For only academic usages of image processing (pixkit-image) and machine learning (pixkit-ml).

Contains image processing and machine learning related methods which had been published (on articles, e.g., journal or conference papers). In addition to above implementations, some frequently utilized tools, i.e., attack simulation and quality assessment, are also involved as expected.

The repo, OpenCV, is used to build up all the pixkit functions: https://github.com/Itseez/opencv

By far, the current version, /modules/pixkit-image involves the following function groups:

** attack: Usually used to simulate attack for "watermarking".

** comp: Image compression.

** filtering: Image filtering.

** edgedetection: Edge detection.

** halftoning: Image halftoning. Turn images into binary (halftone) form.

** enhancement::local: Image contrast enhancement methods, regional methods.

** enhancement::global: Image contrast enhancement methods, global methods.

** qualityassessment: Image quality assessment.

Also, /modules/pixkit-ml involves the following function groups:

** clustering: Data clustering.

** labeling: Image labeling.

See manual for details.

Please read CONTRIBUTING.md in this directory.
Many thanks to our contributors.

Please "report bugs on GitHub https://github.com/yunfuliu/pixkit/issues".

Please read LICENSE.txt in this directory.

Semantic Versioning is used in our version control.