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.

Do not use this skill for live RTSP streams or `rtsp://...` URLs; route those requests to `skills/amc-run-rtsp-calibration/SKILL.md`.

## 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:

| File | Candidate filenames | UI fallback |
|---|---|---|
| Calibration settings | `settings.json`, `config.json`, `calibration_config.json` | UI Step 3: Parameters |
| Alignment JSON | `alignment_data.json` | UI Step 4: Alignment |
| Layout PNG | `layout.png` | UI Step 4: Alignment |

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](#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:

| File | Endpoint | Notes |
|---|---|---|
| Calibration settings | `POST /v1/config/<project_id>` (JSON, posted as-is) | Replaces UI Step 3 (rectification, bundle-adjustment, evaluation, detector, …). Non-2xx is surfaced — never silently fall back. Skip on the UI-fallback path. |
| Alignment | `POST /v1/upload_alignment/<project_id>` (`alignment_data.json`) | |
| Layout | `POST /v1/upload_layout/<project_id>` (`layout.png`) | |
| Ground truth (optional) | `POST /v1/upload_gt_file/<project_id>` (`GT.zip`) | Enables evaluation metrics |
| Focal lengths (optional) | `POST /v1/upload_focal_length/<project_id>` (repeated `focal_length=`) | Overrides GeoCalib estimates |

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:

```bash
: "${REPO_ROOT:?set REPO_ROOT to the auto-magic-calib checkout. Run amc-setup-calibrati