Omnipose (cellpose v0.7.2)

Introducing Omnipose, a collaboration between the Stringer, Wiggins, and Mougous labs written by @kevinjohncutler. Read more about it in our preprint and on the Omnipose README. Important new features are:

• cyto2_omni model for slight improvement over the 'cyto2' Cellpose model
• bact_omni model for bacteria phase contrast segmentation (huge improvement over Cellpose models trained on bacteria, which you can demo with the bact model)
• omni option to use Omnipose mask reconstruction with your Cellpose model to help reduce over-segmentation (off by default)
• cluster option to force DBSCAN clustering in Omnipose mask reconstruction. This is off by default and turned on automatically when the average cell diameter is less than diam_threshold. Note theat scikit-learn is necessary for DBSCAN, and a CLI prompt will ask you to download it when you run --omni.

Several saving options have been included as well:

• in_folders saves outputs into separate folders named masks, outlines, etc. (off by default)
• dir_above saves output in the directory above the image directory (useful to have images next to masks etc.) (off by default)
• save_txt turns on ImageJ outline saving (now off by default)
• save_ncolor uses @kevinjohncutler's N-color algorithm to save masks with repeating but non-touching integers (typically 4 or fewer, 5 or 6 when necessary), which allows segmentations of thousands of cells to be presented without as many colors (which can become very hard to distinguish otherwise). Use in combination with a color map to visualize output.

Several bug fixes and pull requests are included in this release as well.