Metadata-Version: 2.1
Name: Multi-Template-Matching
Version: 1.2
Summary: Object-recognition in images using multiple templates
Home-page: https://github.com/LauLauThom/MultiTemplateMatching-Python
Author: Laurent Thomas
Author-email: laurent132.thomas@laposte.net
License: UNKNOWN
Description: # Multi-Template-Matching
        Multi-Template-Matching is a package to perform object-recognition in images using one or several smaller template images.  
        The template and images should have the same bitdepth (8,16,32-bit) and number of channels (single/Grayscale or RGB).  
        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.  
        
        # Installation
        Using pip, `pip install Multi-Template-Matching`  
        Once installed, `import MTM`should work.
        
        # Documentaion
        Check out the [jupyter notebook tutorial](https://github.com/LauLauThom/Multi-Template-Matching/blob/master/Tutorial.ipynb) for some example of how to use the package.  
        The [wiki](https://github.com/LauLauThom/MultiTemplateMatching/wiki) section of this related repository also provides some information about the implementation.
        
        # Citation
        If you use this implementation for your research, please cite:
          
        _Multi-Template Matching: a versatile tool for object-localization in microscopy images;_  
        _Laurent SV Thomas, Jochen Gehrig_  
        bioRxiv 619338; doi: https://doi.org/10.1101/619338
        
        # Related projects
        See this [repo](https://github.com/LauLauThom/MultiTemplateMatching) for the implementation as a Fiji plugin.  
        [Here](https://nodepit.com/workflow/com.nodepit.space%2Flthomas%2Fpublic%2FMulti-Template%20Matching.knwf) 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 a division of DITABIS AG  
        Digital Biomedical Imaging Systems AG  
        Freiburger Str. 3  
        75179 Pforzheim  
        
        <img src="https://github.com/LauLauThom/MultiTemplateMatching-Python/blob/master/images/Acquifer_Logo_60k_cmyk_300dpi.png" alt="Fish" width="400" height="80">     
        
        # Funding
        This project has received funding from the European Unionâ€™s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 721537 ImageInLife.  
        
        <p float="left">
        <img src="https://github.com/LauLauThom/MultiTemplateMatching-Python/blob/master/images/ImageInlife.png" alt="ImageInLife" width="130" height="100">
        <img src="https://github.com/LauLauThom/MultiTemplateMatching-Python/blob/master/images/MarieCurie.jpg" alt="MarieCurie" width="130" height="130">
        </p>
        
Keywords: object-recognition object-localization
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Description-Content-Type: text/markdown
