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amc-run-sample-calibration

Execute end-to-end camera calibration workflows on synthetic warehouse datasets.

Updated Nov 21, 2025
Search Tags:#python#validation#calibration#microservice#computer-vision#deepstream
One-click install
npx skills add https://github.com/olibartfast/deep-infer --skill amc-run-sample-calibration
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: amc-run-sample-calibration
Source: https://github.com/olibartfast/deep-infer/tree/main/skills/amc-run-sample-calibration
Command: npx skills add https://github.com/olibartfast/deep-infer --skill amc-run-sample-calibration

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes scripts (resource) and assets (resource) components.

What problem does it solve?

This skill provides a reliable way to verify that an AutoMagicCalib (AMC) microservice stack is correctly deployed and functional by running an end-to-end calibration workflow against a known, ground-truth-verified sample dataset.

Core Features & Use Cases

  • End-to-End Validation: Automatically executes the full calibration pipeline, including project creation, data upload, and metric evaluation.
  • Automated Reporting: Generates L2 distance and reprojection error metrics to confirm system accuracy.
  • Use Case: Use this skill immediately after launching a new AMC stack to ensure the environment is configured correctly before processing your own production video data.

Quick Start

Run the amc-run-sample-calibration skill to test the currently running AMC microservice using the bundled sample dataset.

Frequently Asked Questions about amc-run-sample-calibration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate a camera calibration microservice deployment?▼

To validate a camera calibration microservice deployment, run an end-to-end workflow against a ground-truth-verified sample dataset to automatically generate L2 distance and reprojection error metrics.

What is the best way to test an AutoMagicCalib stack after launch?▼

Testing an AutoMagicCalib stack requires executing a full calibration pipeline—project creation, data upload, and metric evaluation—on synthetic warehouse datasets to confirm the environment is configured correctly.

Do I need a specific backend environment to run sample calibration workflows?▼

Yes, running sample calibration workflows requires an active backend environment listening on ports 8000-8009 and the presence of the bundled sample zip archive to execute successfully.

Can I verify VGGT refinement using synthetic warehouse datasets?▼

Yes, you can verify VGGT refinement using synthetic warehouse datasets by executing the end-to-end validation workflow to ensure automated metric generation meets expected accuracy thresholds.

Why does my automated calibration validation fail to generate metrics?▼

Automated calibration validation fails to generate metrics if the backend is not active on ports 8000-8009 or if the required bundled sample zip archive is missing from the environment.

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