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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