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PyGOFPID\n\n[![Code PythonVersion](https://img.shields.io/badge/python-3.8+-blue)](https://img.shields.io/badge/python-3.7+-blue)\n[![License](https://img.shields.io/badge/licence-BSD--3--Clause-green)](https://img.shields.io/badge/license-BSD--3--Clause-green)\n\nPyGOFPID is a Python package implementing\n[perimeter intrusion detection (PID)](https://www.mdpi.com/1424-8220/22/9/3601)\nsystems for video protection.\n\nLeveraging [OpenCV](https://github.com/opencv/opencv-python), you can easily\ndefine a pipeline using good old fashioned (GOF) computer vision methods, like\n[foreground detection](https://en.wikipedia.org/wiki/Foreground_detection) and\n[mathematical morphology](https://en.wikipedia.org/wiki/Mathematical_morphology).\n\nPyGOFPID is distributed under the open source 3-clause BSD license.\n\n## Description\n\nClass `GOFPID` is a versatile tool allowing to build a parameterizable PID\npipeline, composed of:\n\n1. input frame denoising by spatial blurring;\n2. foreground detection by background subtraction or frame differencing;\n3. foreground mask denoising by mathematical morphology;\n4. foreground blob creation;\n5. blob tracking (WIP);\n6. post-filtering;\n7. intrusion detection.\n\nSee `examples/video.py` for a simple example.\n\n## Comparison on i-LIDS\n\nGOFPID is useful for comparison between ancient and new generations of PID,\nbetween good old fashioned and deep learning based PID.\n\n[i-LIDS](https://ieeexplore.ieee.org/document/4105319) is a standard dataset\nfor testing video based PID.\n\nSee [pyPID](https://gitlab.liris.cnrs.fr/dlohani/pypid) for a benchmark between\nPID systems on i-LIDS dataset, with results given for\n[edge-level evaluation after 1s](https://www.mdpi.com/1424-8220/22/9/3601)\n(precision - recall - F1-score):\n\n| Methods | i-LIDS view 1 | i-LIDS view 2 |\n| ------- | ------------- | ------------- |\n| YOLOv2 + SORT [[Nayak2019]](https://ieeexplore.ieee.org/document/9117960) | 0.62 - 0.52 - 0.57 | 0.99 - 0.47 - 0.64 |\n| 3DCAE + MZ [[Lohani2022]](https://ieeexplore.ieee.org/document/9897472) | 0.90 - 0.74 - 0.81 | 0.83 - 0.64 - 0.73 |\n| LUSS-AE + MZ [[Lohani2023]](https://theses.hal.science/tel-04108165/document) | 0.91 - 0.78 - 0.84 | 0.85 - 0.70 - 0.77 |\n| GOFPID v1.0.0 | 0.69 - 0.61 - 0.65 | 0.80 - 0.59 - 0.68 |\n\nSee `examples/dataset_ilids.py` for GOFPID configuration of i-LIDS views.\n\n## Installation\n\n#### From sources\n\nTo install PyGOFPID as a standard module:\n```shell \npip install git+https://github.com/qbarthelemy/PyGOFPID\n```\n\nTo install PyGOFPID in editable / development mode, in the folder:\n```shell\npython setup.py develop\n```\n\n## Testing\n\nUse `pytest`.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqbarthelemy%2Fpygofpid","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqbarthelemy%2Fpygofpid","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqbarthelemy%2Fpygofpid/lists"}