nanodevice_flakedetect_align

To align nanoscale microscope images, you register source images to the full_stack coordinate system using SIFT for same-substrate alignment or Chamfer+DE for cross-substrate alignment, generating warp matrices and diagnostic visuals.

The best way to align nanodevice flakes across different substrates is using Chamfer+DE cross-substrate alignment, which handles rotation and scale search to align top_part images to the full_stack reference.

Yes, you need Python 3.10+ with OpenCV, NumPy, SciPy, and scikit-learn installed to run the nanodevice image alignment workflow and generate alignment reports, warp matrices, and footprint masks.

Yes, SIFT supports same-substrate alignment by registering bottom_part or reference images to the full_stack coordinate system when the images share the same substrate material.

Nanodevice image alignment generates alignment_report.json, warp matrices, footprint masks, footprint contours, and diagnostic visuals to verify registration accuracy and support downstream GDS export.

Cross-substrate image alignment requires Chamfer+DE because SIFT feature matching fails across different materials, while Chamfer distance with Differential Evolution handles rotation and scale variations between substrates.