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double-take

skrashevich/double-take

skrashevich

UI for processing and training images for facial recognition with fixes for non-latin names

3 次收藏下载次数: 0状态:社区镜像维护者:skrashevich仓库类型:镜像最近更新:1 年前
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![CodeFactor]([***]

Community-owned resources:

WorldWide *** server

Frigate/DoubleTake CIS Region *** chat 🇺🇦🇰🇿🇧🇾🇷🇺🇺🇳 make love, not war

Double Take

Unified UI and API for processing and training images for facial recognition.

Why?

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.

Features

  • Responsive UI and API bundled into single https://hub.docker.com/r/skrashevich/double-take
  • 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
  • https://github.com/skrashevich/hassio-addons
  • Preprocess images with OpenCV

Supported Architecture

  • amd64
  • arm64

Supported Detectors

  • https://github.com/exadel-inc/CompreFace
  • Amazon Rekognition
  • DeepStack
  • CodeProject.AI Server
  • Facebox

Supported NVRs

  • https://github.com/blakeblackshear/frigate

Installation

Docker

shell
docker run -d -v $(pwd)/.double-take:/.storage -p 3000:3000 skrashevich/double-take:latest

Docker Compose

yaml
version: '3.7'

volumes:
  double-take:

services:
  double-take:
    container_name: double-take
    image: skrashevich/double-take
    restart: unless-stopped
    volumes:
      - double-take:/.storage
    ports:
      - 3000:3000

Docker (Windows)

To run the Double Take application in Docker on Windows, follow the below instructions:

  1. Install Docker Desktop on Windows system if not already installed.

  2. Open Command Prompt logged in as an administrator.

  3. Pull the Double Take Docker image with the command:

    docker pull skrashevich/double-take:latest
    
  4. Determine the location you wish to use for the configuration folder. For example: C:\Users\YourUsername\double-take-config .

  5. 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.

  1. 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.

Integrations

https://github.com/blakeblackshear/frigate

Subscribe to Frigate's MQTT topics and process images for analysis.

yaml
mqtt:
  host: localhost

frigate:
  url: http://localhost:5000

When 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.

yaml
cameras:
  front-door:
    mqtt:
      timestamp: False
      bounding_box: False
      crop: True
      quality: 100
      height: 500

If a match is found the image is saved to /.storage/matches/<filename>.

Home Assistant

Trigger automations / notifications when images are processed.

If the MQTT integration is configured within Home Assistant, then sensors will automatically be created.

Notification Automation

This notification will work for both matches and unknown results. The message can be customized with any of the attributes from the entity.

yaml
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: 10

MQTT

Publish 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.

yaml
mqtt:
  host: localhost

double-take/matches/david

json
{
  "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
  }
}

double-take/cameras/back-door

json
{
  "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 Services

Gotify

yaml
notify:
  gotify:
    url: http://localhost:8080
    token:

***

yaml
notify:
  telegram:
    token: 
    chat_id: "12345678"

chat_id must be in quotes

API Images

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 ParametersDescriptionDefault
boxShow bounding box around facesfalse
tokenAccess token

UI

The UI is accessible via http://localhost:3000.

  • Matches: /
  • Train: /train
  • Config: /config
  • Access Tokens: /tokens (if authentication is enabled)

Authentication

Enable authentication to password protect the UI. This is recommended if running Double Take behind a reverse proxy which is exposed to the internet.

yaml
auth: true

API

Documentation can be viewed on Here.

Configuration

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.

auth

yaml
# enable authentication for ui and api (default: shown below)
auth: false

token

yaml
# 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

mqtt

yaml
# enable mqtt subscribing and publishing (default: shown below)
mqtt:
  host:
  username:
  password:
  client_id:
  protocol: mqtt # set to mqtts to enables TLS
  port: -1 # default value, expands to 1883 for mqtt or 8883 for mqtts
  expire_after: 600 # timeout (in seconds) of home-assistant "recognised" state. appears to sensor.double_take_{person} sensors. 0 for never expires
  
  tls: # don't forget to set protocol: mqtts if you need secure connection
    # 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

detect

yaml
# 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

yaml
# 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

cameras

yaml
# 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:

detectors

yaml
# detector settings (default: shown below)
detectors:
  compreface:
    # Enable this detector. The default is true (enabled) if the URL below is defined.
    enabled: true
    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,pose
    # only process images from specific cameras, if omitted then all cameras will be processed
    # cameras:
    #   - front-door
    #   - garage

  rekognition:
    # Enable this detector. The default is true (enabled) if the URL below is defined.
    enabled: true
    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:
    # Enable this detector. The default is true (enabled) if the URL below is defined.
    enabled: true
    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:
    # Enable this detector. The default is true (enabled) if the URL below is defined.
    enabled: true
    url:
    # 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: 0.4
    # 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:
    # Enable this detector. The default is true (enabled) if the URL below is defined.
    enabled: true
    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

yaml
# 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

yaml
# 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

yaml
# 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

yaml
# 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

logs

yaml
# log settings (default: shown below)
# options: silent, error, warn, info, http, verbose, debug, silly
logs:
  level: info
  sql: false # trace sql queries

ui

yaml
# 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

yaml
# 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: true

Storing Secrets

Note: If using one of the https://github.com/jakowenko/double-take-hassio-addons then the default Home Assistant /config/secrets.yaml file is used.

yaml
mqtt:
  host: localhost
  username: mqtt
  password: !secret mqtt_password

detectors:
  compreface:
    url: localhost:8000
    key: !secret compreface_key

The secrets.yml file contains the corresponding value assigned to the identifier.

yaml
mqtt_password: <password>
compreface_key: <api-key>

Development

Run Local Containers

Service
UIlocalhost:8080
APIlocalhost:3000
MQTTlocalhost:1883
bash
# start development containers
./.develop/docker up

# remove development containers
./.develop/docker down

Build Local Image

bash
./.develop/build

Star History

![Star History Chart]([***]

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镜像拉取常见问题

使用与功能问题

配置了专属域名后,docker search 为什么会报错?

docker search 限制

Docker Hub 上有的镜像,为什么在轩辕镜像网站搜不到?

站内搜不到镜像

机器不能直连外网时,怎么用 docker save / load 迁镜像?

离线 save/load

docker pull 拉插件报错(plugin v1+json)怎么办?

插件要用 plugin install

WSL 里 Docker 拉镜像特别慢,怎么排查和优化?

WSL 拉取慢

轩辕镜像安全吗?如何用 digest 校验镜像没被篡改?

安全与 digest

第一次用轩辕镜像拉 Docker 镜像,要怎么登录和配置?

新手拉取配置

错误码与失败问题

docker pull 提示 manifest unknown 怎么办?

manifest unknown

docker pull 提示 no matching manifest 怎么办?

no matching manifest(架构)

镜像已拉取完成,却提示 invalid tar header 或 failed to register layer 怎么办?

invalid tar header(解压)

Docker pull 时 HTTPS / TLS 证书验证失败怎么办?

TLS 证书失败

Docker pull 时 DNS 解析超时或连不上仓库怎么办?

DNS 超时

Docker 拉取出现 410 Gone 怎么办?

410 Gone 排查

出现 402 或「流量用尽」提示怎么办?

402 与流量用尽

Docker 拉取提示 UNAUTHORIZED(401)怎么办?

401 认证失败

遇到 429 Too Many Requests(请求太频繁)怎么办?

429 限流

docker login 提示 Cannot autolaunch D-Bus,还算登录成功吗?

D-Bus 凭证提示

为什么会出现「单层超过 20GB」或 413,无法加速拉取?

413 与超大单层

账号 / 计费 / 权限

轩辕镜像免费版和专业版有什么区别?

免费版与专业版区别

轩辕镜像支持哪些 Docker 镜像仓库?

支持的镜像仓库

镜像拉取失败还会不会扣流量?

失败是否计费

麒麟 V10 / 统信 UOS 提示 KYSEC 权限不够怎么办?

KYSEC 拦截脚本

如何在轩辕镜像申请开具发票?

申请开票

怎么修改轩辕镜像的网站登录和仓库登录密码?

修改登录密码

如何注销轩辕镜像账户?要注意什么?

注销账户

配置与原理类

写了 registry-mirrors,为什么还是走官方或仍然报错?

mirrors 不生效

怎么用 docker tag 去掉镜像名里的轩辕域名前缀?

去掉域名前缀

如何拉取指定 CPU 架构的镜像(如 ARM64、AMD64)?

指定架构拉取

用轩辕镜像拉镜像时快时慢,常见原因有哪些?

拉取速度原因

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oldzhang

运维工程师

Linux服务器

5

"Docker访问体验非常流畅,大镜像也能快速完成下载。"

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