Example config.yaml for a new Ambianic Edge install

config.yaml

######################################
#  Ambianic main configuration file  #
######################################
version: '1.2.4'

# path to the data directory
data_dir: &data_dir ./data

# Set logging level to one of DEBUG, INFO, WARNING, ERROR
logging:
  file: ./data/ambianic-log.txt
  level: DEBUG

# Pipeline event timeline configuration
timeline:
  event_log: ./data/timeline-event-log.yaml

# Cameras and other input data sources
sources:
  # replace rtsp uri below with the one for your own camera
  front_door_cam_feed: &src_recorded_cam_feed
    uri: rtsp://admin:[email protected]/media/video1
    type: video
    live: true
#  entry_area_cam_feed: &src_recorded_cam_feed
#    uri: rtsp://admin:[email protected]/media/video1
#    type: video
#    live: true

ai_models:
  image_detection: &tfm_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: &tfm_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

# A named pipeline defines an ordered sequence of operations
# such as reading from a data source, AI model inference, saving samples and others.
pipelines:
  # sequence of piped operations for use in daytime front door watch
   front_door_watch:
     - source: *src_recorded_cam_feed
     - detect_objects: # run ai inference on the input data
        <<: *tfm_image_detection
        confidence_threshold: 0.6
     - save_detections: # save samples from the inference results
        positive_interval: 2 # 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_faces: # run ai inference on the samples from the previous element output
        <<: *tfm_face_detection
        confidence_threshold: 0.6
     - save_detections: # save samples from the inference results
        positive_interval: 2
        idle_interval: 600

  # sequence of piped operations for use in daytime front door watch
#  entry_area_watch:
#    - source: *src_entry_area_cam
#    - detect_objects: # run ai inference on the input data
#        <<: *tfm_image_detection
#        confidence_threshold: 0.8
#    - save_detections: # save samples from the inference results
#        output_directory: *ea_object_detect_dir
#        positive_interval: 2 # 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_faces: # run ai inference on the samples from the previous element output
#        <<: *tfm_face_detection
#        confidence_threshold: 0.8
#    - save_detections: # save samples from the inference results
#        output_directory: *ea_face_detect_dir
#        positive_interval: 2
#        idle_interval: 600