Cellpose

pip install cellpose --upgrade


Check out the twitter [thread](https://twitter.com/marius10p/status/1511415409047650307?s=20&t=umTVIG1CFKIWHYMrQqFKyQ) and [preprint](https://www.biorxiv.org/content/10.1101/2022.04.01.486764v1) for details.

Read how to use human-in-the-loop yourself in the [gui docs](https://cellpose.readthedocs.io/en/latest/gui.html#training-your-own-cellpose-model), and check out the full human-in-the-loop [video](https://youtu.be/3Y1VKcxjNy4).

Also, read more about the model zoo and how to use user-trained models in the [models docs](https://cellpose.readthedocs.io/en/latest/models.html).

stable release
* fixes some bugs in 0.8 with plotting and flow visualization in GUI
* merges pull request for stitch_threshold bug (390)
* allows images with empty masks, depending on new flag `--min_train_masks` that is set to 5 by default. if fewer than `min_train_masks` in an image it is not used
* resample set to default `True` as in older releases
* model reloading per image turned off in CLI and class so that model is faster
* optional `_masks.tif` loading in GUI
* warning for user if masks are to be saved in `np.uint32`

* now install omnipose with pip! `pip install omnipose`
* added PR 416 which removed global logging settings, now turn on logging in a notebook with `from cellpose.io import logger_setup; logger,log_file=logger_setup()`, and from the command line with `--verbose`
* added support for >2^16 masks with np.uint32, if there are <2^16 masks then the masks are returned as np.uint16 still
* fixed bug with torch.long on windows

Fixes to the following bugs:
- `--no_npy` inverted settings
- `omni` not passed through to `remove_bad_flow_masks`

Introducing Omnipose, a collaboration between the Stringer, Wiggins, and Mougous labs written by kevinjohncutler. Read more about it in our [preprint](http://biorxiv.org/content/early/2021/11/04/2021.11.03.467199) and on the Omnipose [README](cellpose/omnipose/README.md). 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.