-
Download and unzip FIJI, move the
FiJji.app
folder toC:\Users\Public\Downloads\
-
Install BaSiC by following the instructions under the Install via the Fiji Updater section on this page
-
Save the
imagej_basic_ashlar.py
toC:\Users\Public\Downloads\Fiji.app\plugins\
-
Launch command prompt
-
In command prompt, change directory to the demo data folder
d:
cd d:\ashlar-batch-csv-demo\demo_data-20181001_Jerry
-
In command prompt, list all folderpaths in the current directory and write it to folderslist.txt
dir /s /b /o:n /a:d > folderslist.txt
-
Create a csv file with three required headings - Directory, Correction, Pyramid
-
Enter the paths for the slide directories, each contains multiple cycles of
rcpnl
files - you can copy the list from thefolderslist.txt
you just created -
Declare whether you want Correction and/or Pyramid for your images
1, yes, y, true or t means yes (case insensitive), others are no
-
Save the csv file, it's
20181001-demo.csv
for this demo -
Make sure you have the
run_ashlar_csv_batch.py
file in the current directory. -
In command prompt, call python to run the script and take in the csv file as config
python run_ashlar_csv_batch.py 20181001-demo.csv
-
When the task is done, the ome-tif (if you choose to generate pyramid images) will be in the directories you specified in the csv file, the filenames are the folders' names. the ffps and dfps (if you turn on correction) will also be in the same folders.
Last active
March 10, 2021 00:21
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Save Yu-AnChen/f9030c9d6425cb293846a729d1b224ed to your computer and use it in GitHub Desktop.
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from __future__ import print_function | |
import csv | |
from subprocess import call, check_output | |
try: | |
import pathlib | |
except ImportError: | |
import pathlib2 as pathlib | |
import argparse | |
import os | |
import datetime | |
def text_to_bool(text): | |
return False \ | |
if not str(text) \ | |
else str(text).lower() in '1,yes,y,true,t' | |
def path_to_date(path): | |
return os.path.getctime(str(path)) | |
parser = argparse.ArgumentParser( | |
description='Read a csv config file and run ashlar' | |
) | |
parser.add_argument( | |
'csv_filepath', metavar='CSVFILE', | |
help='a csv file with header row: Directory, Correction, Pyramid' | |
) | |
parser.add_argument( | |
'-f', '--from-dir', dest='from_dir', default=0, type=int, metavar='FROM', | |
help='starting directory; numbering starts at 0' | |
) | |
parser.add_argument( | |
'-t', '--to-dir', dest='to_dir', default=None, type=int, metavar='TO', | |
help='ending directory; numbering starts at 0' | |
) | |
parser.add_argument( | |
'--sort-by-filename', dest='sort_by_name', default=False, action='store_true', | |
help='sort files by filename; default is sorting files by created date' | |
) | |
args = parser.parse_args() | |
csv_path = pathlib.Path(args.csv_filepath) | |
if not csv_path.is_file() or csv_path.suffix != '.csv': | |
print('A csv file is required to proceed.') | |
parser.print_usage() | |
parser.exit() | |
with open(str(csv_path)) as exp_config: | |
exp_config_reader = csv.DictReader(exp_config) | |
exps = [dict(row) for row in exp_config_reader] | |
# Check the raw_files subfolder for either rcpnl or xdce files | |
for exp in exps[args.from_dir : args.to_dir]: | |
path_exp = pathlib.Path(exp['Directory']) | |
raw_dir = path_exp / 'raw_images' | |
files_exp = sorted(raw_dir.glob('*rcpnl')) | |
file_type = 'rcpnl' | |
if len(files_exp) == 0: | |
files_exp = sorted(raw_dir.rglob('*xdce')) | |
file_type = 'xdce' | |
if len(files_exp) == 0: | |
print('No rcpnl or xdce files found in', str(raw_dir)) | |
continue | |
sorting_method = 'filenames' | |
if not args.sort_by_name: | |
files_exp.sort(key=path_to_date) | |
sorting_method = 'created dates' | |
print('Processing files in', str(raw_dir)) | |
print('Files are sorted by {}'.format(sorting_method)) | |
print(datetime.datetime.now()) | |
print() | |
if text_to_bool(exp['Correction']): | |
lambda_flat = '0.1' | |
lambda_dark = '0.01' | |
ffp_list = [] | |
dfp_list = [] | |
for j in files_exp: | |
print('\r ' + 'Generating ffp and dfp for ' + j.name) | |
ffp_file_name = j.name.replace('.' + file_type, '-ffp.tif') | |
dfp_file_name = j.name.replace('.' + file_type, '-dfp.tif') | |
illumination_dir = path_exp / 'illumination_profiles' | |
if (path_exp / 'illumination_profiles' /ffp_file_name).exists() and (path_exp / 'illumination_profiles' / dfp_file_name).exists(): | |
print('\r ' + ffp_file_name + ' already exists') | |
print('\r ' + dfp_file_name + ' already exists') | |
else: | |
if not illumination_dir.exists(): | |
illumination_dir.mkdir() | |
call("C:/Users/Public/Downloads/Fiji.app/ImageJ-win64.exe --ij2 --headless --run C:/Users/Public/Downloads/Fiji.app/plugins/imagej_basic_ashlar.py \"filename='%s', output_dir='%s', experiment_name='%s', lambda_flat=%s, lambda_dark=%s\"" %(str(j), str(illumination_dir), j.name.replace('.'+file_type, ''), lambda_flat, lambda_dark)) | |
print('\r ' + ffp_file_name + ' generated') | |
print('\r ' + dfp_file_name + ' generated') | |
ffp_list.append(str(illumination_dir / ffp_file_name)) | |
dfp_list.append(str(illumination_dir / dfp_file_name)) | |
print('Run ashlar') | |
print(datetime.datetime.now()) | |
print() | |
out_dir = path_exp / 'registration' | |
if not out_dir.exists(): | |
out_dir.mkdir() | |
input_files = ' '.join([str(f) for f in files_exp]) | |
command = 'ashlar ' + input_files + ' -m 30 -o ' + str(out_dir) | |
if text_to_bool(exp['Pyramid']): | |
command += ' --pyramid -f ' + path_exp.name + '.ome.tif' | |
if text_to_bool(exp['Correction']): | |
ffps = ' '.join(ffp_list) | |
dfps = ' '.join(dfp_list) | |
command += ' --ffp ' + ffps + ' --dfp ' + dfps | |
with open(str(out_dir / path_exp.name) + '-ashlar_command.log', 'a+') as f: | |
f.write('\n=== {} ===\n'.format(str(datetime.datetime.now()))) | |
f.write('ashlar version:\n') | |
f.write(check_output('ashlar --version').decode("utf-8")) | |
f.write('ashlar command:\n') | |
f.write(command) | |
call(command) |
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List folders and subfolders path and write to f.txt (Windows command prompt)
dir /s /b /o:n /a:d > f.txt
source