Object/pattern detection using a Marked Point Process
Looking for disk-like objects (other shapes available) centered in the green region-of-interest:
The Obj.MPP project is published on the Python Package Index (PyPI) at: https://pypi.org/project/obj.mpp. It requires version 3.8, or newer, of the interpreter. It should be installable from Python distribution platforms or Integrated Development Environments (IDEs). Otherwise, it can be installed from a command-line console:
- For all users, after acquiring administrative rights:
First installation: pip install obj.mpp
Installation update: pip install --upgrade obj.mpp
- For the current user (no administrative rights required):
First installation: pip install --user obj.mpp
Installation update: pip install --user --upgrade obj.mpp
The Obj.MPP documentation is hosted at edebreuv.gitlabpages.inria.fr/Obj.MPP.
The optional, periodic detection refinement step is not part of the original Marked Point Process object detection method (see the Gamal Eldin et al reference in the documentation). It is an heuristic addition. While using Obj.MPP on images containing many objects with this refinement step on, Xavier Descombes noticed that, after some iterations, each iteration was taking very long to complete. He hypothesized that the refinement step was applied in each iteration instead of happening with the specified period. He was right since I had forgotten to reset the refinement-related counter after application.
Some features of Obj.MPP were added upon request:
Florence Besse and Fabienne de Graeve requested the possibility to ignore some image segments in the computation of an object quality. Note that this can also be done by writing a dedicated quality function, although it requires coding skills of course.
Xavier Descombes requested the possibility to combine the object quality with the object area in order to favor a larger, encompassing object over smaller adjacent objects, or not. Note that this can also be done by writing a dedicated quality function, although it requires coding skills of course.
The project is developed with PyCharm Community.
The development relies on several open-source packages (see install_requires in setup.py).
The code is formatted by Black, The Uncompromising Code Formatter.
The imports are ordered by isort… your imports, so you don’t have to.
The HTML documentation is generated by SPHINX, Python Documentation Generator.
The package requirement list is generated by pigar.
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