Newer
Older
#! /g/kreshuk/pape/Work/software/conda/miniconda3/envs/cluster_env37/bin/python
from scripts.files import make_folder_structure
from scripts.export import export_segmentation
from scripts.files import copy_xml_with_abspath
from scripts.attributes import make_nucleus_tables
def make_segmentations(old_folder, folder):
path = '/g/kreshuk/data/arendt/platyneris_v1/data.n5'
# export nucleus segemntation
tmp_nuclei = 'tmp_export_nuclei'
key_nuclei = 'volumes/paintera/nuclei'
nuclei_name = 'em-segmented-nuclei-labels'
res_nuclei = [.1, .08, .08]
export_segmentation(path, key_nuclei, old_folder, folder, nuclei_name, res_nuclei, tmp_nuclei)
# export cell segemntation
key_cells = 'volumes/paintera/proofread_cells'
cells_name = 'em-segmented-cells-labels'
res_cells = [.025, .02, .02]
export_segmentation(path, key_cells, old_folder, folder, cells_name, res_cells, tmp_cells)
def make_image_data(old_folder, folder):
data_folder = os.path.join(folder, 'images')
# start by copying the raw data
raw_name = 'em-raw-full-res.xml'
raw_in = os.path.join(old_folder, raw_name)
raw_out = os.path.join(data_folder, raw_name)
copy_xml_with_abspath(raw_in, raw_out)
# TODO
# copy MEDs and SPMs
# copy cellular models
# copy additional segmentations from tischi and ariadne
# (neuropil, muscle, ...)
def make_tables(folder):
# TODO make cell segmentation tables
# name_cells = 'em-segmented-cells-labels'
# res_cells = [.025, .02, .02]
# make_cell_tables(folder, name_cells, 'tmp_tables_cells', res_cells)
# make nucleus segmentation tables
name_nuclei = 'em-segmented-nuclei-labels'
res_nuclei = [.1, .08, .08]
make_nucleus_tables(folder, name_nuclei, 'tmp_tables_nuclei',
res_nuclei, target='local', max_jobs=8)
def make_initial_version():
old_folder = '/g/arendt/EM_6dpf_segmentation/EM-Prospr'
tag = '0.0.0'
folder = os.path.join('data', tag)
# make_segmentations(old_folder, folder)
# make xmls for all necessary image data
# make_image_data(old_folder, folder)
if __name__ == '__main__':
make_initial_version()