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# pca_align.py - Free & Open Source import numpy as np import trimesh def align_to_principal_axes(mesh_path, output_path): # Load mesh mesh = trimesh.load(mesh_path) vertices = mesh.vertices

Download any of these, perform PCA alignment using the script above, and run a cloud-to-mesh comparison. You now have a legitimate "measuring contest" with principal axes. Risk analysis for obscure filenames from peer-to-peer networks: file serge3dxmeasuringcontestandprincipa free

# Sort eigenvectors by eigenvalue (principal = largest) idx = np.argsort(eigenvalues)[::-1] principal_axes = eigenvectors[:, idx] # pca_align

# Ensure right-handed coordinate system if np.linalg.det(principal_axes) < 0: principal_axes[:,2] *= -1 eigenvectors = np.linalg.eig(cov)

# Transform mesh mesh.apply_transform(np.linalg.inv(principal_axes.T)) mesh.export(output_path) print(f"Aligned mesh saved to output_path") align_to_principal_axes("input.stl", "aligned_principal.stl")

# Compute PCA (Principal Component Analysis) centroid = vertices.mean(axis=0) centered = vertices - centroid cov = np.cov(centered.T) eigenvalues, eigenvectors = np.linalg.eig(cov)