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__main__.py
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__main__.py
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import os, argparse, glob, pathlib, time
import subprocess
import numpy as np
from natsort import natsorted
from tqdm import tqdm
from . import utils, models, io
try:
from cellpose import gui
GUI_ENABLED = True
except ImportError as err:
GUI_ERROR = err
GUI_ENABLED = False
GUI_IMPORT = True
except Exception as err:
GUI_ENABLED = False
GUI_ERROR = err
GUI_IMPORT = False
raise
def main():
parser = argparse.ArgumentParser(description='cellpose parameters')
parser.add_argument('--check_mkl', action='store_true', help='check if mkl working')
parser.add_argument('--mkldnn', action='store_true', help='force MXNET_SUBGRAPH_BACKEND = "MKLDNN"')
parser.add_argument('--train', action='store_true', help='train network using images in dir')
parser.add_argument('--dir', required=False,
default=[], type=str, help='folder containing data to run or train on')
parser.add_argument('--img_filter', required=False,
default=[], type=str, help='end string for images to run on')
parser.add_argument('--use_gpu', action='store_true', help='use gpu if mxnet with cuda installed')
parser.add_argument('--do_3D', action='store_true',
help='process images as 3D stacks of images (nplanes x nchan x Ly x Lx')
# settings for running cellpose
parser.add_argument('--pretrained_model', required=False,
default='cyto', type=str, help='model to use')
parser.add_argument('--unet', required=False,
default=0, type=int, help='run standard unet instead of cellpose flow output')
parser.add_argument('--nclasses', required=False,
default=3, type=int, help='if running unet, choose 2 or 3, otherwise not used')
parser.add_argument('--chan', required=False,
default=0, type=int, help='channel to segment; 0: GRAY, 1: RED, 2: GREEN, 3: BLUE')
parser.add_argument('--chan2', required=False,
default=0, type=int, help='nuclear channel (if cyto, optional); 0: NONE, 1: RED, 2: GREEN, 3: BLUE')
parser.add_argument('--all_channels', action='store_true', help='use all channels in image if using own model and images with special channels')
parser.add_argument('--diameter', required=False,
default=30., type=float, help='cell diameter, if 0 cellpose will estimate for each image')
parser.add_argument('--flow_threshold', required=False,
default=0.4, type=float, help='flow error threshold, 0 turns off this optional QC step')
parser.add_argument('--cellprob_threshold', required=False,
default=0.0, type=float, help='cell probability threshold, centered at 0.0')
parser.add_argument('--save_png', action='store_true', help='save masks as png and outlines as text file for ImageJ')
parser.add_argument('--save_tif', action='store_true', help='save masks as tif and outlines as text file for ImageJ')
parser.add_argument('--fast_mode', action='store_true', help="make code run faster by turning off 4 network averaging")
parser.add_argument('--resample', action='store_true', help="run dynamics on full image (slower for images with large diameters)")
parser.add_argument('--no_npy', action='store_true', help='suppress saving of npy')
# settings for training
parser.add_argument('--train_size', action='store_true', help='train size network at end of training')
parser.add_argument('--mask_filter', required=False,
default='_masks', type=str, help='end string for masks to run on')
parser.add_argument('--test_dir', required=False,
default=[], type=str, help='folder containing test data (optional)')
parser.add_argument('--learning_rate', required=False,
default=0.2, type=float, help='learning rate')
parser.add_argument('--n_epochs', required=False,
default=500, type=int, help='number of epochs')
parser.add_argument('--batch_size', required=False,
default=8, type=int, help='batch size')
parser.add_argument('--residual_on', required=False,
default=1, type=int, help='use residual connections')
parser.add_argument('--style_on', required=False,
default=1, type=int, help='use style vector')
parser.add_argument('--concatenation', required=False,
default=0, type=int, help='concatenate downsampled layers with upsampled layers (off by default which means they are added)')
args = parser.parse_args()
if args.check_mkl:
mkl_enabled = models.check_mkl()
else:
mkl_enabled = True
if not args.train and (mkl_enabled and args.mkldnn):
os.environ["MXNET_SUBGRAPH_BACKEND"]="MKLDNN"
else:
os.environ["MXNET_SUBGRAPH_BACKEND"]=""
import mxnet as mx
if len(args.dir)==0:
if not GUI_ENABLED:
print('ERROR: %s'%GUI_ERROR)
if GUI_IMPORT:
print('GUI FAILED: GUI dependencies may not be installed, to install, run')
print(' pip install cellpose[gui]')
else:
gui.run()
else:
use_gpu = False
channels = [args.chan, args.chan2]
# find images
if len(args.img_filter)>0:
imf = args.img_filter
else:
imf = None
if args.use_gpu:
use_gpu = models.use_gpu()
if use_gpu:
device = mx.gpu()
else:
device = mx.cpu()
print('>>>> using %s'%(['CPU', 'GPU'][use_gpu]))
model_dir = pathlib.Path.home().joinpath('.cellpose', 'models')
if not args.train and not args.train_size:
tic = time.time()
if not (args.pretrained_model=='cyto' or args.pretrained_model=='nuclei'):
cpmodel_path = args.pretrained_model
if not os.path.exists(cpmodel_path):
print('model path does not exist, using cyto model')
args.pretrained_model = 'cyto'
image_names = io.get_image_files(args.dir, args.mask_filter, imf=imf)
nimg = len(image_names)
if args.diameter==0:
if args.pretrained_model=='cyto' or args.pretrained_model=='nuclei':
diameter = None
print('>>>> estimating diameter for each image')
else:
print('>>>> using user-specified model, no auto-diameter estimation available')
diameter = model.diam_mean
else:
diameter = args.diameter
print('>>>> using diameter %0.2f for all images'%diameter)
cstr0 = ['GRAY', 'RED', 'GREEN', 'BLUE']
cstr1 = ['NONE', 'RED', 'GREEN', 'BLUE']
print('>>>> running cellpose on %d images using chan_to_seg %s and chan (opt) %s'%
(nimg, cstr0[channels[0]], cstr1[channels[1]]))
for image_name in tqdm(image_names):
image = io.imread(image_name)
if args.pretrained_model=='cyto' or args.pretrained_model=='nuclei':
model = models.Cellpose(device=device, model_type=args.pretrained_model)
masks, flows, _, diams = model.eval(image, channels=channels, diameter=diameter,
do_3D=args.do_3D, net_avg=(not args.fast_mode),
augment=False,
resample=args.resample,
flow_threshold=args.flow_threshold,
cellprob_threshold=args.cellprob_threshold,
batch_size=args.batch_size)
else:
if args.all_channels:
channels = None
model = models.CellposeModel(device=device, pretrained_model=cpmodel_path)
rescale = model.diam_mean / diameter
masks, flows, _ = model.eval(image, channels=channels, rescale=rescale,
do_3D=args.do_3D,
augment=False,
resample=args.resample,
flow_threshold=args.flow_threshold,
cellprob_threshold=args.cellprob_threshold,
batch_size=args.batch_size)
if not args.no_npy:
io.masks_flows_to_seg(image, masks, flows, diams, image_name, channels)
if args.save_png or args.save_tif:
io.save_masks(image, masks, flows, image_name, png=args.save_png, tif=args.save_tif)
print('>>>> completed in %0.3f sec'%(time.time()-tic))
else:
if args.pretrained_model=='cyto' or args.pretrained_model=='nuclei':
cpmodel_path = os.fspath(model_dir.joinpath('%s_0'%(args.pretrained_model)))
if args.pretrained_model=='cyto':
szmean = 30.
else:
szmean = 17.
else:
cpmodel_path = os.fspath(args.pretrained_model)
szmean = 30.
if args.all_channels:
channels = None
test_dir = None if len(args.test_dir)==0 else args.test_dir
output = io.load_train_test_data(args.dir, test_dir, imf, args.mask_filter, args.unet)
images, labels, image_names, test_images, test_labels, image_names_test = output
# model path
if not os.path.exists(cpmodel_path):
if not args.train:
raise ValueError('ERROR: model path missing or incorrect - cannot train size model')
cpmodel_path = False
print('>>>> training from scratch')
if args.diameter==0:
rescale = False
print('>>>> median diameter set to 0 => no rescaling during training')
else:
rescale = True
szmean = args.diameter
else:
rescale = True
args.diameter = szmean
print('>>>> pretrained model %s is being used'%cpmodel_path)
args.residual_on = 1
args.style_on = 1
args.concatenation = 0
if rescale and args.train:
print('>>>> during training rescaling images to fixed diameter of %0.1f pixels'%args.diameter)
# initialize model
if args.unet:
model = models.UnetModel(device=device,
pretrained_model=cpmodel_path,
diam_mean=szmean,
residual_on=args.residual_on,
style_on=args.style_on,
concatenation=args.concatenation,
nclasses=args.nclasses)
else:
model = models.CellposeModel(device=device,
pretrained_model=cpmodel_path,
diam_mean=szmean,
residual_on=args.residual_on,
style_on=args.style_on,
concatenation=args.concatenation)
# train segmentation model
if args.train:
cpmodel_path = model.train(images, labels, train_files=image_names,
test_data=test_images, test_labels=test_labels, test_files=image_names_test,
learning_rate=args.learning_rate, channels=channels,
save_path=os.path.realpath(args.dir), rescale=rescale, n_epochs=args.n_epochs,
batch_size=args.batch_size)
print('>>>> model trained and saved to %s'%cpmodel_path)
# train size model
if args.train_size:
sz_model = models.SizeModel(model, device=device)
sz_model.train(images, labels, test_images, test_labels, channels=channels, batch_size=args.batch_size)
if test_images is not None:
predicted_diams, diams_style = sz_model.eval(test_images, channels=channels)
if test_labels[0].ndim>2:
tlabels = [lbl[0] for lbl in test_labels]
else:
tlabels = test_labels
ccs = np.corrcoef(diams_style, np.array([utils.diameters(lbl)[0] for lbl in tlabels]))[0,1]
cc = np.corrcoef(predicted_diams, np.array([utils.diameters(lbl)[0] for lbl in tlabels]))[0,1]
print('style test correlation: %0.4f; final test correlation: %0.4f'%(ccs,cc))
np.save(os.path.join(args.test_dir, '%s_predicted_diams.npy'%os.path.split(cpmodel_path)[1]),
{'predicted_diams': predicted_diams, 'diams_style': diams_style})
if __name__ == '__main__':
main()