- 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)
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
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.
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