def cell_processor(imp_particles, imp_measure, folder, impname, channel_name):
# Get ROI manager instance, save to zip
rm = RoiManager.getInstance()
if not rm:
rm = RoiManager()
rm.reset()
imp_particles.show()
# Wait for user to add ROIs to manager
Prefs.multiPointMode = True
pause = WaitForUserDialog(
"Select ROIs of interest and add to manager \n \nPress OK to continue")
pause.show()
# get the ROI manager and save
rm = RoiManager.getInstance()
rm.runCommand("save selected",
os.path.join(folder, impname + "_cells_ROIs.zip"))
# select image to measure and show
imp_measure.show()
pixel_collector(rm, imp_measure, channel_name, impname, folder)
return rm
def assisted_SNTtrace(context, imp):
'''
Calls SNT in a given img for user assisted tracing.
Uses WaitForUserDialog to confirm the end of tracing operations.
Returns list of SNT Paths.
'''
snt = prepare_SNT(context, imp)
pafm = snt.getPathAndFillManager()
dialog = WaitForUserDialog("Go to next step...", "Press ok when tracing is done.")
dialog.show()
if dialog.escPressed():
raise RuntimeError("Canceled by the user.")
SNTpaths = pafm.getPaths()
return SNTpaths
def auto_threshold(imp, ch_name, is_auto_thresh, method="IsoData"):
if is_auto_thresh:
IJ.setAutoThreshold(imp, "{} dark".format(method))
thres_min = imp.getProcessor().getMinThreshold()
else:
imp.show()
IJ.run("Threshold...")
# this is the method to wait the user set up the threshold
wu = WaitForUserDialog("Set manual threshold for {}".format(ch_name), "Use slider to adjust threshold and press OK")
wu.show()
thres_min = imp.getProcessor().getMinThreshold()
thres_max = imp.getProcessor().getMaxThreshold()
IJ.log(" -- {}: Min threshold {}".format(ch_name, thres_min))
IJ.setThreshold(imp, thres_min, thres_max)
imp.hide()
WindowManager.getWindow("Threshold").close()
return thres_min
def filter_original():
# Filter out noise, keep edges
IJ.run("Median...", "radius=4")
# Enhance contrast
IJ.run("Enhance Local Contrast (CLAHE)", "blocksize=19 histogram=256 maximum=3 mask=*None*");
# Find edges
IJ.run("Find Edges");
# Threshold...conservatively!
IJ.run('Threshold...', 'Default Dark')
dial = WaitForUserDialog('Please threshold conservatively.')
dial.show()
# Now run the hough circles
IJ.run("Hough Circles", "minimum=8 maximum=9 increment=1 number=0 threshold=0")
image.changes = False
image.close()
# Select the hough space image
dial = WaitForUserDialog('Select the hough space image.')
dial.show()
# Filter out all but the small points
#IJ.run("Bandpass Filter...", "filter_large=7 filter_small=4 suppress=None tolerance=5 autoscale saturate")
# Threshold the centers of the points
IJ.run('Threshold...', 'Default Dark')
dial = WaitForUserDialog('Please threshold circle centers. Should be near max.')
dial.show()
# Select all the particles, deal with ROI's
IJ.run("Analyze Particles...", "size=0-Infinity include add");
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 main():
# define here which membrane indices will be used in the analysis, with last index the "control" index
membrane_indices = [-1, 0, 1, 3]
# for now, work with frontmost open image...
imp = IJ.getImage()
im_title = imp.getTitle()
settings = MembraneEvolutionAnalysisSettings(
membrane_indices=membrane_indices)
settings.loadPersistedSettings()
timestamp = datetime.strftime(datetime.now(), '%Y-%m-%d %H-%M-%S')
DirectoryChooser.setDefaultDirectory((settings.output_path))
dc = DirectoryChooser('Select the root folder for saving output')
output_root = dc.getDirectory()
if output_root is None:
raise IOError('no output path chosen')
settings.output_path = output_root
# get calibration
cal = imp.getCalibration()
if cal.getTimeUnit() == "sec":
cal.setTimeUnit('s')
# pop up a dialog prompting for selection of zero time point, frame interval, and time step for analysis
time_steps_not_ok = True
while time_steps_not_ok:
dialog = NonBlockingGenericDialog("Determine time parameters...")
dialog.addNumericField("0 timepoint frame (1-index): ",
settings.zero_timepoint_frame, 0)
dialog.addNumericField("Acquisition time step (s): ",
cal.frameInterval,
2) # assume stored in seconds
dialog.addNumericField(
"Time step for analysis (s): ",
cal.frameInterval * settings.analysis_frame_step, 2)
dialog.showDialog()
if dialog.wasCanceled():
return
zero_f = dialog.getNextNumber()
acq_t_step = dialog.getNextNumber()
analysis_t_step = dialog.getNextNumber()
if acq_t_step != 0 and analysis_t_step != 0:
analysis_frame_step = analysis_t_step / acq_t_step
if round(analysis_frame_step) == analysis_frame_step:
time_steps_not_ok = False
settings.zero_timepoint_frame = zero_f
settings.analysis_frame_step = analysis_frame_step
if time_steps_not_ok:
warning_dlg = GenericDialog("Error!")
warning_dlg.addMessage(
"Analysis time step must be an integer multiple of acquisition time steps, and neither should be zero!!"
)
warning_dlg.setOKLabel("Try again...")
warning_dlg.showDialog()
if warning_dlg.wasCanceled():
return
start_frame = int(((zero_f - 1) % analysis_frame_step) + 1)
end_frame = int(imp.getNFrames() -
(imp.getNFrames() - zero_f) % analysis_frame_step)
frames = [
f + 1
for f in range(start_frame - 1, end_frame, int(analysis_frame_step))
]
print("frames = " + str(frames))
imp.killRoi()
analysis_imp = SubstackMaker().makeSubstack(
imp,
str(start_frame) + "-" + str(end_frame) + "-" +
str(int(analysis_frame_step)))
imp.changes = False
imp.close()
analysis_imp.show()
drawn_membranes = [
TimepointsMembranes(input_image_title=im_title,
time_point_s=(t - 1) * acq_t_step) for t in frames
]
membranes_listener = UpdateRoiImageListener(drawn_membranes)
analysis_imp.addImageListener(membranes_listener)
# now attach roi listener to store all 0th membranes after showing a waitforuserdialog to prompt continuation
IJ.setTool("freeline")
for membrane_idx in membrane_indices:
# if membrane_idx>50:
# IJ.setTool("line");
analysis_imp.killRoi()
membranes_listener.resetLastFrame()
membranes_listener.setCurrentMembraneIndex(membrane_idx)
analysis_imp.setZ(1)
continue_dlg = WaitForUserDialog(
"Continue?", "Click OK once all the " + str(membrane_idx) +
"-index membranes have been drawn")
continue_dlg.show()
membranes_listener.imageUpdated(analysis_imp)
drawn_membranes = membranes_listener.getDrawnMembraneTimepointsList()
json_path = os.path.join(output_root,
"Membranes " + timestamp + ".json")
f = open(json_path, 'w+')
try:
json.dump(drawn_membranes, f, default=encode_membrane)
finally:
f.close()
# save csv containing mebrane measurements for current membrane index
csv_path = os.path.join(
output_root, ("Membrane measurements " + timestamp + ".csv"))
if membrane_idx == membrane_indices[0]:
try:
f = open(csv_path, 'wb')
writer = csv.writer(f)
writer.writerow([
"Membrane index", ("Time point, " + cal.getTimeUnit()),
("Membrane length, " + cal.getUnit()),
("Euclidean length, " + cal.getUnit()),
"Membrane sinuoisty"
])
finally:
f.close()
try:
f = open(csv_path, 'ab')
writer = csv.writer(f)
for mems in drawn_membranes:
mem = mems.getMembrane(membrane_idx)
if mem is not None:
writer.writerow([
membrane_idx, mems.time_point_s,
mem.getPathLength() * cal.pixelWidth,
mem.getEuclidean() * cal.pixelWidth,
mem.getSinuosity()
])
finally:
f.close()
settings.persistSettings()
settings.save_settings()
print("Finished getting all membranes with indices " +
str(membrane_indices))
analysis_imp.close()
def runTrackMate(imp):
import fiji.plugin.trackmate.Settings as Settings
import fiji.plugin.trackmate.Model as Model
import fiji.plugin.trackmate.SelectionModel as SelectionModel
import fiji.plugin.trackmate.TrackMate as TrackMate
import fiji.plugin.trackmate.Logger as Logger
import fiji.plugin.trackmate.detection.DetectorKeys as DetectorKeys
import fiji.plugin.trackmate.detection.DogDetectorFactory as DogDetectorFactory
import fiji.plugin.trackmate.tracking.sparselap.SparseLAPTrackerFactory as SparseLAPTrackerFactory
import fiji.plugin.trackmate.tracking.LAPUtils as LAPUtils
import fiji.plugin.trackmate.visualization.hyperstack.HyperStackDisplayer as HyperStackDisplayer
import fiji.plugin.trackmate.features.FeatureFilter as FeatureFilter
import fiji.plugin.trackmate.features.FeatureAnalyzer as FeatureAnalyzer
import fiji.plugin.trackmate.features.spot.SpotContrastAndSNRAnalyzerFactory as SpotContrastAndSNRAnalyzerFactory
import fiji.plugin.trackmate.action.ExportStatsToIJAction as ExportStatsToIJAction
import fiji.plugin.trackmate.io.TmXmlReader as TmXmlReader
import fiji.plugin.trackmate.action.ExportTracksToXML as ExportTracksToXML
import fiji.plugin.trackmate.io.TmXmlWriter as TmXmlWriter
import fiji.plugin.trackmate.features.ModelFeatureUpdater as ModelFeatureUpdater
import fiji.plugin.trackmate.features.SpotFeatureCalculator as SpotFeatureCalculator
import fiji.plugin.trackmate.features.spot.SpotContrastAndSNRAnalyzer as SpotContrastAndSNRAnalyzer
import fiji.plugin.trackmate.features.spot.SpotIntensityAnalyzerFactory as SpotIntensityAnalyzerFactory
import fiji.plugin.trackmate.features.track.TrackSpeedStatisticsAnalyzer as TrackSpeedStatisticsAnalyzer
import fiji.plugin.trackmate.util.TMUtils as TMUtils
import fiji.plugin.trackmate.visualization.trackscheme.TrackScheme as TrackScheme
import fiji.plugin.trackmate.visualization.PerTrackFeatureColorGenerator as PerTrackFeatureColorGenerator
#-------------------------
# Instantiate model object
#-------------------------
nFrames = imp.getNFrames()
model = Model()
# Set logger
#model.setLogger(Logger.IJ_LOGGER)
#------------------------
# Prepare settings object
#------------------------
settings = Settings()
settings.setFrom(imp)
# Configure detector
settings.detectorFactory = DogDetectorFactory()
settings.detectorSettings = {
DetectorKeys.KEY_DO_SUBPIXEL_LOCALIZATION: True,
DetectorKeys.KEY_RADIUS: 12.30,
DetectorKeys.KEY_TARGET_CHANNEL: 1,
DetectorKeys.KEY_THRESHOLD: 100.,
DetectorKeys.KEY_DO_MEDIAN_FILTERING: False,
}
# Configure tracker
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap()
settings.trackerSettings['LINKING_MAX_DISTANCE'] = 10.0
settings.trackerSettings['GAP_CLOSING_MAX_DISTANCE'] = 10.0
settings.trackerSettings['MAX_FRAME_GAP'] = 3
# Add the analyzers for some spot features.
# You need to configure TrackMate with analyzers that will generate
# the data you need.
# Here we just add two analyzers for spot, one that computes generic
# pixel intensity statistics (mean, max, etc...) and one that computes
# an estimate of each spot's SNR.
# The trick here is that the second one requires the first one to be in
# place. Be aware of this kind of gotchas, and read the docs.
settings.addSpotAnalyzerFactory(SpotIntensityAnalyzerFactory())
settings.addSpotAnalyzerFactory(SpotContrastAndSNRAnalyzerFactory())
# Add an analyzer for some track features, such as the track mean speed.
settings.addTrackAnalyzer(TrackSpeedStatisticsAnalyzer())
settings.initialSpotFilterValue = 1
print(str(settings))
#----------------------
# Instantiate trackmate
#----------------------
trackmate = TrackMate(model, settings)
#------------
# Execute all
#------------
ok = trackmate.checkInput()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
ok = trackmate.process()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
#----------------
# Display results
#----------------
selectionModel = SelectionModel(model)
displayer = HyperStackDisplayer(model, selectionModel, imp)
displayer.render()
displayer.refresh()
#---------------------
# Select correct spots
#---------------------
# Prepare display.
sm = SelectionModel(model)
color = PerTrackFeatureColorGenerator(model, 'TRACK_INDEX')
# launch TrackScheme to select spots and tracks
trackscheme = TrackScheme(model, sm)
trackscheme.setDisplaySettings('TrackColoring', color)
trackscheme.render()
# Update image with TrackScheme commands
view = HyperStackDisplayer(model, sm, imp)
view.setDisplaySettings('TrackColoring', color)
view.render()
# Wait for the user to select correct spots and tracks before collecting data
dialog = WaitForUserDialog(
"Spots",
"Delete incorrect spots and edit tracks if necessary. (Press ESC to cancel analysis)"
)
dialog.show()
if dialog.escPressed():
IJ.run("Remove Overlay", "")
imp.close()
return ([], nFrames)
# The feature model, that stores edge and track features.
#model.getLogger().log('Found ' + str(model.getTrackModel().nTracks(True)) + ' tracks.')
fm = model.getFeatureModel()
crds_perSpot = []
for id in model.getTrackModel().trackIDs(True):
# Fetch the track feature from the feature model.(remove """ to enable)
"""v = fm.getTrackFeature(id, 'TRACK_MEAN_SPEED')
model.getLogger().log('')
model.getLogger().log('Track ' + str(id) + ': mean velocity = ' + str(v) + ' ' + model.getSpaceUnits() + '/' + model.getTimeUnits())"""
trackID = str(id)
track = model.getTrackModel().trackSpots(id)
spot_track = {}
for spot in track:
sid = spot.ID()
# Fetch spot features directly from spot.
x = spot.getFeature('POSITION_X')
y = spot.getFeature('POSITION_Y')
t = spot.getFeature('FRAME')
q = spot.getFeature('QUALITY')
snr = spot.getFeature('SNR')
mean = spot.getFeature('MEAN_INTENSITY')
#model.getLogger().log('\tspot ID = ' + str(sid) + ', x='+str(x)+', y='+str(y)+', t='+str(t)+', q='+str(q) + ', snr='+str(snr) + ', mean = ' + str(mean))
spot_track[t] = (x, y)
crds_perSpot.append(spot_track)
#print ("Spot", crds_perSpot.index(spot_track),"has the following coordinates:", crds_perSpot[crds_perSpot.index(spot_track)])
return (crds_perSpot, nFrames)
img_width = imp.getWidth()
img_height = imp.getHeight()
bound_width = d['bounds']['maxX'] - d['bounds']['minX']
bound_height = d['bounds']['maxY'] - d['bounds']['minY']
scale = img_width * 1.0 / bound_width
print 'scale', scale
xvals = [(x - d['bounds']['minX']) * scale for x, y in coords[:-1]]
yvals = [(y - d['bounds']['minY']) * scale for x, y in coords[:-1]]
print xvals, yvals
fpoly = PolygonRoi(xvals, yvals, Roi.POLYGON)
imp.setRoi(fpoly)
dlg = WaitForUserDialog('testing')
dlg.show()
roi = imp.getRoi()
fpoly = roi.getFloatPolygon()
coords = [[]]
for x, y in zip(fpoly.xpoints, fpoly.ypoints):
x = (x / scale) + d['bounds']['minX']
y = (y / scale) + d['bounds']['minY']
coords[0].append([x, y])
for x, y in zip(fpoly.xpoints[:1], fpoly.ypoints[:1]):
x = (x / scale) + d['bounds']['minX']
y = (y / scale) + d['bounds']['minY']
coords[0].append([x, y])
d['roi']['coordinates'] = coords
imp.close()
fp = open(f, 'w')
def wait(message=None):
if message == None:
message = 'press ok to continue'
wfug = WaitForUserDialog('Waiting for user', message)
wfug.show()
imp = IJ.getImage()
directory = IJ.getDirectory("image")
f = str(imp.getTitle())
date, rows, columns, time_tif = str.split(f) # deconstruct filename
time = time_tif.replace('tif','jpg') # need to remove .tif from time
row_IDs = list(6*rows[0] + 6*rows[1])
column_IDs = [str(i) for i in 2*(range(int(columns[0]), int(columns[0]) + 6))]
zipped = zip(row_IDs, column_IDs)
sample_names = ["".join(values) for values in zipped]
IJ.setMinAndMax(1900, 11717) # adjust brightness and contrast
IJ.run("Apply LUT")
IJ.makeRectangle(200, 50, 730, 950) # initialize selection
dial = WaitForUserDialog("Adjust selection area")
dial.show() # ask user to place selection appropriately
IJ.run("Crop")
adjDir = os.path.join(directory, "Adjusted")
if not os.path.exists(adjDir):
os.mkdir(adjDir)
adjImage = os.path.join(adjDir, "Adj " + f)
IJ.saveAs("Jpeg", adjImage)
## make ROI list
w = 130
h = 480
x_vals = 2*[10, 128, 246, 360, 478, 596]
y1 = 0
y2 = 470
y_vals = [y1, y1, y1, y1, y1, y1, y2, y2, y2, y2, y2, y2]
ROIs = zip(x_vals, y_vals)
from ij import IJ
from ij.io import SaveDialog
from ij.gui import WaitForUserDialog
theImage = IJ.getImage()
ip = theImage.getProcessor()
sd = SaveDialog("Select file to save results","injury",".csv")
saveFileName = sd.getFileName()
saveFileDir = sd.getDirectory()
if (saveFileName is not None):
saveFilePath = saveFileDir + saveFileName
savefilehandler = open(saveFilePath,"w")
waitDialog = WaitForUserDialog("Use freeform tool to outline the piece of tissue")
waitDialog.show()
roi = theImage.getRoi()
if (roi is not None):
print type(roi)
thePolygon = roi.getPolygon()
boundRect = thePolygon.getBounds()
for i in range(boundRect.x,boundRect.x+boundRect.width):
pos_pixels = 0
tot_pixels = 0
IJ.showProgress(i-boundRect.x,boundRect.width)
for j in range(boundRect.y,boundRect.y+boundRect.height):
if thePolygon.contains(i,j):
value = ip.getPixelValue(i,j)
tot_pixels = tot_pixels + 1
def run():
### Default arguments
two_sarcomere_size = 25 # determined by filter used.
rotation_angle = 0.0
### Get the image we'd like to work with.
# Don't need to ask where to save the intermediate image since it'll just be saved in the matchedmyo folder anyway
# may change this though. In case they want to keep that image around.
this_img = WindowManager.getCurrentImage()
if this_img == None:
ud = WaitForUserDialog(
"Please select the image you would like to analyze.")
ud.show()
this_img = WindowManager.getCurrentImage()
img_name = this_img.getTitle()
matchedmyo_path = "/home/AD/dfco222/scratchMarx/matchedmyo/" # this would be grabbed from the prompt
gd = GenericDialog("Preprocessing Options")
gd.addCheckbox("Automatic Resizing/Rotation", True)
gd.addCheckbox("CLAHE", True)
gd.addCheckbox("Normalization to Transverse Tubules", True)
gd.showDialog()
if gd.wasCanceled():
return
auto_resize_rotate = gd.getNextBoolean()
clahe = gd.getNextBoolean()
normalize = gd.getNextBoolean()
if auto_resize_rotate:
# Clear selection so it takes FFT of full image
rotation_angle = auto_resize_angle_measure(this_img,
two_sarcomere_size)
if clahe:
clahe_args = "blocksize={} histogram=256 maximum=3 mask=*None* fast_(less_accurate)".format(
two_sarcomere_size)
IJ.run(this_img, "Enhance Local Contrast (CLAHE)", clahe_args)
if normalize:
# Ask the user to select a subsection of the image that looks healthy-ish.
ud = WaitForUserDialog(
"Please select a subsection exhibiting a measure of healthy TT structure using the Rectangle tool.\n"
+ " Only a single cell or several are needed.\n\n" +
" Press 'OK' when finished.")
ud.show()
IJ.setTool("rectangle")
# Duplicate the selected subsection.
selection = this_img.crop()
IJ.run(selection, "Duplicate...", "title=subsection.tif")
# Grab the subsection image and rotate it.
selection = WindowManager.getImage("subsection.tif")
IJ.run(
selection, "Rotate...",
"angle={} grid=1 interpolation=Bicubic enlarge".format(
rotation_angle))
# Ask the user to select a bounding box that contains only tubules
# NOTE: Need to get rid of initial selection since it's the entire image and it's annoying to click out of
IJ.setTool("rectangle")
IJ.run(selection, "Select None", "")
ud = WaitForUserDialog(
"Select a subsection of the image that contains only tubules and no membrane."
)
ud.show()
# Grab the subsection ImagePlus
selection = WindowManager.getCurrentImage()
this_window = WindowManager.getActiveWindow()
selection_small = selection.crop()
IJ.run(selection, "Close", "")
# NOTE: May not actually display this depending on how the macros work
IJ.run(selection_small, "Duplicate...", "title=subsection_small.tif")
# Smooth the selection using the single TT filter.
# NOTE: It won't read in so we're just going to hard code it in since it's simple
tt_filt_row = "0 0 0 1 1 1 1 1 1 0 0 0 0\n"
tt_filt = ""
for i in range(21):
tt_filt += tt_filt_row
IJ.run("Convolve...", "text1=[" + tt_filt + "] normalize")
# Segment out the TTs from the 'gaps' using Gaussian Adaptive Thresholding.
selection_small = WindowManager.getImage("subsection_small.tif")
IJ.run(selection_small, "Duplicate...", "title=thresholded.tif")
threshed = WindowManager.getImage("thresholded.tif")
IJ.run(threshed, "Auto Local Threshold",
"method=Bernsen radius=7 parameter_1=1 parameter_2=0 white")
# Select the TTs from the thresholded image.
IJ.run(threshed, "Create Selection", "")
tt_selection = WindowManager.getImage("subsection_small.tif")
IJ.selectWindow("thresholded.tif")
IJ.selectWindow("subsection_small.tif")
IJ.run(tt_selection, "Restore Selection", "")
# Get TT intensity statistics.
stat_options = IS.MEAN | IS.MIN_MAX | IS.STD_DEV
stats = IS.getStatistics(tt_selection.getProcessor(), stat_options,
selection_small.getCalibration())
# Calculate pixel ceiling intensity value based on heuristic.
# TODO: Add a catch for data type overflow.
pixel_ceiling = stats.mean + 3 * stats.stdDev
print "px ceil:", pixel_ceiling
# Invert selection to get inter-sarcomeric gap intensity statistics.
IJ.run(tt_selection, "Make Inverse", "")
stat_options = IS.MEAN | IS.MIN_MAX | IS.STD_DEV
stats = IS.getStatistics(tt_selection.getProcessor(), stat_options,
selection_small.getCalibration())
# Calculate pixel floor intensity value based on heuristic.
pixel_floor = stats.mean - stats.stdDev
# TODO: Add a catch for data type underflow.
print "px floor:", pixel_floor
# Threshold original image based on these values.
IJ.selectWindow(this_img.getTitle())
IJ.run(this_img, "Select All", "")
IJ.setMinAndMax(pixel_floor, pixel_ceiling)
IJ.run(this_img, "Apply LUT", "")
## Ask if it is acceptable.
gd = GenericDialog("Acceptable?")
gd.addMessage(
"If the preprocessed image is acceptable for analysis, hit 'OK' to being analysis.\n"
+ " If the image is unacceptable or an error occurred, hit 'Cancel'")
gd.showDialog()
if gd.wasCanceled():
return
## Save the preprocessed image.
imp = IJ.getImage()
fs = FileSaver(imp)
img_save_dir = matchedmyo_path + "myoimages/" # actually get from user at some point
img_file_path = img_save_dir + img_name[:-4] + "_preprocessed.tif"
if os.path.exists(img_save_dir) and os.path.isdir(img_save_dir):
print "Saving image as:", img_file_path
if os.path.exists(img_file_path):
# use dialog box to ask if they want to overwrite
gd = GenericDialog("Overwrite?")
gd.addMessage(
"A file exists with the specified path, \"{}\". Would you like to overwrite it?"
.format(img_file_path))
gd.enableYesNoCancel()
gd.showDialog()
if gd.wasCanceled():
return
elif fs.saveAsTiff(img_file_path):
print "Preprocessed image saved successfully at:", '"' + img_file_path + '"'
else:
print "Folder does not exist or is not a folder!"
### Create the YAML file containing the parameters for classification
## Ask user for YAML input
gd = GenericDialog("YAML Input")
gd.addStringField("imageName", img_file_path, 50)
#gd.addStringField("maskName", "None")
gd.addStringField("outputParams_fileRoot", img_file_path[:-4], 50)
gd.addStringField("outputParams_fileType", "tif")
gd.addNumericField("outputParams_dpi", 300, 0)
gd.addCheckbox("outputParams_saveHitsArray", False)
gd.addStringField("outputParams_csvFile", matchedmyo_path + "results/")
gd.addCheckbox("TT Filtering", True)
gd.addToSameRow()
gd.addCheckbox("LT Filtering", True)
gd.addToSameRow()
gd.addCheckbox("TA Filtering", True)
gd.addNumericField("scopeResolutions_x", 5.0, 3)
gd.addToSameRow()
gd.addNumericField("scopeResolutions_y", 5.0, 3)
gd.addMessage("Enter in filter rotation angles separated by commas.")
gd.addStringField("", "-25, -20, -15, -10, -5, 0, 5, 10, 15, 20, 25", 50)
gd.addCheckbox("returnAngles", False)
gd.addCheckbox("returnPastedFilter", True)
gd.showDialog()
if gd.wasCanceled():
return
strings = [st.text for st in gd.getStringFields()]
#if strings[1] == "None" or "":
# strings[1] = None
nums = [float(num.text) for num in gd.getNumericFields()]
nums[0] = int(nums[0]) # Have to make sure the dpi variable is an integer
checks = [str(bool(boo.state)) for boo in gd.getCheckboxes()]
iter_argument = ','.join(
[str(float(it) - rotation_angle) for it in strings[4].split(',')])
string_block = """imageName: {0[0]}
outputParams:
fileRoot: {0[1]}
fileType: {0[2]}
dpi: {1[0]}
saveHitsArray: {2[0]}
csvFile: {0[3]}
preprocess: False
filterTypes:
TT: {2[1]}
LT: {2[2]}
TA: {2[3]}
scopeResolutions:
x: {1[1]}
y: {1[2]}
iters: [{3}]
returnAngles: {2[4]}
returnPastedFilter: {2[5]}""".format(strings, nums, checks, iter_argument)
im_title = this_img.getTitle()
with cd(matchedmyo_path):
yaml_file_path = "./YAML_files/" + im_title[:-4] + ".yml"
with open("./YAML_files/" + im_title[:-4] + ".yml", "w") as ym:
ym.write(string_block)
print "Wrote YAML file to:", matchedmyo_path + yaml_file_path[2:]
### Run the matchedmyo code on the preprocessed image
with cd(matchedmyo_path):
#os.chdir(matchedmyo_path)
#subprocess.call(["python3", matchedmyo_path+"matchedmyo.py", "fullValidation"])
subprocess.call(
["python3", "matchedmyo.py", "run", "--yamlFile", yaml_file_path])
raise Exception("Couldn't find the EmissionWavelength info in the metadata")
# Make the maximum intensity projection
# and show it for user input.
project = ZProjector()
project.setMethod(ZProjector.MAX_METHOD)
project.setImage(imp)
project.doProjection()
projection = project.getProjection()
projection.show()
# Wait for the user to draw a line
# and check if line ROI.
IJ.setTool("line");
myWait = WaitForUserDialog ("waitForUser", "Make a line with the region of interest and press OK")
myWait.show()
roi = projection.getRoi()
# Check if roi is a line
# otherwise exit
if not (roi.isLine()):
raise Exception("Line selection required")
# Loop through existing files to avoid
# overwriting. Will increment the number
# at the end. Saves the MIP with input.
fileName = shortTitle + "ROI" + str(num);
overlayOutput = os.path.join(output,"MAX_" + fileName)
while(os.path.isfile(overlayOutput + ".png")):
gd.addNumericField("Slice start:",1,0)
gd.addNumericField("Slice end:",theImage.getNSlices(),0)
gd.showDialog()
if (gd.wasOKed()):
startSlice = gd.getNextNumber()
endSlice = gd.getNextNumber()
width = theImage.getWidth()
height = theImage.getHeight()
newStack = ImageStack(width,height)
t_line = 240
for i in range(startSlice,endSlice+1):
theImage.setSlice(i)
waiter = WaitForUserDialog("Set ROI","Set ROI for thresholding region")
waiter.show()
roi = theImage.getRoi()
newip = theImage.getProcessor().duplicate()
newip.setColor(0)
newip.fillOutside(roi)
newip.snapshot()
newip.setThreshold(0,t_line,ImageProcessor.BLACK_AND_WHITE_LUT)
newImage = ImagePlus("toggler",newip)
newImage.show()
doSameSlice = True
while (doSameSlice):
accept_waiter = NonBlockingGenericDialog("Thresholding...")
accept_waiter.addNumericField("Threshold:",t_line,0)
accept_waiter.setCancelLabel("Apply new threshold")
return RGBStackMerge.mergeChannels(chimps, False)
manders = MandersColocalization()
results = ResultsTable()
for imageFile in os.listdir(inputDir):
print "Opening " + imageFile
try:
images = BF.openImagePlus(inputDir + imageFile)
image = images[0]
except UnknownFormatException:
continue
preview = getPreview(image)
preview.show()
rm = RoiManager()
dialog = WaitForUserDialog("Action required", "Please select regions of interest in this image. Click OK when done.")
dialog.show()
rm.close()
splitter = ChannelSplitter()
imp1 = ImagePlus("CH1", splitter.getChannel(image, imageA))
imp2 = ImagePlus("CH2", splitter.getChannel(image, imageB))
title = image.getTitle()
title = title[:title.rfind('.')]
image.close()
preview.close()
ch1 = ImagePlusAdapter.wrap(imp1)
ch2 = ImagePlusAdapter.wrap(imp2)
for roi in rm.getRoisAsArray():
container = createContainer(roi, ch1, ch2)
img1 = container.getSourceImage1()
img2 = container.getSourceImage2()
IJ.showStatus("Need to pick an ROI first before running this option")
self.workRoiCheckbox.setState(False)
## Main body of script starts here
od = OpenDialog("Select parent LSM file...")
parentLSMFilePath = od.getPath()
if parentLSMFilePath is not None:
tileConfigFilePath = parentLSMFilePath + "_tiles/resized/TileConfiguration.registered.txt"
scale_info = estimate_scale_multiplier(parentLSMFilePath+"_tiles/tile_1.ome.tif",parentLSMFilePath+"_tiles/resized/tile_1.tif")
print scale_info
coords = read_tileconfig_file(tileConfigFilePath)
full_keys = ["tile_"+str(i) for i in range(1,len(coords.keys())+1)]
coords_vals = normalize_coords_in_list(get_list_from_dict(full_keys,coords))
im = IJ.getImage()
wfud = WaitForUserDialog("Select a freehand ROI, then click here")
wfud.show()
roi = im.getRoi()
if (roi is not None):
float_poly = roi.getFloatPolygon()
containing_tiles_superlist = []
for i in range(float_poly.npoints):
containing_tiles_superlist.append(find_containing_tiles([float_poly.xpoints[i],float_poly.ypoints[i],0],coords_vals,scale_info[1],scale_info[2]))
upscaled_coords = upscale_coords(coords,scale_info[0])
containing_tiles_dict = rearrange_tile_list(containing_tiles_superlist)
copy_fullsize_tiles(["tile_"+key for key in containing_tiles_dict.keys()],parentLSMFilePath+"_tiles/",parentLSMFilePath+"_tiles/subtiles/",".ome.tif")
subupcoords_vals = normalize_coords_in_list(get_list_from_dict(["tile_"+key for key in containing_tiles_dict.keys()],upscaled_coords))
hadStitched = False
if len(containing_tiles_dict.keys())>1:
for i in containing_tiles_dict.keys():
IJ.log("Opened tile " + str(i))
write_tileconfig_file(parentLSMFilePath+"_tiles/subtiles/TileConfiguration.subtiles.txt",subupcoords_vals,".ome.tif")
from ij import IJ
from ij.gui import WaitForUserDialog
# Draw a line
dial = WaitForUserDialog('Draw a line that will be rotated to horizontal.')
dial.show()
cur_image = IJ.getImage()
cur_roi = cur_image.getRoi()
cur_angle = cur_roi.getAngle()
print cur_angle
# Angles must be between 90 and -90 degrees...currently are not but it's fine as my
# system has inversion symmetry along the y-axis
IJ.run(cur_image, "Rotate... ", "angle=" +str(cur_angle) + " grid=0 interpolation=Bicubic stack")
