Command line

Input settings

• dir: (string)

  directory of images

• img_filter: (string)

  (optional) ending of filenames (excluding extension) for processing

Run settings

These are the same settings, but set up for the command line, e.g. channels = [chan, chan2].

• chan: (int)

  0 = grayscale; 1 = red; 2 = green; 3 = blue

• chan2: (int)

  (optional); 0 = None (will be set to zero); 1 = red; 2 = green; 3 = blue

• pretrained_model: (string)

  cyto = cellpose cytoplasm model; nuclei = cellpose nucleus model; can also specify absolute path to model file

• diameter: (float)

  average diameter of objects in image, if 0 cellpose will estimate for each image, default is 30

• use_gpu: (bool)

  run network on GPU

• save_png: FLAG

  save masks as png and outlines as text file for ImageJ

• save_tif: FLAG

  save masks as tif and outlines as text file for ImageJ

• fast_mode: FLAG

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

• all_channels: FLAG

  run cellpose on all image channels (use for custom models ONLY)

• no_npy: FLAG

  turn off saving of _seg.npy file

• batch_size: (int, optional 8)

  batch size to run tiles of size 224 x 224

Command line examples

Run python -m cellpose and specify parameters as below. For instance to run on a folder with images where cytoplasm is green and nucleus is blue and save the output as a png (using default diameter 30):

python -m cellpose --dir ~/images_cyto/test/ --pretrained_model cyto --chan 2 --chan2 3 --save_png

You can specify the diameter for all the images or set to 0 if you want the algorithm to estimate it on an image by image basis. Here is how to run on nuclear data (grayscale) where the diameter is automatically estimated:

python -m cellpose --dir ~/images_nuclei/test/ --pretrained_model nuclei --diameter 0. --save_png

Warning

The path given to --dir must be an absolute path.

Options

You can run the help string and see all the options:

::

usage: __main__.py [-h] [–use_gpu] [–check_mkl] [–mkldnn] [–dir DIR] [–look_one_level_down] [–mxnet]

[–img_filter IMG_FILTER] [–channel_axis CHANNEL_AXIS] [–z_axis Z_AXIS] [–chan CHAN] [–chan2 CHAN2] [–invert] [–all_channels] [–pretrained_model PRETRAINED_MODEL] [–unet UNET] [–nclasses NCLASSES] [–omni] [–cluster] [–fast_mode] [–resample] [–no_interp] [–do_3D] [–diameter DIAMETER] [–stitch_threshold STITCH_THRESHOLD] [–flow_threshold FLOW_THRESHOLD] [–mask_threshold MASK_THRESHOLD] [–anisotropy ANISOTROPY] [–diam_threshold DIAM_THRESHOLD] [–exclude_on_edges] [–save_png] [–save_tif] [–no_npy] [–savedir SAVEDIR] [–dir_above] [–in_folders] [–save_flows] [–save_outlines] [–save_ncolor] [–save_txt] [–train] [–train_size] [–mask_filter MASK_FILTER] [–test_dir TEST_DIR] [–learning_rate LEARNING_RATE] [–n_epochs N_EPOCHS] [–batch_size BATCH_SIZE] [–residual_on RESIDUAL_ON] [–style_on STYLE_ON] [–concatenation CONCATENATION] [–save_every SAVE_EVERY] [–save_each] [–verbose] [–testing]

cellpose parameters

optional arguments: -h, –help show this help message and exit –pretrained_model PRETRAINED_MODEL

model to use

--unet UNET

run standard unet instead of cellpose flow output

--omni

Omnipose algorithm (disabled by default)

--cluster

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

--fast_mode

make code run faster by turning off 4 network averaging

--resample

run dynamics on full image (slower for images with large diameters)

--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 DIAMETER

cell diameter, if 0 cellpose will estimate for each image

--stitch_threshold STITCH_THRESHOLD

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

--anisotropy ANISOTROPY

anisotropy of volume in 3D

--diam_threshold DIAM_THRESHOLD

cell diameter threshold for upscaling before mask rescontruction, default 12.

--exclude_on_edges

discard masks which touch edges of image

--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

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”

input image arguments: –dir DIR folder containing data to run or train on. –look_one_level_down run processing on all subdirectories of current folder –mxnet use mxnet –img_filter IMG_FILTER

end string for images to run on

--channel_axis CHANNEL_AXIS

axis of image which corresponds to image channels

--z_axis Z_AXIS

axis of image which corresponds to Z dimension

--chan CHAN

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

--chan2 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

model arguments: –nclasses NCLASSES if running unet, choose 2 or 3; if training omni, choose 4; standard Cellpose uses 3

algorithm arguments: –flow_threshold FLOW_THRESHOLD

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

--mask_threshold MASK_THRESHOLD

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

output arguments: –save_png save masks as png and outlines as text file for ImageJ –save_tif save masks as tif and outlines as text file for ImageJ –no_npy suppress saving of npy –savedir 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)

training arguments: –train train network using images in dir –train_size train size network at end of training –mask_filter MASK_FILTER

end string for masks to run on. Default: _masks

--test_dir TEST_DIR

folder containing test data (optional)

--learning_rate LEARNING_RATE

learning rate. Default: 0.2

--n_epochs N_EPOCHS

number of epochs. Default: 500

--batch_size BATCH_SIZE

batch size. Default: 8

--residual_on RESIDUAL_ON

use residual connections

--style_on STYLE_ON

use style vector

--concatenation CONCATENATION

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

--save_every 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