The TarMass desktop application enables automatic detection, classification, measurement and biomass estimation of tardigrade species in microscopic images. The integrated tools allow the user to manually correct the detections and generate PDF report with statistics.
Biomass estimation is done automatically, using few different algorithms:
- specie classification, detection and keypoints estimation -
Yolov8-pose
[1],Keypoint RCNN
[2] - specie length spline fitting -
quadratic
,cubic
,slinear
- scale detection -
cv2.findContours
[3] +custom contour selector
Tardigrade mass approximation equations used, were introduced by Hallas & Yeates [4], [5]:
- Eutardigrada
- Heterotardigrada
Where L is the length in μm, while R is the length-to-width ratio.
To automatically build and run the docker container, run:
docker-compose build
./run_docker.sh
To run GUI application:
cd src/
./run_gui_app.py
Executable generation is done by cx_freeze
package in setup.py
file:
python3 setup.py build
[1] https://github.com/ultralytics/ultralytics
[2] He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).
[3] Suzuki, S. (1985). Topological structural analysis of digitized binary images by border following. Computer vision, graphics, and image processing, 30(1), 32-46.
[4] Hallas, T. E., & Yeates, G. W. (1972). Tardigrada of the soil and litter of a Danish beech forest. Pedobiologia.
[5] Jennings, P. G. (1976). The Tardigrada of Signy Island, South Orkney Islands, with a note on the Rotifera. British Antarctic Survey Bulletin, 44, 1-25.