def detectCells(source, sink = None, method ="SpotDetection", processMethod = all, verbose = False, **parameter):
"""Detect cells in data
This is a main script to start running the cell detection.
Arguments:
source (str or array): Image source
sink (str or None): destination for the results
method (str or function):
================ ============================================================
Method Description
================ ============================================================
"SpotDetection" uses predefined spot detection pipline
"Ilastik" uses predefined pipline with cell classification via Ilastik
function a user defined function
================ ============================================================
processMethod (str or all): 'sequential' or 'parallel'. if all its choosen
automatically
verbose (bool): print info
**parameter (dict): parameter for the image procesing sub-routines
Returns:
"""
timer = Timer();
# run segmentation
if method == "SpotDetection":
detectCells = ClearMap.ImageProcessing.SpotDetection.detectSpots;
elif method == 'Ilastik':
if ClearMap.ImageProcessing.Ilastik.Initialized:
detectCells = ClearMap.ImageProcessing.IlastikClassification.classifyCells;
processMethod = 'sequential'; #ilastik does parallel processing so force sequential processing here
else:
raise RuntimeError("detectCells: Ilastik not initialized, fix in Settings.py or use SpotDectection method instead!");
else:
raise RuntimeError("detectCells: invalid method %s" % str(method));
if processMethod == 'sequential':
result = sequentiallyProcessStack(source, sink = sink, function = detectCells, verbose = verbose, **parameter);
elif processMethod is all or processMethod == 'parallel':
result = parallelProcessStack(source, sink = sink, function = detectCells, verbose = verbose, **parameter);
else:
raise RuntimeError("detectCells: invalid processMethod %s" % str(processMethod));
if verbose:
timer.printElapsedTime("Total Cell Detection");
return result;
def detectCells(source, sink = None, method ="SpotDetection", processMethod = all, verbose = False, **parameter):
"""Detect cells in data
This is a main script to start running the cell detection.
Arguments:
source (str or array): Image source
sink (str or None): destination for the results
method (str or function):
================ ============================================================
Method Description
================ ============================================================
"SpotDetection" uses predefined spot detection pipline
"Ilastik" uses predefined pipline with cell classification via Ilastik
function a user defined function
================ ============================================================
processMethod (str or all): 'sequential' or 'parallel'. if all its choosen
automatically
verbose (bool): print info
**parameter (dict): parameter for the image procesing sub-routines
Returns:
"""
timer = Timer();
# run segmentation
if method == "SpotDetection":
detectCells = ClearMap.ImageProcessing.SpotDetection.detectSpots;
elif method == 'Ilastik':
if ClearMap.ImageProcessing.Ilastik.Initialized:
detectCells = ClearMap.ImageProcessing.IlastikClassification.classifyCells;
processMethod = 'sequential'; #ilastik does parallel processing so force sequential processing here
else:
raise RuntimeError("detectCells: Ilastik not initialized, fix in Settings.py or use SpotDectection method instead!");
else:
raise RuntimeError("detectCells: invalid method %s" % str(method));
if processMethod == 'sequential':
result = sequentiallyProcessStack(source, sink = sink, function = detectCells, verbose = verbose, **parameter);
elif processMethod is all or processMethod == 'parallel':
result = parallelProcessStack(source, sink = sink, function = detectCells, verbose = verbose, **parameter);
else:
raise RuntimeError("detectCells: invalid processMethod %s" % str(processMethod));
if verbose:
timer.printElapsedTime("Total Cell Detection");
return result;
def detectCells(jobid,
source,
sink=None,
method="SpotDetection",
processMethod="sequential",
verbose=False,
**parameter):
"""Detect cells in data
This is a main script to start running the cell detection.
Arguments:
source (str or array): Image source
sink (str or None): destination for the results
method (str or function):
================ ============================================================
Method Description
================ ============================================================
"SpotDetection" uses predefined spot detection pipline
"Ilastik" uses predefined pipline with cell classification via Ilastik
function a user defined function
================ ============================================================
processMethod (str or all): 'sequential' or 'parallel'. if all its choosen
automatically
verbose (bool): print info
**parameter (dict): parameter for the image procesing sub-routines
TP: added JOBID for cluster jobs
Returns:
"""
timer = Timer()
# run segmentation
if method == "SpotDetection":
detectCells = ClearMap.ImageProcessing.SpotDetection.detectSpots
elif method == 'Ilastik':
if ClearMap.ImageProcessing.Ilastik.Initialized:
detectCells = ClearMap.ImageProcessing.IlastikClassification.classifyCells
processMethod = 'sequential'
#ilastik does parallel processing so force sequential processing here
else:
raise RuntimeError(
"detectCells: Ilastik not initialized, fix in Settings.py or use SpotDectection method instead!"
)
print('Ilastik functionality disabled')
else:
raise RuntimeError("detectCells: invalid method %s" % str(method))
print('ProcessMethod = {}'.format(processMethod))
if processMethod == 'cluster':
result, substack = sequentiallyProcessStack_usingCluster(
jobid,
pth_update(source),
sink=pth_update(sink),
function=detectCells,
verbose=verbose,
**parameter)
if result == 'ENDPROCESS': return 'ENDPROCESS', 'ENDPROCESS'
elif processMethod == 'sequential':
result = sequentiallyProcessStack(source,
sink=sink,
function=detectCells,
verbose=verbose,
**parameter)
elif processMethod is all or processMethod == 'parallel':
result = parallelProcessStack(source,
sink=sink,
function=detectCells,
verbose=verbose,
**parameter)
else:
raise RuntimeError("detectCells: invalid processMethod %s" %
str(processMethod))
if verbose:
timer.printElapsedTime("Total Cell Detection")
if processMethod == 'cluster':
return result, substack
else:
return result
def calculateStatistics(
source,
sink=None,
calculateStatisticsParameter=None,
method="Max",
remove=True,
processMethod=all,
verbose=False,
**parameter
):
"""Calculate statisticsfrom image data
This is a main script to start extracting statistics of volumetric image data.
Arguments:
source (str or array): Image source
sink (str or None): destination for the results
calculateStatisticsParameter (dict):
========= ==================== ===========================================================
Name Type Descritption
========= ==================== ===========================================================
*method* (str or function) function to extract statistic, must be trivially distributable
if None, do not extract information
*remove* (bool) remove redundant overlap
*verbose* (bool or int) print / plot information about this step
========= ==================== ===========================================================
method (str or function):
processMethod (str or all): 'sequential' or 'parallel'. if all its choosen automatically
verbose (bool): print info
**parameter (dict): parameter for the image processing sub-routines
Returns:
list of statistics
"""
timer = Timer()
method = getParameter(calculateStatisticsParameter, "method", method)
remove = getParameter(calculateStatisticsParameter, "remove", remove)
verbose = getParameter(calculateStatisticsParameter, "verbose", verbose)
# run segmentation
if remove:
parameter = joinParameter({"chunkOverlap": 0}, parameter)
if processMethod == "sequential":
result = sequentiallyProcessStack(
source,
sink=sink,
function=calculateStatisticsOnStack,
join=joinStatistics,
method=method,
remove=remove,
verbose=verbose,
**parameter
)
elif processMethod is all or processMethod == "parallel":
result = parallelProcessStack(
source,
sink=sink,
function=calculateStatisticsOnStack,
join=joinStatistics,
method=method,
remove=remove,
verbose=verbose,
**parameter
)
else:
raise RuntimeError("calculateStatistics: invalid processMethod %s" % str(processMethod))
if verbose:
timer.printElapsedTime("Total Time Image Statistics")
return result
def calculateStatistics(source,
sink=None,
calculateStatisticsParameter=None,
method="Max",
remove=True,
processMethod=all,
verbose=False,
**parameter):
"""Calculate statisticsfrom image data
This is a main script to start extracting statistics of volumetric image data.
Arguments:
source (str or array): Image source
sink (str or None): destination for the results
calculateStatisticsParameter (dict):
========= ==================== ===========================================================
Name Type Descritption
========= ==================== ===========================================================
*method* (str or function) function to extract statistic, must be trivially distributable
if None, do not extract information
*remove* (bool) remove redundant overlap
*verbose* (bool or int) print / plot information about this step
========= ==================== ===========================================================
method (str or function):
processMethod (str or all): 'sequential' or 'parallel'. if all its choosen automatically
verbose (bool): print info
**parameter (dict): parameter for the image processing sub-routines
Returns:
list of statistics
"""
timer = Timer()
method = getParameter(calculateStatisticsParameter, "method", method)
remove = getParameter(calculateStatisticsParameter, "remove", remove)
verbose = getParameter(calculateStatisticsParameter, "verbose", verbose)
# run segmentation
if remove:
parameter = joinParameter({"chunkOverlap": 0}, parameter)
if processMethod == 'sequential':
result = sequentiallyProcessStack(source,
sink=sink,
function=calculateStatisticsOnStack,
join=joinStatistics,
method=method,
remove=remove,
verbose=verbose,
**parameter)
elif processMethod is all or processMethod == 'parallel':
result = parallelProcessStack(source,
sink=sink,
function=calculateStatisticsOnStack,
join=joinStatistics,
method=method,
remove=remove,
verbose=verbose,
**parameter)
else:
raise RuntimeError("calculateStatistics: invalid processMethod %s" %
str(processMethod))
if verbose:
timer.printElapsedTime("Total Time Image Statistics")
return result