GUI to facilitate the calculation of optical flow velocity fields in biological images
Project description
OpFLowLab is a user-friendly motion estimation framework that seeks to help cell biologist try out optical flow algorithms on their own dataset.
Key Features
Graphical interface to assess CUDA optimized optical flow algorithms provided by OpenCV
Post processing of velocities using FlowMatch, an object matching routine.
Velocity field validation using artificial tracers and image warping
Visualization of velocity pattern using pathlines
Calculation of velocity field derivatives
Installing
OpFLowLab can be installed with pip:
$ python -m pip install OpFLowLab
Alternatively, the latest source code is available from GitLab:
$ git clone git@gitlab.com:xianbin.yong13/OpFlowLab.git $ python setup.py install
Graphical user interface
To start OpFlowLab:
$ opflowlab
Documentation
Documentation for OpFlowLab can be found at https://opflowlab.readthedocs.io/en/latest/.
How to cite
If you use OpFlowLab, we would appreciate if you could cite the following paper:
License
OpFlowLab is provided under the GPLv3 license.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for OpFlowLab-0.1.1-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | dc6f6d6921cb447bb85db6d0f8725c3820978000acdd1fbfe3f4c176446f584b |
|
| MD5 | 8398694ad7b8ef31981cc869d6cd60c6 |
|
| BLAKE2b-256 | 7d2e49ffa289391971e24d341475dc35c2707c8f0024ae4f20729c50afec0441 |
