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Amc Run Sample Calibration

NVIDIAOfficialCreated by Shubham AgrawalDistributed by NVIDIA

  • Skill
  • Workflow
  • Software development
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
— the description by Shubham Agrawal

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides an automated workflow to verify that a running AutoMagicCalib (AMC) microservice stack is functioning correctly. By processing a specific bundled sample dataset (sdg_08_2_sample_data_010926.zip) containing synthetic warehouse camera data and ground truth, it performs a full calibration cycle. The process includes project creation, data uploading, calibration execution, and evaluation metric reporting, ensuring the environment is ready for production data.

What it does

  • Detects a running AMC microservice on local ports 8000-8009.
  • Extracts and prepares the bundled sample dataset for processing.
  • Automates the full calibration pipeline including video, alignment, and ground truth uploads.
  • Executes calibration and reports performance metrics like L2 distance and reprojection error.
  • Attempts VGGT refinement if the project state allows.
  • Provides a manual Swagger UI walkthrough alternative for non-scripted interaction.

When to use it

  • Immediately after deploying an AMC stack to verify successful installation.
  • When troubleshooting a suspected configuration issue in the AMC microservice.
  • Before running calibration on custom video data to ensure the environment is stable.
  • When a user requests a sanity check of the system using provided sample assets.

Advantages

  • Validates the entire end-to-end stack, not just individual components.
  • Uses ground truth data to provide objective performance metrics.
  • Automates repetitive API sequences, reducing manual configuration errors.
  • Includes built-in checks for prerequisites like backend connectivity and file availability.

Usage examples

“verify AMC install”
The agent scans for a running AMC backend, extracts the sample dataset, runs the calibration script, and reports the final evaluation metrics.
“test sample dataset”
The agent executes the automated calibration workflow and provides a summary of the L2 distance and reprojection error results.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/amc-run-sample-calibration/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip amc-run-sample-calibration.zip -d .agents/skills

Documentation by Shubham Agrawal

Skill: Calibrate Sample Dataset

When to Use This Skill

Activate this skill when the user wants to sanity-check a running AMC stack with the bundled sample dataset. Typical prompts:

  • "test the sample dataset" / "run sample calibration"

  • "verify AMC install"

  • "launch and test" (chain with amc-setup-calibration-stack if the MS isn't already running)

Do NOT use this skill when:

  • The user references their own video paths (e.g. /data/videos/, cam_*.mp4 not from the bundled zip) — route to amc-run-video-calibration.

  • The user provides live RTSP streams or rtsp://... URLs — route to amc-run-rtsp-calibration.

  • This skill is exclusively for assets/sdg_08_2_sample_data_010926.zip.

Prerequisite: AMC microservice running on a port in 8000-8009. If no backend is detected, delegate to amc-setup-calibration-stack first.

If execution cannot proceed in the current environment (no backend, missing sample data, etc.), surface the blocker AND describe the expected workflow + API sequence concisely so the user understands what will run once prerequisites are met. Do not fabricate calibration outputs, evaluation metrics, or trajectories.

Overview

Run a full calibration on the bundled sample dataset (sdg_08_2_sample_data_010926.zip, 4 synthetic warehouse cameras with ground truth) against a running AutoMagicCalib microservice. Useful for verifying that a freshly-launched stack works end-to-end before throwing real data at it.

The sample includes GT, so the run produces evaluation metrics (L2 distance, reprojection error) — no calibration parameter tuning needed.

Prerequisites

  • [ ] AMC microservice running (follow skills/amc-setup-calibration-stack/SKILL.md if not)

  • [ ] Sample zip present at assets/sdg_08_2_sample_data_010926.zip

  • [ ] Python 3 with requests installed, or use the Swagger UI path below

    • Install it explicitly before running the script: python3 -m pip install requests

    • If pip is unavailable, install your distro's Python packaging support first

Instructions

"launch AMC and test sample dataset" (or similar):

  1. Run skills/amc-setup-calibration-stack/SKILL.md first.

  2. Wait for /v1/ready to return OK.

  3. Extract sample data (snippet below) — idempotent, safe to re-run.

  4. Run the bundled script in Run Script.

  5. Report final metrics + UI URL for manual inspection.

  6. VGGT refinement is attempted by default when the project reports vggt_state: READY; otherwise the script explains that VGGT setup is optional and can be enabled later for refinement.

"test sample dataset" (MS already running):

  1. Detect backend: scan ports 8000–8009 for a /v1/ready response.

  2. If none → point to the setup skill.

  3. Extract sample data if not already cached.

  4. Run the bundled script.

  5. Report metrics.

Detect Running Backend
MS_PORT=""
for port in {8000..8009}; do
  if curl -s "http://localhost:$port/v1/ready" | grep -q '"code":0'; then
    MS_PORT=$port; break
  fi
done
[ -z "$MS_PORT" ] && { echo "No running backend. Run amc-setup-calibration-stack skill first."; exit 1; }
echo "Backend on port $MS_PORT"
Locate + Extract Sample Data (idempotent)
: "${REPO_ROOT:?set REPO_ROOT to the auto-magic-calib checkout. Run amc-setup-calibration-stack Step 0b first.}"
grep -q "AutoMagicCalib" "$REPO_ROOT/README.md" 2>/dev/null && grep -q "auto-magic-calib-ms" "$REPO_ROOT/compose/ms/compose.yml" 2>/dev/null || { echo "ERROR: REPO_ROOT is not an auto-magic-calib checkout: $REPO_ROOT" >&2; exit 1; }

SAMPLE_ZIP="$REPO_ROOT/assets/sdg_08_2_sample_data_010926.zip"
[ -f "$SAMPLE_ZIP" ] || { echo "Sample zip not found at $SAMPLE_ZIP"; exit 1; }

# Cache directory next to the zip.
SAMPLE_DIR="$(dirname "$SAMPLE_ZIP")/.cache/sdg_08_2_sample_data_010926"

if [ ! -d "$SAMPLE_DIR" ]; then
  mkdir -p "$SAMPLE_DIR"
  unzip -q "$SAMPLE_ZIP" -d "$SAMPLE_DIR"
fi
ls "$SAMPLE_DIR"
# Expected (possibly inside a wrapper folder): alignment_data/  GT.zip  videos/

Run Script

Run the bundled script from the amc-run-sample-calibration skill package, not from the auto-magic-calib repo root. If the user points the agent at this skill folder directly instead of installing it, set AMC_SAMPLE_SKILL_DIR to the directory containing this SKILL.md, or run the command from that directory. Set REPO_ROOT to the AutoMagicCalib checkout resolved by amc-setup-calibration-stack; the script auto-detects a running backend on localhost ports 8000-8009 when BASE_URL / MS_PORT are not set, accepts BASE_URL, MS_PORT, SAMPLE_DIR, and RUN_VGGT overrides, creates a fresh project each run, attempts VGGT when ready, and prints the NGC warehouse dataset note at the end.

# REPO_ROOT must point to the auto-magic-calib checkout, not the DeepStream repo.
: "${REPO_ROOT:?set REPO_ROOT to the auto-magic-calib checkout. Run amc-setup-calibration-stack Step 0b first.}"
grep -q "AutoMagicCalib" "$REPO_ROOT/README.md" 2>/dev/null && grep -q "auto-magic-calib-ms" "$REPO_ROOT/compose/ms/compose.yml" 2>/dev/null || { echo "ERROR: REPO_ROOT is not an auto-magic-calib checkout: $REPO_ROOT" >&2; exit 1; }

# If AMC was resolved from DeepStream's tools/auto-magic-calib submodule,
# derive the DeepStream root so the unpacked repo skill can be used directly.
if [ -z "${DEEPSTREAM_REPO_ROOT:-}" ] && [ -d "$REPO_ROOT/../../skills/amc-run-sample-calibration" ]; then
  DEEPSTREAM_REPO_ROOT="$(cd "$REPO_ROOT/../.." && pwd)"
fi

SCRIPT_PATH=""
for candidate in \
  "${AMC_SAMPLE_SKILL_DIR:+$AMC_SAMPLE_SKILL_DIR/scripts/run_sample_calibration.py}" \
  "$PWD/scripts/run_sample_calibration.py" \
  "${DEEPSTREAM_REPO_ROOT:+$DEEPSTREAM_REPO_ROOT/skills/amc-run-sample-calibration/scripts/run_sample_calibration.py}" \
  "$PWD/skills/amc-run-sample-calibration/scripts/run_sample_calibration.py" \
  "$HOME/.claude/skills/amc-run-sample-calibration/scripts/run_sample_calibration.py" \
  "$HOME/.codex/skills/amc-run-sample-calibration/scripts/run_sample_calibration.py" \
  "$HOME/.cursor/skills/amc-run-sample-calibration/scripts/run_sample_calibration.py"; do
  if [ -f "$candidate" ]; then
    SCRIPT_PATH="$candidate"
    break
  fi
done

[ -n "$SCRIPT_PATH" ] || {
  echo "ERROR: could not find amc-run-sample-calibration/scripts/run_sample_calibration.py" >&2
  echo "Set AMC_SAMPLE_SKILL_DIR to the amc-run-sample-calibration skill directory, or run this block from that directory." >&2
  exit 1
}

python3 "$SCRIPT_PATH"

Alternative: Swagger UI Walkthrough

Agent shortcut: if the user explicitly requested a Swagger UI walkthrough (or said "no Python"), emit the table below and stop — do not invoke shell tooling, read other sections, or run the bundled Python script.

The microservice exposes an interactive OpenAPI UI at http://<HOST_IP>:<MS_PORT>/docs. If you prefer clicking through the API by hand:

  1. Open http://<HOST_IP>:<MS_PORT>/docs in a browser.

  2. Unzip sdg_08_2_sample_data_010926.zip into a cache directory next to it.

  3. Execute these endpoints in order, copying the project_id from step 1 into subsequent paths:

    #EndpointBody / Files
    1POST /v1/create_projectproject_name: any string
    2POST /v1/upload_video_files/{project_id}files: upload all 4 videos/cam_0*.mp4 sorted by name
    3POST /v1/upload_alignment/{project_id}alignment_file: alignment_data/alignment_data.json
    4POST /v1/upload_layout/{project_id}layout_file: alignment_data/layout.png
    5POST /v1/upload_gt_file/{project_id}gt_file: GT.zip
    6POST /v1/verify_project/{project_id}— (expect project_state: READY)
    7POST /v1/calibrate/{project_id}JSON: {"detector_type": "resnet"}
    8GET /v1/get_project_info/{project_id}Refresh every ~10 s until project_state = COMPLETED
    9GET /v1/result/{project_id}/evaluation_statisticsRead L2 distance + reprojection error
    10 optionalPOST /v1/vggt/calibrate/{project_id} then GET /v1/vggt_results/{project_id}/evaluation_statisticsRun only when vggt_state is READY; poll vggt_state until COMPLETED

This is the same sequence the bundled Python script runs, just executed manually. Step 10 is attempted by default when vggt_state is READY; otherwise it is skipped with setup guidance.

Status Fields from get_project_info

project_info.project_state is the AMC calibration lifecycle for the project. Poll it until it reaches COMPLETED (or stop on ERROR).

project_info.vggt_state is a per-project VGGT refinement lifecycle, a project-scoped status 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 lifecycle is INIT → READY after AMC calibration completes → RUNNING while VGGT refinement runs → COMPLETED (or ERROR). 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.

Success Criteria

  • Project reaches project_state == "COMPLETED" within ~30 min.

  • /v1/result/{id}/evaluation_statistics returns non-empty statistics (GT was uploaded).

  • VGGT either runs to vggt_state == "COMPLETED" and reports /v1/vggt_results/{id}/evaluation_statistics, or is skipped with setup guidance because the project is not READY for VGGT.

  • No ERROR state encountered.

Representative metrics for the sample (yours should be similar):

Average L2 distance(m)               : < 1.5
Average reprojection error 0(px)     : < 10

Key Output Files (on the server)

Results persist under $REPO_ROOT/projects/project_<project_id>/:

projects/project_<project_id>/
├── output/
│   ├── single_view_results/cam_XX/
│   │   ├── camInfo_hyper_XX.yaml
│   │   └── trajDump_Stream_0_3d.txt
│   └── multi_view_results/BA_output/results_ba/refined/
│       └── camInfo_XX.yaml          # ← final calibration (use this)
└── calibration.log

Monitoring Progress

PROJECT_ID=<id_from_step_1>
: "${REPO_ROOT:?set REPO_ROOT to the auto-magic-calib checkout. Run amc-setup-calibration-stack Step 0b first.}"
grep -q "AutoMagicCalib" "$REPO_ROOT/README.md" 2>/dev/null && grep -q "auto-magic-calib-ms" "$REPO_ROOT/compose/ms/compose.yml" 2>/dev/null || { echo "ERROR: REPO_ROOT is not an auto-magic-calib checkout: $REPO_ROOT" >&2; exit 1; }
tail -F --retry "$REPO_ROOT/projects/project_${PROJECT_ID}/calibration.log"

Or stream MS logs:

: "${REPO_ROOT:?set REPO_ROOT to the auto-magic-calib checkout. Run amc-setup-calibration-stack Step 0b first.}"
grep -q "AutoMagicCalib" "$REPO_ROOT/README.md" 2>/dev/null && grep -q "auto-magic-calib-ms" "$REPO_ROOT/compose/ms/compose.yml" 2>/dev/null || { echo "ERROR: REPO_ROOT is not an auto-magic-calib checkout: $REPO_ROOT" >&2; exit 1; }
docker compose -f "$REPO_ROOT/compose/compose.yml" logs -f auto-magic-calib-ms

Troubleshooting

IssueFix
requests not installedInstall it before running the script: python3 -m pip install requests
[2] Uploaded N videos where N >> 4SAMPLE_DIR resolved to the repo root (or another over-broad path) and rglob("cam_*.mp4") swept stale videos from .cache/, projects/, etc. Correct SAMPLE_DIR, then start a fresh project instead of trying to salvage the bad upload set. The script anchors on videos/ and asserts len(videos) <= 16 to fail loud
verify_project returns state != READYConfirm all 4 videos + alignment + layout + GT uploaded; inspect GET /v1/get_project_info/{id} response
Sample not extractedunzip <repo_root>/assets/sdg_08_2_sample_data_010926.zip -d <repo_root>/assets/.cache/sdg_08_2_sample_data_010926/
cam_*.mp4 glob finds 0 filesCheck wrapper-folder depth: find <sample_dir> -name "cam_*.mp4"
Calibration times out (>60 min)Check calibration.log for "insufficient tracklets"; see root README.md guidelines on input videos
Upload returns 413Raise server upload limit, or split files (sample files are <200 MB total so this is unusual)
Port scan finds no backendBackend not running — run amc-setup-calibration-stack skill

Additional Sample Dataset

The root README.md also documents nv_warehouse_032326.zip, a real-world warehouse dataset available from NGC. Download it with ngc registry resource download-version "nvidia/amc-nv-warehouse"; then use amc-run-video-calibration, upload nv_warehouse_config.json in the config step, and run with the transformer detector. It does not include ground-truth data.

  • skills/amc-setup-calibration-stack/SKILL.md — launch MS + UI (prerequisite).

  • skills/amc-run-video-calibration/SKILL.md — run calibration on your own pre-recorded MP4s.

  • skills/amc-run-rtsp-calibration/SKILL.md — run calibration from live RTSP streams through VIOS capture.

Root README.md "Sample Data Setup" and "Calibration Workflow (UI)" sections cover the human-oriented path through the same sample.

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs network access, Needs a command-line tool, Needs Python

Security

What our scanner noticed· Medium risk

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This skill includes scripts that your agent may run. Read them before you use it.

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Attribution and licence

Created by
Shubham Agrawal
Publisher
NVIDIA (Official)
Distributed by
NVIDIA
Source
NVIDIA/skills · skills/amc-run-sample-calibration · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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