amc-run-video-calibration

Files (6)

• BENCHMARK.md
• SKILL.md
• evals.json · evals/
• run_video_calibration.py · scripts/
• skill-card.md
• skill.oms.sig

SKILL.md (first lines)

# 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`.

## Purpose

Guide the agent through project creation, sorted MP4 upload, local asset resolution, UI fallback only when necessary, project verification, calibration, polling, evaluation, and optional VGGT refinement for a user-provided multi-camera dataset.

## Prerequisites

- [ ] AMC microservice **and** UI running (follow `skills/amc-setup-calibration-stack/SKILL.md`)
- [ ] You know the microservice URL (use `https://<HOST_IP>:<MS_PORT>` for remote AMC, or `http://localhost:<MS_PORT>` on loopback) and UI URL
- [ ] Video files locally as contiguous `cam_00.mp4`, `cam_01.mp4`, … time-synchronized, ~1920×1080
- [ ] Python 3 with `requests`
- [ ] If AMC stores project outputs outside the default `projects/` directory, you know the host `PROJECTS_DIR`

## Interaction Model

- The "host's question mechanism" means the runtime's built-in prompt API for short user decisions, such as terminal stdin, an IDE ask tool, or an equivalent interactive dialog.
- If that mechanism is unavailable, ask in chat and wait before any guarded step that requires user confirmation or a missing-file decision.
- For unattended runs, the bundled script requires all non-UI inputs up front and exits before `/calibrate` unless `CONFIRM_CALIBRATION=true` is set. `RUN_VGGT=true` remains a separate opt-in for the optional VGGT step.

## 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.

## Inputs

- Required inputs: `VIDEO_DIR`, `BASE_URL`, and `PROJECT_NAME`.
- Optional local inputs: `CONFIG_FILE`, `ALIGNMENT_JSON`, `LAYOUT_PNG`, `GT_ZIP`, `FOCAL_LENGTHS`, and `DETECTOR_TYPE`.
- Optional control inputs: `CONFIRM_CALIBRATION`, `RUN_VGGT`, `PROJECTS_DIR`, `CALIBRATION_TIMEOUT_SECONDS`, and `VGGT_TIMEOUT_SECONDS`.
- `BASE_URL` should use HTTPS for non-loopback hosts. Set `ALLOW_INSECURE_HTTP=true` only for trusted development setups that intentionally use remote plain HTTP.
- Resolution precedence for settings, alignment, and layout: explicit path, single local auto-detected match, then UI fallback.

## 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.

---

## Available Scripts

| Script | Purpose | Key inputs |
|---|---|---|
| [run_video_calibration.py](scripts/run_video_calibration.py) | Executes create-project, upload, verify, calibrate, poll, evaluate, and optional VGGT refinement for a local MP4 dataset. | Required: `BASE_URL`, `PROJECT_NAME`, `VIDEO_DIR`. Optional: `CONFIG_FILE`, `ALIGNMENT_JSON`, `LAYOUT_PNG`, `GT_ZIP`, `FOCAL_LENGTHS`, `DETECTOR_TYPE`, `CONFIRM_CALIBRATION`, `RUN_VGGT`, `PROJECTS_DIR`, `CALIBRATION_TIMEOUT_SECONDS`, `VGGT_TIMEOUT_SECONDS`, `ALLOW_INSECURE_HTTP`. |

## 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. The bundled script rejects non-contiguous or non-zero-based camera sequences up front; the directory must contain `cam_00.mp4`, `cam_01.mp4`, ... with no gaps.

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

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

**Auto-use rule:** if exactly one match is found, the script uses it automatically and prints the resolved path. No extra prompt occurs for that file.

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; then continue to Step 6 and treat `verify_project` as the source of truth for whether the UI-supplied alignment/layout data is complete.

### 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 |

Upload all resolved local files first, in any order. After the local uploads are complete, continue to Step 5 only for unresolved files, then run Step 6 exactly once to verify the assembled project.

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)

From NVIDIA/skills, Apache-2.0.

More from NVIDIA/skills