def segmentChannel_Weka(image, **kwargs):
""" SegmentChannel using a Weka Classification"""
ch = kwargs['channel']
if ch > len(image):
raise Exception('Expecting at least ' + str(ch) + ' channels. Image has only ' + str(len(imageC)) + ' channel(s)')
imp = image[ch-1].duplicate()
ws = WekaSegmentation(imp) # create an instance
ws.loadClassifier(kwargs['clspath']) # load classifier
impProb = ws.applyClassifier(imp, 0, True)
impMetaProb = extractChannel(impProb,1,1)
impMetaProb.setTitle("MetaProb")
# segmentation
impBW = threshold(impMetaProb, kwargs['probThr'])
impMetaProb.show()
IJ.run("Set Measurements...", " mean center shape area redirect=MetaProb decimal=2");
# particle analysis
IJ.run(impBW, "Analyze Particles...", "size=10-10000 pixel area circularity=0.00-1.00 display exclude clear stack add");
rt = Analyzer.getResultsTable()
validParticles = []
roim = RoiManager.getInstance()
if roim == None:
raise Exception('Fiji error segmentNuclei.py: no RoiManager!')
if roim.getCount() > 0:
rois = roim.getRoisAsArray()
else:
IJ.log("# particles = 0")
return impMetaProb, impBW, None, None
X = rt.getColumn(rt.getColumnIndex("XM"))
Y = rt.getColumn(rt.getColumnIndex("YM"))
Mean = rt.getColumn(rt.getColumnIndex("Mean"))
Circ = rt.getColumn(rt.getColumnIndex("Circ."))
Area = rt.getColumn(rt.getColumnIndex("Area"))
print "# particles = " + str(len(Mean))
nValid = 0
for i in range(len(Mean)):
valid = (Mean[i]>kwargs['minProb']) and (Circ[i]<kwargs['maxCirc']) # filter particles post detection
if(valid):
validParticles.append([i, X[i], Y[i], Mean[i]])
print validParticles
IJ.log("# valid particles = %d " % len(validParticles))
# sort particles according to Mean
validParticlesSorted = sorted(validParticles, key=itemgetter(3))
# only keep the three with the largest Mean
validParticles = validParticlesSorted[-int(kwargs["nrPart"]):]
#random.shuffle(validParticles)
IJ.log("# valid particles = %d " % len(validParticles))
if len(validParticles) == 0:
validParticles = None
return impMetaProb, impBW, validParticles, rois
# Set the options.
options = ['size=%s' % size]
circularity_str = '%.3f-%.3f' % (circularity_min, circularity_max)
options.append('circularity=%s' % circularity_str)
if show.find('_') >= 0:
show_str = '[%s]' % show.replace('_', ' ')
else:
show_str = show
options.append('show=%s' % show_str)
if display_results:
options.append('display')
if not all_results:
options.append('summarize')
if exclude_edges:
options.append('exclude')
if include_holes:
options.append('include')
# Always run "in_situ".
options.append('in_situ')
# Run the command.
IJ.run(input_image_plus_copy, "Analyze Particles...", " ".join(options))
# Save outputs.
if len(output_filename) > 0:
# Save the ImagePlus object as a new image.
IJ.saveAs(input_image_plus_copy, output_datatype, output_filename)
if display_results and len(results_path) > 0:
results_table = analyzer.getResultsTable()
results_table.saveAs(results_path)
ws = WekaSegmentation(imp) # create an instance
ws.loadClassifier(classifierPath) # load classifier
impProb = ws.applyClassifier(imp, 0, True)
#impProb.show()
impMetaProb = extractChannel(impProb,1,1)
impMetaProb.setTitle("MetaProb")
# segmentation
impBW = threshold(impMetaProb,0.6)
impMetaProb.show()
IJ.run("Set Measurements...", " mean center shape area redirect=MetaProb decimal=2");
impBW.show()
# particle analysis
IJ.run("Analyze Particles...", "size=10-10000 pixel area circularity=0.00-1.00 display exclude clear stack add");
rt = Analyzer.getResultsTable()
validParticles = []
if(rt.getColumnIndex("XM")==-1):
print "# particles = 0"
else:
X = rt.getColumn(rt.getColumnIndex("XM"))
Y = rt.getColumn(rt.getColumnIndex("YM"))
Mean = rt.getColumn(rt.getColumnIndex("Mean"))
Circ = rt.getColumn(rt.getColumnIndex("Circ."))
Area = rt.getColumn(rt.getColumnIndex("Area"))
print "# particles = " + str(len(Mean))
for i in range(len(Mean)):
valid = (Mean[i]>0.8) and (Circ[i]<0.8) # filter particles post detection
if(valid):
validParticles.append(i)
circularity_str = '%.3f-%.3f' % ( circularity_min, circularity_max )
options.append( 'circularity=%s' % circularity_str )
if show.find( '_' ) >= 0:
show_str = '[%s]' % show.replace( '_', ' ' )
else:
show_str = show
options.append( 'show=%s' % show_str )
if display_results:
options.append( 'display' )
if not all_results:
options.append( 'summarize' )
if exclude_edges:
options.append( 'exclude' )
if include_holes:
options.append( 'include' )
# Always run "in_situ".
options.append( 'in_situ' )
# Run the command.
IJ.run( input_image_plus_copy, "Analyze Particles...", " ".join( options ) )
# Save outputs.
if tmp_output_path not in [ None, 'None' ]:
# Save the ImagePlus object as a new image.
IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path )
if display_results and results_path not in [ None, 'None' ]:
results_table = analyzer.getResultsTable()
results_table.saveAs( results_path )
except Exception, e:
jython_utils.handle_error( error_log, str( e ) )
from ij import IJ
from ij.gui import OvalRoi
from ij.plugin.frame import RoiManager
from ij.plugin.filter import Analyzer
from ij.measure import Measurements, ResultsTable
imp = IJ.getImage()
rm = RoiManager() # instantiate manager # throws exception if it doesn't exist
#rm = RoiManager.getInstance() # if manager exists
bright_roi = OvalRoi(118,94,12,12); # define and add ROI
imp.setRoi(bright_roi) # make active on image
rm.addRoi(bright_roi) # add
#rm.select(0) # select the ROI
dark_roi = OvalRoi(138,144,12,12)
imp.setRoi(dark_roi) # make active on image
rm.addRoi(dark_roi) # add
rm.runCommand(imp,"Measure") # this will create a new results table
rm.runCommand(imp,"Show All") # show all ROI
rt = Analyzer.getResultsTable()
bright_mean = rt.getValueAsDouble(rt.getColumnIndex("Max"),0)
dark_mean = rt.getValueAsDouble(rt.getColumnIndex("Mean"),1)
print "Bright :" + str(bright_mean) + " Dark: " + str(dark_mean) + "the contrast value is :" + str(bright_mean/dark_mean) #access table by col and row
