def __init__(self, experiment_directory=None, config=None):
super(PostProcessingExperiment, self).__init__(experiment_directory)
# Privates
self._meta_config['exclude_attrs_from_save'] = [
'data_loader', '_device'
]
if config is not None:
self.read_config_file(config)
self.auto_setup()
# Where the segmentation will be saved: (each experiment is in a sub-folder)
proj_dir_path = self.get(
'proj_dir_path',
os.path.join(get_trendytukan_drive_path(),
"projects/pixel_embeddings"))
self.set("proj_dir_path", proj_dir_path)
# Load the file with all the presets of postprocessing:
postproc_presets_path = self.get(
'postproc_presets_path',
os.path.join(
get_abailoni_hci_home_path(),
"pyCharm_projects/uppsala_hackathon/experiments/cremi/"
"postproc_configs/postproc_presets.yml"))
self.set("postproc_presets",
segm_utils.yaml2dict(postproc_presets_path))
self.build_experiment()
self.build_offsets()
def save_infer_output(self, output):
import h5py
import numpy as np
print("Saving....")
from segmfriends.utils.various import check_dir_and_create
dir_path = os.path.join(
get_trendytukan_drive_path(), "projects/pixel_embeddings",
self.get("name_experiment", default="generic_experiment"))
check_dir_and_create(dir_path)
with h5py.File(
os.path.join(
dir_path, "predictions_sample_{}.h5".format(
self.get("loaders/infer/name"))), 'w') as f:
f.create_dataset('data',
data=output.astype(np.float16),
compression='gzip')
def save_infer_output(self, output):
import h5py
import numpy as np
print("Saving....")
from segmfriends.utils.various import check_dir_and_create
dir_path = os.path.join(
get_trendytukan_drive_path(), "projects/pixel_embeddings",
self.get("name_experiment", default="generic_experiment"))
check_dir_and_create(dir_path)
filename = os.path.join(
dir_path,
"predictions_sample_{}.h5".format(self.get("loaders/infer/name")))
print("Writing to ", self.get("inner_path_output", 'data'))
from segmfriends.utils.various import writeHDF5
writeHDF5(output.astype(np.float16), filename,
self.get("inner_path_output", 'data'))
print("Saved to ", filename)
# Dump configuration to export folder:
self.dump_configuration(os.path.join(dir_path,
"prediction_config.yml"))
This could create an effect with thin processes, but apparently it doesn't
"""
# -----------
# LOAD data
# -----------
from vaeAffs.utils.path_utils import get_abailoni_hci_home_path, get_trendytukan_drive_path
from segmfriends.utils.various import parse_data_slice, readHDF5, writeHDF5, readHDF5_from_volume_config
import os
import json
import h5py
from scipy.ndimage import zoom
import nifty.graph.rag as nrag
project_dir = os.path.join(get_trendytukan_drive_path(),
"projects/pixel_embeddings")
IGNORE_LABELS = [0, 1]
nb_threads = 6
EXP_NAMES = [
# TEST:
"v4_addSparseAffs_fullGT_eff",
"v4_addSparseAffs_fullGT_avgDirectVar",
# VAL:
# "v4_addSparseAffs_eff",
# "v4_onlySparseAffs_eff",
# "v4_main_avgDirectVar",
# "v4_addSparseAffs_avgDirectVar",
}
parsed_slice = parse_data_slice(slices[sample])
data_path = os.path.join(get_abailoni_hci_home_path(),
"datasets/new_cremi/sample{}.h5".format(sample))
# data_path = os.path.join(get_trendytukan_drive_path(), "datasets/new_cremi/fib25/sample{}.h5".format(sample))
with h5py.File(data_path, 'r') as f:
print([atr for atr in f['volumes/labels']])
# glia = f['volumes/labels/glia'][:]
raw = f['volumes/raw'][parsed_slice[1:]]
GT = f['volumes/labels/neuron_ids_fixed'][parsed_slice[1:]]
# Load affs:
from segmfriends.utils.various import writeHDF5, readHDF5
affs_path = os.path.join(
get_trendytukan_drive_path(),
"projects/pixel_embeddings/{}/predictions_sample_{}.h5".format(
"v4_addSparseAffs_eff", sample))
affs_dice = 1. - readHDF5(
affs_path, "data", crop_slice="3:5,70:-6,25:-25,25:-25")
affs_path = os.path.join(
get_trendytukan_drive_path(),
"projects/pixel_embeddings/{}/predictions_sample_{}.h5".format(
"v4_addSparseAffs_avgDirectVar", sample))
affs_avg = readHDF5(affs_path, "data", crop_slice="3:5,70:-6,25:-25,25:-25")
# Load segm:
folder_path = os.path.join(get_trendytukan_drive_path(),
"projects/pixel_embeddings/")
segm_path = os.path.join(
folder_path, "{}/out_segms/{}.h5".format(
sample = "C"
# slices = {'A': ":,:,:,:", 'B': ":, :, 90:, 580: 1900", 'C': ":, :, 70:1450, 95:1425"}
slices = {
'A': ":,:,:,:",
'B': ":,:,:,:",
'C': ":,70:-6,50:-50,50:-50",
"0": ":,:,:,:",
"1": ":,:,:,:",
"2": ":,:,:,:",
"3": ":,:,:,:",
}
parsed_slice = parse_data_slice(slices[sample])
conf_folder_path = os.path.join(
get_trendytukan_drive_path(),
"projects/pixel_embeddings/v4_addSparseAffs_avgDirectVar/scores")
prefix_math = [
"C__MEAN___",
# "C__MutexWatershed___",
"C__MWS__stride10___"
]
labels = {
"C__MEAN___": "Gasp Average",
"C__MWS__stride10___": "Mutex Watershed"
}
keys = [
"nb_nodes",
copy_from_previous = {
"A": [],
"B": [],
"C": [22, 82],
"0": [122],
"1": [122],
"2": [],
}
# for sample in ["A", "B", "C"]:
for sample in ["C", "0", "1", "2"]:
print("Sample", sample)
raise DeprecationWarning("Fix bug of glia and boundary label")
data_path = os.path.join(get_trendytukan_drive_path(), "datasets/new_cremi/sample{}.h5".format(sample))
with h5py.File(data_path, 'r') as f:
print([atr for atr in f['volumes/labels']])
# glia = f['volumes/labels/glia'][:]
# raw = f['volumes/raw'][:]
GT = f['volumes/labels/neuron_ids'][:]
glia = f['volumes/labels/glia'][:]
from affogato.affinities import compute_affinities
offsets = [
[0, 1, 0],
[0, 0, 1],
]
print(GT.max())
import os
from copy import deepcopy
from vaeAffs.utils.path_utils import get_trendytukan_drive_path, get_abailoni_hci_home_path
from segmfriends.utils.config_utils import assign_color_to_table_value, return_recursive_key_in_dict
import json
import numpy as np
from segmfriends.utils.various import yaml2dict
# -----------------------
# Script options:
# -----------------------
project_dir = os.path.join(get_trendytukan_drive_path(),"projects/pixel_embeddings")
EXP_NAMES = [
"v4_addSparseAffs_eff",
"v4_onlySparseAffs_eff",
"v4_main_avgDirectVar",
"v4_main_eff",
"v4_addSparseAffs_avgDirectVar",
]
REQUIRED_STRINGS = [
# "_mergedGlia",
# "affs_withLR_z"
]
EXCLUDE_STRINGS = [
"_mergedGlia",
"multicut_kerLin",
