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config.defaults.yaml
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config.defaults.yaml
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#################################################################
# Ambianic main configuration file with default settings #
# This file is periodically committed to the git repo. #
# It is also shipped with updates of the ambianic-edge binary. #
#################################################################
version: '2021.11.11'
# path to the data directory
data_dir: ./data
# Set logging level to one of DEBUG, INFO, WARNING, ERROR
logging:
file: ./data/ambianic-log.txt
level: INFO
# set a less noisy log level for the console output
# console_level: WARNING
# settings related to PWA UI
ui:
baseurl: "https://ui.ambianic.ai"
# Notification provider settings
# see https://github.com/caronc/apprise#popular-notification-services for syntax examples
notifications:
default:
enabled: false # boolean; default = true; enables / disables notifications with 'default' tag
templates:
title: "${event_labels}"
message: "${event_details_url}"
include_attachments: false
providers:
# Using Dynaconf template substitutions
# ref: https://www.dynaconf.com/dynamic/#template-substitutions
- "@format ifttt://{this.ifttt_webhook_id}@ambianic"
# catch_all_email:
# enabled: false # boolean; default = true; enables / disables notifications with 'catch_all_email' tag
# include_attachments: true; default = false
# providers:
# - mailto://userid:[email protected]
# alert_fall:
# providers:
# - mailto://userid:[email protected]
# - json://hostname/a/path/to/post/to
ifttt_webhook_id: "" # None by default
# Pipeline event timeline configuration
timeline:
event_log: ./data/timeline-event-log.yaml
# Cameras and other input data sources
# Using Home Assistant conventions to ease upcoming integration
sources:
# direct support for raspberry picamera
picamera:
uri: picamera
type: video
live: true
# local video device integration example
webcam:
uri: /dev/video0
type: video
live: true
recorded_cam_feed:
uri: file:///workspace/tests/pipeline/avsource/test2-cam-person1.mkv
type: video
live: false # set to true for a continuous loop over the recorded file
ai_models:
image_detection:
model:
tflite: /opt/ambianic-edge/ai_models/mobilenet_ssd_v2_coco_quant_postprocess.tflite
edgetpu: /opt/ambianic-edge/ai_models/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite
labels: /opt/ambianic-edge/ai_models/coco_labels.txt
face_detection:
model:
tflite: /opt/ambianic-edge/ai_models/mobilenet_ssd_v2_face_quant_postprocess.tflite
edgetpu: /opt/ambianic-edge/ai_models/mobilenet_ssd_v2_face_quant_postprocess_edgetpu.tflite
labels: /opt/ambianic-edge/ai_models/coco_labels.txt
top_k: 2
fall_detection:
model:
tflite: /opt/ambianic-edge/ai_models/posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite
edgetpu: /opt/ambianic-edge/ai_models/posenet_mobilenet_v1_075_721_1281_quant_decoder_edgetpu.tflite
labels: /opt/ambianic-edge/ai_models/pose_labels.txt
# A named pipeline defines an ordered sequence of operations
# such as reading from a data source, AI model inference, saving samples and others.
pipelines:
# Pipeline names could be descriptive, e.g. front_door_watch or entry_room_watch.
area_watch:
- source: picamera
- detect_objects: # run ai inference on the input data
ai_model: image_detection
confidence_threshold: 0.6
# Watch for any of the labels listed below. The labels must be from the model trained label set.
# If no labels are listed, then watch for all model trained labels.
label_filter:
- person
- car
- save_detections: # save samples from the inference results
positive_interval: 300 # how often (in seconds) to save samples with ANY results above the confidence threshold
idle_interval: 6000 # how often (in seconds) to save samples with NO results above the confidence threshold
- detect_falls: # look for falls
ai_model: fall_detection
confidence_threshold: 0.6
- save_detections: # save samples from the inference results
positive_interval: 10
idle_interval: 600000
# notify: # notify a thirdy party service
# providers:
# - alert_fall