Since mere thresholding was not good on the dark image and very bright cells, I tried a simple gradient-based approach. The image gradient is computed and labels extracted using a threshold based on Otsu. From the labels regions are extracted if they fit certain criteria similar to the thresholding method used before. The procedure is in procedure.py. On normal images is works good:
The dark image was a problem before. This method finds a much more cells than simple thresholding.
The capped image is still a problem. Caps cannot be found this way and even a lot of big larva are not found.
For validation I use some images which I randomly picked from broodmapper.com (under ../data/broodmapper/). All cells in the honeycomb images are labeled by hand. The validation algorithm checks whether each labeled cell is fully captured by a segment of the segmentation. From that TPR and PPV are calculated. This is done in test_procedure.py, segmentations are shown in segmentations/, statistics are in results.json.