TemplateMatching

Documentation for TemplateMatching.

TemplateMatching is a Julia package designed to offer a native Julia implementation of template matching functionalities similar to those available in OpenCV. This package aims to provide an easy-to-use interface for image processing and computer vision applications, allowing users to leverage the high-performance capabilities of Julia for template matching operations. The package offers performance slightly below that of OpenCV but significantly better than a naive implementation.

Features

Masks are not yet supported in the current version of the package. Unlike OpenCV, TemplateMatching.jl supports n-dimensional arrays^[1].

Below is a table summarising available methods and their equivalent in opencv.

problem in most cases.

Installation

To install TemplateMatching, use the Julia package manager. Open your Julia command-line interface and run:

using Pkg
Pkg.add("TemplateMatching")

Usage

Almost everything you need is the function match_template and its inplace counterpart match_template!.

Below is a quick start example on how to use the TemplateMatching package to perform template matching:

using TemplateMatching
using Images

# Load your source image and template
source = rand(1000, 1000)
template = source[400:500, 100:150]

# Perform template matching using square difference
result = match_template(source, template, SquareDiff())

# Get the best match
argmin(result) # CartesianIndex(400, 100)

# Perform template matching using normalized correlation coefficient
result = match_template(source, template, NormalizedCorrCoeff())

# Get the best match
argmax(result) # CartesianIndex(400, 100)