config.yaml

######################################
#  Ambianic main configuration file  #
######################################
version: '0.10.21'

# 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
# Using Home Assistant conventions to ease upcoming integration
sources:
  # front_door_camera: &src_front_door_cam
    # uri: *secret_uri_front_door_camera
    # type: video, audio, or auto
    # type: video
    # live: true
  # entry_area_camera: &src_entry_area_cam
    # uri: *secret_uri_entry_area_camera
    # type: video
  # recorded front door cam feed for quick testing
    # live: is this a live source or a recording
    # when live is True, the AVSource element will try to reconnect
    # if the stream is interrupted due to network disruption or another reason.
    # live: True
  recorded_cam_feed: &src_recorded_cam_feed
    uri: file:///workspace/tests/pipeline/avsource/test2-cam-person1.mkv
    type: video

ai_models:
#  image_classification:
#    tf_graph:
#    labels:
  image_detection: &tfm_image_detection
    model:
      tflite: ai_models/mobilenet_ssd_v2_coco_quant_postprocess.tflite
      edgetpu: ai_models/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite
    labels: ai_models/coco_labels.txt
  face_detection: &tfm_face_detection
    model:
      tflite: ai_models/mobilenet_ssd_v2_face_quant_postprocess.tflite
      edgetpu: ai_models/mobilenet_ssd_v2_face_quant_postprocess_edgetpu.tflite
    labels: 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: 0.8
     - 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: 0.8
     - 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
    # - mask: svg # apply a mask to the input data
#    - 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

    # - gate: [category: person] # select the labels that will be used as input by the next ai model
    # - ai: # run the next ai model inference on data from the previous section of the pipeline
        # <<: *tfm_face_detection
        # confidence_threshold: 0.6
        # save_samples:
        # location: *data_dir
        # positive_interval: 2 # how often (in seconds) to save samples with positive label detections
      # - gate: [category: face] # select detected faces and pass on to face recognition model
      # - ai: *tfm_face_recognition
          # confidence_threshold: 0.6
      # - save_samples:
          # location: *data_dir
          # positive_interval: 2 # how often (in seconds) to save samples with positive label detections
    # - tee: # a tee splits the pipeline into independent streams, each with its own thread and copy of the input data from the previous pipe
    #   - gate: [category: vehicle]
    #   - ai: *detect_license_plate

#  nighttime_front_door_watch:
#    source: @front_door
#    -
#      mask: svg
#        - uri: ...
#      inference: @find_person_or_vehicle
#    -
#      mask:
#
#  vacation_front_door_watch:

# Policies group pipeline and are mutually exclusive.
#  Only one policy is active at a time.
#  When a specific policy is activated, all other policies are idle
# policies:
#   daytime:
#     - daytime_front_door_watch
#     - daytime_backyard_door_watch
#   nighttime:
#   vacation:

# Masks allow filtering out input signal before an AI model sees it
# masks:
  # svg mask applies an SVG graphics onto an input image
#  svg: ambianic.masks.SvgMask
#    arguments:
#      - uri # of the svg file
      # scale_to_fit indicates whether to scale the mask to fit the stream source shape
      # default True
#      - scale_to_fit
  # audio_hz_trim is a simple mask that cuts out frequency ranges from an input audio stream
  # audio_hz_trim: ambian.masks.AudioHzTrim
  #  arguments:
  #    - hz_band # lower and upper frequency range to cut out

# print a heartbeat message to the log
# helps debug why some threads hang up or go idle for a variety of potential reasons
#heartbeat:
#  interval: 60 # once a minute