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Merge pull request #697 from mrariden/auto_cli_docs
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Auto cli docs
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@carsen-stringer
carsen-stringer authored May 1, 2023
2 parents e559dae + 36f3d64 commit 3ca6a8f
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112 changes: 6 additions & 106 deletions cellpose/__main__.py
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import sys, os, argparse, glob, pathlib, time
import sys, os, glob, pathlib, time
import numpy as np
from natsort import natsorted
from tqdm import tqdm
from cellpose import utils, models, io, core, version_str
from cellpose.cli import get_arg_parser

try:
from cellpose.gui import gui
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import logging

# settings re-grouped a bit
def main():
parser = argparse.ArgumentParser(description='cellpose parameters')

# misc settings
parser.add_argument('--version', action='store_true', help='show cellpose version info')
parser.add_argument('--verbose', action='store_true', help='show information about running and settings and save to log')

# settings for CPU vs GPU
hardware_args = parser.add_argument_group("hardware arguments")
hardware_args.add_argument('--use_gpu', action='store_true', help='use gpu if torch with cuda installed')
hardware_args.add_argument('--gpu_device', required=False, default='0', type=str, help='which gpu device to use, use an integer for torch, or mps for M1')
hardware_args.add_argument('--check_mkl', action='store_true', help='check if mkl working')

# settings for locating and formatting images
input_img_args = parser.add_argument_group("input image arguments")
input_img_args.add_argument('--dir',
default=[], type=str, help='folder containing data to run or train on.')
input_img_args.add_argument('--image_path',
default=[], type=str, help='if given and --dir not given, run on single image instead of folder (cannot train with this option)')
input_img_args.add_argument('--look_one_level_down', action='store_true', help='run processing on all subdirectories of current folder')
input_img_args.add_argument('--img_filter',
default=[], type=str, help='end string for images to run on')
input_img_args.add_argument('--channel_axis',
default=None, type=int, help='axis of image which corresponds to image channels')
input_img_args.add_argument('--z_axis',
default=None, type=int, help='axis of image which corresponds to Z dimension')
input_img_args.add_argument('--chan',
default=0, type=int, help='channel to segment; 0: GRAY, 1: RED, 2: GREEN, 3: BLUE. Default: %(default)s')
input_img_args.add_argument('--chan2',
default=0, type=int, help='nuclear channel (if cyto, optional); 0: NONE, 1: RED, 2: GREEN, 3: BLUE. Default: %(default)s')
input_img_args.add_argument('--invert', action='store_true', help='invert grayscale channel')
input_img_args.add_argument('--all_channels', action='store_true', help='use all channels in image if using own model and images with special channels')

# model settings
model_args = parser.add_argument_group("model arguments")
model_args.add_argument('--pretrained_model', required=False, default='cyto', type=str, help='model to use for running or starting training')
model_args.add_argument('--add_model', required=False, default=None, type=str, help='model path to copy model to hidden .cellpose folder for using in GUI/CLI')
model_args.add_argument('--unet', action='store_true', help='run standard unet instead of cellpose flow output')
model_args.add_argument('--nclasses',default=3, type=int, help='if running unet, choose 2 or 3; cellpose always uses 3')

# algorithm settings
algorithm_args = parser.add_argument_group("algorithm arguments")
algorithm_args.add_argument('--no_resample', action='store_true', help="disable dynamics on full image (makes algorithm faster for images with large diameters)")
algorithm_args.add_argument('--net_avg', action='store_true', help='run 4 networks instead of 1 and average results')
algorithm_args.add_argument('--no_interp', action='store_true', help='do not interpolate when running dynamics (was default)')
algorithm_args.add_argument('--no_norm', action='store_true', help='do not normalize images (normalize=False)')
algorithm_args.add_argument('--do_3D', action='store_true', help='process images as 3D stacks of images (nplanes x nchan x Ly x Lx')
algorithm_args.add_argument('--diameter', required=False, default=30., type=float,
help='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')
algorithm_args.add_argument('--stitch_threshold', required=False, default=0.0, type=float, help='compute masks in 2D then stitch together masks with IoU>0.9 across planes')
algorithm_args.add_argument('--min_size', required=False, default=15, type=int, help='minimum number of pixels per mask, can turn off with -1')
algorithm_args.add_argument('--fast_mode', action='store_true', help='now equivalent to --no_resample; make code run faster by turning off resampling')

algorithm_args.add_argument('--flow_threshold', default=0.4, type=float, help='flow error threshold, 0 turns off this optional QC step. Default: %(default)s')
algorithm_args.add_argument('--cellprob_threshold', default=0, type=float, help='cellprob threshold, default is 0, decrease to find more and larger masks')

algorithm_args.add_argument('--anisotropy', required=False, default=1.0, type=float,
help='anisotropy of volume in 3D')
algorithm_args.add_argument('--exclude_on_edges', action='store_true', help='discard masks which touch edges of image')

# output settings
output_args = parser.add_argument_group("output arguments")
output_args.add_argument('--save_png', action='store_true', help='save masks as png and outlines as text file for ImageJ')
output_args.add_argument('--save_tif', action='store_true', help='save masks as tif and outlines as text file for ImageJ')
output_args.add_argument('--no_npy', action='store_true', help='suppress saving of npy')
output_args.add_argument('--savedir',
default=None, type=str, help='folder to which segmentation results will be saved (defaults to input image directory)')
output_args.add_argument('--dir_above', action='store_true', help='save output folders adjacent to image folder instead of inside it (off by default)')
output_args.add_argument('--in_folders', action='store_true', help='flag to save output in folders (off by default)')
output_args.add_argument('--save_flows', action='store_true', help='whether or not to save RGB images of flows when masks are saved (disabled by default)')
output_args.add_argument('--save_outlines', action='store_true', help='whether or not to save RGB outline images when masks are saved (disabled by default)')
output_args.add_argument('--save_ncolor', action='store_true', help='whether or not to save minimal "n-color" masks (disabled by default')
output_args.add_argument('--save_txt', action='store_true', help='flag to enable txt outlines for ImageJ (disabled by default)')
def main():
""" Run cellpose from command line
"""

# training settings
training_args = parser.add_argument_group("training arguments")
training_args.add_argument('--train', action='store_true', help='train network using images in dir')
training_args.add_argument('--train_size', action='store_true', help='train size network at end of training')
training_args.add_argument('--test_dir',
default=[], type=str, help='folder containing test data (optional)')
training_args.add_argument('--mask_filter',
default='_masks', type=str, help='end string for masks to run on. use "_seg.npy" for manual annotations from the GUI. Default: %(default)s')
training_args.add_argument('--diam_mean',
default=30., type=float, help='mean diameter to resize cells to during training -- if starting from pretrained models it cannot be changed from 30.0')
training_args.add_argument('--learning_rate',
default=0.2, type=float, help='learning rate. Default: %(default)s')
training_args.add_argument('--weight_decay',
default=0.00001, type=float, help='weight decay. Default: %(default)s')
training_args.add_argument('--n_epochs',
default=500, type=int, help='number of epochs. Default: %(default)s')
training_args.add_argument('--batch_size',
default=8, type=int, help='batch size. Default: %(default)s')
training_args.add_argument('--min_train_masks',
default=5, type=int, help='minimum number of masks a training image must have to be used. Default: %(default)s')
training_args.add_argument('--residual_on',
default=1, type=int, help='use residual connections')
training_args.add_argument('--style_on',
default=1, type=int, help='use style vector')
training_args.add_argument('--concatenation',
default=0, type=int, help='concatenate downsampled layers with upsampled layers (off by default which means they are added)')
training_args.add_argument('--save_every',
default=100, type=int, help='number of epochs to skip between saves. Default: %(default)s')
training_args.add_argument('--save_each', action='store_true', help='save the model under a different filename per --save_every epoch for later comparsion')

args = parser.parse_args()
args = get_arg_parser().parse_args() # this has to be in a seperate file for autodoc to work


if args.version:
print(version_str)
return
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