Introduction

dictk (Digital Image Correlation Toolkit) is a Python library for digital image correlation (DIC) — comparing images of a specimen before and after deformation to measure displacement and strain fields.

This guide is a work in progress. For now, it covers the one primitive the toolkit ships with.

Installation

pip install dictk

Getting started

dictk currently provides a single building block: zero-normalized cross-correlation (ZNCC), the similarity metric DIC template matching is built on.

import numpy as np
from dictk import zero_normalized_cross_correlation

a = np.array([[1.0, 2.0], [3.0, 4.0]])
b = np.array([[2.0, 4.0], [6.0, 8.0]])

score = zero_normalized_cross_correlation(a, b)
print(score)  # 1.0 -- correlated up to a brightness/contrast rescale