v1.0.0

This is the first release of dash-doodler, to coincide with the first version that appears to work well for multiple data sets and class sets, using both versions (appCRF.py and appRF.py).

This serves to snapshot this version of the program ahead of major planned changes, involving a database backend, user ID, and web serving.

Subsequent updates will follow major.minor.build convention

the CRF implementation is now faster and better. The RF implementation works well on all tested data, too. the defaults for CRF parameters have changed. Also, the image data representation it is using has changed, and the segmentation now uses nearest-neighbour rather than linear interpolation, which makes more sense for these label images with discrete classes. New sliders are now available to control the data density (increase the downsample factor for larger images). Note the new behavior of the median filter slider – median filtering still occurs if the value > 1, but won’t automatically redo the segmentation when its value is changed (to do that, you should recheck the compute/show segmentation box). The program now automatically selects a new colormap if you have more than 10 classes.