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Python bindings for DIPlib, the quantitative image analysis library

Project description

About DIPlib 3

Introduction

The purpose of the DIPlib project is to provide a one-stop library and development environment for quantitative image analysis, be it applied to microscopy, radiology, astronomy, or anything in between.

There are other image processing/analysis libraries available, some of them hugely popular. Why do we keep investing time in developing and improving DIPlib? The short answer is that we believe DIPlib offers things that are not available elsewhere. The library is built on the following three principles:

  1. Precision:

    We implement the most precise known methods, and output often defaults to floating-point samples. The purpose of these algorithms is quantification, not approximation.

  2. Ease of use

    We use modern C++ features to provide a simple and intuitive interface to algorithms, with expressive syntax, default values, and little boiler-plate code required from the user. There is no need to be aware of an image's data type to use the algorithms effectively.

    Furthermore, developing an image analysis program involves a lot of trial-and-error, rapid prototyping approaches are applicable: the edit-compile-run loop should be quick. We aim for short compile times with pre-compiled algorithms and few public templates.

  3. Efficiency

    We implement the most efficient known algorithms, as long as they don't compromise precision. Ease-of-use features might also incur a slight overhead in execution times. The library can be used in high-throughput quantitative analysis pipelines, but is not designed for real-time video processing.

Project details


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