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Frigate/DoubleTake CIS Region Telegram chat 🇺🇦🇰🇿🇧🇾🇷🇺🇺🇳 make love, not war
Unified UI and API for processing and training images for facial recognition.
There's a lot of great open source software to perform facial recognition, but each of them behave differently. Double Take was created to abstract the complexities of the detection services and combine them into an easy to use UI and API.
- Responsive UI and API bundled into single Docker image
- Ability to password protect UI and API
- Support for multiple detectors
- Train and untrain images for subjects
- Process images from NVRs
- Publish results to MQTT topics
- REST API can be invoked by other applications
- Disable detection based on a schedule
- Home Assistant Add-ons
- Preprocess images with OpenCV
- amd64
- arm64
docker run -d -v $(pwd)/.double-take:/.storage -p 3000:3000 skrashevich/double-take:latestversion: '3.7'
volumes:
double-take:
services:
double-take:
container_name: double-take
image: skrashevich/double-take
restart: unless-stopped
volumes:
- double-take:/.storage
ports:
- 3000:3000To run the Double Take application in Docker on Windows, follow the below instructions:
-
Install Docker Desktop on Windows system if not already installed.
-
Open Command Prompt logged in as an administrator.
-
Pull the Double Take Docker image with the command:
docker pull skrashevich/double-take:latest -
Determine the location you wish to use for the configuration folder. For example: C:\Users\YourUsername\double-take-config .
-
Run the Docker command to start the Double Take container, replacing the default configuration folder location with your new location:
docker run -d -v C:\Users\YourUsername\double-take-config:/.storage -p 3000:3000 skrashevich/double-take:latest
Make sure that the C:\Users\YourUsername\double-take-config directory exists and you have the necessary permissions for that folder. If the folder does not exist, create it before running the Docker command.
- If all went well, your NodeJS app should now be up and running inside a Docker container. You can check your application by visiting
http://localhost:3000.
Subscribe to Frigate's MQTT topics and process images for analysis.
mqtt:
host: localhost
frigate:
url: http://localhost:5000When the frigate/events topic is updated the API begins to process the snapshot.jpg and latest.jpg images from Frigate's API. These images are passed from the API to the configured detector(s) until a match is found that meets the configured requirements. To improve the chances of finding a match, the processing of the images will repeat until the amount of retries is exhausted or a match is found.
When the frigate/+/person/snapshot topic is updated the API will process that image with the configured detector(s). It is recommended to increase the MQTT snapshot size in the Frigate camera config.
cameras:
front-door:
mqtt:
timestamp: False
bounding_box: False
crop: True
quality: 100
height: 500If a match is found the image is saved to /.storage/matches/<filename>.
Trigger automations / notifications when images are processed.
If the MQTT integration is configured within Home Assistant, then sensors will automatically be created.
This notification will work for both matches and unknown results. The message can be customized with any of the attributes from the entity.
alias: Notify
trigger:
- platform: state
entity_id: sensor.double_take_david
- platform: state
entity_id: sensor.double_take_unknown
condition:
- condition: template
value_template: '{{ trigger.to_state.state != trigger.from_state.state }}'
action:
- service: notify.mobile_app
data:
message: |-
{% if trigger.to_state.attributes.match is defined %}
{{trigger.to_state.attributes.friendly_name}} is near the {{trigger.to_state.state}} @ {{trigger.to_state.attributes.match.confidence}}% by {{trigger.to_state.attributes.match.detector}}:{{trigger.to_state.attributes.match.type}} taking {{trigger.to_state.attributes.attempts}} attempt(s) @ {{trigger.to_state.attributes.duration}} sec
{% elif trigger.to_state.attributes.unknown is defined %}
unknown is near the {{trigger.to_state.state}} @ {{trigger.to_state.attributes.unknown.confidence}}% by {{trigger.to_state.attributes.unknown.detector}}:{{trigger.to_state.attributes.unknown.type}} taking {{trigger.to_state.attributes.attempts}} attempt(s) @ {{trigger.to_state.attributes.duration}} sec
{% endif %}
data:
attachment:
url: |-
{% if trigger.to_state.attributes.match is defined %}
http://localhost:3000/api/storage/matches/{{trigger.to_state.attributes.match.filename}}?box=true&token={{trigger.to_state.attributes.token}}
{% elif trigger.to_state.attributes.unknown is defined %}
http://localhost:3000/api/storage/matches/{{trigger.to_state.attributes.unknown.filename}}?box=true&token={{trigger.to_state.attributes.token}}
{% endif %}
actions:
- action: URI
title: View Image
uri: |-
{% if trigger.to_state.attributes.match is defined %}
http://localhost:3000/api/storage/matches/{{trigger.to_state.attributes.match.filename}}?box=true&token={{trigger.to_state.attributes.token}}
{% elif trigger.to_state.attributes.unknown is defined %}
http://localhost:3000/api/storage/matches/{{trigger.to_state.attributes.unknown.filename}}?box=true&token={{trigger.to_state.attributes.token}}
{% endif %}
mode: parallel
max: 10Publish results to double-take/matches/<name> and double-take/cameras/<camera>. The number of results will also be published to double-take/cameras/<camera>/person and will reset back to 0 after 30 seconds.
Errors from the API will be published to double-take/errors.
mqtt:
host: localhost{
"id": "1623906078.684285-5l9hw6",
"duration": 1.26,
"timestamp": "2021-06-17T05:01:36.030Z",
"attempts": 3,
"camera": "living-room",
"zones": [],
"match": {
"name": "david",
"confidence": 66.07,
"match": true,
"box": { "top": 308, "left": 1018, "width": 164, "height": 177 },
"type": "latest",
"duration": 0.28,
"detector": "compreface",
"filename": "2f07d1ad-9252-43fd-9233-2786a36a15a9.jpg",
"base64": null
}
}{
"id": "ff894ff3-2215-4cea-befa-43fe00898b65",
"duration": 4.25,
"timestamp": "2021-06-17T03:19:55.695Z",
"attempts": 5,
"camera": "back-door",
"zones": [],
"matches": [
{
"name": "david",
"confidence": 100,
"match": true,
"box": { "top": 286, "left": 744, "width": 319, "height": 397 },
"type": "manual",
"duration": 0.8,
"detector": "compreface",
"filename": "dcb772de-d8e8-4074-9bce-15dbba5955c5.jpg",
"base64": null
}
],
"misses": [],
"unknowns": [],
"counts": { "person": 1, "match": 1, "miss": 0, "unknown": 0 }
}notify:
gotify:
url: http://localhost:8080
token:notify:
telegram:
token:
chat_id: "12345678"chat_id must be in quotes
Match images are saved to /.storage/matches and can be accessed via http://localhost:3000/api/storage/matches/<filename>.
Training images are saved to /.storage/train and can be accessed via http://localhost:3000/api/storage/train/<name>/<filename>.
Latest images are saved to /.storage/latest and can be accessed via http://localhost:3000/api/storage/latest/<name|camera>.jpg.
| Query Parameters | Description | Default |
|---|---|---|
box |
Show bounding box around faces | false |
token |
Access token |
The UI is accessible via http://localhost:3000.
- Matches:
/ - Train:
/train - Config:
/config - Access Tokens:
/tokens(if authentication is enabled)
Enable authentication to password protect the UI. This is recommended if running Double Take behind a reverse proxy which is exposed to the internet.
auth: trueDocumentation can be viewed on Here.
Configurable options are saved to /.storage/config/config.yml and are editable via the UI at http://localhost:3000/config. Default values do not need to be specified in configuration unless they need to be overwritten.
# enable authentication for ui and api (default: shown below)
auth: false# if authentication is enabled
# age of access token in api response and mqtt topics (default: shown below)
# expressed in seconds or a string describing a time span zeit/ms
# https://github.com/vercel/ms
token:
image: 24h# enable mqtt subscribing and publishing (default: shown below)
mqtt:
host:
username:
password:
client_id:
tls:
# cert chains in PEM format: /path/to/client.crt
cert:
# private keys in PEM format: /path/to/client.key
key:
# optionally override the trusted CA certificates: /path/to/ca.crt
ca:
# if true the server will reject any connection which is not authorized with the list of supplied CAs
reject_unauthorized: false
topics:
# mqtt topic for frigate message subscription
frigate: frigate/events
# mqtt topic for home assistant discovery subscription
homeassistant: homeassistant
# mqtt topic where matches are published by name
matches: double-take/matches
# mqtt topic where matches are published by camera name
cameras: double-take/cameras# global detect settings (default: shown below)
detect:
match:
# save match images
save: true
# include base64 encoded string in api results and mqtt messages
# options: true, false, box
base64: false
# minimum confidence needed to consider a result a match
confidence: 60
# hours to keep match images until they are deleted
purge: 168
# minimum area in pixels to consider a result a match
min_area: 10000
unknown:
# save unknown images
save: true
# include base64 encoded string in api results and mqtt messages
# options: true, false, box
base64: false
# minimum confidence needed before classifying a name as unknown
confidence: 40
# hours to keep unknown images until they are deleted
purge: 8
# minimum area in pixels to keep an unknown result
min_area: 0# frigate settings (default: shown below)
frigate:
url:
# if double take should send matches back to frigate as a sub label
# NOTE: requires frigate 0.11.0+
update_sub_labels: false
# stop the processing loop if a match is found
# if set to false all image attempts will be processed before determining the best match
stop_on_match: true
# ignore detected areas so small that face recognition would be difficult
# quadrupling the min_area of the detector is a good start
# does not apply to MQTT events
min_area: 0
# object labels that are allowed for facial recognition
labels:
- person
attempts:
# number of times double take will request a frigate latest.jpg for facial recognition
latest: 10
# number of times double take will request a frigate snapshot.jpg for facial recognition
snapshot: 10
# process frigate images from frigate/+/person/snapshot topics
mqtt: true
# add a delay expressed in seconds between each detection loop
delay: 0
image:
# height of frigate image passed for facial recognition
height: 500
# only process images from specific cameras
cameras:
# - front-door
# - garage
# only process images from specific zones
zones:
# - camera: garage
# zone: driveway
# override frigate attempts and image per camera
events:
# front-door:
# attempts:
# # number of times double take will request a frigate latest.jpg for facial recognition
# latest: 5
# # number of times double take will request a frigate snapshot.jpg for facial recognition
# snapshot: 5
# # process frigate images from frigate/<camera-name>/person/snapshot topic
# mqtt: false
# # add a delay expressed in seconds between each detection loop
# delay: 1
# image:
# # height of frigate image passed for facial recognition (only if using default latest.jpg and snapshot.jpg)
# height: 1000
# # custom image that will be used in place of latest.jpg
# latest: http://camera-url.com/image.jpg
# # custom image that will be used in place of snapshot.jpg
# snapshot: http://camera-url.com/image.jpg
# This option allows setting a custom time delay for the MQTT home
# assistant device tracker.
# By adjusting device_tracker_timeout , users can determine how long they
# want to wait before receiving a 'not_home' message when no person is
# recognized. The time delay is implemented in minutes and the default value
# is set to 30 minutes
device_tracker_timeout: 30# camera settings (default: shown below)
cameras:
front-door:
# apply masks before processing image
# masks:
# # list of x,y coordinates to define the polygon of the zone
# coordinates:
# - 1920,0,1920,328,1638,305,1646,0
# # show the mask on the final saved image (helpful for debugging)
# visible: false
# # size of camera stream used in resizing masks
# size: 1920x1080
# override global detect variables per camera
# detect:
# match:
# # save match images
# save: true
# # include base64 encoded string in api results and mqtt messages
# # options: true, false, box
# base64: false
# # minimum confidence needed to consider a result a match
# confidence: 60
# # minimum area in pixels to consider a result a match
# min_area: 10000
# unknown:
# # save unknown images
# save: true
# # include base64 encoded string in api results and mqtt messages
# # options: true, false, box
# base64: false
# # minimum confidence needed before classifying a match name as unknown
# confidence: 40
# # minimum area in pixels to keep an unknown result
# min_area: 0
# snapshot:
# # process any jpeg encoded mqtt topic for facial recognition
# topic:
# # process any http image for facial recognition
# url:# detector settings (default: shown below)
detectors:
compreface:
url:
# recognition api key
key:
# number of seconds before the request times out and is aborted
timeout: 15
# minimum required confidence that a recognized face is actually a face
# value is between 0.0 and 1.0
det_prob_threshold: 0.8
# require opencv to find a face before processing with detector
opencv_face_required: false
# comma-separated slugs of face plugins
# https://github.com/exadel-inc/CompreFace/blob/master/docs/Face-services-and-plugins.md)
# face_plugins: mask,gender,age
# only process images from specific cameras, if omitted then all cameras will be processed
# cameras:
# - front-door
# - garage
rekognition:
aws_access_key_id: !secret aws_access_key_id
aws_secret_access_key: !secret aws_secret_access_key
aws_region:
collection_id: double-take
# require opencv to find a face before processing with detector
opencv_face_required: true
# only process images from specific cameras, if omitted then all cameras will be processed
# cameras:
# - front-door
# - garage
deepstack:
url:
key:
# number of seconds before the request times out and is aborted
timeout: 15
# require opencv to find a face before processing with detector
opencv_face_required: false
# only process images from specific cameras, if omitted then all cameras will be processed
# cameras:
# - front-door
# - garage
aiserver:
url:
# number of seconds before the request times out and is aborted
timeout: 15
# require opencv to find a face before processing with detector
opencv_face_required: false
# only process images from specific cameras, if omitted then all cameras will be processed
# cameras:
# - front-door
# - garage
facebox:
url:
# number of seconds before the request times out and is aborted
timeout: 15
# require opencv to find a face before processing with detector
opencv_face_required: false
# only process images from specific cameras, if omitted then all cameras will be processed
# cameras:
# - front-door
# - garage# opencv settings (default: shown below)
# docs: https://docs.opencv.org/4.6.0/d1/de5/classcv_1_1CascadeClassifier.html
opencv:
scale_factor: 1.05
min_neighbors: 4.5
min_size_width: 30
min_size_height: 30# schedule settings (default: shown below)
schedule:
# disable recognition if conditions are met
disable:
# - days:
# - monday
# - tuesday
# times:
# - 20:00-23:59
# cameras:
# - office
# - days:
# - tuesday
# - wednesday
# times:
# - 13:00-15:00
# - 18:00-20:00
# cameras:
# - living-room# notify settings (default: shown below)
notify:
gotify:
url:
token:
priority: 5
# only notify from specific cameras
# cameras:
# - front-door
# - garage
# only notify from specific zones
# zones:
# - camera: garage
# zone: driveway# time settings (default: shown below)
time:
# defaults to iso 8601 format with support for token-based formatting
# https://github.com/moment/luxon/blob/master/docs/formatting.md#table-of-tokens
format:
# time zone used in logs
timezone: UTC# log settings (default: shown below)
# options: silent, error, warn, info, http, verbose, debug, silly
logs:
level: info
sql: false # trace sql queries# ui settings (default: shown below)
ui:
# base path of ui
path:
pagination:
# number of results per page
limit: 50
thumbnails:
# value between 0-100
quality: 95
# value in pixels
width: 500
logs:
# number of lines displayed
lines: 500# telemetry settings (default: shown below)
# self hosted version of plausible.io
# 100% anonymous, used to help improve project
# no cookies and fully compliant with GDPR, CCPA and PECR
telemetry: trueNote: If using one of the Home Assistant Add-ons then the default Home Assistant /config/secrets.yaml file is used.
mqtt:
host: localhost
username: mqtt
password: !secret mqtt_password
detectors:
compreface:
url: localhost:8000
key: !secret compreface_keyThe secrets.yml file contains the corresponding value assigned to the identifier.
mqtt_password: <password>
compreface_key: <api-key>| Service | |
|---|---|
| UI | localhost:8080 |
| API | localhost:3000 |
| MQTT | localhost:1883 |
# start development containers
./.develop/docker up
# remove development containers
./.develop/docker down./.develop/build
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