Deterministic segmentation of grains in dense Laue microdiffraction datasets

Although Laue microdiffraction is an essential non-destructive technique for probing crystallographic orientation and strain at submicrometre resolution, the extreme density of overlapping Bragg reflections often hinders its application to polycrystalline materials. We present a novel analysis pipeline, implemented in the open-source Python package graintools, designed to segment these complex datasets without prior knowledge of the microstructure. The workflow uses a multi-round peak search to handle diverse spot morphologies and constructs a look-up table to identify recurring reflections across raster-scan positions. These reflections are organized into spot families and represented as 2D binary appearance maps, which indicate the spatial occurrence of specific grains. To enhance data fidelity, connected component analysis is applied to clean these maps before they are grouped into clusters using cosine similarity. The method was successfully validated on a 3C-SiC polycrystalline sample, demonstrating its ability to isolate grain footprints, extract accurate orientations and reconstruct intra-grain deviatoric strain maps. This deterministic approach effectively circumvents the combinatorial and memory bottlenecks of traditional indexing and dictionary-based methods, providing a robust solution for analyzing highly populated diffraction patterns.