mm3_Track-Standard.py

#!/usr/bin/env python3
from __future__ import print_function, division
import six

# import modules
import sys
import os
import time
import inspect
import argparse
import yaml
from pprint import pprint # for human readable file output
try:
    import cPickle as pickle
except:
    import pickle
import numpy as np
from scipy.io import savemat

# user modules
# realpath() will make your script run, even if you symlink it
cmd_folder = os.path.realpath(os.path.abspath(
                              os.path.split(inspect.getfile(inspect.currentframe()))[0]))
if cmd_folder not in sys.path:
    sys.path.insert(0, cmd_folder)

# This makes python look for modules in ./external_lib
cmd_subfolder = os.path.realpath(os.path.abspath(
                                 os.path.join(os.path.split(inspect.getfile(
                                 inspect.currentframe()))[0], "external_lib")))
if cmd_subfolder not in sys.path:
    sys.path.insert(0, cmd_subfolder)

import mm3_helpers as mm3

# when using this script as a function and not as a library the following will execute
if __name__ == "__main__":

    # set switches and parameters
    parser = argparse.ArgumentParser(prog='python mm3_Segment.py',
                                     description='Segment cells and create lineages.')
    parser.add_argument('-f', '--paramfile',  type=str,
                        required=True, help='Yaml file containing parameters.')
    parser.add_argument('-o', '--fov',  type=str,
                        required=False, help='List of fields of view to analyze. Input "1", "1,2,3", or "1-10", etc.')
    parser.add_argument('-j', '--nproc',  type=int,
                        required=False, help='Number of processors to use.')
    parser.add_argument('-s', '--segmentsource', type=str,
                        required=False, help='Segmented images to use for tracking. "seg_otsu", "seg_unet", etc.')
    namespace = parser.parse_args()

    # Load the project parameters file
    mm3.information('Loading experiment parameters.')
    if namespace.paramfile:
        param_file_path = namespace.paramfile
    else:
        mm3.warning('No param file specified. Using 100X template.')
        param_file_path = 'yaml_templates/params_SJ110_100X.yaml'
    p = mm3.init_mm3_helpers(param_file_path) # initialized the helper library

    if namespace.fov:
        if '-' in namespace.fov:
            user_spec_fovs = range(int(namespace.fov.split("-")[0]),
                                   int(namespace.fov.split("-")[1])+1)
        else:
            user_spec_fovs = [int(val) for val in namespace.fov.split(",")]
    else:
        user_spec_fovs = []

    # number of threads for multiprocessing
    if namespace.nproc:
        p['num_analyzers'] = namespace.nproc
    mm3.information('Using {} threads for multiprocessing.'.format(p['num_analyzers']))

    # segmentation plane to be used for tracking
    if namespace.segmentsource:
        p['track']['seg_img'] = namespace.segmentsource
    else:
        if 'seg_img' not in p['track'].keys():
            p['track']['seg_img'] = 'seg_otsu' # default to otsu. Good chance of error.
    mm3.information("Using {} images for tracking.".format(p['track']['seg_img']))

    # create segmenteation and cell data folder if they don't exist
    if not os.path.exists(p['seg_dir']) and p['output'] == 'TIFF':
        os.makedirs(p['seg_dir'])
    if not os.path.exists(p['cell_dir']):
        os.makedirs(p['cell_dir'])

    # load specs file
    specs = mm3.load_specs()

    # make list of FOVs to process (keys of channel_mask file)
    fov_id_list = sorted([fov_id for fov_id in specs.keys()])

    # remove fovs if the user specified so
    if user_spec_fovs:
        fov_id_list[:] = [fov for fov in fov_id_list if fov in user_spec_fovs]

    ### Create cell lineages from segmented images
    mm3.information("Creating cell lineages using standard algorithm.")

    # Load time table, which goes into params
    mm3.load_time_table()

    # This dictionary holds information for all cells
    Cells = {}

    # do lineage creation per fov, so pooling can be done by peak
    for fov_id in fov_id_list:
        # update will add the output from make_lineages_function, which is a
        # dict of Cell entries, into Cells
        Cells.update(mm3.make_lineages_fov(fov_id, specs))

    mm3.information("Finished lineage creation.")

    ### Now prune and save the data.
    mm3.information("Curating and saving cell data.")

    # this returns only cells with a parent and daughters
    Complete_Cells = mm3.find_complete_cells(Cells)

    ### save the cell data. Use the script mm3_OutputData for additional outputs.
    # All cell data (includes incomplete cells)
    with open(p['cell_dir'] + '/all_cells.pkl', 'wb') as cell_file:
        pickle.dump(Cells, cell_file, protocol=pickle.HIGHEST_PROTOCOL)

    # Just the complete cells, those with mother and daugther
    # This is a dictionary of cell objects.
    with open(os.path.join(p['cell_dir'],'complete_cells.pkl'), 'wb') as cell_file:
        pickle.dump(Complete_Cells, cell_file, protocol=pickle.HIGHEST_PROTOCOL)

    mm3.information("Finished curating and saving cell data.")