菜单
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'.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/NVIDIA/DeepStream --skill amc-run-sample-calibration命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP/live streams, use amc-run-rtsp-calibration instead.
Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
NVIDIA DeepStream SDK development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.
基于 SOC 职业分类
正在显示 SKILL.md
| name | amc-run-sample-calibration |
| description | 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'. |
| owner | NVIDIA CORPORATION |
| service | auto-magic-calib |
| version | 1.0.0 |
| reviewed | 2026-04-28 |
| license | Apache-2.0 |
| metadata | {"author":"Shubham Agrawal <shuagrawal@nvidia.com>","tags":["amc","calibration","sample","rest-api","validation","python"]} |
Activate this skill when the user wants to sanity-check a running AMC stack with the bundled sample dataset. Typical prompts:
amc-setup-calibration-stack if the MS isn't already running)Do NOT use this skill when:
/data/videos/, cam_*.mp4 not from the bundled zip) — route to amc-run-video-calibration.rtsp://... URLs — route to amc-run-rtsp-calibration.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.
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.
skills/amc-setup-calibration-stack/SKILL.md if not)assets/sdg_08_2_sample_data_010926.ziprequests available, or use the Swagger UI path below
requests is missing it creates a throwaway venv under ${TMPDIR:-/tmp}/amc-sample-test-venv (nothing written to the repo)python3 -m venv itself fails with : ensurepip not availablesudo apt install -y python3-venv python3-pip"launch AMC and test sample dataset" (or similar):
skills/amc-setup-calibration-stack/SKILL.md first./v1/ready to return OK.vggt_state: READY; otherwise the script explains that VGGT setup is optional and can be enabled later for refinement."test sample dataset" (MS already running):
/v1/ready response.MS_PORT=""
for port in {8000..8009}; do
if curl -s "http://localhost:$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 $MS_PORT"
: "${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 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 reads compose/.env from that checkout for the backend port, 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"
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:
Open http://<HOST_IP>:<MS_PORT>/docs in a browser.
Unzip sdg_08_2_sample_data_010926.zip into a cache directory next to it.
Execute these endpoints in order, copying the project_id from step 1 into subsequent paths:
| # | Endpoint | Body / Files |
|---|---|---|
| 1 | POST /v1/create_project | project_name: any string |
| 2 | POST /v1/upload_video_files/{project_id} | files: upload all 4 videos/cam_0*.mp4 sorted by name |
| 3 | POST /v1/upload_alignment/{project_id} | alignment_file: alignment_data/alignment_data.json |
| 4 | POST /v1/upload_layout/{project_id} | layout_file: alignment_data/layout.png |
| 5 | POST /v1/upload_gt_file/{project_id} | gt_file: GT.zip |
| 6 | POST /v1/verify_project/{project_id} | — (expect project_state: READY) |
| 7 | POST /v1/calibrate/{project_id} | JSON: {"detector_type": "resnet"} |
| 8 | GET /v1/get_project_info/{project_id} | Refresh every ~10 s until project_state = COMPLETED |
| 9 | GET /v1/result/{project_id}/evaluation_statistics | Read L2 distance + reprojection error |
| 10 optional | POST /v1/vggt/calibrate/{project_id} then GET /v1/vggt_results/{project_id}/evaluation_statistics | Run 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.
get_project_infoproject_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.
project_state == "COMPLETED" within ~30 min./v1/result/{id}/evaluation_statistics returns non-empty statistics (GT was uploaded).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.ERROR state encountered.Representative metrics for the sample (yours should be similar):
Average L2 distance(m) : < 1.5
Average reprojection error 0(px) : < 10
Results persist under $REPO_ROOT/projects/project_<project_id>/:
projects/project_<project_id>/
├── output/
│ ├── single_view_results/cam_XX/
│ │ ├── camInfo_hyper_XX.yaml
│ │ └── trajDump_Stream_0_3d.txt
│ └── multi_view_results/BA_output/results_ba/refined/
│ └── camInfo_XX.yaml # ← final calibration (use this)
└── calibration.log
PROJECT_ID=<id_from_step_1>
: "${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
| Issue | Fix |
|---|---|
requests not installed | Inside a venv: python3 -m venv venv && ./venv/bin/pip install requests. If python3 -m venv fails: sudo apt install -y python3-venv python3-pip first |
[2] Uploaded N videos where N >> 4 | SAMPLE_DIR resolved to the repo root (or another over-broad path) and rglob("cam_*.mp4") swept stale videos from .cache/, projects/, etc. Stop the run (POST /v1/stop_calibration/{id}), delete the project (DELETE /v1/delete_project/{id}), set SAMPLE_DIR explicitly to the extracted sample dir, re-run. The script anchors on videos/ and asserts len(videos) <= 16 to fail loud |
verify_project returns state != READY | Confirm all 4 videos + alignment + layout + GT uploaded; inspect GET /v1/get_project_info/{id} response |
| Sample not extracted | unzip <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 files | Check 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 413 | Raise server upload limit, or split files (sample files are <200 MB total so this is unusual) |
| Port scan finds no backend | Backend not running — run amc-setup-calibration-stack skill |
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.