config-sample.yml

# VisionAlert Configuration File
# Note, use the syntax ${FOO} to load values from environment variables

# Maximum backlog of frames for object detection.  If we try to enqueue
# more than this, we'll log a warning and drop the oldest frames from the
# queue.  This parameter has a direct impact on the amount of RAM the
# application will use.  For example, 20 1920x1080 frames will consume
# about 125MB of RAM.
input_queue_maximum_frames: 20

# If unable to connect to the camera in this amount of time, give up and
# try again.
connection_timeout_seconds: 3

# If we don't receive any bytes from the camera in this amount of time,
# give up and try again.
read_timeout_seconds: 3

# AWS credentials for uploading images displayed in push notifications
# send via Gotify.
aws_access_key: ${AWS_ACCESS_KEY}
aws_secret_key: ${AWS_SECRET_KEY}

# This is the base URL that your uploaded images can be reached at.  This
# is normally something like https://s3.amazon.com/bucket-name
# This bucket must allow public read access of the images uploaded to it
# for them to display in Gotify.
aws_image_base_url: https://s3.amazon.com/gotify-images

# S3 bucket your images should be uploaded to.
aws_image_bucket: gotify-images

# S3 endpoint URL for uploads.  You can override this here if you use MinIO
# instead of S3
aws_s3_url: https://minio.example.com

# App key for Gotify push notifications
gotify_key: 78sadf0fdh_sdf

# Model and labelmap file that are used by Tensorflow.  Google Coral EdgeTPU
# is supported, but ensure you're using a value model for it, otherwise
# the application won't start.
tensorflow_model_file: detect.tflite
tensorflow_label_map: labelmap.txt

# List of cameras to monitor.  Each has various parameters that are described
# inline.
cameras:
    # All cameras must have a unique name.
  - name: Front Door

    # URL to capture frames from.  Many cameras offer a 'sub-stream' URL with
    # images at a lower resolution or frame rate.  Using this will conserve
    # bandwidth and RAM.  Many cameras allow the username and password as shown
    # here.  Note: For RTSP we will attempt to use TCP but fall back to UDP if
    # unable.
    url: rtsp://user:${CAMERA_PASSWORD}@192.168.1.31/Streaming/Channels/1

    # This is the frames per second that will be submitted for object detection.
    # If this is higher than the FPS received from the camera, then the camera's
    # native frame rate will be used.
    fps: 3

    # A single channel (grayscale) file that is the same resolution as your camera
    # that can be used to mask off areas where you don't want to be alerted when
    # objects are detected.  White pixels of the mask are regions you're
    # are interested in, black pixels are regions you don't want to be alerted of.
    # Note, the entire bounding box must be in the black area to prevent alerts.
    # Any part of the bottom of it in the white area will trigger an alert.
    mask: frontdoor.jpg

    # Types of objects you're interested in detecting and alerting on.  These
    # would come from the label map for whatever tensorflow model you're using.
    # Typically 'person', 'car', & 'truck' are what you're looking for.
    interests:

      person:
        # Minimum confidence percent that should trigger a detection event for
        # this interest class.  Valid values are from 0.0 to 1.0
        confidence: 0.60

      car:
        confidence: 0.55

      dog:
        confidence: 0.65