amc-run-sample-calibration

You can validate camera calibration by automating an end-to-end REST API sequence against the AutoMagicCalib microservice using a bundled synthetic warehouse dataset with known ground truth to verify performance metrics.

Test your REST API microservice deployment by executing a predefined sequence for project creation, video upload, and evaluation against synthetic datasets to confirm environment configuration and system readiness.

Yes, end-to-end calibration validation requires a running AMC backend and the bundled sample zip archive to perform project creation, video upload, and metric evaluation correctly.

The best way to verify system readiness is running an automated calibration pipeline immediately after deploying the stack to confirm dependencies, environment, and microservice configuration using ground-truth-verified samples.

Yes, the automated calibration workflow handles the complete lifecycle from project initialization and data upload through VGGT refinement to final metric evaluation and result reporting.

Yes, you can use Python with the requests dependency to automate REST API calls executing the full calibration workflow, including project creation, video upload, and performance metric evaluation.