Open-Source 2D Vision-Based Dimensional and Strain Measurement of Mechanical Components: A Practical Review and Experimental Validation Using Python OpenCV and MATLAB NCORR

Abstract

Accurate dimensional and deformation measurement of mechanical components is central to manufacturing quality control and experimental mechanics. Contact-based instruments such as Vernier calipers, micrometers, and coordinate measuring machines (CMMs) are widely used but introduce measurement loading and cannot simultaneously capture multi-point displacements. Commercial optical alternatives, such as Vic-2D and GOM Aramis for Digital Image Correlation (DIC) and HALCON for machine vision, provide high accuracy but carry license costs that restrict adoption in academic laboratories and small engineering workshops. This review examines two freely available, open-source platforms that together provide comprehensive non-contact 2D measurement of planar mechanical components: Python OpenCV, which enables camera calibration, homography-based planar reconstruction, adaptive Gaussian thresholding for metallic surfaces, and sub-pixel edge detection; and MATLAB NCORR, an open-source Digital Image Correlation (DIC) package that computes full-field displacement and strain maps from random speckle patterns. The review is directed at general planar mechanical components as: flat rectangular parts, cylindrical fasteners, threaded screws, gears, and beam specimens.Experimental results obtained using a mobile phone camera and the Python-OpenCV pipeline demonstrate dimensional measurement errors below 1.5% relative to Vernier caliper measurements for a flat rectangular component (scientific calculator) and below 1% for length and outer diameter of a metallic machine screw, confirming sub-millimetre accuracy for components in the 10–200 mm size range. Thread pitch measurement via horizontal edge projection is reported as a specific limitation at standard mobile phone image magnification, with error exceeding 89%, and a Hough Transform solution is proposed. An integrated two-tool workflow combining OpenCV discrete-point tracking with NCORR full-field DIC strain mapping is proposed for comprehensive mechanical component characterization at near-zero software cost. Keywords: Python; DIC; MATLAB; OpenCV; machine vision; NCORR; digital image correlation; dimensional measurement; strain measurement; adaptive Gaussian thresholding; open-source metrology; non-contact measurement