def pixel_collector(rm, channel_imp, channel_name, impname, folder):
# define new Results table
rt = ResultsTable()
IndRois = rm.getIndexes()
for index in IndRois:
ROI = rm.getRoi(index)
ROI_name = ROI.getName()
coords = ROI.getContainedPoints()
row = 0
for pixel in coords:
x_coord = pixel.getX()
y_coord = pixel.getY()
rt.setValue(ROI_name + "_X_pos", row, int(x_coord))
rt.setValue(ROI_name + "_Y_pos", row, int(y_coord))
pixel_2 = channel_imp.getProcessor().getPixel(
int(x_coord), int(y_coord))
rt.setValue(ROI_name + "_" + channel_name, row, pixel_2)
row = row + 1
rt.show("Results")
rt.save(os.path.join(folder, impname + '_' + channel_name + "_pixels.csv"))
print "Pixel collection done!"
def run():
global srcFile, ext, numberOfWidthMeasurements
IJ.run("Set Measurements...", "area mean standard modal min centroid center perimeter bounding fit shape feret's integrated median skewness kurtosis area_fraction display redirect=None decimal=3");
IJ.setForegroundColor(255,255,255);
IJ.setBackgroundColor(0,0,0);
IJ.run("Options...", "iterations=1 count=1 black");
table = ResultsTable()
srcDir = srcFile.getAbsolutePath()
for root, directories, filenames in os.walk(srcDir):
for filename in filenames:
# Check for file extension
if not filename.endswith(ext):
continue
# Check for file name pattern
process(srcDir, root, filename, table, numberOfWidthMeasurements)
table.save(os.path.join(srcDir, 'Results.xls'))
def process(self,imp):
# extract nucleus channel, 8-bit and twice binned
imp.setC(self.nucleusChannel)
ip = imp.getChannelProcessor().duplicate()
ip = ip.convertToByteProcessor()
ip = ip.bin(4)
nucleus = ImagePlus("nucleus_channel", ip)
# threshold image and separate clumped nuclei
IJ.run(nucleus, "Auto Threshold", "method=Otsu white setthreshold show");
IJ.run(nucleus, "Make Binary", "thresholded remaining black");
IJ.run(nucleus, "Watershed", "");
directory = imp.getTitle()
directory = directory.replace(" ", "_")\
.replace(",", "_")\
.replace("#", "_series")\
.replace("...", "")\
.replace(".","_")
directory = os.path.join(self.exportDir, directory)
sliceDirectory = os.path.join(directory, "slices")
print directory
print sliceDirectory
if not os.path.exists(sliceDirectory):
os.makedirs(sliceDirectory)
# Create a table to store the results
table = ResultsTable()
# Create a hidden ROI manager, to store a ROI for each blob or cell
#roim = RoiManager(True)
# remove small particles and border particles
pa = ParticleAnalyzer(\
ParticleAnalyzer.ADD_TO_MANAGER | ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES,\
Measurements.CENTER_OF_MASS,\
table,\
self.minArea, self.maxArea,\
0.0,1.0)
if pa.analyze(nucleus):
print "All ok, number of particles: ", table.size()
else:
print "There was a problem in analyzing", imp, nucleus
table.save(os.path.join(directory, "rt.csv"))
# read the center of mass coordinates
cmx = table.getColumn(0)
cmy = table.getColumn(1)
if self.debug:
imp.show()
i=0
for i in range(0, min(self.nCells,table.size())):
# ROI around the cell
cmx = table.getValue("XM",i)
cmy = table.getValue("YM",i)
x = 4 * cmx - (self.boxSize - 1) / 2
y = 4 * cmy - (self.boxSize - 1) / 2
if (x < self.edge or y < self.edge or x > imp.getWidth() - self.edge or y > imp.getHeight() - self.edge):
continue
roi = Roi(x,y,self.boxSize,self.boxSize)
imp.setRoi(roi, False)
cellStack = ImageStack(self.boxSize, self.boxSize)
for z in range(1, imp.getNSlices() + 1):
imp.setSlice(z)
for c in range(1, imp.getNChannels() + 1):
imp.setC(c)
# copy ROI to stack
imp.copy()
impSlice = imp.getClipboard()
cellStack.addSlice(impSlice.getProcessor())
if self.slices:
sliceTitle = "cell_%s_z%s_c%s" % (str(i).zfill(4), str(z).zfill(3), str(c))
print sliceTitle
IJ.saveAsTiff(impSlice, os.path.join(sliceDirectory, sliceTitle))
impSlice.close()
title = "cell_" + str(i).zfill(4)
cell = ImagePlus(title, cellStack)
# save ROI image
IJ.saveAsTiff(cell, os.path.join(directory, title))
cell.close()
if self.debug:
imp.updateAndDraw()
wait = Wait("particle done")
wait.show()
def saveresults(dir, name):
outfile = os.path.join(dir, "{}.csv".format(name))
res = ResultsTable.getResultsTable()
ResultsTable.save(res, outfile)
ResultsTable.reset(res)
def main():
# Prepare directory tree for output.
indir = IJ.getDirectory("input directory")
outdir = IJ.getDirectory(".csv output directory")
nucdir = os.path.join(outdir, "nuclei")
bacdir = os.path.join(outdir, "bacteria")
rufdir = os.path.join(outdir, "ruffles")
gfpdir = os.path.join(outdir, "gfp")
channelsdir = os.path.join(outdir, "channels")
if not os.path.isdir(nucdir):
os.mkdir(nucdir)
if not os.path.isdir(bacdir):
os.mkdir(bacdir)
if not os.path.isdir(rufdir):
os.mkdir(rufdir)
if not os.path.isdir(gfpdir):
os.mkdir(gfpdir)
if not os.path.isdir(channelsdir):
os.mkdir(channelsdir)
# Collect all file paths in the input directory
files = readdirfiles(indir)
nucResults = ResultsTable()
bacResults = ResultsTable()
rufResults = ResultsTable()
gfpResults = ResultsTable()
for file in files:
if file.endswith('ome.tif') or file.endswith('ome.tiff'):
imp = stackprocessor(file,
nChannels=4,
nSlices=7,
nFrames=1)
channels = ChannelSplitter.split(imp)
name = imp.getTitle()
IJ.log("Processing image: {}".format(name))
for c in range(len(channels)):
IJ.run(channels[c], "Grays", "")
IJ.run(channels[c], "Invert", "")
jpgname = channels[c].getShortTitle()
jpgoutfile = os.path.join(channelsdir, "{}.jpg".format(jpgname))
IJ.saveAs(channels[c].flatten(), "Jpeg", jpgoutfile)
IJ.run(channels[c], "Invert", "")
nuc = countobjects(channels[0], nucResults,
threshMethod="Triangle",
subtractBackground=True,
# dilate=True,
watershed=True,
minSize=3.00,
maxSize=100,
minCirc=0.00,
maxCirc=1.00)
bac = countobjects(channels[1], bacResults,
threshMethod="RenyiEntropy",
subtractBackground=False,
watershed=False,
minSize=0.20,
maxSize=30.00,
minCirc=0.00,
maxCirc=1.00)
ruf = countobjects(channels[2], rufResults,
threshMethod="RenyiEntropy",
minSize=2.00,
maxSize=30.00,
minCirc=0.20,
maxCirc=1.00)
gfp = countobjects(channels[3], gfpResults,
threshMethod="RenyiEntropy",
subtractBackground=False,
watershed=True,
minSize=0.20,
maxSize=30.00,
minCirc=0.00,
maxCirc=1.00)
# binaries = [nuc, bac, ruf, gfp]
# channels[0].show()
# binaries[0].show()
# binMontage = RGBStackMerge().mergeChannels(binaries, False)
# binMontage.show()
# chsMontage = RGBStackMerge().mergeChannels(channels, False)
# binMontage = MontageMaker().makeMontage2(binMontage,
# 4, # int columns
# 4, # int rows
# 1.00, # double scale
# 1, # int first
# 16, # int last
# 1, # int inc
# 0, # int borderWidth
# False) # boolean labels)
# chsMontage = MontageMaker().makeMontage2(chsMontage,
# 4, # int columns
# 4, # int rows
# 1.00, # double scale
# 1, # int first
# 16, # int last
# 1, # int inc
# 0, # int borderWidth
# False) # boolean labels)
#
# binMontage.show()
# chsMontage.show()
outfilenuc = os.path.join(nucdir, "threshold_nuc_{}".format(name))
outfilebac = os.path.join(bacdir, "threshold_bac_{}".format(name))
outfileruf = os.path.join(rufdir, "threshold_ruf_{}".format(name))
outfilegfp = os.path.join(gfpdir, "threshold_gfp_{}".format(name))
IJ.saveAs(nuc.flatten(), "Tiff", outfilenuc)
IJ.saveAs(bac.flatten(), "Tiff", outfilebac)
IJ.saveAs(ruf.flatten(), "Tiff", outfileruf)
IJ.saveAs(gfp.flatten(), "Tiff", outfilegfp)
nucResults.show("nuclei")
bacResults.show("bacteria")
rufResults.show("ruffles")
gfpResults.show("gfp")
nucout = os.path.join(outdir, "nuclei.csv")
bacout = os.path.join(outdir, "bacteria.csv")
rufout = os.path.join(outdir, "ruffles.csv")
gfpout = os.path.join(outdir, "gfp.csv")
ResultsTable.save(nucResults, nucout)
ResultsTable.save(bacResults, bacout)
ResultsTable.save(rufResults, rufout)
ResultsTable.save(gfpResults, gfpout)
def main():
# Prepare directory tree for output.
indir = IJ.getDirectory("input directory")
outdir = IJ.getDirectory(".csv output directory")
c1dir = os.path.join(outdir, "Channel1")
c2dir = os.path.join(outdir, "Channel2")
c3dir = os.path.join(outdir, "Channel3")
c4dir = os.path.join(outdir, "Channel4")
channelsdir = os.path.join(outdir, "Channels")
if not os.path.isdir(c1dir):
os.mkdir(c1dir)
if not os.path.isdir(c2dir):
os.mkdir(c2dir)
if not os.path.isdir(c3dir):
os.mkdir(c3dir)
if not os.path.isdir(c4dir):
os.mkdir(c4dir)
if not os.path.isdir(channelsdir):
os.mkdir(channelsdir)
# Collect all file paths in the input directory
files = readdirfiles(indir)
# Initialize the results tables.
c1Results = ResultsTable()
c2Results = ResultsTable()
c3Results = ResultsTable()
c4Results = ResultsTable()
for file in files:
IJ.log("File: {}/{}".format(files.index(file) + 1, len(files)))
if file.endswith('.tif'):
# Open .tiff file as ImagePlus.
imp = Opener().openImage(file)
imp = ZProjector.run(imp, "max")
# imp = stackprocessor(file,
# nChannels=4,
# nSlices=7,
# nFrames=1)
channels = ChannelSplitter.split(imp)
name = imp.getTitle()
# For every channel, save the inverted channel in grayscale as .jpg.
for channel in channels:
IJ.run(channel, "Grays", "")
IJ.run(channel, "Invert", "")
jpgname = channel.getShortTitle()
jpgoutfile = os.path.join(channelsdir,
"{}.jpg".format(jpgname))
IJ.saveAs(channel.flatten(), "Jpeg", jpgoutfile)
IJ.run(channel, "Invert", "")
# OPTIONAL - Perform any other operations (e.g. crossexcitation compensation tasks) before object count.
c2name = channels[2].getTitle()
cal = channels[2].getCalibration()
channels[2] = ImagePlus(
c2name,
ImageCalculator().run("divide create 32-bit", channels[2],
channels[3]).getProcessor(
) # This removes AF647 bleed-through
)
channels[2].setCalibration(cal)
# Settings for channel1 threshold.
c1 = countobjects(channels[0],
c1Results,
threshMethod="Triangle",
subtractBackground=True,
watershed=True,
minSize=0.00,
maxSize=100,
minCirc=0.00,
maxCirc=1.00)
# Settings for channel2 threshold.
c2 = countobjects(channels[1],
c2Results,
threshMethod="RenyiEntropy",
subtractBackground=True,
watershed=False,
minSize=0.00,
maxSize=30.00,
minCirc=0.00,
maxCirc=1.00)
# Settings for channel3 threshold.
c3 = countobjects(channels[2],
c3Results,
threshMethod="RenyiEntropy",
subtractBackground=True,
watershed=False,
minSize=0.00,
maxSize=30.00,
minCirc=0.00,
maxCirc=1.00)
# Settings for channel4 threshold.
c4 = countobjects(channels[3],
c4Results,
threshMethod="RenyiEntropy",
subtractBackground=True,
watershed=False,
minSize=0.20,
maxSize=100.00,
minCirc=0.00,
maxCirc=1.00)
# Format filenames for thresholded .tiff files.
outfileC1 = os.path.join(c1dir, "threshold_c1_{}".format(name))
outfileC2 = os.path.join(c2dir, "threshold_c2_{}".format(name))
outfileC3 = os.path.join(c3dir, "threshold_c3_{}".format(name))
outfileC4 = os.path.join(c4dir, "threshold_c4_{}".format(name))
# Save thresholded .tiff files.
IJ.saveAs(c1.flatten(), "Tiff", outfileC1)
IJ.saveAs(c2.flatten(), "Tiff", outfileC2)
IJ.saveAs(c3.flatten(), "Tiff", outfileC3)
IJ.saveAs(c4.flatten(), "Tiff", outfileC4)
# Show results tables.
# c1Results.show("channel1")
# c2Results.show("channel2")
# c3Results.show("channel3")
# c4Results.show("channel4")
# Prepare results table filenames.
c1out = os.path.join(outdir, "channel1.csv")
c2out = os.path.join(outdir, "channel2.csv")
c3out = os.path.join(outdir, "channel3.csv")
c4out = os.path.join(outdir, "channel4.csv")
# Save results tables.
ResultsTable.save(c1Results, c1out)
ResultsTable.save(c2Results, c2out)
ResultsTable.save(c3Results, c3out)
ResultsTable.save(c4Results, c4out)
def process(srcDir, dstDir, currentDir, fileName, keepDirectories, Channel_1, Channel_2, radius_background, sigmaSmaller, sigmaLarger, minPeakValue, min_dist):
IJ.run("Close All", "")
# Opening the image
IJ.log("Open image file:" + fileName)
#imp = IJ.openImage(os.path.join(currentDir, fileName))
#imp = IJ.getImage()
imp = BF.openImagePlus(os.path.join(currentDir, fileName))
imp = imp[0]
# getDimensions(width, height, channels, slices, frames)
IJ.log("Computing Max Intensity Projection")
if imp.getDimensions()[3] > 1:
imp_max = ZProjector.run(imp,"max")
else:
imp_max = imp
ip1, ip2 = extract_channel(imp_max, Channel_1, Channel_2)
IJ.log("Substract background")
imp1, imp2 = back_substraction(ip1, ip2, radius_background)
IJ.log("Finding Peaks")
ip1_1, ip2_1, peaks_1, peaks_2 = find_peaks(imp1, imp2, sigmaSmaller, sigmaLarger, minPeakValue)
# Create a PointRoi from the DoG peaks, for visualization
roi_1 = PointRoi(0, 0)
roi_2 = PointRoi(0, 0)
roi_3 = PointRoi(0, 0)
roi_4 = PointRoi(0, 0)
# A temporary array of integers, one per dimension the image has
p_1 = zeros(ip1_1.numDimensions(), 'i')
p_2 = zeros(ip2_1.numDimensions(), 'i')
# Load every peak as a point in the PointRoi
for peak in peaks_1:
# Read peak coordinates into an array of integers
peak.localize(p_1)
roi_1.addPoint(imp1, p_1[0], p_1[1])
for peak in peaks_2:
# Read peak coordinates into an array of integers
peak.localize(p_2)
roi_2.addPoint(imp2, p_2[0], p_2[1])
# Chose minimum distance in pixel
#min_dist = 20
for peak_1 in peaks_1:
peak_1.localize(p_1)
for peak_2 in peaks_2:
peak_2.localize(p_2)
d1 = distance(p_1, p_2)
if d1 < min_dist:
roi_3.addPoint(imp1, p_2[0], p_2[1])
break
for peak_2 in peaks_2:
peak_2.localize(p_2)
for peak_1 in peaks_1:
peak_1.localize(p_1)
d2 = distance(p_2, p_1)
if d2 < min_dist:
roi_4.addPoint(imp1, p_2[0], p_2[1])
break
cal = imp.getCalibration()
min_distance = str(round((cal.pixelWidth * min_dist),1))
table = ResultsTable()
table.incrementCounter()
table.addValue("Numbers of Neuron Markers", roi_1.getCount(0))
table.addValue("Numbers of Glioma Markers", roi_2.getCount(0))
table.addValue("Numbers of Glioma within %s um of Neurons" %(min_distance), roi_3.getCount(0))
table.addValue("Numbers of Neurons within %s um of Glioma" %(min_distance), roi_4.getCount(0))
#table.show("Results Analysis")
saveDir = currentDir.replace(srcDir, dstDir) if keepDirectories else dstDir
if not os.path.exists(saveDir):
os.makedirs(saveDir)
IJ.log("Saving to" + saveDir)
table.save(os.path.join(saveDir, fileName + ".csv"))
IJ.selectWindow("Log")
IJ.saveAs("Text", os.path.join(saveDir, fileName + ".csv"));
resultsTable.incrementCounter()
resultsTable.addValue("Threshold", localBackground)
resultsTable.addValue("Seed radius", seedRadius)
resultsTable.addValue("GXY", gaussXY)
resultsTable.addValue("GZ", gaussZ)
resultsTable.addValue("TOTAL", results[i]['all'])
resultsTable.addValue("0-250", results[i]['0'])
resultsTable.addValue("251-500", results[i]['250'])
resultsTable.addValue("501-750", results[i]['500'])
resultsTable.addValue("751-1000", results[i]['750'])
resultsTable.addValue("1001-1500", results[i]['1000'])
resultsTable.addValue(">1501", results[i]['1500'])
resultsTable.addValue("Skipped", results[i]['edge'])
resultsTable.save(options['outputDir'] + options['outputFile'])
else:
parametersTable = ResultsTable()
parametersTable.showRowNumbers(False)
parameters = makeParameters(options)
for i in range(0, len(parameters)):
parametersTable.incrementCounter()
parametersTable.addValue("Threshold", parameters[i]['localBackground'])
parametersTable.addValue("Seed radius", parameters[i]['seedRadius'])
parametersTable.addValue("GXY", parameters[i]['gaussXY'])
parametersTable.addValue("GZ", parameters[i]['gaussZ'])
parametersTable.save(options['paramsOut'])
#consol.setValue("foci_count", currcell, int(consol.getValue("foci_count", currcell))+1)
IJ.selectWindow("Results")
IJ.run("Close")
for count in range(consol.size()):
inrange = consol.getValue("is_in_range", count)
if (inrange == 1):
count_range = count_range + 1
foci = consol.getValue("foci_count", count)
if foci <= 3:
cellsperfoci_range[foci] = cellsperfoci_range[foci] + 1
else:
cellsperfoci_range[
"more"] = cellsperfoci_range["more"] + 1
# save the summary results table
consol.save(directory + "/" + filename + "_summary.csv")
# reset the ROI Manager, close it and go to next file (if there is one)
rm.runCommand("Reset")
rm.close()
if any(x.endswith(ext) for x in filenamelist[counter]):
fp = open(srcDir + "/total_summary.csv", "w")
fp.write("total cells, " + str(countcells) + "\n\n")
if (linechannel > 0):
fp.write("cells in range, " + str(count_range) + "\n")
fp.write("cells with foci and in range, " +
str(cellsperfoci_range[1] + cellsperfoci_range[2] +
cellsperfoci_range[3]) + "\n\n")
for i in range(4):
fp.write("cells with " + str(i) + " foci and in range, " +
str(cellsperfoci_range[i]) + "\n")
def main():
rt = RT.open2(table_file.getAbsolutePath())
if not rt: return
log(" --- --- --- ")
log("Loaded %s" % table_file.getAbsolutePath())
log("Loading column lists...")
# Get column indices from imported file
headings = getColumnHeadings(rt)
id_col = getColumnIndex(headings, "TID")
t_col = getColumnIndex(headings, "t [")
d2p_col = getColumnIndex(headings, "D2P [")
angle_col = getColumnIndex(headings, u'\u03B1 [deg]')
delta_col = getColumnIndex(headings, u'\u0394\u03B1 [deg]')
if angle_col == RT.COLUMN_NOT_FOUND:
log("Failed to detect index for angle column. Re-trying...")
angle_col = getColumnIndex(headings, u'? [deg]')
if delta_col == RT.COLUMN_NOT_FOUND:
log("Failed to detect index for delta angle column. Re-trying...")
delta_col = getColumnIndex(headings, u'?? [deg]')
log("Last column index is %s" % rt.getLastColumn())
if RT.COLUMN_NOT_FOUND in (id_col, d2p_col, delta_col, angle_col):
uiservice.showDialog("Error: Some key columns were not found!",
"Invalid Table?")
return
log("Settings: BOUT_WINDOW= %s, MIN_D2P= %s, DEF_FRAME_INTERVAL= %s" %
(BOUT_WINDOW, '{0:.4f}'.format(MIN_D2P), DEF_FRAME_INTERVAL))
# Store all data on dedicated lists
track_id_rows = rt.getColumnAsDoubles(id_col)
d2p_rows = rt.getColumnAsDoubles(d2p_col)
angle_rows = rt.getColumnAsDoubles(angle_col)
delta_rows = rt.getColumnAsDoubles(delta_col)
t_rows = rt.getColumnAsDoubles(t_col)
# Assess n of data points and extract unique path ids
n_rows = len(track_id_rows)
row_indices = range(n_rows)
track_ids = set(track_id_rows)
n_tracks = len(track_ids)
log("Table has %g rows" % n_rows)
log("Table has %g tracks" % n_tracks)
log("Parsing tracks...")
for track_id in track_ids:
for row, next_row in zip(row_indices, row_indices[1:]):
if track_id_rows[row] != track_id:
continue
if not isNumber(angle_rows[row]):
rt.setValue("FLAG", row, "NA")
continue
lower_bound = max(0, row - BOUT_WINDOW + 1)
upper_bound = min(n_rows - 1, row + BOUT_WINDOW)
win_d2p = []
for _ in range(lower_bound, upper_bound):
win_d2p.append(d2p_rows[row])
if sum(win_d2p) <= MIN_D2P * len(win_d2p):
rt.setValue("FLAG", row, 0)
else:
current_angle = angle_rows[row]
next_angle = angle_rows[next_row]
current_delta = delta_rows[row]
flag = -1 if current_angle < 0 else 1
delta_change = (abs(current_delta) > 90)
same_sign = ((current_angle < 0) == (next_angle < 0))
if delta_change and not same_sign:
flag *= -1
rt.setValue("FLAG", row, flag)
if next_row == n_rows - 1:
rt.setValue("FLAG", next_row, flag)
if rt.save(table_file.getAbsolutePath()):
log("Processed table successfully saved (file overwritten)")
else:
log("Could not override input file. Displaying it...")
rt.show(table_file.name)
log("Creating onset table...")
onset_rt = RT()
onset_rt.showRowNumbers(False)
frame_int = DEF_FRAME_INTERVAL
if "table" in frame_rate_detection:
frame_int = getFrameIntervalFromTable(row_indices, track_id_rows,
t_rows)
elif "image" in frame_rate_detection:
frame_int = getFrameIntervalFromImage(image_file.getAbsolutePath())
else:
log("Using default frame rate")
for track_id in track_ids:
for prev_row, row in zip(row_indices, row_indices[1:]):
if not track_id in (track_id_rows[prev_row], track_id_rows[row]):
continue
flag = rt.getValue("FLAG", row)
if not isNumber(flag):
continue
flag = int(flag)
if flag == 0:
continue
if flag == 1 or flag == -1:
srow = onset_rt.getCounter()
onset_rt.incrementCounter()
onset_rt.setValue("TID", srow, track_id)
from_frame = int(t_rows[prev_row] / frame_int) + 1
to_frame = int(t_rows[row] / frame_int) + 1
onset_rt.setValue("First disp. [t]", srow,
"%s to %s" % (t_rows[prev_row], t_rows[row]))
onset_rt.setValue("First disp. [frames]", srow,
"%s to %s" % (from_frame, to_frame))
onset_rt.setValue("ManualTag", srow, "")
break
out_path = suffixed_path(table_file.getAbsolutePath(), "ManualTagging")
if onset_rt.save(out_path):
log("Summary table successfully saved: %s" % out_path)
else:
log("File not saved... Displaying onset table")
onset_rt.show("Onsets %s" % table_file.name)
img410PA.show()
img470PA.show()
# MORPHOLOGICAL MEASUREMENTS
# These are done on the rotated image mask
scales = {
"4x4": 2.58,
"2x2": 1.29
}
morphTable = ResultsTable()
PA.setResultsTable(morphTable)
maskPA.show()
IJ.run(maskPA, "Select None", "")
IJ.run(maskPA, "Set Scale...", "distance=1 known=" + str(scales[binning]) + " pixel=1 unit=μm"); #TODO scale?
IJ.run(maskPA, "Set Measurements...", "area centroid center perimeter bounding fit shape feret's median skewness kurtosis scientific redirect=None decimal=7");
IJ.run(maskPA, "Analyze Particles...", "size=0-Infinity circularity=0.00-1.00 show=Nothing stack");
morph_headings = morphTable.getColumnHeadings().strip(' ').split('\t')
for m_heading in morph_headings[1:]: # because of formatting, the first is empty
addValues(dataTable, m_heading, [morphTable.getValue(m_heading, i) for i in range(morphTable.size())])
for imPlus in [img410PA, img470PA, maskPA]:
cropStack(imPlus)
IJ.saveAsTiff(imPlus, os.path.join(PARENT_DIR, imPlus.getTitle()))
dataTable.show("Measurements")
dataTable.save(os.path.join(PARENT_DIR, 'measurements.csv'))
def main():
rt = RT.open2(table_file.getAbsolutePath())
if not rt: return
log(" --- --- --- ")
log("Loaded %s" % table_file.getAbsolutePath())
log("Loading column lists...")
# Get column indices from imported file
headings = getColumnHeadings(rt)
id_col = getColumnIndex(headings, "TID")
t_col = getColumnIndex(headings, "t [")
d2p_col = getColumnIndex(headings, "D2P [")
angle_col = getColumnIndex(headings, u'\u03B1 [deg]')
delta_col = getColumnIndex(headings, u'\u0394\u03B1 [deg]')
if angle_col == RT.COLUMN_NOT_FOUND:
log("Failed to detect index for angle column. Re-trying...")
angle_col = getColumnIndex(headings, u'? [deg]')
if delta_col == RT.COLUMN_NOT_FOUND:
log("Failed to detect index for delta angle column. Re-trying...")
delta_col = getColumnIndex(headings, u'?? [deg]')
log("Last column index is %s" % rt.getLastColumn())
if RT.COLUMN_NOT_FOUND in (id_col, d2p_col, delta_col, angle_col):
uiservice.showDialog("Error: Some key columns were not found!", "Invalid Table?")
return
log("Settings: BOUT_WINDOW= %s, MIN_D2P= %s, DEF_FRAME_INTERVAL= %s"
% (BOUT_WINDOW, '{0:.4f}'.format(MIN_D2P), DEF_FRAME_INTERVAL))
# Store all data on dedicated lists
track_id_rows = rt.getColumnAsDoubles(id_col)
d2p_rows = rt.getColumnAsDoubles(d2p_col)
angle_rows = rt.getColumnAsDoubles(angle_col)
delta_rows = rt.getColumnAsDoubles(delta_col)
t_rows = rt.getColumnAsDoubles(t_col)
# Assess n of data points and extract unique path ids
n_rows = len(track_id_rows)
row_indices = range(n_rows)
track_ids = set(track_id_rows)
n_tracks = len(track_ids)
log("Table has %g rows" % n_rows)
log("Table has %g tracks" % n_tracks)
log("Parsing tracks...")
for track_id in track_ids:
for row, next_row in zip(row_indices, row_indices[1:]):
if track_id_rows[row] != track_id:
continue
if not isNumber(angle_rows[row]):
rt.setValue("FLAG", row, "NA")
continue
lower_bound = max(0, row - BOUT_WINDOW + 1)
upper_bound = min(n_rows-1, row + BOUT_WINDOW)
win_d2p = []
for _ in range(lower_bound, upper_bound):
win_d2p.append(d2p_rows[row])
if sum(win_d2p) <= MIN_D2P * len(win_d2p):
rt.setValue("FLAG", row, 0)
else:
current_angle = angle_rows[row]
next_angle = angle_rows[next_row]
current_delta = delta_rows[row]
flag = -1 if current_angle < 0 else 1
delta_change = (abs(current_delta) > 90)
same_sign = ((current_angle<0) == (next_angle<0))
if delta_change and not same_sign:
flag *= -1
rt.setValue("FLAG", row, flag)
if next_row == n_rows - 1:
rt.setValue("FLAG", next_row, flag)
if rt.save(table_file.getAbsolutePath()):
log("Processed table successfully saved (file overwritten)")
else:
log("Could not override input file. Displaying it...")
rt.show(table_file.name)
log("Creating onset table...")
onset_rt = RT()
onset_rt.showRowNumbers(False)
frame_int = DEF_FRAME_INTERVAL
if "table" in frame_rate_detection:
frame_int = getFrameIntervalFromTable(row_indices, track_id_rows, t_rows)
elif "image" in frame_rate_detection:
frame_int = getFrameIntervalFromImage(image_file.getAbsolutePath())
else:
log("Using default frame rate")
for track_id in track_ids:
for prev_row, row in zip(row_indices, row_indices[1:]):
if not track_id in (track_id_rows[prev_row], track_id_rows[row]):
continue
flag = rt.getValue("FLAG", row)
if not isNumber(flag):
continue
flag = int(flag)
if flag == 0:
continue
if flag == 1 or flag == -1:
srow = onset_rt.getCounter()
onset_rt.incrementCounter()
onset_rt.setValue("TID", srow, track_id)
from_frame = int(t_rows[prev_row]/frame_int) + 1
to_frame = int(t_rows[row]/frame_int) + 1
onset_rt.setValue("First disp. [t]", srow,
"%s to %s" % (t_rows[prev_row], t_rows[row]))
onset_rt.setValue("First disp. [frames]", srow,
"%s to %s" % (from_frame, to_frame))
onset_rt.setValue("ManualTag", srow, "")
break
out_path = suffixed_path(table_file.getAbsolutePath(), "ManualTagging")
if onset_rt.save(out_path):
log("Summary table successfully saved: %s" % out_path)
else:
log("File not saved... Displaying onset table")
onset_rt.show("Onsets %s" % table_file.name)
# Parse spots to insert values as objects
for trackID in trackIDs:
track = model.getTrackModel().trackSpots(trackID)
# Sort by frame
sortedTrack = list(track)
sortedTrack.sort(key=lambda s: s.getFeature("FRAME"))
for spot in sortedTrack:
results.incrementCounter()
results.addValue(ID_COLUMN, "" + str(spot.ID()))
# results.addValue(CELL_LABEL_COLUMN,str(int(spot.getFeature("MAX_INTENSITY"))))
results.addValue(CELL_LABEL_COLUMN, spot.getName())
results.addValue(TRACK_ID_COLUMN, "" + str(trackID))
for feature in FEATURES:
val = spot.getFeature(feature)
if math.isnan(val):
results.addValue(feature.lower(), "None")
else:
results.addValue(feature.lower(), "" + str(int(val)))
parents = []
children = []
for edge in model.getTrackModel().edgesOf(spot):
source, target = model.getTrackModel().getEdgeSource(
edge), model.getTrackModel().getEdgeTarget(edge)
if source != spot:
parents.append(source.ID())
results.addValue("parent_ids", str(parents))
results.save(output_path)