Optical recordings are ubiquitous in current scientific research. As recording and storing images and videos have become simpler and cheaper over the past years, new and more efficient tools are needed to analyse the growing amount of data. While current scientific image analysis tools focus on medical 3D data sets, a tool for efficiently handling large-scale 2D time-series recordings is still missing.
We developed ClickPoints, an expandable, open-source, Python-based software to fill this gap. ClickPoints combines and streamlines the three main steps of image analysis – visualization, annotation and evaluation – in one program. ClickPoints enables the user to (i) efficiently review large data sets of images and videos, (ii) annotate interesting findings and (iii) facilitate manual and automatic evaluations. ClickPoints is highly versatile, ranging from clicking and selecting objects with simple markers, drawing masks and computing trajectories, up to (iv) custom-written Python add-ons for adapting or extending the available features. These add-ons (v) reduce development time by utilizing ClickPoints’ display, interface and storage solutions. In addition, ClickPoints provides an (vi) extensive data base application programming interface to facilitate efficient storage and retrieval of results.
ClickPoints is designed to simplify repetitive and labour-intensive scientific tasks, especially when dealing with large image and video time-series recordings. ClickPoints offers cross-platform support for Windows and Linux and is released under the GPLv3 license. Download, documentation and numerous examples are available at http://clickpoints.readthedocs.io.
Read the full publication in Methods in Ecology and Evolution