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Object-recognition in images using multiple templates

Project description

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Multi-Template-Matching

Multi-Template-Matching is a python package to perform object-recognition in images using one or several smaller template images.
The main function MTM.matchTemplates returns the best predicted locations provided either a score_threshold and/or the expected number of objects in the image.

The branch opencl contains some test using the UMat object to run on GPU, but it is actually slow, which can be expected for small dataset as the transfer of the data between the CPU and GPU is slow.

Installation

Using pip in a python environment, pip install Multi-Template-Matching
Once installed, import MTMshould work.
Example jupyter notebooks can be downloaded from the tutorial folder of the github repository and executed in the newly configured python environement.

Documentation

The wiki section of the repo contains a mini API documentation with description of the key functions of the package.

Examples

Check out the jupyter notebook tutorial for some example of how to use the package.
You can run the tutorials online using Binder, no configuration needed ! (click the Binder banner on top of this page).
To run the tutorials locally, install the package using pip as described above, then clone the repository and unzip it.
Finally open a jupyter-notebook session in the unzipped folder to be able to open and execute the notebook.
The wiki section of this related repository also provides some information about the implementation.

Citation

If you use this implementation for your research, please cite:

Thomas, L.S.V., Gehrig, J. Multi-template matching: a versatile tool for object-localization in microscopy images.
BMC Bioinformatics 21, 44 (2020). https://doi.org/10.1186/s12859-020-3363-7

Releases

Previous github releases were archived to Zenodo, but the best is to use pip to install specific versions.
DOI

Related projects

See this repo for the implementation as a Fiji plugin.
Here for a KNIME workflow using Multi-Template-Matching.

Origin of the work

This work has been part of the PhD project of Laurent Thomas under supervision of Dr. Jochen Gehrig at ACQUIFER.

ACQUIFER

Funding

This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 721537 ImageInLife.

ImageInLife MarieCurie

Project details


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