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 = cytoplasm; nuclei = nucleus

  • 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

Options

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

usage: __main__.py [-h] [--check_mkl] [--mkldnn] [--train] [--dir DIR]
               [--img_filter IMG_FILTER] [--use_gpu] [--do_3D]
               [--pretrained_model PRETRAINED_MODEL] [--chan CHAN]
               [--chan2 CHAN2] [--all_channels] [--diameter DIAMETER]
               [--flow_threshold FLOW_THRESHOLD]
               [--cellprob_threshold CELLPROB_THRESHOLD] [--save_png]
               [--save_tif] [--fast_mode] [--no_npy]
               [--mask_filter MASK_FILTER] [--test_dir TEST_DIR]
               [--learning_rate LEARNING_RATE] [--n_epochs N_EPOCHS]
               [--batch_size BATCH_SIZE]

cellpose parameters

optional arguments:
-h, --help            show this help message and exit
--check_mkl           check if mkl working
--mkldnn              force MXNET_SUBGRAPH_BACKEND = "MKLDNN"
--train               train network using images in dir (not yet
                        implemented)
--dir DIR             folder containing data to run or train on
--img_filter IMG_FILTER
                        end string for images to run on
--use_gpu             use gpu if mxnet with cuda installed
--do_3D               process images as 3D stacks of images (nplanes x nchan
                        x Ly x Lx
--pretrained_model PRETRAINED_MODEL
                        model to use
--chan CHAN           channel to segment; 0: GRAY, 1: RED, 2: GREEN, 3: BLUE
--chan2 CHAN2         nuclear channel (if cyto, optional); 0: NONE, 1: RED,
                        2: GREEN, 3: BLUE
--all_channels        use all channels in image if using own model and
                        images with special channels
--diameter DIAMETER   cell diameter, if 0 cellpose will estimate for each
                        image
--flow_threshold FLOW_THRESHOLD
                        flow error threshold, 0 turns off this optional QC
                        step
--cellprob_threshold CELLPROB_THRESHOLD
                        cell probability threshold, centered at 0.0
--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
--fast_mode           make code run faster by turning off augmentations and
                        4 network averaging
--no_npy              suppress saving of npy
--mask_filter MASK_FILTER
                        end string for masks to run on
--test_dir TEST_DIR   folder containing test data (optional)
--learning_rate LEARNING_RATE
                        learning rate
--n_epochs N_EPOCHS   number of epochs
--batch_size BATCH_SIZE
                        batch size