highlights

Purpose

Generates derived video products from recorded footage: a daily highlight reel (events scored and assembled into a short FFmpeg-concatenated clip) and scheduled per-camera timelapses. These are offline/batch jobs that read the events and recordings tables and write new rows into recordings tagged with a synthetic trigger.

Key files

vigilar/highlights/reel.py — event scoring map (PET_ESCAPE=10, WILDLIFE_PREDATOR=9, ...), clip selection, FFmpeg concat assembly, inserts resulting file via insert_recording
vigilar/highlights/timelapse.py — per-camera timelapse generator; reads timelapse_schedules, walks recordings within the configured hour window, stitches with FFmpeg, inserts a new recordings row
vigilar/highlights/__init__.py

MQTT topics

Subscribes: No MQTT subscribers found at time of writing. Publishes: No MQTT publishers found at time of writing.

Database tables

events — read-only, source for reel scoring
recordings — read (source clips) and write (new reel / timelapse entries via insert_recording)
timelapse_schedules — read by timelapse.py to determine which cameras / hours / generation times are active

Depends on

storage — all table access via vigilar.storage.queries and vigilar.storage.schema
FFmpeg subprocess for clip concatenation and timelapse assembly

Consumed by

web — the recordings blueprint surfaces reels and timelapses alongside regular recordings

Notes

Scoring is a hand-tuned dict in reel.py. The top-scored events of the day are pulled, their surrounding recordings are concatenated via FFmpeg, and the result is persisted as a new recordings row with a RecordingTrigger tag so the UI can distinguish generated from captured clips. Timelapse generation is driven by timelapse_schedules rows — each row defines a camera, an hour window, and a generate_time at which the day's frames are stitched.

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