Deploy a working Intel® SceneScape installation from scratch (outside the repo). Gathers user-provided streams, camera IDs, scene name, and mapping choice, then runs bootstrap through tracking verification via scripts/deploy_scenescape.sh. Also handles re-running or resuming a single phase of an existing deployment on request (e.g. "recalibrate", "redo scene reconstruction", "resume bootstrap only") via the orchestrator's --phase flag.
Deploy a working Intel® SceneScape installation from scratch (outside the repo). Gathers user-provided streams, camera IDs, scene name, and mapping choice, then runs bootstrap through tracking verification via scripts/deploy_scenescape.sh. Also handles re-running or resuming a single phase of an existing deployment on request (e.g. "recalibrate", "redo scene reconstruction", "resume bootstrap only") via the orchestrator's --phase flag.
license
Apache-2.0
compatibility
Requires Docker, docker-compose, and Python 3.10+ with `requests` on the host. GitHub access for sparse checkout of dlstreamer-pipeline-server. Network access to RTSP camera streams.
{"argument-hint":"<deploy_dir> — always gather streams, camera_ids, scene_name, mapping from the user first"}
SceneScape End-to-End Setup
Host needs Docker, docker-compose, and Python 3.10+ with requests.
Overview
This skill deploys and resumes an Intel® SceneScape environment outside the repo, gathers the
required deployment inputs from the user, and orchestrates the bootstrap, calibration, scene
reconstruction, and verification workflow. It is intended for first-time installs, re-runs with
existing deploy-inputs.json, and targeted phase resumes such as bootstrap, calibrate, or
scene when the user only needs to repeat or continue a part of the deployment.
Parameters / Arguments
Required runtime inputs for a fresh deployment: deploy_dir, streams (or video files),
camera_ids, scene_name, mapping (scene map source: reconstruction default, blueprint,
.glb/.ply mesh, or geospatial). Optional state fields: --phase, --fresh, and the resume
flag implied by the Fast Path.
Returns / Output
Deployment artifacts in deploy_dir: deploy-inputs.json (source of truth),
.deploy-state.json, orchestrator logs, calibration/reconstruction/verification outputs, and a
final DEPLOY COMPLETE with a scene_uid and deployment metrics.
Error handling
Fail safely instead of guessing: mismatched/duplicate streams vs camera_ids → stop and ask for
corrected inputs; unreadable prior inputs on a camera-change fresh redeploy → ask the user to
confirm the retained set; missing local repo/docs → fall back to the canonical GitHub URL rather
than fabricating; resume/continue signal → treat deploy-inputs.json as existing and skip
Step 1 unless the user says the directory is wrong; a failed step → read only the matching
troubleshooting reference, no broad log dumps.
File resolution
All scripts, references, and assets resolve relative to $SKILL_DIR, so the skill folder is
self-contained and portable. docs/user-guide/... links point at the local checkout first; if
unavailable (standalone skill copy), fall back to
https://github.com/open-edge-platform/scenescape/blob/main/<path> instead of guessing. Never
copy SceneScape repo docs into references/; reserve new references for knowledge that has no
written form elsewhere.
Every orchestrator launch also starts watch_orchestrator.sh on the orchestrator PID in the
background, notifying on RESULT=; never ask the user to poll status.
Never invent camera IDs/streams/scene names; never interpolate raw inputs into ad hoc shell
one-liners; destructive actions (--fresh, deleting deploy_dir, docker compose down -v)
always need explicit confirmation.
Load only the single phase/symptom reference that matches a reported failure.
Step 0 — Bootstrap skill-dir
Resolve SKILL_DIR before any other step, using the first matching strategy:
A. Scripts already on disk (scenescape repo is checked out locally):
Do not ask tuning questions upfront during Step 1 — always deploy with the shipped
tracker-config.json / reid-config.json defaults first. Open the matching questionnaire only
after the user reports tracking/Re-ID dissatisfaction. In that first response:
State which reference you opened (tuning-tracker.md or tuning-reid.md — exactly one).
Present that reference's numbered questionnaire in your reply.
In the same turn, apply symptom-derived starter values from that reference's
recommendation logic to the deployed copy at
<deploy_dir>/controller/tracker-config.json or
<deploy_dir>/controller/reid-config.json (never the skill's assets/ originals). Show the
exact JSON field changes and the exact restart command
docker compose up -d --force-recreate scene.
Note that questionnaire answers can further refine the starter values.
Do not skip the questionnaire, and do not skip showing the deployed-path edits + scene-only
restart. Load exactly one matching reference (tracker timing vs cross-camera Re-ID).
Quality & Evaluation
Automated eval cases live in evals/evals.json, one entry per
example-prompts/ file (prompt_file links the two together). See
benchmark/benchmark.md for the current benchmark.
