Python ResultsTable.getColumnIndex方法代码示例

# 需要导入模块: from ij.measure import ResultsTable [as 别名]
# 或者: from ij.measure.ResultsTable import getColumnIndex [as 别名]
### Compute area fraction of oriented regions ###
# Compute area of oriented mask
newMaskImp = ImageJFunctions.wrapUnsignedByte(newMask, "New mask");
newMaskImp.getProcessor().setThreshold(1.0, 1.5, False); # [0.5, 1.0] does not work due to rounding problems
rt = ResultsTable();
analyzer = Analyzer(newMaskImp, Measurements.AREA | Measurements.LIMIT, rt);
analyzer.measure();

# Compute area of overall selection mask 
energyMaskImp = ImageJFunctions.wrapUnsignedByte(overallSelectionMask, "Energy mask");
energyMaskImp.getProcessor().setThreshold(1.0, 1.5, False); # [0.5, 1.0] does not work due to rounding problems
rtEnergy = ResultsTable();
analyzerEnergy = Analyzer(energyMaskImp, Measurements.AREA | Measurements.LIMIT, rtEnergy);
analyzerEnergy.measure();

# Print Area% (through SciJava OUTPUT, see L5)
if IJ.debugMode:
  print("Coherency area: "+str(rt.getValueAsDouble(rt.getColumnIndex("Area"), rt.size()-1)));
  print("Energy area: "+str(rtEnergy.getValueAsDouble(rtEnergy.getColumnIndex("Area"), rtEnergy.size()-1)));

areaCoherency = rt.getValueAsDouble(rt.getColumnIndex("Area"), rt.size()-1);
areaEnergy = rtEnergy.getValueAsDouble(rtEnergy.getColumnIndex("Area"), rtEnergy.size()-1);
areaFraction = str(areaCoherency/areaEnergy*100.0)+"%";

# Close "S-Mask"
if not IJ.debugMode:
  maskImp.close();
# Close "Energy"
if not IJ.debugMode and useEnergy:
  energyImp.close();

# 需要导入模块: from ij.measure import ResultsTable [as 别名]
# 或者: from ij.measure.ResultsTable import getColumnIndex [as 别名]
def procOneImage(pathpre, wnumber, endings):
  """ Analyzes a single image set (Dapi, VSVG, PM images)
  pathpre: fullpath prefix, down till "endings". 
  endings: a dictionary with signiture for three different channels. 
  wnumber: a number in string, indicating the spot ID.
  Returns three results tables. 
  """
  imp = IJ.openImage(pathpre + endings['dapi'] + '.tif')
  impVSVG = IJ.openImage(pathpre + endings['vsvg'] + '.tif')
  impPM = IJ.openImage(pathpre + endings['pm'] + '.tif')
  imp2 = imp.duplicate()

  rtallcellPM = ResultsTable()
  rtjnucVSVG = ResultsTable()
  rtallcellVSVG = ResultsTable()

  backVSVG = backgroundSubtraction(impVSVG)
  backPM = backgroundSubtraction(impPM)
  impfilteredNuc = nucleusSegmentation(imp2)

  intmax = impfilteredNuc.getProcessor().getMax()
  if intmax == 0:
    return rtallcellPM, rtjnucVSVG, rtallcellVSVG

  impfilteredNuc.getProcessor().setThreshold(1, intmax, ImageProcessor.NO_LUT_UPDATE)
  nucroi = ThresholdToSelection().convert(impfilteredNuc.getProcessor())
  nucroiA = ShapeRoi(nucroi).getRois()
#print nucroiA
  allcellA = [roiEnlarger(r) for r in nucroiA]
  jnucroiA = [roiRingGenerator(r) for r in nucroiA]
#print allcellA
  print 'Detected Cells: ', len(jnucroiA)  
  if len(jnucroiA) <2:
      print "measurement omitted, as there is only on nucleus detected"
      return  rtallcellPM, rtjnucVSVG, rtallcellVSVG
  if (GUIMODE):
    rm = RoiManager()
    for r in jnucroiA:
      rm.addRoi(r)
    rm.show()
    impfilteredNuc.show()
  

  measOpt = PA.AREA + PA.MEAN + PA.CENTROID + PA.STD_DEV + PA.SHAPE_DESCRIPTORS + PA.INTEGRATED_DENSITY + PA.MIN_MAX +\
    PA.SKEWNESS + PA.KURTOSIS + PA.MEDIAN + PA.MODE

## All Cell Plasma Membrane intensity
  measureROIs(impPM, measOpt, rtallcellPM, allcellA, backPM, True)
  meanInt_Cell = rtallcellPM.getColumn(rtallcellPM.getColumnIndex('Mean'))
  print "Results Table rownumber:", len(meanInt_Cell)
# JuxtaNuclear VSVG intensity 
  measureROIs(impVSVG, measOpt, rtjnucVSVG, jnucroiA, backVSVG, False)    
  meanInt_jnuc = rtjnucVSVG.getColumn(rtjnucVSVG.getColumnIndex('Mean'))

# AllCell VSVG intensity 
  measureROIs(impVSVG, measOpt, rtallcellVSVG, allcellA, backVSVG, True)    
  meanInt_vsvgall = rtallcellVSVG.getColumn(rtallcellVSVG.getColumnIndex('Mean'))
  
#Calculation of Transport Ratio JuxtaNuclear VSVG intensity / All Cell Plasma Membrane intensity results will be appended to PM results table.
  for i in range(len(meanInt_Cell)):
    if meanInt_Cell[i] != 0.0:
      transportR = meanInt_jnuc[i] / meanInt_Cell[i]
      transportRall = meanInt_vsvgall[i] / meanInt_Cell[i]
    else:
      transportR = float('inf')
      transportRall = float('inf')
    rtjnucVSVG.setValue('TransportRatio', i, transportR)
    rtallcellVSVG.setValue('TransportRatio', i, transportRall)
    rtjnucVSVG.setValue('WellNumber', i, int(wnumber)) 
    rtallcellVSVG.setValue('WellNumber', i, int(wnumber))
    rtallcellPM.setValue('WellNumber', i, int(wnumber)) 
  return rtallcellPM, rtjnucVSVG, rtallcellVSVG

# 需要导入模块: from ij.measure import ResultsTable [as 别名]
# 或者: from ij.measure.ResultsTable import getColumnIndex [as 别名]

#.........这里部分代码省略.........
  imp = ic.run("Subtract create 32-bit stack", imp, imp_avg);
 
  #
  # REGION SEGMENTATION
  #
  
  imp1 = Duplicator().run(imp, 1, imp.getImageStackSize()-1)
  imp2 = Duplicator().run(imp, 2, imp.getImageStackSize())
  imp_diff = ic.run("Subtract create 32-bit stack", imp1, imp2);
  #imp_diff.show()

  IJ.run(imp_diff, "Z Project...", "projection=[Standard Deviation]");
  imp_diff_sd = IJ.getImage()
 
  # save
  IJ.run(imp_diff_sd, "Gaussian Blur...", "sigma=5");
  output_file = filename+"--sd.tif"
  IJ.saveAs(imp_diff_sd, "TIFF", os.path.join(output_folder, output_file))
  tbModel.setFileAPth(output_folder, output_file, iDataSet, "SD","IMG")

  IJ.run(imp_diff_sd, "Enhance Contrast", "saturated=0.35");
  IJ.run(imp_diff_sd, "8-bit", "");
  IJ.run(imp_diff_sd, "Properties...", "unit=p pixel_width=1 pixel_height=1 voxel_depth=1");
  IJ.run(imp_diff_sd, "Auto Local Threshold", "method=Niblack radius=60 parameter_1=2 parameter_2=0 white");
  
  rm = ROIManipulator.getEmptyRm()
  IJ.run(imp_diff_sd, "Analyze Particles...", "add");


  # select N largest Rois
  diameter_roi = []
  for i in range(rm.getCount()):
    roi = rm.getRoi(i)
    diameter_roi.append([roi.getFeretsDiameter(), roi])
  diameter_roi = sorted(diameter_roi, reverse=True)
  #print diameter_roi

  rm.reset()
  for i in range(min(len(diameter_roi), p["n_rois"])):
    rm.addRoi(diameter_roi[i][1]) 
  
  # save 
  output_file = filename+"--rois"
  ROIManipulator.svRoisToFl(output_folder, output_file, rm.getRoisAsArray())  
  tbModel.setFileAPth(output_folder, output_file+".zip", iDataSet, "REGIONS","ROI")

   
  #
  # FFT in each region
  #

  IJ.run(imp, "Variance...", "radius=2 stack");
  output_file = filename+"--beats.tif"
  IJ.saveAs(imp, "TIFF", os.path.join(output_folder, output_file))
  tbModel.setFileAPth(output_folder, output_file, iDataSet, "BEATS","IMG")
  
  n = rm.getCount()
  for i_roi in range(n):
    imp_selection = Duplicator().run(imp)
    rm.select(imp_selection, i_roi)
    IJ.run(imp_selection, "Clear Outside", "stack");
    imp_selection.show()
    
    # FFT using Parallel FFTJ
    transformer = FloatTransformer(imp_selection.getStack())
    transformer.fft()
    imp_fft = transformer.toImagePlus(SpectrumType.FREQUENCY_SPECTRUM)
    imp_fft.show()

    # Analyze FFt
    IJ.run(imp_fft, "Gaussian Blur 3D...", "x=0 y=0 z=1.5");
    IJ.run(imp_fft, "Plot Z-axis Profile", "");
    output_file = filename+"--Region"+str(i_roi+1)+"--fft.tif"
    IJ.saveAs(IJ.getImage(), "TIFF", os.path.join(output_folder, output_file))
    tbModel.setFileAPth(output_folder, output_file, iDataSet, "FFT_R"+str(i_roi+1),"IMG")

    IJ.run(imp_fft, "Select All", "");
    rm.addRoi(imp_fft.getRoi())
    rm.select(rm.getCount())
    rt = ResultsTable()
    rt = rm.multiMeasure(imp_fft); #print(rt.getColumnHeadings);
    x = rt.getColumn(rt.getColumnIndex("Mean1"))
    #rm.runCommand("delete")
    
    peak_height_pos = []
    x_min = 10
    for i in range(x_min,len(x)/2):
      before = x[i-1]
      center = x[i]
      after = x[i+1]
      if (center>before) and (center>after):
        peak_height_pos.append([float(x[i]),i])
        
    if len(peak_height_pos)>0:
      peak_height_pos = sorted(peak_height_pos, reverse=True)
    
    n_max = 3
    for i_max in range(min(len(peak_height_pos),n_max)):
      tbModel.setNumVal(round(float(len(x))/float(peak_height_pos[i_max][1]),2), iDataSet, "F"+str(i_max+1)+"_R"+str(i_roi+1))
      tbModel.setNumVal(int(peak_height_pos[i_max][0]), iDataSet, "A"+str(i_max+1)+"_R"+str(i_roi+1))

# 需要导入模块: from ij.measure import ResultsTable [as 别名]
# 或者: from ij.measure.ResultsTable import getColumnIndex [as 别名]
def nucleusSegmentation(imp2):
    """ Segmentation of nucleus image. 
    Nucleus are selected that:
    1. No overlapping with dilated regions
    2. close to circular shape. Deformed nuclei are rejected.
    Outputs a binary image.
    """
#Convert to 8bit
    ImageConverter(imp2).convertToGray8()
#blur slightly using Gaussian Blur 
    radius = 2.0
    accuracy = 0.01
    GaussianBlur().blurGaussian( imp2.getProcessor(), radius, radius, accuracy)
# Auto Local Thresholding
    imps = ALT().exec(imp2, "Bernsen", 15, 0, 0, True)
    imp2 = imps[0]


#ParticleAnalysis 0: prefiltering by size and circularity
    rt = ResultsTable()
    paOpt = PA.CLEAR_WORKSHEET +\
                    PA.SHOW_MASKS +\
                    PA.EXCLUDE_EDGE_PARTICLES +\
                    PA.INCLUDE_HOLES #+ \
#		PA.SHOW_RESULTS 
    measOpt = PA.AREA + PA.STD_DEV + PA.SHAPE_DESCRIPTORS + PA.INTEGRATED_DENSITY
    MINSIZE = 20
    MAXSIZE = 10000
    pa0 = PA(paOpt, measOpt, rt, MINSIZE, MAXSIZE, 0.8, 1.0)
    pa0.setHideOutputImage(True)
    pa0.analyze(imp2)
    imp2 = pa0.getOutputImage() # Overwrite 
    imp2.getProcessor().invertLut()
#impNuc = imp2.duplicate()	## for the ring. 
    impNuc = Duplicator().run(imp2)

#Dilate the Nucleus Area
## this should be 40 pixels in Cihan's method, but should be smaller. 
#for i in range(20):
#	IJ.run(imp2, "Dilate", "")
    rf = RankFilters()
    rf.rank(imp2.getProcessor(), RIMSIZE, RankFilters.MAX)

#Particle Analysis 1: get distribution of sizes. 

    paOpt = PA.CLEAR_WORKSHEET +\
                    PA.SHOW_NONE +\
                    PA.EXCLUDE_EDGE_PARTICLES +\
                    PA.INCLUDE_HOLES #+ \
#		PA.SHOW_RESULTS 
    measOpt = PA.AREA + PA.STD_DEV + PA.SHAPE_DESCRIPTORS + PA.INTEGRATED_DENSITY
    rt1 = ResultsTable()
    MINSIZE = 20
    MAXSIZE = 10000
    pa = PA(paOpt, measOpt, rt1, MINSIZE, MAXSIZE)
    pa.analyze(imp2)
    #rt.show('after PA 1')
#particle Analysis 2: filter nucleus by size and circularity. 
    #print rt1.getHeadings()
    if (rt1.getColumnIndex('Area') > -1):
      q1, q3, outlier_offset = getOutlierBound(rt1)
    else:
      q1 = MINSIZE
      q3 = MAXSIZE
      outlier_offset = 0
      print imp2.getTitle(), ": no Nucleus segmented,probably too many overlaps"

    paOpt = PA.CLEAR_WORKSHEET +\
                    PA.SHOW_MASKS +\
                    PA.EXCLUDE_EDGE_PARTICLES +\
                    PA.INCLUDE_HOLES #+ \
#		PA.SHOW_RESULTS 
    rt2 = ResultsTable()
    #pa = PA(paOpt, measOpt, rt, q1-outlier_offset, q3+outlier_offset, circq1-circoutlier_offset, circq3+circoutlier_offset)
    pa = PA(paOpt, measOpt, rt2, q1-outlier_offset, q3+outlier_offset, 0.8, 1.0)
    pa.setHideOutputImage(True)
    pa.analyze(imp2)
    impDilatedNuc = pa.getOutputImage() 

#filter original nucleus

    filteredNuc = ImageCalculator().run("AND create", impDilatedNuc, impNuc)
    return filteredNuc