Amc Run Video Calibration

Skill: Calibrate from Video Files

When to Use This Skill

Activate this skill when the user has pre-recorded MP4 files and wants to calibrate them via the AMC REST API. Typical prompts:

• "calibrate my videos" / "run AMC on these videos"
• "calibrate from video files"

Drives calibration through the REST API on user-supplied pre-recorded MP4 files — no CLI scripts or Docker bind-mounts required, just a running microservice and your files.

Prerequisites

• AMC microservice and UI running (follow skills/amc-setup-calibration-stack/SKILL.md)
• You know the microservice URL (e.g. http://<HOST_IP>:<MS_PORT>) and UI URL
• Video files locally as cam_00.mp4, cam_01.mp4, … time-synchronized, ~1920×1080
• Python 3 with requests

Data Privacy

Video files uploaded via this skill are transmitted to the AutoMagicCalib backend (REST endpoint). Only use this skill when the backend is deployed on a trusted platform / network.

What to Ask the User

Required

(Video-file naming and the microservice URL are specified under Prerequisites above — collect the inputs below.)

1. Videos directory — the folder the skill globs for cam_*.mp4, uploaded sorted alphabetically.
2. Microservice URL
3. Project name — short descriptive string

Auto-Detected (ask only if not found)

The script searches the videos dir, its first-level subdirectories, and its parent. If exactly one match is found, it is used; otherwise the script prints the searched locations and continues to explicit path or UI fallback:

Posting the settings file replaces UI Step 3 and may pin the detector (resnet/transformer), which is passed to /calibrate separately — see Step 4.

Optional

4. Ground truth zip — GT.zip with _World_Cameras_Camera_XX/ folders (enables evaluation metrics)
5. Focal lengths — one per camera, e.g. 1269.0, 1099.5, 1099.5
6. Detector type — resnet (default, fast) or transformer (slower, better under occlusion)
7. Run VGGT refinement? — if VGGT is ready after AMC completes, ask the user whether to run refinement (see setup skill)

See root README.md "Custom Dataset" section for input-video guidelines and ground-truth format.

Instructions

All endpoints below are implemented end-to-end in the Complete Python Script — the prose is the workflow plus the decisions the agent must make; the script is the authoritative runnable.

Step 1 — Create Project

POST /v1/create_project (form field project_name) → save the returned project_id.

Step 2 — Upload Videos (required)

POST /v1/upload_video_files/<project_id> (multipart files). Upload sorted alphabetically — the server assigns camera indices by upload order.

Step 3 — Resolve Local Files (Auto-Scan, Ask, or UI)

For each of calibration-settings, alignment, and layout, run this resolution:

1. Auto-scan VIDEO_DIR, one level of subdirectories under VIDEO_DIR, and VIDEO_DIR.parent for the candidate filenames (table above).
2. If exactly one match, use it and print what was found.
3. If zero or multiple matches, print the searched locations, then ask the user for an explicit path using the host's question mechanism; if none is available, ask in chat and wait. If they don't have the file, mark it for UI fallback.
4. UI fallback: tell the user to complete the corresponding UI step; wait for confirmation; for alignment/layout also verify files landed in projects/project_<id>/manual_adjustment/.

Step 4 — Upload Resolved Files

Upload each file resolved locally:

After a successful settings POST, parse the file for "detector" / "detector_type" — if it's "resnet" or "transformer", use that value for the /calibrate call in Step 7 (detector is a separate API parameter, not consumed by /config).

Step 5 — UI Fallback (only for files the user doesn't have locally)

If any of settings / alignment / layout was not resolved in Step 3, direct the user to the appropriate UI step:

• Settings missing → "Open UI project <project_id>, go to Step 3: Parameters, tune via the settings dialog (or accept defaults), click Save." Also: before the /calibrate call, ask the user which detector to use (resnet or transformer) using the host's question mechanism; if none is available, ask in chat and wait. UI Step 3 does not cover detector choice.
• Alignment or layout missing → "Open UI project <project_id>, go to Step 4: Alignment, upload layout, mark correspondence points, click Save."

Wait for user confirmation. For non-interactive script runs, provide the needed files up front; the script exits with a clear message rather than waiting on input. For alignment/layout, verify on disk before continuing:

REPO_ROOT=$(git rev-parse --show-toplevel)
# Resolve PROJECT_DIR from compose/.env (default: projects/ at repo root).
PROJECT_DIR_REL=$(grep ^PROJECT_DIR "$REPO_ROOT/compose/.env" 2>/dev/null | cut -d= -f2 | tr -d '[:space:]')
HOST_PROJECTS=$(cd "$REPO_ROOT/compose" && realpath "${PROJECT_DIR_REL:-../../projects}")

ls "$HOST_PROJECTS/project_<project_id>/manual_adjustment/"
# Expected: alignment_data.json, layout.png

Step 6 — Verify Project

POST /v1/verify_project/<project_id> → must return {"project_state": "READY"} before calibrating.

Step 7 — Start Calibration

Confirm the plan before calibrating. Whether the settings file and detector were auto-detected or asked, present a short summary and get explicit user confirmation before POST /calibrate using the host's question mechanism; if none is available, ask in chat and wait. The resolved values are the defaults, so confirming is one click, but the user can switch the detector or skip an auto-detected settings file. The standalone Python script prints the same plan and prompts only when stdin is interactive. Summarize:

• Detector — resnet or transformer (the value to be sent).
• Calibration settings — the file being applied (path), or "defaults" if none.
• Optional overrides — ground-truth zip and focal lengths, if any.

POST /v1/calibrate/<project_id>
Content-Type: application/json

{"detector_type": "resnet"}

Step 8 — Poll for Completion

GET /v1/get_project_info/<project_id> every 10 s — project_info.project_state goes RUNNING → COMPLETED (or ERROR, pull the log). Typical time: 10–60 min depending on video length and detector.

Step 9 — Get Results

GET /v1/result/<project_id>/evaluation_statistics (only if GT was uploaded; includes Average L2 distance(m) and Average reprojection error 0(px)), and GET /v1/amc/calibrate/<project_id>/log for the calibration log.

Status Fields from get_project_info

project_info.project_state is the AMC calibration lifecycle for the project: RUNNING → COMPLETED (or ERROR).

project_info.vggt_state is also per-project, a project-scoped VGGT refinement lifecycle rather than a direct global service or model-load status. A newly created project can report vggt_state: "INIT" even when the VGGT model is present and mounted. The expected VGGT lifecycle is INIT → READY after AMC calibration completes → RUNNING while VGGT refinement runs → COMPLETED (or ERROR).

Use vggt_state == "READY" only as the gate for optional VGGT refinement in Step 10. Interpret INIT on a new or uncalibrated project as normal project state. If AMC calibration is complete and the project remains in a non-ready VGGT state, confirm VGGT setup and model availability with the setup skill checks and service logs.

Step 10 — (Optional) VGGT Refinement

After AMC calibration completes, read vggt_state from GET /v1/get_project_info/<project_id>.

• If the project reports vggt_state == "READY", ask the user whether to run VGGT refinement using the host's question mechanism; if none is available, ask in chat and wait.
• If the user confirms, POST /v1/vggt/calibrate/<project_id>, poll vggt_state via get_project_info, then GET /v1/vggt_results/<project_id>/evaluation_statistics.
• If VGGT is not ready, skip refinement and explain that the user can set up VGGT with amc-setup-calibration-stack and rerun this optional step later.

The standalone Python script prompts only when stdin is interactive. In non-interactive runs, set RUN_VGGT = True to opt in; otherwise the script prints that VGGT is ready and continues without blocking.

Complete Python Script

Use scripts/run_video_calibration.py for the runnable implementation. Set BASE_URL, PROJECT_NAME, and VIDEO_DIR; optional env vars are CONFIG_FILE, ALIGNMENT_JSON, LAYOUT_PNG, GT_ZIP, FOCAL_LENGTHS, DETECTOR_TYPE, RUN_VGGT, REPO_ROOT, and PROJECTS_DIR. The script implements UI fallback, plan confirmation, VGGT prompt/opt-in behavior, polling, and refined statistics retrieval.

Success Criteria

• project_state == "COMPLETED" after polling.
• If manual alignment was used: projects/project_<id>/manual_adjustment/ contains alignment_data.json + layout.png.
• If GT was uploaded: evaluation returns typical thresholds:
  • Average L2 distance(m) < 1.5
  • Average reprojection error 0(px) < 5
• No ERROR state.

Key Output Files (on server)

projects/project_<project_id>/
├── manual_adjustment/
│   ├── alignment_data.json
│   └── layout.png
├── output/
│   ├── single_view_results/cam_XX/
│   │   ├── camInfo_hyper_XX.yaml
│   │   └── trajDump_Stream_0_3d.txt
│   └── multi_view_results/BA_output/results_ba/
│       ├── initial/camInfo_XX.yaml
│       └── refined/camInfo_XX.yaml          # ← final calibration
└── calibration.log

Troubleshooting

For Downstream Skills — MV3DT Export

A downstream Multi-View 3D Tracking skill fetches the MV3DT-format calibration directly from the microservice (this skill does not download it; it returns the project_id). After this skill reports COMPLETED:

• GET /v1/result/{project_id}/mv3dt_result?result_type=amc → mv3dt_output.zip (contains transforms.yml).
• If VGGT ran to COMPLETED (Step 10): ?result_type=vggt → vggt_mv3dt_output.zip.

Related Skills

• skills/amc-setup-calibration-stack/SKILL.md — start MS + UI first.
• skills/amc-run-sample-calibration/SKILL.md — verify the stack with the bundled sample before trying your own.

Root README.md "Custom Dataset" and "Calibration Workflow (UI)" sections document input-video guidelines and the UI-driven alternative to this API flow.