- dir: (string)
directory of images
- img_filter: (string)
(optional) ending of filenames (excluding extension) for processing
These are the same 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_outlines: FLAG
save outlines as text file for ImageJ
- save_png: FLAG
save masks as png
- save_tif: FLAG
save masks as tif
- 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
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_pngYou 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_pngWarning
The path given to --dir must be an absolute path.
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