weitsicht documentation#
Date: May 29, 2026 Version: 0.0.4
Useful links: Source Repository | Issues & Ideas | Q&A Support |
Unlock Your Geo-Referenced Images
weitsicht is an open source, Apache 2.0 library for Python.
The library allows to work with geo-referenced image data. Image pixel coordinates can be mapped to 3D or 3D points can be projected
into images to get pixel coordinates.
The package bridges computer-vision/photogrammetry outputs and GIS workflows, carrying data from direct or indirect georeferencing into downstream uses like monitoring and digitization.
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Capabilities:
Mapping, map pixels or image’s center-point and footprint (image extent) easily.
Projection, get the pixel position of 3D coordinates.
CRS, weitsicht handles coordinate system conversions (to some extent)
Perspective Image and Camera, mathematic model of your digital camera and pose.
Ortho imagery, use ortho imagery to map content or convert 2D coordinates to 3D.
Mapper Classes, several mapper classes can be used to map your pixel data: HorizontalPlane, Raster, Mesh
ImageBatch, container class to perform tasks on multiple images. Find all images where coordinates are visible. Map for all images footprint and centerpoint.
Meta-Data, use image’s meta-data (EXIF, XMP) to estimate camera model and image pose.
weitsicht is developed to provide an easy to use package for all levels of experience,
from scientist in environmental science without computer vision or photogrammetric background,
for teaching and students as well for computer vision/photogrammetry experts.
Its structure is kept as modular as possible to easily extend new mathematical models for cameras, images, or mappers.
weitsicht is not a Structure from Motion (SFM) package.
Note
As of the nature of python, do not expect to perform super fast. Especially the processing intensive operations on raster and mesh. Nevertheless some effort was put into optimization and reduction of loops.
The user guide provides information about installation and how to use the package.
The documentation provides background, definitions, and mathematical context.
The reference guide contains a detailed description of the API. The reference describes how the methods work and which parameters can be used. It assumes that you have an understanding of the key concepts.
Contributions are highly welcome. There are many ways to contribute. Fixing typos, extending functionalities, implementing new classes…
WISDAMapp#
WISDAMapp is the software packages which provides a GUI to work with environmental images. Images can be loaded and digitized, for example to monitor marine mammals. There are workflows for metadata enrichment as well as workflows to work with AI detections.
WISDAMapp was one of the main roots for that package. Originally it was implemented completely inside WISDAMapp.
As there was interest in the mathematical/geometrical core behind WISDAMapp, the first major refactoring of WISDAMapp in late 2024 was the right time to split the GUI and the core into two parts.
Project website: www.wisdamapp.org Repository: github.com/WISDAMapp/WISDAM |
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Examples#
