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    "info": {
        "author": "Laurent Thomas",
        "author_email": "laurent132.thomas@laposte.net",
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        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Image Recognition"
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        "description": "\u00ef\u00bb\u00bf# Multi-Template-Matching\nMulti-Template-Matching is a package to perform object-recognition in images using one or several smaller template images.  \nThe template and images should have the same bitdepth (8,16,32-bit) and number of channels (single/Grayscale or RGB).  \nThe main function `MTM.matchTemplates` returns the best predicted locations provided either a score_threshold and/or the expected number of objects in the image.  \n\n# Installation\nUsing pip in a python environment, `pip install Multi-Template-Matching`  \nOnce installed, `import MTM`should work.  \nExample jupyter notebooks can be downloaded from the tutorial folder of the github repository and executed in the newly configured python environement.  \n\n# Documentation\nThe package MTM contains mostly 2 important functions:  \n\n## matchTemplates  \n`matchTemplates(listTemplates, image, method=cv2.TM_CCOEFF_NORMED, N_object=float(\"inf\"), score_threshold=0.5, maxOverlap=0.25, searchBox=None)`  \n\nThis function searches each template in the image, and return the best N_object location which offer the best scores and which do not overlap above the `maxOverlap` threshold.  \n\n__Parameters__\n- _listTemplates_:   \n            list of tuples (LabelString, Grayscale or RGB numpy array) templates to search in each image, associated to a label \n\n- _image_  : Grayscale or RGB numpy array  \n           image in which to perform the search, it should be the same bitDepth and number of channels than the templates\n\n- _method_ : int   \n            one of OpenCV template matching method (0 to 5), default 5=0-mean cross-correlation\n\n- _N_object_: int  \n            expected number of objects in the image\n\n- score_threshold: float in range [0,1]  \n            if N>1, returns local minima/maxima respectively below/above the score_threshold\n\n- _maxOverlap_: float in range [0,1]  \n            This is the maximal value for the ratio of the Intersection Over Union (IoU) area between a pair of bounding boxes.\n            If the ratio is over the maxOverlap, the lower score bounding box is discarded.\n\n- _searchBox_ : tuple (X, Y, Width, Height) in pixel unit  \n            optional rectangular search region as a tuple\n\n__Returns__\n- Pandas DataFrame with 1 row per hit and column \"TemplateName\"(string), \"BBox\":(X, Y, Width, Height), \"Score\":float             \n            - if N=1, return the best match independently of the score_threshold  \n            - if N<inf, returns up to N best matches that passed the score_threshold  \n            - if N=inf, returns all matches that passed the score_threshold  \n\n\nThe function `findMatches` performs the same detection without the Non-Maxima Supression.  \n\n## drawBoxesOnRGB\nThe 2nd important function is `drawBoxesOnRGB` to display the detections as rectangular bounding boxes on the initial image.  \nTo be able to visualise the detection as colored bounding boxes, the function return a RGB copy of the image if a grayscale image is provided.  \nIt is also possible to draw the detection bounding boxes on the grayscale image using drawBoxesOnGray (for instance to generate a mask of the detections).      \n`drawBoxesOnRGB(image, hits, boxThickness=2, boxColor=(255, 255, 00), showLabel=True, labelColor=(255, 255, 0), labelScale=0.5 )`\n\nThis function returns a copy of the image with predicted template locations as bounding boxes overlaid on the image\nThe name of the template can also be displayed on top of the bounding boxes with showLabel=True.\n\n__Parameters__\n- _image_  : numpy array  \n        image in which the search was performed  \n\n- _hits_ :  \n        (pandas DataFrame) hits as returned by matchTemplates or findMatches  \n\n- _boxThickness_: int  \n        thickness of bounding box contour in pixels. -1 will fill the bounding box (useful for masks).  \n\n- _boxColor_: (int, int, int)  \n        RGB color for the bounding box  \n\n- _showLabel_: Boolean, default True  \n        Display label of the bounding box (field TemplateName)\n\n- _labelColor_: (int, int, int)  \n          RGB color for the label  \n\n- _labelScale_: float, default=0.5\n\tscale for the label sizes\n\n__Returns__\n- _outImage_: RGB image  \n        original image with predicted template locations depicted as bounding boxes  \n\n# Examples\nCheck out the [jupyter notebook tutorial](https://github.com/multi-template-matching/MultiTemplateMatching-Python/tree/master/tutorials) for some example of how to use the package.  \nThe [wiki](https://github.com/multi-template-matching/MultiTemplateMatching-Fiji/wiki) section of this related repository also provides some information about the implementation.\n\n# Citation\nIf you use this implementation for your research, please cite:\n\n_Multi-Template Matching: a versatile tool for object-localization in microscopy images;_  \n_Laurent SV Thomas, Jochen Gehrig_  \nbioRxiv 619338; doi: https://doi.org/10.1101/619338\n\n# Releases\nNew github releases are automatically archived to Zenodo.  \n[![DOI](https://zenodo.org/badge/197186256.svg)](https://zenodo.org/badge/latestdoi/197186256)\n\n# Related projects\nSee this [repo](https://github.com/multi-template-matching/MultiTemplateMatching-Fiji) for the implementation as a Fiji plugin.  \n[Here](https://nodepit.com/workflow/com.nodepit.space%2Flthomas%2Fpublic%2FMulti-Template%20Matching.knwf) for a KNIME workflow using Multi-Template-Matching.\n\n\n# Origin of the work\nThis work has been part of the PhD project of **Laurent Thomas** under supervision of **Dr. Jochen Gehrig** at:  \n\nACQUIFER a division of DITABIS AG  \nDigital Biomedical Imaging Systems AG  \nFreiburger Str. 3  \n75179 Pforzheim  \n\n<img src=\"https://github.com/multi-template-matching/MultiTemplateMatching-Python/blob/master/images/Acquifer_Logo_60k_cmyk_300dpi.png\" alt=\"Fish\" width=\"400\" height=\"80\">     \n\n# Funding\nThis project has received funding from the European Union\u00e2\u20ac\u2122s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 721537 ImageInLife.  \n\n<p float=\"left\">\n<img src=\"https://github.com/multi-template-matching/MultiTemplateMatching-Python/blob/master/images/ImageInlife.png\" alt=\"ImageInLife\" width=\"130\" height=\"100\">\n<img src=\"https://github.com/multi-template-matching/MultiTemplateMatching-Python/blob/master/images/MarieCurie.jpg\" alt=\"MarieCurie\" width=\"130\" height=\"130\">\n</p>\n\n\n",
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