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# Multi-Template-Matching (mtm) : the object-oriented python implementation
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Multi-Template-Matching is an accessible method to perform object-detection in images using one or several template images for the search.
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The strength of the method compared to previously available single-template matching, is that by combining the detections from multiple templates,
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one can improve the range of detectable patterns. This helps if you expect variability of the object-perspective in your images, such as rotation, flipping...
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Contrary to `mtm.matchTemplates`, __it does not perform NMS__ so you will potentially get overlapping detections.
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Usually one should use directly `mtm.matchTemplates`.
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The website of the project https://multi-template-matching.github.io/Multi-Template-Matching/ references most of the information, including presetnations, posters and recorded talks/tutorials.
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The website of the project https://multi-template-matching.github.io/Multi-Template-Matching/ references most of the information, including presentations, posters and recorded talks/tutorials.
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The [wiki](https://github.com/multi-template-matching/MultiTemplateMatching-Fiji/wiki) section of this related repository also provides some information about the implementation.
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# Installation
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Open a command prompt (or Anaconda prompt if using Anaconda) and type
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# Examples
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Check out the [jupyter notebook tutorial](https://github.com/multi-template-matching/mtm-python-oop/tree/master/tutorials) for some example of how to use the package.
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You can run the tutorials online using Binder, no configuration needed ! (click the Binder banner on top of this page).
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To run the tutorials locally, install the package using pip as described above, then clone the repository and unzip it.
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Finally open a jupyter-notebook session in the unzipped folder to be able to open and execute the notebook.
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The [wiki](https://github.com/multi-template-matching/MultiTemplateMatching-Fiji/wiki) section of this related repository also provides some information about the implementation.
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To run the tutorials locally, install the package using pip as described above, then clone/download the repository and unzip it.
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Finally open a jupyter-notebook session in the unzipped folder to be able to open and execute the notebooks.
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# Citation
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If you use this implementation for your research, please cite:
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