintainer.** If you can't explain why something works the way it does, you're not ready to submit it.
|
||||
4. **Check for an existing pull request addressing the same change.** If one exists, comment there and work with its author instead of opening a duplicate.
|
||||
5. **It is strictly prohibited to use AI to write your posts for you** (bug reports, feature requests, pull request descriptions, GitHub discussions, responding to humans, etc.). We need to hear from _you_, not your AI assistant. These are the spaces where we build trust and understanding with contributors, and that only works if we're talking to each other.
|
||||
|
||||
### Established contributors
|
||||
|
||||
Contributors with a long history of thoughtful, quality contributions to Frigate have earned trust through that track record. The level of scrutiny we apply to AI usage naturally reflects that trust. This isn't a formal exemption, it's just how trust works. If you've been around, we know how you think and how you work. If you're new, we're still getting to know you, and clear disclosure helps build that relationship.
|
||||
|
||||
### What this means in practice
|
||||
|
||||
We're not trying to gatekeep how you write code. Use whatever tools make you productive. But there's a difference between using AI as a tool to implement something you understand and handing a feature request to an AI and submitting whatever comes back. The former is fine. The latter creates maintenance risk for the project.
|
||||
|
||||
Some honest context: when we review a PR, we're not just evaluating whether the code works today. We're evaluating whether we can maintain it, debug it, and extend it long-term, often without the original author's involvement. Code that the author doesn't deeply understand is code that nobody understands, and that's a liability.
|
||||
|
||||
One more thing worth saying directly: most maintainers already have access to the same AI tools you do. A PR that's entirely AI-generated, where the author can't explain the design, debug issues independently, or engage substantively in design discussions, doesn't offer something we couldn't produce ourselves. What makes a contribution genuinely valuable is the human judgment and domain understanding behind it, as well as the engagement during review that shapes it into something we can confidently take on long-term.
|
||||
|
||||
## Our use of AI
|
||||
|
||||
The Frigate documentation site has an "Ask AI" search that answers questions from the docs, and we may use AI tooling to help with triage and project management. Like any automated tooling, it is not always right.
|
||||
|
||||
If an AI tool leaves a comment on your contribution, treat it the way you would any other comment. If you think it is wrong, say so, and a brief explanation is enough. Maintainers always have the final say.
|
||||
|
||||
## Enforcement
|
||||
|
||||
Contributions and posts that do not follow this policy will be closed. Depending on the situation, maintainers may also:
|
||||
|
||||
- Hide or delete comments that appear to be unreviewed AI output
|
||||
- Mark automated content as spam
|
||||
- Close an issue, discussion, or pull request without further review
|
||||
- Lock a conversation
|
||||
- Temporarily or permanently block an account from participating in the project
|
||||
|
||||
Repeated violations may result in being blocked from contributing to Frigate.
|
||||
|
||||
### When we get it wrong
|
||||
|
||||
There is no reliable way to detect this, and we're not going to pretend otherwise. Whether something reads as unreviewed AI output is a judgment call, usually made quickly, by a volunteer with limited time and no way to know for certain. These calls are subjective and we won't always get them right.
|
||||
|
||||
If it happens to you, just say so. A short reply telling us you wrote it yourself is enough, and we'll take you at your word and pick the conversation back up. We would much rather occasionally reopen something we misjudged than treat everyone who posts here as a suspect.
|
||||
|
||||
We'd ask for some understanding in return. These calls get made quickly because the volume is real, and time spent second-guessing them is time not spent helping the person in the next thread.
|
||||
|
||||
## Attribution
|
||||
|
||||
Portions of this policy are adapted from the [Open Home Foundation AI Policy](https://developers.home-assistant.io/docs/ai_policy/).
|
||||
+20
-15
@@ -2,8 +2,6 @@
|
||||
|
||||
Thank you for your interest in contributing to Frigate. This document covers the expectations and guidelines for contributions. Please read it before submitting a pull request.
|
||||
|
||||
All participation in this project, including pull requests, issues, and discussions, is covered by our [AI policy](AI_POLICY.md).
|
||||
|
||||
## Before you start
|
||||
|
||||
### Bugfixes
|
||||
@@ -12,27 +10,34 @@ If you've found a bug and want to fix it, go for it. Link to the relevant issue
|
||||
|
||||
### New features
|
||||
|
||||
A pull request is more than just code — it's a request for the maintainers to review, integrate, and support the change long-term. We're selective about what we take on, and prioritize changes that align with the project's direction and can be responsibly maintained in the long term.
|
||||
Every new feature adds scope that the maintainers must test, maintain, and support long-term. Before writing code for a new feature:
|
||||
|
||||
**Large or highly-requested features** raise the bar even higher. Popularity signals demand, but it doesn't pre-approve any particular implementation. The bigger the change, the higher the long-term cost, and the more important it is that we're aligned on scope and approach before any code is written. A large PR that lands without prior discussion is unlikely to be merged as-is, no matter how well it's implemented.
|
||||
|
||||
Before writing code for a new feature:
|
||||
|
||||
1. **Check for existing discussion.** Search [feature requests](https://github.com/blakeblackshear/frigate/issues) and [discussions](https://github.com/blakeblackshear/frigate/discussions) to see if it's been proposed or discussed. Feature requests tagged with "planned" are on our radar — we plan to get to them, but we don't maintain a public roadmap or timeline. Check in with us first if you have interest in contributing to one.
|
||||
1. **Check for existing discussion.** Search [feature requests](https://github.com/blakeblackshear/frigate/issues) and [discussions](https://github.com/blakeblackshear/frigate/discussions) to see if it's been proposed or discussed. Pinned feature requests are on our radar — we plan to get to them, but we don't maintain a public roadmap or timeline. Check in with us first if you have interest in contributing to one.
|
||||
2. **Start a discussion or feature request first.** This helps ensure your idea aligns with Frigate's direction before you invest time building it. Community interest in a feature request helps us gauge demand, though a great idea is a great idea even without a crowd behind it.
|
||||
3. **Be open to "no".** We try to be thoughtful about what we take on, and sometimes that means saying no to good code if the feature isn't the right fit for the project. These calls are sometimes subjective, and we won't always get them right. We're happy to discuss and reconsider.
|
||||
|
||||
## AI usage policy
|
||||
|
||||
AI tools are a reality of modern development and we're not opposed to their use. But we need to understand your relationship with the code you're submitting, and we need to hear from you rather than from your AI assistant.
|
||||
AI tools are a reality of modern development and we're not opposed to their use. But we need to understand your relationship with the code you're submitting. The more AI was involved, the more important it is that you've genuinely reviewed, tested, and understood what it produced.
|
||||
|
||||
**Read the [AI policy](AI_POLICY.md) before you open a pull request.** It is short, and it applies to everything you post here. The parts that most often catch people out:
|
||||
### Requirements when AI is used
|
||||
|
||||
- A person has to be in the loop. Don't wire a bot or agent up to open pull requests, issues, or discussions on your behalf.
|
||||
- Disclose how AI was used. The PR template asks for this. Be honest, it won't automatically disqualify your PR.
|
||||
- Review and test everything you submit, and be prepared to explain every line when asked.
|
||||
- Don't use AI to write your PR description or your replies to maintainers.
|
||||
If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
|
||||
|
||||
Pull requests that appear to be unreviewed AI output will be closed without review.
|
||||
1. **Explicitly disclose the manner in which AI was employed.** The PR template asks for this. Be honest — this won't automatically disqualify your PR. We'd rather have an honest disclosure than find out later. Trust matters more than method.
|
||||
2. **Perform a comprehensive manual review prior to submitting the pull request.** Don't submit code you haven't read carefully and tested locally.
|
||||
3. **Be prepared to explain every line of code they submitted when asked about it by a maintainer.** If you can't explain why something works the way it does, you're not ready to submit it.
|
||||
4. **It is strictly prohibited to use AI to write your posts for you** (bug reports, feature requests, pull request descriptions, GitHub discussions, responding to humans, etc.). We need to hear from _you_, not your AI assistant. These are the spaces where we build trust and understanding with contributors, and that only works if we're talking to each other.
|
||||
|
||||
### Established contributors
|
||||
|
||||
Contributors with a long history of thoughtful, quality contributions to Frigate have earned trust through that track record. The level of scrutiny we apply to AI usage naturally reflects that trust. This isn't a formal exemption — it's just how trust works. If you've been around, we know how you think and how you work. If you're new, we're still getting to know you, and clear disclosure helps build that relationship.
|
||||
|
||||
### What this means in practice
|
||||
|
||||
We're not trying to gatekeep how you write code. Use whatever tools make you productive. But there's a difference between using AI as a tool to implement something you understand and handing a feature request to an AI and submitting whatever comes back. The former is fine. The latter creates maintenance risk for the project.
|
||||
|
||||
Some honest context: when we review a PR, we're not just evaluating whether the code works today. We're evaluating whether we can maintain it, debug it, and extend it long-term — often without the original author's involvement. Code that the author doesn't deeply understand is code that nobody understands, and that's a liability.
|
||||
|
||||
## Pull request guidelines
|
||||
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
default_target: local
|
||||
|
||||
COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
|
||||
VERSION = 0.19.0
|
||||
VERSION = 0.18.0
|
||||
IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
|
||||
GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
|
||||
BOARDS= #Initialized empty
|
||||
|
||||
+1
-1
@@ -24,7 +24,7 @@ yell
|
||||
sigh
|
||||
singing
|
||||
choir
|
||||
yodeling
|
||||
sodeling
|
||||
chant
|
||||
mantra
|
||||
child_singing
|
||||
|
||||
+7
-29
@@ -60,10 +60,10 @@ ARG DEBIAN_FRONTEND
|
||||
RUN --mount=type=bind,source=docker/main/build_intel_media_driver.sh,target=/deps/build_intel_media_driver.sh \
|
||||
/deps/build_intel_media_driver.sh
|
||||
|
||||
FROM wget AS go2rtc
|
||||
FROM scratch AS go2rtc
|
||||
ARG TARGETARCH
|
||||
RUN --mount=type=bind,source=docker/main/install_go2rtc.sh,target=/deps/install_go2rtc.sh \
|
||||
/deps/install_go2rtc.sh
|
||||
WORKDIR /rootfs/usr/local/go2rtc/bin
|
||||
ADD --link --chmod=755 "https://github.com/AlexxIT/go2rtc/releases/download/v1.9.13/go2rtc_linux_${TARGETARCH}" go2rtc
|
||||
|
||||
FROM wget AS tempio
|
||||
ARG TARGETARCH
|
||||
@@ -81,10 +81,10 @@ RUN --mount=type=bind,source=docker/main/install_tempio.sh,target=/deps/install_
|
||||
FROM base_host AS ov-converter
|
||||
ARG DEBIAN_FRONTEND
|
||||
|
||||
# Install OpenVINO for model conversion
|
||||
# Install OpenVino Runtime and Dev library
|
||||
COPY docker/main/requirements-ov.txt /requirements-ov.txt
|
||||
RUN apt-get -qq update \
|
||||
&& apt-get -qq install -y wget python3 python3-distutils \
|
||||
&& apt-get -qq install -y wget python3 python3-dev python3-distutils gcc pkg-config libhdf5-dev \
|
||||
&& wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
|
||||
&& sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
|
||||
&& python3 get-pip.py "pip" \
|
||||
@@ -265,25 +265,8 @@ ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PA
|
||||
RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_deps.sh \
|
||||
/deps/install_deps.sh
|
||||
|
||||
# Runtime users. frigate may be remapped at start via PUID/PGID (init-usermod)
|
||||
# or replaced entirely with docker's --user. go2rtc is intentionally separate
|
||||
# and more restricted. frigate-data is the shared group for /config access.
|
||||
# -o tolerates variant base images that already contain uid/gid 1000.
|
||||
RUN groupadd -o --gid 1000 frigate \
|
||||
&& useradd -o --uid 1000 --gid frigate --no-create-home --shell /usr/sbin/nologin frigate \
|
||||
&& groupadd --system go2rtc \
|
||||
&& useradd --system --gid go2rtc --no-create-home --shell /usr/sbin/nologin go2rtc \
|
||||
&& groupadd --system frigate-data \
|
||||
&& usermod -aG frigate-data frigate \
|
||||
&& usermod -aG frigate-data go2rtc \
|
||||
&& for grp in video render plugdev audio; do \
|
||||
if getent group "$grp" >/dev/null; then \
|
||||
usermod -aG "$grp" frigate && usermod -aG "$grp" go2rtc; \
|
||||
fi; \
|
||||
done
|
||||
|
||||
ENV DEFAULT_FFMPEG_VERSION="8.0"
|
||||
ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:7.0:5.0"
|
||||
ENV DEFAULT_FFMPEG_VERSION="7.0"
|
||||
ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:5.0"
|
||||
|
||||
RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
|
||||
&& sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
|
||||
@@ -324,11 +307,6 @@ HEALTHCHECK --start-period=300s --start-interval=5s --interval=15s --timeout=5s
|
||||
# Frigate deps with Node.js and NPM for devcontainer
|
||||
FROM deps AS devcontainer
|
||||
|
||||
# /config here is the developer's bind-mounted checkout, not a data volume, so
|
||||
# the prepare ownership sweep must not run: it would chown the source tree to
|
||||
# the runtime uid and lock out any container user that isn't 1000.
|
||||
ENV FRIGATE_RUN_AS_ROOT=true
|
||||
|
||||
# Do not start the actual Frigate service on devcontainer as it will be started by VS Code
|
||||
# But start a fake service for simulating the logs
|
||||
COPY docker/main/fake_frigate_run /etc/s6-overlay/s6-rc.d/frigate/run
|
||||
|
||||
@@ -1,106 +1,11 @@
|
||||
"""Convert the default SSDLite MobileNet v2 model to OpenVINO IR.
|
||||
|
||||
Replaces the legacy openvino-dev Model Optimizer conversion. The TensorFlow
|
||||
frontend translates the Object Detection API pre and post processors literally,
|
||||
producing per-class NonMaxSuppression, NonZero ops and map loops with data
|
||||
dependent shapes that the GPU plugin handles very badly. Both are cut out the
|
||||
way ssd_v2_support.json used to do it: the preprocessor is an identity at the
|
||||
native 300x300 input, and the postprocessor becomes a single fused
|
||||
DetectionOutput. The result is the [1, 1, 100, 7] tensor that Frigate's
|
||||
OpenVINO detector expects, with the input flipped to BGR to match the legacy
|
||||
reverse_input_channels behavior.
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import openvino as ov
|
||||
from openvino import opset8 as ops
|
||||
from openvino.preprocess import PrePostProcessor
|
||||
from openvino.tools import mo
|
||||
|
||||
MODEL_DIR = "/models/ssdlite_mobilenet_v2_coco_2018_05_09"
|
||||
OUTPUT_PATH = "/models/ssdlite_mobilenet_v2.xml"
|
||||
INPUT_SHAPE = [1, 300, 300, 3]
|
||||
|
||||
# faster_rcnn_box_coder divides the deltas by pipeline.config's y/x/height/width
|
||||
# scales of 10/10/5/5, which DetectionOutput expresses as per-prior variances.
|
||||
BOX_VARIANCES = np.float32([0.1, 0.1, 0.2, 0.2])
|
||||
|
||||
model = ov.convert_model(
|
||||
f"{MODEL_DIR}/frozen_inference_graph.pb",
|
||||
input=[("image_tensor:0", INPUT_SHAPE)],
|
||||
ov_model = mo.convert_model(
|
||||
"/models/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb",
|
||||
compress_to_fp16=True,
|
||||
transformations_config="/usr/local/lib/python3.11/dist-packages/openvino/tools/mo/front/tf/ssd_v2_support.json",
|
||||
tensorflow_object_detection_api_pipeline_config="/models/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config",
|
||||
reverse_input_channels=True,
|
||||
)
|
||||
|
||||
nodes = {op.get_friendly_name(): op for op in model.get_ordered_ops()}
|
||||
parameter = model.get_parameters()[0]
|
||||
|
||||
preprocessor = nodes["Preprocessor/map/TensorArrayStack/TensorArrayGatherV3"]
|
||||
box_deltas = nodes["Postprocessor/Reshape_1"].output(0)
|
||||
class_scores = nodes["Postprocessor/convert_scores"].output(0)
|
||||
anchors_output = nodes["Postprocessor/Reshape"].output(0)
|
||||
|
||||
# The anchors only depend on the static input shape, so fold them into a
|
||||
# constant and drop the generator subgraph with the rest of the postprocessor.
|
||||
probe = ov.Core().compile_model(
|
||||
ov.Model([anchors_output, preprocessor.output(0)], [parameter], "probe"), "CPU"
|
||||
)
|
||||
probe_input = np.random.default_rng(0).integers(0, 255, INPUT_SHAPE, dtype=np.uint8)
|
||||
anchors, resized = (out.copy() for out in probe([probe_input]).values())
|
||||
|
||||
assert np.allclose(resized, probe_input, atol=1e-3), (
|
||||
"preprocessor is not an identity at 300x300, it cannot be bypassed"
|
||||
)
|
||||
|
||||
image = ops.convert(parameter, "f32")
|
||||
|
||||
for consumer in list(preprocessor.output(0).get_target_inputs()):
|
||||
consumer.replace_source_output(image.output(0))
|
||||
|
||||
# (ymin, xmin, ymax, xmax) -> (xmin, ymin, xmax, ymax)
|
||||
priors = anchors[:, [1, 0, 3, 2]].astype(np.float32).reshape(-1)
|
||||
variances = np.tile(BOX_VARIANCES, len(anchors))
|
||||
proposals = ops.constant(np.stack([priors, variances])[np.newaxis])
|
||||
|
||||
# (ty, tx, th, tw) -> (dx, dy, dw, dh) for the CENTER_SIZE decode
|
||||
box_logits = ops.reshape(ops.gather(box_deltas, [1, 0, 3, 2], 1), [1, -1], False)
|
||||
class_preds = ops.reshape(class_scores, [1, -1], False)
|
||||
|
||||
detections = ops.detection_output(
|
||||
box_logits,
|
||||
class_preds,
|
||||
proposals,
|
||||
{
|
||||
"background_label_id": 0,
|
||||
"top_k": 100,
|
||||
"keep_top_k": [100],
|
||||
"nms_threshold": 0.6,
|
||||
"confidence_threshold": 0.3,
|
||||
"code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
|
||||
"share_location": True,
|
||||
"variance_encoded_in_target": False,
|
||||
"normalized": True,
|
||||
"clip_before_nms": False,
|
||||
"clip_after_nms": True,
|
||||
"decrease_label_id": False,
|
||||
},
|
||||
)
|
||||
detections.output(0).get_tensor().set_names({"detection_out"})
|
||||
|
||||
model = ov.Model([detections], [parameter], "ssdlite_mobilenet_v2")
|
||||
|
||||
ppp = PrePostProcessor(model)
|
||||
ppp.input().tensor().set_layout(ov.Layout("NHWC"))
|
||||
ppp.input().preprocess().reverse_channels()
|
||||
model = ppp.build()
|
||||
|
||||
# Fail the build rather than silently ship the dynamically shaped graph again.
|
||||
op_types = [op.get_type_name() for op in model.get_ordered_ops()]
|
||||
assert op_types.count("DetectionOutput") == 1, "postprocessor was not fused"
|
||||
|
||||
for dynamic_op in ("NonMaxSuppression", "NonZero", "Loop", "TensorIterator"):
|
||||
assert dynamic_op not in op_types, f"{dynamic_op} left in the graph"
|
||||
|
||||
output_shape = model.outputs[0].get_partial_shape()
|
||||
assert output_shape.is_static and list(output_shape) == [1, 1, 100, 7], (
|
||||
f"unexpected detector output shape {output_shape}"
|
||||
)
|
||||
|
||||
ov.save_model(model, OUTPUT_PATH, compress_to_fp16=True)
|
||||
ov.save_model(ov_model, "/models/ssdlite_mobilenet_v2.xml")
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
set -euxo pipefail
|
||||
|
||||
SQLITE_VEC_VERSION="0.1.9"
|
||||
SQLITE_VEC_VERSION="0.1.3"
|
||||
|
||||
source /etc/os-release
|
||||
|
||||
|
||||
+38
-97
@@ -28,13 +28,7 @@ update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
|
||||
mkdir -p -m 600 /root/.gnupg
|
||||
|
||||
# install coral runtime
|
||||
# sha256 digests of the release debs; update when bumping the libedgetpu release.
|
||||
declare -A edgetpu_checksums=(
|
||||
["amd64"]="63fd00989d29160fa9894e115156a9abe456e88751fc9be89d26e4696200441b"
|
||||
["arm64"]="eab8aa4576b4dbf738135d8094f32270b24117f77147d25cbe0f49d0144d85f2"
|
||||
)
|
||||
wget -q -O /tmp/libedgetpu1-max.deb "https://github.com/feranick/libedgetpu/releases/download/16.0TF2.17.1-1/libedgetpu1-max_16.0tf2.17.1-1.bookworm_${TARGETARCH}.deb"
|
||||
echo "${edgetpu_checksums[${TARGETARCH}]} /tmp/libedgetpu1-max.deb" | sha256sum -c -
|
||||
unset DEBIAN_FRONTEND
|
||||
yes | dpkg -i /tmp/libedgetpu1-max.deb && export DEBIAN_FRONTEND=noninteractive
|
||||
rm /tmp/libedgetpu1-max.deb
|
||||
@@ -51,41 +45,28 @@ if [[ "${TARGETARCH}" == "arm64" ]]; then
|
||||
fi
|
||||
fi
|
||||
|
||||
# sha256 digests of the ffmpeg builds, keyed "<install dir>-<arch>".
|
||||
# Upstream publishes no checksums; these come from a one-time fetch and guard
|
||||
# against later substitution. Update when bumping a build URL.
|
||||
declare -A ffmpeg_checksums=(
|
||||
["5.0-amd64"]="377abec133f9d9e8014dee1b91c9684ac8bb0b5b7d80100a57116ff837c4c0d4"
|
||||
["7.0-amd64"]="e13860eb90409c8218319c928067834ce450128e86f24cfed5cfe91ce6e31037"
|
||||
["8.0-amd64"]="9bac85054d351cdc89c0a4f45c8ea5c44df94009aabd964b719bbadd56aedae9"
|
||||
["5.0-arm64"]="57ee475407bad49910ba9b946428396e30cf075ea28a7912fbe1aa2578085af0"
|
||||
["7.0-arm64"]="16c8b04e9d0ea9c769ad964c4c453fcf05121a1947237329d2e9d8a5e43e2a3c"
|
||||
["8.0-arm64"]="cd91948468d0f11ce795a2cdaa0c69911bd1db313b49bb19c22512beb88cde69"
|
||||
)
|
||||
|
||||
# the tarballs nest their binaries under a directory named for the arch, which
|
||||
# matches TARGETARCH for both builds we consume
|
||||
install_ffmpeg() {
|
||||
local dir="$1" url="$2"
|
||||
mkdir -p "/usr/lib/ffmpeg/${dir}"
|
||||
wget -qO ffmpeg.tar.xz "${url}"
|
||||
echo "${ffmpeg_checksums[${dir}-${TARGETARCH}]} ffmpeg.tar.xz" | sha256sum -c -
|
||||
tar -xf ffmpeg.tar.xz -C "/usr/lib/ffmpeg/${dir}" --strip-components 1 "${TARGETARCH}/bin/ffmpeg" "${TARGETARCH}/bin/ffprobe"
|
||||
rm -f ffmpeg.tar.xz
|
||||
}
|
||||
|
||||
# ffmpeg -> amd64
|
||||
if [[ "${TARGETARCH}" == "amd64" ]]; then
|
||||
install_ffmpeg 5.0 "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linux64-gpl-5.1.tar.xz"
|
||||
install_ffmpeg 7.0 "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz"
|
||||
install_ffmpeg 8.0 "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-06-02-14-20/ffmpeg-n8.1.1-9-g58d4114d36-linux64-gpl-8.1.tar.xz"
|
||||
mkdir -p /usr/lib/ffmpeg/5.0
|
||||
wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linux64-gpl-5.1.tar.xz"
|
||||
tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
|
||||
rm -rf ffmpeg.tar.xz
|
||||
mkdir -p /usr/lib/ffmpeg/7.0
|
||||
wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-03-19-13-03/ffmpeg-n7.1.3-43-g5a1f107b4c-linux64-gpl-7.1.tar.xz"
|
||||
tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
|
||||
rm -rf ffmpeg.tar.xz
|
||||
fi
|
||||
|
||||
# ffmpeg -> arm64
|
||||
if [[ "${TARGETARCH}" == "arm64" ]]; then
|
||||
install_ffmpeg 5.0 "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linuxarm64-gpl-5.1.tar.xz"
|
||||
install_ffmpeg 7.0 "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linuxarm64-gpl-7.0.tar.xz"
|
||||
install_ffmpeg 8.0 "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-06-02-14-20/ffmpeg-n8.1.1-9-g58d4114d36-linuxarm64-gpl-8.1.tar.xz"
|
||||
mkdir -p /usr/lib/ffmpeg/5.0
|
||||
wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linuxarm64-gpl-5.1.tar.xz"
|
||||
tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
|
||||
rm -f ffmpeg.tar.xz
|
||||
mkdir -p /usr/lib/ffmpeg/7.0
|
||||
wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-03-19-13-03/ffmpeg-n7.1.3-43-g5a1f107b4c-linuxarm64-gpl-7.1.tar.xz"
|
||||
tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
|
||||
rm -f ffmpeg.tar.xz
|
||||
fi
|
||||
|
||||
# arch specific packages
|
||||
@@ -106,89 +87,49 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
|
||||
# intel packages use zst compression so we need to update dpkg
|
||||
apt-get install -y dpkg
|
||||
|
||||
# use intel apt repo for libmfx1 (legacy QSV, pre-Gen12)
|
||||
# use intel apt intel packages
|
||||
wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
|
||||
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | tee /etc/apt/sources.list.d/intel-gpu-jammy.list
|
||||
apt-get -qq update
|
||||
|
||||
# intel-media-va-driver-non-free is built from source in the
|
||||
# intel-media-driver Dockerfile stage for Battlemage (Xe2) support
|
||||
apt-get -qq install --no-install-recommends --no-install-suggests -y \
|
||||
libmfx1
|
||||
rm -f /usr/share/keyrings/intel-graphics.gpg
|
||||
rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
|
||||
libmfx1 libmfxgen1 libvpl2
|
||||
|
||||
# upgrade libva2, oneVPL runtime, and libvpl2 from trixie for Battlemage support
|
||||
echo "deb http://deb.debian.org/debian trixie main" > /etc/apt/sources.list.d/trixie.list
|
||||
apt-get -qq update
|
||||
apt-get -qq install -y -t trixie libva2 libva-drm2 libzstd1
|
||||
apt-get -qq install -y -t trixie libmfx-gen1.2 libvpl2
|
||||
rm -f /etc/apt/sources.list.d/trixie.list
|
||||
apt-get -qq update
|
||||
apt-get -qq install -y ocl-icd-libopencl1
|
||||
|
||||
# install libtbb12 for NPU support
|
||||
apt-get -qq install -y libtbb12
|
||||
|
||||
# install legacy and standard intel compute packages
|
||||
# sha256 digests of the driver debs, taken from the ww<week>.sum asset
|
||||
# compute-runtime ships per release and the checksum.sha256 on npu-driver
|
||||
# v1.19.0; intel-graphics-compiler and level-zero publish none, so those
|
||||
# five are hash-what-you-get. Refresh after a version bump with
|
||||
# `curl -sL <url> | sha256sum`, cross-checking upstream's sum where the
|
||||
# release still has one. npu-driver stopped publishing them after v1.19.0.
|
||||
declare -A intel_checksums=(
|
||||
["libigdgmm12_22.9.0_amd64.deb"]="9d712f71c18baee076de9961dda71e8089291e1bd0deb5d649ab5ba5de114f97"
|
||||
["intel-opencl-icd-legacy1_24.35.30872.36_amd64.deb"]="bbe71e4f414259e06a10cde72c29a2bd78d41b2bb2f6f8463b1806797fe66e85"
|
||||
["intel-level-zero-gpu-legacy1_1.5.30872.36_amd64.deb"]="40dfbd15ab62de036a00824b304a2aa1fa2d81ad60ef83da09cfe3c5a80c429f"
|
||||
["intel-igc-opencl_1.0.17537.24_amd64.deb"]="dd016400f87fa2b6a9fa9fbcca7eb4a2629174a29de679709f9bec5cede88b0e"
|
||||
["intel-igc-core_1.0.17537.24_amd64.deb"]="c1e1ecdfe2064c047c552651cfdcdafc504f2033afafba65654338b880048b67"
|
||||
["intel-opencl-icd_26.14.37833.4-0_amd64.deb"]="2e15eeb4fe9c1bba467a655967373eec6a20dd04cc7159de53c359f17ab53e41"
|
||||
["libze-intel-gpu1_26.14.37833.4-0_amd64.deb"]="34ce5791160d87ce6d54edb558a4030858ee1dad2afb067b9c5c58d4cde774c6"
|
||||
["intel-igc-opencl-2_2.32.7+21184_amd64.deb"]="3c9bddbfe558279402bbeaabcf9c63b8de46b956b0ad9625415fd35dda53ad52"
|
||||
["intel-igc-core-2_2.32.7+21184_amd64.deb"]="64e5230788e3a31e611e8d815a141b1facb91e5f0ef239233ef3f0614bfe3fd6"
|
||||
["level-zero_1.28.2+u22.04_amd64.deb"]="9015a579abef960166f8e943858d5c81fd4199a960f07260c1da66038257effb"
|
||||
["intel-driver-compiler-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb"]="8087bfcc0872d7976d0163203c7c783a4176f813c473766587e86c7b34135dff"
|
||||
["intel-fw-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb"]="740219c03495f8812c03ab74baf8199acf17d13929001105418d4ba226ba2290"
|
||||
["intel-level-zero-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb"]="f4f5eb97aa7da52c7fec97e4ddfb43aae01703bbadc767bae1f2d4faf342ba42"
|
||||
)
|
||||
|
||||
fetch_intel_deb() {
|
||||
local url="$1" name
|
||||
name=$(basename "$url")
|
||||
wget -q "$url"
|
||||
echo "${intel_checksums[${name}]} ${name}" | sha256sum -c -
|
||||
}
|
||||
rm -f /usr/share/keyrings/intel-graphics.gpg
|
||||
rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
|
||||
|
||||
# install legacy and standard intel icd and level-zero-gpu
|
||||
# see https://github.com/intel/compute-runtime/blob/master/LEGACY_PLATFORMS.md for more info
|
||||
# needed core package
|
||||
fetch_intel_deb https://github.com/intel/compute-runtime/releases/download/26.14.37833.4/libigdgmm12_22.9.0_amd64.deb
|
||||
dpkg -i libigdgmm12_22.9.0_amd64.deb
|
||||
rm libigdgmm12_22.9.0_amd64.deb
|
||||
wget https://github.com/intel/compute-runtime/releases/download/25.13.33276.19/libigdgmm12_22.7.0_amd64.deb
|
||||
dpkg -i libigdgmm12_22.7.0_amd64.deb
|
||||
rm libigdgmm12_22.7.0_amd64.deb
|
||||
|
||||
# legacy compute-runtime packages
|
||||
fetch_intel_deb https://github.com/intel/compute-runtime/releases/download/24.35.30872.36/intel-opencl-icd-legacy1_24.35.30872.36_amd64.deb
|
||||
fetch_intel_deb https://github.com/intel/compute-runtime/releases/download/24.35.30872.36/intel-level-zero-gpu-legacy1_1.5.30872.36_amd64.deb
|
||||
fetch_intel_deb https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.17537.24/intel-igc-opencl_1.0.17537.24_amd64.deb
|
||||
fetch_intel_deb https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.17537.24/intel-igc-core_1.0.17537.24_amd64.deb
|
||||
# standard compute-runtime packages
|
||||
fetch_intel_deb https://github.com/intel/compute-runtime/releases/download/26.14.37833.4/intel-opencl-icd_26.14.37833.4-0_amd64.deb
|
||||
fetch_intel_deb https://github.com/intel/compute-runtime/releases/download/26.14.37833.4/libze-intel-gpu1_26.14.37833.4-0_amd64.deb
|
||||
fetch_intel_deb https://github.com/intel/intel-graphics-compiler/releases/download/v2.32.7/intel-igc-opencl-2_2.32.7+21184_amd64.deb
|
||||
fetch_intel_deb https://github.com/intel/intel-graphics-compiler/releases/download/v2.32.7/intel-igc-core-2_2.32.7+21184_amd64.deb
|
||||
# legacy packages
|
||||
wget https://github.com/intel/compute-runtime/releases/download/24.35.30872.36/intel-opencl-icd-legacy1_24.35.30872.36_amd64.deb
|
||||
wget https://github.com/intel/compute-runtime/releases/download/24.35.30872.36/intel-level-zero-gpu-legacy1_1.5.30872.36_amd64.deb
|
||||
wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.17537.24/intel-igc-opencl_1.0.17537.24_amd64.deb
|
||||
wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.17537.24/intel-igc-core_1.0.17537.24_amd64.deb
|
||||
# standard packages
|
||||
wget https://github.com/intel/compute-runtime/releases/download/25.13.33276.19/intel-opencl-icd_25.13.33276.19_amd64.deb
|
||||
wget https://github.com/intel/compute-runtime/releases/download/25.13.33276.19/intel-level-zero-gpu_1.6.33276.19_amd64.deb
|
||||
wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.10.10/intel-igc-opencl-2_2.10.10+18926_amd64.deb
|
||||
wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.10.10/intel-igc-core-2_2.10.10+18926_amd64.deb
|
||||
# npu packages
|
||||
fetch_intel_deb https://github.com/oneapi-src/level-zero/releases/download/v1.28.2/level-zero_1.28.2+u22.04_amd64.deb
|
||||
fetch_intel_deb https://github.com/intel/linux-npu-driver/releases/download/v1.19.0/intel-driver-compiler-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb
|
||||
fetch_intel_deb https://github.com/intel/linux-npu-driver/releases/download/v1.19.0/intel-fw-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb
|
||||
fetch_intel_deb https://github.com/intel/linux-npu-driver/releases/download/v1.19.0/intel-level-zero-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb
|
||||
wget https://github.com/oneapi-src/level-zero/releases/download/v1.28.2/level-zero_1.28.2+u22.04_amd64.deb
|
||||
wget https://github.com/intel/linux-npu-driver/releases/download/v1.19.0/intel-driver-compiler-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb
|
||||
wget https://github.com/intel/linux-npu-driver/releases/download/v1.19.0/intel-fw-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb
|
||||
wget https://github.com/intel/linux-npu-driver/releases/download/v1.19.0/intel-level-zero-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb
|
||||
|
||||
dpkg -i *.deb
|
||||
rm *.deb
|
||||
apt-get -qq install -f -y
|
||||
|
||||
# Battlemage uses the xe kernel driver, but the VA-API driver is still iHD.
|
||||
# The oneVPL runtime may look for a driver named after the kernel module.
|
||||
ln -sf /usr/lib/x86_64-linux-gnu/dri/iHD_drv_video.so /usr/lib/x86_64-linux-gnu/dri/xe_drv_video.so
|
||||
fi
|
||||
|
||||
if [[ "${TARGETARCH}" == "arm64" ]]; then
|
||||
|
||||
@@ -1,19 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -euxo pipefail
|
||||
|
||||
go2rtc_version="1.9.14"
|
||||
|
||||
# sha256 digests of the release binaries; update when bumping go2rtc_version.
|
||||
declare -A go2rtc_checksums=(
|
||||
["amd64"]="32d616af226bd731678ffde328b94cfb94e30339bfefc469cfb76323144615a6"
|
||||
["arm64"]="359fabade8a7a51e81a55fe6df6b0ef81764a5e1d63179577534eaaa71904b50"
|
||||
)
|
||||
|
||||
dest_dir="/rootfs/usr/local/go2rtc/bin"
|
||||
mkdir -p "${dest_dir}"
|
||||
|
||||
wget -qO "${dest_dir}/go2rtc" \
|
||||
"https://github.com/AlexxIT/go2rtc/releases/download/v${go2rtc_version}/go2rtc_linux_${TARGETARCH}"
|
||||
echo "${go2rtc_checksums[${TARGETARCH}]} ${dest_dir}/go2rtc" | sha256sum -c -
|
||||
chmod 755 "${dest_dir}/go2rtc"
|
||||
@@ -4,29 +4,11 @@ set -euxo pipefail
|
||||
|
||||
hailo_version="4.21.0"
|
||||
|
||||
# sha256 digests of the release artifacts; update when bumping hailo_version.
|
||||
# The runtime tarball is keyed by TARGETARCH, the wheel by the python arch tag.
|
||||
declare -A hailort_checksums=(
|
||||
["amd64"]="0a57ac5f7cc8c2c3668133189d9285b55f498e8cb219797e203f6f5015fec4b3"
|
||||
["arm64"]="dd840548eb5d0d147c99aee2cb013d39d64be09c5bc63061171fcfacf4547b3f"
|
||||
["x86_64"]="8112a973ab48095399b29d883f31987828df5861b8553f614c89f098a67b3fb6"
|
||||
["aarch64"]="658432a43573280d472f6402d7934669effe7f163ba3dffa31c50bbeeaa7c01d"
|
||||
)
|
||||
|
||||
if [[ "${TARGETARCH}" == "amd64" ]]; then
|
||||
arch="x86_64"
|
||||
elif [[ "${TARGETARCH}" == "arm64" ]]; then
|
||||
arch="aarch64"
|
||||
fi
|
||||
|
||||
# downloaded rather than streamed into tar because streaming and verifying the
|
||||
# digest before extraction are mutually exclusive
|
||||
wget -qO /tmp/hailort.tar.gz "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-debian12-${TARGETARCH}.tar.gz"
|
||||
echo "${hailort_checksums[${TARGETARCH}]} /tmp/hailort.tar.gz" | sha256sum -c -
|
||||
tar -C / -xzf /tmp/hailort.tar.gz
|
||||
rm -f /tmp/hailort.tar.gz
|
||||
|
||||
wheel="/wheels/hailort-${hailo_version}-cp311-cp311-linux_${arch}.whl"
|
||||
mkdir -p /wheels
|
||||
wget -qO "${wheel}" "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${hailo_version}-cp311-cp311-linux_${arch}.whl"
|
||||
echo "${hailort_checksums[${arch}]} ${wheel}" | sha256sum -c -
|
||||
wget -qO- "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-debian12-${TARGETARCH}.tar.gz" | tar -C / -xzf -
|
||||
wget -P /wheels/ "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${hailo_version}-cp311-cp311-linux_${arch}.whl"
|
||||
|
||||
@@ -4,15 +4,6 @@ set -euxo pipefail
|
||||
|
||||
s6_version="3.2.1.0"
|
||||
|
||||
# sha256 digests of the release artifacts, from the .sha256 files published at
|
||||
# https://github.com/just-containers/s6-overlay/releases/tag/v3.2.1.0
|
||||
# Update these when bumping s6_version.
|
||||
declare -A s6_checksums=(
|
||||
["noarch"]="42e038a9a00fc0fef70bf0bc42f625a9c14f8ecdfe77d4ad93281edf717e10c5"
|
||||
["x86_64"]="8bcbc2cada58426f976b159dcc4e06cbb1454d5f39252b3bb0c778ccf71c9435"
|
||||
["aarch64"]="c8fd6b1f0380d399422fc986a1e6799f6a287e2cfa24813ad0b6a4fb4fa755cc"
|
||||
)
|
||||
|
||||
if [[ "${TARGETARCH}" == "amd64" ]]; then
|
||||
s6_arch="x86_64"
|
||||
elif [[ "${TARGETARCH}" == "arm64" ]]; then
|
||||
@@ -21,15 +12,8 @@ fi
|
||||
|
||||
mkdir -p /rootfs/
|
||||
|
||||
download_and_extract() {
|
||||
local arch="$1"
|
||||
local tarball="/tmp/s6-overlay-${arch}.tar.xz"
|
||||
wget -qO "${tarball}" \
|
||||
"https://github.com/just-containers/s6-overlay/releases/download/v${s6_version}/s6-overlay-${arch}.tar.xz"
|
||||
echo "${s6_checksums[${arch}]} ${tarball}" | sha256sum -c -
|
||||
tar -C /rootfs/ -Jxpf "${tarball}"
|
||||
rm -f "${tarball}"
|
||||
}
|
||||
wget -qO- "https://github.com/just-containers/s6-overlay/releases/download/v${s6_version}/s6-overlay-noarch.tar.xz" |
|
||||
tar -C /rootfs/ -Jxpf -
|
||||
|
||||
download_and_extract "noarch"
|
||||
download_and_extract "${s6_arch}"
|
||||
wget -qO- "https://github.com/just-containers/s6-overlay/releases/download/v${s6_version}/s6-overlay-${s6_arch}.tar.xz" |
|
||||
tar -C /rootfs/ -Jxpf -
|
||||
|
||||
@@ -4,14 +4,6 @@ set -euxo pipefail
|
||||
|
||||
tempio_version="2021.09.0"
|
||||
|
||||
# sha256 digests of the release binaries; update when bumping tempio_version.
|
||||
# Upstream publishes no checksums, so these come from a one-time fetch and
|
||||
# guard against later substitution rather than the original download.
|
||||
declare -A tempio_checksums=(
|
||||
["amd64"]="b7b93ebfd24c1161cec7aecfad62ab51f2241149358cef354b86cdbc6a60546f"
|
||||
["aarch64"]="3a5c32981ba68b75ed9b28497429e5a5cecbeb74c3b821b035a48b37609bb895"
|
||||
)
|
||||
|
||||
if [[ "${TARGETARCH}" == "amd64" ]]; then
|
||||
arch="amd64"
|
||||
elif [[ "${TARGETARCH}" == "arm64" ]]; then
|
||||
@@ -21,5 +13,4 @@ fi
|
||||
mkdir -p /rootfs/usr/local/tempio/bin
|
||||
|
||||
wget -q -O /rootfs/usr/local/tempio/bin/tempio "https://github.com/home-assistant/tempio/releases/download/${tempio_version}/tempio_${arch}"
|
||||
echo "${tempio_checksums[${arch}]} /rootfs/usr/local/tempio/bin/tempio" | sha256sum -c -
|
||||
chmod 755 /rootfs/usr/local/tempio/bin/tempio
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
ruff == 0.15.20
|
||||
ruff
|
||||
|
||||
# types
|
||||
types-peewee == 3.17.*
|
||||
|
||||
@@ -1,2 +1,3 @@
|
||||
numpy
|
||||
openvino >= 2026.2.0
|
||||
tensorflow
|
||||
openvino-dev>=2024.0.0
|
||||
@@ -11,7 +11,7 @@ joserfc == 1.2.*
|
||||
cryptography == 44.0.*
|
||||
pathvalidate == 3.3.*
|
||||
markupsafe == 3.0.*
|
||||
python-multipart == 0.0.26
|
||||
python-multipart == 0.0.20
|
||||
# Classification Model Training
|
||||
tensorflow == 2.19.* ; platform_machine == 'aarch64'
|
||||
tensorflow-cpu == 2.19.* ; platform_machine == 'x86_64'
|
||||
@@ -42,7 +42,7 @@ opencv-python-headless == 4.11.0.*
|
||||
opencv-contrib-python == 4.11.0.*
|
||||
scipy == 1.16.*
|
||||
# OpenVino & ONNX
|
||||
openvino == 2025.4.*
|
||||
openvino == 2025.3.*
|
||||
onnxruntime == 1.22.*
|
||||
# Embeddings
|
||||
transformers == 4.45.*
|
||||
@@ -79,5 +79,7 @@ sherpa-onnx==1.12.*
|
||||
faster-whisper==1.1.*
|
||||
librosa==0.11.*
|
||||
soundfile==0.13.*
|
||||
# DeGirum detector
|
||||
degirum == 0.16.*
|
||||
# Memory profiling
|
||||
memray == 1.15.*
|
||||
|
||||
@@ -1,12 +1,4 @@
|
||||
#!/command/with-contenv bash
|
||||
# shellcheck shell=bash
|
||||
|
||||
if [[ "$(id -u)" -eq 0 ]]; then
|
||||
# logutil-service drops to nobody and applies S6_LOGGING_SCRIPT
|
||||
exec logutil-service /dev/shm/logs/certsync
|
||||
fi
|
||||
|
||||
# Non-root (--user) fallback: logutil-service cannot change UID, so run
|
||||
# s6-log directly with the same directives S6_LOGGING_SCRIPT configures.
|
||||
# shellcheck disable=SC2086
|
||||
exec s6-log ${S6_LOGGING_SCRIPT:-T 1 n0 s10000000 T} /dev/shm/logs/certsync
|
||||
exec logutil-service /dev/shm/logs/certsync
|
||||
|
||||
@@ -1,12 +1,4 @@
|
||||
#!/command/with-contenv bash
|
||||
# shellcheck shell=bash
|
||||
|
||||
if [[ "$(id -u)" -eq 0 ]]; then
|
||||
# logutil-service drops to nobody and applies S6_LOGGING_SCRIPT
|
||||
exec logutil-service /dev/shm/logs/frigate
|
||||
fi
|
||||
|
||||
# Non-root (--user) fallback: logutil-service cannot change UID, so run
|
||||
# s6-log directly with the same directives S6_LOGGING_SCRIPT configures.
|
||||
# shellcheck disable=SC2086
|
||||
exec s6-log ${S6_LOGGING_SCRIPT:-T 1 n0 s10000000 T} /dev/shm/logs/frigate
|
||||
exec logutil-service /dev/shm/logs/frigate
|
||||
|
||||
@@ -1,12 +1,4 @@
|
||||
#!/command/with-contenv bash
|
||||
# shellcheck shell=bash
|
||||
|
||||
if [[ "$(id -u)" -eq 0 ]]; then
|
||||
# logutil-service drops to nobody and applies S6_LOGGING_SCRIPT
|
||||
exec logutil-service /dev/shm/logs/go2rtc
|
||||
fi
|
||||
|
||||
# Non-root (--user) fallback: logutil-service cannot change UID, so run
|
||||
# s6-log directly with the same directives S6_LOGGING_SCRIPT configures.
|
||||
# shellcheck disable=SC2086
|
||||
exec s6-log ${S6_LOGGING_SCRIPT:-T 1 n0 s10000000 T} /dev/shm/logs/go2rtc
|
||||
exec logutil-service /dev/shm/logs/go2rtc
|
||||
|
||||
@@ -1,61 +0,0 @@
|
||||
#!/command/with-contenv bash
|
||||
# shellcheck shell=bash
|
||||
# Remap the frigate user to PUID/PGID and register EXTRA_GROUPS.
|
||||
# No-op when: started with --user (euid != 0), FRIGATE_RUN_AS_ROOT=true,
|
||||
# or PUID/PGID already match.
|
||||
|
||||
set -o errexit -o nounset -o pipefail
|
||||
|
||||
if [[ "$(id -u)" -ne 0 ]]; then
|
||||
# Started with docker --user; the host owns UID mapping entirely.
|
||||
exit 0
|
||||
fi
|
||||
|
||||
if [[ "${FRIGATE_RUN_AS_ROOT:-false}" == "true" ]]; then
|
||||
echo "[INFO] FRIGATE_RUN_AS_ROOT=true: skipping user remapping"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
puid="${PUID:-1000}"
|
||||
pgid="${PGID:-1000}"
|
||||
|
||||
if ! [[ "$puid" =~ ^[0-9]+$ && "$pgid" =~ ^[0-9]+$ ]]; then
|
||||
echo "[ERROR] PUID and PGID must be numeric, got '${puid}' and '${pgid}'" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Remapping to 0 would make the frigate user root, so every service would keep
|
||||
# full privilege while reporting a successful migration.
|
||||
if [[ "$puid" -eq 0 || "$pgid" -eq 0 ]]; then
|
||||
echo "[ERROR] PUID/PGID 0 would run the services as root and defeat the privilege separation." >&2
|
||||
echo "[ERROR] Set FRIGATE_RUN_AS_ROOT=true if you want to keep running as root." >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
current_uid="$(id -u frigate)"
|
||||
current_gid="$(id -g frigate)"
|
||||
|
||||
if [[ "$puid" != "$current_uid" || "$pgid" != "$current_gid" ]]; then
|
||||
if [[ ! -w /etc/passwd ]]; then
|
||||
echo "[ERROR] PUID/PGID remapping needs a writable /etc and is not compatible with read_only: true." >&2
|
||||
echo "[ERROR] Either remove read_only and keep PUID, or drop PUID/PGID and use docker's user: ${puid}:${pgid} instead." >&2
|
||||
echo "[ERROR] See https://docs.frigate.video/configuration/non_root for the compatibility matrix." >&2
|
||||
exit 1
|
||||
fi
|
||||
echo "[INFO] Remapping frigate user to ${puid}:${pgid}"
|
||||
groupmod -o -g "$pgid" frigate
|
||||
usermod -o -u "$puid" frigate
|
||||
fi
|
||||
|
||||
# EXTRA_GROUPS: numeric host GIDs granting device access (e.g. host render/video)
|
||||
if [[ -n "${EXTRA_GROUPS:-}" ]]; then
|
||||
for gid in ${EXTRA_GROUPS//,/ }; do
|
||||
if ! getent group "$gid" >/dev/null; then
|
||||
groupadd -o -g "$gid" "frigate-extra-${gid}"
|
||||
fi
|
||||
group_name="$(getent group "$gid" | cut -d: -f1)"
|
||||
usermod -aG "$group_name" frigate
|
||||
usermod -aG "$group_name" go2rtc
|
||||
echo "[INFO] Added frigate and go2rtc to supplementary group ${group_name} (gid ${gid})"
|
||||
done
|
||||
fi
|
||||
@@ -1 +0,0 @@
|
||||
oneshot
|
||||
@@ -1 +0,0 @@
|
||||
/etc/s6-overlay/s6-rc.d/init-usermod/run
|
||||
@@ -7,12 +7,5 @@ set -o errexit -o nounset -o pipefail
|
||||
dirs=(/dev/shm/logs/frigate /dev/shm/logs/go2rtc /dev/shm/logs/nginx /dev/shm/logs/certsync)
|
||||
|
||||
mkdir -p "${dirs[@]}"
|
||||
|
||||
# logutil-service drops s6-log to nobody, so the dirs must stay nobody-owned
|
||||
# in root mode. Under docker --user we are already the (only) target user,
|
||||
# chown would fail, and the plain s6-log fallback in the *-log services
|
||||
# writes as us (the mkdir above is sufficient, /dev/shm is 1777).
|
||||
if [[ "$(id -u)" -eq 0 ]]; then
|
||||
chown nobody:nogroup "${dirs[@]}"
|
||||
fi
|
||||
chown nobody:nogroup "${dirs[@]}"
|
||||
chmod 02755 "${dirs[@]}"
|
||||
|
||||
@@ -1,12 +1,4 @@
|
||||
#!/command/with-contenv bash
|
||||
# shellcheck shell=bash
|
||||
|
||||
if [[ "$(id -u)" -eq 0 ]]; then
|
||||
# logutil-service drops to nobody and applies S6_LOGGING_SCRIPT
|
||||
exec logutil-service /dev/shm/logs/nginx
|
||||
fi
|
||||
|
||||
# Non-root (--user) fallback: logutil-service cannot change UID, so run
|
||||
# s6-log directly with the same directives S6_LOGGING_SCRIPT configures.
|
||||
# shellcheck disable=SC2086
|
||||
exec s6-log ${S6_LOGGING_SCRIPT:-T 1 n0 s10000000 T} /dev/shm/logs/nginx
|
||||
exec logutil-service /dev/shm/logs/nginx
|
||||
|
||||
@@ -77,20 +77,15 @@ if [ ! \( -f "$letsencrypt_path/privkey.pem" -a -f "$letsencrypt_path/fullchain.
|
||||
openssl req -new -newkey rsa:4096 -days 365 -nodes -x509 \
|
||||
-subj "/O=FRIGATE DEFAULT CERT/CN=*" \
|
||||
-keyout "$letsencrypt_path/privkey.pem" -out "$letsencrypt_path/fullchain.pem" 2>/dev/null
|
||||
chmod 600 "$letsencrypt_path/privkey.pem"
|
||||
chmod 644 "$letsencrypt_path/fullchain.pem"
|
||||
fi
|
||||
|
||||
# nginx settings are read once; both templates consume them
|
||||
nginx_settings=$(python3 /usr/local/nginx/get_nginx_settings.py)
|
||||
|
||||
# build templates for optional FRIGATE_BASE_PATH environment variable
|
||||
echo "$nginx_settings" | \
|
||||
python3 /usr/local/nginx/get_nginx_settings.py | \
|
||||
tempio -template /usr/local/nginx/templates/base_path.gotmpl \
|
||||
-out /usr/local/nginx/conf/base_path.conf
|
||||
|
||||
# build templates for additional network settings
|
||||
echo "$nginx_settings" | \
|
||||
python3 /usr/local/nginx/get_nginx_settings.py | \
|
||||
tempio -template /usr/local/nginx/templates/listen.gotmpl \
|
||||
-out /usr/local/nginx/conf/listen.conf
|
||||
|
||||
|
||||
@@ -144,16 +144,3 @@ rm -f /dev/shm/.frigate-is-stopping
|
||||
|
||||
migrate_addon_config_dir
|
||||
migrate_db_from_media_to_config
|
||||
|
||||
# Align volume ownership with the runtime user (one sweep per PUID/schema
|
||||
# change, guarded by the sentinel; see fix-ownership). The escape hatch
|
||||
# deletes the sentinel instead: ownership is never mutated while it is on,
|
||||
# so the next non-root boot must re-sweep whatever root created meanwhile.
|
||||
if [[ "$(id -u)" -eq 0 ]]; then
|
||||
if [[ "${FRIGATE_RUN_AS_ROOT:-false}" == "true" ]]; then
|
||||
rm -f /config/.permissions_version
|
||||
else
|
||||
/usr/local/bin/fix-ownership --sentinel /config/.permissions_version \
|
||||
"${PUID:-1000}" "${PGID:-1000}" /config /media/frigate
|
||||
fi
|
||||
fi
|
||||
|
||||
@@ -1,129 +0,0 @@
|
||||
#!/bin/bash
|
||||
# Single source of truth for aligning volume ownership with the runtime user.
|
||||
#
|
||||
# Usage: fix-ownership [--dry-run] [--sentinel FILE] UID GID PATH [PATH...]
|
||||
#
|
||||
# --dry-run report what would change, touch nothing
|
||||
# --sentinel skip entirely when FILE already records "SCHEMA:UID:GID";
|
||||
# write it after a successful run (used by the boot path so
|
||||
# multi-TB volumes are swept once per UID/schema change, not
|
||||
# on every boot)
|
||||
#
|
||||
# Only files whose uid OR gid differs are touched, so re-runs are cheap.
|
||||
# Top-level /config additionally grants group frigate-data TRAVERSE ONLY
|
||||
# (g+rx) so the separate go2rtc user can reach its pre-created HomeKit file
|
||||
# on hosts where /config is mounted 0700. Never g+w: directory write means
|
||||
# unlink rights over frigate.db/config.yml, and would let a compromised
|
||||
# go2rtc plant /config/go2rtc, which the go2rtc run script executes
|
||||
# preferentially, as root under the escape hatch.
|
||||
|
||||
set -o errexit -o nounset -o pipefail
|
||||
|
||||
# Permissions-layout epoch. Bump to force a one-time re-sweep on upgrade
|
||||
# (e.g. when the privilege-drop release must capture files created as root
|
||||
# since the previous sweep).
|
||||
schema=1
|
||||
|
||||
dry_run=0
|
||||
sentinel=""
|
||||
|
||||
while [[ "${1:-}" == --* ]]; do
|
||||
case "$1" in
|
||||
--dry-run) dry_run=1; shift ;;
|
||||
--sentinel)
|
||||
if [[ -z "${2:-}" ]]; then
|
||||
echo "[ERROR] fix-ownership: --sentinel requires a file argument" >&2
|
||||
exit 2
|
||||
fi
|
||||
sentinel="$2"; shift 2 ;;
|
||||
*) echo "[ERROR] fix-ownership: unknown option $1" >&2; exit 2 ;;
|
||||
esac
|
||||
done
|
||||
|
||||
if [[ $# -lt 3 ]]; then
|
||||
echo "Usage: fix-ownership [--dry-run] [--sentinel FILE] UID GID PATH..." >&2
|
||||
exit 2
|
||||
fi
|
||||
|
||||
target_uid="$1"
|
||||
target_gid="$2"
|
||||
shift 2
|
||||
|
||||
if [[ "$(id -u)" -ne 0 ]]; then
|
||||
echo "[INFO] fix-ownership: not running as root, skipping (ownership is managed by the host in --user mode)"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# A dry run always inspects: the sentinel records what a past sweep did, not
|
||||
# what the volume looks like now, and reporting from it would hide later drift.
|
||||
if [[ "$dry_run" -eq 0 && -n "$sentinel" && -f "$sentinel" && "$(cat "$sentinel")" == "${schema}:${target_uid}:${target_gid}" ]]; then
|
||||
echo "[INFO] fix-ownership: ${target_uid}:${target_gid} (schema ${schema}) already applied, skipping"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# A sweep that could not chown everything must not be recorded as complete:
|
||||
# the sentinel would make every later boot skip it and the entries would stay
|
||||
# unreachable once services run unprivileged.
|
||||
swept_clean=1
|
||||
|
||||
for path in "$@"; do
|
||||
# An absent root is an incomplete sweep, not a finished one: /media/frigate
|
||||
# is not in the image, so a boot before the volume is mounted would
|
||||
# otherwise record success and the volume would never be swept once added.
|
||||
if [[ ! -d "$path" ]]; then
|
||||
swept_clean=0
|
||||
echo "[WARN] fix-ownership: $path does not exist, skipping; will retry on next boot"
|
||||
continue
|
||||
fi
|
||||
|
||||
# find may fail mid-walk on a live volume (file deleted under it) or on a
|
||||
# stale mount. Tolerate it rather than aborting under errexit, but never
|
||||
# read a failed scan as "nothing to do": that would record the sweep as
|
||||
# complete without having looked.
|
||||
if ! count=$(find "$path" \( -not -uid "$target_uid" -o -not -gid "$target_gid" \) -printf '.' 2>/dev/null | wc -c); then
|
||||
swept_clean=0
|
||||
echo "[WARN] fix-ownership: could not scan ${path}; will retry on next boot"
|
||||
continue
|
||||
fi
|
||||
|
||||
if [[ "$count" -eq 0 ]]; then
|
||||
echo "[INFO] fix-ownership: $path already owned by ${target_uid}:${target_gid}, nothing to do"
|
||||
continue
|
||||
fi
|
||||
|
||||
# find does not descend symlinks and chown -h retargets the link itself, so
|
||||
# anything behind a symlinked directory is outside this sweep. Following
|
||||
# them is not an option: a link could walk the chown out of the volume.
|
||||
if [[ -n "$(find "$path" -type l -xtype d -print -quit 2>/dev/null)" ]]; then
|
||||
echo "[WARN] fix-ownership: ${path} contains symlinked directories; ownership behind them is not managed and must be aligned by hand"
|
||||
fi
|
||||
|
||||
echo "[WARN] fix-ownership: adjusting ownership of ${count} entries under ${path}; on large recordings volumes this can take a long time"
|
||||
if [[ "$dry_run" -eq 1 ]]; then
|
||||
echo "[INFO] fix-ownership: dry run, not changing ${path}"
|
||||
continue
|
||||
fi
|
||||
|
||||
find "$path" \( -not -uid "$target_uid" -o -not -gid "$target_gid" \) \
|
||||
-exec chown -h "${target_uid}:${target_gid}" {} + || {
|
||||
swept_clean=0
|
||||
echo "[WARN] fix-ownership: some entries under ${path} could not be updated (deleted mid-sweep or chown denied); will retry on next mismatch"
|
||||
}
|
||||
done
|
||||
|
||||
# go2rtc (separate user) must be able to REACH its HomeKit state in /config.
|
||||
# Write access is per-file, not per-directory: go2rtc's PatchConfig rewrites
|
||||
# the first -config file via os.WriteFile (in-place truncate, no rename,
|
||||
# verified against go2rtc v1.9.14 internal/app/config.go), and the file is
|
||||
# always pre-created by setup_homekit_config before go2rtc starts, so
|
||||
# O_CREATE never needs directory write. See header comment for why g+w is
|
||||
# forbidden here.
|
||||
if [[ "$dry_run" -eq 0 && -d /config ]]; then
|
||||
chgrp frigate-data /config 2>/dev/null || true
|
||||
chmod g+rx /config 2>/dev/null || true
|
||||
fi
|
||||
|
||||
if [[ "$dry_run" -eq 0 && -n "$sentinel" && "$swept_clean" -eq 1 ]]; then
|
||||
echo "${schema}:${target_uid}:${target_gid}" > "$sentinel" || \
|
||||
echo "[WARN] fix-ownership: could not write ${sentinel}; the sweep will run again on next boot"
|
||||
fi
|
||||
@@ -5,7 +5,11 @@ from typing import Any
|
||||
from ruamel.yaml import YAML
|
||||
|
||||
sys.path.insert(0, "/opt/frigate")
|
||||
from frigate.util.config import find_config_file, resolve_ffmpeg_path
|
||||
from frigate.const import (
|
||||
DEFAULT_FFMPEG_VERSION,
|
||||
INCLUDED_FFMPEG_VERSIONS,
|
||||
)
|
||||
from frigate.util.config import find_config_file
|
||||
|
||||
sys.path.remove("/opt/frigate")
|
||||
|
||||
@@ -25,4 +29,9 @@ except FileNotFoundError:
|
||||
config: dict[str, Any] = {}
|
||||
|
||||
path = config.get("ffmpeg", {}).get("path", "default")
|
||||
print(resolve_ffmpeg_path(path, "ffmpeg"))
|
||||
if path == "default":
|
||||
print(f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg")
|
||||
elif path in INCLUDED_FFMPEG_VERSIONS:
|
||||
print(f"/usr/lib/ffmpeg/{path}/bin/ffmpeg")
|
||||
else:
|
||||
print(f"{path}/bin/ffmpeg")
|
||||
|
||||
@@ -3,27 +3,52 @@
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from ruamel.yaml import YAML
|
||||
|
||||
sys.path.insert(0, "/opt/frigate")
|
||||
from frigate.config.env import apply_config_env_vars, substitute_frigate_vars
|
||||
from frigate.config.env import substitute_frigate_vars
|
||||
from frigate.const import (
|
||||
BIRDSEYE_PIPE,
|
||||
DEFAULT_FFMPEG_VERSION,
|
||||
INCLUDED_FFMPEG_VERSIONS,
|
||||
LIBAVFORMAT_VERSION_MAJOR,
|
||||
)
|
||||
from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
|
||||
from frigate.util.config import find_config_file, resolve_ffmpeg_path
|
||||
from frigate.util.services import (
|
||||
is_go2rtc_arbitrary_exec_allowed,
|
||||
is_restricted_go2rtc_source,
|
||||
)
|
||||
from frigate.util.config import find_config_file
|
||||
|
||||
sys.path.remove("/opt/frigate")
|
||||
|
||||
yaml = YAML()
|
||||
|
||||
# Check if arbitrary exec sources are allowed (defaults to False for security)
|
||||
allow_arbitrary_exec = None
|
||||
if "GO2RTC_ALLOW_ARBITRARY_EXEC" in os.environ:
|
||||
allow_arbitrary_exec = os.environ.get("GO2RTC_ALLOW_ARBITRARY_EXEC")
|
||||
elif (
|
||||
os.path.isdir("/run/secrets")
|
||||
and os.access("/run/secrets", os.R_OK)
|
||||
and "GO2RTC_ALLOW_ARBITRARY_EXEC" in os.listdir("/run/secrets")
|
||||
):
|
||||
allow_arbitrary_exec = (
|
||||
Path(os.path.join("/run/secrets", "GO2RTC_ALLOW_ARBITRARY_EXEC"))
|
||||
.read_text()
|
||||
.strip()
|
||||
)
|
||||
# check for the add-on options file
|
||||
elif os.path.isfile("/data/options.json"):
|
||||
with open("/data/options.json") as f:
|
||||
raw_options = f.read()
|
||||
options = json.loads(raw_options)
|
||||
allow_arbitrary_exec = options.get("go2rtc_allow_arbitrary_exec")
|
||||
|
||||
ALLOW_ARBITRARY_EXEC = allow_arbitrary_exec is not None and str(
|
||||
allow_arbitrary_exec
|
||||
).lower() in ("true", "1", "yes")
|
||||
|
||||
|
||||
config_file = find_config_file()
|
||||
|
||||
try:
|
||||
@@ -37,20 +62,6 @@ try:
|
||||
except FileNotFoundError:
|
||||
config: dict[str, Any] = {}
|
||||
|
||||
# No validator runs here, so install environment_vars ourselves. FRIGATE_
|
||||
# names only: anything else lands in os.environ, where the exec gate reads
|
||||
# GO2RTC_ALLOW_ARBITRARY_EXEC.
|
||||
config_env_vars = config.get("environment_vars")
|
||||
apply_config_env_vars(
|
||||
{
|
||||
key: value
|
||||
for key, value in config_env_vars.items()
|
||||
if str(key).startswith("FRIGATE_")
|
||||
}
|
||||
if isinstance(config_env_vars, dict)
|
||||
else {}
|
||||
)
|
||||
|
||||
go2rtc_config: dict[str, Any] = config.get("go2rtc", {})
|
||||
|
||||
# Need to enable CORS for go2rtc so the frigate integration / card work automatically
|
||||
@@ -96,7 +107,12 @@ if go2rtc_config.get("rtsp", {}).get("password") is not None:
|
||||
|
||||
# ensure ffmpeg path is set correctly
|
||||
path = config.get("ffmpeg", {}).get("path", "default")
|
||||
ffmpeg_path = resolve_ffmpeg_path(path, "ffmpeg")
|
||||
if path == "default":
|
||||
ffmpeg_path = f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg"
|
||||
elif path in INCLUDED_FFMPEG_VERSIONS:
|
||||
ffmpeg_path = f"/usr/lib/ffmpeg/{path}/bin/ffmpeg"
|
||||
else:
|
||||
ffmpeg_path = f"{path}/bin/ffmpeg"
|
||||
|
||||
if go2rtc_config.get("ffmpeg") is None:
|
||||
go2rtc_config["ffmpeg"] = {"bin": ffmpeg_path}
|
||||
@@ -112,13 +128,18 @@ if LIBAVFORMAT_VERSION_MAJOR < 59:
|
||||
go2rtc_config["ffmpeg"]["rtsp"] = rtsp_args
|
||||
|
||||
|
||||
def is_restricted_source(stream_source: str) -> bool:
|
||||
"""Check if a stream source is restricted (echo, expr, or exec)."""
|
||||
return stream_source.strip().startswith(("echo:", "expr:", "exec:"))
|
||||
|
||||
|
||||
for name in list(go2rtc_config.get("streams", {})):
|
||||
stream = go2rtc_config["streams"][name]
|
||||
|
||||
if isinstance(stream, str):
|
||||
try:
|
||||
formatted_stream = substitute_frigate_vars(stream)
|
||||
if is_restricted_go2rtc_source(formatted_stream):
|
||||
if not ALLOW_ARBITRARY_EXEC and is_restricted_source(formatted_stream):
|
||||
print(
|
||||
f"[ERROR] Stream '{name}' uses a restricted source (echo/expr/exec) which is disabled by default for security. "
|
||||
f"Set GO2RTC_ALLOW_ARBITRARY_EXEC=true to enable arbitrary exec sources."
|
||||
@@ -126,7 +147,7 @@ for name in list(go2rtc_config.get("streams", {})):
|
||||
del go2rtc_config["streams"][name]
|
||||
continue
|
||||
go2rtc_config["streams"][name] = formatted_stream
|
||||
except ValueError as e:
|
||||
except KeyError as e:
|
||||
print(
|
||||
"[ERROR] Invalid substitution found, see https://docs.frigate.video/configuration/restream#advanced-restream-configurations for more info."
|
||||
)
|
||||
@@ -137,7 +158,7 @@ for name in list(go2rtc_config.get("streams", {})):
|
||||
for i, stream_item in enumerate(stream):
|
||||
try:
|
||||
formatted_stream = substitute_frigate_vars(stream_item)
|
||||
if is_restricted_go2rtc_source(formatted_stream):
|
||||
if not ALLOW_ARBITRARY_EXEC and is_restricted_source(formatted_stream):
|
||||
print(
|
||||
f"[ERROR] Stream '{name}' item {i + 1} uses a restricted source (echo/expr/exec) which is disabled by default for security. "
|
||||
f"Set GO2RTC_ALLOW_ARBITRARY_EXEC=true to enable arbitrary exec sources."
|
||||
@@ -145,7 +166,7 @@ for name in list(go2rtc_config.get("streams", {})):
|
||||
continue
|
||||
|
||||
filtered_streams.append(formatted_stream)
|
||||
except ValueError as e:
|
||||
except KeyError as e:
|
||||
print(
|
||||
"[ERROR] Invalid substitution found, see https://docs.frigate.video/configuration/restream#advanced-restream-configurations for more info."
|
||||
)
|
||||
@@ -160,20 +181,6 @@ for name in list(go2rtc_config.get("streams", {})):
|
||||
)
|
||||
del go2rtc_config["streams"][name]
|
||||
|
||||
elif isinstance(stream, dict):
|
||||
# The map form ({"url": ...}) lets go2rtc resolve the source
|
||||
# recursively, so it is effectively a dynamic way to generate the URL
|
||||
# for a stream. That can only be backed by an exec source, so it cannot
|
||||
# be allowed unless arbitrary exec is explicitly enabled. When it is
|
||||
# enabled, leave the map untouched for go2rtc to resolve.
|
||||
if not is_go2rtc_arbitrary_exec_allowed():
|
||||
print(
|
||||
f"[ERROR] Stream '{name}' uses a dynamic source format which is disabled by default for security. "
|
||||
f"Set GO2RTC_ALLOW_ARBITRARY_EXEC=true to enable arbitrary exec sources."
|
||||
)
|
||||
del go2rtc_config["streams"][name]
|
||||
continue
|
||||
|
||||
# add birdseye restream stream if enabled
|
||||
if config.get("birdseye", {}).get("restream", False):
|
||||
birdseye: dict[str, Any] = config.get("birdseye")
|
||||
@@ -189,6 +196,3 @@ if config.get("birdseye", {}).get("restream", False):
|
||||
# Write go2rtc_config to /dev/shm/go2rtc.yaml
|
||||
with open("/dev/shm/go2rtc.yaml", "w") as f:
|
||||
yaml.dump(go2rtc_config, f)
|
||||
|
||||
# config contains camera credentials; do not leave it world-readable
|
||||
os.chmod("/dev/shm/go2rtc.yaml", 0o640)
|
||||
|
||||
@@ -11,7 +11,6 @@ events {
|
||||
|
||||
http {
|
||||
map_hash_bucket_size 256;
|
||||
server_tokens off;
|
||||
|
||||
include mime.types;
|
||||
default_type application/octet-stream;
|
||||
@@ -63,7 +62,6 @@ http {
|
||||
|
||||
server {
|
||||
include listen.conf;
|
||||
include security_headers.conf;
|
||||
|
||||
# enable HTTP/2 for TLS connections to eliminate browser 6-connection limit
|
||||
http2 on;
|
||||
@@ -77,12 +75,6 @@ http {
|
||||
vod_align_segments_to_key_frames on;
|
||||
vod_manifest_segment_durations_mode accurate;
|
||||
vod_ignore_edit_list on;
|
||||
# short leading segments at each playlist start; sources start at
|
||||
# the seek target, so the ladder applies to every seek. Only
|
||||
# effective when clips declare real keyFrameDurations
|
||||
vod_bootstrap_segment_durations 1000;
|
||||
vod_bootstrap_segment_durations 2000;
|
||||
vod_bootstrap_segment_durations 4000;
|
||||
vod_segment_duration 10000;
|
||||
|
||||
# MPEG-TS settings (not used when fMP4 is enabled, kept for reference)
|
||||
@@ -125,7 +117,6 @@ http {
|
||||
secure_token $args;
|
||||
secure_token_types application/vnd.apple.mpegurl;
|
||||
|
||||
include security_headers.conf;
|
||||
add_header Cache-Control "no-store";
|
||||
expires off;
|
||||
|
||||
@@ -142,7 +133,6 @@ http {
|
||||
|
||||
location /stream/ {
|
||||
include auth_request.conf;
|
||||
include security_headers.conf;
|
||||
add_header Cache-Control "no-store";
|
||||
expires off;
|
||||
|
||||
@@ -164,7 +154,6 @@ http {
|
||||
}
|
||||
|
||||
expires 7d;
|
||||
include security_headers.conf;
|
||||
add_header Cache-Control "public";
|
||||
autoindex on;
|
||||
root /media/frigate;
|
||||
@@ -257,14 +246,12 @@ http {
|
||||
|
||||
location /api/ {
|
||||
include auth_request.conf;
|
||||
include security_headers.conf;
|
||||
add_header Cache-Control "no-store";
|
||||
expires off;
|
||||
proxy_pass http://frigate_api/;
|
||||
include proxy.conf;
|
||||
|
||||
proxy_cache api_cache;
|
||||
proxy_cache_key "$scheme$proxy_host$request_uri|$role|$groups|$user";
|
||||
proxy_cache_lock on;
|
||||
proxy_cache_use_stale updating;
|
||||
proxy_cache_valid 200 5s;
|
||||
@@ -286,13 +273,6 @@ http {
|
||||
include proxy.conf;
|
||||
}
|
||||
|
||||
location /api/logout {
|
||||
auth_request off;
|
||||
rewrite ^/api(/.*)$ $1 break;
|
||||
proxy_pass http://frigate_api;
|
||||
include proxy.conf;
|
||||
}
|
||||
|
||||
# Allow unauthenticated access to the first_time_login endpoint
|
||||
# so the login page can load help text before authentication.
|
||||
location /api/auth/first_time_login {
|
||||
@@ -324,34 +304,29 @@ http {
|
||||
|
||||
location / {
|
||||
# do not require auth for static assets
|
||||
include security_headers.conf;
|
||||
add_header Cache-Control "no-store";
|
||||
expires off;
|
||||
|
||||
location /assets/ {
|
||||
access_log off;
|
||||
expires 1y;
|
||||
include security_headers.conf;
|
||||
add_header Cache-Control "public";
|
||||
}
|
||||
|
||||
location /fonts/ {
|
||||
access_log off;
|
||||
expires 1y;
|
||||
include security_headers.conf;
|
||||
add_header Cache-Control "public";
|
||||
}
|
||||
|
||||
location /locales/ {
|
||||
access_log off;
|
||||
include security_headers.conf;
|
||||
add_header Cache-Control "public";
|
||||
}
|
||||
|
||||
location ~ ^/.*-([A-Za-z0-9]+)\.webmanifest$ {
|
||||
access_log off;
|
||||
expires 1y;
|
||||
include security_headers.conf;
|
||||
add_header Cache-Control "public";
|
||||
default_type application/json;
|
||||
proxy_set_header Accept-Encoding "";
|
||||
|
||||
@@ -1,5 +0,0 @@
|
||||
# Deliberately no X-Frame-Options or CSP frame-ancestors: HA's Webpage card and
|
||||
# iframe panels frame Frigate cross-origin, and either would break them
|
||||
# silently. Bind-mount this file to add your own.
|
||||
add_header X-Content-Type-Options "nosniff" always;
|
||||
add_header Referrer-Policy "strict-origin-when-cross-origin" always;
|
||||
@@ -1,45 +0,0 @@
|
||||
#!/bin/bash
|
||||
# Ahead-of-time volume ownership migration for switching Frigate to non-root.
|
||||
# Run from the host BEFORE enabling PUID/PGID or --user:
|
||||
#
|
||||
# ./fix-permissions.sh [--dry-run] <config_dir> <media_dir> [PUID] [PGID]
|
||||
#
|
||||
# Wraps the image's fix-ownership helper so there is exactly one
|
||||
# implementation of the chown logic. Requires an image that contains the
|
||||
# helper (any release that includes non-root support).
|
||||
|
||||
set -o errexit -o nounset -o pipefail
|
||||
|
||||
IMAGE="${FRIGATE_IMAGE:-ghcr.io/blakeblackshear/frigate:stable}"
|
||||
|
||||
dry_run_flag=""
|
||||
if [[ "${1:-}" == "--dry-run" ]]; then
|
||||
dry_run_flag="--dry-run"
|
||||
shift
|
||||
fi
|
||||
|
||||
if [[ $# -lt 2 ]]; then
|
||||
echo "Usage: $0 [--dry-run] <config_dir> <media_dir> [PUID] [PGID]" >&2
|
||||
exit 2
|
||||
fi
|
||||
|
||||
config_dir="$1"
|
||||
media_dir="$2"
|
||||
puid="${3:-1000}"
|
||||
pgid="${4:-1000}"
|
||||
|
||||
# The ids are interpolated into the container's bash -c source below, so
|
||||
# anything but digits would be reparsed as shell rather than passed through
|
||||
if ! [[ "$puid" =~ ^[0-9]+$ && "$pgid" =~ ^[0-9]+$ ]]; then
|
||||
echo "[ERROR] PUID and PGID must be numeric, got '${puid}' and '${pgid}'" >&2
|
||||
exit 2
|
||||
fi
|
||||
|
||||
echo "[INFO] Using image ${IMAGE} (override with FRIGATE_IMAGE=...)"
|
||||
# shellcheck disable=SC2086
|
||||
docker run --rm \
|
||||
-v "${config_dir}:/config" \
|
||||
-v "${media_dir}:/media/frigate" \
|
||||
--entrypoint bash \
|
||||
"${IMAGE}" \
|
||||
-c "command -v fix-ownership >/dev/null || { echo '[ERROR] this Frigate image predates non-root support; set FRIGATE_IMAGE to a release that includes it' >&2; exit 1; }; exec fix-ownership ${dry_run_flag} ${puid} ${pgid} /config /media/frigate"
|
||||
@@ -11,10 +11,10 @@ except FileNotFoundError:
|
||||
pass
|
||||
|
||||
try:
|
||||
with open("/config/conv2rknn.yaml") as config_file:
|
||||
with open("/config/conv2rknn.yaml", "r") as config_file:
|
||||
configuration = yaml.safe_load(config_file)
|
||||
except FileNotFoundError:
|
||||
raise Exception("Please place a config file at /config/conv2rknn.yaml") from None
|
||||
raise Exception("Please place a config file at /config/conv2rknn.yaml")
|
||||
|
||||
if configuration["config"] != None:
|
||||
rknn_config = configuration["config"]
|
||||
@@ -31,7 +31,7 @@ if "soc" not in configuration:
|
||||
with open("/proc/device-tree/compatible") as file:
|
||||
soc = file.read().split(",")[-1].strip("\x00")
|
||||
except FileNotFoundError:
|
||||
raise Exception("Make sure to run docker in privileged mode.") from None
|
||||
raise Exception("Make sure to run docker in privileged mode.")
|
||||
|
||||
configuration["soc"] = [
|
||||
soc,
|
||||
|
||||
+4
-11
@@ -13,7 +13,7 @@ ARG ROCM
|
||||
|
||||
RUN apt update -qq && \
|
||||
apt install -y wget gpg && \
|
||||
wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.2.3/ubuntu/jammy/amdgpu-install_7.2.3.70203-1_all.deb && \
|
||||
wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.2/ubuntu/jammy/amdgpu-install_7.2.70200-1_all.deb && \
|
||||
apt install -y ./rocm.deb && \
|
||||
apt update && \
|
||||
apt install -qq -y rocm
|
||||
@@ -32,14 +32,11 @@ RUN echo /opt/rocm/lib|tee /opt/rocm-dist/etc/ld.so.conf.d/rocm.conf
|
||||
FROM deps AS deps-prelim
|
||||
|
||||
COPY docker/rocm/debian-backports.sources /etc/apt/sources.list.d/debian-backports.sources
|
||||
# install_deps.sh upgraded libstdc++6 from trixie for Battlemage; the matching
|
||||
# -dev package must also come from trixie or apt refuses to satisfy it.
|
||||
RUN echo "deb http://deb.debian.org/debian trixie main" > /etc/apt/sources.list.d/trixie.list && \
|
||||
apt-get update && \
|
||||
RUN apt-get update && \
|
||||
apt-get install -y libnuma1 && \
|
||||
apt-get install -qq -y -t bookworm-backports mesa-va-drivers mesa-vulkan-drivers && \
|
||||
apt-get install -qq -y -t trixie libstdc++-14-dev && \
|
||||
rm -f /etc/apt/sources.list.d/trixie.list && \
|
||||
# Install C++ standard library headers for HIPRTC kernel compilation fallback
|
||||
apt-get install -qq -y libstdc++-12-dev && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
WORKDIR /opt/frigate
|
||||
@@ -78,10 +75,6 @@ ENV MIGRAPHX_DISABLE_MIOPEN_FUSION=1
|
||||
ENV MIGRAPHX_DISABLE_SCHEDULE_PASS=1
|
||||
ENV MIGRAPHX_DISABLE_REDUCE_FUSION=1
|
||||
ENV MIGRAPHX_ENABLE_HIPRTC_WORKAROUNDS=1
|
||||
ENV MIOPEN_CUSTOM_CACHE_DIR=/config/model_cache/migraphx
|
||||
ENV MIOPEN_USER_DB_PATH=/config/model_cache/migraphx
|
||||
ENV AMD_COMGR_CACHE=1
|
||||
ENV AMD_COMGR_CACHE_DIR=/config/model_cache/migraphx
|
||||
|
||||
COPY --from=rocm-dist / /
|
||||
|
||||
|
||||
@@ -1 +1 @@
|
||||
onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.2.3-1/onnxruntime_migraphx-1.24.4-cp311-cp311-linux_x86_64.whl
|
||||
onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.2.0/onnxruntime_migraphx-1.23.1-cp311-cp311-linux_x86_64.whl
|
||||
@@ -1,5 +1,5 @@
|
||||
variable "ROCM" {
|
||||
default = "7.2.3"
|
||||
default = "7.2.0"
|
||||
}
|
||||
variable "HSA_OVERRIDE_GFX_VERSION" {
|
||||
default = ""
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,6 +1,7 @@
|
||||
---
|
||||
id: system
|
||||
title: System
|
||||
id: advanced
|
||||
title: Advanced Options
|
||||
sidebar_label: Advanced Options
|
||||
---
|
||||
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
@@ -63,28 +64,34 @@ go2rtc:
|
||||
|
||||
### `environment_vars`
|
||||
|
||||
This section sets environment variables in the Frigate process for those unable to modify the environment of the container, like within Home Assistant OS. It's meant for process settings such as `LIBVA_DRIVER_NAME` or the TensorFlow thread counts below. Docker users should set environment variables in their `docker run` command (`-e LIBVA_DRIVER_NAME=i965`) or `docker-compose.yml` file (`environment:` section) instead. Values set here are stored in plain text in your config file, so credentials belong in `secrets.yaml`, Docker environment variables, or Docker secrets instead.
|
||||
This section can be used to set environment variables for those unable to modify the environment of the container, like within Home Assistant OS. Docker users should set environment variables in their `docker run` command (`-e FRIGATE_MQTT_PASSWORD=secret`) or `docker-compose.yml` file (`environment:` section) instead. Note that values set here are stored in plain text in your config file, so if the goal is to keep credentials out of your configuration, use Docker environment variables or Docker secrets instead.
|
||||
|
||||
Names prefixed with `FRIGATE_` set here also take part in `{FRIGATE_VARIABLE_NAME}` substitution (see [below](#substitution-sources-and-precedence)), but `secrets.yaml` is the better home for them.
|
||||
Variables prefixed with `FRIGATE_` can be referenced in config fields that support environment variable substitution (such as MQTT host and credentials, camera stream URLs, and ONVIF host and credentials) using the `{FRIGATE_VARIABLE_NAME}` syntax.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Environment variables" /> to add or edit environment variables.
|
||||
|
||||
| Field | Description |
|
||||
| ----------------- | --------------------------------------------------------- |
|
||||
| **Variable name** | The environment variable name (e.g., `LIBVA_DRIVER_NAME`) |
|
||||
| **Value** | The value for the variable |
|
||||
| Field | Description |
|
||||
| --------- | --------------------------------------------------------- |
|
||||
| **Key** | The environment variable name (e.g., `FRIGATE_MQTT_USER`) |
|
||||
| **Value** | The value for the variable |
|
||||
|
||||
Names prefixed with `FRIGATE_` can also be referenced elsewhere in your configuration using the `{FRIGATE_VARIABLE_NAME}` syntax.
|
||||
Variables defined here can be referenced elsewhere in your configuration using the `{FRIGATE_VARIABLE_NAME}` syntax.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
environment_vars:
|
||||
LIBVA_DRIVER_NAME: i965
|
||||
FRIGATE_MQTT_USER: my_mqtt_user
|
||||
FRIGATE_MQTT_PASSWORD: my_mqtt_password
|
||||
|
||||
mqtt:
|
||||
host: "{FRIGATE_MQTT_HOST}"
|
||||
user: "{FRIGATE_MQTT_USER}"
|
||||
password: "{FRIGATE_MQTT_PASSWORD}"
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -118,51 +125,6 @@ environment_vars:
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
### `secrets.yaml`
|
||||
|
||||
A `secrets.yaml` file next to your `config.yml` is an additional source of `FRIGATE_` variables, for installs that can't set container environment variables or mount Docker secrets. It's a flat map of names to values, and it is never read or written by the Frigate UI:
|
||||
|
||||
```yaml
|
||||
FRIGATE_CAM_USER: viewer
|
||||
FRIGATE_CAM_PASS: "p@ss w0rd"
|
||||
FRIGATE_MQTT_HOST: mqtt.internal.example
|
||||
```
|
||||
|
||||
For Docker this is `/config/secrets.yaml` inside the container, so it lives in whatever host directory you mounted at `/config`. For the Home Assistant App it's `/addon_configs/<addon_directory>/secrets.yaml`, in the same folder as your `config.yml`; see [the App config directory](../config.md#accessing-app-config-dir) for the directory name for your variant.
|
||||
|
||||
Names must start with `FRIGATE_`, and nesting is not supported. `secrets.yaml` feeds `{FRIGATE_VARIABLE_NAME}` substitution, so the handful of variables Frigate reads straight from the process environment, such as `FRIGATE_JWT_SECRET`, still need a container environment variable or a Docker secret.
|
||||
|
||||
### Substitution sources and precedence
|
||||
|
||||
The same `{FRIGATE_VARIABLE_NAME}` placeholder resolves from four sources. When a name is defined in more than one, the higher one wins and a warning at startup names which source was used.
|
||||
|
||||
| Priority | Source | Where it's set | Who can use it |
|
||||
| ----------- | --------------------- | -------------------------------------------------------------------------- | ------------------------------ |
|
||||
| 1 (highest) | Docker secrets | Files in `/run/secrets`, or the directory named by `CREDENTIALS_DIRECTORY` | Docker, systemd |
|
||||
| 2 | Container environment | `docker run -e`, the `environment:` section of `docker-compose.yml` | Docker |
|
||||
| 3 | `secrets.yaml` | Next to `config.yml`, see above | Everyone, including the HA App |
|
||||
| 4 (lowest) | `environment_vars` | The block in `config.yml` described above | Everyone, including the HA App |
|
||||
|
||||
For example, with this `secrets.yaml`:
|
||||
|
||||
```yaml
|
||||
FRIGATE_MQTT_PASSWORD: from_secrets
|
||||
```
|
||||
|
||||
and this `config.yml`:
|
||||
|
||||
```yaml
|
||||
environment_vars:
|
||||
FRIGATE_MQTT_PASSWORD: from_config
|
||||
|
||||
mqtt:
|
||||
password: "{FRIGATE_MQTT_PASSWORD}"
|
||||
```
|
||||
|
||||
the password resolves to `from_secrets`, and the log shows `FRIGATE_MQTT_PASSWORD is defined in more than one place, using the value from secrets.yaml`. Add `-e FRIGATE_MQTT_PASSWORD=from_env` to the container and it resolves to `from_env` instead.
|
||||
|
||||
Referencing a name that no source defines is a config validation error naming the field.
|
||||
|
||||
### `database`
|
||||
|
||||
Tracked object and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
|
||||
@@ -210,7 +172,7 @@ Custom models may also require different input tensor formats. The colorspace co
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Detection models" /> and, on the model you want to change, open the **Custom Model** tab to configure the model path, dimensions, and input format.
|
||||
Navigate to <NavPath path="Settings > System > Detection model" /> to configure the model path, dimensions, and input format.
|
||||
|
||||
| Field | Description |
|
||||
| --------------------------------------------- | ------------------------------------ |
|
||||
@@ -225,14 +187,12 @@ Navigate to <NavPath path="Settings > System > Detection models" /> and, on the
|
||||
|
||||
```yaml
|
||||
# Optional: model config
|
||||
models:
|
||||
- devices:
|
||||
- openvino:GPU
|
||||
path: /path/to/model
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: "nhwc"
|
||||
input_pixel_format: "bgr"
|
||||
model:
|
||||
path: /path/to/model
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: "nhwc"
|
||||
input_pixel_format: "bgr"
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -242,22 +202,22 @@ models:
|
||||
|
||||
:::warning
|
||||
|
||||
If the labelmap is customized then the labels used for alerts will need to be adjusted as well. See [alert labels](../review.md#restricting-alerts-to-specific-labels) for more info.
|
||||
If the labelmap is customized then the labels used for alerts will need to be adjusted as well. See [alert labels](../configuration/review.md#restricting-alerts-to-specific-labels) for more info.
|
||||
|
||||
:::
|
||||
|
||||
The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- labelmap:
|
||||
2: vehicle
|
||||
3: vehicle
|
||||
5: vehicle
|
||||
7: vehicle
|
||||
15: animal
|
||||
16: animal
|
||||
17: animal
|
||||
model:
|
||||
labelmap:
|
||||
2: vehicle
|
||||
3: vehicle
|
||||
5: vehicle
|
||||
7: vehicle
|
||||
15: animal
|
||||
16: animal
|
||||
17: animal
|
||||
```
|
||||
|
||||
Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.
|
||||
@@ -274,16 +234,26 @@ Some labels have special handling and modifications can disable functionality.
|
||||
|
||||
## Network Configuration
|
||||
|
||||
Frigate exposes a few networking options. IPv6 and the listen ports are set in the `networking` configuration (or from the Settings UI); more advanced changes require [customizing the bundled Nginx configuration](#customizing-the-nginx-configuration).
|
||||
Changes to Frigate's internal network configuration can be made by bind mounting nginx.conf into the container. For example:
|
||||
|
||||
```yaml
|
||||
services:
|
||||
frigate:
|
||||
container_name: frigate
|
||||
...
|
||||
volumes:
|
||||
...
|
||||
- /path/to/your/nginx.conf:/usr/local/nginx/conf/nginx.conf
|
||||
```
|
||||
|
||||
### Enabling IPv6
|
||||
|
||||
By default Frigate listens on IPv4 only. To also listen on IPv6 (on port `5000`, and on `8971` when TLS is configured), enable it in the `networking` configuration.
|
||||
IPv6 is disabled by default. Enable it in the Frigate configuration.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Networking" /> and enable **IPv6**.
|
||||
Navigate to <NavPath path="Settings > System > Networking" /> and expand **IPv6 configuration**, then enable **Enable IPv6**.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -291,7 +261,7 @@ Navigate to <NavPath path="Settings > System > Networking" /> and enable **IPv6*
|
||||
```yaml
|
||||
networking:
|
||||
ipv6:
|
||||
enabled: true
|
||||
enabled: True
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -328,26 +298,8 @@ networking:
|
||||
|
||||
This setting is for advanced users. For the majority of use cases it's recommended to change the `ports` section of your Docker compose file or use the Docker `run` `--publish` option instead, e.g. `-p 443:8971`. Changing Frigate's ports may break some integrations.
|
||||
|
||||
The internal and external ports must be different port numbers, and Frigate will refuse to start otherwise. Requests arriving on the internal port are treated as authenticated admins, so pointing both at the same port would remove authentication from the external one.
|
||||
|
||||
Nginx binds these ports when it starts, so port changes only take effect after Frigate restarts.
|
||||
|
||||
:::
|
||||
|
||||
### Customizing the Nginx configuration
|
||||
|
||||
More advanced changes to Frigate's internal network configuration can be made by bind mounting your own `nginx.conf` into the container. For example:
|
||||
|
||||
```yaml
|
||||
services:
|
||||
frigate:
|
||||
container_name: frigate
|
||||
...
|
||||
volumes:
|
||||
...
|
||||
- /path/to/your/nginx.conf:/usr/local/nginx/conf/nginx.conf
|
||||
```
|
||||
|
||||
## Base path
|
||||
|
||||
By default, Frigate runs at the root path (`/`). However some setups require to run Frigate under a custom path prefix (e.g. `/frigate`), especially when Frigate is located behind a reverse proxy that requires path-based routing.
|
||||
@@ -374,7 +326,7 @@ For example:
|
||||
```
|
||||
services:
|
||||
frigate:
|
||||
image: ghcr.io/blakeblackshear/frigate:stable
|
||||
image: blakeblackshear/frigate:latest
|
||||
environment:
|
||||
- FRIGATE_BASE_PATH=/frigate
|
||||
```
|
||||
@@ -399,7 +351,7 @@ To do this:
|
||||
|
||||
### Custom go2rtc version
|
||||
|
||||
Frigate currently includes go2rtc v1.9.14, there may be certain cases where you want to run a different version of go2rtc.
|
||||
Frigate currently includes go2rtc v1.9.13, there may be certain cases where you want to run a different version of go2rtc.
|
||||
|
||||
To do this:
|
||||
|
||||
@@ -54,7 +54,7 @@ The ffmpeg process for capturing audio will be a separate connection to the came
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add an input with the `audio` role pointing to a stream that includes audio.
|
||||
Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add an input with the `audio` role pointing to a stream that includes audio.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -78,7 +78,7 @@ cameras:
|
||||
|
||||
### Configuring Minimum Volume
|
||||
|
||||
The audio detector uses volume levels in the same way that motion in a camera feed is used for object detection. This means that Frigate will not run audio detection unless the audio volume is above the configured level in order to reduce resource usage. Audio levels can vary widely between camera models so it is important to run tests to see what volume levels are. The [Debug view](/usage/live#the-single-camera-view) in the Frigate UI has an Audio tab for cameras that have the `audio` role assigned where a graph and the current levels are displayed. The `min_volume` parameter should be set to the minimum the `RMS` level required to run audio detection.
|
||||
The audio detector uses volume levels in the same way that motion in a camera feed is used for object detection. This means that Frigate will not run audio detection unless the audio volume is above the configured level in order to reduce resource usage. Audio levels can vary widely between camera models so it is important to run tests to see what volume levels are. The Debug view in the Frigate UI has an Audio tab for cameras that have the `audio` role assigned where a graph and the current levels are is displayed. The `min_volume` parameter should be set to the minimum the `RMS` level required to run audio detection.
|
||||
|
||||
:::tip
|
||||
|
||||
@@ -88,7 +88,7 @@ Volume is considered motion for recordings, this means when the `record -> retai
|
||||
|
||||
### Configuring Audio Events
|
||||
|
||||
The included audio model has over [500 different types](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) of audio that can be detected, many of which are not practical. By default `bark`, `fire_alarm`, `speech`, and `yell` are enabled but these can be customized.
|
||||
The included audio model has over [500 different types](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) of audio that can be detected, many of which are not practical. By default `bark`, `fire_alarm`, `scream`, `speech`, and `yell` are enabled but these can be customized.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
@@ -107,6 +107,7 @@ audio:
|
||||
listen:
|
||||
- bark
|
||||
- fire_alarm
|
||||
- scream
|
||||
- speech
|
||||
- yell
|
||||
```
|
||||
@@ -114,97 +115,9 @@ audio:
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
#### Grouping Audio Labels
|
||||
|
||||
Related audio classes can be grouped under one label by mapping their numeric
|
||||
class IDs to the same name. Add the grouped name to `listen` and use it for any
|
||||
corresponding filter:
|
||||
|
||||
```yaml
|
||||
audio:
|
||||
listen:
|
||||
- dogs
|
||||
labelmap:
|
||||
69: dogs # dog
|
||||
70: dogs # bark
|
||||
75: dogs # whimper_dog
|
||||
filters:
|
||||
dogs:
|
||||
threshold: 0.8
|
||||
```
|
||||
|
||||
Class IDs are zero-based indices in
|
||||
[`audio-labelmap.txt`](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt),
|
||||
so each ID is one less than the displayed file line number.
|
||||
Audio label mappings are separate from the object detector's `model.labelmap`.
|
||||
|
||||
### Common Audio Labels
|
||||
|
||||
The labelmap includes hundreds of sound types. The labels below are the ones most users may find practical, grouped by what they're typically used for. Use the exact label string from the left column in your `listen` config, or search for the label in the Frigate UI directly.
|
||||
|
||||
Some labels cover several related sounds: `yell` is triggered by shouting, yelling, children shouting, and screaming; `crying` covers baby cries, sobbing, and whimpering; and `speech` covers ordinary talking and conversation.
|
||||
|
||||
**Safety and security**
|
||||
|
||||
| Label | Detects |
|
||||
| ---------------- | ---------------------------------- |
|
||||
| `yell` | Shouting, yelling, screaming |
|
||||
| `fire_alarm` | Fire and smoke alarm sirens |
|
||||
| `smoke_detector` | Smoke detector beeps |
|
||||
| `alarm` | General alarm sounds |
|
||||
| `car_alarm` | Car alarms |
|
||||
| `siren` | Emergency vehicle and civil sirens |
|
||||
| `glass` | Glass clinking |
|
||||
| `shatter` | Breaking glass |
|
||||
| `breaking` | Something breaking |
|
||||
| `gunshot` | Gunshots |
|
||||
| `explosion` | Explosions |
|
||||
|
||||
**People and activity**
|
||||
|
||||
| Label | Detects |
|
||||
| ----------- | ------------------------ |
|
||||
| `speech` | Talking and conversation |
|
||||
| `laughter` | Laughing |
|
||||
| `crying` | Baby crying and sobbing |
|
||||
| `cough` | Coughing |
|
||||
| `footsteps` | Footsteps and walking |
|
||||
| `knock` | Knocking on a door |
|
||||
| `doorbell` | Doorbell |
|
||||
| `ding-dong` | Doorbell chime |
|
||||
|
||||
**Pets and animals**
|
||||
|
||||
| Label | Detects |
|
||||
| ---------- | ---------------- |
|
||||
| `bark` | Dog barking |
|
||||
| `dog` | Other dog sounds |
|
||||
| `howl` | Howling |
|
||||
| `growling` | Growling |
|
||||
| `meow` | Cat meowing |
|
||||
| `cat` | Other cat sounds |
|
||||
| `hiss` | Hissing |
|
||||
|
||||
**Vehicles and driveway**
|
||||
|
||||
| Label | Detects |
|
||||
| ----------------- | -------------------- |
|
||||
| `car` | Passing cars |
|
||||
| `honk` | Car horns |
|
||||
| `truck` | Trucks |
|
||||
| `reversing_beeps` | Vehicle backup beeps |
|
||||
| `motorcycle` | Motorcycles |
|
||||
| `engine_starting` | Engines starting |
|
||||
|
||||
:::tip
|
||||
|
||||
Frequently-heard labels like `speech` can generate a lot of events, and each event could save a snapshot and recording based on your configuration, so start with a focused set and expand from there. The defaults (`bark`, `fire_alarm`, `speech`, `yell`) plus a few of the safety labels above cover most needs. See the [full audio labelmap](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) or the Frigate UI for every available type.
|
||||
|
||||
:::
|
||||
|
||||
### Audio Transcription
|
||||
|
||||
Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service. Automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
|
||||
Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service — automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
|
||||
|
||||
:::info
|
||||
|
||||
@@ -280,7 +193,7 @@ The only field that is valid at the camera level is `enabled`.
|
||||
|
||||
#### Live transcription
|
||||
|
||||
The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing, or toggle it outside of the UI with the [`frigate/<camera_name>/audio_transcription/set`](/integrations/mqtt#frigatecamera_nameaudio_transcriptionset) MQTT topic or the HTTP API. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate/<camera_name>/audio/transcription` will also be updated in real-time with transcribed text.
|
||||
The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate/<camera_name>/audio/transcription` will also be updated in real-time with transcribed text.
|
||||
|
||||
Results can be error-prone due to a number of factors, including:
|
||||
|
||||
@@ -296,7 +209,7 @@ If you have CUDA hardware, you can experiment with the `large` `whisper` model o
|
||||
|
||||
#### Transcription and translation of `speech` audio events
|
||||
|
||||
Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button (the microphone icon) in the Tracked Object Details pane.
|
||||
Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button in the Tracked Object Details pane.
|
||||
|
||||
In order to use transcription and translation for past events, you must enable audio detection and define `speech` as an audio type to listen for. To have `speech` events translated into the language of your choice, set the `language` config parameter with the correct [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
|
||||
|
||||
@@ -318,7 +231,7 @@ Recorded `speech` events will always use a `whisper` model, regardless of the `m
|
||||
|
||||
Because transcription is **serialized (one event at a time)** and speech events can be generated far faster than they can be processed, an auto-transcribe toggle would very quickly create an ever-growing backlog and degrade core functionality. For the amount of engineering and risk involved, it adds **very little practical value** for the majority of deployments, which are often on low-powered, edge hardware.
|
||||
|
||||
If you hear speech that's actually important and worth saving/indexing for the future, **just press the transcribe button (the microphone icon) in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
|
||||
If you hear speech that's actually important and worth saving/indexing for the future, **just press the transcribe button in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
|
||||
|
||||
Other options are being considered for future versions of Frigate to add transcription options that support external `whisper` Docker containers. A single transcription service could then be shared by Frigate and other applications (for example, Home Assistant Voice), and run on more powerful machines when available.
|
||||
|
||||
|
||||
@@ -91,7 +91,7 @@ auth:
|
||||
|
||||
## Session Length
|
||||
|
||||
The default session length for user authentication in Frigate is 24 hours. This setting determines how long a user's authenticated session remains active before a token refresh is required. Otherwise, the user will need to log in again.
|
||||
The default session length for user authentication in Frigate is 24 hours. This setting determines how long a user's authenticated session remains active before a token refresh is required — otherwise, the user will need to log in again.
|
||||
|
||||
While the default provides a balance of security and convenience, you can customize this duration to suit your specific security requirements and user experience preferences. The session length is configured in seconds.
|
||||
|
||||
@@ -141,7 +141,7 @@ Changing the secret will invalidate current tokens.
|
||||
|
||||
## Proxy configuration
|
||||
|
||||
Frigate can be configured to leverage features of common upstream authentication proxies such as Authelia, Authentik, oauth2_proxy, or traefik-forward-auth. Frigate does not implement OIDC, SAML, or LDAP natively; as an NVR focused on recording and object detection, it relies on robust, battle-tested proxies to handle those protocols and passes the authenticated user and role through via headers (see below).
|
||||
Frigate can be configured to leverage features of common upstream authentication proxies such as Authelia, Authentik, oauth2_proxy, or traefik-forward-auth.
|
||||
|
||||
If you are leveraging the authentication of an upstream proxy, you likely want to disable Frigate's authentication as there is no correspondence between users in Frigate's database and users authenticated via the proxy. Optionally, if communication between the reverse proxy and Frigate is over an untrusted network, you should set an `auth_secret` in the `proxy` config and configure the proxy to send the secret value as a header named `X-Proxy-Secret`. Assuming this is an untrusted network, you will also want to [configure a real TLS certificate](tls.md) to ensure the traffic can't simply be sniffed to steal the secret.
|
||||
|
||||
@@ -262,19 +262,6 @@ In this example:
|
||||
|
||||
- Admin precedence: if the `admin` mapping matches, Frigate resolves the session to `admin` to avoid accidental downgrade when a user belongs to multiple groups (for example both `admin` and `viewer` groups).
|
||||
|
||||
:::note
|
||||
|
||||
If a user isn't getting the role you expect, enable debug logging to see exactly what headers Frigate is receiving from your proxy:
|
||||
|
||||
```yaml
|
||||
logger:
|
||||
default: info
|
||||
logs:
|
||||
frigate.api.auth: debug
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
#### Port Considerations
|
||||
|
||||
**Authenticated Port (8971)**
|
||||
|
||||
@@ -6,7 +6,6 @@ title: Camera Autotracking
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
import FaqItem from "@site/src/components/FaqItem";
|
||||
|
||||
An ONVIF-capable, PTZ (pan-tilt-zoom) camera that supports relative movement within the field of view (FOV) can be configured to automatically track moving objects and keep them in the center of the frame.
|
||||
|
||||
@@ -162,13 +161,13 @@ Every PTZ camera is different, so autotracking may not perform ideally in every
|
||||
|
||||
The object tracker in Frigate estimates the motion of the PTZ so that tracked objects are preserved when the camera moves. In most cases 5 fps is sufficient, but if you plan to track faster moving objects, you may want to increase this slightly. Higher frame rates (> 10fps) will only slow down Frigate and the motion estimator and may lead to dropped frames, especially if you are using experimental zooming.
|
||||
|
||||
A fast [detector](object_detectors.md) is recommended. CPU detectors will not perform well or won't work at all. You can watch Frigate's [debug viewer](/usage/live#the-single-camera-view) for your camera to see a thicker colored box around the object currently being autotracked.
|
||||
A fast [detector](object_detectors.md) is recommended. CPU detectors will not perform well or won't work at all. You can watch Frigate's debug viewer for your camera to see a thicker colored box around the object currently being autotracked.
|
||||
|
||||
|
||||
|
||||
A full-frame zone in `required_zones` is not recommended, especially if you've calibrated your camera and there are `movement_weights` defined in the configuration file. Frigate will continue to autotrack an object that has entered one of the `required_zones`, even if it moves outside of that zone.
|
||||
|
||||
Some users have found it helpful to adjust the zone `inertia` value. See the [configuration reference](advanced/reference.md).
|
||||
Some users have found it helpful to adjust the zone `inertia` value. See the [configuration reference](index.md).
|
||||
|
||||
## Zooming
|
||||
|
||||
@@ -188,96 +187,30 @@ In security and surveillance, it's common to use "spotter" cameras in combinatio
|
||||
|
||||
## Troubleshooting and FAQ
|
||||
|
||||
### Camera Compatibility
|
||||
|
||||
<FaqItem id="which-ptz-camera-should-i-use-for-autotracking" question="Which PTZ camera should I use for autotracking?">
|
||||
|
||||
See the community-maintained list of [ONVIF PTZ camera recommendations](cameras.md#onvif-ptz-camera-recommendations) for cameras and brands reported to work (and not work) with autotracking. This is not an exhaustive list that is frequently updated, so other cameras not listed may also work well. Frigate's autotracking was developed with a Dahua SD1A404XB-GNR (now sold as the EmpireTech PTZ1A4M-4X-S2), and Dahua / EmpireTech PTZs are the most consistently reported as working well.
|
||||
|
||||
When comparing models:
|
||||
|
||||
- Verify ONVIF support first. See [Checking ONVIF camera support](#checking-onvif-camera-support) above.
|
||||
- Favor a camera with a fast PTZ motor. Cameras with slow motors may fail [calibration](#calibration) and will struggle to keep up with objects that move across the field of view quickly.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="does-autotracking-work-with-reolink-ptz-cameras" question="Does autotracking work with Reolink PTZ cameras?">
|
||||
|
||||
No. Reolink cameras (including the TrackMix series) lack the ONVIF FOV RelativeMove firmware support that Frigate's autotracker requires, so autotracking will not work with any current Reolink PTZ. Their video streams and basic PTZ controls still work in Frigate. If you want object tracking on a Reolink PTZ, you will need to use the tracking feature built into the camera's firmware, which is proprietary and operates independently of Frigate.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="im-seeing-an-error-in-the-logs-that-my-camera-is-still-in-onvif-moving-status-what-does-this-mean" question={"I'm seeing an error in the logs that my camera \"is still in ONVIF 'MOVING' status.\" What does this mean?"}>
|
||||
|
||||
There are two possible known reasons for this (and perhaps others yet unknown): a slow PTZ motor or buggy camera firmware. Frigate uses an ONVIF parameter provided by the camera, `MoveStatus`, to determine when the PTZ's motor is moving or idle. According to some users, Hikvision PTZs (even with the latest firmware), are not updating this value after PTZ movement. Unfortunately there is no workaround to this bug in Hikvision firmware, so autotracking will not function correctly and should be disabled in your config. This may also be the case with other non-Hikvision cameras utilizing Hikvision firmware, such as some Annke models. In rare cases the vendor may provide fixed firmware on request; for example, Annke has supplied firmware that resolves this for the CZ504 (see the [camera recommendations list](cameras.md#onvif-ptz-camera-recommendations)).
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="calibration-seems-to-have-completed-but-the-camera-is-not-actually-moving-to-track-my-object-why" question="Calibration seems to have completed, but the camera is not actually moving to track my object. Why?">
|
||||
|
||||
Some cameras have firmware that reports that FOV RelativeMove, the ONVIF command that Frigate uses for autotracking, is supported. However, if the camera does not pan or tilt when an object comes into the required zone, your camera's firmware does not actually support FOV RelativeMove. One such camera is the Uniview IPC672LR-AX4DUPK. It actually moves its zoom motor instead of panning and tilting and does not follow the ONVIF standard whatsoever.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
### Calibration Issues
|
||||
|
||||
<FaqItem id="i-tried-calibrating-my-camera-but-the-logs-show-that-it-is-stuck-at-0-and-frigate-is-not-starting-up" question="I tried calibrating my camera, but the logs show that it is stuck at 0% and Frigate is not starting up.">
|
||||
|
||||
This is often caused by the same reason as the "MOVING" status error above - the `MoveStatus` ONVIF parameter is not changing due to a bug in your camera's firmware. Also, see the note above: Frigate's web UI and all other cameras will be unresponsive while calibration is in progress. This is expected and normal. But if you don't see log entries every few seconds for calibration progress, your camera is not compatible with autotracking.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="frigate-reports-an-error-saying-that-calibration-has-failed-why" question="Frigate reports an error saying that calibration has failed. Why?">
|
||||
|
||||
Calibration measures the amount of time it takes for Frigate to make a series of movements with your PTZ. This error message is recorded in the log if these values are too high for Frigate to support calibrated autotracking. This is often the case when your camera's motor or network connection is too slow or your camera's firmware doesn't report the motor status in a timely manner.
|
||||
|
||||
Some things to try:
|
||||
|
||||
- If your camera's firmware has a PTZ or motor speed setting, set it to the fastest available speed and calibrate again.
|
||||
- Run without calibration: remove the `movement_weights` line from your config, set `calibrate_on_startup` to `False`, and restart.
|
||||
|
||||
If calibration consistently fails, this often means your camera's motor is too slow and autotracking will behave unpredictably or won't be able to keep up with moving objects.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="autotracking-is-erratic-or-moves-the-camera-in-the-wrong-direction" question="Autotracking is erratic, moves the camera in the wrong direction, or zooms past my object. Why?">
|
||||
|
||||
Frigate uses the `movement_weights` measured during calibration to predict how far the camera needs to move to keep an object centered, so inaccurate values produce movements that don't seem to make sense: overshooting, moving the opposite direction, or zooming in on an object's last known position and losing it entirely. This is almost always a calibration issue.
|
||||
|
||||
- Remove the `movement_weights` entry from your config and restart Frigate to run without calibration. If tracking improves, try recalibrating.
|
||||
- Recalibrate several times. The `movement_weights` values should be close to each other after each run. If they vary significantly between runs, your camera may not be reporting its motor status reliably, and you may get better results without calibration.
|
||||
- If you are using zooming, a high `zoom_factor` can cause the camera to zoom in too far and lose the object. Try a lower value.
|
||||
|
||||
Remember to recalibrate whenever you change your `return_preset`, change your camera's detect `fps`, or enable zooming after calibrating with it disabled.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
### Tracking Behavior
|
||||
|
||||
<FaqItem id="the-autotracker-loses-track-of-my-object-why" question="The autotracker loses track of my object. Why?">
|
||||
### The autotracker loses track of my object. Why?
|
||||
|
||||
There are many reasons this could be the case. If you are using experimental zooming, your `zoom_factor` value might be too high, the object might be traveling too quickly, the scene might be too dark, there are not enough details in the scene (for example, a PTZ looking down on a driveway or other monotone background without a sufficient number of hard edges or corners), or the scene is otherwise less than optimal for Frigate to maintain tracking.
|
||||
|
||||
Your camera's shutter speed may also be set too low so that blurring occurs with motion. Check your camera's firmware to see if you can increase the shutter speed.
|
||||
|
||||
Watching Frigate's debug view can help to determine a possible cause. The autotracked object will have a thicker colored box around it. If the camera consistently zooms in on the object and then loses it, see [Autotracking is erratic, moves the camera in the wrong direction, or zooms past my object. Why?](#autotracking-is-erratic-or-moves-the-camera-in-the-wrong-direction) above.
|
||||
Watching Frigate's debug view can help to determine a possible cause. The autotracked object will have a thicker colored box around it.
|
||||
|
||||
</FaqItem>
|
||||
### I'm seeing an error in the logs that my camera "is still in ONVIF 'MOVING' status." What does this mean?
|
||||
|
||||
<FaqItem id="im-seeing-this-error-in-the-logs-autotracker-motion-estimator-couldnt-get-transformations-what-does-this-mean" question={"I'm seeing this error in the logs: \"Autotracker: motion estimator couldn't get transformations\". What does this mean?"}>
|
||||
There are two possible known reasons for this (and perhaps others yet unknown): a slow PTZ motor or buggy camera firmware. Frigate uses an ONVIF parameter provided by the camera, `MoveStatus`, to determine when the PTZ's motor is moving or idle. According to some users, Hikvision PTZs (even with the latest firmware), are not updating this value after PTZ movement. Unfortunately there is no workaround to this bug in Hikvision firmware, so autotracking will not function correctly and should be disabled in your config. This may also be the case with other non-Hikvision cameras utilizing Hikvision firmware.
|
||||
|
||||
### I tried calibrating my camera, but the logs show that it is stuck at 0% and Frigate is not starting up.
|
||||
|
||||
This is often caused by the same reason as above - the `MoveStatus` ONVIF parameter is not changing due to a bug in your camera's firmware. Also, see the note above: Frigate's web UI and all other cameras will be unresponsive while calibration is in progress. This is expected and normal. But if you don't see log entries every few seconds for calibration progress, your camera is not compatible with autotracking.
|
||||
|
||||
### I'm seeing this error in the logs: "Autotracker: motion estimator couldn't get transformations". What does this mean?
|
||||
|
||||
To maintain object tracking during PTZ moves, Frigate tracks the motion of your camera based on the details of the frame. If you are seeing this message, it could mean that your `zoom_factor` may be set too high, the scene around your detected object does not have enough details (like hard edges or color variations), or your camera's shutter speed is too slow and motion blur is occurring. Try reducing `zoom_factor`, finding a way to alter the scene around your object, or changing your camera's shutter speed.
|
||||
|
||||
</FaqItem>
|
||||
### Calibration seems to have completed, but the camera is not actually moving to track my object. Why?
|
||||
|
||||
<FaqItem id="why-does-object-detection-pause-briefly-when-the-camera-moves" question="Why does object detection pause briefly when the camera moves?">
|
||||
Some cameras have firmware that reports that FOV RelativeMove, the ONVIF command that Frigate uses for autotracking, is supported. However, if the camera does not pan or tilt when an object comes into the required zone, your camera's firmware does not actually support FOV RelativeMove. One such camera is the Uniview IPC672LR-AX4DUPK. It actually moves its zoom motor instead of panning and tilting and does not follow the ONVIF standard whatsoever.
|
||||
|
||||
When the PTZ moves, the entire frame changes at once. Frigate's motion detection treats sudden scene-wide changes (like a lightning flash, an infrared mode switch, or a camera move) specially and pauses detection momentarily until the scene stabilizes. This is expected and normal, and detection resumes shortly after the camera stops moving. If detection does not resume once the camera is stationary, use the [debug view](/usage/live#the-single-camera-view) to see what is happening.
|
||||
### Frigate reports an error saying that calibration has failed. Why?
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="can-i-turn-autotracking-on-and-off-automatically" question="Can I turn autotracking on and off automatically?">
|
||||
|
||||
Yes. Autotracking can be toggled per camera at runtime over MQTT with the [`frigate/<camera_name>/ptz_autotracker/set`](../integrations/mqtt.md#frigatecamera_nameptz_autotrackerset) topic, and the [Home Assistant integration](../integrations/home-assistant.md) exposes a switch for it. This pairs well with the "spotter" camera automations described in [Usage applications](#usage-applications) above, for example only enabling autotracking at night or when nobody is home.
|
||||
|
||||
</FaqItem>
|
||||
Calibration measures the amount of time it takes for Frigate to make a series of movements with your PTZ. This error message is recorded in the log if these values are too high for Frigate to support calibrated autotracking. This is often the case when your camera's motor or network connection is too slow or your camera's firmware doesn't report the motor status in a timely manner. You can try running without calibration (just remove the `movement_weights` line from your config and restart), but if calibration fails, this often means that autotracking will behave unpredictably.
|
||||
|
||||
@@ -6,29 +6,19 @@ import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
In addition to Frigate's Live camera dashboard, Birdseye allows a portable heads-up view of your cameras to see what is going on around your property / space without having to watch all cameras that may have nothing happening. Birdseye allows specific modes that intelligently show and disappear based on what you care about.
|
||||
|
||||
Birdseye can be viewed by adding the "Birdseye" camera to a Camera Group in the Web UI. Add a Camera Group by pressing the pencil icon in the sidebar on the Live page, and choose "Birdseye" as one of the cameras.
|
||||
Birdseye can be viewed by adding the "Birdseye" camera to a Camera Group in the Web UI. Add a Camera Group by pressing the "+" icon on the Live page, and choose "Birdseye" as one of the cameras.
|
||||
|
||||
Birdseye can also be used in Home Assistant dashboards, cast to media devices, etc.
|
||||
|
||||
:::note
|
||||
|
||||
Each camera tile in Birdseye is composed from the frames of the stream assigned the `detect` role, so a camera's image quality in Birdseye matches its detect stream resolution rather than a higher-resolution recording stream. If a camera looks low quality in Birdseye, increasing the detect width and height (or assigning the `detect` role to a higher-resolution stream) is what affects it. See [setting up camera inputs](./cameras.md#setting-up-camera-inputs) for how roles are assigned.
|
||||
|
||||
:::
|
||||
|
||||
## Birdseye Behavior
|
||||
|
||||
### Birdseye Activity Types
|
||||
### Birdseye Modes
|
||||
|
||||
Birdseye offers independent activity types that control when cameras are shown. Multiple activity types can be listed together.
|
||||
Birdseye offers different modes to customize which cameras show under which circumstances.
|
||||
|
||||
- **continuous:** The camera is always included
|
||||
- **motion:** The camera is included when motion was detected within the last 30 seconds
|
||||
- **all_objects:** The camera is included when a tracked object is present, active or stationary
|
||||
- **alerts:** The camera is included while an alert review item is in progress
|
||||
- **detections:** The camera is included while a detection review item is in progress
|
||||
|
||||
`alerts` and `detections` follow the review item's own lifetime, so the camera is removed as soon as the review item ends. Which objects qualify for each is set in [review configuration](./review.md).
|
||||
- **continuous:** All cameras are always included
|
||||
- **motion:** Cameras that have detected motion within the last 30 seconds are included
|
||||
- **objects:** Cameras that have tracked an active object within the last 30 seconds are included
|
||||
|
||||
### Custom Birdseye Icon
|
||||
|
||||
@@ -43,29 +33,27 @@ To include a camera in Birdseye view only for specific circumstances, or exclude
|
||||
|
||||
**Global settings:** Navigate to <NavPath path="Settings > System > Birdseye" /> to configure the default Birdseye behavior for all cameras.
|
||||
|
||||
**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the activity types or disable Birdseye for a specific camera.
|
||||
**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the mode or disable Birdseye for a specific camera.
|
||||
|
||||
| Field | Description |
|
||||
| ---------------------- | ---------------------------------------------------------- |
|
||||
| **Enable Birdseye** | Whether this camera appears in Birdseye view |
|
||||
| **Activity types** | Conditions that determine when to show the camera |
|
||||
| Field | Description |
|
||||
|-------|-------------|
|
||||
| **Enable Birdseye** | Whether this camera appears in Birdseye view |
|
||||
| **Tracking mode** | When to show the camera: `continuous`, `motion`, or `objects` |
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml {10-12,15-16}
|
||||
```yaml {8-10,12-14}
|
||||
# Include all cameras by default in Birdseye view
|
||||
birdseye:
|
||||
enabled: True
|
||||
modes:
|
||||
- continuous
|
||||
mode: continuous
|
||||
|
||||
cameras:
|
||||
front:
|
||||
# Only include the "front" camera in Birdseye view when an alert is in progress
|
||||
# Only include the "front" camera in Birdseye view when objects are detected
|
||||
birdseye:
|
||||
modes:
|
||||
- alerts
|
||||
mode: objects
|
||||
back:
|
||||
# Exclude the "back" camera from Birdseye view
|
||||
birdseye:
|
||||
@@ -77,15 +65,15 @@ cameras:
|
||||
|
||||
### Birdseye Inactivity
|
||||
|
||||
By default birdseye shows all cameras that have had the configured activity in the last 30 seconds. This threshold can be configured, and applies to the `motion` and `all_objects` activity types only.
|
||||
By default birdseye shows all cameras that have had the configured activity in the last 30 seconds. This threshold can be configured.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Birdseye" />.
|
||||
|
||||
| Field | Description |
|
||||
| ------------------------ | --------------------------------------------------------------------------- |
|
||||
| Field | Description |
|
||||
|-------|-------------|
|
||||
| **Inactivity threshold** | Seconds of inactivity before a camera is hidden from Birdseye (default: 30) |
|
||||
|
||||
</TabItem>
|
||||
@@ -112,9 +100,9 @@ The resolution and aspect ratio of birdseye can be configured. Resolution will i
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Birdseye" />.
|
||||
|
||||
| Field | Description |
|
||||
| ---------- | ----------------------------------------------- |
|
||||
| **Width** | Birdseye output width in pixels (default: 1280) |
|
||||
| Field | Description |
|
||||
|-------|-------------|
|
||||
| **Width** | Birdseye output width in pixels (default: 1280) |
|
||||
| **Height** | Birdseye output height in pixels (default: 720) |
|
||||
|
||||
</TabItem>
|
||||
@@ -132,12 +120,12 @@ birdseye:
|
||||
|
||||
### Sorting cameras in the Birdseye view
|
||||
|
||||
It is possible to override the order of cameras that are being shown in the Birdseye view. The order is set at the camera level (when using YAML).
|
||||
It is possible to override the order of cameras that are being shown in the Birdseye view. The order is set at the camera level.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Birdseye" /> and in the **Camera order** field, use the drag handle next to each camera name to control the display order.
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> for each camera and set the **Position** field to control the display order.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -146,8 +134,7 @@ Navigate to <NavPath path="Settings > System > Birdseye" /> and in the **Camera
|
||||
# Include all cameras by default in Birdseye view
|
||||
birdseye:
|
||||
enabled: True
|
||||
modes:
|
||||
- continuous
|
||||
mode: continuous
|
||||
|
||||
cameras:
|
||||
front:
|
||||
@@ -174,8 +161,8 @@ It is possible to limit the number of cameras shown on birdseye at one time. Whe
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Birdseye" />.
|
||||
|
||||
| Field | Description |
|
||||
| ------------------------ | ----------------------------------------------------------------------------------- |
|
||||
| Field | Description |
|
||||
|-------|-------------|
|
||||
| **Layout > Max cameras** | Maximum number of cameras shown at once (e.g., `1` for only the most active camera) |
|
||||
|
||||
</TabItem>
|
||||
@@ -200,8 +187,8 @@ By default birdseye tries to fit 2 cameras in each row and then double in size u
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Birdseye" />.
|
||||
|
||||
| Field | Description |
|
||||
| --------------------------- | -------------------------------------------------------- |
|
||||
| Field | Description |
|
||||
|-------|-------------|
|
||||
| **Layout > Scaling factor** | Camera scaling factor between 1.0 and 5.0 (default: 2.0) |
|
||||
|
||||
</TabItem>
|
||||
|
||||
@@ -3,8 +3,6 @@ id: camera_specific
|
||||
title: Camera Specific Configurations
|
||||
---
|
||||
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
:::note
|
||||
|
||||
This page makes use of presets of FFmpeg args. For more information on presets, see the [FFmpeg Presets](/configuration/ffmpeg_presets) page.
|
||||
@@ -150,34 +148,19 @@ WEB Digest Algorithm - MD5
|
||||
|
||||
Reolink has many different camera models with inconsistently supported features and behavior. The below table shows a summary of various features and recommendations.
|
||||
|
||||
| Camera Resolution | Camera Generation | Recommended Stream Type | Additional Notes |
|
||||
| ----------------- | ------------------------- | --------------------------------- | ------------------------------------------------------------------------------------------- |
|
||||
| 5MP or lower | All | http-flv | Stream is h264 |
|
||||
| 6MP or higher | Latest (ex: Duo3, CX-8##) | http-flv with ffmpeg 8.0, or rtsp | This uses the new http-flv-enhanced over H265 which requires ffmpeg 8.0 (Frigate's default) |
|
||||
| 6MP or higher | Older (ex: RLC-8##) | rtsp | |
|
||||
| Camera Resolution | Camera Generation | Recommended Stream Type | Additional Notes |
|
||||
| ----------------- | ------------------------- | --------------------------------- | ----------------------------------------------------------------------- |
|
||||
| 5MP or lower | All | http-flv | Stream is h264 |
|
||||
| 6MP or higher | Latest (ex: Duo3, CX-8##) | http-flv with ffmpeg 8.0, or rtsp | This uses the new http-flv-enhanced over H265 which requires ffmpeg 8.0 |
|
||||
| 6MP or higher | Older (ex: RLC-8##) | rtsp | |
|
||||
|
||||
Frigate works much better with newer Reolink cameras that are setup with the below options:
|
||||
Frigate works much better with newer reolink cameras that are setup with the below options:
|
||||
|
||||
If available, recommended settings are:
|
||||
|
||||
- `On, fluency first` this sets the camera to CBR (constant bit rate)
|
||||
- `Interframe Space 1x` this sets the iframe interval to the same as the frame rate
|
||||
|
||||
#### Setup via the Add Camera Wizard
|
||||
|
||||
The [Add Camera Wizard](cameras.md#adding-a-camera-with-the-add-camera-wizard) is the recommended way to add a standard Reolink camera. Before starting, make sure [HTTP is enabled](https://support.reolink.com/articles/360003452893-How-to-Access-Reolink-Cameras-NVRs-Home-Hub-Locally-via-Web-Browsers/) in the camera's advanced network settings. The wizard uses the camera's HTTP API to determine its resolution and choose the recommended stream type from the table above.
|
||||
|
||||
1. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />.
|
||||
2. Choose **Manual selection** as the stream detection method and select **Reolink** as the camera brand.
|
||||
3. The wizard queries the camera and automatically uses an http-flv stream for cameras 5MP and lower, or an RTSP stream for higher resolution cameras.
|
||||
4. In the validation step, enable **Use stream compatibility mode** for http-flv streams when the wizard recommends it.
|
||||
|
||||
If you use the **Probe camera** method instead, the discovered stream URLs will be RTSP. For Reolink cameras where http-flv is recommended, the wizard will show a warning in the validation step.
|
||||
|
||||
The wizard covers standard single-camera setups. For two way talk, cameras connected through a Reolink NVR, or audio transcoding for WebRTC live view, configure the camera manually as shown below.
|
||||
|
||||
#### Manual configuration
|
||||
|
||||
According to [this discussion](https://github.com/blakeblackshear/frigate/issues/3235#issuecomment-1135876973), the http video streams seem to be the most reliable for Reolink.
|
||||
|
||||
Cameras connected via a Reolink NVR can be connected with the http stream, use `channel[0..15]` in the stream url for the additional channels.
|
||||
@@ -192,7 +175,7 @@ Reolink's latest cameras support two way audio via go2rtc and other applications
|
||||
|
||||
NOTE: The RTSP stream can not be prefixed with `ffmpeg:`, as go2rtc needs to handle the stream to support two way audio.
|
||||
|
||||
Ensure [HTTP is enabled](https://support.reolink.com/articles/360003452893-How-to-Access-Reolink-Cameras-NVRs-Home-Hub-Locally-via-Web-Browsers/) in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
|
||||
Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
|
||||
|
||||
:::
|
||||
|
||||
@@ -204,7 +187,7 @@ go2rtc:
|
||||
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
|
||||
your_reolink_camera_sub:
|
||||
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
|
||||
# example for connecting to a Reolink camera that supports two way talk
|
||||
# example for connectin to a Reolink camera that supports two way talk
|
||||
your_reolink_camera_twt:
|
||||
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
|
||||
- "rtsp://username:password@reolink_ip/Preview_01_sub"
|
||||
@@ -242,14 +225,13 @@ cameras:
|
||||
roles:
|
||||
- detect
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
### Unifi Protect Cameras
|
||||
|
||||
:::note
|
||||
:::note
|
||||
|
||||
Unifi G5s cameras and newer need a Unifi Protect server to enable rtsps stream, it's not possible to enable it in standalone mode.
|
||||
Unifi G5s cameras and newer need a Unifi Protect server to enable rtsps stream, it's not posible to enable it in standalone mode.
|
||||
|
||||
:::
|
||||
|
||||
@@ -264,7 +246,7 @@ go2rtc:
|
||||
- rtspx://192.168.1.1:7441/abcdefghijk
|
||||
```
|
||||
|
||||
[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#source-rtsp)
|
||||
[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-rtsp)
|
||||
|
||||
In the Unifi 2.0 update Unifi Protect Cameras had a change in audio sample rate which causes issues for ffmpeg. The input rate needs to be set for record if used directly with unifi protect.
|
||||
|
||||
@@ -287,6 +269,7 @@ Some community members have found better performance on Wyze cameras by using an
|
||||
To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's [FFmpeg Device](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg-device) support:
|
||||
|
||||
- Preparation outside of Frigate:
|
||||
|
||||
- Get USB camera path. Run `v4l2-ctl --list-devices` to get a listing of locally-connected cameras available. (You may need to install `v4l-utils` in a way appropriate for your Linux distribution). In the sample configuration below, we use `video=0` to correlate with a detected device path of `/dev/video0`
|
||||
- Get USB camera formats & resolutions. Run `ffmpeg -f v4l2 -list_formats all -i /dev/video0` to get an idea of what formats and resolutions the USB Camera supports. In the sample configuration below, we use a width of 1024 and height of 576 in the stream and detection settings based on what was reported back.
|
||||
- If using Frigate in a container (e.g. Docker on TrueNAS), ensure you have USB Passthrough support enabled, along with a specific Host Device (`/dev/video0`) + Container Device (`/dev/video0`) listed.
|
||||
|
||||
@@ -7,74 +7,6 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
## Adding a camera with the Add Camera Wizard
|
||||
|
||||
The Add Camera Wizard is the recommended way to add a camera. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />. The wizard connects to your camera, tests each stream, and writes the camera's configuration for you, including the [go2rtc](go2rtc.md) restream and the live view stream mapping, so a standard setup needs no hand-written YAML.
|
||||
|
||||
### Step 1: Name and connection
|
||||
|
||||
Enter a name for the camera along with its host or IP address and credentials, then choose how the wizard should find the camera's streams:
|
||||
|
||||
- **Probe camera** queries the camera over ONVIF (the ONVIF port is usually 80 or 8080) and asks it for its stream URLs. Some cameras use a separate ONVIF/service account rather than the device admin user, and some require **Use digest authentication** to be enabled.
|
||||
- **Manual selection** builds a stream URL from a template for the camera brand you pick (Dahua/Amcrest/EmpireTech, Hikvision/Uniview/Annke, Ubiquiti, Reolink, Axis, TP-Link, or Foscam). Choose **Other** to enter a custom RTSP URL directly. Non-RTSP stream types must be [configured manually](#setting-up-camera-inputs).
|
||||
|
||||
The name you enter is lowercased and spaces become underscores. If the result still isn't a valid config key, the wizard generates a safe name and stores what you typed as `friendly_name`.
|
||||
|
||||
### Step 2: Probe or snapshot
|
||||
|
||||
In probe mode, the wizard reports what the camera returned (manufacturer, model, firmware, profile count, and whether PTZ, presets, and [autotracking](autotracking.md) are supported) along with the RTSP URLs it discovered. Test each candidate to see its resolution, frame rate, and codecs together with a snapshot, then select the one you want to use.
|
||||
|
||||
In manual mode, the wizard tests the templated URL and shows the same metadata and snapshot.
|
||||
|
||||
If no RTSP URLs are found, the credentials may be wrong or the camera may not support ONVIF. Go back and use manual selection instead.
|
||||
|
||||
### Step 3: Stream configuration
|
||||
|
||||
Assign [roles](#setting-up-camera-inputs) to the stream, and use **Add Another Stream** to add the camera's other streams, for example a substream for `detect` alongside the main stream for `record`. At least one stream must have the `detect` role before you can continue.
|
||||
|
||||
**Reduce connections to camera** routes that input through the go2rtc restream so Frigate and the live view share a single connection to the camera instead of each opening their own. See [restream](restream.md) for more detail.
|
||||
|
||||
### Step 4: Validation and testing
|
||||
|
||||
Connect each stream to get a live preview, an estimated bandwidth figure, and a list of validation results. The wizard checks for the most common misconfigurations, including:
|
||||
|
||||
- A detect resolution that is too high (increased resource usage) or too low for reliable detection, or one it could not probe at all
|
||||
- A stream marked `record` whose audio codec is not AAC, or that has no audio at all
|
||||
- A stream marked `audio` that carries no audio stream
|
||||
- Using a restreamed input for the `record` role
|
||||
- Brand-specific issues, such as an RTSP stream on a Reolink camera that should use http-flv, or a Dahua/Hikvision substream selected for `detect`
|
||||
|
||||
**Use stream compatibility mode** passes the stream through go2rtc's ffmpeg module. Enable it if a stream fails to load after several attempts. Note that this also prevents [two way talk](/configuration/live#two-way-talk) from being detected for that stream.
|
||||
|
||||
**Save New Camera** writes the configuration and starts the camera right away. No restart is required.
|
||||
|
||||
Other features, including [hardware acceleration](hardware_acceleration_video.md), [two way talk](/configuration/live#two-way-talk), and audio transcoding, is configured after the camera has been added. For camera model specific quirks, see the [camera specific](camera_specific.md) docs.
|
||||
|
||||
## Deleting a camera
|
||||
|
||||
Click **Delete Camera** in <NavPath path="Settings > Global configuration > Camera management" />, choose the camera, and confirm. Deleting a camera requires the `admin` role and cannot be undone.
|
||||
|
||||
:::warning
|
||||
|
||||
Deleting a camera permanently removes its recordings, tracked objects, and configuration. If you only want to stop processing a camera, set its state to **Off** or **Disabled** in <NavPath path="Settings > Global configuration > Camera management" /> instead. See [camera state](/configuration/live#camera-state).
|
||||
|
||||
:::
|
||||
|
||||
Deleting a camera removes:
|
||||
|
||||
- The camera's section of your config file, along with its entries in any [role](authentication.md#user-roles) camera list. A custom role left with no cameras is removed as well.
|
||||
- Every database record for the camera: tracked objects, review items, recordings, previews, timeline entries, the saved region grid, and [triggers](semantic_search.md#triggers).
|
||||
- Every media file for the camera: recordings, snapshots, thumbnails, and preview clips.
|
||||
|
||||
[Exports](/usage/exports) are kept by default, so saved footage survives the deletion of the camera it came from. Turn on **Also delete exports for this camera** in the confirmation step to remove those too.
|
||||
|
||||
The camera's processes are stopped and the change takes effect immediately, so no restart is required. If the resulting config cannot be parsed, Frigate restores the previous config and reports an error instead of leaving Frigate in a broken state.
|
||||
|
||||
Two things are not cleaned up for you:
|
||||
|
||||
- **go2rtc streams.** Frigate makes a best effort to stop a running [go2rtc](go2rtc.md) stream named after the camera, but stream entries in your config file remain and are recreated on the next restart. Remove them in <NavPath path="Settings > System > go2rtc streams" /> or in your config file.
|
||||
- **Camera groups.** A deleted camera stays listed in any [camera group](#setting-up-camera-groups) that referenced it. The group skips the missing camera, so this is harmless, but you can edit the group to drop the stale entry.
|
||||
|
||||
## Setting Up Camera Inputs
|
||||
|
||||
Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.
|
||||
@@ -83,24 +15,21 @@ A camera is enabled by default but can be disabled by using `enabled: False`. Ca
|
||||
|
||||
Each role can only be assigned to one input per camera. The options for roles are as follows:
|
||||
|
||||
| Role | Description |
|
||||
| ------------ | ------------------------------------------------------------------------------------------------------------ |
|
||||
| `detect` | Main feed for object detection. [docs](object_detectors.md) |
|
||||
| `record` | Saves segments of the video feed based on configuration settings. [docs](record.md) |
|
||||
| `record_sub` | Saves segments of a second, lower quality stream with its own retention. [docs](record.md#sub-stream-recording) |
|
||||
| `audio` | Feed for audio based detection. [docs](audio_detectors.md) |
|
||||
| Role | Description |
|
||||
| -------- | ----------------------------------------------------------------------------------- |
|
||||
| `detect` | Main feed for object detection. [docs](object_detectors.md) |
|
||||
| `record` | Saves segments of the video feed based on configuration settings. [docs](record.md) |
|
||||
| `audio` | Feed for audio based detection. [docs](audio_detectors.md) |
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
|
||||
Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
|
||||
|
||||
| Field | Description |
|
||||
| ----------------- | ------------------------------------------------------------------- |
|
||||
| **Camera inputs** | List of input stream definitions (paths and roles) for this camera. |
|
||||
|
||||
For each input you can choose its source: select **Restream (go2rtc)** to pick an existing [go2rtc stream](restream.md) from a dropdown (Frigate uses the `rtsp://127.0.0.1:8554/<stream>` path and `preset-rtsp-restream` input args for that input automatically), or **Manual input path** to type the stream URL directly.
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
|
||||
|
||||
| Field | Description |
|
||||
@@ -138,7 +67,7 @@ Additional cameras are simply added under the camera configuration section.
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and use the [Add Camera Wizard](#adding-a-camera-with-the-add-camera-wizard) to configure each additional camera.
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Management" /> and use the add camera button to configure each additional camera.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -214,11 +143,6 @@ If your ONVIF camera does not require authentication credentials, you may still
|
||||
|
||||
:::
|
||||
|
||||
If a camera connects but fails to authenticate, two optional fields can help:
|
||||
|
||||
- `tls_insecure`: Skips TLS certificate verification and sends the ONVIF password as plaintext (`PasswordText`) instead of a hashed digest (`PasswordDigest`). Some cameras reject the digest token and only accept plaintext. This weakens connection security, so only enable it on a trusted local network.
|
||||
- `ignore_time_mismatch`: ONVIF authentication tokens include a timestamp, and a camera will reject the token if its clock differs too much from Frigate's. Enabling this makes Frigate compensate for the time offset so authentication can still succeed. Running NTP on both the camera and the Frigate host is the recommended fix; only use this in a "safe" environment, as it slightly weakens token validation.
|
||||
|
||||
If your camera has multiple ONVIF profiles, you can specify which one to use for PTZ control with the `profile` option, matched by token or name. When not set, Frigate selects the first profile with a valid PTZ configuration. Check the Frigate debug logs (`frigate.ptz.onvif: debug`) to see available profile names and tokens for your camera.
|
||||
|
||||
An ONVIF-capable camera that supports relative movement within the field of view (FOV) can also be configured to automatically track moving objects and keep them in the center of the frame. For autotracking setup, see the [autotracking](autotracking.md) docs.
|
||||
@@ -250,7 +174,7 @@ The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.or
|
||||
| Hikvision DS-2DE3A404IWG-E/W | ✅ | ✅ | |
|
||||
| Reolink | ✅ | ❌ | |
|
||||
| Speco O8P32X | ✅ | ❌ | |
|
||||
| Sunba 405-D20X | ✅ | ❌ | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatible. |
|
||||
| Sunba 405-D20X | ✅ | ❌ | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatable. |
|
||||
| Tapo | ✅ | ❌ | Many models supported, ONVIF Service Port: 2020 |
|
||||
| Uniview IPC672LR-AX4DUPK | ✅ | ❌ | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands |
|
||||
| Uniview IPC6612SR-X33-VG | ✅ | ✅ | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working. |
|
||||
@@ -263,7 +187,7 @@ Camera groups let you organize cameras together with a shared name and icon, mak
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
On the Live dashboard, press the **pencil icon** in the main navigation to add a new camera group. Configure the group name, select which cameras to include, choose an icon, and set the display order.
|
||||
On the Live dashboard, press the **+** icon in the main navigation to add a new camera group. Configure the group name, select which cameras to include, choose an icon, and set the display order.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
@@ -1,244 +0,0 @@
|
||||
---
|
||||
id: config_overrides
|
||||
title: Global and Camera-Level Configuration
|
||||
---
|
||||
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
Most of Frigate's configuration can be set once for all cameras and then adjusted for individual cameras. The global value acts as the default for every camera, and any camera can override it.
|
||||
|
||||
This page explains how that inheritance works. For a tour of the Settings UI itself, see [Frigate Configuration](./config.md).
|
||||
|
||||
## The basics
|
||||
|
||||
Set a value globally and every camera uses it. Set the same value on a camera and that camera uses its own value instead.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Global configuration > Object detection" /> and set **Detect FPS** to `5`. Every camera now detects at 5 fps.
|
||||
2. Navigate to <NavPath path="Settings > Camera configuration > Object detection" />, select the `driveway` camera, and set **Detect FPS** to `10`.
|
||||
|
||||
The `driveway` camera now detects at 10 fps. Every other camera still uses the global value of 5.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
detect:
|
||||
fps: 5 # every camera detects at 5 fps
|
||||
|
||||
cameras:
|
||||
front_door:
|
||||
ffmpeg: ...
|
||||
driveway:
|
||||
ffmpeg: ...
|
||||
detect:
|
||||
fps: 10 # except this one
|
||||
```
|
||||
|
||||
`front_door` inherits `fps: 5`, and `driveway` uses `10`.
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
## Overrides apply per value, not per section
|
||||
|
||||
Overriding one value in a section does not detach the rest of that section. Everything you don't set on the camera still comes from the global configuration.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
If you set a camera's **Motion threshold** but leave **Contour area** alone, only the threshold is overridden. The contour area continues to follow <NavPath path="Settings > Global configuration > Motion detection" />, and changing it there still affects that camera.
|
||||
|
||||
Open a section to see which values are overridden: the section header indicates how many fields differ from the global configuration.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
motion:
|
||||
threshold: 30
|
||||
contour_area: 10
|
||||
|
||||
cameras:
|
||||
driveway:
|
||||
motion:
|
||||
threshold: 40
|
||||
```
|
||||
|
||||
The `driveway` camera ends up with `threshold: 40` and `contour_area: 10`. Only the value you wrote was overridden.
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
## Returning a camera to the global value
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
A camera section that has its own values shows an **Overridden** badge. To remove the override and go back to inheriting, use the **Reset to Global** button at the bottom of the section.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
Frigate treats a camera value as an override because it is written in the config file, not because it differs from the global value. Repeating the global value under a camera still creates an override:
|
||||
|
||||
```yaml
|
||||
snapshots:
|
||||
enabled: true
|
||||
|
||||
cameras:
|
||||
driveway:
|
||||
snapshots:
|
||||
enabled: true # this is an override, even though it matches
|
||||
```
|
||||
|
||||
If you later change the global `snapshots.enabled` to `false`, `driveway` keeps saving snapshots, because it has its own value. To make a camera follow the global value again, delete the key from the camera rather than setting it to match.
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
## Lists replace, maps merge
|
||||
|
||||
This is the distinction that surprises people most.
|
||||
|
||||
**Lists are replaced entirely.** A camera's list does not add to the global list, it takes its place.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
The camera page shows the objects the camera is currently tracking, starting from the global list. Changing that selection under <NavPath path="Settings > Camera configuration > Objects" /> replaces the list for that camera, so make sure every object you want tracked is selected, not just the ones you are adding.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
objects:
|
||||
track:
|
||||
- person
|
||||
- car
|
||||
|
||||
cameras:
|
||||
backyard:
|
||||
objects:
|
||||
track:
|
||||
- dog # backyard tracks ONLY dog, not person or car
|
||||
```
|
||||
|
||||
To track `dog` in addition to the global objects, list all of them on the camera.
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
An empty list is a valid override, and is the normal way to opt a camera out of something:
|
||||
|
||||
```yaml
|
||||
review:
|
||||
alerts:
|
||||
labels:
|
||||
- person
|
||||
|
||||
cameras:
|
||||
street:
|
||||
review:
|
||||
alerts:
|
||||
labels: [] # this camera never creates alerts
|
||||
```
|
||||
|
||||
**Maps are merged key by key.** A camera can add an entry without redeclaring the others.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Adding a filter for one object under <NavPath path="Settings > Camera configuration > Objects" /> does not remove the filters inherited from <NavPath path="Settings > Global configuration > Objects" />. The camera keeps both.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
objects:
|
||||
filters:
|
||||
person:
|
||||
min_area: 5000
|
||||
|
||||
cameras:
|
||||
driveway:
|
||||
objects:
|
||||
filters:
|
||||
car:
|
||||
min_area: 10000
|
||||
```
|
||||
|
||||
The `driveway` camera ends up with both the `car` filter it defined and the `person` filter from the global configuration.
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
## Which settings can be overridden
|
||||
|
||||
Most, but not all. The [full reference config](./advanced/reference.md) is the authoritative source: sections that support camera-level overrides are marked with the comment `# NOTE: Can be overridden at the camera level`. In the UI, a setting can be overridden if it appears under both <NavPath path="Settings > Global configuration" /> and <NavPath path="Settings > Camera configuration" />.
|
||||
|
||||
A few things worth knowing beyond that:
|
||||
|
||||
- Some sections are **global only** and have no camera-level equivalent, including `go2rtc`, `genai` providers, `classification`, `telemetry`, `camera_groups`, and `ui`.
|
||||
- Some sections exist **only at the camera level**, such as `zones` and `onvif`.
|
||||
- Some sections are **partially overridable**, meaning a camera accepts only a few of the keys available globally. `face_recognition`, `lpr`, and `audio_transcription` work this way, and the reference config notes which keys apply.
|
||||
|
||||
## Enrichments that must be enabled globally first
|
||||
|
||||
License plate recognition and face recognition are special: the global setting is not just a default, it is a switch that must be on before any camera can use the feature. Enabling one on a camera while it is disabled globally is a configuration error, and Frigate will refuse to start:
|
||||
|
||||
```
|
||||
Camera driveway has lpr enabled but lpr is disabled at the global level of the config. You must enable lpr at the global level.
|
||||
```
|
||||
|
||||
Enable the feature globally, then turn it off on the cameras that don't need it.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Global configuration > License plate recognition" /> and enable **LPR**.
|
||||
2. Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" />, select each camera that should not run LPR, and disable the **Enable LPR** toggle.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
lpr:
|
||||
enabled: true
|
||||
|
||||
cameras:
|
||||
driveway:
|
||||
ffmpeg: ... # inherits lpr, enabled
|
||||
backyard:
|
||||
ffmpeg: ...
|
||||
lpr:
|
||||
enabled: false # opted out
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
:::note
|
||||
|
||||
This applies only to `lpr` and `face_recognition`, because the global setting controls whether the supporting background process starts at all. Other features do not work this way. Audio transcription, for example, can be enabled on a single camera without being enabled globally.
|
||||
|
||||
:::
|
||||
|
||||
## Profiles
|
||||
|
||||
[Profiles](./profiles.md) add a further layer on top of everything described above. A profile is a named set of camera overrides that you can switch on and off while Frigate is running, for example to change detection and recording behavior when you leave the house.
|
||||
|
||||
Profiles are applied on top of a camera's already-resolved configuration, so a profile value wins over both the camera and the global value while that profile is active. Profiles cover a subset of the camera sections and do not modify your config file.
|
||||
|
||||
## Summary
|
||||
|
||||
- A camera inherits every value you don't set on it.
|
||||
- Overriding one value does not detach the rest of the section.
|
||||
- Writing a value on a camera overrides it, even if it matches the global value. Remove it to inherit again.
|
||||
- Lists replace the global list. Maps merge into it.
|
||||
- An empty list is an override, not an omission.
|
||||
- `lpr` and `face_recognition` must be enabled globally before a camera can use them.
|
||||
@@ -11,7 +11,7 @@ Object classification allows you to train a custom MobileNetV2 classification mo
|
||||
|
||||
:::info
|
||||
|
||||
Training a custom object classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
|
||||
Training a custom object classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
|
||||
|
||||
:::
|
||||
|
||||
@@ -137,7 +137,7 @@ If examples for some of your classes do not appear in the grid, you can continue
|
||||
|
||||
:::tip Diversity matters far more than volume
|
||||
|
||||
Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data. The model learns what _that exact moment_ looked like rather than what actually defines the class. **This is why Frigate does not implement bulk training in the UI.**
|
||||
Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data — the model learns what _that exact moment_ looked like rather than what actually defines the class. **This is why Frigate does not implement bulk training in the UI.**
|
||||
|
||||
For more detail, see [Frigate Tip: Best Practices for Training Face and Custom Classification Models](https://github.com/blakeblackshear/frigate/discussions/21374).
|
||||
|
||||
@@ -149,16 +149,9 @@ For more detail, see [Frigate Tip: Best Practices for Training Face and Custom C
|
||||
- **The wizard is just the starting point**: You don't need to find and label every class upfront. Missing classes will naturally appear in Recent Classifications, and those images tend to be more valuable because they represent new conditions and edge cases.
|
||||
- **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
|
||||
- **Preprocessing**: Ensure examples reflect object crops similar to Frigate's boxes; keep the subject centered.
|
||||
- **Crop size**: Aim for crops of at least 100×100 pixels (a 10,000 pixel area). Crops smaller than ~80×80 get stretched 3-7× by the model's 224×224 input resize and tend to collapse into a generic "blob" region of feature space where identity becomes unreliable. If most of your detections are small because the camera is far from the subject, consider repositioning the camera for closer crops.
|
||||
- **Class balance**: Aim to keep your largest class within ~3× the count of your smallest. Beyond that, the model becomes biased toward the dominant class and tends to default borderline predictions to it (the "everything looks like Buddy" failure mode).
|
||||
- **Labels**: Keep label names short and consistent; include a `none` class if you plan to ignore uncertain predictions for sub labels.
|
||||
- **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.
|
||||
|
||||
:::tip `none` works differently from named classes
|
||||
|
||||
Named classes work best with visually uniform examples. Every Buddy photo should look like Buddy. The `none` class needs the opposite: visual diversity across sizes, framings, and qualities, because at inference it has to absorb everything that isn't one of your named classes. Don't apply the same "only keep large, well-framed images" rule to `none` that you would to a named class. Mix in small crops, partial views, and false positives deliberately - otherwise the model has no signal for "small/ambiguous thing = not one of my known classes" and will force those crops into a named class by default.
|
||||
|
||||
:::
|
||||
|
||||
## Debugging Classification Models
|
||||
|
||||
To troubleshoot issues with object classification models, enable debug logging to see detailed information about classification attempts, scores, and consensus calculations.
|
||||
|
||||
@@ -11,7 +11,7 @@ State classification allows you to train a custom MobileNetV2 classification mod
|
||||
|
||||
:::info
|
||||
|
||||
Training a custom state classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
|
||||
Training a custom state classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
|
||||
|
||||
:::
|
||||
|
||||
@@ -73,13 +73,9 @@ classification:
|
||||
interval: 10 # also run every N seconds (optional)
|
||||
cameras:
|
||||
front:
|
||||
# [x1, y1, x2, y2] as decimals between 0 and 1, relative to the
|
||||
# camera's detect resolution
|
||||
crop: [0.0, 0.25, 0.3, 0.85]
|
||||
crop: [0, 180, 220, 400]
|
||||
```
|
||||
|
||||
Crop coordinates are normalized: each value is a fraction of the camera's `detect` width or height, not a pixel value. Drawing the crop in the UI wizard writes these values for you.
|
||||
|
||||
An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For state classification models, the default is 100.
|
||||
|
||||
</TabItem>
|
||||
@@ -107,7 +103,7 @@ Once some images are assigned, training will begin automatically.
|
||||
|
||||
:::tip Diversity matters far more than volume
|
||||
|
||||
Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data. The model learns what _that exact moment_ looked like rather than what actually defines the state. This often leads to models that work perfectly under the original conditions but become unstable when day turns to night, weather changes, or seasonal lighting shifts. **This is why Frigate does not implement bulk training in the UI.**
|
||||
Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data — the model learns what _that exact moment_ looked like rather than what actually defines the state. This often leads to models that work perfectly under the original conditions but become unstable when day turns to night, weather changes, or seasonal lighting shifts. **This is why Frigate does not implement bulk training in the UI.**
|
||||
|
||||
For more detail, see [Frigate Tip: Best Practices for Training Face and Custom Classification Models](https://github.com/blakeblackshear/frigate/discussions/21374).
|
||||
|
||||
|
||||
@@ -6,7 +6,6 @@ title: Face Recognition
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
import FaqItem from "@site/src/components/FaqItem";
|
||||
|
||||
Face recognition identifies known individuals by matching detected faces with previously learned facial data. When a known `person` is recognized, their name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
|
||||
|
||||
@@ -20,7 +19,7 @@ Face recognition requires a one-time internet connection to download detection a
|
||||
|
||||
### Face Detection
|
||||
|
||||
When running a Frigate+ model (or any custom model that natively detects faces) should ensure that `face` is added to the [list of objects to track](../plus/index.md#available-label-types) either globally or for a specific camera. This will allow face detection to run at the same time as object detection and be more efficient.
|
||||
When running a Frigate+ model (or any custom model that natively detects faces) should ensure that `face` is added to the [list of objects to track](../plus/#available-label-types) either globally or for a specific camera. This will allow face detection to run at the same time as object detection and be more efficient.
|
||||
|
||||
When running a default COCO model or another model that does not include `face` as a detectable label, face detection will run via CV2 using a lightweight DNN model that runs on the CPU. In this case, you should _not_ define `face` in your list of objects to track.
|
||||
|
||||
@@ -87,7 +86,7 @@ Navigate to <NavPath path="Settings > Enrichments > Face recognition" />.
|
||||
- **Detection threshold**: Face detection confidence score required before recognition runs. This field only applies to the standalone face detection model; `min_score` should be used to filter for models that have face detection built in.
|
||||
- Default: `0.7`
|
||||
- **Minimum face area**: Minimum size (in pixels) a face must be before recognition runs. Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant faces.
|
||||
- Default: `750` pixels
|
||||
- Default: `500` pixels
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -96,7 +95,7 @@ Navigate to <NavPath path="Settings > Enrichments > Face recognition" />.
|
||||
face_recognition:
|
||||
enabled: true
|
||||
detection_threshold: 0.7
|
||||
min_area: 750
|
||||
min_area: 500
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -152,14 +151,6 @@ Follow these steps to begin:
|
||||
|
||||
## Creating a Robust Training Set
|
||||
|
||||
:::tip
|
||||
|
||||
**The short version:** Start with a few clear, front-facing photos of each person. As faces are detected in the Recent Recognitions tab, train clear images that scored lower, adding variety (different angles, lighting, and expressions) slowly. Diversity matters far more than volume, and low-quality images hurt recognition more than they help.
|
||||
|
||||
For a step-by-step narrative of these best practices (and the same principles applied to state and object classification), see the [Frigate Tips: Best Practices for Training](https://github.com/blakeblackshear/frigate/discussions/21374) discussion.
|
||||
|
||||
:::
|
||||
|
||||
The number of images needed for a sufficient training set for face recognition varies depending on several factors:
|
||||
|
||||
- Diversity of the dataset: A dataset with diverse images, including variations in lighting, pose, and facial expressions, will require fewer images per person than a less diverse dataset.
|
||||
@@ -180,7 +171,7 @@ When choosing images to include in the face training set it is recommended to al
|
||||
- If it is difficult to make out details in a persons face it will not be helpful in training.
|
||||
- Avoid images with extreme under/over-exposure.
|
||||
- Avoid blurry / pixelated images.
|
||||
- Avoid training on infrared (gray-scale). The models are trained on color images and will not be able to extract features from gray-scale images.
|
||||
- Avoid training on infrared (gray-scale). The models are trained on color images and will be able to extract features from gray-scale images.
|
||||
- Using images of people wearing hats / sunglasses may confuse the model.
|
||||
- Do not upload too many similar images at the same time, it is recommended to train no more than 4-6 similar images for each person to avoid over-fitting.
|
||||
|
||||
@@ -190,27 +181,9 @@ When choosing images to include in the face training set it is recommended to al
|
||||
|
||||
The Recent Recognitions tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
|
||||
|
||||
Each face image is labeled with a name (or `Unknown`) along with the confidence score of that recognition attempt. Images are grouped by the person they were matched against, not by who they actually are, so a group labeled with a person's name can contain a crop that is really someone else but happened to score as a partial match. The name and score shown on each individual crop describe that single attempt.
|
||||
Each face image is labeled with a name (or `Unknown`) along with the confidence score of the recognition attempt. While each image can be used to train the system for a specific person, not all images are suitable for training.
|
||||
|
||||
While each image can be used to train the system for a specific person, not all images are suitable for training. Refer to the guidelines below for best practices on selecting images for training.
|
||||
|
||||
### How Frigate Decides Who a Person Is
|
||||
|
||||
Recognition does not happen one frame at a time. While a `person` is in view, Frigate runs face recognition on many frames, not just a single frame. The final `sub_label` is decided from all of those attempts together, weighted by the area of each face (larger, closer faces count more), not from any single frame.
|
||||
|
||||
This has a few practical consequences:
|
||||
|
||||
- A handful of wrong guesses on blurry or distant frames usually do not change the result. If Frigate sees a person as "Tom, Tom, Sam, Tom, Tom," it will still conclude the person was Tom.
|
||||
- The goal is not for every individual face crop to be correct. The goal is for each person to be recognized correctly overall, across all the faces captured while they were present.
|
||||
- A single very high confidence match will not by itself assign a sub label. Recognition must be consistent. See [I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?](#i-see-scores-above-the-threshold-in-the-recent-recognitions-tab-but-a-sub-label-wasnt-assigned) below.
|
||||
|
||||
### Which Faces Are Worth Training?
|
||||
|
||||
Whether a face is worth training has little to do with what it was recognized as. A crop is a good training candidate when all of these are true:
|
||||
|
||||
- It did not already score high and correctly. Faces that are already recognized confidently add little and increase the risk of over-fitting.
|
||||
- It is clear enough to be useful: not blurry, not heavily off-axis, not infrared (gray-scale). If it is hard for you to make out the face, it will not help the model.
|
||||
- It adds something new: a different angle, lighting, expression, or distance than what you already have.
|
||||
Refer to the guidelines below for best practices on selecting images for training.
|
||||
|
||||
### Step 1 - Building a Strong Foundation
|
||||
|
||||
@@ -226,81 +199,39 @@ Once front-facing images are performing well, start choosing slightly off-angle
|
||||
|
||||
## FAQ
|
||||
|
||||
### Getting Recognition Working
|
||||
|
||||
<FaqItem id="how-do-i-debug-face-recognition-issues" question="How do I debug Face Recognition issues?">
|
||||
### How do I debug Face Recognition issues?
|
||||
|
||||
Start with the [Usage](#usage) section and re-read the [Model Requirements](#model-requirements) above.
|
||||
|
||||
1. Enable debug logs to see exactly what Frigate is doing.
|
||||
- Enable debug logs for face recognition by adding `frigate.data_processing.real_time.face: debug` to your `logger` configuration. Restart Frigate after this change.
|
||||
|
||||
```yaml
|
||||
logger:
|
||||
default: info
|
||||
logs:
|
||||
# highlight-next-line
|
||||
frigate.data_processing.real_time.face: debug
|
||||
```
|
||||
|
||||
- These logs report where the pipeline stopped for each `person` object, such as no face being found within the person's bounding box, the detected face being smaller than `min_area`, or a face being recognized but scoring too low.
|
||||
- If you see no face-related messages at all, also add `frigate.embeddings.maintainer: debug` to confirm that the face processor was created at startup and that `person` updates are reaching it.
|
||||
|
||||
2. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
|
||||
1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
|
||||
|
||||
If you are using a Frigate+ or `face` detecting model:
|
||||
- Watch the [debug view](/usage/live#the-single-camera-view) to ensure that `face` is being detected along with `person`.
|
||||
- Watch the debug view (Settings --> Debug) to ensure that `face` is being detected along with `person`.
|
||||
- You may need to adjust the `min_score` for the `face` object if faces are not being detected.
|
||||
|
||||
If you are **not** using a Frigate+ or `face` detecting model:
|
||||
- Check your `detect` stream resolution and ensure it is sufficiently high enough to capture face details on `person` objects.
|
||||
- You may need to lower your `detection_threshold` if faces are not being detected.
|
||||
|
||||
3. Any detected faces will then be _recognized_.
|
||||
2. Any detected faces will then be _recognized_.
|
||||
- Make sure you have trained at least one face per the recommendations above.
|
||||
- Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
|
||||
|
||||
</FaqItem>
|
||||
### Detection does not work well with blurry images?
|
||||
|
||||
<FaqItem id="does-face-recognition-run-on-the-recording-stream" question="Does face recognition run on the recording stream?">
|
||||
|
||||
Face recognition does not run on the recording stream, this would be suboptimal for many reasons:
|
||||
|
||||
1. The latency of accessing the recordings means the notifications would not include the names of recognized people because recognition would not complete until after.
|
||||
2. The embedding models used run on a set image size, so larger images will be scaled down to match this anyway.
|
||||
3. Motion clarity is much more important than extra pixels, over-compression and motion blur are much more detrimental to results than resolution.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
### Improving Accuracy and Training
|
||||
|
||||
<FaqItem id="detection-does-not-work-well-with-blurry-images" question="Detection does not work well with blurry images?">
|
||||
|
||||
Accuracy is definitely going to be improved with higher quality cameras / streams. It is important to look at the DORI (Detection Observation Recognition Identification) range of your camera, if that specification is posted. This specification explains the distance from the camera that a person can be detected, observed, recognized, and identified. The identification range is the most relevant here, and the distance listed by the camera is the furthest that face recognition will realistically work.
|
||||
Accuracy is definitely a going to be improved with higher quality cameras / streams. It is important to look at the DORI (Detection Observation Recognition Identification) range of your camera, if that specification is posted. This specification explains the distance from the camera that a person can be detected, observed, recognized, and identified. The identification range is the most relevant here, and the distance listed by the camera is the furthest that face recognition will realistically work.
|
||||
|
||||
Some users have also noted that setting the stream in camera firmware to a constant bit rate (CBR) leads to better image clarity than with a variable bit rate (VBR).
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="can-i-train-faces-for-people-who-only-appear-at-night" question="Can I train faces for people who only appear at night?">
|
||||
|
||||
The embedding models are trained on color images, so gray-scale and infrared (IR) faces sit in a different feature distribution and are more easily confused with other people. Prefer color images, and avoid mixing gray-scale samples in early while you are building a foundation. If someone only ever appears at night, gray-scale training is acceptable, but keep those samples limited and as clear as possible, and add them only once color recognition is stable for your other people.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="why-cant-i-bulk-upload-photos" question="Why can't I bulk upload photos?">
|
||||
### Why can't I bulk upload photos?
|
||||
|
||||
It is important to methodically add photos to the library, bulk importing photos (especially from a general photo library) will lead to over-fitting in that particular scenario and hurt recognition performance.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="why-cant-i-bulk-reprocess-faces" question="Why can't I bulk reprocess faces?">
|
||||
### Why can't I bulk reprocess faces?
|
||||
|
||||
Face embedding models work by breaking apart faces into different features. This means that when reprocessing an image, only images from a similar angle will have its score affected.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="why-do-unknown-people-score-similarly-to-known-people" question="Why do unknown people score similarly to known people?">
|
||||
### Why do unknown people score similarly to known people?
|
||||
|
||||
This can happen for a few different reasons, but this is usually an indicator that the training set needs to be improved. This is often related to over-fitting:
|
||||
|
||||
@@ -310,54 +241,33 @@ This can happen for a few different reasons, but this is usually an indicator th
|
||||
|
||||
Review your face collections and remove most of the unclear or low-quality images. Then, use the **Reprocess** button on each face in the **Train** tab to evaluate how the changes affect recognition scores.
|
||||
|
||||
Avoid training on images that already score highly, as this can lead to over-fitting. Instead, focus on relatively clear images that score lower (ideally with different lighting, angles, and conditions) to help the model generalize more effectively.
|
||||
Avoid training on images that already score highly, as this can lead to over-fitting. Instead, focus on relatively clear images that score lower - ideally with different lighting, angles, and conditions—to help the model generalize more effectively.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="should-i-correct-a-face-that-was-recognized-as-the-wrong-person" question="Should I correct a face that was recognized as the wrong person?">
|
||||
|
||||
Only if it is a good image. Reassigning a face does add it to that person's training set, but two things are true at once:
|
||||
|
||||
- Reassigning a single misclassified frame has a small effect. The image is weighted against every other sample for that person, so correcting 1 frame out of 20 will not move recognition much. Occasional wrong guesses on poor frames are normal and do not need to be fixed.
|
||||
- Reassigning a poor image (blurry, off-angle, low-resolution, gray-scale) can hurt more than the misidentification did, because low-quality samples degrade recognition for that whole person.
|
||||
|
||||
So the decision is about image quality, not about the wrong label. If the crop is clear, well-lit, and reasonably front-facing, and it scored low or was wrong, assigning it to the correct person is useful. If you can barely make out the face yourself, ignore it; do not train it just to correct the label.
|
||||
|
||||
If a person is repeatedly misidentified, do not keep reassigning the same frame. Instead, remove low-quality or misleading images and add a few high-quality samples to the correct person. See [Why do unknown people score similarly to known people?](#why-do-unknown-people-score-similarly-to-known-people) above.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="frigate-misidentified-a-face-can-i-tell-it-that-a-face-is-not-a-specific-person" question={'Frigate misidentified a face. Can I tell it that a face is "not" a specific person?'}>
|
||||
### Frigate misidentified a face. Can I tell it that a face is "not" a specific person?
|
||||
|
||||
No, face recognition does not support negative training (i.e., explicitly telling it who someone is _not_). Instead, the best approach is to improve the training data by using a more diverse and representative set of images for each person.
|
||||
For more guidance, refer to the section above on improving recognition accuracy.
|
||||
|
||||
This also applies to a stranger who is repeatedly matched to a known person (for example, a delivery driver recognized as you). Do not create a profile for them and do not reassign their faces to yourself, as this pollutes your training set and makes recognition worse. Leave the detection as unknown and improve the known person's training set instead. Face recognition learns who someone is, not who they are not.
|
||||
### I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?
|
||||
|
||||
</FaqItem>
|
||||
The Frigate considers the recognition scores across all recognition attempts for each person object. The scores are continually weighted based on the area of the face, and a sub label will only be assigned to person if a person is confidently recognized consistently. This avoids cases where a single high confidence recognition would throw off the results.
|
||||
|
||||
<FaqItem id="i-see-scores-above-the-threshold-in-the-recent-recognitions-tab-but-a-sub-label-wasnt-assigned" question="I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?">
|
||||
|
||||
Frigate considers the recognition scores across all recognition attempts for each person object. The scores are continually weighted based on the area of the face, and a sub label will only be assigned to person if a person is confidently recognized consistently. This avoids cases where a single high confidence recognition would throw off the results.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
### Compatibility and Maintenance
|
||||
|
||||
<FaqItem id="can-i-use-other-face-recognition-software-like-doubletake-at-the-same-time-as-the-built-in-face-recognition" question="Can I use other face recognition software like DoubleTake at the same time as the built in face recognition?">
|
||||
### Can I use other face recognition software like DoubleTake at the same time as the built in face recognition?
|
||||
|
||||
No, using another face recognition service will interfere with Frigate's built in face recognition. When using double-take the sub_label feature must be disabled if the built in face recognition is also desired.
|
||||
|
||||
</FaqItem>
|
||||
### Does face recognition run on the recording stream?
|
||||
|
||||
<FaqItem id="i-get-an-unknown-error-when-taking-a-photo-directly-with-my-iphone" question="I get an unknown error when taking a photo directly with my iPhone">
|
||||
Face recognition does not run on the recording stream, this would be suboptimal for many reasons:
|
||||
|
||||
1. The latency of accessing the recordings means the notifications would not include the names of recognized people because recognition would not complete until after.
|
||||
2. The embedding models used run on a set image size, so larger images will be scaled down to match this anyway.
|
||||
3. Motion clarity is much more important than extra pixels, over-compression and motion blur are much more detrimental to results than resolution.
|
||||
|
||||
### I get an unknown error when taking a photo directly with my iPhone
|
||||
|
||||
By default iOS devices will use HEIC (High Efficiency Image Container) for images, but this format is not supported for uploads. Choosing `large` as the format instead of `original` will use JPG which will work correctly.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="how-can-i-delete-the-face-database-and-start-over" question="How can I delete the face database and start over?">
|
||||
### How can I delete the face database and start over?
|
||||
|
||||
Frigate does not store anything in its database related to face recognition. You can simply delete all of your faces through the Frigate UI or remove the contents of the `/media/frigate/clips/faces` directory.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
@@ -7,33 +7,33 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
Frigate ships with a set of FFmpeg presets to keep your configuration short and readable. Each preset expands to a longer list of FFmpeg arguments at runtime. You can see exactly what every preset expands to in [this file](https://github.com/blakeblackshear/frigate/blob/master/frigate/ffmpeg_presets.py).
|
||||
Some presets of FFmpeg args are provided by default to make the configuration easier. All presets can be seen in [this file](https://github.com/blakeblackshear/frigate/blob/master/frigate/ffmpeg_presets.py).
|
||||
|
||||
In the config file you reference a preset by its name (for example, `preset-vaapi`). In the UI, the same preset is shown with a friendly label (for example, **VAAPI (Intel/AMD GPU)**). Both refer to the same thing: the tables below list the config name alongside the label you'll see in the UI.
|
||||
### Hwaccel Presets
|
||||
|
||||
### Hwaccel (Hardware Acceleration) Presets {#hwaccel-presets}
|
||||
It is highly recommended to use hwaccel presets in the config. These presets not only replace the longer args, but they also give Frigate hints of what hardware is available and allows Frigate to make other optimizations using the GPU such as when encoding the birdseye restream or when scaling a stream that has a size different than the native stream size.
|
||||
|
||||
Hardware acceleration arguments tell FFmpeg to decode your camera's video stream on a GPU or integrated graphics chip instead of the CPU, which dramatically lowers CPU usage. Using a preset is highly recommended. Beyond replacing a long list of arguments, each preset also tells Frigate what hardware is available so it can offload additional work to the GPU, for example, encoding the Birdseye restream or scaling a stream whose resolution differs from the camera's native size.
|
||||
See [the hwaccel docs](/configuration/hardware_acceleration_video.md) for more info on how to setup hwaccel for your GPU / iGPU.
|
||||
|
||||
See [the hardware acceleration docs](/configuration/hardware_acceleration_video.md) for details on setting up hardware acceleration for your GPU / iGPU, then select the preset that matches your hardware.
|
||||
| Preset | Usage | Other Notes |
|
||||
| --------------------- | ------------------------------ | ----------------------------------------------------- |
|
||||
| preset-rpi-64-h264 | 64 bit Rpi with h264 stream | |
|
||||
| preset-rpi-64-h265 | 64 bit Rpi with h265 stream | |
|
||||
| preset-vaapi | Intel & AMD VAAPI | Check hwaccel docs to ensure correct driver is chosen |
|
||||
| preset-intel-qsv-h264 | Intel QSV with h264 stream | If issues occur recommend using vaapi preset instead |
|
||||
| preset-intel-qsv-h265 | Intel QSV with h265 stream | If issues occur recommend using vaapi preset instead |
|
||||
| preset-nvidia | Nvidia GPU | |
|
||||
| preset-jetson-h264 | Nvidia Jetson with h264 stream | |
|
||||
| preset-jetson-h265 | Nvidia Jetson with h265 stream | |
|
||||
| preset-rkmpp | Rockchip MPP | Use image with \*-rk suffix and privileged mode |
|
||||
|
||||
| Preset (YAML config) | UI Label | Usage | Notes |
|
||||
| --------------------- | ----------------------- | --------------------------------- | --------------------------------------------------------------- |
|
||||
| preset-rpi-64-h264 | Raspberry Pi (H.264) | 64-bit Raspberry Pi, H.264 stream | |
|
||||
| preset-rpi-64-h265 | Raspberry Pi (H.265) | 64-bit Raspberry Pi, H.265 stream | |
|
||||
| preset-vaapi | VAAPI (Intel/AMD GPU) | Intel or AMD GPU via VAAPI | Check the hwaccel docs to ensure the correct driver is selected |
|
||||
| preset-intel-qsv-h264 | Intel QuickSync (H.264) | Intel QuickSync, H.264 stream | If you have issues, use the VAAPI preset instead |
|
||||
| preset-intel-qsv-h265 | Intel QuickSync (H.265) | Intel QuickSync, H.265 stream | If you have issues, use the VAAPI preset instead |
|
||||
| preset-nvidia | NVIDIA GPU | NVIDIA GPU | |
|
||||
| preset-jetson-h264 | NVIDIA Jetson (H.264) | NVIDIA Jetson, H.264 stream | |
|
||||
| preset-jetson-h265 | NVIDIA Jetson (H.265) | NVIDIA Jetson, H.265 stream | |
|
||||
| preset-rkmpp | Rockchip RKMPP | Rockchip MPP | Use an image with the `-rk` suffix and run in privileged mode |
|
||||
Select the appropriate hwaccel preset for your hardware.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to the appropriate preset for your hardware.
|
||||
2. To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and set **Hardware acceleration arguments** for that camera.
|
||||
2. To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and set **Hardware acceleration arguments** for that camera.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -53,25 +53,25 @@ cameras:
|
||||
|
||||
### Input Args Presets
|
||||
|
||||
Input arguments are passed to FFmpeg before your camera source and control how Frigate connects to and reads the stream: the transport protocol, timeouts, reconnection behavior, and how the stream is probed. The right input args ensure a reliable connection and maximum compatibility for each type of stream.
|
||||
Input args presets help make the config more readable and handle use cases for different types of streams to ensure maximum compatibility.
|
||||
|
||||
See [the camera-specific docs](/configuration/camera_specific.md) for more on non-standard cameras and recommendations for using them in Frigate.
|
||||
See [the camera specific docs](/configuration/camera_specific.md) for more info on non-standard cameras and recommendations for using them in Frigate.
|
||||
|
||||
| Preset (config) | UI Label | Usage | Notes |
|
||||
| -------------------------------- | ----------------------------------------- | --------------------------- | ------------------------------------------------------------------------------- |
|
||||
| preset-http-jpeg-generic | HTTP JPEG (Generic) | HTTP live JPEG | Restreaming the live JPEG is recommended instead |
|
||||
| preset-http-mjpeg-generic | HTTP MJPEG (Generic) | HTTP MJPEG stream | Restreaming the MJPEG stream is recommended instead |
|
||||
| preset-http-reolink | HTTP - Reolink Cameras | Reolink HTTP-FLV stream | Only for Reolink HTTP, not when restreaming as RTSP |
|
||||
| preset-rtmp-generic | RTMP (Generic) | RTMP stream | |
|
||||
| preset-rtsp-generic | RTSP (Generic) | RTSP stream | The default when no input args are specified |
|
||||
| preset-rtsp-restream | RTSP - Restream from go2rtc | RTSP stream from a restream | Use when a go2rtc restream is the source for Frigate |
|
||||
| preset-rtsp-restream-low-latency | RTSP - Restream from go2rtc (Low Latency) | RTSP stream from a restream | Lowers latency for a go2rtc restream source; may cause issues with some cameras |
|
||||
| preset-rtsp-udp | RTSP - UDP | RTSP stream over UDP | Use when the camera only supports UDP |
|
||||
| preset-rtsp-blue-iris | RTSP - Blue Iris | Blue Iris RTSP stream | Use when consuming a stream from Blue Iris |
|
||||
| Preset | Usage | Other Notes |
|
||||
| -------------------------------- | ------------------------- | ------------------------------------------------------------------------------------------------ |
|
||||
| preset-http-jpeg-generic | HTTP Live Jpeg | Recommend restreaming live jpeg instead |
|
||||
| preset-http-mjpeg-generic | HTTP Mjpeg Stream | Recommend restreaming mjpeg stream instead |
|
||||
| preset-http-reolink | Reolink HTTP-FLV Stream | Only for reolink http, not when restreaming as rtsp |
|
||||
| preset-rtmp-generic | RTMP Stream | |
|
||||
| preset-rtsp-generic | RTSP Stream | This is the default when nothing is specified |
|
||||
| preset-rtsp-restream | RTSP Stream from restream | Use for rtsp restream as source for frigate |
|
||||
| preset-rtsp-restream-low-latency | RTSP Stream from restream | Use for rtsp restream as source for frigate to lower latency, may cause issues with some cameras |
|
||||
| preset-rtsp-udp | RTSP Stream via UDP | Use when camera is UDP only |
|
||||
| preset-rtsp-blue-iris | Blue Iris RTSP Stream | Use when consuming a stream from Blue Iris |
|
||||
|
||||
:::warning
|
||||
|
||||
Be mindful of input arguments when restreaming, because you can end up with a mix of protocols. The `http` and `rtmp` presets cannot be used with `rtsp` streams. For example, using a Reolink camera with an RTSP restream as the recording source while `preset-http-reolink` is applied will cause a crash. In cases like this, set the preset at the stream level instead. See the example below.
|
||||
It is important to be mindful of input args when using restream because you can have a mix of protocols. `http` and `rtmp` presets cannot be used with `rtsp` streams. For example, when using a reolink cam with the rtsp restream as a source for record the preset-http-reolink will cause a crash. In this case presets will need to be set at the stream level. See the example below.
|
||||
|
||||
:::
|
||||
|
||||
@@ -96,15 +96,13 @@ cameras:
|
||||
|
||||
### Output Args Presets
|
||||
|
||||
Output arguments are passed to FFmpeg after your camera source and control how recordings are written: which codecs are used and whether audio and video are copied as-is or re-encoded. The right output args ensure consistent, playable recordings for each type of stream.
|
||||
Output args presets help make the config more readable and handle use cases for different types of streams to ensure consistent recordings.
|
||||
|
||||
| Preset (config) | UI Label | Usage | Notes |
|
||||
| -------------------------------- | ------------------------------- | ----------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| preset-record-generic | Record (Generic, no audio) | Record without audio | Use this if your camera has no audio, or if you don't want to record audio |
|
||||
| preset-record-generic-audio-copy | Record (Generic + Copy Audio) | Record with the original audio | Use this to keep the camera's audio in recordings without re-encoding |
|
||||
| preset-record-generic-audio-aac | Record (Generic + Audio to AAC) | Record with audio transcoded to AAC | The default when no output args are specified. Transcodes audio to AAC. If the source is already AAC, use `preset-record-generic-audio-copy` to avoid re-encoding |
|
||||
| preset-record-mjpeg | Record - MJPEG Cameras | Record an MJPEG stream | Restreaming the MJPEG stream is recommended instead |
|
||||
| preset-record-jpeg | Record - JPEG Cameras | Record a live JPEG | Restreaming the live JPEG is recommended instead |
|
||||
| preset-record-ubiquiti | Record - Ubiquiti Cameras | Record a Ubiquiti stream with audio | Handles Ubiquiti's non-standard audio format |
|
||||
|
||||
These presets apply to the `record` output args. If [sub stream recording](/configuration/record#sub-stream-recording) is enabled, the same args are used for the `record_sub` role unless `output_args.record_sub` is set, which accepts the same presets and manual args.
|
||||
| Preset | Usage | Other Notes |
|
||||
| -------------------------------- | --------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| preset-record-generic | Record WITHOUT audio | If your camera doesn't have audio, or if you don't want to record audio, use this option |
|
||||
| preset-record-generic-audio-copy | Record WITH original audio | Use this to enable audio in recordings |
|
||||
| preset-record-generic-audio-aac | Record WITH transcoded aac audio | This is the default when no option is specified. Use it to transcode audio to AAC. If the source is already in AAC format, use preset-record-generic-audio-copy instead to avoid unnecessary re-encoding |
|
||||
| preset-record-mjpeg | Record an mjpeg stream | Recommend restreaming mjpeg stream instead |
|
||||
| preset-record-jpeg | Record live jpeg | Recommend restreaming live jpeg instead |
|
||||
| preset-record-ubiquiti | Record ubiquiti stream with audio | Recordings with ubiquiti non-standard audio |
|
||||
|
||||
@@ -6,46 +6,12 @@ title: Configuring Generative AI
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
import FaqItem from "@site/src/components/FaqItem";
|
||||
|
||||
## Configuration
|
||||
|
||||
A Generative AI provider can be configured in the global config, which will make the Generative AI features available for use. There are currently 5 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI-Compatible section below.
|
||||
A Generative AI provider can be configured in the global config, which will make the Generative AI features available for use. There are currently 4 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI-Compatible section below.
|
||||
|
||||
`genai` is a map of named providers. Each key under `genai` is a name you choose, and its value is that provider's settings:
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
|
||||
- Click **Add** and enter a **Provider name**. Any name of letters, numbers, hyphens, and underscores is accepted, but it cannot be changed from the UI after the provider is created.
|
||||
- Set **Provider** to the service you are using (e.g., `ollama`)
|
||||
- Set **Base URL**, **API key**, and **Model** as required by that provider
|
||||
- Set **Roles** to the roles this provider should handle.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider: # any name you like
|
||||
provider: ollama
|
||||
base_url: http://localhost:11434
|
||||
model: qwen3-vl:4b
|
||||
roles:
|
||||
- descriptions
|
||||
- embeddings
|
||||
- chat
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
The examples on this page all use `my_provider`, but the name is arbitrary and is only used to reference the provider elsewhere in the config (for example, `semantic_search.model`).
|
||||
|
||||
Each provider handles one or more **roles**: `chat`, `descriptions`, and `embeddings`. A provider handles all three by default, and each role may be assigned to exactly one provider. Define a single provider if you want it to do everything, or split the roles across several providers using the `roles` option.
|
||||
|
||||
If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
|
||||
To use Generative AI, you must define a single provider at the global level of your Frigate configuration. If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
|
||||
|
||||
## Local Providers
|
||||
|
||||
@@ -59,23 +25,15 @@ Running Generative AI models on CPU is not recommended, as high inference times
|
||||
|
||||
### Recommended Local Models
|
||||
|
||||
#### Vision models
|
||||
You must use a vision-capable model with Frigate. The following models are recommended for local deployment:
|
||||
|
||||
You must use a vision-capable model with Frigate. The following models are recommended for local deployment of the `descriptions` and `chat` roles:
|
||||
|
||||
| Model | Notes |
|
||||
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `qwen3-vl` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object. |
|
||||
| `qwen3.6`/`qwen3.8` | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
|
||||
| `gemma4` | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action. |
|
||||
|
||||
#### Embedding models
|
||||
|
||||
The `embeddings` role needs a different kind of model. Text queries are matched against the stored image embeddings, so the model must be trained to place images and text into the same vector space. A chat or description model will still return vectors when asked, but those vectors are not trained for retrieval and text searches will return poor matches with no error to indicate why.
|
||||
|
||||
| Model | Notes |
|
||||
| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `qwen3-vl-embedding` | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Must be served by llama.cpp started with `--embeddings` and `--mmproj`. |
|
||||
| Model | Notes |
|
||||
| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `qwen3-vl` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object. |
|
||||
| `qwen3.5` | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
|
||||
| `gemma4` | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action. |
|
||||
| `Intern3.5VL` | Relatively fast with good vision comprehension |
|
||||
| `gemma3` | Slower model with good vision and temporal understanding |
|
||||
|
||||
:::info
|
||||
|
||||
@@ -91,14 +49,15 @@ You should have at least 8 GB of RAM available (or VRAM if running on GPU) to ru
|
||||
|
||||
### Model Types: Instruct vs Thinking
|
||||
|
||||
Vision-language models come in **instruct** variants (fine-tuned to follow instructions and respond concisely), **thinking** variants (fine-tuned for free-form, speculative reasoning), and **hybrid** variants that support both modes per request. Most modern vision-language models are hybrid.
|
||||
Most vision-language models are available as **instruct** models, which are fine-tuned to follow instructions and respond concisely to prompts. However, some models (such as certain Qwen-VL or minigpt variants) offer both **instruct** and **thinking** versions.
|
||||
|
||||
Frigate manages reasoning per task automatically:
|
||||
- **Instruct models** are always recommended for use with Frigate. These models generate direct, relevant, actionable descriptions that best fit Frigate's object and event summary use case.
|
||||
- **Reasoning / Thinking models** are fine-tuned for more free-form, open-ended, and speculative outputs, which are typically not concise and may not provide the practical summaries Frigate expects. For this reason, Frigate does **not** recommend or support using thinking models.
|
||||
|
||||
- **Description tasks** (object descriptions, review descriptions, review summaries) are synthesis-only and benefit from concise, direct output, so Frigate disables thinking for these calls when the model exposes a per-request toggle.
|
||||
- **Chat** lets you toggle thinking on or off from the composer when the configured model supports it.
|
||||
Some models are labeled as **hybrid** (capable of both thinking and instruct tasks). In these cases, it is recommended to disable reasoning / thinking, which is generally model specific (see your models documentation).
|
||||
|
||||
You can use a pure instruct, hybrid, or thinking-capable model with Frigate. No extra configuration is required to disable thinking for descriptions.
|
||||
**Recommendation:**
|
||||
Always select the `-instruct` or documented instruct/tagged variant of any model you use in your Frigate configuration. If in doubt, refer to your model provider's documentation or model library for guidance on the correct model variant to use.
|
||||
|
||||
### llama.cpp
|
||||
|
||||
@@ -121,26 +80,23 @@ All llama.cpp native options can be passed through `provider_options`, including
|
||||
- Set **Provider** to `llamacpp`
|
||||
- Set **Base URL** to your llama.cpp server address (e.g., `http://localhost:8080`)
|
||||
- Set **Model** to the name of your model
|
||||
- Optionally, under **Provider Options**, set `context_size` to override the context size Frigate detects from the server
|
||||
- Under **Provider Options**, set `context_size` to tell Frigate your context size so it can send the appropriate amount of information
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider:
|
||||
provider: llamacpp
|
||||
base_url: http://localhost:8080
|
||||
model: your-model-name
|
||||
provider_options:
|
||||
context_size: 16000 # Optional, overrides the context size reported by the server.
|
||||
provider: llamacpp
|
||||
base_url: http://localhost:8080
|
||||
model: your-model-name
|
||||
provider_options:
|
||||
context_size: 16000 # Tell Frigate your context size so it can send the appropriate amount of information.
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
Frigate queries the llama.cpp server for the model's context size at startup and logs it along with the other detected capabilities. If `context_size` is set in `provider_options`, that value is always used instead, even when the server reports its own.
|
||||
|
||||
### Ollama
|
||||
|
||||
[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
|
||||
@@ -173,14 +129,13 @@ Note that Frigate will not automatically download the model you specify in your
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider:
|
||||
provider: ollama
|
||||
base_url: http://localhost:11434
|
||||
model: qwen3-vl:4b
|
||||
provider_options: # other Ollama client options can be defined
|
||||
keep_alive: -1
|
||||
options:
|
||||
num_ctx: 8192 # make sure the context matches other services that are using ollama
|
||||
provider: ollama
|
||||
base_url: http://localhost:11434
|
||||
model: qwen3-vl:4b
|
||||
provider_options: # other Ollama client options can be defined
|
||||
keep_alive: -1
|
||||
options:
|
||||
num_ctx: 8192 # make sure the context matches other services that are using ollama
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -196,12 +151,11 @@ For OpenAI-compatible servers (such as llama.cpp) that don't expose the configur
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider:
|
||||
provider: openai
|
||||
base_url: http://your-llama-server
|
||||
model: your-model-name
|
||||
provider_options:
|
||||
context_size: 8192 # Specify the configured context size
|
||||
provider: openai
|
||||
base_url: http://your-llama-server
|
||||
model: your-model-name
|
||||
provider_options:
|
||||
context_size: 8192 # Specify the configured context size
|
||||
```
|
||||
|
||||
This ensures Frigate uses the correct context window size when generating prompts.
|
||||
@@ -224,11 +178,10 @@ This ensures Frigate uses the correct context window size when generating prompt
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider:
|
||||
provider: openai
|
||||
base_url: http://your-server:port
|
||||
api_key: your-api-key # May not be required for local servers
|
||||
model: your-model-name
|
||||
provider: openai
|
||||
base_url: http://your-server:port
|
||||
api_key: your-api-key # May not be required for local servers
|
||||
model: your-model-name
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -248,7 +201,7 @@ Cloud Generative AI providers require an active internet connection to send imag
|
||||
|
||||
### Ollama Cloud
|
||||
|
||||
Ollama also supports [cloud models](https://ollama.com/cloud), where model inference is performed in the cloud. You can connect directly to Ollama Cloud by setting `base_url` to `https://ollama.com` and providing an API key. Alternatively, you can run Ollama locally and use a cloud model name so your local instance forwards requests to the cloud. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
|
||||
Ollama also supports [cloud models](https://ollama.com/cloud), where your local Ollama instance handles requests from Frigate, but model inference is performed in the cloud. Set up Ollama locally, sign in with your Ollama account, and specify the cloud model name in your Frigate config. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
|
||||
|
||||
#### Configuration
|
||||
|
||||
@@ -257,8 +210,7 @@ Ollama also supports [cloud models](https://ollama.com/cloud), where model infer
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
|
||||
- Set **Provider** to `ollama`
|
||||
- Set **Base URL** to your local Ollama address (e.g., `http://localhost:11434`) or `https://ollama.com` for direct cloud inference
|
||||
- Set **API key** if required by your endpoint (e.g., when using `https://ollama.com`)
|
||||
- Set **Base URL** to your local Ollama address (e.g., `http://localhost:11434`)
|
||||
- Set **Model** to the cloud model name
|
||||
|
||||
</TabItem>
|
||||
@@ -266,21 +218,9 @@ Ollama also supports [cloud models](https://ollama.com/cloud), where model infer
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider:
|
||||
provider: ollama
|
||||
base_url: http://localhost:11434
|
||||
model: cloud-model-name
|
||||
```
|
||||
|
||||
or when using Ollama Cloud directly
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider:
|
||||
provider: ollama
|
||||
base_url: https://ollama.com
|
||||
model: cloud-model-name
|
||||
api_key: your-api-key
|
||||
provider: ollama
|
||||
base_url: http://localhost:11434
|
||||
model: cloud-model-name
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -318,10 +258,9 @@ To start using Gemini, you must first get an API key from [Google AI Studio](htt
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider:
|
||||
provider: gemini
|
||||
api_key: "{FRIGATE_GEMINI_API_KEY}"
|
||||
model: gemini-2.5-flash
|
||||
provider: gemini
|
||||
api_key: "{FRIGATE_GEMINI_API_KEY}"
|
||||
model: gemini-2.5-flash
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -331,13 +270,12 @@ genai:
|
||||
|
||||
To use a different Gemini-compatible API endpoint, set the `provider_options` with the `base_url` key to your provider's API URL. For example:
|
||||
|
||||
```yaml {5,6}
|
||||
```yaml {4,5}
|
||||
genai:
|
||||
my_provider:
|
||||
provider: gemini
|
||||
...
|
||||
provider_options:
|
||||
base_url: https://...
|
||||
provider: gemini
|
||||
...
|
||||
provider_options:
|
||||
base_url: https://...
|
||||
```
|
||||
|
||||
Other HTTP options are available, see the [python-genai documentation](https://github.com/googleapis/python-genai).
|
||||
@@ -346,7 +284,7 @@ Other HTTP options are available, see the [python-genai documentation](https://g
|
||||
|
||||
### OpenAI
|
||||
|
||||
OpenAI does not have a free tier for their API.
|
||||
OpenAI does not have a free tier for their API. With the release of gpt-4o, pricing has been reduced and each generation should cost fractions of a cent if you choose to go this route.
|
||||
|
||||
#### Supported Models
|
||||
|
||||
@@ -371,10 +309,9 @@ To start using OpenAI, you must first [create an API key](https://platform.opena
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider:
|
||||
provider: openai
|
||||
api_key: "{FRIGATE_OPENAI_API_KEY}"
|
||||
model: gpt-4o
|
||||
provider: openai
|
||||
api_key: "{FRIGATE_OPENAI_API_KEY}"
|
||||
model: gpt-4o
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -390,14 +327,13 @@ To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` env
|
||||
|
||||
For OpenAI-compatible servers (such as llama.cpp) that don't expose the configured context size in the API response, you can manually specify the context size in `provider_options`:
|
||||
|
||||
```yaml {6,7}
|
||||
```yaml {5,6}
|
||||
genai:
|
||||
my_provider:
|
||||
provider: openai
|
||||
base_url: http://your-llama-server
|
||||
model: your-model-name
|
||||
provider_options:
|
||||
context_size: 8192 # Specify the configured context size
|
||||
provider: openai
|
||||
base_url: http://your-llama-server
|
||||
model: your-model-name
|
||||
provider_options:
|
||||
context_size: 8192 # Specify the configured context size
|
||||
```
|
||||
|
||||
This ensures Frigate uses the correct context window size when generating prompts.
|
||||
@@ -432,91 +368,11 @@ To start using Azure OpenAI, you must first [create a resource](https://learn.mi
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider:
|
||||
provider: azure_openai
|
||||
base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
|
||||
model: gpt-5-mini
|
||||
api_key: "{FRIGATE_OPENAI_API_KEY}"
|
||||
provider: azure_openai
|
||||
base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
|
||||
model: gpt-5-mini
|
||||
api_key: "{FRIGATE_OPENAI_API_KEY}"
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
## FAQ
|
||||
|
||||
<FaqItem id="how-do-i-debug-genai-issues" question="How do I debug GenAI issues?">
|
||||
|
||||
Frigate's Generative AI features are configured and enabled separately. [Review descriptions and summaries](/configuration/genai/genai_review) live under `review.genai`, and [object descriptions](/configuration/genai/genai_objects) live under `objects.genai`. Configuring a provider on this page does not enable either feature, and enabling one does not enable the other. Decide which of the two is not working, then work through the steps below.
|
||||
|
||||
1. Confirm a provider is available and holds the `descriptions` role.
|
||||
- Review descriptions, review summaries, and object descriptions all use the provider that has the `descriptions` role assigned in <NavPath path="Settings > Enrichments > Generative AI > Roles" /> (`genai.<provider>.roles`).
|
||||
- A provider is contacted the first time one of its roles is actually used. A provider holding the `embeddings` role for semantic search is initialized during startup, while a `descriptions` provider is not initialized until the first description is requested, which may be well after boot.
|
||||
- In <NavPath path="Settings > Enrichments > Generative AI" />, use **Refresh models** next to the model field. It queries the provider for its model list and is a quick way to verify that the base URL, API key, and network path between Frigate and your provider are correct.
|
||||
|
||||
2. Confirm the feature you expect is actually enabled.
|
||||
- Object descriptions are disabled by default. Turn on <NavPath path="Settings > Global configuration > Objects > GenAI object config > Enable GenAI" /> (`objects.genai.enabled`), either globally or per camera. This is the most common reason custom prompts appear to be ignored while review summaries are still being generated.
|
||||
- Review descriptions are disabled by default. Turn on <NavPath path="Settings > Global configuration > Review > GenAI config > Enable GenAI descriptions" /> (`review.genai.enabled`). Once enabled, alerts are described by default but detections are not, so a detection-only review item will never get a summary unless **Enable GenAI for detections** (`review.genai.detections`) is also on.
|
||||
|
||||
3. If object descriptions are never requested, check the filters that skip generation.
|
||||
- <NavPath path="Settings > Global configuration > Objects > GenAI object config > GenAI objects" /> (`objects.genai.objects`) limits generation to specific labels, and **Required zones** (`objects.genai.required_zones`) requires the object to have entered one of those zones. If either is set and does not match, Frigate skips the request silently.
|
||||
- Thumbnails are only collected while an object is moving. Objects that go stationary early contribute fewer frames.
|
||||
- **Use snapshots** (`objects.genai.use_snapshot`) requires snapshots to be enabled for the camera. If the snapshot cannot be read, Frigate logs `Cannot load snapshot for <id>, file not found` and no description is generated.
|
||||
- **Send on end** (`objects.genai.send_triggers.tracked_object_end`) is on by default. If you have turned it off in favor of **Early GenAI trigger** (`objects.genai.send_triggers.after_significant_updates`), descriptions are only requested once that number of updates is reached.
|
||||
|
||||
4. Enable debug logs to see exactly what Frigate is doing. Restart Frigate after this change. The next step also requires a restart, so turn both on at the same time to avoid restarting twice.
|
||||
|
||||
```yaml
|
||||
logger:
|
||||
default: info
|
||||
logs:
|
||||
# highlight-start
|
||||
frigate.genai: debug
|
||||
frigate.data_processing.post.object_descriptions: debug
|
||||
frigate.data_processing.post.review_descriptions: debug
|
||||
# highlight-end
|
||||
```
|
||||
|
||||
5. Save the exact images and prompts that were sent to your provider.
|
||||
- Turn on **Save thumbnails** for the feature you are debugging (`review.genai.debug_save_thumbnails` or `objects.genai.debug_save_thumbnails`). Both features write to `/media/frigate/clips/genai-requests/`, and these files are admin-only.
|
||||
- Review descriptions write `genai-requests/<review_id>/` containing the numbered frames that were sent, plus `prompt.txt` and `response.txt` with the exact prompt and the raw, unparsed model response.
|
||||
- Review summary reports write `genai-requests/<start_ts>-<end_ts>/prompt.txt` and `response.txt`. No images are involved, since a report summarizes existing review descriptions.
|
||||
- Object descriptions write `genai-requests/<event_id>/` containing the numbered thumbnails. The prompt for object descriptions is not written to a file, it is only visible in the debug logs from step 4.
|
||||
- Look at the saved images before blaming the model. If the object is small, blurry, or out of frame, no prompt will fix the result. For object descriptions, consider turning on **Use snapshots** (`objects.genai.use_snapshot`) to send a higher quality image. For review items, consider setting **Review image source** (`review.genai.image_source`) to `recordings` for 480p frames instead of the lower resolution preview frames.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
For review descriptions, navigate to <NavPath path="Settings > Global configuration > Review" /> and set **GenAI config > Save thumbnails** to on.
|
||||
|
||||
For object descriptions, navigate to <NavPath path="Settings > Global configuration > Objects" />, expand **GenAI object config**, and set **Save thumbnails** to on.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
review:
|
||||
genai:
|
||||
enabled: true
|
||||
# highlight-next-line
|
||||
debug_save_thumbnails: true
|
||||
|
||||
objects:
|
||||
genai:
|
||||
enabled: true
|
||||
# highlight-next-line
|
||||
debug_save_thumbnails: true
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
6. Verify the prompt is what you think it is.
|
||||
- Object description prompts are the ones you control directly. A camera-level <NavPath path="Settings > Camera configuration > Objects > GenAI object config > Caption prompt" /> (`objects.genai.prompt`) overrides the global one, and an entry in **Object prompts** (`objects.genai.object_prompts`) for a label overrides both for that label. Only `{label}`, `{sub_label}`, and `{camera}` are substituted.
|
||||
- Review description prompts are built by Frigate and request a structured JSON response, so they are not fully replaceable. The parts you control are <NavPath path="Settings > Global configuration > Review > GenAI config > Activity context prompt" /> (`review.genai.activity_context_prompt`) and **Additional concerns** (`review.genai.additional_concerns`). Keep the activity context prompt general, since overly specific rules will sway the model's threat level scoring.
|
||||
|
||||
7. If descriptions are generated but the results are poor or inconsistent, look at the model and the context window.
|
||||
- Empty fields, missing `shortSummary` values, or `Failed to parse review description` errors usually mean the model is not following the requested JSON schema. Smaller models struggle with structured output. Try a larger parameter size or one of the [recommended models](#recommended-local-models).
|
||||
- Frigate calculates how many frames to send from the context size the provider reports. If your server reports a different value than it is actually running with, frames will be truncated or the request will fail. Pin the value by adding `context_size` under <NavPath path="Settings > Enrichments > Generative AI > Provider options" /> (`genai.<provider>.provider_options`), and for Ollama also confirm `options.num_ctx` there matches the context you have configured.
|
||||
- Check **Review Description Speed** and **Object Description Speed** in <NavPath path="System metrics > Enrichments" />. If inference takes tens of seconds, requests will queue behind each other and descriptions will appear to stop. For Ollama, review `OLLAMA_NUM_PARALLEL`, `OLLAMA_MAX_QUEUE`, and `OLLAMA_MAX_LOADED_MODELS` so that concurrent requests from Frigate are handled the way you expect.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
@@ -52,10 +52,9 @@ You can define custom prompts at the global level and per-object type. To config
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider:
|
||||
provider: ollama
|
||||
base_url: http://localhost:11434
|
||||
model: qwen3-vl:8b-instruct
|
||||
provider: ollama
|
||||
base_url: http://localhost:11434
|
||||
model: qwen3-vl:8b-instruct
|
||||
|
||||
objects:
|
||||
genai:
|
||||
@@ -113,7 +112,3 @@ Many providers also have a public facing chat interface for their models. Downlo
|
||||
- OpenAI - [ChatGPT](https://chatgpt.com)
|
||||
- Gemini - [Google AI Studio](https://aistudio.google.com)
|
||||
- Ollama - [Open WebUI](https://docs.openwebui.com/)
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
If descriptions are not being generated, or the generated descriptions are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).
|
||||
|
||||
@@ -201,7 +201,3 @@ Along with individual review item summaries, Generative AI can also produce a si
|
||||
Review reports can be requested via the [API](/integrations/api/generate-review-summary-review-summarize-start-start-ts-end-end-ts-post) by sending a POST request to `/api/review/summarize/start/{start_ts}/end/{end_ts}` with Unix timestamps.
|
||||
|
||||
For Home Assistant users, there is a built-in service (`frigate.review_summarize`) that makes it easy to request review reports as part of automations or scripts. This allows you to automatically generate daily summaries, vacation reports, or custom time period reports based on your specific needs.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
If summaries are not being generated, or the generated summaries are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).
|
||||
|
||||
@@ -1,72 +0,0 @@
|
||||
---
|
||||
id: go2rtc
|
||||
title: go2rtc
|
||||
---
|
||||
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
Frigate uses the bundled go2rtc to power a number of key features:
|
||||
|
||||
- WebRTC or MSE for live viewing with audio, higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream and does not support audio
|
||||
- Live stream support for cameras in Home Assistant Integration
|
||||
- RTSP relay for use with other consumers to reduce the number of connections to your camera streams
|
||||
|
||||
:::tip[Most users no longer need to configure go2rtc by hand]
|
||||
|
||||
The [**camera setup wizard**](cameras.md#adding-a-camera-with-the-add-camera-wizard) is the recommended way to add cameras. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />, and the wizard probes your camera and writes its configuration for you, including the go2rtc restream and the live stream mapping, so go2rtc is set up automatically.
|
||||
|
||||
This guide is mainly useful if you are **upgrading from an older version and have existing cameras that don't yet use go2rtc**, or if you want to fine-tune a stream by hand (for example, to transcode a codec your browser can't play). The [go2rtc troubleshooting guide](/troubleshooting/go2rtc) applies regardless of how your cameras were added.
|
||||
|
||||
:::
|
||||
|
||||
## Adding a go2rtc stream manually
|
||||
|
||||
If you added your cameras with the wizard, go2rtc is already configured. You can skip straight to [troubleshooting](/troubleshooting/go2rtc). The steps below are for upgrading users with existing cameras that aren't using go2rtc yet, or for anyone who prefers to configure a stream by hand.
|
||||
|
||||
Configure go2rtc to connect to your camera by adding the stream you want to use for live view. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#module-streams), not just rtsp.
|
||||
|
||||
:::tip
|
||||
|
||||
For the best experience, set the stream name under `go2rtc` to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera.
|
||||
|
||||
See [the live view docs](/configuration/live#setting-streams-for-live-ui) for more information.
|
||||
|
||||
:::
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > go2rtc Streams" /> and click **Add stream**. Give the stream a name (use the camera's name so Frigate can auto-map it - for example, if your camera's name is `back`, use `back` as the go2rtc stream name), then paste the camera's stream URL into the **Source** field. Save the section.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
go2rtc:
|
||||
streams:
|
||||
back:
|
||||
- rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
After adding this to the config, restart Frigate and try to watch the live stream for a single camera by clicking on it from the dashboard. It should look much clearer and more fluent than the original jsmpeg stream.
|
||||
|
||||
### Next steps
|
||||
|
||||
1. If the stream you added to go2rtc is also used by Frigate for the `record` or `detect` role, you can migrate your config to pull from the RTSP restream to reduce the number of connections to your camera as shown [here](/configuration/restream#reduce-connections-to-camera).
|
||||
2. You can [set up WebRTC](/configuration/live#webrtc-extra-configuration) if your camera supports two-way talk. Note that WebRTC only supports specific audio formats and may require opening ports on your router.
|
||||
3. If your camera supports two-way talk, you must configure your stream with `#backchannel=0` to prevent go2rtc from blocking other applications from accessing the camera's audio output. See [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream) in the restream documentation.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
If your stream won't play, has no audio, uses excessive CPU, or otherwise misbehaves, see the dedicated [go2rtc troubleshooting guide](/troubleshooting/go2rtc). It walks through how to isolate where the problem is and covers the most common issues: unsupported codecs, H.265/HEVC, audio, WebRTC and two-way talk, hardware-accelerated transcoding with FFmpeg 8, and camera-specific quirks.
|
||||
|
||||
## Homekit Configuration
|
||||
|
||||
To export camera streams to HomeKit, Frigate must be configured in docker to use `host` networking mode. HomeKit settings are stored in `/config/go2rtc_homekit.yml` rather than in your Frigate config, and are edited through the go2rtc config editor at `http://<frigate_host>:1984/editor.html`. Pairings are saved back to that file automatically.
|
||||
|
||||
See the [HomeKit integration docs](/integrations/homekit) for the full setup, including the video and audio requirements HomeKit places on the stream.
|
||||
@@ -17,6 +17,8 @@ Some types of hardware acceleration are detected and used automatically, but you
|
||||
- Check the logs: A message will either say that hardware acceleration was automatically detected, or there will be a warning that no hardware acceleration was automatically detected
|
||||
- If hardware acceleration is specified in the config, verification can be done by ensuring the logs are free from errors. There is no CPU fallback for hardware acceleration.
|
||||
|
||||
:::info
|
||||
|
||||
Frigate supports presets for optimal hardware accelerated video decoding:
|
||||
|
||||
**AMD**
|
||||
@@ -47,10 +49,14 @@ Frigate supports presets for optimal hardware accelerated video decoding:
|
||||
|
||||
Depending on your system, these presets may not be compatible, and you may need to use manual hwaccel args to take advantage of your hardware. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
|
||||
|
||||
:::
|
||||
|
||||
## Intel-based CPUs
|
||||
|
||||
Frigate can utilize most Intel integrated GPUs and Arc GPUs to accelerate video decoding.
|
||||
|
||||
:::info
|
||||
|
||||
**Recommended hwaccel Preset**
|
||||
|
||||
| CPU Generation | Intel Driver | Recommended Preset | Notes |
|
||||
@@ -62,9 +68,11 @@ Frigate can utilize most Intel integrated GPUs and Arc GPUs to accelerate video
|
||||
| Intel Arc A-series | iHD / Xe | preset-intel-qsv-\* | |
|
||||
| Intel Arc B-series | iHD / Xe | preset-intel-qsv-\* | Requires host kernel 6.12+ |
|
||||
|
||||
:::
|
||||
|
||||
:::note
|
||||
|
||||
The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `config.yml` for HA App users](advanced/system.md#environment_vars).
|
||||
The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `config.yml` for HA App users](advanced.md#environment_vars).
|
||||
|
||||
See [The Intel Docs](https://www.intel.com/content/www/us/en/support/articles/000005505/processors.html) to figure out what generation your CPU is.
|
||||
|
||||
@@ -77,7 +85,7 @@ VAAPI supports automatic profile selection so it will work automatically with bo
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -97,7 +105,7 @@ ffmpeg:
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.264)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.264)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -115,7 +123,7 @@ ffmpeg:
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.265)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.265)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -128,32 +136,90 @@ ffmpeg:
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
### Configuring Intel GPU Stats
|
||||
### Configuring Intel GPU Stats in Docker
|
||||
|
||||
Frigate reads Intel GPU utilization directly from the kernel's per-client DRM usage counters exposed at `/proc/<pid>/fdinfo/<fd>`. This requires:
|
||||
Additional configuration is needed for the Docker container to be able to access the `intel_gpu_top` command for GPU stats. There are two options:
|
||||
|
||||
- Linux kernel **5.19 or newer** for the `i915` driver, or any release of the `xe` driver.
|
||||
- Frigate running with permission to read other processes' fdinfo. Running as root inside the container (the default) satisfies this; non-root setups may need `CAP_SYS_PTRACE`.
|
||||
1. Run the container as privileged.
|
||||
2. Add the `CAP_PERFMON` capability (note: you might need to set the `perf_event_paranoid` low enough to allow access to the performance event system.)
|
||||
|
||||
No `intel_gpu_top` binary, `CAP_PERFMON`, privileged mode, or `perf_event_paranoid` tuning is required.
|
||||
#### Run as privileged
|
||||
|
||||
#### Stats for SR-IOV or specific devices
|
||||
This method works, but it gives more permissions to the container than are actually needed.
|
||||
|
||||
If the host has more than one Intel GPU (e.g. an iGPU plus a discrete GPU, or SR-IOV virtual functions), pin stats collection to a specific device by setting `intel_gpu_device` to either its PCI bus address or a DRM card/render-node path:
|
||||
##### Docker Compose - Privileged
|
||||
|
||||
```yaml
|
||||
services:
|
||||
frigate:
|
||||
...
|
||||
image: ghcr.io/blakeblackshear/frigate:stable
|
||||
# highlight-next-line
|
||||
privileged: true
|
||||
```
|
||||
|
||||
##### Docker Run CLI - Privileged
|
||||
|
||||
```bash {4}
|
||||
docker run -d \
|
||||
--name frigate \
|
||||
...
|
||||
--privileged \
|
||||
ghcr.io/blakeblackshear/frigate:stable
|
||||
```
|
||||
|
||||
#### CAP_PERFMON
|
||||
|
||||
Only recent versions of Docker support the `CAP_PERFMON` capability. You can test to see if yours supports it by running: `docker run --cap-add=CAP_PERFMON hello-world`
|
||||
|
||||
##### Docker Compose - CAP_PERFMON
|
||||
|
||||
```yaml {5,6}
|
||||
services:
|
||||
frigate:
|
||||
...
|
||||
image: ghcr.io/blakeblackshear/frigate:stable
|
||||
cap_add:
|
||||
- CAP_PERFMON
|
||||
```
|
||||
|
||||
##### Docker Run CLI - CAP_PERFMON
|
||||
|
||||
```bash {4}
|
||||
docker run -d \
|
||||
--name frigate \
|
||||
...
|
||||
--cap-add=CAP_PERFMON \
|
||||
ghcr.io/blakeblackshear/frigate:stable
|
||||
```
|
||||
|
||||
#### perf_event_paranoid
|
||||
|
||||
_Note: This setting must be changed for the entire system._
|
||||
|
||||
For more information on the various values across different distributions, see https://askubuntu.com/questions/1400874/what-does-perf-paranoia-level-four-do.
|
||||
|
||||
Depending on your OS and kernel configuration, you may need to change the `/proc/sys/kernel/perf_event_paranoid` kernel tunable. You can test the change by running `sudo sh -c 'echo 2 >/proc/sys/kernel/perf_event_paranoid'` which will persist until a reboot. Make it permanent by running `sudo sh -c 'echo kernel.perf_event_paranoid=2 >> /etc/sysctl.d/local.conf'`
|
||||
|
||||
#### Stats for SR-IOV or other devices
|
||||
|
||||
When using virtualized GPUs via SR-IOV, you need to specify the device path to use to gather stats from `intel_gpu_top`. This example may work for some systems using SR-IOV:
|
||||
|
||||
```yaml
|
||||
telemetry:
|
||||
stats:
|
||||
intel_gpu_device: "0000:00:02.0"
|
||||
intel_gpu_device: "sriov"
|
||||
```
|
||||
|
||||
For other virtualized GPUs, try specifying the direct path to the device instead:
|
||||
|
||||
```yaml
|
||||
telemetry:
|
||||
stats:
|
||||
intel_gpu_device: "/dev/dri/card1"
|
||||
intel_gpu_device: "drm:/dev/dri/card0"
|
||||
```
|
||||
|
||||
When passing a device path, make sure the device is also passed through to the container.
|
||||
If you are passing in a device path, make sure you've passed the device through to the container.
|
||||
|
||||
## AMD-based CPUs
|
||||
|
||||
@@ -161,7 +227,7 @@ Frigate can utilize modern AMD integrated GPUs and AMD GPUs to accelerate video
|
||||
|
||||
### Configuring Radeon Driver
|
||||
|
||||
You need to change the driver to `radeonsi` by adding the following environment variable `LIBVA_DRIVER_NAME=radeonsi` to your docker-compose file or [in the `config.yml` for HA App users](advanced/system.md#environment_vars).
|
||||
You need to change the driver to `radeonsi` by adding the following environment variable `LIBVA_DRIVER_NAME=radeonsi` to your docker-compose file or [in the `config.yml` for HA App users](advanced.md#environment_vars).
|
||||
|
||||
### Via VAAPI
|
||||
|
||||
@@ -170,7 +236,7 @@ VAAPI supports automatic profile selection so it will work automatically with bo
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -185,7 +251,7 @@ ffmpeg:
|
||||
|
||||
## NVIDIA GPUs
|
||||
|
||||
While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced/system#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.
|
||||
While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.
|
||||
|
||||
A more complete list of cards and their compatible drivers is available in the [driver release readme](https://download.nvidia.com/XFree86/Linux-x86_64/525.85.05/README/supportedchips.html).
|
||||
|
||||
@@ -229,7 +295,7 @@ Using `preset-nvidia` ffmpeg will automatically select the necessary profile for
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA GPU`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA GPU`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -292,7 +358,7 @@ If you are using the HA App, you may need to use the full access variant and tur
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)` (for H.264 streams) or `Raspberry Pi (H.265)` (for H.265/HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)` (for H.264 streams) or `Raspberry Pi (H.265)` (for H.265/HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -312,9 +378,8 @@ ffmpeg:
|
||||
|
||||
:::note
|
||||
|
||||
If running Frigate through Docker, map the relevant `/dev/video*` devices into
|
||||
the container. Running in privileged mode also works but grants far more access
|
||||
than needed. With Docker Compose add:
|
||||
If running Frigate through Docker, you either need to run in privileged mode or
|
||||
map the `/dev/video*` devices to Frigate. With Docker Compose add:
|
||||
|
||||
```yaml {4-5}
|
||||
services:
|
||||
@@ -413,7 +478,7 @@ For example, for H264 video, you'll select `preset-jetson-h264`.
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA Jetson (H.264)` (or `NVIDIA Jetson (H.265)` for HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA Jetson (H.264)` (or `NVIDIA Jetson (H.265)` for HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -445,7 +510,7 @@ Set the FFmpeg hwaccel preset to enable hardware video processing.
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Rockchip RKMPP`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Rockchip RKMPP`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -478,7 +543,7 @@ Error marking filters as finished
|
||||
Restarting ffmpeg...
|
||||
```
|
||||
|
||||
you should try to upgrade to FFmpeg 7. This can be done using this config option:
|
||||
you should try to uprade to FFmpeg 7. This can be done using this config option:
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
@@ -512,7 +577,7 @@ Set the FFmpeg hwaccel args to enable hardware video processing.
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and configure the hardware acceleration args and input args manually for Synaptics hardware. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
|
||||
Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and configure the hardware acceleration args and input args manually for Synaptics hardware. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
---
|
||||
id: config
|
||||
id: index
|
||||
title: Frigate Configuration
|
||||
---
|
||||
|
||||
@@ -7,58 +7,15 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
Frigate can be configured through the **Settings UI** or by editing the YAML configuration file directly. The Settings UI is the recommended approach. It provides validation and a guided experience for all configuration options.
|
||||
Frigate can be configured through the **Settings UI** or by editing the YAML configuration file directly. The Settings UI is the recommended approach — it provides validation and a guided experience for all configuration options.
|
||||
|
||||
## Using the Settings UI
|
||||
|
||||
The Settings UI groups every configuration option into sections that are listed in the left-hand menu. Each section presents a guided form with validation, so you don't need to remember the structure of the YAML or look up option names by hand.
|
||||
|
||||
### Global vs. camera-level configuration
|
||||
|
||||
Settings are organized into two scopes:
|
||||
|
||||
- **Global configuration**: values under <NavPath path="Settings > Global configuration" /> apply to every camera by default. This is where you set the baseline behavior for object detection, recording, snapshots, motion, and so on.
|
||||
- **Camera configuration**: values under <NavPath path="Settings > Camera configuration" /> apply to a single camera. Use the camera selector button at the top of these pages to choose which camera you are editing.
|
||||
|
||||
When a camera-level section is left untouched, the camera simply inherits the global values. Changing a value on a camera page **overrides** the global value for that camera only: the global setting and every other camera are unaffected. This mirrors how the YAML works, where a value set under `cameras.<name>` takes precedence over the same value set at the top level. See [Global and Camera-Level Configuration](./config_overrides.md) for the full details, including how lists and maps are handled and which settings must be enabled globally first.
|
||||
|
||||
To undo an override and go back to inheriting from the parent scope, use the reset button at the bottom of the section:
|
||||
|
||||
- On a camera section, the button is labeled **Reset to Global** and restores the camera to the global value.
|
||||
- On a global section, the button is labeled **Reset to Default** and restores Frigate's built-in default.
|
||||
|
||||
Resetting asks for confirmation and cannot be undone once applied.
|
||||
|
||||
### Saving changes and the Save All button
|
||||
|
||||
Edits are not applied until you save them. As soon as you change a value, the UI tracks it as a pending change:
|
||||
|
||||
- The edited section shows a **Modified** badge, and the changed fields are highlighted.
|
||||
- A **You have unsaved changes** notice appears above the section's **Save** and **Undo** buttons. **Save** commits just that section; **Undo** discards its pending edits.
|
||||
|
||||
Because pending changes can span multiple sections (and multiple cameras), the header provides a **Save All** button that writes every pending change at once. Next to it, **Review pending changes** opens a summary that lists each pending edit with its scope (Global or a specific camera), the affected field, and the new value, so you can confirm exactly what will be written before committing. **Undo All** discards every pending change across all sections.
|
||||
|
||||
### Restart-required indicators
|
||||
|
||||
Most settings take effect immediately, but some require Frigate to restart before they apply. Fields that require a restart are marked with a small restart icon and a **Restart required** tooltip next to the field label.
|
||||
|
||||
When you save a change that touches one of these fields, Frigate confirms the save and reminds you that a restart is needed (for example, _"Settings saved successfully. Restart Frigate to apply your changes."_). The notification includes a one-click **Restart Frigate** action so you can apply the change right away, or you can continue editing and restart later.
|
||||
|
||||
### The colored dots in the camera configuration menu
|
||||
|
||||
When you are working under <NavPath path="Settings > Camera configuration" />, small colored dots can appear next to a section's name in the menu. They give you an at-a-glance summary of that section's state for the selected camera:
|
||||
|
||||
- **Blue dot**: this section **overrides the global configuration**. One or more values in the section have been set specifically for this camera and differ from the global defaults.
|
||||
- **Profile-colored dot**: when you are viewing a [camera profile](./profiles.md), a dot in that profile's assigned color indicates the section is **overridden by that profile**. Each profile is given its own distinct color so you can tell at a glance which sections it changes.
|
||||
- **Amber dot**: this section has **unsaved changes**. It appears alongside the **Modified** badge whenever you have pending edits in the section that haven't been saved yet.
|
||||
|
||||
Hover over any dot to see a tooltip describing what it means. Open a section to see exactly which fields are overridden: the section header indicates how many fields differ from the global (or base) configuration.
|
||||
It is recommended to start with a minimal configuration and add to it as described in [the getting started guide](../guides/getting_started.md).
|
||||
|
||||
## Configuration File Location
|
||||
|
||||
For users who prefer to edit the YAML configuration file directly, it is recommended to start with a minimal configuration and add to it as described in [the getting started guide](../guides/getting_started.md).
|
||||
For users who prefer to edit the YAML configuration file directly:
|
||||
|
||||
- **Home Assistant App:** `/addon_configs/<addon_directory>/config.yml` (see [directory list](#accessing-app-config-dir))
|
||||
- **Home Assistant App:** `/addon_configs/<addon_directory>/config.yml` — see [directory list](#accessing-app-config-dir)
|
||||
- **All other installations:** Map to `/config/config.yml` inside the container
|
||||
|
||||
It can be named `config.yml` or `config.yaml`, but if both files exist `config.yml` will be preferred and `config.yaml` will be ignored.
|
||||
@@ -100,7 +57,7 @@ VS Code supports JSON schemas for automatically validating configuration files.
|
||||
|
||||
## Environment Variable Substitution
|
||||
|
||||
Frigate supports the use of environment variables starting with `FRIGATE_` **only** where specifically indicated in the [reference config](./advanced/reference.md). See [substitution sources and precedence](./advanced/system.md#substitution-sources-and-precedence) for where those values can come from, including `secrets.yaml`. For example, the following values can be replaced at runtime by using environment variables:
|
||||
Frigate supports the use of environment variables starting with `FRIGATE_` **only** where specifically indicated in the [reference config](./reference.md). For example, the following values can be replaced at runtime by using environment variables:
|
||||
|
||||
```yaml
|
||||
mqtt:
|
||||
@@ -130,13 +87,12 @@ go2rtc:
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider:
|
||||
api_key: "{FRIGATE_GENAI_API_KEY}"
|
||||
api_key: "{FRIGATE_GENAI_API_KEY}"
|
||||
```
|
||||
|
||||
## Common configuration examples
|
||||
|
||||
Here are some common starter configuration examples. These can be configured through the Settings UI or via YAML. Refer to the [reference config](./advanced/reference.md) for detailed information about all config values.
|
||||
Here are some common starter configuration examples. These can be configured through the Settings UI or via YAML. Refer to the [reference config](./reference.md) for detailed information about all config values.
|
||||
|
||||
### Raspberry Pi Home Assistant App with USB Coral
|
||||
|
||||
@@ -154,10 +110,10 @@ Here are some common starter configuration examples. These can be configured thr
|
||||
|
||||
1. Navigate to <NavPath path="Settings > System > MQTT" /> and configure the MQTT connection to your Home Assistant Mosquitto broker
|
||||
2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)`
|
||||
3. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown
|
||||
3. Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`
|
||||
4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
|
||||
5. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
|
||||
6. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
|
||||
6. Navigate to <NavPath path="Settings > Camera configuration > Management" /> and add your camera with the appropriate RTSP stream URL
|
||||
7. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp
|
||||
|
||||
</TabItem>
|
||||
@@ -172,9 +128,10 @@ mqtt:
|
||||
ffmpeg:
|
||||
hwaccel_args: preset-rpi-64-h264
|
||||
|
||||
models:
|
||||
- devices:
|
||||
- edgetpu:usb
|
||||
detectors:
|
||||
coral:
|
||||
type: edgetpu
|
||||
device: usb
|
||||
|
||||
record:
|
||||
enabled: True
|
||||
@@ -232,10 +189,10 @@ cameras:
|
||||
|
||||
1. Navigate to <NavPath path="Settings > System > MQTT" /> and set **Enable MQTT** to off
|
||||
2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
|
||||
3. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown
|
||||
3. Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`
|
||||
4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
|
||||
5. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
|
||||
6. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
|
||||
6. Navigate to <NavPath path="Settings > Camera configuration > Management" /> and add your camera with the appropriate RTSP stream URL
|
||||
7. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp
|
||||
|
||||
</TabItem>
|
||||
@@ -248,9 +205,10 @@ mqtt:
|
||||
ffmpeg:
|
||||
hwaccel_args: preset-vaapi
|
||||
|
||||
models:
|
||||
- devices:
|
||||
- edgetpu:usb
|
||||
detectors:
|
||||
coral:
|
||||
type: edgetpu
|
||||
device: usb
|
||||
|
||||
record:
|
||||
enabled: True
|
||||
@@ -308,11 +266,11 @@ cameras:
|
||||
|
||||
1. Navigate to <NavPath path="Settings > System > MQTT" /> and configure the connection to your MQTT broker
|
||||
2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
|
||||
3. Navigate to <NavPath path="Settings > System > Detection models" /> and select **Intel GPU** from the **Hardware** dropdown
|
||||
4. On the same model, open the **Custom Model** tab and configure the OpenVINO model path and settings
|
||||
3. Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `openvino` and **Device** `AUTO`
|
||||
4. Navigate to <NavPath path="Settings > System > Detection model" /> and configure the OpenVINO model path and settings
|
||||
5. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
|
||||
6. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
|
||||
7. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
|
||||
7. Navigate to <NavPath path="Settings > Camera configuration > Management" /> and add your camera with the appropriate RTSP stream URL
|
||||
8. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp
|
||||
|
||||
</TabItem>
|
||||
@@ -327,12 +285,15 @@ mqtt:
|
||||
ffmpeg:
|
||||
hwaccel_args: preset-vaapi
|
||||
|
||||
models:
|
||||
- devices:
|
||||
- openvino:AUTO
|
||||
width: 300
|
||||
height: 300
|
||||
input_tensor: nhwc
|
||||
detectors:
|
||||
ov:
|
||||
type: openvino
|
||||
device: AUTO
|
||||
|
||||
model:
|
||||
width: 300
|
||||
height: 300
|
||||
input_tensor: nhwc
|
||||
input_pixel_format: bgr
|
||||
path: /openvino-model/ssdlite_mobilenet_v2.xml
|
||||
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
|
||||
@@ -6,7 +6,6 @@ title: License Plate Recognition (LPR)
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
import FaqItem from "@site/src/components/FaqItem";
|
||||
|
||||
Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
|
||||
|
||||
@@ -284,8 +283,8 @@ Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /
|
||||
| Field | Description |
|
||||
| ------------------------------ | ----------------------------------------------------------------------------------------------------- |
|
||||
| **Enable LPR** | Set to on |
|
||||
| **Minimum plate area** | Set to `1500` to ignore plates with an area (length x width) smaller than 1500 pixels |
|
||||
| **Min plate length** | Set to `4` to only recognize plates with 4 or more characters |
|
||||
| **Minimum plate area** | Set to `1500` — ignore plates with an area (length x width) smaller than 1500 pixels |
|
||||
| **Min plate length** | Set to `4` — only recognize plates with 4 or more characters |
|
||||
| **Known plates > Wife's Car** | `ABC-1234`, `ABC-I234` (accounts for potential confusion between the number one and capital letter I) |
|
||||
| **Known plates > Johnny** | `J*N-*234` (matches JHN-1234 and JMN-I234; `*` matches any number of characters) |
|
||||
| **Known plates > Sally** | `[S5]LL 1234` (matches both SLL 1234 and 5LL 1234) |
|
||||
@@ -364,7 +363,7 @@ An example configuration for a dedicated LPR camera using a `license_plate`-dete
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /> and set **Enable LPR** to on. Set **Device** to `CPU` (can also be `GPU` if available).
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add your camera streams.
|
||||
Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add your camera streams.
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
|
||||
|
||||
@@ -474,15 +473,15 @@ Navigate to <NavPath path="Settings > Camera configuration > License plate recog
|
||||
| Field | Description |
|
||||
| --------------------- | -------------------------------------------------------------------------------- |
|
||||
| **Enable LPR** | Set to on |
|
||||
| **Enhancement level** | Set to `3` (optional, enhances the image before trying to recognize characters) |
|
||||
| **Enhancement level** | Set to `3` (optional — enhances the image before trying to recognize characters) |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add your camera streams.
|
||||
Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add your camera streams.
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
|
||||
|
||||
| Field | Description |
|
||||
| --------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
|
||||
| **Enable object detection** | Set to off to disable Frigate's standard object detection pipeline |
|
||||
| **Enable object detection** | Set to off — disables Frigate's standard object detection pipeline |
|
||||
| **Detect FPS** | Set to `5`. Increase if necessary, though high values may slow down Frigate's enrichments pipeline and use considerable CPU. |
|
||||
| **Detect width** | Set to `1920` (recommended value, but depends on your camera) |
|
||||
| **Detect height** | Set to `1080` (recommended value, but depends on your camera) |
|
||||
@@ -491,7 +490,7 @@ Navigate to <NavPath path="Settings > Camera configuration > Objects" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | -------------------------------------------------------------------------------------- |
|
||||
| **Objects to track** | Set to an empty list, required when not using a Frigate+ model for dedicated LPR mode |
|
||||
| **Objects to track** | Set to an empty list — required when not using a Frigate+ model for dedicated LPR mode |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
|
||||
|
||||
@@ -592,9 +591,7 @@ By selecting the appropriate configuration, users can optimize their dedicated L
|
||||
|
||||
## FAQ
|
||||
|
||||
### Detection and Recognition
|
||||
|
||||
<FaqItem id="why-isnt-my-license-plate-being-detected-and-recognized" question="Why isn't my license plate being detected and recognized?">
|
||||
### Why isn't my license plate being detected and recognized?
|
||||
|
||||
Ensure that:
|
||||
|
||||
@@ -609,43 +606,29 @@ Recognized plates will show as object labels in the debug view and will appear i
|
||||
|
||||
If you are still having issues detecting plates, start with a basic configuration and see the debugging tips below.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting <code>car</code> or <code>motorcycle</code> objects?</>}>
|
||||
### Can I run LPR without detecting `car` or `motorcycle` objects?
|
||||
|
||||
In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="how-can-i-improve-detection-accuracy" question="How can I improve detection accuracy?">
|
||||
### How can I improve detection accuracy?
|
||||
|
||||
- Use high-quality cameras with good resolution.
|
||||
- Adjust `detection_threshold` and `recognition_threshold` values.
|
||||
- Define a `format` regex to filter out invalid detections.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="does-lpr-work-at-night" question="Does LPR work at night?">
|
||||
### Does LPR work at night?
|
||||
|
||||
Yes, but performance depends on camera quality, lighting, and infrared capabilities. Make sure your camera can capture clear images of plates at night.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="can-i-limit-lpr-to-specific-zones" question="Can I limit LPR to specific zones?">
|
||||
### Can I limit LPR to specific zones?
|
||||
|
||||
LPR, like other Frigate enrichments, runs at the camera level rather than the zone level. While you can't restrict LPR to specific zones directly, you can control when recognition runs by setting a `min_area` value to filter out smaller detections.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="how-can-i-match-known-plates-with-minor-variations" question="How can I match known plates with minor variations?">
|
||||
### How can I match known plates with minor variations?
|
||||
|
||||
Use `match_distance` to allow small character mismatches. Alternatively, define multiple variations in `known_plates`.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
### Performance and Troubleshooting
|
||||
|
||||
<FaqItem id="how-do-i-debug-lpr-issues" question="How do I debug LPR issues?">
|
||||
### How do I debug LPR issues?
|
||||
|
||||
Start with ["Why isn't my license plate being detected and recognized?"](#why-isnt-my-license-plate-being-detected-and-recognized). If you are still having issues, work through these steps.
|
||||
|
||||
@@ -688,7 +671,7 @@ lpr:
|
||||
3. Ensure your plates are being _detected_.
|
||||
|
||||
If you are using a Frigate+ or `license_plate` detecting model:
|
||||
- Watch the [Debug view](/usage/live#the-single-camera-view) to ensure that `license_plate` is being detected.
|
||||
- Watch the debug view (Settings --> Debug) to ensure that `license_plate` is being detected.
|
||||
- View MQTT messages for `frigate/events` to verify detected plates.
|
||||
- You may need to adjust your `min_score` and/or `threshold` for the `license_plate` object if your plates are not being detected.
|
||||
|
||||
@@ -697,28 +680,21 @@ lpr:
|
||||
- You may need to adjust your `detection_threshold` if your plates are not being detected.
|
||||
|
||||
4. Ensure the characters on detected plates are being _recognized_.
|
||||
- Check the **Plate recognition** inference time in Enrichment metrics (<NavPath path="System metrics > Enrichments" />). High inference times (> 100ms) could lead to poor recognition results, especially for dedicated LPR cameras where the plate crosses the frame quickly.
|
||||
- Enable `debug_save_plates` to save images of detected text on plates to the clips directory (`/media/frigate/clips/lpr`). Ensure these images are readable and the text is clear.
|
||||
- Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` or `motorcycle` label will change to the recognized plate when LPR is enabled and working.
|
||||
- Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="will-lpr-slow-down-my-system" question="Will LPR slow down my system?">
|
||||
### Will LPR slow down my system?
|
||||
|
||||
LPR's performance impact depends on your hardware. Ensure you have at least 4GB RAM and a capable CPU or GPU for optimal results. If you are running the Dedicated LPR Camera mode, resource usage will be higher compared to users who run a model that natively detects license plates. Tune your motion detection settings for your dedicated LPR camera so that the license plate detection model runs only when necessary.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="i-am-seeing-a-yolov9-plate-detection-metric-in-enrichment-metrics-but-i-have-a-frigate-or-custom-model-that-detects-license_plate-why-is-the-yolov9-model-running" question={<>I am seeing a YOLOv9 plate detection metric in Enrichment Metrics, but I have a Frigate+ or custom model that detects <code>license_plate</code>. Why is the YOLOv9 model running?</>}>
|
||||
### I am seeing a YOLOv9 plate detection metric in Enrichment Metrics, but I have a Frigate+ or custom model that detects `license_plate`. Why is the YOLOv9 model running?
|
||||
|
||||
The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.
|
||||
|
||||
If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="it-looks-like-frigate-picked-up-my-cameras-timestamp-or-overlay-text-as-the-license-plate-how-can-i-prevent-this" question="It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?">
|
||||
### It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?
|
||||
|
||||
This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
|
||||
|
||||
@@ -726,10 +702,6 @@ If you are using a model that natively detects `license_plate`, add an _object m
|
||||
|
||||
If you are not using a model that natively detects `license_plate` or you are using dedicated LPR camera mode, only a _motion mask_ over your text is required.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="i-see-error-running--model-in-my-logs-or-my-inference-time-is-very-high-how-can-i-fix-this" question={'I see "Error running ... model" in my logs, or my inference time is very high. How can I fix this?'}>
|
||||
### I see "Error running ... model" in my logs, or my inference time is very high. How can I fix this?
|
||||
|
||||
This usually happens when your GPU is unable to compile or use one of the LPR models. Set your `device` to `CPU` and try again. GPU acceleration only provides a slight performance increase, and the models are lightweight enough to run without issue on most CPUs.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
+75
-175
@@ -6,13 +6,12 @@ title: Live View
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
import FaqItem from "@site/src/components/FaqItem";
|
||||
|
||||
Frigate intelligently displays your camera streams on the Live view dashboard. By default, Frigate employs "smart streaming" where camera images update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any motion or active objects are detected, cameras seamlessly switch to a live stream.
|
||||
|
||||
### Live View technologies
|
||||
|
||||
Frigate intelligently uses three different streaming technologies to display your camera streams on the dashboard and the single camera view, switching between available modes based on network bandwidth, player errors, or required features like two-way talk. The highest quality and fluency of the Live view requires the bundled `go2rtc` to be [configured](/configuration/go2rtc).
|
||||
Frigate intelligently uses three different streaming technologies to display your camera streams on the dashboard and the single camera view, switching between available modes based on network bandwidth, player errors, or required features like two-way talk. The highest quality and fluency of the Live view requires the bundled `go2rtc` to be configured as shown in the [step by step guide](/guides/configuring_go2rtc).
|
||||
|
||||
The jsmpeg live view will use more browser and client GPU resources. Using go2rtc is highly recommended and will provide a superior experience.
|
||||
|
||||
@@ -34,7 +33,7 @@ If you are using go2rtc, you should adjust the following settings in your camera
|
||||
|
||||
- Video codec: **H.264** - provides the most compatible video codec with all Live view technologies and browsers. Avoid any kind of "smart codec" or "+" codec like _H.264+_ or _H.265+_. as these non-standard codecs remove keyframes (see below).
|
||||
- Audio codec: **AAC** - provides the most compatible audio codec with all Live view technologies and browsers that support audio.
|
||||
- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://web.archive.org/web/20251213190836/https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
|
||||
- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
|
||||
|
||||
The default video and audio codec on your camera may not always be compatible with your browser, which is why setting them to H.264 and AAC is recommended. See the [go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness) for codec support information.
|
||||
|
||||
@@ -89,18 +88,8 @@ Configure a "friendly name" for your stream followed by the go2rtc stream name.
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Camera configuration > Live playback" /> and select your camera.
|
||||
2. Under **Live stream names**, click **Add stream** to add a new entry.
|
||||
3. In the **Stream name** field, enter a friendly name that will appear in the Live UI's stream dropdown (e.g., `Main Stream`).
|
||||
4. In the **go2rtc stream** field, open the dropdown and select the go2rtc stream this name should map to (e.g., `test_cam`). The dropdown lists every stream configured under `go2rtc.streams`. If the go2rtc stream hasn't been created yet, you can type the name and choose **Use "..."** to save a custom value.
|
||||
5. Repeat for each additional stream you want to expose (e.g., `Sub Stream` → `test_cam_sub`).
|
||||
6. Use the trash icon on a row to remove a stream, then **Save** the section.
|
||||
|
||||
:::tip
|
||||
|
||||
Configure your go2rtc streams first under <NavPath path="Settings > System > go2rtc streams" /> so the dropdown is populated with valid options.
|
||||
|
||||
:::
|
||||
1. Navigate to <NavPath path="Settings > Camera configuration > Live playback" />, then select your camera.
|
||||
- Under **Live stream names**, add entries mapping a friendly name to each go2rtc stream name (e.g., `Main Stream` mapped to `test_cam`, `Sub Stream` mapped to `test_cam_sub`).
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -196,7 +185,7 @@ services:
|
||||
|
||||
:::
|
||||
|
||||
See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#module-webrtc) for more information about this.
|
||||
See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.8.3#module-webrtc) for more information about this.
|
||||
|
||||
### Two way talk
|
||||
|
||||
@@ -268,47 +257,19 @@ cameras:
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
### Camera state
|
||||
### Disabling cameras
|
||||
|
||||
Each camera has three possible states, surfaced as a status selector in **Settings → Global configuration → Camera management**:
|
||||
Cameras can be temporarily disabled through the Frigate UI and through [MQTT](/integrations/mqtt#frigatecamera_nameenabledset) to conserve system resources. When disabled, Frigate's ffmpeg processes are terminated — recording stops, object detection is paused, and the Live dashboard displays a blank image with a disabled message. Review items, tracked objects, and historical footage for disabled cameras can still be accessed via the UI.
|
||||
|
||||
- **On**: streams are processed normally. Object detection, recording, and Live view are active.
|
||||
- **Off**: Frigate's ffmpeg processes are paused. Recording stops, object detection is paused, and the Live dashboard displays a blank image with a "Camera is off" message. The camera is still visible in the Live dashboard and its past review items, tracked objects, and historical footage remain accessible via the UI. The Off state persists across Frigate restarts via a `.runtime_state.json` file alongside `config.yml` (see [Runtime toggle persistence](#runtime-toggle-persistence)).
|
||||
- **Disabled**: the change is saved to your configuration file (`enabled: False`). The camera stops immediately, Frigate stops ffmpeg processes, and all live and historical UI elements for the camera are no longer visible but remains retained on disk. The camera is still listed in **Settings → Global configuration → Camera management** so it can be re-enabled. **A restart of Frigate is required to bring a disabled camera back to On.**
|
||||
:::note
|
||||
|
||||
#### Turning a camera on or off
|
||||
Disabling a camera via the Frigate UI or MQTT is temporary and does not persist through restarts of Frigate.
|
||||
|
||||
Turning a camera off is temporary and does not require a restart. The available controls are:
|
||||
:::
|
||||
|
||||
- The power button in the single-camera Live view header
|
||||
- The right-click context menu on a camera tile on the Live dashboard
|
||||
- The Camera management settings pane (status set to **Off**)
|
||||
- The mobile settings drawer on the single-camera Live view (admin users only)
|
||||
- The [MQTT topic](/integrations/mqtt#frigatecamera_nameenabledset) `frigate/<camera_name>/enabled/set` with payload `ON` or `OFF`
|
||||
- The Home Assistant integration via the [`camera.turn_on` / `camera.turn_off` actions](/integrations/home-assistant#camera-api)
|
||||
For restreamed cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source).
|
||||
|
||||
#### Disabling a camera
|
||||
|
||||
Disabling a camera saves the change to your configuration file. Navigate to **Settings → Global configuration → Camera management** and set the camera's status to **Disabled**. Runtime processing stops immediately; the change persists across restarts.
|
||||
|
||||
Re-enabling a disabled camera requires a restart of Frigate so that the ffmpeg processes and other camera-scoped resources can be initialized. The UI will prompt you to restart when you switch a disabled camera back to On.
|
||||
|
||||
#### Restream behavior
|
||||
|
||||
For both Off and Disabled cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source).
|
||||
|
||||
#### Choosing Off versus Disabled
|
||||
|
||||
If you want a camera's historical data (review items, tracked objects, footage) to stay accessible in the UI while you stop processing, set the camera to **Off**. If you want the camera fully removed from the Live dashboard, review filters, and other UI surfaces, set it to **Disabled**. The Disabled state still keeps the camera in Camera management so it can be re-enabled later; if you want to remove all traces of a camera including its configuration, delete it via Camera management instead.
|
||||
|
||||
#### Runtime toggle persistence
|
||||
|
||||
The Live view toggles for **camera on/off**, **detect**, **recordings**, **snapshots**, and **audio detection** (along with the equivalent MQTT `/set` topics) write the new state to `.runtime_state.json` next to your `config.yml`. The file is replayed on Frigate startup so your last-known toggle states survive a restart. Two interactions worth knowing:
|
||||
|
||||
- **Settings UI saves win.** When you save a field through **Settings → Global configuration**, the matching entry is cleared from `.runtime_state.json` so the new value in your config file is the durable source.
|
||||
- **Switching profiles clears all runtime overrides.** Activating or deactivating a [profile](/configuration/profiles) is treated as a deliberate state change, so the file is wiped to avoid stale overrides replaying on top of the new profile.
|
||||
|
||||
If you hand-edit `config.yml` while runtime overrides exist, the overrides will still replay on restart. Delete `.runtime_state.json` to reset to the YAML-defined defaults.
|
||||
Note that disabling a camera through the config file (`enabled: False`) removes all related UI elements, including historical footage access. To retain access while disabling the camera, keep it enabled in the config and use the UI or MQTT to disable it temporarily.
|
||||
|
||||
### Live player error messages
|
||||
|
||||
@@ -334,7 +295,7 @@ When your browser runs into problems playing back your camera streams, it will l
|
||||
|
||||
- **stalled**
|
||||
- What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
|
||||
- What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in <NavPath path="Settings > UI" /> .
|
||||
- What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval — shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
|
||||
|
||||
- Possible console messages from the player code:
|
||||
- `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
|
||||
@@ -342,155 +303,94 @@ When your browser runs into problems playing back your camera streams, it will l
|
||||
|
||||
## Live view FAQ
|
||||
|
||||
### Getting Live View Working
|
||||
1. **Why don't I have audio in my Live view?**
|
||||
|
||||
<FaqItem id="why-dont-i-have-audio-in-my-live-view" question="Why don't I have audio in my Live view?">
|
||||
You must use go2rtc to hear audio in your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc.
|
||||
|
||||
You must use go2rtc to hear audio in your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc.
|
||||
Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc.
|
||||
|
||||
If the audio controls don't appear in the UI at all, verify that the Live view is actually using your go2rtc stream. If your go2rtc stream names don't match your Frigate camera name, you must map them with the `live -> streams` config (see [Setting Streams For Live UI](#setting-streams-for-live-ui) above); otherwise the UI falls back to the video-only jsmpeg player.
|
||||
2. **Frigate shows that my live stream is in "low bandwidth mode". What does this mean?**
|
||||
|
||||
Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc.
|
||||
Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
|
||||
|
||||
</FaqItem>
|
||||
When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. Continuous streaming mode does not have an automatic reset mechanism, but you can use the _Reset_ option to force a reload of your stream.
|
||||
|
||||
<FaqItem id="i-have-unmuted-some-cameras-on-my-dashboard-but-i-do-not-hear-sound-why" question="I have unmuted some cameras on my dashboard, but I do not hear sound. Why?">
|
||||
If you are using continuous streaming or you are loading more than a few high resolution streams at once on the dashboard, your browser may struggle to begin playback of your streams before the timeout. Frigate always prioritizes showing a live stream as quickly as possible, even if it is a lower quality jsmpeg stream. You can use the "Reset" link/button to try loading your high resolution stream again.
|
||||
|
||||
If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.
|
||||
Errors in stream playback (e.g., connection failures, codec issues, or buffering timeouts) that cause the fallback to low bandwidth mode (jsmpeg) are logged to the browser console for easier debugging. These errors may include:
|
||||
- Network issues (e.g., MSE or WebRTC network connection problems).
|
||||
- Unsupported codecs or stream formats (e.g., H.265 in WebRTC, which is not supported in some browsers).
|
||||
- Buffering timeouts or low bandwidth conditions causing fallback to jsmpeg.
|
||||
- Browser compatibility problems (e.g., iOS Safari limitations with MSE).
|
||||
|
||||
</FaqItem>
|
||||
To view browser console logs:
|
||||
1. Open the Frigate Live View in your browser.
|
||||
2. Open the browser's Developer Tools (F12 or right-click > Inspect > Console tab).
|
||||
3. Reproduce the error (e.g., load a problematic stream or simulate network issues).
|
||||
4. Look for messages prefixed with the camera name.
|
||||
|
||||
<FaqItem id="my-live-view-shows-a-black-screen-or-doesnt-load-but-the-debug-view-works-why" question="My live view shows a black screen or doesn't load, but the debug view works. Why?">
|
||||
These logs help identify if the issue is player-specific (MSE vs. WebRTC) or related to camera configuration (e.g., go2rtc streams, codecs). If you see frequent errors:
|
||||
- Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera-settings-recommendations)).
|
||||
- Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
|
||||
- Test with a different stream via the UI dropdown (if `live -> streams` is configured).
|
||||
- For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see (WebRTC Extra Configuration)(#webrtc-extra-configuration)).
|
||||
- If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.
|
||||
|
||||
The debug view plays the `detect` stream processed by Frigate itself, while the Live view plays your go2rtc stream directly in the browser. If the debug view works but the Live view doesn't, your browser usually can't decode what the camera is sending, most often H.265 video or an incompatible audio track.
|
||||
3. **It doesn't seem like my cameras are streaming on the Live dashboard. Why?**
|
||||
|
||||
Work through the [go2rtc troubleshooting guide](/troubleshooting/go2rtc#live-view-is-black-buffering-or-stuck-in-low-bandwidth-mode) to isolate the problem. Two fixes resolve the majority of cases:
|
||||
On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group.
|
||||
|
||||
1. Restream through go2rtc's FFmpeg module by prefixing your source with `ffmpeg:`, for example `- ffmpeg:rtsp://user:password@192.168.1.5:554/stream`.
|
||||
2. If that doesn't help, transcode to compatible codecs: `- ffmpeg:rtsp://user:password@192.168.1.5:554/stream#video=h264#audio=aac#hardware`.
|
||||
4. **I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it?**
|
||||
|
||||
</FaqItem>
|
||||
This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line.
|
||||
|
||||
<FaqItem id="how-do-i-get-the-best-live-view-experience-in-home-assistant" question="How do I get the best live view experience in Home Assistant?">
|
||||
5. **How does "smart streaming" work?**
|
||||
|
||||
For a full-resolution, low-latency live view in Home Assistant dashboards, use the [Advanced Camera Card](https://card.camera) with the [go2rtc live provider](https://card.camera/#/configuration/cameras/live-provider?id=go2rtc), which streams directly from Frigate's bundled go2rtc. This also supports audio and [two-way talk](#two-way-talk) on capable cameras. See the [Home Assistant integration docs](/integrations/home-assistant) for setup.
|
||||
Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream.
|
||||
|
||||
</FaqItem>
|
||||
This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
|
||||
|
||||
### Streaming Behavior
|
||||
Smart streaming depends on having your camera's motion `threshold` and `contour_area` config values dialed in. Use the Motion Tuner in Settings in the UI to tune these values in real-time.
|
||||
|
||||
<FaqItem id="how-does-smart-streaming-work" question={'How does "smart streaming" work?'}>
|
||||
This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
|
||||
|
||||
Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream.
|
||||
6. **I have unmuted some cameras on my dashboard, but I do not hear sound. Why?**
|
||||
|
||||
This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
|
||||
If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.
|
||||
|
||||
Smart streaming depends on having your camera's motion `threshold` and `contour_area` config values dialed in. Use the Motion Tuner in Settings in the UI to tune these values in real-time.
|
||||
7. **My camera streams have lots of visual artifacts / distortion.**
|
||||
|
||||
This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
|
||||
Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
|
||||
|
||||
</FaqItem>
|
||||
8. **Why does my camera stream switch aspect ratios on the Live dashboard?**
|
||||
|
||||
<FaqItem id="it-doesnt-seem-like-my-cameras-are-streaming-on-the-live-dashboard-why" question="It doesn't seem like my cameras are streaming on the Live dashboard. Why?">
|
||||
Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
|
||||
|
||||
On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group.
|
||||
To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
|
||||
|
||||
</FaqItem>
|
||||
Example: Resolutions from two streams
|
||||
- Mismatched (may cause aspect ratio switching on the dashboard):
|
||||
- Live/go2rtc stream: 1920x1080 (16:9)
|
||||
- Detect stream: 640x352 (~1.82:1, not 16:9)
|
||||
|
||||
<FaqItem id="frigate-shows-that-my-live-stream-is-in-low-bandwidth-mode-what-does-this-mean" question={'Frigate shows that my live stream is in "low bandwidth mode". What does this mean?'}>
|
||||
- Matched (prevents switching):
|
||||
- Live/go2rtc stream: 1920x1080 (16:9)
|
||||
- Detect stream: 640x360 (16:9)
|
||||
|
||||
Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
|
||||
You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
|
||||
|
||||
When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. Continuous streaming mode does not have an automatic reset mechanism, but you can use the _Reset_ option to force a reload of your stream.
|
||||
|
||||
If you are using continuous streaming or you are loading more than a few high resolution streams at once on the dashboard, your browser may struggle to begin playback of your streams before the timeout. Frigate always prioritizes showing a live stream as quickly as possible, even if it is a lower quality jsmpeg stream. You can use the "Reset" link/button to try loading your high resolution stream again.
|
||||
|
||||
Errors in stream playback (e.g., connection failures, codec issues, or buffering timeouts) that cause the fallback to low bandwidth mode (jsmpeg) are logged to the browser console for easier debugging. These errors may include:
|
||||
|
||||
- Network issues (e.g., MSE or WebRTC network connection problems).
|
||||
- Unsupported codecs or stream formats (e.g., H.265 in WebRTC, which is not supported in some browsers).
|
||||
- Buffering timeouts or low bandwidth conditions causing fallback to jsmpeg.
|
||||
- Browser compatibility problems (e.g., iOS Safari limitations with MSE).
|
||||
|
||||
To view browser console logs:
|
||||
|
||||
1. Open the Frigate Live View in your browser.
|
||||
2. Open the browser's Developer Tools (F12 or right-click > Inspect > Console tab).
|
||||
3. Reproduce the error (e.g., load a problematic stream or simulate network issues).
|
||||
4. Look for messages prefixed with the camera name.
|
||||
|
||||
These logs help identify if the issue is player-specific (MSE vs. WebRTC) or related to camera configuration (e.g., go2rtc streams, codecs). If you see frequent errors:
|
||||
|
||||
- Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera-settings-recommendations)).
|
||||
- Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
|
||||
- Test with a different stream via the UI dropdown (if `live -> streams` is configured).
|
||||
- For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see [WebRTC Extra Configuration](#webrtc-extra-configuration)).
|
||||
- If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="why-is-my-live-view-delayed-or-lagging-behind-real-time" question="Why is my live view delayed or lagging behind real time?">
|
||||
|
||||
A delay when a stream first starts is usually caused by your camera's I-frame (keyframe) interval. Playback cannot begin until a keyframe arrives, so an interval set higher than your camera's frame rate makes the stream take longer to start. Set the I-frame interval to match the frame rate (or "1x" on Reolink) per the [camera settings recommendations](#camera-settings-recommendations).
|
||||
|
||||
A stream that starts on time but falls further behind live is buffering, which is usually the browser struggling to decode too many high-resolution streams at once. Select a lower-bandwidth substream for your dashboards (see [Setting Streams For Live UI](#setting-streams-for-live-ui)), reduce the number of streams open at once, or improve the network connection between your browser and Frigate. Frigate's player automatically speeds up playback to catch up to live after buffering, and falls back to low bandwidth mode if it stalls for too long. The _Reset_ option forces a fresh connection at the live edge.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="why-does-frigate-prefer-mse-over-webrtc-for-live-view" question="Why does Frigate prefer MSE over WebRTC for live view?">
|
||||
|
||||
Frigate prefers MSE because it delivers a better out-of-the-box experience than WebRTC on nearly every axis that matters for a security camera system. MSE is an open standard optimized and supported by all modern browsers, works without any extra configuration (WebRTC requires port forwarding and candidate setup, and lacks H.265 support in some browsers), and requires no internet access for NAT traversal. More importantly, MSE runs over TCP, so every frame arrives and is decoded in order, so nothing is ever silently skipped. WebRTC optimizes for latency over UDP by discarding late or incomplete frames, which works against you on cellular or spotty Wi-Fi: you can end up with frozen video, visual corruption, or gaps in the feed without ever knowing you missed something. Frigate's enhanced MSE player has adaptive speed playback and has been tuned for latency and connection robustness that meets or exceeds WebRTC, so you get near-real-time playback with a guarantee that when the video plays, every frame is actually there - which, for an NVR whose whole purpose is letting you see what happened, matters more than shaving fractions of a second off a latency number. That's why Frigate defaults to MSE and reserves WebRTC for cases that require it, like two-way talk.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
### Video Quality Issues
|
||||
|
||||
<FaqItem id="i-see-a-strange-diagonal-line-on-my-live-view-but-my-recordings-look-fine-how-can-i-fix-it" question="I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it?">
|
||||
|
||||
This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="my-camera-streams-have-lots-of-visual-artifacts-or-distortion" question="My camera streams have lots of visual artifacts / distortion.">
|
||||
|
||||
Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="why-does-my-camera-stream-switch-aspect-ratios-on-the-live-dashboard" question="Why does my camera stream switch aspect ratios on the Live dashboard?">
|
||||
|
||||
Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
|
||||
|
||||
To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
|
||||
|
||||
Example: Resolutions from two streams
|
||||
|
||||
- Mismatched (may cause aspect ratio switching on the dashboard):
|
||||
- Live/go2rtc stream: 1920x1080 (16:9)
|
||||
- Detect stream: 640x352 (~1.82:1, not 16:9)
|
||||
|
||||
- Matched (prevents switching):
|
||||
- Live/go2rtc stream: 1920x1080 (16:9)
|
||||
- Detect stream: 640x360 (16:9)
|
||||
|
||||
You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
|
||||
|
||||
```yaml
|
||||
cameras:
|
||||
front_door:
|
||||
detect:
|
||||
width: 640
|
||||
height: 360 # set this to 360 instead of 352
|
||||
ffmpeg:
|
||||
inputs:
|
||||
- path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
|
||||
roles:
|
||||
- record
|
||||
- path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
|
||||
roles:
|
||||
- detect
|
||||
```
|
||||
|
||||
The same applies to your `record` stream: if its aspect ratio differs from your `detect` stream, your recordings will appear in a different shape than the live view. For consistent framing across live view and recordings, use the same aspect ratio for all of a camera's streams (the resolution can still differ).
|
||||
|
||||
</FaqItem>
|
||||
```yaml
|
||||
cameras:
|
||||
front_door:
|
||||
detect:
|
||||
width: 640
|
||||
height: 360 # set this to 360 instead of 352
|
||||
ffmpeg:
|
||||
inputs:
|
||||
- path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
|
||||
roles:
|
||||
- record
|
||||
- path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
|
||||
roles:
|
||||
- detect
|
||||
```
|
||||
|
||||
@@ -7,11 +7,9 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
Frigate has two kinds of masks: motion masks and object filter masks. Both are narrow tools for fine-tuning, **not for hiding an area from Frigate**. Masks should be used sparingly; in most cases where users reach for one, a [zone](zones.md) with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) is the right tool instead. See [Which tool do I need?](#which-tool-do-i-need) and [Common mistakes](#common-mistakes) below if you're new to Frigate's mask behavior.
|
||||
|
||||
## Motion masks
|
||||
|
||||
Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the [Debug view](/usage/live#the-single-camera-view) with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. _Over-masking will make it more difficult for objects to be tracked._
|
||||
Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the Debug feed (Settings --> Debug) with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. _Over-masking will make it more difficult for objects to be tracked._
|
||||
|
||||
See [further clarification](#further-clarification) below on why you may not want to use a motion mask.
|
||||
|
||||
@@ -23,16 +21,7 @@ Object filter masks can be used to filter out stubborn false positives in fixed
|
||||
|
||||
|
||||
|
||||
## Which tool do I need?
|
||||
|
||||
| What you're trying to do | Recommended tool | How it works |
|
||||
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Only get alerts/detections for activity in the areas you care about, ignoring activity elsewhere (e.g., alert when someone enters your yard, but not when they walk past on the sidewalk) | A [zone](zones.md) combined with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) | Frigate keeps detecting and tracking activity everywhere in the frame, but a review item is only created once the bottom-center of an object's bounding box enters a required zone. |
|
||||
| Stop a stubborn false positive at a specific fixed spot (e.g., a tree base that keeps being detected as a person) | An **object filter mask** for that object type | Any detection of that object type whose bounding-box bottom-center lands inside the mask is treated as a false positive and discarded. |
|
||||
| Ignore motion in an area that obviously isn't an object of interest (e.g., the camera timestamp, sky, flags, treetops swaying) | A **motion mask** | Motion inside the mask is ignored when deciding whether to run object detection. Objects can still be detected in a motion masked area if motion elsewhere in the frame triggers detection. |
|
||||
| Stop tracking an object type altogether on this camera (e.g., you never care about cats) | Remove the object from the camera's [`objects.track`](objects.md) list | Frigate skips this object type entirely on this camera, regardless of where it appears. |
|
||||
|
||||
## Using the mask creator
|
||||
## Creating masks
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
@@ -135,14 +124,3 @@ This is what `required_zones` are for. You should define a zone (remember this i
|
||||
> Maybe my specific situation just warrants this. I've just been having a hard time understanding the relevance of this information - it seems to be that it's exactly what would be expected when "masking out" an area of ANY image.
|
||||
|
||||
That may be the case for you. Frigate will definitely work harder tracking people on the sidewalk to make sure it doesn't miss anyone who steps foot on your stoop. The trade off with the way you have it now is slower recognition of objects and potential misses. That may be acceptable based on your needs. Also, if your resolution is low enough on the detect stream, your regions may already be so big that they grab the entire object anyway.
|
||||
|
||||
## Common mistakes
|
||||
|
||||
**"I added a motion mask to ignore my driveway/sidewalk."**
|
||||
A motion mask doesn't hide an area from Frigate. Objects can still be detected and tracked inside a masked area. The mask only stops motion _in that area_ from triggering object detection. If you want activity on the sidewalk to never produce a review item, define a [zone](zones.md) over the area you DO care about (your stoop, your driveway) and add it to [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones). Frigate will still see people on the sidewalk, but it won't create an alert until they cross into the zone.
|
||||
|
||||
**"I added an object filter mask because I don't care about cars in my yard."**
|
||||
Object filter masks are for stubborn false positives at fixed locations, not for filtering whole areas or whole object types. If you only want alerts when a car enters the driveway, use a [zone](zones.md) with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones). If you don't care about a whole object type on this camera, remove it from [`objects.track`](objects.md).
|
||||
|
||||
**"I masked everything except a thin strip on my stoop."**
|
||||
Heavy masking hurts tracking. Frigate uses motion near a tracked object's previous bounding box to decide where to look in the next frame; with most of the frame masked, an object walking from an unmasked area into a masked one effectively disappears and gets picked up as a "new" object when it reappears. For example: someone walks down your sidewalk, stops under a tree (masked area) to tie their shoe, then continues. Frigate sees that as two separate people and can create two separate review items. Because Frigate needs several consecutive frames above the confidence threshold to commit to a detection, each re-appearance can also delay or miss alerts. Use [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) for "only alert me about this spot" and leave the surrounding area unmasked so tracking stays intact.
|
||||
|
||||
@@ -59,8 +59,6 @@ Metrics are available at `/api/metrics` by default. No additional Frigate config
|
||||
- `frigate_storage_used_bytes{storage=""}` - Storage used bytes
|
||||
- `frigate_storage_mount_type{mount_type="", storage=""}` - Storage mount type info
|
||||
|
||||
These gauges report the operating system's figures for the whole filesystem (the same numbers as `df`), not Frigate's own recording footprint. For how this differs from the recordings usage shown in the UI, see [Understanding storage usage](/configuration/record#understanding-storage-usage).
|
||||
|
||||
### Service Metrics
|
||||
|
||||
- `frigate_service_uptime_seconds` - Uptime in seconds
|
||||
|
||||
@@ -11,7 +11,7 @@ import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
Frigate uses motion detection as a first line check to see if there is anything happening in the frame worth checking with object detection.
|
||||
|
||||
Once motion is detected, it tries to group up nearby areas of motion together in hopes of identifying a rectangle in the image that will capture the area worth inspecting. These are the red "motion boxes" you see in the [debug viewer](/usage/live#the-single-camera-view).
|
||||
Once motion is detected, it tries to group up nearby areas of motion together in hopes of identifying a rectangle in the image that will capture the area worth inspecting. These are the red "motion boxes" you see in the debug viewer.
|
||||
|
||||
## The Goal
|
||||
|
||||
@@ -66,7 +66,7 @@ motion:
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
Lower values mean motion detection is more sensitive to changes in color, making it more likely for example to detect motion when a brown dog blends in with a brown fence or a person wearing a red shirt blends in with a red car. If the threshold is too low however, it may detect things like grass blowing in the wind, shadows, etc. to be detected as motion.
|
||||
Lower values mean motion detection is more sensitive to changes in color, making it more likely for example to detect motion when a brown dogs blends in with a brown fence or a person wearing a red shirt blends in with a red car. If the threshold is too low however, it may detect things like grass blowing in the wind, shadows, etc. to be detected as motion.
|
||||
|
||||
Watching the motion boxes in the debug view, increase the threshold until you only see motion that is visible to the eye. Once this is done, it is important to test and ensure that desired motion is still detected.
|
||||
|
||||
@@ -151,7 +151,7 @@ motion:
|
||||
|
||||
Large changes in motion like PTZ moves and camera switches between Color and IR mode should result in a pause in object detection. `lightning_threshold` defines the percentage of the image used to detect these substantial changes. Increasing this value makes motion detection more likely to treat large changes (like IR mode switches) as valid motion. Decreasing it makes motion detection more likely to ignore large amounts of motion, such as a person approaching a doorbell camera.
|
||||
|
||||
Note that `lightning_threshold` does **not** stop motion-based recordings from being saved. It only prevents additional motion analysis after the threshold is exceeded, reducing false positive object detections during high-motion periods (e.g. storms or PTZ sweeps) without interfering with recordings.
|
||||
Note that `lightning_threshold` does **not** stop motion-based recordings from being saved — it only prevents additional motion analysis after the threshold is exceeded, reducing false positive object detections during high-motion periods (e.g. storms or PTZ sweeps) without interfering with recordings.
|
||||
|
||||
:::warning
|
||||
|
||||
@@ -194,10 +194,6 @@ This option is handy when you want to prevent large transient changes from trigg
|
||||
|
||||
:::warning
|
||||
|
||||
When the skip threshold is exceeded, **no motion is reported** for that frame, meaning **nothing is recorded** for that frame. That means you can miss something important, like a PTZ camera auto-tracking an object or activity while the camera is moving. If you prefer to guarantee that every frame is saved, leave this unset and accept occasional recordings containing scene noise. They typically only take up a few megabytes and are quick to scan in the timeline UI.
|
||||
When the skip threshold is exceeded, **no motion is reported** for that frame, meaning **nothing is recorded** for that frame. That means you can miss something important, like a PTZ camera auto-tracking an object or activity while the camera is moving. If you prefer to guarantee that every frame is saved, leave this unset and accept occasional recordings containing scene noise — they typically only take up a few megabytes and are quick to scan in the timeline UI.
|
||||
|
||||
:::
|
||||
|
||||
## Reviewing Detected Motion
|
||||
|
||||
To review what the detector picked up, or to search past recordings for motion in a specific region, see [Reviewing Motion](/usage/review#reviewing-motion) on the Review page.
|
||||
|
||||
@@ -6,7 +6,6 @@ title: Notifications
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
import FaqItem from "@site/src/components/FaqItem";
|
||||
|
||||
# Notifications
|
||||
|
||||
@@ -22,7 +21,7 @@ Push notifications require internet access from the Frigate server to the browse
|
||||
|
||||
In order to use notifications the following requirements must be met:
|
||||
|
||||
- Frigate must be accessed via a secure `https` connection while signed in as a Frigate user ([see the authorization docs](/configuration/authentication)).
|
||||
- Frigate must be accessed via a secure `https` connection ([see the authorization docs](/configuration/authentication)).
|
||||
- A supported browser must be used. Currently Chrome, Firefox, and Safari are known to be supported.
|
||||
- In order for notifications to be usable externally, Frigate must be accessible externally.
|
||||
- For iOS devices, some users have also indicated that the Notifications switch needs to be enabled in iOS Settings --> Apps --> Safari --> Advanced --> Features.
|
||||
@@ -86,13 +85,7 @@ cameras:
|
||||
|
||||
### Registration
|
||||
|
||||
Once notifications are enabled, press the `Register This Device` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
|
||||
|
||||
:::warning
|
||||
|
||||
Each registration is attached to the Frigate user account you are signed in as, so you must register over a secure connection to the authenticated port (`8971`). Reverse proxies and tunnels should point at port `8971`.
|
||||
|
||||
:::
|
||||
Once notifications are enabled, press the `Register for Notifications` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
|
||||
|
||||
## Supported Notifications
|
||||
|
||||
@@ -111,62 +104,3 @@ Different platforms handle notifications differently, some settings changes may
|
||||
### Android
|
||||
|
||||
Most Android phones have battery optimization settings. To get reliable Notification delivery the browser (Chrome, Firefox) should have battery optimizations disabled. If Frigate is running as a PWA then the Frigate app should have battery optimizations disabled as well.
|
||||
|
||||
## Notifications FAQ
|
||||
|
||||
<FaqItem id="how-do-i-debug-notifications-issues" question="How do I debug notifications issues?">
|
||||
|
||||
Push notifications involve Frigate, your browser, and your browser vendor's push service, so it helps to work from the server outward.
|
||||
|
||||
1. Enable debug logs for the push client by adding `frigate.comms.webpush: debug` to your `logger` configuration. Restart Frigate after this change.
|
||||
|
||||
```yaml
|
||||
logger:
|
||||
default: info
|
||||
logs:
|
||||
# highlight-next-line
|
||||
frigate.comms.webpush: debug
|
||||
```
|
||||
|
||||
These logs show exactly where a notification stopped, including:
|
||||
- `Email must be provided for push notifications to be sent` means the global `email` field is empty and nothing will ever be sent.
|
||||
- `Sending test notification` and `Sending push notification for <camera>, review ID <id>` mean Frigate handed the message off to the push service.
|
||||
- `Skipping notification for <camera> - in global cooldown period` (or `camera-specific cooldown period`) means your [cooldown](#configuration) values suppressed it.
|
||||
- `Notifications for <camera> are currently suspended` means notifications were suspended from <NavPath path="Settings > Notifications" /> or MQTT.
|
||||
- `Notification endpoint expired for <user>, received 410` means that device's subscription is no longer valid and it must be re-registered.
|
||||
- `Failed to send notification to <user> :: <status>` means the push service rejected the message. A `401` or `403` usually points at a VAPID or `email` problem, and a `5xx` is a problem on the push service's end.
|
||||
- If you see no messages at all when an alert occurs, the notification was never queued. Confirm an actual **alert** was created (notifications are not sent for detections), and that notifications are enabled both globally and for that camera.
|
||||
|
||||
2. Verify the basics that most reports come down to:
|
||||
- Frigate must be reached over `https` with a certificate your device trusts. Browsers silently refuse to register a service worker otherwise, and a self-signed certificate that is not installed as trusted on the device will fail.
|
||||
- On iOS, notifications only work when Frigate has been installed to the Home Screen via **Share > Add to Home Screen** and opened from that icon. Safari and Chrome tabs cannot receive web push on iOS.
|
||||
- Each device must be registered individually, and Frigate must be restarted after registering before anything can be sent, including test notifications.
|
||||
- The Frigate server needs outbound internet access to the browser vendor's push service. See [Network Requirements](/frigate/network_requirements#push-notifications).
|
||||
|
||||
3. Test from the UI. Use the `Send a test notification` button in <NavPath path="Settings > Notifications" />. If the log shows `Sending test notification` but nothing arrives on the device, the problem is between the push service and your device rather than in Frigate.
|
||||
|
||||
4. Check the browser side on the device that is not receiving notifications:
|
||||
- Confirm the site's notification permission is set to **Allow** in your browser or OS settings, and that a focus/do not disturb mode is not hiding them.
|
||||
- In desktop browsers, open Developer Tools > Application > Service Workers and confirm `notifications-worker.js` is registered and activated. Unregistering it and registering the device again will rebuild a broken subscription.
|
||||
- Check the browser console and your reverse proxy logs for failures loading `/notifications-worker.js` or errors on `/api/notifications/register`.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="why-did-notifications-stop-arriving-after-working-for-a-while" question="Why did notifications stop arriving after working for a while?">
|
||||
|
||||
Push subscriptions are issued by the browser vendor and can be revoked, most often after a browser update, after clearing site data, or when a device has been offline for an extended period. When this happens the device still appears registered in Frigate, but the push service rejects the message. The debug logs will show `Notification endpoint expired` with a `404` or `410` status.
|
||||
|
||||
Unregister and re-register the affected device from <NavPath path="Settings > Notifications" />, then restart Frigate.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="why-am-i-not-getting-notifications-for-one-specific-camera" question="Why am I not getting notifications for one specific camera?">
|
||||
|
||||
Work through these in order:
|
||||
|
||||
- Notifications are only sent for **alerts**. If the camera is producing detections instead, adjust the camera's `review > alerts > labels` so the objects you care about are classified as alerts.
|
||||
- Confirm notifications are enabled for that camera in <NavPath path="Settings > Camera configuration > Notifications" />.
|
||||
- Check the camera's `cooldown` value, and remember that the global cooldown applies across all cameras. A busy camera can consume the global cooldown and suppress a quieter one.
|
||||
- If [authentication](/configuration/authentication) is enabled with roles, users only receive notifications for the cameras their role grants access to.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -7,7 +7,7 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
There are several types of object filters that can be used to reduce [false positive](/frigate/glossary#false-positive) rates.
|
||||
There are several types of object filters that can be used to reduce false positive rates.
|
||||
|
||||
## Object Scores
|
||||
|
||||
@@ -26,9 +26,9 @@ In frame 2, the score is below the `min_score` value, so Frigate ignores it and
|
||||
|
||||
The **top score** is the highest computed score the tracked object has ever reached during its lifetime. Because the computed score rises and falls as new frames come in, the top score can be thought of as the peak confidence Frigate had in the object. In Frigate's UI (such as the Tracking Details pane in Explore), you may see all three values:
|
||||
|
||||
- **Score**: the raw detector score for that single frame.
|
||||
- **Computed Score**: the median of the most recent score history at that moment. This is the value compared against `threshold`.
|
||||
- **Top Score**: the highest computed score reached so far for the tracked object.
|
||||
- **Score** — the raw detector score for that single frame.
|
||||
- **Computed Score** — the median of the most recent score history at that moment. This is the value compared against `threshold`.
|
||||
- **Top Score** — the highest computed score reached so far for the tracked object.
|
||||
|
||||
### Minimum Score
|
||||
|
||||
@@ -36,7 +36,7 @@ Any detection below `min_score` will be immediately thrown out and never tracked
|
||||
|
||||
### Threshold
|
||||
|
||||
`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create a tracked object. If `threshold` is too high then true positive tracked objects may be missed due to the object never scoring high enough.
|
||||
`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create an tracked object. If `threshold` is too high then true positive tracked objects may be missed due to the object never scoring high enough.
|
||||
|
||||
## Configuring Object Scores
|
||||
|
||||
@@ -144,8 +144,8 @@ cameras:
|
||||
|
||||
### Zones
|
||||
|
||||
[Required zones](/configuration/zones.md#restricting-alerts-and-detections-to-specific-zones) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create tracked objects for objects that enter the zone.
|
||||
[Required zones](/configuration/zones.md) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create tracked objects for objects that enter the zone.
|
||||
|
||||
### Object Masks
|
||||
|
||||
[Object Filter Masks](/configuration/masks#object-filter-masks) are a last resort but can be useful when false positives are in the relatively same place but can not be filtered due to their size or shape. Object filter masks can be configured in <NavPath path="Settings > Camera configuration > Masks / Zones" />.
|
||||
[Object Filter Masks](/configuration/masks) are a last resort but can be useful when false positives are in the relatively same place but can not be filtered due to their size or shape. Object filter masks can be configured in <NavPath path="Settings > Camera configuration > Masks / Zones" />.
|
||||
|
||||
@@ -158,4 +158,4 @@ Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use yo
|
||||
- EdgeTPU Model: `/edgetpu_model.tflite`
|
||||
- Labels: `/labelmap.txt`
|
||||
|
||||
You also need to update the [model config](advanced/system.md#model) if they differ from the defaults.
|
||||
You also need to update the [model config](advanced.md#model) if they differ from the defaults.
|
||||
|
||||
@@ -14,13 +14,13 @@ Profiles allow you to define named sets of camera configuration overrides that c
|
||||
Profiles operate as a two-level system:
|
||||
|
||||
1. **Profile definitions** are declared at the top level of your config under `profiles`. Each definition has a machine name (the key) and a `friendly_name` for display in the UI.
|
||||
2. **Camera profile overrides** are declared under each camera's `profiles` section, keyed by the profile name. Only the settings you want to change need to be specified. Everything else is inherited from the camera's base configuration.
|
||||
2. **Camera profile overrides** are declared under each camera's `profiles` section, keyed by the profile name. Only the settings you want to change need to be specified — everything else is inherited from the camera's base configuration.
|
||||
|
||||
When a profile is activated, Frigate merges each camera's profile overrides on top of its base config. When the profile is deactivated, all cameras revert to their original settings. Only one profile can be active at a time.
|
||||
|
||||
:::info
|
||||
|
||||
Profile changes are applied in-memory and take effect immediately. No restart is required. The active profile is persisted across Frigate restarts (stored in the `/config/.profiles` file).
|
||||
Profile changes are applied in-memory and take effect immediately — no restart is required. The active profile is persisted across Frigate restarts (stored in the `/config/.profiles` file).
|
||||
|
||||
:::
|
||||
|
||||
@@ -33,10 +33,10 @@ The easiest way to define profiles is to use the Frigate UI. Profiles can also b
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. **Create a profile**: Navigate to <NavPath path="Settings > Global configuration > Profiles" />. Click the **Add Profile** button, enter a name (and optionally a profile ID).
|
||||
2. **Configure overrides**: Navigate to a camera configuration section (e.g. Motion detection, Record, Notifications). In the top right, two buttons will appear - choose a camera and a profile from the profile selector to edit overrides for that camera and section. Only the fields you change will be stored as overrides. Fields that require a restart are hidden since profiles are applied at runtime. You can click the **Remove Profile Override** button to clear overrides.
|
||||
3. **Activate a profile**: Use the **Profiles** option in Frigate's main menu to choose a profile. Alternatively, in Settings, navigate to <NavPath path="Settings > Global configuration > Profiles" />, then choose a profile in the Active Profile dropdown to activate it. The active profile is also shown in the status bar at the bottom of the screen on desktop browsers.
|
||||
4. **Delete a profile**: Navigate to <NavPath path="Settings > Global configuration > Profiles" />, then click the trash icon for a profile. This removes the profile definition and all camera overrides associated with it.
|
||||
1. **Create a profile** — Navigate to <NavPath path="Settings > Camera configuration > Profiles" />. Click the **Add Profile** button, enter a name (and optionally a profile ID).
|
||||
2. **Configure overrides** — Navigate to a camera configuration section (e.g. Motion detection, Record, Notifications). In the top right, two buttons will appear - choose a camera and a profile from the profile selector to edit overrides for that camera and section. Only the fields you change will be stored as overrides — fields that require a restart are hidden since profiles are applied at runtime. You can click the **Remove Profile Override** button to clear overrides.
|
||||
3. **Activate a profile** — Use the **Profiles** option in Frigate's main menu to choose a profile. Alternatively, in Settings, navigate to <NavPath path="Settings > Camera configuration > Profiles" />, then choose a profile in the Active Profile dropdown to activate it. The active profile is also shown in the status bar at the bottom of the screen on desktop browsers.
|
||||
4. **Delete a profile** — Navigate to <NavPath path="Settings > Camera configuration > Profiles" />, then click the trash icon for a profile. This removes the profile definition and all camera overrides associated with it.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -126,11 +126,7 @@ Only the fields you explicitly set in a profile override are applied. All other
|
||||
|
||||
## Activating Profiles
|
||||
|
||||
Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), the [HTTP API](../integrations/api/camera-set-camera-camera-name-set-feature-sub-command-put.api.mdx), or the Home Assistant integration.
|
||||
|
||||
In the Frigate UI, open the Settings cog and select **Profiles** from the submenu to see all defined profiles. From there you can activate any profile or deactivate the current one. The active profile is indicated in the UI so you always know which profile is in effect.
|
||||
|
||||
Activating or deactivating a profile clears any [runtime toggle overrides](/configuration/live#runtime-toggle-persistence) so the profile's settings aren't silently undone by a stale toggle from before the switch.
|
||||
Profiles can be activated and deactivated from the Frigate UI. Open the Settings cog and select **Profiles** from the submenu to see all defined profiles. From there you can activate any profile or deactivate the current one. The active profile is indicated in the UI so you always know which profile is in effect.
|
||||
|
||||
## Example: Home / Away Setup
|
||||
|
||||
@@ -139,10 +135,10 @@ A common use case is having different detection and notification settings based
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Global configuration > Profiles" /> and create two profiles: **Home** and **Away**.
|
||||
1. Navigate to <NavPath path="Settings > Camera configuration > Profiles" /> and create two profiles: **Home** and **Away**.
|
||||
2. From to the Camera configuration section in Settings, choose the **front_door** camera, and select the **Away** profile from the profile dropdown. Then, enable notifications from the Notifications pane, and set alert labels to `person` and `car` from the Review pane. Then, from the profile dropdown choose **Home** profile, then navigate to Notifications to disable notifications.
|
||||
3. For the **indoor_cam** camera, perform similar steps - configure the **Away** profile to enable the camera, detection, and recording. Configure the **Home** profile to disable the camera entirely for privacy.
|
||||
4. Activate the desired profile from <NavPath path="Settings > Global configuration > Profiles" /> or from the **Profiles** option in Frigate's main menu.
|
||||
4. Activate the desired profile from <NavPath path="Settings > Camera configuration > Profiles" /> or from the **Profiles** option in Frigate's main menu.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -211,48 +207,3 @@ In this example:
|
||||
- **Away profile**: The front door camera enables notifications and tracks specific alert labels. The indoor camera is fully enabled with detection and recording.
|
||||
- **Home profile**: The front door camera disables notifications. The indoor camera is completely disabled for privacy.
|
||||
- **No profile active**: All cameras use their base configuration values.
|
||||
|
||||
## FAQ
|
||||
|
||||
### Can I define a zone or mask in a profile but not have it in the base config?
|
||||
|
||||
No. Profiles are pure overrides. Every zone and mask defined under a profile must reference an entry that already exists on the base camera config. Configurations that introduce profile-only zones or masks are rejected at startup.
|
||||
|
||||
If you want a zone or mask to be active only under a specific profile, define it on the base config with `enabled: false`, then enable it in that profile's overrides.
|
||||
|
||||
### How do I revert a profile zone or mask override back to the base configuration?
|
||||
|
||||
Delete the override. In the Frigate UI, edit the profile and use the "Revert override" action (the trash can icon) on the zone or mask. The base entry is left untouched, and once the override is removed the profile inherits the base values for that zone or mask.
|
||||
|
||||
### Can multiple profiles be active at the same time?
|
||||
|
||||
No. Only one profile can be active at a time. Activating a new profile automatically deactivates the current one.
|
||||
|
||||
### What happens to my profile overrides if I delete a zone or mask from the base?
|
||||
|
||||
When you delete a base zone or mask in the Frigate UI, any profile overrides for that entry are deleted automatically as part of the same operation. If you remove a base entry by editing your config file directly and leave a profile override behind, the config will fail validation at startup until the orphaned override is removed as well.
|
||||
|
||||
### How do I make a YAML profile track no objects at all?
|
||||
|
||||
Set the tracked object list explicitly to an empty list in the profile:
|
||||
|
||||
```yaml
|
||||
cameras:
|
||||
front_door:
|
||||
profiles:
|
||||
home:
|
||||
objects:
|
||||
track: []
|
||||
```
|
||||
|
||||
Leaving the `objects` section empty (or omitting `track`) does not clear the list. Empty sections set no fields, so the profile inherits the full tracked object list from the base config, including anything set at the global level. The same applies to other lists, such as `audio.listen`.
|
||||
|
||||
### Why are some settings missing when I configure a profile override?
|
||||
|
||||
Fields that require a Frigate restart to take effect cannot be overridden by profiles, since profiles are applied at runtime without restarting. Those fields are hidden when editing a profile override and can only be changed on the base configuration.
|
||||
|
||||
### Can I schedule profiles to be enabled or disabled at certain times?
|
||||
|
||||
Not within Frigate itself. Frigate is an NVR, not an automation platform, so it intentionally does not include a scheduler for activating profiles. Instead, activate profiles from an automation platform that already handles time- and event-based triggers well, such as [Home Assistant](https://www.home-assistant.io/) or [Node-RED](https://nodered.org/). These integrate with Frigate and give you far more robust and flexible scheduling than a built-in scheduler could.
|
||||
|
||||
If you prefer something lightweight, a simple script driven by a cron job that toggles profiles on a schedule works too.
|
||||
|
||||
@@ -11,12 +11,6 @@ Recordings can be enabled and are stored at `/media/frigate/recordings`. The fol
|
||||
|
||||
New recording segments are written from the camera stream to cache, they are only moved to disk if they match the setup recording retention policy.
|
||||
|
||||
:::tip
|
||||
|
||||
To keep a specific clip beyond your retention window, [export](/usage/exports) it rather than increasing retention for the whole camera. Exports are saved separately and are never removed by retention.
|
||||
|
||||
:::
|
||||
|
||||
H265 recordings can be viewed in Chrome 108+, Edge and Safari only. All other browsers require recordings to be encoded with H264.
|
||||
|
||||
## Common recording configurations
|
||||
@@ -170,9 +164,9 @@ record:
|
||||
|
||||
The `pre_capture` and `post_capture` values define the **time window** around a review item, but only recording segments that also match the configured **retention mode** are actually kept on disk.
|
||||
|
||||
- **`mode: all`**: Retains every segment within the capture window, regardless of whether motion was detected.
|
||||
- **`mode: motion`** (default): Only retains segments within the capture window that contain motion. This includes segments with active tracked objects, since object motion implies motion. Segments without any motion are discarded even if they fall within the pre/post capture range.
|
||||
- **`mode: active_objects`**: Only retains segments within the capture window where tracked objects were actively moving. Segments with general motion but no active objects are discarded.
|
||||
- **`mode: all`** — Retains every segment within the capture window, regardless of whether motion was detected.
|
||||
- **`mode: motion`** (default) — Only retains segments within the capture window that contain motion. This includes segments with active tracked objects, since object motion implies motion. Segments without any motion are discarded even if they fall within the pre/post capture range.
|
||||
- **`mode: active_objects`** — Only retains segments within the capture window where tracked objects were actively moving. Segments with general motion but no active objects are discarded.
|
||||
|
||||
This means that with the default `motion` mode, you may see less footage than the configured pre/post capture duration if parts of the capture window had no motion.
|
||||
|
||||
@@ -197,7 +191,11 @@ Because recording segments are written in 10 second chunks, pre-capture timing d
|
||||
|
||||
### Where to view pre/post capture footage
|
||||
|
||||
Pre and post capture footage is included in the **recording timeline**, visible in the History view. Note that pre/post capture settings only affect which recording segments are **retained on disk**. They do not change the start and end points shown in the UI. The History view will still center on the review item's actual time range, but you can scrub backward and forward through the retained pre/post capture footage on the timeline. The Explore view shows object-specific clips that are trimmed to when the tracked object was actually visible, so pre/post capture time will not be reflected there.
|
||||
Pre and post capture footage is included in the **recording timeline**, visible in the History view. Note that pre/post capture settings only affect which recording segments are **retained on disk** — they do not change the start and end points shown in the UI. The History view will still center on the review item's actual time range, but you can scrub backward and forward through the retained pre/post capture footage on the timeline. The Explore view shows object-specific clips that are trimmed to when the tracked object was actually visible, so pre/post capture time will not be reflected there.
|
||||
|
||||
## Will Frigate delete old recordings if my storage runs out?
|
||||
|
||||
As of Frigate 0.12 if there is less than an hour left of storage, the oldest 2 hours of recordings will be deleted.
|
||||
|
||||
## Configuring Recording Retention
|
||||
|
||||
@@ -275,163 +273,6 @@ record:
|
||||
|
||||
This configuration will retain recording segments that overlap with alerts and detections for 10 days. Because multiple tracked objects can reference the same recording segments, this avoids storing duplicate footage for overlapping tracked objects and reduces overall storage needs.
|
||||
|
||||
## Sub Stream Recording
|
||||
|
||||
In addition to the main recording stream, Frigate can record a second, lower quality stream for each camera. This serves two purposes:
|
||||
|
||||
- **Quality selection during playback**: A quality selector (`Auto`, `Original`, or `Low`) appears in History view for cameras with sub stream recording enabled. `Original` and `Low` play only that stream's recordings. Time ranges where the selected stream has no footage are skipped during playback, and the selector notes when the selected stream has no recordings at all in the viewed time range. With `Auto` (the default), playback prefers the original quality and automatically falls back to the low quality stream when the connection cannot keep up, or for time ranges where the original recordings have expired. The selector shows each stream's video codec and audio details beneath the options; footage recorded by older Frigate versions shows no details.
|
||||
- **Extended retention**: Sub stream recordings have their own retention settings, fully independent of the main recordings. By giving the low quality recordings a longer retention period, you can keep weeks or months of low quality history using a fraction of the storage, and that history remains playable after the main recordings expire. Playback falls back to the low quality recordings automatically, and the timeline shows a muted treatment for time ranges where only low quality footage remains. Timeline previews are kept for as long as either stream still has recordings, so scrubbing works across the whole retained history.
|
||||
|
||||
### Configuring sub stream recording
|
||||
|
||||
Sub stream recording uses the `record_sub` input role. This role can be assigned to the same input as `detect`, so in the common case where detect already uses the camera's sub stream, no additional camera connection is needed. Like the main recording stream, sub stream segments are copied directly from the camera stream without re-encoding, so the recording quality is determined by the source stream.
|
||||
|
||||
The following examples keep 7 days of full quality continuous recordings and 60 days of low quality continuous recordings:
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and select the camera.
|
||||
|
||||
- In **Camera inputs**, enable the **Record (Sub Stream)** role on the stream you want to record at low quality, commonly the same stream that has the **Detect** role. Only one stream may have this role, and it cannot be assigned to the same stream as the **Record** role.
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Recording" /> and select the camera.
|
||||
|
||||
- Set **Enable recording** to on
|
||||
- Set **Continuous retention > Retention days** to `7`
|
||||
- Set **Sub stream recording > Enable sub stream recording** to on
|
||||
- Set **Sub stream recording > Sub stream continuous retention > Retention days** to `60`
|
||||
|
||||
The camera setup wizard also offers the **Record (Sub Stream)** role when assigning stream roles for a newly added camera.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
cameras:
|
||||
front_door:
|
||||
ffmpeg:
|
||||
inputs:
|
||||
- path: rtsp://camera/main
|
||||
roles:
|
||||
- record
|
||||
- path: rtsp://camera/sub
|
||||
roles:
|
||||
- detect
|
||||
- record_sub
|
||||
record:
|
||||
enabled: true
|
||||
continuous:
|
||||
days: 7
|
||||
sub:
|
||||
enabled: true
|
||||
continuous:
|
||||
days: 60
|
||||
```
|
||||
|
||||
If your camera does not provide a suitable sub stream (or the sub stream is already used at a resolution you don't want to record), you can use a go2rtc transcode as the source for `record_sub` instead:
|
||||
|
||||
```yaml
|
||||
go2rtc:
|
||||
streams:
|
||||
front_door: rtsp://camera/main
|
||||
front_door_lq: ffmpeg:front_door#video=h264#width=854#hardware
|
||||
|
||||
cameras:
|
||||
front_door:
|
||||
ffmpeg:
|
||||
inputs:
|
||||
- path: rtsp://127.0.0.1:8554/front_door
|
||||
input_args: preset-rtsp-restream
|
||||
roles:
|
||||
- detect
|
||||
- record
|
||||
- path: rtsp://127.0.0.1:8554/front_door_lq
|
||||
input_args: preset-rtsp-restream
|
||||
roles:
|
||||
- record_sub
|
||||
record:
|
||||
enabled: true
|
||||
continuous:
|
||||
days: 7
|
||||
sub:
|
||||
enabled: true
|
||||
continuous:
|
||||
days: 60
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
The `record.sub` config supports the same retention structure as the main recording config: `continuous`, `motion`, `alerts`, and `detections` each with their own `days` (and `mode` for alerts and detections). The pre-capture and post-capture windows for alerts and detections are taken from the main `record.alerts` and `record.detections` config. Extending `sub.alerts.days` or `sub.detections.days` beyond the main values also keeps those review items visible in the review timeline for the longer window, with playback falling back to the low quality stream once the main recordings expire.
|
||||
|
||||
:::note
|
||||
|
||||
Recording must be enabled (`record.enabled`) for sub stream recording to run, and Frigate will fail to start if `record.sub.enabled` is set without a `record_sub` role assigned to one of the camera's inputs.
|
||||
|
||||
:::
|
||||
|
||||
### How Auto picks a quality
|
||||
|
||||
`Auto` measures throughput on every segment download and compares it against the original stream's bitrate (computed from the recorded footage itself). Playback drops to the low quality stream when any of these happen:
|
||||
|
||||
- A freeze lasts 4 seconds (10 seconds when it starts within 2 seconds of a seek, since the seek target is rarely buffered), or freezes total 7 seconds within the last minute.
|
||||
- 3 downloads in a row measure below the original bitrate plus 10%, dropping quality before a stall ever becomes visible.
|
||||
- No first frame appears within 10 seconds, or loading fails outright.
|
||||
|
||||
Playback returns to full quality only when measured throughput exceeds the original bitrate by 50%, checked continuously while playing the low quality stream and again at each new hour. The asymmetric thresholds (1.1x to drop, 1.5x to return) keep a borderline connection from switching back and forth.
|
||||
|
||||
The most recent measurement is remembered on the device: a connection last measured below the original bitrate (or below 3 Mbps when the bitrate is not yet known) starts playback on the low quality stream so a first frame appears immediately, then upgrades within a few segments if the speed allows.
|
||||
|
||||
The quality selector shows which stream Auto is currently playing and why. A browser with Data Saver enabled stays on the low quality stream, a browser that cannot decode the original stream's codec (for example H.265 without HEVC support) plays the low quality stream for that camera, and pinning `Original` or `Low` bypasses Auto entirely.
|
||||
|
||||
### Sub stream output args
|
||||
|
||||
By default the sub stream is recorded with the same [output args](/configuration/ffmpeg_presets#output-args-presets) as the main recording stream, so it inherits any customization made to `ffmpeg.output_args.record`. Setting `ffmpeg.output_args.record_sub` gives the sub stream its own args instead. Like all `ffmpeg` config, this can be set globally or per camera.
|
||||
|
||||
The most common reason to set this is a pair of streams whose audio differs. Many cameras send AAC on the main stream but PCM on the sub stream, and PCM cannot be copied into an mp4 recording. Copying the main stream's audio avoids re-encoding audio that is already AAC, while the sub stream still needs to be transcoded:
|
||||
|
||||
```yaml
|
||||
ffmpeg:
|
||||
output_args:
|
||||
# main stream audio is already AAC, so copy it
|
||||
record: preset-record-generic-audio-copy
|
||||
# sub stream audio is PCM, so transcode it to AAC
|
||||
record_sub: preset-record-generic-audio-aac
|
||||
```
|
||||
|
||||
Other reasons to set this are recording a sub stream whose codec needs a different preset than the main stream, such as `preset-record-mjpeg`, or forcing a matching audio sample rate across the two streams with manual args ending in `-c:a aac -ar 16000`.
|
||||
|
||||
:::warning
|
||||
|
||||
Avoid removing audio from only one of the two streams (for example with `-an`). When one stream has audio and the other does not, playback of time ranges that combine both qualities is silent, so stripping audio from the sub stream also silences the merged timeline.
|
||||
|
||||
:::
|
||||
|
||||
### Which stream do features use?
|
||||
|
||||
As a general rule, features that read recordings prefer the main stream and fall back to the sub stream for time ranges where the main recordings have expired. Analytics features use only the main stream.
|
||||
|
||||
| Feature | Stream used |
|
||||
| ---------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
|
||||
| Recording playback (History and Review) | Both (main preferred with sub fallback by default), or exactly one stream when a quality is selected manually |
|
||||
| Tracking details and Explore clip playback | Main, falling back to sub where the main recordings have expired |
|
||||
| Exports and clip downloads | Main; sub is used when no main recordings remain in the range (streams are never mixed in one file) |
|
||||
| Frames grabbed from a recording in History (download snapshot, submit frame to Frigate+) | Main preferred, sub fallback |
|
||||
| Audio extraction (e.g., transcription) | Main preferred, sub fallback |
|
||||
| Motion search | Main only |
|
||||
| Review timeline motion data | Main only |
|
||||
| Storage usage statistics | Both streams counted, and listed separately per camera |
|
||||
|
||||
This table covers only features that read recordings from disk. Tracked object snapshots and thumbnails (the images shown in Explore and sent with notifications, and the images submitted to Frigate+ from a tracked object) are captured live from the `detect` stream as the object is tracked, never from recordings, so sub stream recording does not affect them.
|
||||
|
||||
### Trade-offs
|
||||
|
||||
- Recording a second stream increases overall storage use. The increase is typically small relative to the main recordings, since the low quality stream is much smaller. Both streams are cached before being written to disk, so cache use goes up as well. See [the `/tmp/cache` area is separate](#the-tmpcache-area-is-separate) if you start seeing `No space left on device` errors after enabling it.
|
||||
- The go2rtc transcode approach continuously encodes the low quality stream, which uses CPU or GPU resources. This cost only applies to the transcode path; recording the camera's native sub stream does not re-encode. See the [go2rtc hardware acceleration documentation](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) for accelerating the transcode.
|
||||
- Many camera sub streams do not include audio. If the source stream has no audio, the low quality recordings will not have audio.
|
||||
- **Matching video codecs and audio settings between the two streams gives the smoothest playback.** When playback combines both qualities on one timeline (the default `Auto` behavior: for example original quality during events with low quality in between, or low quality history after the original recordings expire) and the streams use different video codecs or audio settings, for example H.265 on the main stream and H.264 on the sub stream, or 16 kHz audio on one and 8 kHz on the other, playback still works: Frigate inserts a decoder reset at each quality transition, which can cause a barely-perceptible pause there. Configuring both streams in the camera's firmware to use the same video codec, audio codec, and sample rate makes transitions fully seamless, and a mismatched audio sample rate can also be corrected with [sub stream output args](#sub-stream-output-args). If one stream has audio and the other does not, combined time ranges play **without audio**; selecting a single quality with the playback selector always keeps that stream's audio.
|
||||
|
||||
## Can I have "continuous" recordings, but only at certain times?
|
||||
|
||||
Using Frigate UI, Home Assistant, or MQTT, cameras can be automated to only record in certain situations or at certain times.
|
||||
@@ -508,63 +349,3 @@ Setting `verbose: true` writes a detailed report of every orphaned file and data
|
||||
This operation uses considerable CPU resources and includes a safety threshold that aborts if more than 50% of files would be deleted. Only run when necessary. If you set `force: true` the safety threshold will be bypassed; do not use `force` unless you are certain the deletions are intended.
|
||||
|
||||
:::
|
||||
|
||||
## Understanding storage usage
|
||||
|
||||
The storage usage Frigate reports will not exactly match what the operating system reports with `df` or `du`. This is expected, not a bug. The sections below explain how Frigate derives its storage figures and why they differ from the disk's own accounting.
|
||||
|
||||
### How Frigate measures recording usage
|
||||
|
||||
The **Recordings** value on the Storage Metrics page (<NavPath path="System > Storage" />), and the per-camera **Camera Storage** breakdown, is the sum of the recording segment sizes Frigate has written, taken from Frigate's database. It is **not** computed by a scan of the disk. Frigate tracks usage this way by design: repeatedly walking the entire drive to total its size would keep hard drives spun up and add unnecessary I/O.
|
||||
|
||||
The disk **total** shown beside it, and the free-space figure Frigate uses to decide when to delete recordings, instead come from the operating system's report for the whole filesystem mounted at `/media/frigate`. As a result, the **Unused** value on the page is _total disk capacity minus Frigate's recordings_, not the drive's real free space, which will be lower whenever anything else is stored on the disk.
|
||||
|
||||
### What counts toward usage, and why it won't match `df`
|
||||
|
||||
Only **recording segments** (`/media/frigate/recordings`) are included in the recordings storage total. Plenty of other things consume real disk space but are **not** part of that number:
|
||||
|
||||
- **Snapshots and thumbnails** (`/media/frigate/clips`): see [Snapshots](/configuration/snapshots). These are retained independently of recordings.
|
||||
- **Preview videos** and **review thumbnails** (also under `/media/frigate/clips`).
|
||||
- **Exports** (`/media/frigate/exports`): exports are never removed by retention.
|
||||
- **The database, downloaded detection models, and face / license plate training images** (stored under `/config`).
|
||||
- **Debug images from enrichments** (`/media/frigate/clips`): when enabled, License Plate Recognition's `debug_save_plates` and GenAI's `debug_save_thumbnails` save plate crops and request images for troubleshooting.
|
||||
|
||||
These files are the usual explanation for an "other" or seemingly unaccounted bucket of space: it is real, it is Frigate's, and it simply isn't part of the _recordings_ total. They are also why comparing the **Recordings** figure to `df -h` always shows a gap: `df` additionally counts any non-Frigate data on the disk, filesystem overhead and reserved blocks (ext4 reserves ~5% for root by default, so a disk can read "full" before recordings approach the total), and recently deleted recordings whose space has not yet been reclaimed.
|
||||
|
||||
:::tip
|
||||
|
||||
The Storage page is not intended to be a system-wide disk monitor: it shows how much space _Frigate's recordings_ use. To see true disk usage, use `df -h` (free space) and `du -sh` (per-directory usage) on the host.
|
||||
|
||||
:::
|
||||
|
||||
### Free space and the `/media/frigate` mount
|
||||
|
||||
Frigate reports the capacity and free space of whatever filesystem is actually mounted at `/media/frigate` **inside the container**. If an external drive or network share isn't truly mounted there (a missing `/etc/fstab` entry, a share that was offline when the container started, or a host that doesn't pass the path through), the container falls back to the host's OS disk, and Frigate will correctly report that smaller disk instead of the drive you intended.
|
||||
|
||||
If the reported capacity doesn't match your drive, the mount is the place to look, not Frigate. Verify what is actually mounted from inside the container:
|
||||
|
||||
```bash
|
||||
docker exec -it frigate df -h /media/frigate
|
||||
docker exec -it frigate mount | grep media
|
||||
```
|
||||
|
||||
See the [storage mount layout](/frigate/installation#storage) for how the volumes are expected to be configured.
|
||||
|
||||
### The `/tmp/cache` area is separate
|
||||
|
||||
Recording segments are first written to `/tmp/cache`, a small, in-memory (`tmpfs`) area, before being checked and moved to `/media/frigate/recordings`. Because it is separate and small, `/tmp/cache` can fill up and produce `No space left on device` errors even when the recordings disk has plenty of room. They are different storage areas. See [Recordings troubleshooting](/troubleshooting/recordings) for diagnosing cache and slow-storage issues.
|
||||
|
||||
### When the metrics don't match what's on disk
|
||||
|
||||
Because usage is tracked in the database, deleting recording files directly on disk, or files left behind after an upgrade, will not update the reported usage, and can even push it above 100%. Frigate is unaware of files it didn't record and won't count or remove them automatically. Use [Syncing Media Files With Disk](#syncing-media-files-with-disk) to reconcile the database with what is actually on disk.
|
||||
|
||||
## Will Frigate delete old recordings if my storage runs out?
|
||||
|
||||
Yes. Frigate continuously checks the **free space of the disk** holding `/media/frigate/recordings`. This is different from adding up the size of every recording: free space is a single number the operating system already tracks, so Frigate can ask for it instantly without reading through your files or spinning up the disk, which is exactly why it relies on this check rather than scanning the drive. When less than roughly one hour of recording space remains (estimated from the current recording bitrate, **not** a fixed percentage), Frigate deletes the oldest recordings to reclaim space and logs a message. This emergency cleanup removes the oldest recordings first **regardless of retention settings**.
|
||||
|
||||
Two consequences follow from this being based on whole-disk free space:
|
||||
|
||||
- Because the check uses the disk's real free space, **anything** filling the drive, including non-Frigate files, can trigger deletion of your oldest recordings.
|
||||
- Cleanup can run while a meaningful percentage of the disk is still free (for example, with high bitrates or many cameras), because the threshold is "less than ~1 hour of recording headroom," not "X% full."
|
||||
|
||||
Frequent emergency cleanups usually mean your configured retention exceeds what the disk can hold. Reduce your retention days so the normal retention cleanup keeps up and the emergency path rarely triggers.
|
||||
|
||||
+92
-168
@@ -11,8 +11,6 @@ It is not recommended to copy this full configuration file. Only specify values
|
||||
|
||||
:::
|
||||
|
||||
Sections marked `# NOTE: Can be overridden at the camera level` can be set globally and then adjusted per camera. See [Global and Camera-Level Configuration](../config_overrides.md) for how that works.
|
||||
|
||||
```yaml
|
||||
mqtt:
|
||||
# Optional: Enable mqtt server (default: shown below)
|
||||
@@ -56,6 +54,17 @@ mqtt:
|
||||
# 2 = exactly once
|
||||
qos: 0
|
||||
|
||||
# Optional: Detectors configuration. Defaults to a single CPU detector
|
||||
detectors:
|
||||
# Required: name of the detector
|
||||
detector_name:
|
||||
# Required: type of the detector
|
||||
# Frigate provides many types, see https://docs.frigate.video/configuration/object_detectors for more details (default: shown below)
|
||||
# Additional detector types can also be plugged in.
|
||||
# Detectors may require additional configuration.
|
||||
# Refer to the Detectors configuration page for more information.
|
||||
type: cpu
|
||||
|
||||
# Optional: Database configuration
|
||||
database:
|
||||
# The path to store the SQLite DB (default: shown below)
|
||||
@@ -138,64 +147,41 @@ auth:
|
||||
# NOTE: changing this value will not automatically update password hashes, you
|
||||
# will need to change each user password for it to apply
|
||||
hash_iterations: 600000
|
||||
# Optional: Map roles to the list of cameras each role can access (default: none)
|
||||
# NOTE: An empty list grants the role access to all cameras. Roles defined here can be
|
||||
# referenced by proxy header role mapping or assigned to native users.
|
||||
roles:
|
||||
my_custom_role:
|
||||
- front_door
|
||||
- back_yard
|
||||
|
||||
# Optional: object detection models. Defaults to a single model on a CPU detector.
|
||||
# Optional: model modifications
|
||||
# NOTE: The default values are for the EdgeTPU detector.
|
||||
# Other detectors will require the model config to be set.
|
||||
models:
|
||||
# Optional: the camera environment this model is for (default: shown below)
|
||||
# Cameras select a model by setting detect -> scene to a matching value, and
|
||||
# a model with a scene of all is used by any camera that does not set one.
|
||||
# Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
|
||||
- scene: all
|
||||
# Required: hardware this model runs on, as <detector> or <detector>:<device>
|
||||
# See https://docs.frigate.video/configuration/object_detectors for the
|
||||
# detectors available and the devices each one accepts. All of a model's
|
||||
# devices must use the same detector. Listing the same device more than once
|
||||
# runs additional inference processes on it.
|
||||
devices:
|
||||
- edgetpu:pci:0
|
||||
# Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
|
||||
path: /edgetpu_model.tflite
|
||||
# Required: path to the labelmap (default: shown below)
|
||||
labelmap_path: /labelmap.txt
|
||||
# Required: Object detection model input width (default: shown below)
|
||||
width: 320
|
||||
# Required: Object detection model input height (default: shown below)
|
||||
height: 320
|
||||
# Required: Object detection model input colorspace
|
||||
# Valid values are rgb, bgr, or yuv. (default: shown below)
|
||||
input_pixel_format: rgb
|
||||
# Required: Object detection model input tensor format
|
||||
# Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
|
||||
input_tensor: nhwc
|
||||
# Optional: Data type of the model input tensor
|
||||
# Valid values are float, float_denorm, or int (default: shown below)
|
||||
input_dtype: int
|
||||
# Required: Object detection model architecture, used by detectors that support more
|
||||
# than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
|
||||
# Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
|
||||
model_type: ssd
|
||||
# Required: Label name modifications. These are merged into the standard labelmap.
|
||||
labelmap:
|
||||
2: vehicle
|
||||
# Optional: Map of object labels to their attribute labels (default: depends on model)
|
||||
attributes_map:
|
||||
person:
|
||||
- amazon
|
||||
- face
|
||||
car:
|
||||
- amazon
|
||||
- fedex
|
||||
- license_plate
|
||||
- ups
|
||||
model:
|
||||
# Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
|
||||
path: /edgetpu_model.tflite
|
||||
# Required: path to the labelmap (default: shown below)
|
||||
labelmap_path: /labelmap.txt
|
||||
# Required: Object detection model input width (default: shown below)
|
||||
width: 320
|
||||
# Required: Object detection model input height (default: shown below)
|
||||
height: 320
|
||||
# Required: Object detection model input colorspace
|
||||
# Valid values are rgb, bgr, or yuv. (default: shown below)
|
||||
input_pixel_format: rgb
|
||||
# Required: Object detection model input tensor format
|
||||
# Valid values are nhwc or nchw (default: shown below)
|
||||
input_tensor: nhwc
|
||||
# Required: Object detection model type, currently only used with the OpenVINO detector
|
||||
# Valid values are ssd, yolox, yolonas (default: shown below)
|
||||
model_type: ssd
|
||||
# Required: Label name modifications. These are merged into the standard labelmap.
|
||||
labelmap:
|
||||
2: vehicle
|
||||
# Optional: Map of object labels to their attribute labels (default: depends on model)
|
||||
attributes_map:
|
||||
person:
|
||||
- amazon
|
||||
- face
|
||||
car:
|
||||
- amazon
|
||||
- fedex
|
||||
- license_plate
|
||||
- ups
|
||||
|
||||
# Optional: Audio Events Configuration
|
||||
# NOTE: Can be overridden at the camera level
|
||||
@@ -210,16 +196,13 @@ audio:
|
||||
# - 500 - medium sensitivity
|
||||
# - 1000 - low sensitivity
|
||||
min_volume: 500
|
||||
# Optional: Number of threads to use for audio detection (default: shown below)
|
||||
num_threads: 2
|
||||
# Optional: Types of audio to listen for (default: shown below)
|
||||
listen:
|
||||
- bark
|
||||
- fire_alarm
|
||||
- scream
|
||||
- speech
|
||||
- yell
|
||||
# Optional: Audio label name modifications. These are merged into the standard audio labelmap.
|
||||
labelmap: {}
|
||||
# Optional: Filters to configure detection.
|
||||
filters:
|
||||
# Label that matches label in listen config.
|
||||
@@ -254,15 +237,11 @@ birdseye:
|
||||
# Optional: Encoding quality of the mpeg1 feed (default: shown below)
|
||||
# 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
|
||||
quality: 8
|
||||
# Optional: Activity types that include cameras in Birdseye (default: shown below)
|
||||
# Multiple activity types can be listed at the same time.
|
||||
# continuous: all cameras are included always
|
||||
# motion: included if motion was detected within the inactivity threshold
|
||||
# all_objects: included if a tracked object was present within the inactivity threshold
|
||||
# alerts: included while an alert review item is in progress
|
||||
# detections: included while a detection review item is in progress
|
||||
modes:
|
||||
- all_objects
|
||||
# Optional: Mode of the view. Available options are: objects, motion, and continuous
|
||||
# objects - cameras are included if they have had a tracked object within the last 30 seconds
|
||||
# motion - cameras are included if motion was detected in the last 30 seconds
|
||||
# continuous - all cameras are included always
|
||||
mode: objects
|
||||
# Optional: Threshold for camera activity to stop showing camera (default: shown below)
|
||||
inactivity_threshold: 30
|
||||
# Optional: Configure the birdseye layout
|
||||
@@ -278,7 +257,7 @@ birdseye:
|
||||
# More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
|
||||
ffmpeg:
|
||||
# Optional: ffmpeg binary path (default: shown below)
|
||||
# can also be set to `8.0` or `5.0` to specify one of the included versions
|
||||
# can also be set to `7.0` or `5.0` to specify one of the included versions
|
||||
# or can be set to any path that holds `bin/ffmpeg` & `bin/ffprobe`
|
||||
path: "default"
|
||||
# Optional: global ffmpeg args (default: shown below)
|
||||
@@ -294,8 +273,6 @@ ffmpeg:
|
||||
detect: -threads 2 -f rawvideo -pix_fmt yuv420p
|
||||
# Optional: output args for record streams (default: shown below)
|
||||
record: preset-record-generic
|
||||
# Optional: output args for sub stream record streams (default: the record output args above)
|
||||
# record_sub: preset-record-generic
|
||||
# Optional: Time in seconds to wait before ffmpeg retries connecting to the camera. (default: shown below)
|
||||
# If set too low, frigate will retry a connection to the camera's stream too frequently, using up the limited streams some cameras can allow at once
|
||||
# If set too high, then if a ffmpeg crash or camera stream timeout occurs, you could potentially lose up to a maximum of retry_interval second(s) of footage
|
||||
@@ -315,10 +292,6 @@ detect:
|
||||
width: 1280
|
||||
# Optional: height of the frame for the input with the detect role (default: use native stream resolution)
|
||||
height: 720
|
||||
# Optional: the environment this camera looks at, which picks the model it runs on
|
||||
# (default: the model with a scene of all)
|
||||
# Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
|
||||
scene: outdoor
|
||||
# Optional: desired fps for your camera for the input with the detect role (default: shown below)
|
||||
# NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
|
||||
fps: 5
|
||||
@@ -355,7 +328,7 @@ detect:
|
||||
# especially when using separate streams for detect and record.
|
||||
# Use this setting to make the timeline bounding boxes more closely align
|
||||
# with the recording. The value can be positive or negative.
|
||||
# TIP: Imagine there is a tracked object clip with a person walking from left to right.
|
||||
# TIP: Imagine there is an tracked object clip with a person walking from left to right.
|
||||
# If the tracked object lifecycle bounding box is consistently to the left of the person
|
||||
# then the value should be decreased. Similarly, if a person is walking from
|
||||
# left to right and the bounding box is consistently ahead of the person
|
||||
@@ -484,8 +457,8 @@ review:
|
||||
detections: False
|
||||
# Optional: Activity Context Prompt to give context to the GenAI what activity is and is not suspicious.
|
||||
# It is important to be direct and detailed. See documentation for the default prompt structure.
|
||||
activity_context_prompt: |
|
||||
Define what is and is not suspicious
|
||||
activity_context_prompt: """Define what is and is not suspicious
|
||||
"""
|
||||
# Optional: Image source for GenAI (default: preview)
|
||||
# Options: "preview" (uses cached preview frames at ~180p) or "recordings" (extracts frames from recordings at 480p)
|
||||
# Using "recordings" provides better image quality but uses more tokens per image.
|
||||
@@ -496,8 +469,6 @@ review:
|
||||
- Animals in the garden
|
||||
# Optional: Preferred response language (default: English)
|
||||
preferred_language: English
|
||||
# Optional: Save thumbnails sent to the GenAI provider for review/debugging purposes (default: shown below)
|
||||
debug_save_thumbnails: False
|
||||
|
||||
# Optional: Motion configuration
|
||||
# NOTE: Can be overridden at the camera level
|
||||
@@ -529,8 +500,6 @@ motion:
|
||||
# - 30 - medium sensitivity
|
||||
# - 50 - low sensitivity
|
||||
contour_area: 10
|
||||
# Optional: Alpha blending factor used in frame differencing for motion calculation (default: shown below)
|
||||
delta_alpha: 0.2
|
||||
# Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
|
||||
# Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
|
||||
# Low values will cause things like moving shadows to be detected as motion for longer.
|
||||
@@ -603,8 +572,6 @@ record:
|
||||
timelapse_args: "-vf setpts=0.04*PTS -r 30"
|
||||
# Optional: Global hardware acceleration settings for timelapse exports. (default: inherit)
|
||||
hwaccel_args: auto
|
||||
# Optional: Maximum number of export jobs to process at the same time (default: shown below)
|
||||
max_concurrent: 3
|
||||
# Optional: Recording Preview Settings
|
||||
preview:
|
||||
# Optional: Quality of recording preview (default: shown below).
|
||||
@@ -650,42 +617,6 @@ record:
|
||||
# For example, if the camera retain mode is "motion", the segments without motion are
|
||||
# never stored, so setting the mode to "all" here won't bring them back.
|
||||
mode: motion
|
||||
# Optional: Sub stream recording settings
|
||||
# Records a second, lower quality stream for quality selection during playback
|
||||
# and extended low quality retention. Requires the record_sub role to be assigned
|
||||
# to one of the camera's inputs.
|
||||
sub:
|
||||
# Optional: Enable sub stream recording (default: shown below)
|
||||
# NOTE: Recording must also be enabled for sub stream recording to run.
|
||||
enabled: False
|
||||
# Optional: Continuous retention settings for sub stream recordings
|
||||
continuous:
|
||||
# Optional: Number of days to retain sub stream recordings regardless of tracked objects or motion (default: shown below)
|
||||
days: 0
|
||||
# Optional: Motion retention settings for sub stream recordings
|
||||
motion:
|
||||
# Optional: Number of days to retain sub stream recordings triggered by motion (default: shown below)
|
||||
days: 0
|
||||
# Optional: Retention settings for sub stream recordings of alerts
|
||||
# NOTE: Pre and post capture windows are taken from the main alerts config above.
|
||||
alerts:
|
||||
# Required: Retention days (default: shown below)
|
||||
days: 10
|
||||
# Optional: Mode for retention. (default: shown below)
|
||||
# all - save all sub stream recording segments for alerts regardless of activity
|
||||
# motion - save all sub stream recording segments for alerts with any detected motion
|
||||
# active_objects - save all sub stream recording segments for alerts with active/moving objects
|
||||
mode: motion
|
||||
# Optional: Retention settings for sub stream recordings of detections
|
||||
# NOTE: Pre and post capture windows are taken from the main detections config above.
|
||||
detections:
|
||||
# Required: Retention days (default: shown below)
|
||||
days: 10
|
||||
# Optional: Mode for retention. (default: shown below)
|
||||
# all - save all sub stream recording segments for detections regardless of activity
|
||||
# motion - save all sub stream recording segments for detections with any detected motion
|
||||
# active_objects - save all sub stream recording segments for detections with active/moving objects
|
||||
mode: motion
|
||||
|
||||
# Optional: Configuration for the snapshots written to the clips directory for each tracked object
|
||||
# Timestamp, bounding_box, crop and height settings are applied by default to API requests for snapshots.
|
||||
@@ -783,42 +714,28 @@ lpr:
|
||||
enhancement: 0
|
||||
# Optional: Save plate images to /media/frigate/clips/lpr for debugging purposes (default: shown below)
|
||||
debug_save_plates: False
|
||||
# Optional: List of regex replacement rules to normalize detected plates before matching (default: none)
|
||||
replace_rules:
|
||||
# Required: regex pattern to match in the detected plate
|
||||
- pattern: "O"
|
||||
# Required: string to replace the matched pattern with
|
||||
replacement: "0"
|
||||
# Optional: List of regex replacement rules to normalize detected plates (default: shown below)
|
||||
replace_rules: {}
|
||||
|
||||
# Optional: Configuration for AI / LLM providers
|
||||
# Optional: Configuration for AI / LLM provider
|
||||
# WARNING: Depending on the provider, this will send thumbnails over the internet
|
||||
# to Google or OpenAI's LLMs to generate descriptions. GenAI features can be configured at
|
||||
# the camera level to enhance privacy for indoor cameras.
|
||||
# NOTE: genai is a map of named providers. Each key is a name you choose for the provider,
|
||||
# and each role (chat, descriptions, embeddings) may be assigned to exactly one provider.
|
||||
genai:
|
||||
# Required: name of the provider (chosen by you, used to reference it elsewhere)
|
||||
my_provider:
|
||||
# Required: Provider must be one of ollama, openai, azure_openai, gemini, or llamacpp
|
||||
provider: ollama
|
||||
# Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
|
||||
base_url: http://localhost::11434
|
||||
# Required if gemini or openai
|
||||
api_key: "{FRIGATE_GENAI_API_KEY}"
|
||||
# Required: The model to use with the provider.
|
||||
model: gemini-1.5-flash
|
||||
# Optional: Roles this provider handles (default: shown below)
|
||||
# Each role (chat, descriptions, embeddings) must be assigned to exactly one provider.
|
||||
roles:
|
||||
- chat
|
||||
- descriptions
|
||||
- embeddings
|
||||
# Optional additional args to pass to the GenAI Provider (default: None)
|
||||
provider_options:
|
||||
keep_alive: -1
|
||||
# Optional: Options to pass during inference calls (default: {})
|
||||
runtime_options:
|
||||
temperature: 0.7
|
||||
# Required: Provider must be one of ollama, gemini, or openai
|
||||
provider: ollama
|
||||
# Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
|
||||
base_url: http://localhost::11434
|
||||
# Required if gemini or openai
|
||||
api_key: "{FRIGATE_GENAI_API_KEY}"
|
||||
# Required: The model to use with the provider.
|
||||
model: gemini-1.5-flash
|
||||
# Optional additional args to pass to the GenAI Provider (default: None)
|
||||
provider_options:
|
||||
keep_alive: -1
|
||||
# Optional: Options to pass during inference calls (default: {})
|
||||
runtime_options:
|
||||
temperature: 0.7
|
||||
|
||||
# Optional: Configuration for audio transcription
|
||||
# NOTE: only the enabled option can be overridden at the camera level
|
||||
@@ -865,15 +782,14 @@ classification:
|
||||
cameras:
|
||||
camera_name:
|
||||
# Required: Crop of image frame on this camera to run classification on
|
||||
# [x1, y1, x2, y2] as decimals between 0 and 1, relative to the detect resolution
|
||||
crop: [0.0, 0.25, 0.3, 0.85]
|
||||
crop: [0, 180, 220, 400]
|
||||
# Optional: If classification should be run when motion is detected in the crop (default: shown below)
|
||||
motion: False
|
||||
# Optional: Interval to run classification on in seconds (default: shown below)
|
||||
interval: None
|
||||
|
||||
# Optional: Restream configuration
|
||||
# Uses https://github.com/AlexxIT/go2rtc (v1.9.14)
|
||||
# Uses https://github.com/AlexxIT/go2rtc (v1.9.13)
|
||||
# NOTE: The default go2rtc API port (1984) must be used,
|
||||
# changing this port for the integrated go2rtc instance is not supported.
|
||||
go2rtc:
|
||||
@@ -924,8 +840,8 @@ cameras:
|
||||
# Required: name of the camera
|
||||
back:
|
||||
# Optional: Enable/Disable the camera (default: shown below).
|
||||
# When False, ffmpeg is not started and the camera is hidden from the UI
|
||||
# (except Camera Management). Re-enabling requires a Frigate restart.
|
||||
# If disabled: config is used but no live stream and no capture etc.
|
||||
# Events/Recordings are still viewable.
|
||||
enabled: True
|
||||
# Optional: camera type used for some Frigate features (default: shown below)
|
||||
# Options are "generic" and "lpr"
|
||||
@@ -937,7 +853,7 @@ cameras:
|
||||
# Required: the path to the stream
|
||||
# NOTE: path may include environment variables or docker secrets, which must begin with 'FRIGATE_' and be referenced in {}
|
||||
- path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
|
||||
# Required: list of roles for this stream. valid values are: audio,detect,record,record_sub
|
||||
# Required: list of roles for this stream. valid values are: audio,detect,record
|
||||
# NOTICE: In addition to assigning the audio, detect, and record roles
|
||||
# they must also be enabled in the camera config.
|
||||
roles:
|
||||
@@ -992,9 +908,6 @@ cameras:
|
||||
inertia: 3
|
||||
# Optional: Number of seconds that an object must loiter to be considered in the zone (default: shown below)
|
||||
loitering_time: 0
|
||||
# Optional: Minimum speed required for an object to be considered present in the zone (default: none)
|
||||
# In real-world units if distances are set. Used for speed-based zone triggers.
|
||||
speed_threshold: 2.5
|
||||
# Optional: List of objects that can trigger this zone (default: all tracked objects)
|
||||
objects:
|
||||
- person
|
||||
@@ -1030,13 +943,8 @@ cameras:
|
||||
# Optional: Adjust sort order of cameras in the UI. Larger numbers come later (default: shown below)
|
||||
# By default the cameras are sorted alphabetically.
|
||||
order: 0
|
||||
# Optional: Whether or not to show the camera on the default All Cameras live dashboard.
|
||||
# The camera is still available everywhere else, including camera groups and settings
|
||||
# (default: shown below)
|
||||
# Optional: Whether or not to show the camera in the Frigate UI (default: shown below)
|
||||
dashboard: True
|
||||
# Optional: Whether this camera is visible in review (the review page and its camera
|
||||
# filter, motion review, and the history view) (default: shown below)
|
||||
review: True
|
||||
|
||||
# Optional: connect to ONVIF camera
|
||||
# to enable PTZ controls.
|
||||
@@ -1175,6 +1083,22 @@ ui:
|
||||
# Optional: Set the time format used.
|
||||
# Options are browser, 12hour, or 24hour (default: shown below)
|
||||
time_format: browser
|
||||
# Optional: Set the date style for a specified length.
|
||||
# Options are: full, long, medium, short
|
||||
# Examples:
|
||||
# short: 2/11/23
|
||||
# medium: Feb 11, 2023
|
||||
# full: Saturday, February 11, 2023
|
||||
# (default: shown below).
|
||||
date_style: short
|
||||
# Optional: Set the time style for a specified length.
|
||||
# Options are: full, long, medium, short
|
||||
# Examples:
|
||||
# short: 8:14 PM
|
||||
# medium: 8:15:22 PM
|
||||
# full: 8:15:22 PM Mountain Standard Time
|
||||
# (default: shown below).
|
||||
time_style: medium
|
||||
# Optional: Set the unit system to either "imperial" or "metric" (default: metric)
|
||||
# Used in the UI and in MQTT topics
|
||||
unit_system: metric
|
||||
@@ -11,7 +11,7 @@ import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
Frigate can restream your video feed as an RTSP feed for other applications such as Home Assistant to utilize it at `rtsp://<frigate_host>:8554/<camera_name>`. Port 8554 must be open. [This allows you to use a video feed for detection in Frigate and Home Assistant live view at the same time without having to make two separate connections to the camera](#reduce-connections-to-camera). The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.
|
||||
|
||||
Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc/tree/v1.9.14) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#configuration) for more advanced configurations and features.
|
||||
Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc/tree/v1.9.13) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#configuration) for more advanced configurations and features.
|
||||
|
||||
:::note
|
||||
|
||||
@@ -61,7 +61,7 @@ Configure the go2rtc stream and point the camera inputs at the local restream.
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera. Then navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> for each camera. For each input, choose **Restream (go2rtc)** and pick the matching stream from the dropdown. Frigate uses the local restream URL (`rtsp://127.0.0.1:8554/<camera_name>`) and the `preset-rtsp-restream` input args for that input automatically. (Choose **Manual input path** instead to type a URL directly.)
|
||||
Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera. Then navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> for each camera and set the input paths to use the local restream URL (`rtsp://127.0.0.1:8554/<camera_name>`).
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -111,7 +111,7 @@ Two connections are made to the camera. One for the sub stream, one for the rest
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera and its sub stream. Then navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> for each camera and add separate inputs for the main and sub streams. Set each input's source to **Restream (go2rtc)** and pick the matching stream from the dropdown. Frigate uses the local restream URL and the `preset-rtsp-restream` input args for that input automatically.
|
||||
Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera and its sub stream. Then navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> for each camera and configure separate inputs for the main and sub streams using the local restream URLs.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -221,7 +221,7 @@ For security reasons, the `echo:`, `expr:`, and `exec:` stream sources are disab
|
||||
|
||||
If you attempt to use these sources in your configuration, the streams will be removed and an error message will be printed in the logs.
|
||||
|
||||
To enable these sources, you must set the environment variable `GO2RTC_ALLOW_ARBITRARY_EXEC=true`. This can be done in your Docker Compose file or container environment, or for Home Assistant App users with the `go2rtc_allow_arbitrary_exec` option in the App's configuration. The `environment_vars` section of the Frigate config can't enable it:
|
||||
To enable these sources, you must set the environment variable `GO2RTC_ALLOW_ARBITRARY_EXEC=true`. This can be done in your Docker Compose file or container environment:
|
||||
|
||||
```yaml
|
||||
environment:
|
||||
@@ -236,7 +236,7 @@ Enabling arbitrary exec sources allows execution of arbitrary commands through g
|
||||
|
||||
## Advanced Restream Configurations
|
||||
|
||||
The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#source-exec) source in go2rtc can be used for custom ffmpeg commands and other applications. An example is below:
|
||||
The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
|
||||
|
||||
:::warning
|
||||
|
||||
@@ -244,11 +244,16 @@ The `exec:`, `echo:`, and `expr:` sources are disabled by default for security.
|
||||
|
||||
:::
|
||||
|
||||
NOTE: RTSP output will need to be passed with two curly braces `{{output}}`, whereas pipe output must be passed without curly braces.
|
||||
:::warning
|
||||
|
||||
The `exec:`, `echo:`, and `expr:` sources are disabled by default for security. You must set `GO2RTC_ALLOW_ARBITRARY_EXEC=true` to use them. See [Security: Restricted Stream Sources](#security-restricted-stream-sources) for more information.
|
||||
|
||||
:::
|
||||
|
||||
NOTE: The output will need to be passed with two curly braces `{{output}}`
|
||||
|
||||
```yaml
|
||||
go2rtc:
|
||||
streams:
|
||||
stream1: exec:ffmpeg -hide_banner -re -stream_loop -1 -i /media/BigBuckBunny.mp4 -c copy -rtsp_transport tcp -f rtsp {{output}}
|
||||
stream2: exec:rpicam-vid -t 0 --libav-format h264 -o -
|
||||
```
|
||||
|
||||
@@ -23,7 +23,7 @@ In 0.14 and later, all of that is bundled into a single review item which starts
|
||||
|
||||
## Alerts and Detections
|
||||
|
||||
Not every segment of video captured by Frigate may be of the same level of interest to you. Video of people who enter your property may be a different priority than those walking by on the sidewalk. For this reason, Frigate categorizes review items as _alerts_ and _detections_. By default, all person and car objects are considered alerts. You can refine categorization of your review items by configuring [required zones](/configuration/zones#restricting-alerts-and-detections-to-specific-zones) for them.
|
||||
Not every segment of video captured by Frigate may be of the same level of interest to you. Video of people who enter your property may be a different priority than those walking by on the sidewalk. For this reason, Frigate 0.14 categorizes review items as _alerts_ and _detections_. By default, all person and car objects are considered alerts. You can refine categorization of your review items by configuring required zones for them.
|
||||
|
||||
:::note
|
||||
|
||||
@@ -121,31 +121,6 @@ cameras:
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
## Categorizing manual events
|
||||
|
||||
Events created with the [create manual event API](../integrations/api/create-event-events-camera-name-label-create-post.api.mdx) are categorized with the same label lists, using the label from the request path:
|
||||
|
||||
1. If alerts are enabled and the label is listed in `review -> alerts -> labels`, the review item is an alert.
|
||||
2. Otherwise, if detections are enabled and the label is listed in `review -> detections -> labels`, the review item is a detection.
|
||||
3. If the label is in neither list, the review item is an alert, or no review item is created if alerts are disabled.
|
||||
|
||||
This means manual events are alerts unless you explicitly list their label as a detection label. For example, to have PIR sensors create detections instead of alerts, post to `/api/events/front_door/pir_sensor/create` with the following config:
|
||||
|
||||
```yaml {5-7}
|
||||
cameras:
|
||||
front_door:
|
||||
review:
|
||||
detections:
|
||||
labels:
|
||||
- pir_sensor
|
||||
```
|
||||
|
||||
:::note
|
||||
|
||||
Required zones do not apply to manual events, since they are created through the API rather than by the object tracker. Setting `review -> alerts -> labels` to an empty list also does not stop manual events from becoming alerts, as a label in neither list still falls back to an alert.
|
||||
|
||||
:::
|
||||
|
||||
## Restricting review items to specific zones
|
||||
|
||||
By default a review item will be created if any `review -> alerts -> labels` and `review -> detections -> labels` are detected anywhere in the camera frame. You will likely want to configure review items to only be created when the object enters an area of interest, [see the zone docs for more information](./zones.md#restricting-alerts-and-detections-to-specific-zones)
|
||||
@@ -155,7 +130,3 @@ By default a review item will be created if any `review -> alerts -> labels` and
|
||||
Because zones don't apply to audio, audio labels will always be marked as a detection by default.
|
||||
|
||||
:::
|
||||
|
||||
## Reviewing Motion
|
||||
|
||||
The Review page can also surface periods of motion that didn't produce a tracked object, and lets you search past recordings for motion in a region you draw. See [Reviewing Motion](/usage/review#reviewing-motion) in the Usage docs for how to use **Motion Previews** and **Motion Search**, and [Tuning Motion Detection](motion_detection.md) for configuring the underlying motion detector.
|
||||
|
||||
@@ -7,7 +7,7 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_, numerical vector representations, for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.
|
||||
Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_ — numerical vector representations — for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.
|
||||
|
||||
Frigate uses models from [Jina AI](https://huggingface.co/jinaai) to create and save embeddings to Frigate's database. All of this runs locally.
|
||||
|
||||
@@ -163,8 +163,8 @@ genai:
|
||||
model: your-model-name
|
||||
roles:
|
||||
- embeddings
|
||||
- descriptions
|
||||
- chat
|
||||
- vision
|
||||
- tools
|
||||
|
||||
semantic_search:
|
||||
enabled: True
|
||||
@@ -222,11 +222,16 @@ See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_
|
||||
|
||||
## Usage and Best Practices
|
||||
|
||||
For tips on getting the best results from Semantic Search (choosing between thumbnail and description search, phrasing queries effectively, and combining search with the other Explore filters), see [Usage and best practices](/usage/explore#usage-and-best-practices) in the Usage docs.
|
||||
1. Semantic Search is used in conjunction with the other filters available on the Explore page. Use a combination of traditional filtering and Semantic Search for the best results.
|
||||
2. Use the thumbnail search type when searching for particular objects in the scene. Use the description search type when attempting to discern the intent of your object.
|
||||
3. Because of how the AI models Frigate uses have been trained, the comparison between text and image embedding distances generally means that with multi-modal (`thumbnail` and `description`) searches, results matching `description` will appear first, even if a `thumbnail` embedding may be a better match. Play with the "Search Type" setting to help find what you are looking for. Note that if you are generating descriptions for specific objects or zones only, this may cause search results to prioritize the objects with descriptions even if the the ones without them are more relevant.
|
||||
4. Make your search language and tone closely match exactly what you're looking for. If you are using thumbnail search, **phrase your query as an image caption**. Searching for "red car" may not work as well as "red sedan driving down a residential street on a sunny day".
|
||||
5. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
|
||||
6. Experiment! Find a tracked object you want to test and start typing keywords and phrases to see what works for you.
|
||||
|
||||
## Triggers
|
||||
|
||||
Triggers utilize Semantic Search to automate actions when a tracked object matches a specified image or description. Triggers can be configured so that Frigate executes specific actions when a tracked object's image or description matches a predefined image or text, based on a similarity threshold. Triggers are managed per camera and can be configured via the Frigate UI in the Settings page under the Triggers tab.
|
||||
Triggers utilize Semantic Search to automate actions when a tracked object matches a specified image or description. Triggers can be configured so that Frigate executes a specific actions when a tracked object's image or description matches a predefined image or text, based on a similarity threshold. Triggers are managed per camera and can be configured via the Frigate UI in the Settings page under the Triggers tab.
|
||||
|
||||
:::note
|
||||
|
||||
|
||||
@@ -7,17 +7,13 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
A snapshot is a single still image that captures a tracked object at its best moment: the clearest frame Frigate saw while following that object across the scene. Unlike a [recording](./record.md), which is continuous video, a snapshot is one representative image saved per tracked object once tracking ends.
|
||||
Frigate can save a snapshot image to `/media/frigate/clips` for each object that is detected named as `<camera>-<id>-clean.webp`. They are also accessible [via the api](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx)
|
||||
|
||||
When snapshots are enabled, Frigate saves one image to `/media/frigate/clips` for each tracked object, named `<camera>-<id>-clean.webp`. A clean image is always stored without any annotations (no timestamp, bounding boxes, or cropping) so you have an unmodified copy of the original frame. Annotations like bounding boxes and timestamps are applied on demand when a snapshot is requested [via the HTTP API](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx). See [Rendering](#rendering) below.
|
||||
Snapshots are accessible in the UI in the Explore pane. This allows for quick submission to the Frigate+ service.
|
||||
|
||||
A few things to keep in mind:
|
||||
To only save snapshots for objects that enter a specific zone, [see the zone docs](./zones.md#restricting-snapshots-to-specific-zones)
|
||||
|
||||
- Snapshots are saved per tracked object, so a camera with no detected objects produces no snapshots even if recording is enabled.
|
||||
- Snapshots and recordings are configured and retained independently. Enabling one does not enable the other.
|
||||
- Snapshots are accessible in the UI in the Explore pane, which allows for quick submission to the Frigate+ service.
|
||||
- To only save snapshots for objects that enter a specific zone, [see the zone docs](./zones.md#restricting-snapshots-to-specific-zones).
|
||||
- Snapshots sent via MQTT are configured separately under the camera MQTT settings, not here.
|
||||
Snapshots sent via MQTT are configured separately under the camera MQTT settings, not here.
|
||||
|
||||
## Enabling Snapshots
|
||||
|
||||
@@ -111,6 +107,7 @@ Navigate to <NavPath path="Settings > Global configuration > Snapshots" />.
|
||||
| Field | Description |
|
||||
| -------------------------------------------------- | ----------------------------------------------------------------------------------- |
|
||||
| **Snapshot retention > Default retention** | Number of days to retain snapshots (default: 10) |
|
||||
| **Snapshot retention > Retention mode** | Retention mode: `all`, `motion`, or `active_objects` |
|
||||
| **Snapshot retention > Object retention > Person** | Per-object overrides for retention days (e.g., keep `person` snapshots for 15 days) |
|
||||
|
||||
</TabItem>
|
||||
@@ -121,6 +118,7 @@ snapshots:
|
||||
enabled: True
|
||||
retain:
|
||||
default: 10
|
||||
mode: motion
|
||||
objects:
|
||||
person: 15
|
||||
```
|
||||
@@ -132,7 +130,7 @@ snapshots:
|
||||
|
||||
Frigate does not save every frame. It picks a single "best" frame for each tracked object based on detection confidence, object size, and the presence of key attributes like faces or license plates. Frames where the object touches the edge of the frame are deprioritized. That best frame is written to disk once tracking ends.
|
||||
|
||||
MQTT snapshots are published more frequently: each time a better thumbnail frame is found during tracking, or when the current best image is older than `best_image_timeout` (default: 60s). These use their own annotation settings configured under the camera MQTT settings.
|
||||
MQTT snapshots are published more frequently — each time a better thumbnail frame is found during tracking, or when the current best image is older than `best_image_timeout` (default: 60s). These use their own annotation settings configured under the camera MQTT settings.
|
||||
|
||||
## Rendering
|
||||
|
||||
|
||||
@@ -43,7 +43,7 @@ Let's look at an example use case: I want to record any cars that enter my drive
|
||||
|
||||
One might simply think "Why not just run object detection any time there is motion around the driveway area and notify if the bounding box is in that zone?"
|
||||
|
||||
With that approach, what video is related to the car that entered the driveway? Did it come from the left or right? Was it parked across the street for an hour before turning into the driveway? One approach is to just record 24/7 or for motion (on any changed pixels) and not attempt to do that at all. This is what most other NVRs do. Just don't even try to identify a start and end for that object since it's hard and you will be wrong some portion of the time.
|
||||
With that approach, what video is related to the car that entered the driveway? Did it come from the left or right? Was it parked across the street for an hour before turning into the driveway? One approach is to just record 24/7 or for motion (on any changed changed pixels) and not attempt to do that at all. This is what most other NVRs do. Just don't even try to identify a start and end for that object since it's hard and you will be wrong some portion of the time.
|
||||
|
||||
Couldn't you just look at when motion stopped and started? Motion for a video feed is nothing more than looking for pixels that are different than they were in previous frames. If the car entered the driveway while someone was mowing the grass, how would you know which motion was for the car and which was for the person when they mow along the driveway or street? What if another car was driving the other direction on the street? Or what if its a windy day and the bush by your mailbox is blowing around?
|
||||
|
||||
@@ -61,4 +61,4 @@ Now you have to determine which of the bounding boxes in this frame should be ma
|
||||
|
||||
Now let's assume that those other 3 cars were already being tracked as stationary objects, so the car driving down the street is a new 4th car. The object tracker knows we have had 3 cars and we now have 4. As the new car approaches the parked cars, the bounding boxes for all 4 cars is predicted based on the previous frames. The predicted boxes for the parked cars is pretty much a 100% overlap with the bounding boxes in the new frame. The parked cars are slam dunk matches to the tracking ids they had before and the only one left is the remaining bounding box which gets assigned to the new car. This results in a much lower error rate. Not perfect, but better.
|
||||
|
||||
The most difficult scenario that causes IDs to be assigned incorrectly is when an object completely occludes another object. When a car drives in front of another car and it's no longer visible, a bounding box disappeared and it's a bit of a toss up when assigning the id since it's difficult to know which one is in front of the other. This happens for cars passing in front of other cars fairly often. It's something that we want to improve in the future.
|
||||
The most difficult scenario that causes IDs to be assigned incorrectly is when an object completely occludes another object. When a car drives in front of another car and its no longer visible, a bounding box disappeared and it's a bit of a toss up when assigning the id since it's difficult to know which one is in front of the other. This happens for cars passing in front of other cars fairly often. It's something that we want to improve in the future.
|
||||
|
||||
@@ -18,7 +18,7 @@ Zones cannot have the same name as a camera. If desired, a single zone can inclu
|
||||
|
||||
Zones can be toggled on or off without removing them from the configuration. Disabled zones are completely ignored at runtime - objects will not be tracked for zone presence, and zones will not appear in the debug view. This is useful for temporarily disabling a zone during certain seasons or times of day without modifying the configuration.
|
||||
|
||||
During testing, enable the Zones option for the [Debug view](/usage/live#the-single-camera-view) of your camera so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
|
||||
During testing, enable the Zones option for the Debug view of your camera (Settings --> Debug) so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
|
||||
|
||||
## Creating a Zone
|
||||
|
||||
@@ -61,7 +61,7 @@ Navigate to <NavPath path="Settings > Camera configuration > Review" />.
|
||||
|
||||
| Field | Description |
|
||||
| ---------------------------------- | ----------------------------------------------------------------------------------------- |
|
||||
| **Alerts config > Required zones** | Set to `entire_yard` so an object must enter that zone to be considered an alert; leave empty to allow alerts anywhere in the frame. |
|
||||
| **Alerts config > Required zones** | Zones that an object must enter to be considered an alert; leave empty to allow any zone. |
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -82,7 +82,7 @@ cameras:
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
You may also want to filter detections to only be created when an object enters a secondary area of interest. For example, to trigger alerts when an object enters the inner area of the yard (an `inner_yard` zone) but detections when an object enters the edge of the yard (an `edge_yard` zone):
|
||||
You may also want to filter detections to only be created when an object enters a secondary area of interest. For example, to trigger alerts when an object enters the inner area of the yard but detections when an object enters the edge of the yard:
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
@@ -91,8 +91,8 @@ Navigate to <NavPath path="Settings > Camera configuration > Review" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------------------------- | -------------------------------------------------------------------------------------------- |
|
||||
| **Alerts config > Required zones** | Set to `inner_yard` so an object must enter that zone to be considered an alert; leave empty to allow alerts anywhere in the frame. |
|
||||
| **Detections config > Required zones** | Set to `edge_yard` so an object must enter that zone to be considered a detection; leave empty to allow detections anywhere in the frame. |
|
||||
| **Alerts config > Required zones** | Zones that an object must enter to be considered an alert; leave empty to allow any zone. |
|
||||
| **Detections config > Required zones** | Zones that an object must enter to be considered a detection; leave empty to allow any zone. |
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -121,7 +121,7 @@ cameras:
|
||||
|
||||
### Restricting snapshots to specific zones
|
||||
|
||||
To only save snapshots when an object enters a specific zone, for example an `entire_yard` zone:
|
||||
To only save snapshots when an object enters a specific zone:
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
@@ -5,7 +5,7 @@ title: Camera setup
|
||||
|
||||
Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but less compatibility. Firefox 134+/136+/137+ (Windows/Mac/Linux & Android), Chrome 108+, Safari and Edge are the only browsers able to play H.265 and only support a limited number of H.265 profiles. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
|
||||
|
||||
- **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The default frame rate of 5fps is correct for almost all cameras and rarely needs to be changed; see [Choosing a detect frame rate](#choosing-a-detect-frame-rate). Higher resolutions and frame rates will drive higher CPU usage on your server.
|
||||
- **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The recommended frame rate is 5fps, but may need to be higher (10fps is the recommended maximum for most users) for very fast moving objects. Higher resolutions and frame rates will drive higher CPU usage on your server.
|
||||
|
||||
- **Recording**: This stream should be the resolution you wish to store for reference. Typically, this will be the highest resolution your camera supports. I recommend setting this feed in your camera's firmware to 15 fps.
|
||||
|
||||
@@ -25,44 +25,6 @@ Larger resolutions **do** improve performance if the objects are very small in t
|
||||
|
||||
|
||||
|
||||
### Choosing a detect frame rate
|
||||
|
||||
`detect.fps` controls how many times per second Frigate runs object detection. It does **not** need to match your camera's frame rate. The default of **5** is correct for the vast majority of cameras.
|
||||
|
||||
:::warning
|
||||
|
||||
Most users who raise `detect.fps` above the default don't need to. Increasing it consumes more CPU/GPU (detection load scales directly with the frame rate) while providing **no benefit to tracking** once objects are already being followed smoothly. Leave it at **5** unless you have a specific scene that fails the test below, and confirm any change actually helps in the [debug view](/usage/live#the-single-camera-view).
|
||||
|
||||
:::
|
||||
|
||||
#### Why 5 is enough for almost everyone
|
||||
|
||||
Frigate follows an object by matching its bounding box from one detection frame to the next, which requires the object to be detected often enough while it is on screen. At 5 fps this is satisfied in normal scenes: an object crossing a yard, porch, driveway, or walkway is in view for several seconds and produces ~15 or more detections, which is more than enough for a reliable track and a good snapshot. This includes fast subjects such as a running person or a bolting pet, which on a wide-angle view remain on screen for several seconds.
|
||||
|
||||
A higher rate helps only when an object crosses the **entire frame in less than two seconds**, which is determined by camera framing rather than object speed - for example, a camera aimed down a street at fast cross-traffic. In those scenes 5 fps may produce too few detections to hold a track. Cameras covering normal approaches and open areas are unaffected.
|
||||
|
||||
#### Checking whether a higher rate is needed
|
||||
|
||||
Estimate how long an object is visible as it crosses the area of interest, aiming for roughly 8–10 detections during the pass:
|
||||
|
||||
> **`detect.fps` ≈ 10 ÷ (seconds the object is in view)**
|
||||
|
||||
Most objects (people walking or running, pets, and vehicles in a yard, driveway, or walkway) stay in view for two seconds or more, so the default of 5 fps is correct. Slowly try raising it to 10 (the recommended maximum) in increments only when objects routinely cross the entire frame in about a second, such as a camera aimed at a street or sidewalk with fast cross-traffic. Objects that transit in under a second cannot be tracked reliably at any practical rate, so reposition the camera instead.
|
||||
|
||||
:::tip
|
||||
|
||||
If the formula calls for more than 10, the fix is **camera placement, not frame rate**. Angle the camera so objects move toward it rather than across the view, or aim it where traffic slows. A higher `detect.fps` increases CPU load proportionally without producing more detections of a too-brief object.
|
||||
|
||||
:::
|
||||
|
||||
#### Verify in the debug view
|
||||
|
||||
Confirm any change in the Debug view or Debug Replay. Watch a typical object cross the scene: if its bounding box follows it smoothly while visible, the rate is sufficient. A box that jumps erratically, drops out, or splits one object into multiple events indicates the rate should be increased one step.
|
||||
|
||||
#### Dedicated LPR cameras
|
||||
|
||||
A dedicated license plate recognition camera is the most common reason to use something higher than 5 fps: the camera is highly zoomed, the plate is small, and it moves at full vehicle speed, so it transits the frame quickly. However, the same ceiling applies: above 10 fps is unnecessary, and **placement matters most**: aim LPR cameras where vehicles slow down, such as gates, driveways, and parking entrances. A tight view of a fast through-road will not likely read plates reliably at any frame rate. See [License Plate Recognition](/configuration/license_plate_recognition) for details.
|
||||
|
||||
### Example Camera Configuration
|
||||
|
||||
For the Dahua/Loryta 5442 camera, I use the following settings:
|
||||
|
||||
@@ -5,40 +5,20 @@ title: Glossary
|
||||
|
||||
The glossary explains terms commonly used in Frigate's documentation.
|
||||
|
||||
## Alert
|
||||
|
||||
The higher-priority of the two [review item](#review-item) severities, the other being a [detection](#detection). By default a review item is an alert when it involves a `person` or `car`; the qualifying [labels](#label) and [zones](#zone) can be configured. [See the review docs for more info](/configuration/review)
|
||||
|
||||
## Attribute
|
||||
|
||||
A property detected on an [object](#object) that exists alongside its [label](#label). Unlike a [sub label](#sub-label), an object can carry several attributes at once. Some attributes come directly from the object detection [model](#model) (for example `face`, `license_plate`, or delivery carrier logos such as `amazon`, `ups`, and `fedex`), while others come from a [custom object classification model](/configuration/custom_classification/object_classification) configured with the `attribute` type. Attributes are visible in the Tracked Object Details pane in Explore, in `frigate/events` MQTT messages, and through the HTTP API.
|
||||
|
||||
## Bounding Box
|
||||
|
||||
A box returned by the object detection [model](#model) that outlines a detected [object](#object) in the frame. In the [Debug view](/usage/live#the-single-camera-view), bounding boxes are colored by object [label](#label).
|
||||
A box returned from the object detection model that outlines an object in the frame. These have multiple colors depending on object type in the debug live view.
|
||||
|
||||
### Bounding Box Colors
|
||||
|
||||
- At startup different colors will be assigned to each object label
|
||||
- A dark blue thin line indicates that object is not detected at this current point in time
|
||||
- A gray thin line indicates that object is detected as being stationary
|
||||
- A thick line indicates that object is the subject of autotracking (when enabled)
|
||||
|
||||
## Class
|
||||
|
||||
The categories a classification [model](#model) is trained to distinguish between. Each class is a distinct visual category the model predicts, plus a `none` class for inputs that don't fit any category. For example, a custom object classification model for `person` objects might use the classes `delivery_person`, `resident`, and `none`. The predicted class is applied to the [object](#object) as either a [sub label](#sub-label) or an [attribute](#attribute), depending on the model's configuration. [See the object classification docs for more info](/configuration/custom_classification/object_classification)
|
||||
|
||||
## Detection
|
||||
|
||||
The lower-priority of the two [review item](#review-item) severities, the other being an [alert](#alert). By default, any review item that does not qualify as an alert is a detection; the qualifying [labels](#label) and [zones](#zone) can be configured. Despite the name, a detection is a category of review item, not the same as the object detection performed by the [model](#model). [See the review docs for more info](/configuration/review)
|
||||
- A thick line indicates that object is the subject of autotracking (when enabled).
|
||||
|
||||
## False Positive
|
||||
|
||||
An incorrect result from the object detection [model](#model), where it assigns the wrong [label](#label) to something in the frame, for example a dog identified as a person, or a chair identified as a dog. A person correctly identified in an area you want to ignore is not a false positive.
|
||||
|
||||
## Label
|
||||
|
||||
The type assigned to a detected [object](#object) by the object detection [model](#model), drawn from the model's labelmap, for example `person`, `car`, or `dog`. Frigate tracks `person` by default; additional labels are tracked by adding them to the objects configuration. [See the available objects docs for the full list](/configuration/objects)
|
||||
An incorrect detection of an object type. For example a dog being detected as a person, a chair being detected as a dog, etc. A person being detected in an area you want to ignore is not a false positive.
|
||||
|
||||
## Mask
|
||||
|
||||
@@ -46,56 +26,44 @@ There are two types of masks in Frigate. [See the mask docs for more info](/conf
|
||||
|
||||
### Motion Mask
|
||||
|
||||
A motion mask stops [motion](#motion) in the masked area from triggering object detection. It does not stop an object from being detected when object detection runs because of motion in a nearby area. Use motion masks for parts of the frame that change constantly but never contain objects you care about: camera timestamps, the sky, the tops of trees, and so on.
|
||||
Motion masks prevent detection of [motion](#motion) in masked areas from triggering Frigate to run object detection, but do not prevent objects from being detected if object detection runs due to motion in nearby areas. For example: camera timestamps, skies, the tops of trees, etc.
|
||||
|
||||
### Object Mask
|
||||
|
||||
An object filter mask drops any [bounding box](#bounding-box) whose bottom center falls inside the masked area (overlap elsewhere doesn't matter). The object is forced to be treated as a [false positive](#false-positive) and ignored.
|
||||
Object filter masks drop any bounding boxes where the bottom center (overlap doesn't matter) is in the masked area. It forces them to be considered a [false positive](#false-positive) so that they are ignored.
|
||||
|
||||
## Min Score
|
||||
|
||||
The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded.
|
||||
|
||||
## Model
|
||||
|
||||
A machine learning model that Frigate uses to detect or classify objects. The object detection model locates [objects](#object) in each frame and returns their [labels](#label) and [bounding boxes](#bounding-box). Additional enrichment models run on tracked objects to add detail: face recognition, license plate recognition, bird classification, custom object and state classification, and the embedding models used for semantic search. [See the object detectors docs for more info](/configuration/object_detectors)
|
||||
The lowest score that an object can be detected with during tracking, any detection with a lower score will be assumed to be a false positive
|
||||
|
||||
## Motion
|
||||
|
||||
A change in pixels between the current camera frame and previous frames. When many nearby pixels change together, they are grouped and shown as a red motion box in the debug live view. [See the motion detection docs for more info](/configuration/motion_detection)
|
||||
|
||||
## Object
|
||||
|
||||
Something Frigate can detect and follow in a camera frame, identified by its [label](#label) (for example a person or a car). The object types Frigate watches for are set in the `objects` configuration. Once an object is detected and followed across frames it becomes a [tracked object](#tracked-object-event-in-previous-versions), which may also carry a [sub label](#sub-label) and [attributes](#attribute). [See the available objects docs for more info](/configuration/objects)
|
||||
When pixels in the current camera frame are different than previous frames. When many nearby pixels are different in the current frame they grouped together and indicated with a red motion box in the live debug view. [See the motion detection docs for more info](/configuration/motion_detection)
|
||||
|
||||
## Region
|
||||
|
||||
A portion of the camera frame sent to the object detection [model](#model). Regions are selected because of [motion](#motion), active objects, or occasionally to recheck stationary objects, and are shown as green boxes in the debug live view.
|
||||
A portion of the camera frame that is sent to object detection, regions can be sent due to motion, active objects, or occasionally for stationary objects. These are represented by green boxes in the debug live view.
|
||||
|
||||
## Review Item
|
||||
|
||||
A period of time during which one or more [tracked objects](#tracked-object-event-in-previous-versions) were active, grouped together for review. Each review item is categorized as either an [alert](#alert) or a [detection](#detection). [See the review docs for more info](/configuration/review)
|
||||
A review item is a time period where any number of events/tracked objects were active. [See the review docs for more info](/configuration/review)
|
||||
|
||||
## Snapshot Score
|
||||
|
||||
The object's score at the specific moment the snapshot was captured.
|
||||
|
||||
## Sub Label
|
||||
|
||||
A more specific identity assigned to a [tracked object](#tracked-object-event-in-previous-versions) in addition to its [label](#label). A `person` may get the name of a recognized face, a `car` may get the name of a known license plate, and a `bird` may get its species. An object can have only one sub label at a time. Sub labels are produced by face recognition, license plate recognition, bird classification, custom object classification configured with the `sub label` type, and semantic search triggers.
|
||||
The score shown in a snapshot is the score of that object at that specific moment in time.
|
||||
|
||||
## Threshold
|
||||
|
||||
The median score an object must reach to be considered a true positive.
|
||||
The threshold is the median score that an object must reach in order to be considered a true positive.
|
||||
|
||||
## Top Score
|
||||
|
||||
The highest median score an object reached over its lifetime.
|
||||
The top score for an object is the highest median score for an object.
|
||||
|
||||
## Tracked Object ("event" in previous versions)
|
||||
|
||||
An [object](#object) followed from the moment it enters the frame until it leaves, including any time it stays still. A tracked object is saved once it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording.
|
||||
The time period starting when a tracked object entered the frame and ending when it left the frame, including any time that the object remained still. Tracked objects are saved when it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording to be saved.
|
||||
|
||||
## Zone
|
||||
|
||||
A user-defined area of interest within the camera frame. Zones can be used for notifications and to limit where Frigate creates a [review item](#review-item). [See the zone docs for more info](/configuration/zones)
|
||||
Zones are areas of interest, zones can be used for notifications and for limiting the areas where Frigate will create a [review item](#review-item). [See the zone docs for more info](/configuration/zones)
|
||||
|
||||
@@ -55,7 +55,7 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
**Most Hardware**
|
||||
|
||||
- [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices offering a wide range of compatibility with devices.
|
||||
- [Supports many model architectures](../../configuration/object_detectors#configuration-hailo)
|
||||
- [Supports many model architectures](../../configuration/object_detectors#configuration)
|
||||
- Runs best with tiny or small size models
|
||||
|
||||
- [Google Coral EdgeTPU](#google-coral-tpu): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
|
||||
@@ -68,26 +68,26 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
**AMD**
|
||||
|
||||
- [ROCm](#rocm---amd-gpu): ROCm can run on AMD Discrete GPUs to provide efficient object detection
|
||||
- [Supports limited model architectures](../../configuration/object_detectors#amdrocm-gpu-detector)
|
||||
- [Supports limited model architectures](../../configuration/object_detectors#rocm-supported-models)
|
||||
- Runs best on discrete AMD GPUs
|
||||
|
||||
**Apple Silicon**
|
||||
|
||||
- [Apple Silicon](#apple-silicon): Apple Silicon is usable on all M1 and newer Apple Silicon devices to provide efficient and fast object detection
|
||||
- [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#apple-silicon-detector)
|
||||
- [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#apple-silicon-supported-models)
|
||||
- Runs well with any size models including large
|
||||
- Runs via ZMQ proxy which adds some latency, only recommended for local connection
|
||||
|
||||
**Intel**
|
||||
|
||||
- [OpenVino](#openvino---intel): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel NPUs to provide efficient object detection.
|
||||
- [Supports majority of model architectures](../../configuration/object_detectors#openvino-detector)
|
||||
- [Supports majority of model architectures](../../configuration/object_detectors#openvino-supported-models)
|
||||
- Runs best with tiny, small, or medium models
|
||||
|
||||
**Nvidia**
|
||||
|
||||
- [Nvidia GPU](#nvidia-gpus): Nvidia GPUs can provide efficient object detection.
|
||||
- [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx)
|
||||
- [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models)
|
||||
- Runs well with any size models including large
|
||||
|
||||
- <CommunityBadge /> [Jetson](#nvidia-jetson): Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6.
|
||||
@@ -111,14 +111,14 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
|
||||
### Hailo-8
|
||||
|
||||
Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms, including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
|
||||
Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms—including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
|
||||
|
||||
**Default Model Configuration:**
|
||||
|
||||
- **Hailo-8L:** Default model is **YOLOv6n**.
|
||||
- **Hailo-8:** Default model is **YOLOv6n**.
|
||||
|
||||
In real-world deployments, even with multiple cameras running concurrently, Frigate has demonstrated consistent performance. Testing on x86 platforms, with dual PCIe lanes, yields further improvements in FPS, throughput, and latency compared to the Raspberry Pi setup.
|
||||
In real-world deployments, even with multiple cameras running concurrently, Frigate has demonstrated consistent performance. Testing on x86 platforms—with dual PCIe lanes—yields further improvements in FPS, throughput, and latency compared to the Raspberry Pi setup.
|
||||
|
||||
| Name | Hailo‑8 Inference Time | Hailo‑8L Inference Time |
|
||||
| ---------------- | ---------------------- | ----------------------- |
|
||||
@@ -223,11 +223,10 @@ Apple Silicon can not run within a container, so a ZMQ proxy is utilized to comm
|
||||
|
||||
With the [ROCm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many discrete AMD GPUs.
|
||||
|
||||
| Name | YOLOv9 Inference Time | YOLO-NAS Inference Time | RF-DETR Inference Time |
|
||||
| -------------- | --------------------------- | ------------------------- | ---------------------- |
|
||||
| AMD 780M | t-320: ~ 14 ms s-320: 20 ms | 320: ~ 25 ms 640: ~ 50 ms | |
|
||||
| AMD 8700G | | 320: ~ 20 ms 640: ~ 40 ms | |
|
||||
| AMD 9060XT 16G | t-320: ~ 4 ms s-320: 5 ms | 320: ~ 6 ms | Nano-320: ~ 90 ms |
|
||||
| Name | YOLOv9 Inference Time | YOLO-NAS Inference Time |
|
||||
| --------- | --------------------------- | ------------------------- |
|
||||
| AMD 780M | t-320: ~ 14 ms s-320: 20 ms | 320: ~ 25 ms 640: ~ 50 ms |
|
||||
| AMD 8700G | | 320: ~ 20 ms 640: ~ 40 ms |
|
||||
|
||||
## Community Supported Detectors
|
||||
|
||||
|
||||
@@ -4,15 +4,12 @@ title: Installation
|
||||
---
|
||||
|
||||
import ShmCalculator from '@site/src/components/ShmCalculator'
|
||||
import DockerComposeGenerator from '@site/src/components/DockerComposeGenerator'
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
|
||||
Frigate is a Docker container that can be run on any Docker host including as a [Home Assistant App](https://www.home-assistant.io/apps/). Note that the Home Assistant App is **not** the same thing as the integration. The [integration](/integrations/home-assistant) is required to integrate Frigate into Home Assistant, whether you are running Frigate as a standalone Docker container or as a Home Assistant App.
|
||||
|
||||
:::tip
|
||||
|
||||
If you already have Frigate installed as a Home Assistant App, check out the [getting started guide](../guides/getting_started.md#configuring-frigate) to configure Frigate.
|
||||
If you already have Frigate installed as a Home Assistant App, check out the [getting started guide](../guides/getting_started#configuring-frigate) to configure Frigate.
|
||||
|
||||
:::
|
||||
|
||||
@@ -78,7 +75,7 @@ Users of the Snapcraft build of Docker cannot use storage locations outside your
|
||||
|
||||
Frigate utilizes shared memory to store frames during processing. The default `shm-size` provided by Docker is **64MB**.
|
||||
|
||||
The default shm size of **128MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in Docker Compose). If raising the shm size does not help, check your [process and file limits](#process-and-file-limits) as well.
|
||||
The default shm size of **128MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in Docker Compose).
|
||||
|
||||
The Frigate container also stores logs in shm, which can take up to **40MB**, so make sure to take this into account in your math as well.
|
||||
|
||||
@@ -86,30 +83,6 @@ The Frigate container also stores logs in shm, which can take up to **40MB**, so
|
||||
|
||||
The shm size cannot be set per container for Home Assistant Apps. However, this is probably not required since by default Home Assistant Supervisor allocates `/dev/shm` with half the size of your total memory. If your machine has 8GB of memory, chances are that Frigate will have access to up to 4GB without any additional configuration.
|
||||
|
||||
### Process and file limits
|
||||
|
||||
Frigate runs many processes and opens a number of shared memory files. Installs with a large number of cameras can exceed the default limits your container runtime applies.
|
||||
|
||||
Hitting the PID limit logs `RuntimeError: can't start new thread`, often followed by a "Bus error" that makes it look like an shm sizing problem. Compare the current count against the max from inside the container:
|
||||
|
||||
```bash
|
||||
cat /sys/fs/cgroup/pids.current
|
||||
cat /sys/fs/cgroup/pids.max
|
||||
```
|
||||
|
||||
If these are close, raise the limit with [`--pids-limit`](https://docs.docker.com/engine/containers/resource_constraints/) (or `service.pids_limit` in Docker Compose).
|
||||
|
||||
Running out of file descriptors logs `OSError: [Errno 24] Too many open files`. Raise the limit in Docker Compose:
|
||||
|
||||
```yaml
|
||||
services:
|
||||
frigate:
|
||||
ulimits:
|
||||
nofile:
|
||||
soft: 65535
|
||||
hard: 65535
|
||||
```
|
||||
|
||||
## Extra Steps for Specific Hardware
|
||||
|
||||
The following sections contain additional setup steps that are only required if you are using specific hardware. If you are not using any of these hardware types, you can skip to the [Docker](#docker) installation section.
|
||||
@@ -118,7 +91,7 @@ The following sections contain additional setup steps that are only required if
|
||||
|
||||
By default, the Raspberry Pi limits the amount of memory available to the GPU. In order to use ffmpeg hardware acceleration, you must increase the available memory by setting `gpu_mem` to the maximum recommended value in `config.txt` as described in the [official docs](https://www.raspberrypi.org/documentation/computers/config_txt.html#memory-options).
|
||||
|
||||
Additionally, the USB Coral draws a considerable amount of power. If using any other USB devices such as an SSD, you will experience instability due to the Pi not providing enough power to USB devices. You will need to purchase an external USB hub with its own power supply. Some have reported success with <a href="https://amzn.to/3a2mH0P" target="_blank" rel="nofollow noopener sponsored">this</a> (affiliate link).
|
||||
Additionally, the USB Coral draws a considerable amount of power. If using any other USB devices such as an SSD, you will experience instability due to the Pi not providing enough power to USB devices. You will need to purchase an external USB hub with it's own power supply. Some have reported success with <a href="https://amzn.to/3a2mH0P" target="_blank" rel="nofollow noopener sponsored">this</a> (affiliate link).
|
||||
|
||||
### Hailo-8
|
||||
|
||||
@@ -313,7 +286,7 @@ The MemryX MX3 Accelerator is available in the M.2 2280 form factor (like an NVM
|
||||
|
||||
#### Installation
|
||||
|
||||
To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/2p1/get_started/install_hardware.html).
|
||||
To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/get_started/hardware_setup.html).
|
||||
|
||||
Then follow these steps for installing the correct driver/runtime configuration:
|
||||
|
||||
@@ -322,12 +295,6 @@ Then follow these steps for installing the correct driver/runtime configuration:
|
||||
3. Run the script with `./user_installation.sh`
|
||||
4. **Restart your computer** to complete driver installation.
|
||||
|
||||
:::warning
|
||||
|
||||
For manual setup, use **MemryX SDK 2.1** only. Other SDK versions are not supported for this setup. See the [SDK 2.1 documentation](https://developer.memryx.com/2p1/index.html)
|
||||
|
||||
:::
|
||||
|
||||
#### Setup
|
||||
|
||||
To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
|
||||
@@ -501,20 +468,11 @@ Finally, configure [hardware object detection](/configuration/object_detectors#a
|
||||
|
||||
Running through Docker with Docker Compose is the recommended install method.
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="domestic" label="Docker Compose Generator" default>
|
||||
|
||||
Generate a Frigate Docker Compose configuration based on your hardware and requirements.
|
||||
|
||||
<DockerComposeGenerator/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="original" label="Example Docker Compose File">
|
||||
```yaml
|
||||
services:
|
||||
frigate:
|
||||
container_name: frigate
|
||||
# privileged: true # ONLY enable if your hardware requires it (see hardware-specific docs); prefer the device mappings below
|
||||
privileged: true # this may not be necessary for all setups
|
||||
restart: unless-stopped
|
||||
stop_grace_period: 30s # allow enough time to shut down the various services
|
||||
image: ghcr.io/blakeblackshear/frigate:stable
|
||||
@@ -543,37 +501,6 @@ services:
|
||||
environment:
|
||||
FRIGATE_RTSP_PASSWORD: "password"
|
||||
```
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
### Recommended security options
|
||||
|
||||
Frigate does not need elevated container privileges for most setups. The
|
||||
following hardens the container; add the `devices`/`group_add` entries your
|
||||
hardware requires (see the hardware acceleration docs):
|
||||
|
||||
```yaml
|
||||
services:
|
||||
frigate:
|
||||
...
|
||||
security_opt:
|
||||
- no-new-privileges:true
|
||||
cap_drop:
|
||||
- ALL
|
||||
```
|
||||
|
||||
:::note
|
||||
|
||||
`telemetry.stats.network_bandwidth` uses nethogs, which requires root with
|
||||
NET_ADMIN/NET_RAW capabilities. If you enable that stat, omit `cap_drop: [ALL]`
|
||||
or add `cap_add: [NET_ADMIN, NET_RAW]`.
|
||||
|
||||
Platforms that genuinely require `privileged: true` (MemryX, some QNAP setups)
|
||||
are called out in their own sections and are unaffected by this guidance.
|
||||
|
||||
:::
|
||||
|
||||
**Docker CLI**
|
||||
|
||||
If you can't use Docker Compose, you can run the container with something similar to this:
|
||||
|
||||
@@ -639,8 +566,6 @@ Home Assistant OS users can install via the App repository.
|
||||
5. Start the App
|
||||
6. Use the _Open Web UI_ button to access the Frigate UI, then click in the _cog icon_ > _Configuration editor_ and configure Frigate to your liking
|
||||
|
||||
App users who can't set container environment variables can put `FRIGATE_` values in a `secrets.yaml` next to `config.yml` in `/addon_configs/<addon_directory>` instead. See [`secrets.yaml`](../configuration/advanced/system.md#secretsyaml).
|
||||
|
||||
There are several variants of the App available:
|
||||
|
||||
| App Variant | Description |
|
||||
@@ -652,7 +577,7 @@ There are several variants of the App available:
|
||||
|
||||
If you are using hardware acceleration for ffmpeg, you **may** need to use the _Full Access_ variant of the App. This is because the Frigate App runs in a container with limited access to the host system. The _Full Access_ variant allows you to disable _Protection mode_ and give Frigate full access to the host system.
|
||||
|
||||
You can also edit the Frigate configuration file through the [VS Code App](https://github.com/hassio-addons/addon-vscode) or similar. In that case, the configuration file will be at `/addon_configs/<addon_directory>/config.yml`, where `<addon_directory>` is specific to the variant of the Frigate App you are running. See the list of directories [here](../configuration/config.md#accessing-app-config-dir).
|
||||
You can also edit the Frigate configuration file through the [VS Code App](https://github.com/hassio-addons/addon-vscode) or similar. In that case, the configuration file will be at `/addon_configs/<addon_directory>/config.yml`, where `<addon_directory>` is specific to the variant of the Frigate App you are running. See the list of directories [here](../configuration/index.md#accessing-app-config-dir).
|
||||
|
||||
## Kubernetes
|
||||
|
||||
@@ -801,7 +726,7 @@ Failure to remap port 5000 on the host will result in the WebUI and all API endp
|
||||
|
||||
:::
|
||||
|
||||
Docker containers on macOS can be orchestrated by either [Docker Desktop](https://docs.docker.com/desktop/setup/install/mac-install/) or [OrbStack](https://orbstack.dev) (native Swift app). The difference in inference speeds is negligible, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application.
|
||||
Docker containers on macOS can be orchestrated by either [Docker Desktop](https://docs.docker.com/desktop/setup/install/mac-install/) or [OrbStack](https://orbstack.dev) (native swift app). The difference in inference speeds is negligable, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application.
|
||||
|
||||
To allow Frigate to use the Apple Silicon Neural Engine / Processing Unit (NPU) the host must be running [Apple Silicon Detector](../configuration/object_detectors.md#apple-silicon-detector) on the host (outside Docker)
|
||||
|
||||
@@ -820,7 +745,7 @@ services:
|
||||
- /path/to/your/recordings:/recordings
|
||||
ports:
|
||||
- "8971:8971"
|
||||
# If exposing on macOS map to a different host port like 5001 or any other port with no conflicts
|
||||
# If exposing on macOS map to a diffent host port like 5001 or any orher port with no conflicts
|
||||
# - "5001:5000" # Internal unauthenticated access. Expose carefully.
|
||||
- "8554:8554" # RTSP feeds
|
||||
extra_hosts:
|
||||
|
||||
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Reference in New Issue
Block a user