def test_normalize_channels_in_place():
a = np.zeros((100, 200, 3), dtype=np.float32)
a[..., 0] = (0.2 / 2)
a[..., 1] = (0.3 / 2)
a[..., 2] = (0.5 / 2)
# erase a pixel entirely
a[50, 50, :] = 0.0
normalize_channels_in_place(a)
assert (a.sum(axis=-1) == 1.0).all()
assert a[50, 50, 0] == a[50, 50, 1] == a[50, 50, 2]
def test_normalize_channels_in_place():
a = np.zeros((100,200,3), dtype=np.float32)
a[..., 0] = (0.2 / 2)
a[..., 1] = (0.3 / 2)
a[..., 2] = (0.5 / 2)
# erase a pixel entirely
a[50,50,:] = 0.0
normalize_channels_in_place(a)
assert (a.sum(axis=-1) == 1.0).all()
assert a[50,50,0] == a[50,50,1] == a[50,50,2]
def ilastik_simple_predict(gray_vol, mask, classifier_path, filter_specs_path, selected_channels=None, normalize=True,
LAZYFLOW_THREADS=0, LAZYFLOW_TOTAL_RAM_MB=None, logfile="/dev/null"):
"""
gray_vol: A 3D numpy array with axes zyx
mask: A binary image where 0 means "no prediction necessary".
'None' can be given, which means "predict everything".
classifier_path: Path to a vigra RandomForest classifier, in HDF5.
Example: /path/to/myclassifier.h5/classifiers/my_rf
filter_specs_path: Path to "filter specs" json file. The json structure is like this:
[ ['GaussianSmoothing', 0.3],
['GaussianSmoothing', 0.7],
['LaplacianOfGaussian', 1.6] ]
(See ilastik's simple_predict.py for valid filter names.)
selected_channels: A list of channel indexes to select and return from the prediction results.
'None' can also be given, which means "return all prediction channels".
You may also return a *nested* list, in which case groups of channels can be
combined (summed) into their respective output channels.
For example: selected_channels=[0,3,[2,4],7] means the output will have 4 channels:
0,3,2+4,7 (channels 5 and 6 are simply dropped).
normalize: Renormalize all outputs so the channels sum to 1 everywhere.
That is, (predictions.sum(axis=-1) == 1.0).all()
Note: Pixels with 0.0 in all channels will be simply given a value of 1/N in all channels.
LAZYFLOW_THREADS, LAZYFLOW_TOTAL_RAM_MB: Same meanings as in ilastik_predict_with_array().
(although we have to configure them in a different way)
"""
print("ilastik_simple_predict(): Starting with raw data: dtype={}, shape={}".format(str(gray_vol.dtype), gray_vol.shape))
import os
from collections import OrderedDict
import uuid
import platform
import vigra
from ilastik.utility.simple_predict import load_and_predict
from lazyflow.request import Request
print("ilastik_simple_predict(): Done with imports")
_prepare_lazyflow_config(LAZYFLOW_THREADS, LAZYFLOW_TOTAL_RAM_MB, 10)
Request.reset_thread_pool(LAZYFLOW_THREADS)
# The process_name argument is prefixed to all log messages.
# For now, just use the machine name and a uuid
# FIXME: It would be nice to provide something more descriptive, like the ROI of the current spark job...
process_name = platform.node() + "-" + str(uuid.uuid1())
# To avoid conflicts between processes, give each process it's own logfile to write to.
if logfile != "/dev/null":
base, ext = os.path.splitext(logfile)
logfile = base + '.' + process_name + ext
_init_logging(logfile, process_name)
# Construct an OrderedDict of role-names -> DatasetInfos
# (See PixelClassificationWorkflow.ROLE_NAMES)
raw_data_array = vigra.taggedView(gray_vol, 'zyx')
print("ilastik_simple_predict(): Starting export...")
predictions = load_and_predict( raw_data_array, classifier_path, filter_specs_path, compute_blockwise=True )
selected_predictions = select_channels(predictions, selected_channels)
if normalize:
normalize_channels_in_place(selected_predictions)
return selected_predictions
def two_stage_voxel_predictions(gray_vol,
mask,
stage_1_ilp_path,
stage_2_ilp_path,
selected_channels=None,
normalize=True,
LAZYFLOW_THREADS=1,
LAZYFLOW_TOTAL_RAM_MB=None,
logfile="/dev/null",
extra_cmdline_args=[]):
"""
Using ilastik's python API, run a two-stage voxel prediction using the two given project files.
The output of the first stage will be saved to a temporary location on disk and used as input to the second stage.
gray_vol: A 3D numpy array with axes zyx
mask: A binary image where 0 means "no prediction necessary".
'None' can be given, which means "predict everything".
(It will only be used during the second stage.)
ilp_stage_1_path: Path to the project file for the first stage. Should accept graystale uint8 data as the input.
ilastik also accepts a url to a DVID key-value, which will be downloaded and opened as an ilp
ilp_stage_1_path: Path to the project file for the second stage. Should take N input channels (uint8) as input,
where N is the number of channels produced in stage 1.
selected_channels: A list of channel indexes to select and return from the prediction results.
'None' can also be given, which means "return all prediction channels".
You may also return a *nested* list, in which case groups of channels can be
combined (summed) into their respective output channels.
For example: selected_channels=[0,3,[2,4],7] means the output will have 4 channels:
0,3,2+4,7 (channels 5 and 6 are simply dropped).
normalize: Renormalize all outputs so the channels sum to 1 everywhere.
That is, (predictions.sum(axis=-1) == 1.0).all()
Note: Pixels with 0.0 in all channels will be simply given a value of 1/N in all channels.
LAZYFLOW_THREADS, LAZYFLOW_TOTAL_RAM_MB: Passed to ilastik via environment variables.
"""
print("two_stage_voxel_predictions(): Starting with raw data: dtype={}, shape={}"\
.format(str(gray_vol.dtype), gray_vol.shape))
import tempfile
import shutil
import numpy as np
import h5py
scratch_dir = tempfile.mkdtemp(prefix='voxel_predictions_')
logger.info("Writing intermediate results to scratch directory: " +
scratch_dir)
#logger.info( "FIXME: Writing grayscale for debug purposes" )
#with h5py.File(scratch_dir + '/grayscale.h5', 'w') as grayscale_file:
# grayscale_file.create_dataset('grayscale', data=gray_vol)
try:
# Run predictions on the in-memory data.
stage_1_output_path = scratch_dir + '/stage_1_predictions.h5'
run_ilastik_stage(1, stage_1_ilp_path, gray_vol, None,
stage_1_output_path, LAZYFLOW_THREADS,
LAZYFLOW_TOTAL_RAM_MB, logfile, extra_cmdline_args)
stage_2_output_path = scratch_dir + '/stage_2_predictions.h5'
run_ilastik_stage(2, stage_2_ilp_path, stage_1_output_path, mask,
stage_2_output_path, LAZYFLOW_THREADS,
LAZYFLOW_TOTAL_RAM_MB, logfile, extra_cmdline_args)
combined_predictions_path = scratch_dir + '/combined_predictions.h5'
# Sadly, we must rewrite the predictions into a single file, because they might be combined together.
# Technically, we could avoid this with some fancy logic, but that would be really annoying.
with h5py.File(combined_predictions_path,
'w') as combined_predictions_file:
with h5py.File(stage_1_output_path, 'r') as stage_1_prediction_file, \
h5py.File(stage_2_output_path, 'r') as stage_2_prediction_file:
stage_1_predictions = stage_1_prediction_file['predictions']
stage_2_predictions = stage_2_prediction_file['predictions']
assert stage_1_predictions.dtype == stage_2_predictions.dtype, \
"Mismatched dtypes: {} vs {}".format( stage_1_predictions.dtype, stage_2_predictions.dtype )
stage_1_channels = stage_1_predictions.shape[-1]
stage_2_channels = stage_2_predictions.shape[-1]
assert stage_1_predictions.shape[:-1] == stage_2_predictions.shape[:-1], \
"Non-channel dimensions must match. shapes were: {} and {}"\
.format(stage_1_predictions.shape, stage_2_predictions.shape)
combined_shape = stage_1_predictions.shape[:-1] + (
(stage_1_channels + stage_2_channels), )
combined_predictions = combined_predictions_file.create_dataset(
'predictions',
dtype=stage_1_predictions.dtype,
shape=combined_shape,
chunks=(64, 64, 64, 1))
# Do this one channel at a time to save RAM
for c in range(stage_1_channels):
combined_predictions[..., c] = stage_1_predictions[..., c]
for c in range(stage_2_channels):
combined_predictions[..., stage_1_channels +
c] = stage_2_predictions[..., c]
num_channels = combined_predictions.shape[-1]
if selected_channels:
assert isinstance(selected_channels, list)
for selection in selected_channels:
if isinstance(selection, list):
assert all(c < num_channels for c in selection), \
"Selected channels ({}) exceed number of prediction classes ({})"\
.format( selected_channels, num_channels )
else:
assert selection < num_channels, \
"Selected channels ({}) exceed number of prediction classes ({})"\
.format( selected_channels, num_channels )
# This will extract the channels we want, converting from hdf5 to numpy along the way.
selected_predictions = select_channels(combined_predictions,
selected_channels)
if normalize:
normalize_channels_in_place(selected_predictions)
assert selected_predictions.dtype == np.float32
return selected_predictions
finally:
shutil.rmtree(scratch_dir)
def two_stage_voxel_predictions(gray_vol, mask, stage_1_ilp_path, stage_2_ilp_path, selected_channels=None, normalize=True,
LAZYFLOW_THREADS=1, LAZYFLOW_TOTAL_RAM_MB=None, logfile="/dev/null", extra_cmdline_args=[]):
"""
Using ilastik's python API, run a two-stage voxel prediction using the two given project files.
The output of the first stage will be saved to a temporary location on disk and used as input to the second stage.
gray_vol: A 3D numpy array with axes zyx
mask: A binary image where 0 means "no prediction necessary".
'None' can be given, which means "predict everything".
(It will only be used during the second stage.)
ilp_stage_1_path: Path to the project file for the first stage. Should accept graystale uint8 data as the input.
ilastik also accepts a url to a DVID key-value, which will be downloaded and opened as an ilp
ilp_stage_1_path: Path to the project file for the second stage. Should take N input channels (uint8) as input,
where N is the number of channels produced in stage 1.
selected_channels: A list of channel indexes to select and return from the prediction results.
'None' can also be given, which means "return all prediction channels".
You may also return a *nested* list, in which case groups of channels can be
combined (summed) into their respective output channels.
For example: selected_channels=[0,3,[2,4],7] means the output will have 4 channels:
0,3,2+4,7 (channels 5 and 6 are simply dropped).
normalize: Renormalize all outputs so the channels sum to 1 everywhere.
That is, (predictions.sum(axis=-1) == 1.0).all()
Note: Pixels with 0.0 in all channels will be simply given a value of 1/N in all channels.
LAZYFLOW_THREADS, LAZYFLOW_TOTAL_RAM_MB: Passed to ilastik via environment variables.
"""
print("two_stage_voxel_predictions(): Starting with raw data: dtype={}, shape={}"\
.format(str(gray_vol.dtype), gray_vol.shape))
import tempfile
import shutil
import numpy as np
import h5py
scratch_dir = tempfile.mkdtemp(prefix='voxel_predictions_')
logger.info( "Writing intermediate results to scratch directory: " + scratch_dir )
#logger.info( "FIXME: Writing grayscale for debug purposes" )
#with h5py.File(scratch_dir + '/grayscale.h5', 'w') as grayscale_file:
# grayscale_file.create_dataset('grayscale', data=gray_vol)
try:
# Run predictions on the in-memory data.
stage_1_output_path = scratch_dir + '/stage_1_predictions.h5'
run_ilastik_stage(1, stage_1_ilp_path, gray_vol, None, stage_1_output_path,
LAZYFLOW_THREADS, LAZYFLOW_TOTAL_RAM_MB, logfile, extra_cmdline_args)
stage_2_output_path = scratch_dir + '/stage_2_predictions.h5'
run_ilastik_stage(2, stage_2_ilp_path, stage_1_output_path, mask, stage_2_output_path,
LAZYFLOW_THREADS, LAZYFLOW_TOTAL_RAM_MB, logfile, extra_cmdline_args)
combined_predictions_path = scratch_dir + '/combined_predictions.h5'
# Sadly, we must rewrite the predictions into a single file, because they might be combined together.
# Technically, we could avoid this with some fancy logic, but that would be really annoying.
with h5py.File(combined_predictions_path, 'w') as combined_predictions_file:
with h5py.File(stage_1_output_path, 'r') as stage_1_prediction_file, \
h5py.File(stage_2_output_path, 'r') as stage_2_prediction_file:
stage_1_predictions = stage_1_prediction_file['predictions']
stage_2_predictions = stage_2_prediction_file['predictions']
assert stage_1_predictions.dtype == stage_2_predictions.dtype, \
"Mismatched dtypes: {} vs {}".format( stage_1_predictions.dtype, stage_2_predictions.dtype )
stage_1_channels = stage_1_predictions.shape[-1]
stage_2_channels = stage_2_predictions.shape[-1]
assert stage_1_predictions.shape[:-1] == stage_2_predictions.shape[:-1], \
"Non-channel dimensions must match. shapes were: {} and {}"\
.format(stage_1_predictions.shape, stage_2_predictions.shape)
combined_shape = stage_1_predictions.shape[:-1] + ((stage_1_channels + stage_2_channels),)
combined_predictions = combined_predictions_file.create_dataset('predictions',
dtype=stage_1_predictions.dtype,
shape=combined_shape,
chunks=(64,64,64,1) )
# Do this one channel at a time to save RAM
for c in range(stage_1_channels):
combined_predictions[..., c] = stage_1_predictions[..., c]
for c in range(stage_2_channels):
combined_predictions[..., stage_1_channels+c] = stage_2_predictions[..., c]
num_channels = combined_predictions.shape[-1]
if selected_channels:
assert isinstance(selected_channels, list)
for selection in selected_channels:
if isinstance(selection, list):
assert all(c < num_channels for c in selection), \
"Selected channels ({}) exceed number of prediction classes ({})"\
.format( selected_channels, num_channels )
else:
assert selection < num_channels, \
"Selected channels ({}) exceed number of prediction classes ({})"\
.format( selected_channels, num_channels )
# This will extract the channels we want, converting from hdf5 to numpy along the way.
selected_predictions = select_channels(combined_predictions, selected_channels)
if normalize:
normalize_channels_in_place(selected_predictions)
assert selected_predictions.dtype == np.float32
return selected_predictions
finally:
shutil.rmtree(scratch_dir)
def ilastik_predict_with_array(gray_vol, mask, ilp_path, selected_channels=None, normalize=True,
LAZYFLOW_THREADS=1, LAZYFLOW_TOTAL_RAM_MB=None, logfile="/dev/null", extra_cmdline_args=[]):
"""
Using ilastik's python API, open the given project
file and run a prediction on the given raw data array.
Other than the project file, nothing is read or written
using the hard disk.
gray_vol: A 3D numpy array with axes zyx
mask: A binary image where 0 means "no prediction necessary".
'None' can be given, which means "predict everything".
ilp_path: Path to the project file. ilastik also accepts a url to a DVID key-value, which will be downloaded and opened as an ilp
selected_channels: A list of channel indexes to select and return from the prediction results.
'None' can also be given, which means "return all prediction channels".
You may also return a *nested* list, in which case groups of channels can be
combined (summed) into their respective output channels.
For example: selected_channels=[0,3,[2,4],7] means the output will have 4 channels:
0,3,2+4,7 (channels 5 and 6 are simply dropped).
normalize: Renormalize all outputs so the channels sum to 1 everywhere.
That is, (predictions.sum(axis=-1) == 1.0).all()
Note: Pixels with 0.0 in all channels will be simply given a value of 1/N in all channels.
LAZYFLOW_THREADS, LAZYFLOW_TOTAL_RAM_MB: Passed to ilastik via environment variables.
"""
print "ilastik_predict_with_array(): Starting with raw data: dtype={}, shape={}".format(str(gray_vol.dtype), gray_vol.shape)
import os
from collections import OrderedDict
import uuid
import multiprocessing
import platform
import psutil
import vigra
import ilastik_main
from ilastik.applets.dataSelection import DatasetInfo
print "ilastik_predict_with_array(): Done with imports"
if LAZYFLOW_TOTAL_RAM_MB is None:
# By default, assume our alotted RAM is proportional
# to the CPUs we've been told to use
machine_ram = psutil.virtual_memory().total
machine_ram -= 1024**3 # Leave 1 GB RAM for the OS.
LAZYFLOW_TOTAL_RAM_MB = LAZYFLOW_THREADS * machine_ram / multiprocessing.cpu_count()
# Before we start ilastik, prepare the environment variable settings.
os.environ["LAZYFLOW_THREADS"] = str(LAZYFLOW_THREADS)
os.environ["LAZYFLOW_TOTAL_RAM_MB"] = str(LAZYFLOW_TOTAL_RAM_MB)
os.environ["LAZYFLOW_STATUS_MONITOR_SECONDS"] = "10"
# Prepare ilastik's "command-line" arguments, as if they were already parsed.
args, extra_workflow_cmdline_args = ilastik_main.parser.parse_known_args(extra_cmdline_args)
args.headless = True
args.debug = True # ilastik's 'debug' flag enables special power features, including experimental workflows.
args.project = ilp_path
args.readonly = True
# By default, all ilastik processes duplicate their console output to ~/.ilastik_log.txt
# Obviously, having all spark nodes write to a common file is a bad idea.
# The "/dev/null" setting here is recognized by ilastik and means "Don't write a log file"
args.logfile = logfile
# The process_name argument is prefixed to all log messages.
# For now, just use the machine name and a uuid
# FIXME: It would be nice to provide something more descriptive, like the ROI of the current spark job...
args.process_name = platform.node() + "-" + str(uuid.uuid1())
print "ilastik_predict_with_array(): Creating shell..."
# Instantiate the 'shell', (in this case, an instance of ilastik.shell.HeadlessShell)
# This also loads the project file into shell.projectManager
shell = ilastik_main.main( args, extra_workflow_cmdline_args )
## Need to find a better way to verify the workflow type
#from ilastik.workflows.pixelClassification import PixelClassificationWorkflow
#assert isinstance(shell.workflow, PixelClassificationWorkflow)
# Construct an OrderedDict of role-names -> DatasetInfos
# (See PixelClassificationWorkflow.ROLE_NAMES)
raw_data_array = vigra.taggedView(gray_vol, 'zyx')
role_data_dict = OrderedDict([ ("Raw Data", [ DatasetInfo(preloaded_array=raw_data_array) ]) ])
if mask is not None:
# If there's a mask, we might be able to save some computation time.
mask = vigra.taggedView(mask, 'zyx')
role_data_dict["Prediction Mask"] = [ DatasetInfo(preloaded_array=mask) ]
print "ilastik_predict_with_array(): Starting export..."
# Sanity checks
opInteractiveExport = shell.workflow.batchProcessingApplet.dataExportApplet.topLevelOperator.getLane(0)
selected_result = opInteractiveExport.InputSelection.value
num_channels = opInteractiveExport.Inputs[selected_result].meta.shape[-1]
# For convenience, verify the selected channels before we run the export.
if selected_channels:
assert isinstance(selected_channels, list)
for selection in selected_channels:
if isinstance(selection, list):
assert all(c < num_channels for c in selection), \
"Selected channels ({}) exceed number of prediction classes ({})"\
.format( selected_channels, num_channels )
else:
assert selection < num_channels, \
"Selected channels ({}) exceed number of prediction classes ({})"\
.format( selected_channels, num_channels )
# Run the export via the BatchProcessingApplet
prediction_list = shell.workflow.batchProcessingApplet.run_export(role_data_dict, export_to_array=True)
assert len(prediction_list) == 1
predictions = prediction_list[0]
assert predictions.shape[-1] == num_channels
selected_predictions = select_channels(predictions, selected_channels)
if normalize:
normalize_channels_in_place(selected_predictions)
return selected_predictions
def ilastik_predict_with_array(gray_vol,
mask,
ilp_path,
selected_channels=None,
normalize=True,
LAZYFLOW_THREADS=1,
LAZYFLOW_TOTAL_RAM_MB=None,
logfile="/dev/null",
extra_cmdline_args=[]):
"""
Using ilastik's python API, open the given project
file and run a prediction on the given raw data array.
Other than the project file, nothing is read or written
using the hard disk.
gray_vol: A 3D numpy array with axes zyx
mask: A binary image where 0 means "no prediction necessary".
'None' can be given, which means "predict everything".
ilp_path: Path to the project file. ilastik also accepts a url to a DVID key-value, which will be downloaded and opened as an ilp
selected_channels: A list of channel indexes to select and return from the prediction results.
'None' can also be given, which means "return all prediction channels".
You may also return a *nested* list, in which case groups of channels can be
combined (summed) into their respective output channels.
For example: selected_channels=[0,3,[2,4],7] means the output will have 4 channels:
0,3,2+4,7 (channels 5 and 6 are simply dropped).
normalize: Renormalize all outputs so the channels sum to 1 everywhere.
That is, (predictions.sum(axis=-1) == 1.0).all()
Note: Pixels with 0.0 in all channels will be simply given a value of 1/N in all channels.
LAZYFLOW_THREADS, LAZYFLOW_TOTAL_RAM_MB: Passed to ilastik via environment variables.
"""
print "ilastik_predict_with_array(): Starting with raw data: dtype={}, shape={}".format(
str(gray_vol.dtype), gray_vol.shape)
import os
from collections import OrderedDict
import uuid
import multiprocessing
import platform
import psutil
import vigra
import ilastik_main
from ilastik.applets.dataSelection import DatasetInfo
from lazyflow.operators.cacheMemoryManager import CacheMemoryManager
import logging
logging.getLogger(__name__).info('status=ilastik prediction')
print "ilastik_predict_with_array(): Done with imports"
if LAZYFLOW_TOTAL_RAM_MB is None:
# By default, assume our alotted RAM is proportional
# to the CPUs we've been told to use
machine_ram = psutil.virtual_memory().total
machine_ram -= 1024**3 # Leave 1 GB RAM for the OS.
LAZYFLOW_TOTAL_RAM_MB = LAZYFLOW_THREADS * machine_ram / multiprocessing.cpu_count(
)
# Before we start ilastik, prepare the environment variable settings.
os.environ["LAZYFLOW_THREADS"] = str(LAZYFLOW_THREADS)
os.environ["LAZYFLOW_TOTAL_RAM_MB"] = str(LAZYFLOW_TOTAL_RAM_MB)
os.environ["LAZYFLOW_STATUS_MONITOR_SECONDS"] = "10"
# Prepare ilastik's "command-line" arguments, as if they were already parsed.
args, extra_workflow_cmdline_args = ilastik_main.parser.parse_known_args(
extra_cmdline_args)
args.headless = True
args.debug = True # ilastik's 'debug' flag enables special power features, including experimental workflows.
args.project = str(ilp_path)
args.readonly = True
# The process_name argument is prefixed to all log messages.
# For now, just use the machine name and a uuid
# FIXME: It would be nice to provide something more descriptive, like the ROI of the current spark job...
args.process_name = platform.node() + "-" + str(uuid.uuid1())
# To avoid conflicts between processes, give each process it's own logfile to write to.
if logfile != "/dev/null":
base, ext = os.path.splitext(logfile)
logfile = base + '.' + args.process_name + ext
# By default, all ilastik processes duplicate their console output to ~/.ilastik_log.txt
# Obviously, having all spark nodes write to a common file is a bad idea.
# The "/dev/null" setting here is recognized by ilastik and means "Don't write a log file"
args.logfile = logfile
print "ilastik_predict_with_array(): Creating shell..."
# Instantiate the 'shell', (in this case, an instance of ilastik.shell.HeadlessShell)
# This also loads the project file into shell.projectManager
shell = ilastik_main.main(args, extra_workflow_cmdline_args)
## Need to find a better way to verify the workflow type
#from ilastik.workflows.pixelClassification import PixelClassificationWorkflow
#assert isinstance(shell.workflow, PixelClassificationWorkflow)
# Construct an OrderedDict of role-names -> DatasetInfos
# (See PixelClassificationWorkflow.ROLE_NAMES)
raw_data_array = vigra.taggedView(gray_vol, 'zyx')
role_data_dict = OrderedDict([
("Raw Data", [DatasetInfo(preloaded_array=raw_data_array)])
])
if mask is not None:
# If there's a mask, we might be able to save some computation time.
mask = vigra.taggedView(mask, 'zyx')
role_data_dict["Prediction Mask"] = [DatasetInfo(preloaded_array=mask)]
print "ilastik_predict_with_array(): Starting export..."
# Sanity checks
opInteractiveExport = shell.workflow.batchProcessingApplet.dataExportApplet.topLevelOperator.getLane(
0)
selected_result = opInteractiveExport.InputSelection.value
num_channels = opInteractiveExport.Inputs[selected_result].meta.shape[-1]
# For convenience, verify the selected channels before we run the export.
if selected_channels:
assert isinstance(selected_channels, list)
for selection in selected_channels:
if isinstance(selection, list):
assert all(c < num_channels for c in selection), \
"Selected channels ({}) exceed number of prediction classes ({})"\
.format( selected_channels, num_channels )
else:
assert selection < num_channels, \
"Selected channels ({}) exceed number of prediction classes ({})"\
.format( selected_channels, num_channels )
# Run the export via the BatchProcessingApplet
prediction_list = shell.workflow.batchProcessingApplet.run_export(
role_data_dict, export_to_array=True)
assert len(prediction_list) == 1
predictions = prediction_list[0]
assert predictions.shape[-1] == num_channels
selected_predictions = select_channels(predictions, selected_channels)
if normalize:
normalize_channels_in_place(selected_predictions)
# Cleanup: kill cache monitor thread
CacheMemoryManager().stop()
CacheMemoryManager.instance = None
# Cleanup environment
del os.environ["LAZYFLOW_THREADS"]
del os.environ["LAZYFLOW_TOTAL_RAM_MB"]
del os.environ["LAZYFLOW_STATUS_MONITOR_SECONDS"]
logging.getLogger(__name__).info('status=ilastik prediction finished')
return selected_predictions
