def main():
if IJ.isResultsWindow():
IJ.selectWindow("Results")
IJ.run("Close")
if (WindowManager.getImageCount() > 0):
#IJ.run("Save")
Commands.closeAll()
selected_dir = str(myDir)
if not selected_dir.endswith(os.path.sep):
selected_dir = selected_dir + os.path.sep
dir_list = list()
file_type = "stitched_images"
if selected_dir.endswith(file_type + os.path.sep):
dir_list.append(selected_dir)
#print(dir_list)
else:
dir_list = listDir(selected_dir, file_type, recursive = True)
# print(dir_list)
if len(dir_list) > 0:
for dir in dir_list:
image_list = listDir(dir, file_type = ".tiff", recursive = False)
remove_marker_dir(dir)
for image in image_list:
IJ.open(image)
processImage()
else:
image_list = listDir(selected_dir, file_type = ".tiff", recursive = False)
remove_marker_dir(selected_dir)
for image in image_list:
IJ.open(image)
processImage()
def run(in_dir, ou_dir, extension, minimum, maximum):
extension = extension.strip()
extension = "*" + extension
for path in glob(os.path.join(in_dir, '*.tif')):
print path + ':',
IJ.open(path)
imp = IJ.getImage()
minima = [x.strip() for x in ch_min.split(';')]
maxima = [x.strip() for x in ch_max.split(';')]
for c in range(0, imp.getNChannels()):
imp.setC(c + 1)
ip = imp.getProcessor()
stats = ip.getStats()
M = 2**ip.getBitDepth()
if minima[c] == '-1':
mi = stats.min
else:
mi = Double.parseDouble(minima[c])
if maxima[c] == '-1':
ma = stats.max
else:
ma = Double.parseDouble(maxima[c])
scale = M / (ma - mi)
ip.subtract(mi)
ip.multiply(scale)
print ' ch' + str(c) + '=[' + str(mi) + ' ... ' + str(ma) + ']',
print ' '
IJ.run('Make Composite')
IJ.run('RGB Color')
IJ.run('Save',
'save=[' + os.path.join(ou_dir, os.path.basename(path)) + ']')
IJ.run('Close All')
def run_it():
#Runs microscope_check and defines the scanList accordingly
microscopeType = microscope_check(experimentFolder)
# Call list_scans and pass it the microscopeType variable. Gets the scanList
if microscopeType == "Olympus":
scanList = list_scans(experimentFolder, microscopeType)
print "The returned scanList is", len(scanList), "item(s) long"
elif microscopeType == "Bruker":
scanList = list_scans(experimentFolder, microscopeType)
print "The returned scanList is", len(scanList), "item(s) long"
# For each scan in the scanList, call the following functions
for scan in scanList:
directories = define_directories(scan) # get paths to the directories
basename = os.path.basename(scan) # get the scan name (basename)
print "Opening " + basename
files = os.listdir(
directories[4]
) # makes a list of all the files in rawMAX directory
for f in files: # finds the MAX projections and only opens them (skips merged)
if fnmatch.fnmatch(
f, myChoice
): #finds whatever string you specify in the param box
IJ.open(os.path.join(directories[4], f))
IJ.run("Out [-]", f) #zoom out one level
def run():
# ensure no other windows are open and clear resultstable
IJ.run("Close All")
IJ.run("Clear Results", "")
#Ask user for file to be converted
srcDir = IJ.getFilePath("Select file to analyse")
if not srcDir:
return
#open file
IJ.open(srcDir)
dstDir = os.path.dirname(srcDir)
print("dstDir = " + dstDir)
#open ROI manager, save ROI names as labels in measurements
rm = RoiManager.getInstance()
if not rm:
rm = RoiManager()
rm.runCommand("UseNames", "true")
# set parameters to be measured
IJ.run("Set Measurements...",
"area mean integrated limit display redirect=None decimal=3")
FOVlist = WindowManager.getIDList()
chnls = IJ.getString(
"Please enter which channels you would like to analyse. Example: 123 analyses channels 1, 2 and 3",
'123')
for FOV in FOVlist:
imp = WindowManager.getImage(FOV)
IJ.run(imp, "Arrange Channels...", "new=" + chnls)
imp.close()
imp2 = WindowManager.getCurrentImage()
imageTitle = WindowManager.getImage(FOV).getTitle()
newDir = imageTitle + ' ROIs'
dirPath = os.path.join(dstDir, newDir)
if not os.path.exists(dirPath):
try:
os.makedirs(dirPath)
except OSError as e:
if e.errno != errno.EEXIST:
raise
# clear ROI list before analysis
rm.runCommand(imp2, "Deselect")
rm.runCommand(imp2, "Delete")
TMdata, nFrames = runTrackMate(imp2)
if TMdata:
iterateCoords(TMdata, nFrames, dirPath, imp2)
imp2.changes = 0
imp2.close()
#relabel()
resultsDir = os.path.splitext(srcDir)[0] + ".csv"
#while os.path.exists(resultsDir):
#resultsDir = resultsDir+"_1"+".csv"
IJ.saveAs("Results", resultsDir)
def run():
roidir = '/Volumes/TRANSCEND/LauraLeopold/Rois/'
maskdir = '/Volumes/TRANSCEND/LauraLeopold/Mask/'
originaldir = '/Volumes/TRANSCEND/LauraLeopold/Original/'
Raw_path = os.path.join(originaldir, '*tif')
X = glob.glob(Raw_path)
axes = 'YX'
for fname in X:
print(fname)
IJ.open(fname)
imp = IJ.getImage()
Name = os.path.basename(os.path.splitext(fname)[0])
RoiName = roidir + Name + '.roi'
Roi = IJ.open(RoiName)
rm = RoiManager.getInstance()
if (rm == None):
rm = RoiManager()
rm.addRoi(Roi)
print(fname, RoiName)
if not rm:
print "Please first add some ROIs to the ROI Manager"
return
impMask = IJ.createImage("Mask", "8-bit grayscale-mode",
imp.getWidth(), imp.getHeight(),
imp.getNChannels(), imp.getNSlices(),
imp.getNFrames())
IJ.setForegroundColor(255, 255, 255)
rm.runCommand(impMask, "Deselect")
rm.runCommand(impMask, "Fill")
rm.runCommand('Delete')
IJ.saveAs(impMask, '.tif', maskdir + Name)
imp.close()
def run():
leftdir = '/Users/rando/Downloads/NLM-MontgomeryCXRSet/MontgomerySet/ManualMask/leftMask/'
rightdir = '/Users/rando/Downloads/NLM-MontgomeryCXRSet/MontgomerySet/ManualMask/rightMask/'
savedir = '/Users/rando/Downloads/NLM-MontgomeryCXRSet/MontgomerySet/ManualMask/BinaryMask/'
Raw_path = os.path.join(leftdir, '*png')
X = glob.glob(Raw_path)
axes = 'YX'
for fname in X:
print(fname)
IJ.open(fname);
imp = IJ.getImage()
width = imp.width
height = imp.height
title_left = imp.getTitle();
print(title_left)
Name = os.path.basename(os.path.splitext(fname)[0])
RightName = rightdir + title_left
IJ.open(RightName)
imp_right = IJ.getImage()
title_right = imp_right.getTitle()
print(title_right)
imp_res = ImageCalculator.run(imp, imp_right, "add create 8-bit");
title = imp_res.getTitle()
IJ.saveAs(imp_res, '.tif', savedir + Name);
imp.close();
imp_right.close();
imp_res.close();
def straighten(stackName):
'''
IJ.run("Set Measurements...", "area mean min center redirect=None decimal=3")
IJ.open(stackName)
imp = IJ.getImage()
stack = imp.getImageStack()
for i in xrange(1, imp.getNSlices()+1):
image = ImagePlus(str(i), stack.getProcessor(i))
xvals = []
yvals = []
maxvals = []
j = 0
for k in xrange(0, 512, 2):
IJ.makeRectangle(k, 0, 4, 512)
IJ.run("Measure")
table = RT.getResultsTable()
x = table.getValue("XM", 0)
y = table.getValue("YM", 0)
#maxi = IJ.getResult("Max")
table = []
xvals.append(k)
yvals.append(y)
#maxvals.append(maxi)
#if maxvals[j] == 0 and j > 0:
#yvals[j] = yvals[j-1]
j += 1
print "xvals:", xvals
print "yvals:", yvals
mr = MR()
IJ.run("Make Selection...", "freeline, "+str(xvals)+" ,"+ str(yvals))
#IJ.runMacro("makeSelection...", "freeline, "+str(xvals)+" ,"+ str(yvals));
#IJ.run("makeSelection(\"freeline\", xvals, yvals);")
#IJ.run("Straighten...", "line = 80")
'''
IJ.open(stackName)
imp = IJ.getImage()
stack = imp.getImageStack()
print imp.getNSlices()
for i in range(1, imp.getNSlices() + 1):
image = ImagePlus(str(i), stack.getProcessor(i))
IJ.runMacroFile(
"/Users/juliansegert/repos/Bio119_root_tracking/straightenOneImage.ijm"
)
newImp = WM.getCurrentImage()
fs = FileSaver(newImp)
fs.saveAsTiff(
"/Users/juliansegert/Documents/Roots/Four_root_image_stacks/Pos01/"
+ str(i) + ".tif")
newImp.close()
def openAtFolder(event):
if 0 == path.getText().find("http"):
IJ.showMessage("Can't open folder: it's an URL")
return
directory = os.path.dirname(path.getText())
od = OpenDialog("Open", directory, None)
filepath = od.getPath()
if filepath:
IJ.open(filepath)
def straighten(stackName):
'''
IJ.run("Set Measurements...", "area mean min center redirect=None decimal=3")
IJ.open(stackName)
imp = IJ.getImage()
stack = imp.getImageStack()
for i in xrange(1, imp.getNSlices()+1):
image = ImagePlus(str(i), stack.getProcessor(i))
xvals = []
yvals = []
maxvals = []
j = 0
for k in xrange(0, 512, 2):
IJ.makeRectangle(k, 0, 4, 512)
IJ.run("Measure")
table = RT.getResultsTable()
x = table.getValue("XM", 0)
y = table.getValue("YM", 0)
#maxi = IJ.getResult("Max")
table = []
xvals.append(k)
yvals.append(y)
#maxvals.append(maxi)
#if maxvals[j] == 0 and j > 0:
#yvals[j] = yvals[j-1]
j += 1
print "xvals:", xvals
print "yvals:", yvals
mr = MR()
IJ.run("Make Selection...", "freeline, "+str(xvals)+" ,"+ str(yvals))
#IJ.runMacro("makeSelection...", "freeline, "+str(xvals)+" ,"+ str(yvals));
#IJ.run("makeSelection(\"freeline\", xvals, yvals);")
#IJ.run("Straighten...", "line = 80")
'''
IJ.open(stackName)
imp = IJ.getImage()
stack = imp.getImageStack()
print imp.getNSlices()
for i in range(1, imp.getNSlices()+1):
image = ImagePlus(str(i), stack.getProcessor(i))
IJ.runMacroFile("/Users/juliansegert/repos/Bio119_root_tracking/straightenOneImage.ijm")
newImp = WM.getCurrentImage()
fs = FileSaver(newImp)
fs.saveAsTiff("/Users/juliansegert/Documents/Roots/Four_root_image_stacks/Pos01/"+str(i)+".tif")
newImp.close()
def openImage():
print rel_path
if rel_path.endswith(".klb"):
try:
klb = KLB.newInstance()
img = klb.readFull(os.path.join(base_path, rel_path))
IL.wrap(img, rel_path).show()
except:
print sys.exc_info()
else:
print "via IJ.open"
IJ.open(os.path.join(base_path, rel_path))
def process(root, f):
print(f)
full_path = os.path.join(root, f)
dest_path = os.path.join(root, "tmp", f)
if not os.path.isdir(os.path.join(root, "tmp")):
os.makedirs(os.path.join(root, "tmp"))
IJ.open(full_path)
imp = IJ.getImage()
IJ.run("Properties...", "frame=5")
IJ.run("Bio-Formats Exporter", "save=" + dest_path + " compression=Uncompressed")
imp.close()
def make_tmp_tifs(images, tmp_dir, copy=False):
file_pattern = '{i}.tif'
files = []
for i, image in enumerate(images):
files += [tmp_dir + '/%d.tif' % i]
if copy:
from ij import IJ
IJ.open(os.path.abspath(image))
IJ.saveAs("Tiff", files[-1])
IJ.run("Close All")
else:
force_symlink(os.path.abspath(image), files[-1])
return files, file_pattern
def openImage():
print rel_path
if rel_path.endswith(".klb"):
if (KLB == None):
print "Cannot open KLB due to missing module"
try:
klb = KLB.newInstance()
img = klb.readFull(os.path.join(base_path, rel_path))
IL.wrap(img, rel_path).show()
except:
print sys.exc_info()
else:
print "via IJ.open"
IJ.open(os.path.join(base_path, rel_path))
def main():
if (WindowManager.getImageCount() > 0):
IJ.run("Save")
processImage()
# if (WindowManager.getImageCount() > 0):
# for image in range(WindowManager.getImageCount()):
# processImage()
else:
selected_dir = str(myDir)
if not selected_dir.endswith(os.path.sep):
selected_dir = selected_dir + os.path.sep
dir_list = list()
file_type = "stitched_images"
if selected_dir.endswith(file_type + os.path.sep):
dir_list.append(selected_dir)
#print(dir_list)
else:
dir_list = listDir(selected_dir, file_type, recursive=True)
print(dir_list)
if len(dir_list) > 0:
for dir in dir_list:
genotype_dirs = list()
filenames = os.listdir(dir)
for filename in filenames:
if os.path.isdir(
os.path.join(os.path.abspath(dir), filename)):
genotype_dirs.append(
os.path.join(os.path.abspath(dir), filename))
for genotype_dir in genotype_dirs:
java.lang.System.gc()
java.lang.System.gc()
java.lang.System.gc()
image_list = listDir(genotype_dir,
file_type=".tiff",
recursive=False)
remove_marker_dir(genotype_dir)
for image in image_list:
IJ.open(image)
processImage()
else:
image_list = listDir(selected_dir,
file_type=".tiff",
recursive=False)
remove_marker_dir(selected_dir)
for image in image_list:
IJ.open(image)
processImage()
def openedImagesmain(channel1, channel2, ignoreString, primarySize,
primaryImageThresh, secondaryImageThresh):
def writeTablesToCSV(id, roiOut, speckleOut):
try:
#append your labels to them before they're written
identifier = id[:-4]
speckleOut_ = RTC.appendColToFront(identifier, speckleOut)
roiOut_ = RTC.appendColToFront(identifier, roiOut)
#save the tables to csvs -- they will append to a currently existing csv
#or create a new one
Saves.table2CSV("speckleOutput.csv", speckleOut_)
Saves.table2CSV("AnalysisOutput.csv", roiOut_)
print("finished writing tables to csvs.")
except:
print("failed to write to csvs.")
finally:
print("")
#Begin
try:
RTC = ResultsTableToCSV(channel1, channel2, ignoreString)
primary, secondary, images = RTC.getOpenImageNames()
Binarize(primaryImageThresh, secondaryImageThresh, primary, secondary)
speckleInputs = "primary=[{}] secondary=[{}] " \
"redirect=None min_primary_size={} min_secondary_size=0.00000 " \
"show=none exclude speckle statistic secondary_object"
IJ.run("Speckle Inspector",
speckleInputs.format(primary, secondary, primarySize))
Saves = SaveStuff(primary)
Saves.saveLogs()
#Saves.saveNewImages(images)
Saves.saveAllImages(images)
speckleTableName = "Speckle List " + primary
roiTableName = "Roi Analysis"
roiOut = RTC.roiAnalysisToWrite(roiTableName)
speckleOut = RTC.readResultsTablesOfNumbers(speckleTableName)
WindowManager.closeAllWindows()
fijiDir = IJ.getDir("imagej")
fijiScriptsDir = join(fijiDir, "scripts")
writeTablesToCSV(primary, roiOut, speckleOut)
IJ.open(join(fijiScriptsDir, "SmootherSpeckling.py"))
print("script completed successfully.")
except:
print("sorry, the script broke :/")
finally:
print("")
def executePairRegistration(prefix, tp, matrix):
saveMatrixForTransformJ(matrix)
infile = prefix + "_" + tp + ".tif"
outfile = "reg_%s_%s.tif" % (prefix, tp)
IJ.open(output_dir + infile)
IJ.run(
"TransformJ Affine", "matrix=" + output_dir +
"TransformJ_matrix.txt interpolation=linear background=0.0")
IJ.run("Save", "save=" + output_dir + outfile)
while IJ.macroRunning():
Thread.sleep(100)
WindowManager.getWindow(infile).close()
WindowManager.getWindow(outfile).close()
def leafDoubleClicked(self, fileOb):
name = fileOb.getName()
idot = name.rfind('.')
if -1 == idot:
print "File name lacks extension"
return
# Open depending on what it is
extension = name[idot+1:].lower() # in lowercase
languages = set(["py", "clj", "js", "java", "bs", "ijm", "r", "rb", "sc", "txt", "md"])
if extension in languages:
self.texteditor.open(fileOb)
else:
# Let ImageJ handle it
print "Asking ImageJ to open file", fileOb
IJ.open(fileOb.getAbsolutePath())
def split_channels_blue(imp):
#Create blue channel
original_blue = ChannelSplitter.getChannel(imp, 3)
original_blue_IP = ImagePlus(filename, original_blue)
fs = FileSaver(original_blue_IP)
folder = "/Users/gceleste/Desktop/test/channels"
filepath = folder + "/" + "{}_bluechannel.tif".format(filename)
fs.saveAsTiff(filepath)
#Open blue channel image.
blue = IJ.open(filepath)
#blue_IP = ImagePlus(filename, blue)
IJ.run(
"3D Objects Counter",
"threshold=100 slice=1 min.=50 max.=1447680 exclude_objects_on_edges objects summary"
)
#Save blue object map
blue_map = IJ.getImage()
fs = FileSaver(blue_map)
folder = "/Users/gceleste/Desktop/test/object maps"
filepath = folder + "/" + "{}_objectmap(blue).jpg".format(filename)
fs.saveAsJpeg(filepath)
#Close blue channel image.
blue_map.close()
blue = IJ.getImage()
blue.close()
def getCompositeImgs():
BF, PI, filepath = getImgs()
for i in range(len(BF)):
IJ.open(filepath + '/' + PI[i])
IJ.run('Auto Threshold', 'method=Yen ignore_black white')
IJ.open(filepath + '/' + BF[i])
IJ.run('8-bit')
IJ.run('Merge Channels...', 'c1=%s c4=%s create'%(PI[i], BF[i]))
IJ.run('Flatten')
if 'processed' not in os.listdir(filepath):
os.mkdir(filepath + '/processed/')
name = BF[i].split('bf')
IJ.saveAs('Tiff', filepath + '/processed/' + name[0] + '_COMPOSITE_' + name[1])
IJ.run('Close')
print '[+] Processed image: %s' %(name[0] + '_COMPOSITE_' + name[1])
return 0
def split_channels_red(imp):
#Create red channel
original_red = ChannelSplitter.getChannel(imp, 1)
original_red_IP = ImagePlus(filename, original_red)
fs = FileSaver(original_red_IP)
folder = "/Users/gceleste/Desktop/test/channels"
filepath = folder + "/" + "{}_redchannel.tif".format(filename)
fs.saveAsTiff(filepath)
#Open red channel image.
red = IJ.open(filepath)
#red_IP = ImagePlus(filename, red)
IJ.run(
"3D Objects Counter",
"threshold=130 slice=1 min.=50 max.=1447680 exclude_objects_on_edges objects summary"
)
#Save red object map.
red_map = IJ.getImage()
fs = FileSaver(red_map)
folder = "/Users/gceleste/Desktop/test/object maps"
filepath = folder + "/" + "{}_objectmap(red).jpg".format(filename)
fs.saveAsJpeg(filepath)
#Close red channel images.
red_map.close()
red = IJ.getImage()
red.close()
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 mousePressed(self, event):
if 2 == event.getClickCount():
# If the file is open, bring it to the front
ids = WindowManager.getIDList()
if ids: # can be null
is_open = False # to allow bringing to front more than one window
# in cases where it has been opened more than once
for ID in ids:
imp = WindowManager.getImage(ID)
fi = imp.getOriginalFileInfo()
filepath = os.path.join(fi.directory, fi.fileName)
if File(filepath).equals(File(
event.getSource().getText())):
imp.getWindow().toFront()
is_open = True
if is_open:
return
# otherwise open it
IJ.open(table_entries[getSelectedRowIndex()][-2])
def preparePairRegistration(tpA, tpB):
fileA = "raw_" + tpA + ".tif"
fileB = "raw_" + tpB + ".tif"
IJ.open(output_dir + fileA)
IJ.open(output_dir + fileB)
IJ.run(
"Rigid Registration",
"initialtransform=[] n=1 tolerance=1.000 level=5 stoplevel=2 materialcenterandbbox=[] "
+ "template=%s measure=Euclidean" % (fileA))
IJ.selectWindow("Matrix")
IJ.saveAs("Text",
"/Users/jug/MPI/ProjectNorden/output/matrix_%s.txt" % (tpB))
window = WindowManager.getWindow("Matrix")
window.close()
window = WindowManager.getWindow(output_dir + fileA)
window.close()
window = WindowManager.getWindow(output_dir + fileB)
window.close()
def main():
IJ.open(dir_path+file_name) #IJ.open("/Users/richardhart/Dropbox/AMRE/2013-08-03-96dpiPrintTesting-DifferentZheights-Sample.png")
IJ.selectWindow(file_name) #IJ.selectWindow("2013-08-03-96dpiPrintTesting-DifferentZheights-Sample.png")
IJ.run("Options...", "iterations=1 count=1 edm=Overwrite") #setting binary mask options
IJ.run("Make Binary") #converting a picture o a binary image.
IJ.run("Watershed") #Watershed breaks appart blobs in a binary image into smaller pieces
IJ.run("Set Measurements...", "area mean standard modal min centroid center perimeter bounding fit shape feret's integrated median skewness kurtosis area_fraction stack redirect=None decimal=3") #Specifying what measurements to take
IJ.run("Analyze Particles...", "size=0-Infinity circularity=0.00-1.00 show=Ellipses display clear include") #taking measurements
IJ.selectWindow("Results") #Making sure the results are selected so the window's data will be saved
IJ.saveAs("Results", dir_path + "Results.txt"); #IJ.saveAs("/Users/richardhart/Dropbox/AMRE/Results.txt")
def runThunderSTORM(filename):
# ---------------------------------------------
# Runs the ThunderSTORM plugin on the filename
# provided with the settings from the top of
# this file.
# ---------------------------------------------
global exportList
if virtualStack:
IJ.openVirtual(filename)
else:
IJ.open(filename)
IJ.run(stormAnalysisCmd, stormAnalysisParams)
dataStoreFilename = os.path.dirname(filename) + os.sep + os.path.splitext(
os.path.basename(filename))[0] + ".csv"
exportList.append(dataStoreFilename)
exportParams = stormExportParams.replace("%filename", dataStoreFilename)
print exportParams
IJ.run(stormExportCmd, exportParams)
WindowManager.closeAllWindows()
return exportList
def run():
Ch0dir = '/Volumes/TRANSCEND/Claudia/LGR5SegmentationTraining/DeformMask/'
Ch1dir = '/Volumes/TRANSCEND/Claudia/LGR5SegmentationTraining/DeformMask/'
DoubleChdir = '/Volumes/TRANSCEND/Claudia/LGR5SegmentationTraining/DeformDoubleChannelMask/'
Raw_path = os.path.join(Ch0dir, '*tif')
X = glob.glob(Raw_path)
axes = 'YX'
for fname in X:
print(fname)
IJ.open(fname);
imp = IJ.getImage()
Name = os.path.basename(os.path.splitext(fname)[0])
Ch1Name = Ch1dir + Name + '.tif'
IJ.open(Ch1Name);
impCh1 = IJ.getImage()
IJ.run("Merge Channels...", "c1="+imp.getTitle()+ " c2="+impCh1.getTitle()+ " create");
IJ.saveAs('.tif', DoubleChdir + Name);
imp.close();
impCh1.close();
composite = wm.getFrontWindow()
if composite is not None: done = composite.close()
def walkAnalysis(fileList, directory, outpath):
from ij import IJ, ImagePlus
from ij.macro import Interpreter as IJ1
from datetime import datetime
IJ1.batchMode = True
current = 0
pnum = 0
nooresults = ''
for files in os.walk(directory, topdown=True):
for file in fileList:
drc = ''.join(directory + file)
cfile = file
imp = IJ.open(drc)
IJ.run("16-bit")
IJ.setThreshold(65409, 65532)
IJ.run("Subtract Background...", "rolling=" + Roll + " dark")
##*LIGHT CONDITIONS MUST BE CONSTANT!!* possibly omit light call in order to bugfix
IJ.run(imp, "Gaussian Blur...", "sigma=" + SigM)
IJ.run("Make Binary")
##increases contrast and allows watershed
IJ.run("Watershed")
dt = str(datetime.now())[:11]
savestring = out + "mask" + dt + cfile
IJ.saveAs(imp, "Jpeg", out + "mask" + cfile)
##jpeg saveas dramatically decreases process time
IJ.run("Set Measurements...",
"integrated area_fraction redirect=None decimal=" + Dec)
IJ.run(
imp, "Analyze Particles...",
"size=10-Infinity circularity=0.05-1.00 show=[Outlines] display clear include summarize in_situ"
)
##most likely the value will change according to desired cell line
current += 1 ##current is a counter for images processed, as well as possible naming convention for renaming images.
numstr = str(current)
#current is a int class and cannot be concat with a string.
dt = str(
datetime.now()
)[:
10] ## this slice truncates the colon that is unacceptable in file naming convention. find an alternative solution if possible.
results = (out + "Results" + dt + ".csv")
if os.path.exists(results):
while os.path.exists(results):
pnum += 1
results = (out + "Results" + dt + '-' + str(pnum) + ".csv")
IJ.saveAs("Results", results)
else:
IJ.saveAs("Results", results)
csvStr = (out + "Results" + dt + ".csv")
IJ1.batchMode = False
return numstr, csvStr
def main():
#summary = False
if (WindowManager.getImageCount() > 0):
IJ.run("Save")
image_dir = processImage()
# if (WindowManager.getImageCount() > 0):
# for image in range(WindowManager.getImageCount()):
# processImage()
else:
#summary = True
#selected_dir = IJ.getDirectory("Select Directory")
if not selected_dir.endswith(os.path.sep):
selected_dir = selected_dir + os.path.sep
dir_list = list()
file_type = [".tif", ".tiff"]
image_list = listDir(selected_dir, file_type = file_type, recursive = False)
for image in image_list:
IJ.open(image)
image_dir = processImage()
summarize(os.path.join(image_dir, "markers" ))
summarize(os.path.join(image_dir, "processed_markers"))
def main(threshold, min_size, max_size, remove_last_slice=False):
IJ.run(
"3D OC Options",
"volume surface nb_of_obj._voxels nb_of_surf._voxels integrated_density mean_gray_value std_dev_gray_value median_gray_value minimum_gray_value maximum_gray_value centroid mean_distance_to_surface std_dev_distance_to_surface median_distance_to_surface centre_of_mass bounding_box dots_size=5 font_size=10 show_numbers white_numbers store_results_within_a_table_named_after_the_image_(macro_friendly) redirect_to=none"
)
IJ.run("Set Measurements...", "area mean centroid redirect=None decimal=6")
Commands.closeAll()
selected_dir = IJ.getDirectory("Select Directory")
print(selected_dir)
selected_dir = selected_dir + "/"
dir_list = list()
file_type = "processed_images"
if selected_dir.endswith(file_type + "/"):
dir_list.append(selected_dir)
else:
dir_list = listDir(selected_dir, file_type, recursive=True)
print(dir_list)
for dir in dir_list:
print(dir)
cd_dir = dir + "/cd/"
print(cd_dir)
makeDir(cd_dir)
image_list = listDir(dir, file_type=".tiff", recursive=False)
for i in image_list:
print(i)
now = time.time()
IJ.open(i)
imp = IJ.getImage()
IJ.run(imp, "Measure", "")
saveResults(imp, cd_dir)
#processImage(imp, threshold, min_size, max_size, remove_last_slice)
later = time.time()
difference = int(later - now)
#time.sleep(difference/10)
print("The time difference is " + str(difference))
"""
Start a Trainable Weka Segmmentation
Modifications
Date Who Ver What
---------- --- ------ -------------------------------------------------
2019-05-17 JRM 0.1.00 Start a segmentation
"""
from org.python.core import codecs
codecs.setDefaultEncoding('utf-8')
import os
from ij import IJ, WindowManager
IJ.run("Close All")
"""
# MacOS
str_path = "/Users/jrminter/Documents/git/tips/ImageJ/jpg/aggregates_w_TWS.jpg"
"""
# Windows
str_path = "C:/Users/jrminter/Documents/git/tips/ImageJ/jpg/aggregates_w_TWS.jpg"
IJ.open(str_path)
IJ.run("Trainable Weka Segmentation")
"""
Start a Trainable Weka Segmmentation
Modifications
Date Who Ver What
---------- --- ------ -------------------------------------------------
2019-05-17 JRM 0.1.00 Start a segmentation
"""
from org.python.core import codecs
codecs.setDefaultEncoding('utf-8')
import os
from ij import IJ, WindowManager
IJ.run("Close All")
"""
# MacOS
str_path = "/Users/jrminter/Documents/git/tips/ImageJ/jpg/aggregates_w_TWS.jpg"
"""
# Windows
str_path = "C:/Users/jrminter/Documents/git/tips/ImageJ/jpg/aggregates_w_TWS.jpg"
IJ.open(str_path)
IJ.run("Trainable Weka Segmentation")
def duty_cycle(i, horizontal):
IJ.open(i)
IJ.run("Enhance Contrast...", "saturated=0.3 equalize")
IJ.run("Smooth", "")
'IJ.run("Anisotropic Diffusion 2D", "number=20 smoothings=1 keep=20 a1=0.50 a2=0.90 dt=20 edge=5") # use instead of smooth for better results, slower
imp = IJ.getImage()
ImageConverter(imp).convertToGray8()
file_base = str(imp.title) # convert name into string
file_base = file_base.split('.', 1)[0] # remove all characters including and after .
sample_id = file_base.split('_', 1)[0]
height = imp.height
width = imp.width
IJ.run(imp, "Set Scale...", "distance=5000 known=1 pixel=1 unit=cm")
IJ.setTool("line")
for scan in range(1, num_scans):
row_count = 0
data=[]
data2=[]
last = 0
filename = file_base + "_" + str(scan)
if not horizontal:
IJ.makeLine(0, scan*height/num_scans, width, scan*height/num_scans) # horizontal line to measure vertical grating profile
else:
IJ.makeLine(width*scan/num_scans, 9*height/10, width*scan/num_scans, 0) # vertical line to measure horizontal grating profile
IJ.run(imp, "Plot Profile", "")
imp_plot = IJ.getImage()
IJ.run(imp, "Find Peaks", setting) # nw07 params: min._peak_amplitude=60 min._peak_distance=0 min._value=100 max._value=0 exclude list
IJ.saveAs("Results", csvpath + filename + "_raw.csv")
imp_plot.close()
imp_plot = IJ.getImage()
IJ.saveAs("Png", csvpath + "Peaks in Plot of " + filename + ".png")
imp_plot.close()
IJ.selectWindow(filename + "_raw.csv")
IJ.run("Close")
with open(csvpath + filename + "_raw.csv", "rb") as fin, open(csvpath + filename + "_peaks.csv", "wb") as fout:
writer = csv.writer(fout)
for row in csv.reader(fin):
if not row[2] == '':
writer.writerow(row)
with open(csvpath + filename + "_peaks.csv", "rb") as File:
reader = csv.reader(File)
for row in reader:
row_count += 1
if row_count == 1:
continue
data.append(tuple(row)) # Append all non-header rows into a list of data as a tuple of cells
for row in sort_table(data, 2):
data2.append(tuple(row))
if len(data2) < 3:
IJ.log(filename + " no peaks detected, skipping...")
continue
else:
peaks = len(data2)
last = data2[0]
last = last[2]
row_count = 0
diff = 0
colG = []
blank = ""
duty = zip(*data2)
for row in data2:
row_count += 1
if row_count == 1:
continue
else:
print row[2]
print last
diff = float(row[2]) - float(last)
colG.append(diff)
last = row[2]
a, b = colG[::2], colG[1::2]
avga = sum(a)/len(a)
avgb = sum(b)/len(b)
a_len_dev = len(a) - 1
b_len_dev = len(b) - 1
if b_len_dev > 1:
a_stdev = sqrt(sum((x - avga)**2 for x in a) / a_len_dev)
b_stdev = sqrt(sum((x - avgb)**2 for x in b) / b_len_dev)
else:
a_stdev = 1
b_stdev = 1
duty_cyc = avga/(avga+avgb)
perc = duty_cyc*100
invperc = 100 - perc
inv_duty = 1 - duty_cyc
duty_max = max(duty_cyc, inv_duty)
duty_min = min(duty_cyc, inv_duty)
percs = round(perc)
percs = int(percs)
percs = str(percs)
invpercs = round(invperc)
invpercs = int(invpercs)
invpercs = str(invpercs)
colG.insert(0, blank)
a.insert(0, blank)
b.insert(0, blank)
b.insert(1, blank)
i = 0
while i < len(a):
i += 2
a.insert(i, blank)
i = 1
while i < len(b):
i += 2
b.insert(i, blank)
duty.append(colG)
duty.append(a)
duty.append(b)
duty = zip(*duty)
result = [sample_id, file_base, scan, duty_cyc, inv_duty, a_stdev, b_stdev, peaks, duty_max, duty_min]
header = ["X0", "Y0", "X1", "Y1", "X2", "Y2", "diff", "a", "b"]
results.append(result)
with open(csvpath + filename + "_duty.csv", 'wb') as myfile:
wr = csv.writer(myfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
wr.writerow(header)
wr.writerows(duty)
print(filename + " duty cycle is " + percs + "/" + invpercs + "%.")
IJ.log(filename + " duty cycle is " + percs + "/" + invpercs + "% (" + str(peaks) + " peaks found)")
with open(csvpath + "results.csv", 'wb') as myfile:
wr = csv.writer(myfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
wr.writerows(results) # Write the result data from all images into a single csv file
# This is not indexed by intellij import net.imagej.legacy.IJ1Helper import org.scijava.log.LogService from net.imglib2.img.display.imagej import ImageJFunctions # log = IJ1Helper.getLegacyContext().getService(LogService.class) # Finds methods but no signatures from ij import IJ IJ.open() repurl = "http://imagej.net/images" IJ.open(repurl + "/FluorescentCells.jpg") imp = IJ.getImage() print imp.getClass() img = ImageJFunctions.wrap(imp) print img.getClass()
filelist = os.listdir(filepath)
metadata = []
imgsdata = []
for i in filelist:
if i.split('_')[-1] == 'Properties.xml':
metadata.append(i)
if i.split('.')[-1] == 'tif':
imgsdata.append(i.split('_')[0])
imgsdata = getUniques(imgsdata)
metadata = getUniques(metadata)
data = []
for i in imgsdata:
scale = getScale(filepath + '/' + i + '_Properties.xml')
img00 = IJ.open(filepath + '/' + i + '_ch00.tif')
IJ.run("Set Scale...", "distance=0 known=0 pixel=1 unit=pixel");
img01 = IJ.open(filepath + '/' + i + '_ch01.tif')
IJ.run("Set Scale...", "distance=0 known=0 pixel=1 unit=pixel");
data01 = getRadius(img01)
cyto_r = scale * math.sqrt(float(data01[0].split('\t')[2])/math.pi)
img02 = IJ.open(filepath + '/' + i + '_ch02.tif')
IJ.run("Set Scale...", "distance=0 known=0 pixel=1 unit=pixel");
data02 = getRadius(img02)
nuc_r = scale * math.sqrt(float(data02[0].split('\t')[2])/math.pi)
NCR = math.sqrt(nuc_r/cyto_r)**3
data.append([data01, data02, scale, nuc_r, cyto_r, NCR])
IJ.run('Close')
# A quick script to calibrate an Oxford AZtec image
# Modifications
# Date Who Ver What
# ---------- --- ------ -------------------------------------------------
# 2015-10-13 JRM 0.1.00 Initial prototype
from org.python.core import codecs
codecs.setDefaultEncoding('utf-8')
import os
from ij import IJ, Prefs
from ij.io import FileSaver
import jmFijiGen as jmg
from ij.measure import Calibration
IJ.open(file.getAbsolutePath())
# load the dataset
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")
fPath = file.getParent()
fPath.replace("\\", "/")
tifName = fName + ".tif"
tifPath = os.path.join(fPath, tifName)
print(fName)
imp.changes = False
sleepTime = 3 # pause to see ong in sec
scaleX = 0.03143555 # um/px X
scaleY = 0.03144531 # um/px y
barW = 1.0 # bar width, microns
barH = 6 # bar height, pts
barF = 24 # bar font, pts
barC = "Black" # bar color
barL = "Lower Right" # bar location
homDir = os.environ['HOME']
relDir = "/Dropbox/datasets/AgX-dat/eia2855ImgSeries"
imgNam = "IMAGE027.tif"
imgPath = homDir + relDir + "/" + imgNam
IJ.open(imgPath);
IJ.run("Properties...", "channels=1 slices=1 frames=1 unit=um pixel_width=0.03143555 pixel_height=0.03144531 voxel_depth=1.0000000");
orig = IJ.getImage();
ti = orig.getShortTitle();
pngPath = homDir + relDir + "/" + ti + ".png"
strBar = "width=%g height=%g font=%g color=%s location=[%s] bold" % (barW, barH, barF, barC, barL)
# a hack to get the scale bars to work reliably
IJ.run(orig, "RGB Color", "")
# dummy to get things set
foo = orig.duplicate()
IJ.run(foo, "Add Scale Bar", strBar)
# explicitly save preferences
Prefs.savePreferences()
from ij import IJ
# from ij.gui import WaitForUserDialog
import glob, os
__author__ = 'Nichole Wespe'
inputDir = IJ.getDirectory("Choose Source Directory ")
pattern = os.path.join(inputDir, "*.tif")
fileList = glob.glob(pattern)
print "Found " + str(len(fileList)) + " tiff files to process."
for f in fileList:
IJ.open(f)
IJ.run("plate cropper")
imp = IJ.getImage()
imp.close()
# Copies tiles for stitching into temp directory and stitches, if necessary
if len(tiles)==1:
params = "open=["+ imgDir +"tile_"+str(tiles[0])+".ome.tif] color_mode=Default view=Hyperstack stack_order=XYCZT"
IJ.run("Bio-Formats Importer", params);
tileImage = WindowManager.getImage("tile_"+str(tiles[0])+".ome.tif")
tileImage.setSlice(int(tileImage.getNSlices()/2))
else:
ind = 1
for t in tiles:
shutil.copyfile(imgDir+"tile_"+str(t)+".ome.tif",imgDir+"temp/tile_"+str(ind)+".ome.tif")
ind = ind + 1
IJ.showStatus("Beginning stitch...")
params = "type=[Grid: row-by-row] order=[Right & Down ] grid_size_x=" + str(xs[2]) + " grid_size_y=" + str(ys[2]) + " tile_overlap=10 first_file_index_i=1 directory=[" + imgDir + "temp] file_names=tile_{i}.ome.tif output_textfile_name=TileConfiguration.txt fusion_method=[Linear Blending] regression_threshold=0.30 max/avg_displacement_threshold=2.50 absolute_displacement_threshold=3.50 compute_overlap ignore_z_stage subpixel_accuracy computation_parameters=[Save memory (but be slower)] image_output=[Fuse and display]"
IJ.run("Grid/Collection stitching", params)
fusedImage = WindowManager.getImage("Fused")
IJ.saveAsTiff(fusedImage,imgDir+"temp/Fused.tif")
fusedImage.close()
IJ.open(imgDir+"temp/Fused.tif")
tileImage = WindowManager.getImage("Fused.tif")
tileImage.setSlice(int(tileImage.getNSlices()/2))
gd = NonBlockingGenericDialog("Explore full resolution...")
gd.addMessage("Select ROI for visualization at full resolution")
gd.showDialog()
doContinue = gd.wasOKed()
##### Removes temporary tile files ######
# filelist = [f for f in os.listdir(imgDir+"temp") if f.endswith(".tif")]
# for f in filelist:
# os.remove(f)
=======
"""
title = imp.getShortTitle()
imp.setTitle(title)
IJ.run("Stack to Images")
impR = WindowManager.getImage("1")
impG = WindowManager.getImage("2")
impB = WindowManager.getImage("3")
IJ.run("Merge Channels...", "c1=1 c2=2 c3=3 ignore")
impRGB = WindowManager.getImage("RGB")
# impRGB.show()
impC = WindowManager.getImage("4")
impC.close()
impR.close()
impG.close()
impB.close()
return (impRGB)
IJ.run("Close All")
imgDir = "/Users/jrminter/Desktop/"
imgNam = "jrm-screen-shot"
imgExt = ".png"
imgPath = imgDir + imgNam + imgExt
imgFullName = imgNam + imgExt
IJ.open(imgPath)
imp = IJ.getImage()
impRGB = convert_stack_4_to_rgb(imp)
impRGB.show()
imp.changes = False
imp.close()
# The size of the Gaussian Blur. Should be on the order of 30% of image width
size = 50
# A boolean to determine if we want to close - without warning - intermediate
# images
closeIntermediate = True
# The base title to give the image
strTitle = "small_rgb_bubbles"
IJ.run("Close All")
strImgPath = "/Users/jrminter/Documents/git/tips/ImageJ/png/small_rgb_bubbles.png"
IJ.open(strImgPath)
impOri = IJ.getImage()
impOri.setTitle(strTitle)
impOri.show()
[impR, impG, impB] = jmg.separate_colors(impOri)
impR.show()
impG.show()
impB.show()
impRb = jmg.gaussian_blur_8_bks(impR, size)
impRb.setTitle("rb")
impRb.show()
impGb = jmg.gaussian_blur_8_bks(impG, size)
impGb.setTitle("gb")
impGb.show()
ic = ImageCalculator()
in_folder = "E:/DataBinge_ImageJ/ImageJ DB/5 files tiff - Copy/"
out_folder = "E:/DataBinge_ImageJ/ImageJ DB/Claire Output/"
the_files = os.listdir(in_folder)
print(the_files)
the_file = the_files[0]
print(the_file)
# open the_file inside in_folder.
dat = IJ.open(
in_folder + the_file
) # Note that strings (string = "stuff") are joined by concatenation
# select the active window
imp = IJ.getImage()
# use the same renaming trick as for the macro recorder
imp.setTitle("current")
# IJ.run executes commands from the macro recorder
IJ.run(
"Scale...",
"x=.5 y=.5 z=1.0 width=128 height=128 depth=930 interpolation=Bilinear average process create"
)
imp.close()
IJ.selectWindow("current-1")
linCol - the color for line/stroke in the overlay (default green)
bDebug - a flag (default False) that, if true, keeps the work image opem
This adds the detected features to the overlay and returns the result table for
processing for output.
"""
title = imp.getTitle()
shortTitle = imp.getShortTitle()
imp.setTitle(shortTitle)
imp.show()
IJ.run(imp,"Duplicate...", "title=work")
wrk = IJ.getImage()
IJ.run(wrk, "Median...", "radius=2")
IJ.run(wrk, "Enhance Contrast", "saturated=0.05")
wrk.setTitle(shortTitle + "-km")
IJ.run(wrk, "Threshold", strThrMeth)
IJ.run(wrk, "Fill Holes", "")
IJ.run(wrk, "Properties...", "channels=1 slices=1 frames=1 unit=px pixel_width=1 pixel_height=1 voxel_depth=1")
IJ.run(wrk, "Set Measurements...", "area centroid center perimeter shape display redirect=None decimal=3")
IJ. run(wrk, "Analyze Particles...", "display exclude clear add");
return wrk
IJ.run("Close All");
closeRW = False
IJ.open("C:\\Data\\images\\key-test\\KMAG\\qm-05227-KMAG-96-19-08.tif");
ori = IJ.getImage()
kmi = anaKmag(ori)
kmi.show()
