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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 :ref:`settings`, but set up for the command line, e.g. channels = [chan, chan2].

  • chan: (int) channel to segment, ones-based because zero is gray (average of all channels)
    0 = grayscale; 1 = red; 2 = green; 3 = blue
  • chan2: (int) nuclear or other channel, ones-based because zero means set to all zeros
    (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
  • 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] [--dir DIR]
[--look_one_level_down] [--img_filter IMG_FILTER] [--channel_axis CHANNEL_AXIS] [--z_axis Z_AXIS] [--chan CHAN] [--chan2 CHAN2] [--invert] [--all_channels] [--pretrained_model PRETRAINED_MODEL] [--unet] [--nclasses NCLASSES] [--no_resample] [--net_avg] [--no_interp] [--do_3D] [--diameter DIAMETER] [--stitch_threshold STITCH_THRESHOLD] [--fast_mode] [--flow_threshold FLOW_THRESHOLD] [--cellprob_threshold CELLPROB_THRESHOLD] [--anisotropy ANISOTROPY] [--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] [--test_dir TEST_DIR] [--mask_filter MASK_FILTER] [--diam_mean DIAM_MEAN] [--learning_rate LEARNING_RATE] [--weight_decay WEIGHT_DECAY] [--n_epochs N_EPOCHS] [--batch_size BATCH_SIZE] [--min_train_masks MIN_TRAIN_MASKS] [--residual_on RESIDUAL_ON] [--style_on STYLE_ON] [--concatenation CONCATENATION] [--save_every SAVE_EVERY] [--save_each] [--verbose]

cellpose parameters

optional arguments: -h, --help show this help message and exit --verbose show information about running and settings and save

to log

hardware arguments: --use_gpu use gpu if torch with cuda installed --check_mkl check if mkl working

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
--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: --pretrained_model PRETRAINED_MODEL

model to use for running or starting training
--unet run standard unet instead of cellpose flow output
--nclasses NCLASSES
 if running unet, choose 2 or 3; cellpose always uses 3

algorithm arguments: --no_resample disable dynamics on full image (makes algorithm faster

for images with large diameters)
--net_avg run 4 networks instead of 1 and average results
--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 will use the diameter of the training labels used in the model, or with built-in model will estimate diameter for each image
--stitch_threshold STITCH_THRESHOLD
 compute masks in 2D then stitch together masks with IoU>0.9 across planes
--fast_mode now equivalent to --no_resample; make code run faster by turning off resampling
--flow_threshold FLOW_THRESHOLD
 flow error threshold, 0 turns off this optional QC step. Default: 0.4
--cellprob_threshold CELLPROB_THRESHOLD
 cellprob threshold, default is 0, decrease to find more and larger masks
--anisotropy ANISOTROPY
 anisotropy of volume in 3D
--exclude_on_edges
 discard masks which touch edges of image

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 --test_dir TEST_DIR folder containing test data (optional) --mask_filter MASK_FILTER

end string for masks to run on. Default: _masks
--diam_mean DIAM_MEAN
 mean diameter to resize cells to during training -- if starting from pretrained models it cannot be changed from 30.0
--learning_rate LEARNING_RATE
 learning rate. Default: 0.2
--weight_decay WEIGHT_DECAY
 weight decay. Default: 1e-05
--n_epochs N_EPOCHS
 number of epochs. Default: 500
--batch_size BATCH_SIZE
 batch size. Default: 8
--min_train_masks MIN_TRAIN_MASKS
 minimum number of masks a training image must have to be used. Default: 5
--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