CLI

See command line examples for typical use cases.

usage: omnipose [image args] [model args] [...]

input image arguments

--dir

folder containing data on which to run or train

--look_one_level_down

run processing on all subdirectories of current folder

--mxnet

use mxnet

--img_filter

filter images by this suffix

--channel_axis

axis of image which corresponds to image channels

--z_axis

axis of image which corresponds to Z dimension

--chan

channel to segment; 0: GRAY, 1: RED, 2: GREEN, 3: BLUE. Default: 0

--chan2

nuclear channel (if cyto, optional); 0: NONE, 1: RED, 2: GREEN, 3: BLUE. Default: 0

--invert

invert grayscale channel

--all_channels

use all channels in image if using own model and images with special channels

--dim

number of spatiotemporal dimensions of images (not counting channels). Default: 2

model arguments

--pretrained_model

model to use

--unet

run standard unet instead of cellpose flow output

--nclasses

number of prediction classes for model (3 for Cellpose, 4 for Omnipose boundary field)

--nchan

number of channels on which model is trained

--kernel_size

kernel size for maskpool. Starts at 2, higher means more aggressive downsampling.

algorithm arguments

--omni

Omnipose algorithm (disabled by default)

--affinity_seg

use new affinity segmentation algorithm (disabled by default)

--cluster

DBSCAN clustering. Reduces oversegmentation of thin features (disabled by default)

--no_suppress

Euler integration 1/t suppression reduces oversegmentation but can give undersegmentation in 3D; this flag disables it.

--fast_mode

make code run faster by turning off 4 network averaging and resampling

--no_resample

disable dynamics on full image (makes algorithm faster for images with large diameters)

--no_net_avg

make code run faster by only running 1 network

--no_interp

do not interpolate when running dynamics (was default)

--do_3D

process images as 3D stacks of images (nplanes x nchan x Ly x Lx

--diameter

cell diameter, 0 disables unless sizemodel is present. Default: 0.0

--rescale

image rescaling factor (r = diameter / model diameter)

--stitch_threshold

compute masks in 2D then stitch together masks with IoU>0.9 across planes

--flow_threshold

flow error threshold, 0 turns off this optional QC step. Default: 0.4

--mask_threshold

mask threshold, default is 0, decrease to find more and larger masks

--niter

Number of Euler iterations, enter value to override Omnipose diameter estimation (under/over-segment)

--anisotropy

anisotropy of volume in 3D

--diam_threshold

cell diameter threshold for upscaling before mask rescontruction, default 12

--exclude_on_edges

discard masks which touch edges of image

--min_size

minimum size for masks, helps if small debris is labeled

--max_size

maximum size for masks, helps if background patches are labeled

output arguments

--save_png

save masks as png

--save_tif

save masks as tif

--no_npy

suppress saving of npy

--savedir

folder to which segmentation results will be saved (defaults to input image directory)

--dir_above

save output folders adjacent to image folder instead of inside it (off by default)

--in_folders

flag to save output in folders (off by default)

--save_flows

whether or not to save RGB images of flows when masks are saved (disabled by default)

--save_outlines

whether or not to save RGB outline images when masks are saved (disabled by default)

--save_ncolor

whether or not to save minimal "n-color" masks (disabled by default

--save_txt

flag to enable txt outlines for ImageJ (disabled by default)

--transparency

store flows with background transparent (alpha=flow magnitude) (disabled by default)

training arguments

--train

train network using images in dir

--train_size

train size network at end of training

--mask_filter

end string for masks to run on. Default: "_masks"

--test_dir

folder containing test data (optional)

--learning_rate

learning rate. Default: 0.2

--n_epochs

number of epochs. Default: 500

--batch_size

batch size. Default: 8

--num_workers

number of dataloader workers. Default: 0

--dataloader

Use pytorch dataloader instead of older manual loading code.

--min_train_masks

minimum number of masks a training image must have to be used. Default: 1

--residual_on

use residual connections

--style_on

use style vector

--concatenation

concatenate downsampled layers with upsampled layers (off by default which means they are added)

--save_every

number of epochs to skip between saves. Default: 100

--save_each

save the model under a different filename per --save_every epoch for later comparsion

--RAdam

use RAdam instead of SGD

--checkpoint

turn on checkpoints to reduce memory usage

--dropout

Use dropout in training

--tyx

list of yx, zyx, or tyx dimensions for training

--links

Search and use link files for multi-label objects.

--amp

Use Automatic Mixed Precision.

--affinity_field

Use summed affinity instead of distance field.

hardware arguments

--use_gpu

use gpu if torch or mxnet with cuda installed

--check_mkl

check if mkl working

--mkldnn

for mxnet, force MXNET_SUBGRAPH_BACKEND = "MKLDNN"

development arguments

--verbose

flag to output extra information (e.g. diameter metrics) for debugging and fine-tuning parameters

--testing

flag to suppress CLI user confirmation for saving output; for test scripts

--timing

flag to output timing information for select modules