def containsRoot_(self):
IJ.run("Clear Results")
IJ.run("Measure")
table = RT.getResultsTable()
if RT.getValue(table, "Max", 0) == 0:
return False
return True
def executeTask_Weka(validParticles, **kwargs):
'''execute task for microscope'''
zk = ZeissKeys()
msg = Message()
if validParticles is None or len(validParticles) < kwargs["nrPart"]:
IJ.run("Read Write Windows Registry", "action=write location=[HKCU\\"+zk.regkey+"] key="+zk.subkey_codemic+" value=["+msg.CODE_NOTHING+"] windows=REG_SZ")
return
def threshold(imp, threshold):
""" Threshold image """
impout = Duplicator().run(imp)
IJ.setThreshold(impout, threshold, 1000000000)
IJ.run(impout, "Convert to Mask", "stack")
impout.setTitle("Threshold");
return impout
def __favg(self) : zp = ZProjector(self.__impRes) zp.setMethod(ZProjector.AVG_METHOD) zp.doProjection() imp = zp.getProjection() imp.show() if self.__fire : IJ.run(imp, "Fire", "")
def run():
gd = GenericDialogPlus("ND2 Conversion Tool")
gd.addMessage("This plugin uses BioFormats to convert ND2 images to JPEG for further processing in ImageJ.")
gd.addDirectoryOrFileField("Input: ", "G:\\Subdir testing\\") #srcDir
gd.addDirectoryOrFileField("Output: ", "G:\\Subdir testing\\") #dstDir
gd.addStringField("File name contains: ", "") #nameContains
gd.addCheckbox("Preserve directory structure?", True) #keepDirs
gd.addCheckbox("Run in headless mode?", True) #anneBoleyn
gd.addCheckbox("Overwrite existing output files?", True)
gd.showDialog()
if gd.wasCanceled():
return
srcDir = gd.getNextString()
dstDir = gd.getNextString()
nameContains = gd.getNextString()
keepDirectories = gd.getNextBoolean()
anneBoleyn = gd.getNextBoolean()
overOut = gd.getNextBoolean()
IJ.run("Input/Output...", "jpeg=100")
for root, directories, filenames in os.walk(srcDir):
for filename in filenames:
# Check for file extension
if not filename.endswith(".nd2"):
continue
# Check for file name pattern
if nameContains not in filename:
continue
process(srcDir, dstDir, nameContains, root, filename, keepDirectories, anneBoleyn, overOut)
def threshold(imp, lower, upper): duplicate=Duplicator().run(imp) duplicate.getProcessor().resetMinAndMax() IJ.setAutoThreshold(duplicate, "Default dark"); IJ.setThreshold(duplicate, lower, upper) IJ.run(duplicate, "Convert to Mask", ""); return duplicate
def run_straighten(roiWindowsize = 4):
""" Original straightening function based on Nick's macro. Used in final version.
Returns coordinate string used to make centerline. """
IJ.run("Set Measurements...", "mean min center redirect=None decimal=3")
IJ.runMacro("//setTool(\"freeline\");")
IJ.run("Line Width...", "line=80");
numPoints = 512/roiWindowsize
xvals = []
yvals = []
maxvals = []
counter = 0
for i in range(0, 512, roiWindowsize):
IJ.run("Clear Results")
IJ.makeRectangle(i, 0, roiWindowsize, 512)
IJ.run("Measure")
table = RT.getResultsTable()
xvals.append(i + roiWindowsize/2)
yvals.append(RT.getValue(table, "YM", 0))
maxvals.append((RT.getValue(table, "Max", 0)))
if maxvals[counter] == 0 and counter > 0:
yvals[counter] = yvals[counter - 1]
counter += 1
coords = ""
for i in range(numPoints - 1):
coords += str(xvals[i]) + ", " + str(yvals[i]) +", "
coords += str(xvals[numPoints-1]) + ", " + str(yvals[numPoints-1])
IJ.runMacro("makeLine("+coords+")")
IJ.run("Straighten...", "line = 80")
return coords
def measureTumor(original, locations):
'''Returns the area from the original image with the
highest kurtosis which generally corresponds to the most
in focus image. Also saves an image corresponding to a mask
of the measurement.'''
# Prevent ROI manager from appearing
roiM = RoiManager(True)
ParticleAnalyzer.setRoiManager(roiM)
# Locate particles above a minimum size and with a desired circularity
IJ.run(locations, "Analyze Particles...", "size=" + str(minimumCancerArea) +"-" + str(maxCancerArea) +" circularity=" + str(circularityCutoff) + "-1.00 show=Nothing exclude add stack");
# Choose ROI with the highest kurtosis
maxKurtosis = None
area = None
selectedROI = None
for roi in roiM.getRoisAsArray():
original.setRoi(roi)
stats = original.getStatistics(Measurements.KURTOSIS, Measurements.AREA)
currentKurtosis = stats.kurtosis
if currentKurtosis > maxKurtosis:
maxKurtosis = stats.kurtosis
area = stats.area
selectedROI = roi
original.killRoi() # Remove the remaining ROI
roiM.runCommand("Reset")
return (area, selectedROI)
def __dia(self, event):
IJ.run("Set Scale...", "distance=0 known=0 pixel=1 unit=pixel")
#IJ.run("Properties...", "channels=1 slices=1 frames=20 unit=pixel pixel_width=1.0000 pixel_height=1.0000 voxel_depth=1.0000 frame=[1 sec] origin=0,0");
self.__impD = IJ.getImage()
self.__isD = self.__impD.getImageStack()
self.__display.text = "DIA="+self.__impD.getTitle()
self.__initDIA = True
def adjustScale(original):
'''Assuming that the units outported by the Zeiss microscope are mm or \mu-m,
adjust the units so they are in mm.'''
pixelWidth = original.getCalibration().pixelWidth
if pixelWidth > 1:
newScale = 1000*(1.0/pixelWidth)
IJ.run("Set Scale...", "distance=" + str(newScale) + " known=1 pixel=1 unit=mm")
def run():
srcDir = DirectoryChooser("Choose!").getDirectory()
if not srcDir:
# user canceled dialog
return
for root, directories, filenames in os.walk(srcDir):
for filename in filenames:
# Skip non-TIFF files
if not filename.endswith(fileID):
continue
path = os.path.join(root, filename)
print path
imp = IJ.openImage(path)
imp.show()
if method=="contrast":
IJ.run(imp,"Enhance Contrast", "saturated=0.35");
time.sleep(0.2)
IJ.save(imp,path + "_auto.jpeg")
imp.changes = False
imp.getWindow().close()
elif method=="scale":
IJ.run(imp, "Scale...", "x=0.2 y=0.2 interpolation=None create title=small");
impScale = IJ.getImage()
time.sleep(0.2)
IJ.save(impScale,path + "_thumb.jpeg")
imp.changes = False
imp.getWindow().close()
impScale.changes = False
impScale.getWindow().close()
def extractRIM(impbin, iteration):
IJ.run(imp, "Options...", "iterations=1 count=1 edm=Overwrite do=Nothing");
impErode = Duplicator().run(impbin)
impDilate = Duplicator().run(impbin)
# resized = CanvasResizer().expandStack(impDilate.getStack(), impDilate.getWidth()+2*iteration, impDilate.getHeight()+2*iteration, iteration, iteration)
# impDilate.setStack(None, resized);
for j in range(impErode.getStackSize()):
ipe = impErode.getStack().getProcessor(j+1)
ipe.setBackgroundValue(0)
ipd = impDilate.getStack().getProcessor(j+1)
ipd.setBackgroundValue(0)
for i in range(iteration):
ipe.erode()
ipd.dilate()
# IJ.run(impErode, "Dilate", "stack")
# IJ.run(impDilate, "Erode", "stack")
# resized = CanvasResizer().expandStack(impDilate.getStack(), impDilate.getWidth()-2*iteration, impDilate.getHeight()-2*iteration, -1*iteration, -1*iteration)
# impDilate.setStack(None, resized);
# impErode.show()
# Duplicator().run(impDilate).show()
for i in range(1, impbin.getStackSize()+1):
impDilate.setSlice(i)
impErode.setSlice(i)
ImageCalculator().calculate("XOR", impDilate, impErode)
return impDilate;
def makeStack(stackDir, stackName = "stack"):
IJ.run("Image Sequence...", "open="+stackDir+" file = (\"img_.*\") sort")
imp = IJ.getImage()
#IJ.run("8-bit")
fs = FileSaver(imp)
print "Saving stack"
fs.saveAsTiff(stackDir + "/" +stackName+".tif")
def gaussian_blur_8_bks(imp, size):
"""
gaussian_blur_8_bks(imp, size)
Perform a Gaussian Blur on an 8 bit gray scale
image, use that to do a background correction,
and return the background corrected image. The
blur size is 0.3*image width
Parameters
==========
imp: ImagePlus
The 8 bit gray scale input image
Returns
=======
bks: The image plus of the backround-subtracted
gray scale image
size: The size in px of the blur. Should be about
40% of the image size
"""
strSize = "sigma=%i" % (size)
imp.show()
IJ.run("Gaussian Blur...", strSize)
ti = imp.getShortTitle()
ti += "_gb"
imp.setTitle(ti)
return(imp)
def run_script():
'''Function to be run when this file is used as a script'''
selected_mode, img1_in, img2_in = get_setup()
if not selected_mode:
return
corrected_stack = drift.get_corrected_stack((img1_in, img2_in), mode=selected_mode)
img1, img2 = tools.stack_to_list_of_imp(corrected_stack)
img_ratio = ImageCalculator().run('Divide create', img2, img1)
img_ratio.setTitle('Jump-ratio [%s divided by %s]' % (img2.getShortTitle(),
img1.getShortTitle())
)
img_ratio.changes = True
img_ratio.copyScale(img1_in)
img_ratio.show()
IJ.run(img_ratio, 'Enhance Contrast', 'saturated=0.35')
# We want to optimise the lower displaylimit:
minimum = img_ratio.getProcessor().getMin()
maximum = img_ratio.getProcessor().getMax()
stat = img_ratio.getStatistics(Stats.MEAN + Stats.STD_DEV)
mean = stat.mean
stdv = stat.stdDev
if minimum < mean - stdv:
if mean - stdv >= 0:
img_ratio.getProcessor().setMinAndMax(mean - stdv, maximum)
else:
img_ratio.getProcessor().setMinAndMax(0, maximum)
img_ratio.updateAndDraw()
def rgb_from_r_g_b(r, g, b, title):
"""
rgb_from_r_g_b(r, g, b, title)
Generate an RGB image from three 8/bit per pixel
gray scale images
Parameters
----------
r: An ImagePlus
The red channel
g: An ImagePlus
The green channel
b: An ImagePlus
The blue channel
title: a string
The title for the image
Returns
-------
impRGB: An ImagePlus
The RGB image
"""
tiR = r.getTitle()
tiG = g.getTitle()
tiB = b.getTitle()
strTwo = "c1=[%s] c2=[%s] c3=[%s]" % (tiR, tiG, tiB)
IJ.run("Merge Channels...", strTwo)
impRecon = IJ.getImage()
impRecon.show()
return(impRecon)
def run_stitch(filePath):
basePath,baseName = os.path.split(filePath)
imagePrefix = os.path.splitext(baseName)[0]
resizeName = basePath + "/" + baseName + "_tiles/resized/"
checkName = basePath + "/" + baseName + "_tiles/resized/" + imagePrefix + "_seq/"
activeName = basePath + "/" + baseName + "_tiles"
IJ.log("Stitching " + filePath + "...")
if (not os.path.isdir(checkName)):
thread = Thread.currentThread()
originalThread = thread.getName()
thread.setName("Run$_stitch_process")
options = "choose=" + resizeName + " select=" + activeName + "/tile_info.txt image_prefix=" + imagePrefix
Macro.setOptions(Thread.currentThread(),options)
try:
IJ.run("Stitch wrapper",options)
IJ.log("succeeded")
returnVal = 0
except:
IJ.log("failed")
os.rmdir(checkName)
returnVal = 1
thread.setName(originalThread)
Macro.setOptions(thread,None)
else:
IJ.log("skipped")
returnVal = 2
return returnVal
def autoProcAZtecImage(fwMicrons, wrkDir, barWid=0.1, barHt=9, barFnt=24, barCol="Black", barLoc="Lower Right"): imp = IJ.getImage() fName = imp.getShortTitle() wid = imp.getWidth() argThree = "distance=%g known=%f pixel=1 unit=um" % (wid, fwMicrons) IJ.run(imp, "Set Scale...", argThree) IJ.run(imp, "Enhance Contrast", "saturated=0.35") fs = FileSaver(imp) if path.exists(wrkDir) and path.isdir(wrkDir): print "folder exists:", wrkDir tifName = fName + ".tif" tifPath = path.join(wrkDir, tifName) print(tifPath) if fs.saveAsTiff(tifPath): print "Tif saved successfully at ", tifPath IJ.run(imp, "RGB Color", "") # dummy to get things set foo = imp.duplicate() s2 = "width=%g height=%g font=%g color=%s location=[%s] bold" % (barWid, barHt, barFnt, barCol, barLoc) IJ.run(foo, "Add Scale Bar", s2) # explicitly save preferences Prefs.savePreferences() foo.changes = False foo.close() IJ.run(imp, "Add Scale Bar", s2) fs = FileSaver(imp) pngName = fName + ".png" pngPath = path.join(wrkDir, pngName) if fs.saveAsPng(pngPath): print "png saved successfully at ", pngPath
fileout = os.path.join(analyzeDir, os.path.basename(filepath))
IJ.saveAs(impOut, "Jpeg", fileout)
def createOutputImg(image, roim, centPart, metaphase):
#maximal projection
imp = image
if image.getNSlices() > 1:
zp = ZProjector()
zp.setMethod(zp.MAX_METHOD)
zp.setImage(image)
zp.doProjection()
imp = zp.getProjection()
else:
imp = image
IJ.run(imp,"Enhance Contrast", "saturated=0.35")
IJ.setForegroundColor(255,0,0)
#need to show the image to draw overlay
imp.show()
IJ.run(imp,"RGB Color", "");
for i in range(roim.getCount()):
roim.select(imp, i)
roim.runCommand("Draw")
if metaphase:
IJ.setForegroundColor(0,255,255)
else:
IJ.setForegroundColor(0,255,0)
roim.select(imp, centPart)
def __init__(self, img_list, params=None):
'''Create an instance of pySIFT.
:param img_list: All SIFT calculations are done with the given list
of ImagePlus objects (Classes that extend ImagePlus are allowed).
:param params: Pass a modified instance of Param
to customize the SIFT detection (default: None).
'''
self.images = []
def check(img):
'''Returns True if img is a an ImagePlus or a class that extends it.'''
# Check if the given object extends ImagePlus:
return isinstance(img, ImagePlus)
for img in img_list:
if check(img):
if img.getBitDepth() == 32:
# For 32-bit the images are normalized
# to bypass a limitation of the SIFT implementation.
img_dup = img.crop()
mean = img_dup.getProcessor().getStatistics().mean
IJ.run(img_dup, "Divide...", "value=%f" % (mean,))
IJ.run(img_dup, "Enhance Contrast", "saturated=0.1")
self.images.append(img_dup)
else:
self.images.append(img)
if params and isinstance(params, Param):
self.param = params
else:
self.param = Param()
self.drift_vector = [None] * len(self.images)
self.drift_matrix = [[]] * len(self.images)
for i, _ in enumerate(self.drift_matrix):
self.drift_matrix[i] = [None] * len(self.images)
self._extract_features_()
def rankFilterMapImage(imp, radius=1.5, no=2):
"""clipNoisePixMapImage(imp, no=2)
Clips noise pixels from an X-ray map image (typically a 16 bit gray image).
First, it sets the display range to a noise offset to max and removes the noise
pixels (to get rid of isolated pixels), then converts to an 8 bit image that spans
0 to 255 and returns an 8 bit gray scale. This is ready for a hueLUT. It peforms
this on a duplicate imp and returns the resultant imp. To the best of my understanding,
this is how Oxford treats their maps w/o a 3x3 smooth.
Inputs:
imp - the input ImagePlus object
no - the noise offset, default = 2, to remove noise pixels
Returns:
ret - an ImapePlus for the 8-bit, scaled, filtered image
"""
stats = imp.getStatistics(Measurements.MIN_MAX)
imp.setDisplayRange(0, stats.max)
ret = imp.duplicate()
ip = ret.getProcessor()
data = ip.getPixels()
l = len(data)
for i in range(l):
val = data[i]
if val < no:
data[i] = 0
rf = RankFilters()
rf.rank(ret.getProcessor(), radius, RankFilters.MEDIAN)
IJ.run(ret, "8-bit", "")
name = imp.getShortTitle()
stats = ret.getStatistics(Measurements.MIN_MAX)
ret.setDisplayRange(0, stats.max)
ret.setTitle(name)
return ret
def anaParticles(imp, minSize, maxSize, minCirc, bHeadless=True):
"""anaParticles(imp, minSize, maxSize, minCirc, bHeadless=True)
Analyze particles using a watershed separation. If headless=True, we cannot
redirect the intensity measurement to the original immage becuae it is never
displayed. If we display the original, we can and get the mean gray level. We
may then compute the particle contrast from the measured Izero value for the image.
No ability here to draw outlines on the original."""
strName = imp.getShortTitle()
imp.setTitle("original")
ret = imp.duplicate()
IJ.run(ret, "Enhance Contrast", "saturated=0.35")
IJ.run(ret, "8-bit", "")
IJ.run(ret, "Threshold", "method=Default white")
IJ.run(ret, "Watershed", "")
rt = ResultsTable()
# strSetMeas = "area mean modal min center perimeter bounding fit shape feret's redirect='original' decimal=3"
# N.B. redirect will not work without a displayed image, so we cannot use a gray level image
if bHeadless == True:
strSetMeas = "area mean modal min center perimeter bounding fit shape feret's decimal=3"
else:
imp.show()
strSetMeas = "area mean modal min center perimeter bounding fit shape feret's redirect='original' decimal=3"
IJ.run("Set Measurements...", strSetMeas)
# note this doies not get passed directly to ParticleAnalyzer, so
# I did this, saved everything and looked for the measurement value in ~/Library/Preferences/IJ_Prefs.txt
# measurements=27355
# meas = Measurements.AREA + Measurements.CIRCULARITY + Measurements.PERIMETER + Measurements.SHAPE_DESCRIPTORS
# didn't work reliably
meas = 27355
pa = ParticleAnalyzer(0, meas, rt, minSize, maxSize, minCirc, 1.0);
pa.analyze(ret);
rt.createTableFromImage(ret.getProcessor())
return [ret, rt]
def smoothMapImage(imp, no=2):
"""smoothMapImage(imp, no=2)
Smooths an X-ray map image (typically a 16 bit gray image). First, it sets the
display range to a noise offset to the max and sets pixels below the noise offset
to zero (to get rid of isolated pixels), converts to an 8
bit image that spans no to 255 and smooths with a 3x3 kernel. and
converts it to an 8 bit gray scale image that spans 0-255. This is
ready for a hueLUT. It peforms this on a duplicate imp and returns
the resultant imp. To the best of my understanding, this is how Oxford
treats their maps.
Inputs:
imp - the input ImagePlus object
no - the noise offset, default = 2, to remove noise pixels
Returns:
ret - an ImapePlus for the 8-bit, scaled, filtered image
"""
stats = imp.getStatistics(Measurements.MIN_MAX)
imp.setDisplayRange(no, stats.max)
ret = imp.duplicate()
ip = ret.getProcessor()
data = ip.getPixels()
l = len(data)
for i in range(l):
val = data[i]
if val < no:
data[i] = 0
IJ.run(ret, "8-bit", "")
name = imp.getShortTitle()
ip = ret.getProcessor()
ip.smooth()
stats = ret.getStatistics(Measurements.MIN_MAX)
ret.setDisplayRange(0, stats.max)
ret.setTitle(name)
return ret
def bSaveZProject(imp, dstFolder, shortname):
#bring to front
impWinStr = imp.getTitle()
IJ.selectWindow(impWinStr)
bPrintLog('Making Z-Project from ' + impWinStr, 2)
madez = 0
z1 = 1
z2 = imp.getNSlices()
if z2>1:
paramStr = 'start=%s stop=%s projection=[Max Intensity]' % (z1, z2)
IJ.run('Z Project...', paramStr) #makes 'MAX_' window
zWinStr = 'MAX_' + impWinStr
zImp = WindowManager.getImage(zWinStr)
madez = 1
else:
zImp = imp
if dstFolder != None:
dstFile = dstFolder + 'max_' + shortname + '.tif'
bPrintLog('Saving Z-Project: ' + dstFile, 2)
bSaveStack(zImp, dstFile)
if madez:
zImp.changes = 0
zImp.close()
def __fluo(self, event):
IJ.run("Set Scale...", "distance=0 known=0 pixel=1 unit=pixel")
IJ.run("Set Measurements...", "area mean standard modal min centroid shape integrated median skewness kurtosis stack limit display redirect=None decimal=3")
self.__impF = IJ.getImage()
self.__isF = self.__impF.getImageStack()
self.__display.text = "FLUO="+self.__impF.getTitle()
self.__initFLUO = True
def Make8bit_UsingMinMax(_imp):
imp = _imp.duplicate()
stats = StackStatistics(imp)
IJ.setMinAndMax(imp, stats.min, stats.max)
IJ.run(imp, "8-bit", "")
IJ.setMinAndMax(imp, 0, 255)
return imp
def createOutputImg(image, rois, particle, ptsFcs=None):
"""create an output image which as overlay of channel"""
IJ.setForegroundColor(255, 0, 0)
IJ.run(image, "Enhance Contrast", "saturated=0")
IJ.run(image, "RGB Color", "")
IJ.setForegroundColor(255, 0, 0)
ip = image.getProcessor()
ip.setColor(Color.red)
for roi in rois:
ip.draw(roi)
ip.setColor(Color.green)
if particle is not None:
for part in particle:
ip.draw(rois[part])
ip.setLineWidth(2)
ip.setColor(Color.white)
if ptsFcs is not None:
print "Some fcs points"
for i, pt in enumerate(ptsFcs):
drawCross(ip, pt, 8)
ip.drawString(str(i + 1), pt[0] + 1, pt[1] - 1)
image.setProcessor(ip)
return image
def run():
print '======================'
print 'running'
if len(sys.argv)<2:
print 'bAlignCrop will align a stack.'
print 'Usage: ./fiji'
filePath = '/Users/cudmore/Desktop/X20150214_a156_010_ch1_c.tif'
#return
else:
filePath = sys.argv[1]
#check that file exists
if not os.path.isfile(filePath):
print 'Error: did not find file ', filePath
return
#open file
print 'opening file:', filePath
imp = IJ.openImage(filePath)
if imp is None:
print 'Error opening file'
return
imp.show()
winTitle = imp.getTitle()
transformationFile = filePath + '.txt'
print 'running multistack reg'
#IJ.run(imp, "MultiStackReg", "stack_1=X20150214_a156_010_ch1_c.tif action_1=Align file_1=[] stack_2=None action_2=Ignore file_2=[] transformation=Translation save");
stackRegParams = 'stack_1=%s action_1=[Align] file_1=[%s] transformation=[Translation] save' %(winTitle, transformationFile)
IJ.run('MultiStackReg', stackRegParams)
def mask(ip, valueA, valueB):
""" Mask the image with a threshold value ranging between valueA and valueB"""
ip.setThreshold(valueA, valueB, ImageProcessor.NO_LUT_UPDATE)
imp = ImagePlus('Mask', ip)
IJ.run(imp, "Convert to Mask", "")
return imp
def test_HD(): imp = IJ.openImage(filePath.getAbsolutePath()) stack = imp.getStack() ip1 = stack.getProcessor(1).duplicate() ip2 = stack.getProcessor(2).duplicate() [ip1, s] = scaleLongSide(ip1, longSide) if (Scale == 1): ip2 = scale(ip2, s) maskA = impMask1.duplicate() maskB = impMask2.duplicate() # IJ.run(maskA, "Invert", "") # IJ.run(maskB, "Invert", "") IJ.run(maskA, "Outline", "") IJ.run(maskB, "Outline", "") # IJ.run(maskA, "Invert", "") # IJ.run(maskB, "Invert", "") maskA.show() maskB.show() impMask1.show() MaskArea1 = maskArea( impMask1.getProcessor() ) impMask2.show() MaskArea2 = maskArea( impMask2.getProcessor() ) hd = Hausdorff_Distance() hd.exec( maskA, maskB ) print(hd.getAveragedHausdorffDistance()) print(hd.getHausdorffDistance())
def XYTCZL(directory_load, directory_save, chl, frames, z_planes, loops, total,
pat, title):
def pattern_closer(pat):
impopen = [WM.getImage(id) for id in WM.getIDList()]
for wind in impopen:
imp2close = wind
if "" + pat + "_R" in str(wind):
imp2close.close()
def hyperSC(num, istr):
imp2 = impl[num]
imp2 = HyperStackConverter.toHyperStack(imp2, 2, 1, 50, "xytcz",
"Color")
if (num - 2) < 10:
numstr = "000" + str(num - 2)
elif (num - 2) < 100:
numstr = "00" + str(num - 2)
elif (num - 2) > 99:
numstr = "0" + str(num - 2)
IJ.saveAs(
imp2, "Tiff",
"" + directory_save + title + "_R" + istr + "_Z" + numstr + ".tif")
IJ.run("Close All", "")
#Calculated variables
range_rep = total / loops
rangeDef = loops + 1
IJ1.batchMode = True
imp = IJ.run(
"Bio-Formats", "open=" + directory_load +
" color_mode=Default rois_import=[ROI manager] view=[Standard ImageJ] stack_order=Default use_virtual_stack series_2"
)
for i in range(loops):
start = str(1 + i * range_rep)
end = str(range_rep + i * range_rep)
if i < 10:
istr = "000" + str(i)
elif i < 100:
istr = "00" + str(i)
elif i > 99:
istr = "0" + str(i)
impDup = IJ.run(
imp, "Duplicate...", "title=" + title + "_R" + istr +
" duplicate range=" + start + "-" + end + "")
IJ.run(impDup, "Stack Splitter", "number=" + str(z_planes) + "")
impl = [WM.getImage(id) for id in WM.getIDList()]
a = [hyperSC(num, istr) for num in range(2, z_planes + 2)]
pattern_closer(pat)
IJ1.batchMode = False
def run():
print '======================'
print 'running'
if len(sys.argv) < 2:
print 'bAlignCrop will align a stack.'
print 'Usage: ./fiji'
filePath = '/Users/cudmore/Desktop/X20150214_a156_010_ch1_c.tif'
#return
else:
filePath = sys.argv[1]
#check that file exists
if not os.path.isfile(filePath):
print 'Error: did not find file ', filePath
return
#open file
print 'opening file:', filePath
imp = IJ.openImage(filePath)
if imp is None:
print 'Error opening file'
return
imp.show()
winTitle = imp.getTitle()
transformationFile = filePath + '.txt'
print 'running multistack reg'
#IJ.run(imp, "MultiStackReg", "stack_1=X20150214_a156_010_ch1_c.tif action_1=Align file_1=[] stack_2=None action_2=Ignore file_2=[] transformation=Translation save");
stackRegParams = 'stack_1=%s action_1=[Align] file_1=[%s] transformation=[Translation] save' % (
winTitle, transformationFile)
IJ.run('MultiStackReg', stackRegParams)
def ana_particles(imp, minSize, maxSize, minCirc, bHeadless=True):
"""ana_particles(imp, minSize, maxSize, minCirc, bHeadless=True)
Analyze particles using a watershed separation. If headless=True, we cannot
redirect the intensity measurement to the original image because it is never
displayed. If we display the original, we can and get the mean gray level. We
may then compute the particle contrast from the measured Izero value for the image.
No ability here to draw outlines on the original.
"""
strName = imp.getShortTitle()
imp.setTitle("original")
ret = imp.duplicate()
ret.setTitle("work")
IJ.run(ret, "Enhance Contrast", "saturated=0.35")
IJ.run(ret, "8-bit", "")
IJ.run(ret, "Threshold", "method=Default white")
IJ.run(ret, "Watershed", "")
rt = ResultsTable()
# strSetMeas = "area mean modal min center perimeter bounding fit shape feret's redirect='original' decimal=3"
# N.B. redirect will not work without a displayed image, so we cannot use a gray level image
if bHeadless == True:
strSetMeas = "area mean modal min center perimeter bounding fit shape feret's decimal=3"
else:
imp.show()
strSetMeas = "area mean modal min center perimeter bounding fit shape feret's redirect='original' decimal=3"
IJ.run("Set Measurements...", strSetMeas)
# note this does not get passed directly to ParticleAnalyzer, so
# I did this, saved everything and looked for the measurement value in ~/Library/Preferences/IJ_Prefs.txt
# measurements=27355
# meas = Measurements.AREA + Measurements.CIRCULARITY + Measurements.PERIMETER + Measurements.SHAPE_DESCRIPTORS
# didn't work reliably
meas = 27355
pa = ParticleAnalyzer(0, meas, rt, minSize, maxSize, minCirc, 1.0)
pa.analyze(ret)
rt.createTableFromImage(ret.getProcessor())
return [ret, rt]
def Generate_segmented_image(imp, channel):
imp_Threshold_1 = ExtractChannel(imp1, channel)
imp_Threshold_1.setTitle(("Threshold" + "Channel" + str(channel)))
imp_Threshold_1.show()
IJ.run(imp_Threshold_1, "Median...", "radius=1")
IJ.run(imp_Threshold_1, "Subtract Background...", "rolling=50")
IJ.setAutoThreshold(imp_Threshold_1, "Triangle dark")
#Prefs.blackBackground = True;
IJ.run(imp_Threshold_1, "Convert to Mask", "")
return imp_Threshold_1
def preprocess(self):
IJ.run(self.imp, "Gaussian Blur...", "sigma=" + str(gaussianVal))
IJ.run(self.imp, "8-bit", "")
imp_tmp = self.imp.duplicate()
IJ.run(self.imp, "Gray Morphology",
"radius=" + str(tophatSize) + " type=circle operator=open")
ImageCalculator().run("Subtract", imp_tmp, self.imp)
self.imp.changes = False
self.imp.close()
self.imp = imp_tmp
def duplicateMaskInRoi(imp, roi, duplicateStack=False):
imp.setRoi(roi)
if duplicateStack:
WindowManager.setCurrentWindow(imp.getWindow())
impMT1 = IJ.run("Duplicate...", "duplicate")
impMT1 = WindowManager.getCurrentImage()
else:
impMT1 = imp.crop()
IJ.run("Set Scale...", "distance=0 known=0 pixel=1 unit=pixel")
IJ.run(impMT1, "Create Selection", "")
roi1 = impMT1.getRoi()
bounds = roi1.getBounds()
if duplicateStack:
WindowManager.setCurrentWindow(impMT1.getWindow())
IJ.run("Duplicate...", "duplicate")
impMT = WindowManager.getCurrentImage()
impMT1.close()
else:
impMT = impMT1.crop()
IJ.run(impMT, "Canvas Size...", "width="+str(bounds.width+2)+" height="+str(bounds.height+2)+" position=Center zero")
return impMT, bounds
def Generate_segmented(imp, channel, threshold_method, filtersize):
imp_Threshold_1 = ExtractChannel(imp1, channel)
imp_Threshold_1.setTitle(("Threshold" + "Channel" + str(channel)))
imp_Threshold_1.show()
IJ.run(imp_Threshold_1, "Median...", "radius=" + str(filtersize))
IJ.run(imp_Threshold_1, "Subtract Background...", "rolling=50")
IJ.setAutoThreshold(imp_Threshold_1, threshold_method + " dark")
#Prefs.blackBackground = True;
IJ.run(imp_Threshold_1, "Convert to Mask", "")
return imp_Threshold_1
def Generate_segmented_fixedThreshold(imp, channel, thres_min):
imp_Threshold_1 = ExtractChannel(imp1, channel)
imp_Threshold_1.setTitle(("Threshold" + "Channel" + str(channel)))
imp_Threshold_1.show()
IJ.run(imp_Threshold_1, "Median...", "radius=1")
IJ.run(imp_Threshold_1, "Subtract Background...", "rolling=50")
IJ.setThreshold(imp_Threshold_1, thres_min, 100000)
#Prefs.blackBackground = True;
IJ.run(imp_Threshold_1, "Convert to Mask", "")
return imp_Threshold_1
def draw(i, j, array, color):
if color == "white":
antiColor = "black"
else:
antiColor = "white"
path(i, j, array)
setColor(color)
IJ.run("Fill")
setColor(antiColor)
IJ.run("Draw")
IJ.run("Select None")
def extract_frame_process_roi(imp, frame, channel, process, background, roi, z_min, z_max):
# extract frame and channel
imp_frame = ImagePlus("", extract_frame(imp, frame, channel, z_min, z_max)).duplicate()
# check for roi and crop
if roi != None:
#print roi.getBounds()
imp_frame.setRoi(roi)
IJ.run(imp_frame, "Crop", "")
# subtract background
if background > 0:
#IJ.log("Subtracting "+str(background));
IJ.run(imp_frame, "Subtract...", "value="+str(background)+" stack");
# enhance edges
if process:
IJ.run(imp_frame, "Mean 3D...", "x=1 y=1 z=0");
IJ.run(imp_frame, "Find Edges", "stack");
# return
return imp_frame
def prepros2(folder_in,folder_out):
for filename in os.listdir(folder_in):
imp =IJ.openImage(os.path.join(folder_in,filename))
IJ.run(imp, "TransformJ Rotate", "z-angle=9 y-angle=-6 x-angle=0.0 interpolation=Linear background=0.0")
imp = IJ.getImage()
stack = imp.getImageStack()
stackcropped = stack.crop(118,98,70,1364,1304,904)
imp = ImagePlus("2",stackcropped)
output = "nrrd=["+folder_out+filename+"]"
IJ.run(imp, "Nrrd ... ", output)
imp.close()
IJ.run("Collect Garbage", "")
def process_folder(foldername, dataset="dataset.xml"):
xmlname = foldername + "/" + dataset
# this one is for a single well of projected images
IJ.run(
"Calculate pairwise shifts ...", "select=" + xmlname +
" process_angle=[All angles] process_channel=[All channels] process_illumination=[All illuminations] process_tile=[All tiles] process_timepoint=[All Timepoints] method=[Phase Correlation] downsample_in_x=4 downsample_in_y=4"
)
IJ.run(
"Filter pairwise shifts ...", "select=" + xmlname +
" min_r=0.6 max_r=1 max_shift_in_x=0 max_shift_in_y=0 max_shift_in_z=0 max_displacement=0"
)
IJ.run(
"Optimize globally and apply shifts ...", "select=" + xmlname +
" process_angle=[All angles] process_channel=[All channels] process_illumination=[All illuminations] process_tile=[All tiles] process_timepoint=[All Timepoints] relative=2.500 absolute=3.500 global_optimization_strategy=[Two-Round using Metadata to align unconnected Tiles] fix_group_0-0"
)
IJ.run(
"Fuse dataset ...", "select=" + xmlname +
" process_angle=[All angles] process_channel=[All channels] process_illumination=[All illuminations] process_tile=[All tiles] process_timepoint=[All Timepoints] bounding_box=[Currently Selected Views] downsampling="
+ str(downsample) +
" pixel_type=[16-bit unsigned integer] interpolation=[Linear Interpolation] image=[Precompute Image] interest_points_for_non_rigid=[-= Disable Non-Rigid =-] blend produce=[Each timepoint & channel] fused_image=[Save as (compressed) TIFF stacks] output_file_directory="
+ foldername + " filename_addition=_larged")
def mkMask(imp, prePick, numRep, nStacks):
file_name = imp.getTitle()
IJ.selectWindow(file_name)
IJ.setSlice(prePick)
tempFileNames = []
for pp in range(0, numRep):
tempFileNames.append('temp' + str(pp))
IJ.run(imp, "Duplicate...", "title=" + tempFileNames[pp])
IJ.run("8-bit", "")
cont_imgs = " ".join('image%d=%s' % (c + 1, w)
for c, w in enumerate(tempFileNames))
IJ.run("Concatenate...", "title=tempStack " + cont_imgs)
tempStack = IJ.getImage()
IJ.run(
tempStack, "Make Montage...", "columns=" + str(numRep / nStacks) +
" rows=" + str(nStacks) + " scale=1 first=1 last=" + str(numRep) +
" increment=1 border=1 font=12")
m2_imp = IJ.getImage()
m2_imp.setTitle("Mask")
tempStack.close()
return m2_imp
def bunwarpjFIJI(ref, reg):
directName = 'direct_transformation'
inverseName = 'inverse_transformation'
regStack = reg.getStack()
sizeZ = reg.getStackSize()
source = ImagePlus('source', ref.duplicate())
for i in range(1, sizeZ):
ip = regStack.getProcessor(i).duplicate()
target = ImagePlus('target', ip)
directFileName = os.path.join(saveDir.getAbsolutePath(),
directName + '_' + str(i))
inverseFileName = os.path.join(saveDir.getAbsolutePath(),
inverseName + '_' + str(i))
imp = IJ.run(
source, "bUnwarpJ",
"source_image=source target_image=target registration=Accurate image_subsample_factor=0 initial_deformation=[Very Coarse] final_deformation=Fine divergence_weight=0 curl_weight=0 landmark_weight=0 image_weight=1 consistency_weight=10 stop_threshold=0.01 save_transformations save_direct_transformation=["
+ directFileName + "] save_inverse_transformation=[" +
inverseFileName + "]")
return imp
def extract_frame_process_roi(imp, frame, channel, process, roi):
# extract frame and channel
imp_frame = ImagePlus("", extract_frame(imp, frame, channel)).duplicate()
# check for roi and crop
if roi != None:
#print roi.getBounds()
imp_frame.setRoi(roi)
IJ.run(imp_frame, "Crop", "")
# process
if process:
IJ.run(imp_frame, "Mean 3D...", "x=1 y=1 z=0");
IJ.run(imp_frame, "Find Edges", "stack");
# return
return imp_frame
def prepros1(folder_in,folder_out,firstFolder):
for x in range(1):
fnum = firstFolder+x
folder = folder_in+str(fnum)
output = "nrrd=["+folder_out+str(fnum)+".nrrd]"
imp = FolderOpener.open(folder)
stack = imp.getImageStack()
stackcropped = stack.crop(404,644,538,1604,1476,1200)
imp = ImagePlus("1",stackcropped)
IJ.run(imp, "Stack Contrast Adjustment", "is")
imp = IJ.getImage()
IJ.run(imp, "Nrrd ... ", output);
imp.close()
IJ.run("Collect Garbage", "")
def extract_frame_process_roi(imp, frame, roi, options):
# extract frame and channel
imp_frame = ImagePlus("", extract_frame(imp, frame, options['channel'], options['z_min'], options['z_max'])).duplicate()
# check for roi and crop
if roi != None:
#print roi.getBounds()
imp_frame.setRoi(roi)
IJ.run(imp_frame, "Crop", "")
# subtract background
if options['background'] > 0:
#IJ.log("Subtracting "+str(background));
IJ.run(imp_frame, "Subtract...", "value="+str(options['background'])+" stack");
# enhance edges
if options['process']:
IJ.run(imp_frame, "Mean 3D...", "x=1 y=1 z=0");
IJ.run(imp_frame, "Find Edges", "stack");
# project into 2D if we only want to correct the drift in x and y
if imp_frame.getNSlices() > 1:
if options['correct_only_xy']:
imp_frame = ZProjector.run(imp_frame, "avg");
# return
return imp_frame
def countobjects(imp, rt,
subtractBackground=False, watershed=False, dilate=False,
threshMethod="Otsu", physicalUnits=True,
minSize=0.00, maxSize=float("inf"),
minCirc=0.00, maxCirc=1.00):
"""Threshold and count objects in channel 'channelNumber'.
This function splits an image in the separate channels, and counts the number of objects in the thresholded
channel.
Args:
imp: An ImagePlus with 1 frame, 1 slice.
Returns:
A list of filepaths.
"""
cal = imp.getCalibration()
if subtractBackground:
IJ.run(imp, "Subtract Background...", "rolling=50")
IJ.setAutoThreshold(imp, "{} dark".format(threshMethod))
IJ.run(imp, "Convert to Mask", "")
if dilate:
IJ.run(imp, "Dilate", "")
if watershed:
IJ.run(imp, "Watershed", "")
if physicalUnits: # Convert physical units to pixels for the current calibration.
minSize = cal.getRawX(math.sqrt(minSize)) ** 2
maxSize = cal.getRawX(math.sqrt(maxSize)) ** 2
pa = ParticleAnalyzer(
ParticleAnalyzer.SHOW_OVERLAY_OUTLINES|ParticleAnalyzer.DISPLAY_SUMMARY, #int options
Measurements.AREA|Measurements.SHAPE_DESCRIPTORS|Measurements.MEAN|Measurements.CENTROID|Measurements.LABELS, #int measurements
rt, #ResultsTable
minSize, #double
maxSize, #double
minCirc, #double
maxCirc) #double
pa.analyze(imp)
return imp
def drawSpot(point, diam):
'''
point: list of 2 integer values
diam: float indicating the diameter of a cell in pixels
Draws a filled circle on the selected channel of the current image at the indicated point
'''
xcenter = point[0]
ycenter = point[1]
# print("Drawing spot at "+str(xcenter)+", "+str(ycenter))
IJ.run(
imp2, "Specify...", "width=" + str(diam) + " height=" + str(diam) +
" x=" + str(xcenter) + " y=" + str(ycenter) + " oval")
IJ.run(imp2, "Fill", "slice")
IJ.run(imp2, "Select None", "")
return
def process(inputpath, outputpath):
imp = IJ.openImage(inputpath)
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
IJ.setThreshold(imp, t1, 255)
#imp.show()
#WaitForUserDialog("Title", "Look at image").show()
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Watershed", "")
# Counts and measures the area of particles and adds them to a table called areas. Also adds them to the ROI manager
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA,
table, 50, 9999999999999999, 0.2, 1.0)
pa.setHideOutputImage(True)
pa.analyze(imp)
imp.changes = False
imp.close()
areas = table.getColumn(0)
summary = {}
if areas:
summary['Image'] = filename
summary['Nuclei.count'] = len(areas)
summary['Area.Covered'] = sum(areas)
fieldnames = list(summary.keys())
with open(outputpath, 'a') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
extrasaction='ignore',
lineterminator='\n')
if os.path.getsize(outputDirectory + "/" + outputname + ".csv") < 1:
writer.writeheader()
writer.writerow(summary)
def identifyRed(imp):
# Clear ROI manager
rm = RoiManager.getInstance()
if rm is not None:
rm.reset()
# Get hue from HSB stack
impHue = ProcessHSB.getHue(imp)
width = impHue.width
height = impHue.height
# Get processor for hue
ipHue = impHue.getProcessor().convertToFloat()
huePixels = ipHue.getPixels()
# Bring brightness into the equation
impBrightness = ProcessHSB.getBrightness(imp)
IJ.run(impBrightness, "Auto Threshold", "method=Default white")
#IJ.run(impBrightness, "Analyze Particles...", "size=10000-Infinity circularity=0.00-1.00 show=Masks in_situ")
# If hue < 30 and hue > 225, True
newPixels = map(ProcessHSB.redHue, huePixels)
# Pause: Do this later
ipNew = FloatProcessor(width, height, newPixels)
impRed = ImagePlus("RedHue", ipNew)
IJ.run(impRed, "8-bit", "") # convert to 8-bit
# Logic AND pictures together
ic = ImageCalculator()
impRedMask = ic.run("AND create", impRed, impBrightness)
IJ.run(impRedMask, "8-bit", "") # convert to 8-bit
IJ.run(impRedMask, "Analyze Particles...", "size=10000-Infinity circularity=0.00-1.00 show=Masks add in_situ") # "add" right after "include" to include roi to manager
impHue.close()
impBrightness.close()
impRed.close()
impRedMask.close()
def rescale(folder_in, folder_out):
"""Function to rescale 3D NRRD image series"""
for filename in os.listdir(folder_in):
imp = IJ.openImage(os.path.join(folder_in, filename))
img = ImgLib.wrap(imp)
img2 = Resample(img, 0.25)
imp = ImgLib.wrap(img2)
output = "nrrd=[" + folder_out + filename + "]"
IJ.run(imp, "Nrrd ... ", output)
imp = None
img = None
img2 = None
gc.collect()
time.sleep(15)
gc.collect()
IJ.run("Collect Garbage", "")
IJ.run("Collect Garbage", "")
def extract_frame_process_roi(imp, frame, channel, process, roi, roiz):
if( imp.getStack().getClass().getName() == "ct.vss.VirtualStackOfStacks"):
imp_frame = extract_cropped_frame_from_VirtualStackOfStacks(imp, frame-1, channel, roi, roiz)
else:
# extract frame and channel
imp_frame = ImagePlus("", extract_frame(imp, frame, channel, roiz)).duplicate()
# check for roi and crop
if roi != None:
#print roi.getBounds()
imp_frame.setRoi(roi)
IJ.run(imp_frame, "Crop", "")
# process
if process:
IJ.run(imp_frame, "Mean 3D...", "x=1 y=1 z=0");
IJ.run(imp_frame, "Find Edges", "stack");
# return
return imp_frame
def pairwise_stitching(tiff_1, tiff_2, temp_dir):
IJ.open(tiff_1)
tiff_1_title = os.path.basename(tiff_1)
IJ.open(tiff_2)
tiff_2_title = os.path.basename(tiff_2)
new_fused_image = "" + tiff_1_title.split("_")[0] + "-" + tiff_2_title.split("_")[0] + "_.tiff"
IJ.run("Pairwise stitching", "first_image=" + tiff_1_title + " second_image=" + tiff_2_title + " fusion_method=[Linear Blending] fused_image=" + new_fused_image + " check_peaks=1 compute_overlap x=0.0000 y=0.0000 z=0.0000 registration_channel_image_1=[Average all channels] registration_channel_image_2=[Average all channels]")
IJ.selectWindow(tiff_1_title)
IJ.run("Close")
os.remove(tiff_1)
IJ.selectWindow(tiff_2_title)
IJ.run("Close")
os.remove(tiff_2)
IJ.selectWindow(new_fused_image)
imp = IJ.getImage()
IJ.saveAsTiff(imp, os.path.join(temp_dir, new_fused_image))
imp.close()
def setThreshold(img, outDir, out_name, thresholdPref):
if thresholdPref == thresholdChoices[2]:
img2 = img.duplicate()
m, f = getThresholdOptions(thresholdChoices[0])
IJ.run(img, "Auto Threshold", "method=" + m)
out_file = os.path.join(outDir, f, out_name)
checkDirs(os.path.join(outDir, f))
saveImage(img, out_file)
m, f = getThresholdOptions(thresholdChoices[1])
IJ.run(img2, "Auto Threshold", "method=" + m)
out_file = os.path.join(outDir, f, out_name)
checkDirs(os.path.join(outDir, f))
saveImage(img2, out_file)
else:
m, f = getThresholdOptions(thresholdPref)
IJ.run(img, "Auto Threshold", "method=" + m)
out_file = os.path.join(outDir, out_name)
saveImage(img, out_file)
def identifyTunicate(imp):
# Clear ROI manager
rm = RoiManager.getInstance()
if rm is not None:
rm.reset()
# Get hue from HSB stack
impHue = ProcessHSB.getHue(imp)
width = impHue.width
height = impHue.height
# Get processor
ipHue = impHue.getProcessor().convertToFloat()
huePixels = ipHue.getPixels()
# Get macro hue because it is the same color
newPixels = map(ProcessHSB.macroHue, huePixels)
ipNew = FloatProcessor(width, height, newPixels)
impTunicate = ImagePlus("MacroHue", ipNew)
# Bring brightness into the equation
impBrightness = ProcessHSB.getBrightness(imp)
IJ.run(impBrightness, "Auto Threshold", "method=MaxEntropy white")
#IJ.run(impBrightness, "Analyze Particles...", "size=10000-Infinity circularity=0.10-1.00 show=Masks in_situ") # "add" right after "include" to include roi to manager
# Logic AND pictures together
ic = ImageCalculator()
impTunicateMask = ic.run("AND create", impTunicate, impBrightness)
IJ.run(impTunicateMask, "8-bit", "") # convert to 8-bit
impTunicate.close()
impBrightness.close()
IJ.run(impTunicateMask, "Analyze Particles...",
"size=10000-Infinity circularity=0.50-1.00 show=Masks add in_situ"
) # "add" right after "include" to include roi to manager
impTunicateMask.close()
#imp.show()
#rm = RoiManager.getInstance()
#return rm
#testImp = IJ.getImage()
#result = identifyTunicate(testImp)
#print type(result)
def processImage():
imp = IJ.getImage()
image_name = imp.title
image_dir = IJ.getDirectory("image")
IJ.run(imp, "Measure", "")
marker_dir = os.path.join(image_dir, "markers" + os.path.sep)
processed_marker_dir = os.path.join(image_dir, "processed_markers" + os.path.sep)
makeDir(marker_dir, processed_marker_dir)
csv_file = saveResults(imp, marker_dir)
imp.deleteRoi()
tiff_save_name = os.path.join(processed_marker_dir, image_name)
IJ.saveAsTiff(imp, tiff_save_name)
remove_duplicate_counter(csv_file, processed_marker_dir)
# file_dir = os.path.dirname(os.path.abspath(__file__))
IJ.run(imp, "setRoi ", "");
IJ.run("Save")
imp.close()
return image_dir
def identifyRed(imp):
# Clear ROI manager
rm = RoiManager.getInstance()
if rm is not None:
rm.reset()
# set results table
# rt = ResultsTable.getResultsTable()
# Get hue from HSB stack
impHue = ProcessHSB.getHue(imp)
width = impHue.width
height = impHue.height
# Get processor for hue
ipHue = impHue.getProcessor().convertToFloat()
huePixels = ipHue.getPixels()
# Bring brightness into the equation
impBrightness = ProcessHSB.getBrightness(imp)
IJ.run(impBrightness, "Auto Threshold", "method=MaxEntropy white")
IJ.run(impBrightness, "Analyze Particles...",
"size=10000-Infinity circularity=0.00-1.00 show=Masks in_situ")
# Get processor for brightness
ipBrightness = impBrightness.getProcessor().convertToFloat()
brightnessPixels = ipBrightness.getPixels()
# Do the thing now
newPixels = map(ProcessHSB.redHueBackground, brightnessPixels, huePixels)
# If hue < 30 and hue > 225, True
#newNewPixels = map(ProcessHSB.redHue, newPixels)
# Pause: Do this later
ipNew = FloatProcessor(width, height, newPixels)
impRed = ImagePlus("RedHue", ipNew)
IJ.run(impRed, "8-bit", "") # convert to 8-bit
#IJ.run(imp, "Options...", "iterations=20 count=1 black do=[Fill Holes]");
impRed.show()
def tag(imp, roiMan, roiTotal, color):
print "Inside of UrchinTag..."
print "imp " + str(imp)
# Set the color and line options for tagging
IJ.run(imp, "Line Width...", "line=10")
IJ.run("Colors...", "foreground=" + color + " background=black selection=magenta")
if roiMan is not None:
# Iterate through the ROIs
for roi in xrange(roiTotal):
# Select the ROI
roiMan.select(roi)
selectRoi = roiMan.getRoi(roi)
# Tag the ROI
IJ.run(imp, "Draw", "slice")
# end for loop
return 1
else:
return 0
def process(filename, output_path):
# if multiple channels
#IJ.run("Make Composite");
IJ.run("Z Project...", "projection=[Max Intensity]")
# the number of channels is one (orig image) less than the number of images opened
# add scale bar to image
IJ.run(
"Scale Bar...",
"width=50 height=20 font=18 color=White background=None location=[Lower Right] hide overlay"
)
# save scale bar to stack
IJ.run("Flatten")
IJ.saveAs("Tiff", os.path.join(output_path, 'maxint_w_scale' + filename))
# close all the images
num_channels = WM.getImageCount()
for i in range(0, num_channels):
curr_img = WM.getCurrentImage()
curr_img.close()
return