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
return SpotDetectionLog(imgBgs32, data, ops, thresholdmethod, dimensions2D, factory) def SpotDetection2(imp): imp=Duplicator().run(imp) # subtract background
return SpotDetectionLog(imgBgs32, data, ops, thresholdmethod, dimensions2D, factory) def SpotDetection2(imp): imp=Duplicator().run(imp) # subtract background bgs=BackgroundSubtracter()
bgs.rollingBallBackground(imp.getProcessor(), 50.0, False, False, True, True, True) IJ.run(imp, "Auto Threshold", "method=Triangle white"); return imp def SpotDetection3(imp, invert=False): # operate on a duplicate as not to change the original imp=Duplicator().run(imp) if (invert): IJ.run(imp, "Invert", ""); # subtract background bgs=BackgroundSubtracter()
IJ.run(imp, "Auto Threshold", "method=Triangle white"); return imp def SpotDetection3(imp, invert=False): # operate on a duplicate as not to change the original imp=Duplicator().run(imp) if (invert): IJ.run(imp, "Invert", ""); # subtract background bgs=BackgroundSubtracter() bgs.rollingBallBackground(imp.getProcessor(), 50.0, False, False, True, True, True)
def boxed_intensities(imp1, width, height):
"""Create a new image with averaged intensity regions.
Parameters
----------
imp1 : ImagePlus
width, height : int
The width and height of the rectangles.
Returns
-------
imp2 : ImagePlus
The resulting ImagePlus, same dimensions as imp1.
"""
imp2 = Duplicator().run(imp1)
imp2.setTitle('heatmap-' + imp1.getTitle())
imw = imp1.getWidth()
imh = imp1.getHeight()
ip1 = imp1.getProcessor()
ip2 = imp2.getProcessor()
if (imw % width + imh % height) > 0:
msg = "WARNING: image size (%dx%d) not dividable by box (%dx%d)!"
IJ.log(msg % (imw, imh, width, height))
for box_y in range(0, imh / height):
start_y = box_y * height
for box_x in range(0, imw / width):
start_x = box_x * width
# print "%d %d" % (start_x, start_y)
bavg = rect_avg(ip1, start_x, start_y, width, height)
# print bavg
rect_set(ip2, start_x, start_y, width, height, bavg)
return imp2
def boxed_intensities(imp1, width, height):
"""Create a new image with averaged intensity regions.
Parameters
----------
imp1 : ImagePlus
width, height : int
The width and height of the rectangles.
Returns
-------
imp2 : ImagePlus
The resulting ImagePlus, same dimensions as imp1.
"""
imp2 = Duplicator().run(imp1)
imp2.setTitle('heatmap-' + imp1.getTitle())
imw = imp1.getWidth()
imh = imp1.getHeight()
ip1 = imp1.getProcessor()
ip2 = imp2.getProcessor()
if (imw % width + imh % height) > 0:
msg = "WARNING: image size (%dx%d) not dividable by box (%dx%d)!"
log.warn(msg % (imw, imh, width, height))
for box_y in range(0, imh / height):
start_y = box_y * height
for box_x in range(0, imw / width):
start_x = box_x * width
# print "%d %d" % (start_x, start_y)
bavg = rect_avg(ip1, start_x, start_y, width, height)
# print bavg
rect_set(ip2, start_x, start_y, width, height, bavg)
return imp2
if chl is None:
maxC = max([i.getNChannels() for i in imL])
else:
maxC = len(chl)
ssL = [] # Same number of slices
nff=True
for im in imL:
if chl is not None: ## Here we extract only the channels we need
im = SubHyperstackMaker().makeSubhyperstack(im, [i+1 for i in chl], range(1, im.getNSlices()+1), range(1, im.getNFrames()+1))
nz = maxZ-im.getNSlices() # Number of z planes to add
if nz==0 and im.getNChannels()==maxC:
ssL.append(im)
continue
if im.getNFrames()==1:
tmp = Duplicator().run(im, 1, im.getNChannels(), 1, 1, 1, 1) # create a duplicated slice
tmp.getProcessor().multiply(0) # make it a dark frame
ssL.append(Concatenator().concatenate([im]+[tmp]*nz, False)) # This deletes references/images in imL
else: # if im.getNFrames()>1 (we need to concatenate time-point-per timepoint :s )
nff=False
allFrames = []
stack = im.getImageStack()
nZ = im.getNSlices()
nC = im.getNChannels()
dark = Duplicator().run(im, 1, 1, 1, 1, 1, 1)
dark.getProcessor().multiply(0)
res = ij.ImageStack(im.width, im.height)
icnt = 0
for ia in range(1, im.getNFrames()+1):
for ib in range(1, maxZ+1):
for ic in range(1,maxC+1):
icnt +=1
def run(imp, preprocessor_path, postprocessor_path, threshold_method,
user_comment):
output_parameters = {
"image title": "",
"preprocessor path": float,
"post processor path": float,
"thresholding op": float,
"use ridge detection": bool,
"high contrast": int,
"low contrast": int,
"line width": int,
"minimum line length": int,
"mitochondrial footprint": float,
"branch length mean": float,
"branch length median": float,
"branch length stdevp": float,
"summed branch lengths mean": float,
"summed branch lengths median": float,
"summed branch lengths stdevp": float,
"network branches mean": float,
"network branches median": float,
"network branches stdevp": float
}
output_order = [
"image title", "preprocessor path", "post processor path",
"thresholding op", "use ridge detection", "high contrast",
"low contrast", "line width", "minimum line length",
"mitochondrial footprint", "branch length mean",
"branch length median", "branch length stdevp",
"summed branch lengths mean", "summed branch lengths median",
"summed branch lengths stdevp", "network branches mean",
"network branches median", "network branches stdevp"
]
# Perform any preprocessing steps...
status.showStatus("Preprocessing image...")
if preprocessor_path != None:
if preprocessor_path.exists():
preprocessor_thread = scripts.run(preprocessor_path, True)
preprocessor_thread.get()
imp = WindowManager.getCurrentImage()
else:
pass
# Store all of the analysis parameters in the table
if preprocessor_path == None:
preprocessor_str = ""
else:
preprocessor_str = preprocessor_path.getCanonicalPath()
if postprocessor_path == None:
postprocessor_str = ""
else:
postprocessor_str = preprocessor_path.getCanonicalPath()
output_parameters["preprocessor path"] = preprocessor_str
output_parameters["post processor path"] = postprocessor_str
output_parameters["thresholding op"] = threshold_method
output_parameters["use ridge detection"] = str(use_ridge_detection)
output_parameters["high contrast"] = rd_max
output_parameters["low contrast"] = rd_min
output_parameters["line width"] = rd_width
output_parameters["minimum line length"] = rd_length
# Create and ImgPlus copy of the ImagePlus for thresholding with ops...
status.showStatus("Determining threshold level...")
imp_title = imp.getTitle()
slices = imp.getNSlices()
frames = imp.getNFrames()
output_parameters["image title"] = imp_title
imp_calibration = imp.getCalibration()
imp_channel = Duplicator().run(imp, imp.getChannel(), imp.getChannel(), 1,
slices, 1, frames)
img = ImageJFunctions.wrap(imp_channel)
# Determine the threshold value if not manual...
binary_img = ops.run("threshold.%s" % threshold_method, img)
binary = ImageJFunctions.wrap(binary_img, 'binary')
binary.setCalibration(imp_calibration)
binary.setDimensions(1, slices, 1)
# Get the total_area
if binary.getNSlices() == 1:
area = binary.getStatistics(Measurements.AREA).area
area_fraction = binary.getStatistics(
Measurements.AREA_FRACTION).areaFraction
output_parameters[
"mitochondrial footprint"] = area * area_fraction / 100.0
else:
mito_footprint = 0.0
for slice in range(binary.getNSlices()):
binary.setSliceWithoutUpdate(slice)
area = binary.getStatistics(Measurements.AREA).area
area_fraction = binary.getStatistics(
Measurements.AREA_FRACTION).areaFraction
mito_footprint += area * area_fraction / 100.0
output_parameters[
"mitochondrial footprint"] = mito_footprint * imp_calibration.pixelDepth
# Generate skeleton from masked binary ...
# Generate ridges first if using Ridge Detection
if use_ridge_detection and (imp.getNSlices() == 1):
skeleton = ridge_detect(imp, rd_max, rd_min, rd_width, rd_length)
else:
skeleton = Duplicator().run(binary)
IJ.run(skeleton, "Skeletonize (2D/3D)", "")
# Analyze the skeleton...
status.showStatus("Setting up skeleton analysis...")
skel = AnalyzeSkeleton_()
skel.setup("", skeleton)
status.showStatus("Analyzing skeleton...")
skel_result = skel.run()
status.showStatus("Computing graph based parameters...")
branch_lengths = []
summed_lengths = []
graphs = skel_result.getGraph()
for graph in graphs:
summed_length = 0.0
edges = graph.getEdges()
for edge in edges:
length = edge.getLength()
branch_lengths.append(length)
summed_length += length
summed_lengths.append(summed_length)
output_parameters["branch length mean"] = eztables.statistical.average(
branch_lengths)
output_parameters["branch length median"] = eztables.statistical.median(
branch_lengths)
output_parameters["branch length stdevp"] = eztables.statistical.stdevp(
branch_lengths)
output_parameters[
"summed branch lengths mean"] = eztables.statistical.average(
summed_lengths)
output_parameters[
"summed branch lengths median"] = eztables.statistical.median(
summed_lengths)
output_parameters[
"summed branch lengths stdevp"] = eztables.statistical.stdevp(
summed_lengths)
branches = list(skel_result.getBranches())
output_parameters["network branches mean"] = eztables.statistical.average(
branches)
output_parameters["network branches median"] = eztables.statistical.median(
branches)
output_parameters["network branches stdevp"] = eztables.statistical.stdevp(
branches)
# Create/append results to a ResultsTable...
status.showStatus("Display results...")
if "Mito Morphology" in list(WindowManager.getNonImageTitles()):
rt = WindowManager.getWindow(
"Mito Morphology").getTextPanel().getOrCreateResultsTable()
else:
rt = ResultsTable()
rt.incrementCounter()
for key in output_order:
rt.addValue(key, str(output_parameters[key]))
# Add user comments intelligently
if user_comment != None and user_comment != "":
if "=" in user_comment:
comments = user_comment.split(",")
for comment in comments:
rt.addValue(comment.split("=")[0], comment.split("=")[1])
else:
rt.addValue("Comment", user_comment)
rt.show("Mito Morphology")
# Create overlays on the original ImagePlus and display them if 2D...
if imp.getNSlices() == 1:
status.showStatus("Generate overlays...")
IJ.run(skeleton, "Green", "")
IJ.run(binary, "Magenta", "")
skeleton_ROI = ImageRoi(0, 0, skeleton.getProcessor())
skeleton_ROI.setZeroTransparent(True)
skeleton_ROI.setOpacity(1.0)
binary_ROI = ImageRoi(0, 0, binary.getProcessor())
binary_ROI.setZeroTransparent(True)
binary_ROI.setOpacity(0.25)
overlay = Overlay()
overlay.add(binary_ROI)
overlay.add(skeleton_ROI)
imp.setOverlay(overlay)
imp.updateAndDraw()
# Generate a 3D model if a stack
if imp.getNSlices() > 1:
univ = Image3DUniverse()
univ.show()
pixelWidth = imp_calibration.pixelWidth
pixelHeight = imp_calibration.pixelHeight
pixelDepth = imp_calibration.pixelDepth
# Add end points in yellow
end_points = skel_result.getListOfEndPoints()
end_point_list = []
for p in end_points:
end_point_list.append(
Point3f(p.x * pixelWidth, p.y * pixelHeight, p.z * pixelDepth))
univ.addIcospheres(end_point_list, Color3f(255.0, 255.0, 0.0), 2,
1 * pixelDepth, "endpoints")
# Add junctions in magenta
junctions = skel_result.getListOfJunctionVoxels()
junction_list = []
for p in junctions:
junction_list.append(
Point3f(p.x * pixelWidth, p.y * pixelHeight, p.z * pixelDepth))
univ.addIcospheres(junction_list, Color3f(255.0, 0.0, 255.0), 2,
1 * pixelDepth, "junctions")
# Add the lines in green
graphs = skel_result.getGraph()
for graph in range(len(graphs)):
edges = graphs[graph].getEdges()
for edge in range(len(edges)):
branch_points = []
for p in edges[edge].getSlabs():
branch_points.append(
Point3f(p.x * pixelWidth, p.y * pixelHeight,
p.z * pixelDepth))
univ.addLineMesh(branch_points, Color3f(0.0, 255.0, 0.0),
"branch-%s-%s" % (graph, edge), True)
# Add the surface
univ.addMesh(binary)
univ.getContent("binary").setTransparency(0.5)
# Perform any postprocessing steps...
status.showStatus("Running postprocessing...")
if postprocessor_path != None:
if postprocessor_path.exists():
postprocessor_thread = scripts.run(postprocessor_path, True)
postprocessor_thread.get()
else:
pass
status.showStatus("Done analysis!")
def generate_background_rois(input_mask_imp,
params,
membrane_edges,
dilations=5,
threshold_method=None,
membrane_imp=None):
"""automatically identify background region based on auto-thresholded image, existing membrane edges and position of midpoint anchor"""
if input_mask_imp is None and membrane_imp is not None:
segmentation_imp = Duplicator().run(membrane_imp)
# do thresholding using either previous method if threhsold_method is None or using (less conservative?) threshold method
if (threshold_method is None
or not (threshold_method in params.listThresholdMethods())):
mask_imp = make_and_clean_binary(segmentation_imp,
params.threshold_method)
else:
mask_imp = make_and_clean_binary(segmentation_imp,
threshold_method)
segmentation_imp.close()
else:
input_mask_imp.killRoi()
mask_imp = Duplicator().run(input_mask_imp)
rois = []
IJ.setForegroundColor(0, 0, 0)
roim = RoiManager(True)
rt = ResultsTable()
for fridx in range(mask_imp.getNFrames()):
mask_imp.setT(fridx + 1)
# add extra bit to binary mask from loaded membrane in case user refined edges...
# flip midpoint anchor across the line joining the two extremes of the membrane,
# and fill in the triangle made by this new point and those extremes
poly = membrane_edges[fridx].getPolygon()
l1 = (poly.xpoints[0], poly.ypoints[0])
l2 = (poly.xpoints[-1], poly.ypoints[-1])
M = (0.5 * (l1[0] + l2[0]), 0.5 * (l1[1] + l2[1]))
Mp1 = (params.manual_anchor_midpoint[0][0] - M[0],
params.manual_anchor_midpoint[0][1] - M[1])
p2 = (M[0] - Mp1[0], M[1] - Mp1[1])
new_poly_x = list(poly.xpoints)
new_poly_x.append(p2[0])
new_poly_y = list(poly.ypoints)
new_poly_y.append(p2[1])
mask_imp.setRoi(PolygonRoi(new_poly_x, new_poly_y, PolygonRoi.POLYGON))
IJ.run(mask_imp, "Fill", "slice")
mask_imp.killRoi()
# now dilate the masked image and identify the unmasked region closest to the midpoint anchor
ip = mask_imp.getProcessor()
dilations = 5
for d in range(dilations):
ip.dilate()
ip.invert()
mask_imp.setProcessor(ip)
mxsz = mask_imp.getWidth() * mask_imp.getHeight()
pa = ParticleAnalyzer(
ParticleAnalyzer.ADD_TO_MANAGER | ParticleAnalyzer.SHOW_PROGRESS,
ParticleAnalyzer.CENTROID, rt, 0, mxsz)
pa.setRoiManager(roim)
pa.analyze(mask_imp)
ds_to_anchor = [
math.sqrt((x - params.manual_anchor_midpoint[0][0])**2 +
(y - params.manual_anchor_midpoint[0][1])**2)
for x, y in zip(
rt.getColumn(rt.getColumnIndex("X")).tolist(),
rt.getColumn(rt.getColumnIndex("Y")).tolist())
]
if len(ds_to_anchor) > 0:
roi = roim.getRoi(ds_to_anchor.index(min(ds_to_anchor)))
rois.append(roi)
else:
rois.append(None)
roim.reset()
rt.reset()
roim.close()
mask_imp.close()
return rois
original) # duplicate the original image, only the CFOV
CFOV.setTitle("CFOV")
CFOV.show()
######### Nema process including Re-bin image to larger pixels ################################################################################
desired_pixel_width = getPixel(
) # 6.4 mm default, remember tolerance is +/-30%
current_pixel_width = CFOV.getCalibration(
).pixelWidth #get pixel width, 1.16 mm
shrink_factor = int(desired_pixel_width /
current_pixel_width) # must be an integer
IJ.run(CFOV, "Bin...", "x=" + str(shrink_factor) + " y=" + str(shrink_factor) +
" bin=Sum") # run the bin plugin
IJ.run(CFOV, "Convolve...",
"text1=[1 2 1\n2 4 2\n1 2 1\n] normalize") # apply the nema filter
######## Analyse pixels
CFOVpixels = CFOV.getProcessor().convertToFloat().getPixels(
) # access processor, get float array of pixels
CFOVmean = sum(CFOVpixels) / len(CFOVpixels)
#pixels_above_threshold = filter(lambda pix: pix > 0.75*mean, pixels) # return a new array with values above zero, use anonymous function
CFOVunifiormity = getUniformity(min(CFOVpixels), max(CFOVpixels))
######## Print results
results_string = "The integral uniformity of the CFOV is: " + str(
CFOVunifiormity) + "% for pixel size of " + str(
desired_pixel_width) + " mm"
IJ.log(results_string)
IJ.showMessage(results_string)
otsu=ops.run("threshold", ops.create( dimensions2D, BitType()), imgBgs, Otsu())
display.createDisplay("thresholded", data.create(ImgPlus(otsu)))
'''
#Utility.clearOutsideRoi(imp, clone)
IJ.run(imp, "Auto Local Threshold",
"method=MidGrey radius=15 parameter_1=0 parameter_2=0 white")
IJ.run(imp, "Fill Holes", "")
IJ.run(imp, "Close-", "")
IJ.run(imp, "Watershed", "")
iplus.updateAndDraw()
# create a hidden roi manager
roim = RoiManager(True)
# count the particles
countParticles(iplus, roim, 10, 200, 0.5, 1.0)
[truecolor1.getProcessor().draw(roi) for roi in roim.getRoisAsArray()]
truecolor1.updateAndDraw()
#Prefs.blackBackground = False;
#IJ.run("Make Binary", "");
#IJ.run("LoG 3D");
#IJ.run("Duplicate...", "title="+"test")
#IJ.run("RGB Stack");
#IJ.run("Convert Stack to Images");
def run(imp, preprocessor_path, postprocessor_path, threshold_method, user_comment):
output_parameters = {"image title" : "",
"preprocessor path" : float,
"post processor path" : float,
"thresholding op" : float,
"use ridge detection" : bool,
"high contrast" : int,
"low contrast" : int,
"line width" : int,
"minimum line length" : int,
"mitochondrial footprint" : float,
"branch length mean" : float,
"branch length median" : float,
"branch length stdevp" : float,
"summed branch lengths mean" : float,
"summed branch lengths median" : float,
"summed branch lengths stdevp" : float,
"network branches mean" : float,
"network branches median" : float,
"network branches stdevp" : float}
output_order = ["image title",
"preprocessor path",
"post processor path",
"thresholding op",
"use ridge detection",
"high contrast",
"low contrast",
"line width",
"minimum line length",
"mitochondrial footprint",
"branch length mean",
"branch length median",
"branch length stdevp",
"summed branch lengths mean",
"summed branch lengths median",
"summed branch lengths stdevp",
"network branches mean",
"network branches median",
"network branches stdevp"]
# Perform any preprocessing steps...
status.showStatus("Preprocessing image...")
if preprocessor_path != None:
if preprocessor_path.exists():
preprocessor_thread = scripts.run(preprocessor_path, True)
preprocessor_thread.get()
imp = WindowManager.getCurrentImage()
else:
pass
# Store all of the analysis parameters in the table
if preprocessor_path == None:
preprocessor_str = ""
else:
preprocessor_str = preprocessor_path.getCanonicalPath()
if postprocessor_path == None:
postprocessor_str = ""
else:
postprocessor_str = preprocessor_path.getCanonicalPath()
output_parameters["preprocessor path"] = preprocessor_str
output_parameters["post processor path"] = postprocessor_str
output_parameters["thresholding op"] = threshold_method
output_parameters["use ridge detection"] = str(use_ridge_detection)
output_parameters["high contrast"] = rd_max
output_parameters["low contrast"] = rd_min
output_parameters["line width"] = rd_width
output_parameters["minimum line length"] = rd_length
# Create and ImgPlus copy of the ImagePlus for thresholding with ops...
status.showStatus("Determining threshold level...")
imp_title = imp.getTitle()
slices = imp.getNSlices()
frames = imp.getNFrames()
output_parameters["image title"] = imp_title
imp_calibration = imp.getCalibration()
imp_channel = Duplicator().run(imp, imp.getChannel(), imp.getChannel(), 1, slices, 1, frames)
img = ImageJFunctions.wrap(imp_channel)
# Determine the threshold value if not manual...
binary_img = ops.run("threshold.%s"%threshold_method, img)
binary = ImageJFunctions.wrap(binary_img, 'binary')
binary.setCalibration(imp_calibration)
binary.setDimensions(1, slices, 1)
# Get the total_area
if binary.getNSlices() == 1:
area = binary.getStatistics(Measurements.AREA).area
area_fraction = binary.getStatistics(Measurements.AREA_FRACTION).areaFraction
output_parameters["mitochondrial footprint"] = area * area_fraction / 100.0
else:
mito_footprint = 0.0
for slice in range(binary.getNSlices()):
binary.setSliceWithoutUpdate(slice)
area = binary.getStatistics(Measurements.AREA).area
area_fraction = binary.getStatistics(Measurements.AREA_FRACTION).areaFraction
mito_footprint += area * area_fraction / 100.0
output_parameters["mitochondrial footprint"] = mito_footprint * imp_calibration.pixelDepth
# Generate skeleton from masked binary ...
# Generate ridges first if using Ridge Detection
if use_ridge_detection and (imp.getNSlices() == 1):
skeleton = ridge_detect(imp, rd_max, rd_min, rd_width, rd_length)
else:
skeleton = Duplicator().run(binary)
IJ.run(skeleton, "Skeletonize (2D/3D)", "")
# Analyze the skeleton...
status.showStatus("Setting up skeleton analysis...")
skel = AnalyzeSkeleton_()
skel.setup("", skeleton)
status.showStatus("Analyzing skeleton...")
skel_result = skel.run()
status.showStatus("Computing graph based parameters...")
branch_lengths = []
summed_lengths = []
graphs = skel_result.getGraph()
for graph in graphs:
summed_length = 0.0
edges = graph.getEdges()
for edge in edges:
length = edge.getLength()
branch_lengths.append(length)
summed_length += length
summed_lengths.append(summed_length)
output_parameters["branch length mean"] = eztables.statistical.average(branch_lengths)
output_parameters["branch length median"] = eztables.statistical.median(branch_lengths)
output_parameters["branch length stdevp"] = eztables.statistical.stdevp(branch_lengths)
output_parameters["summed branch lengths mean"] = eztables.statistical.average(summed_lengths)
output_parameters["summed branch lengths median"] = eztables.statistical.median(summed_lengths)
output_parameters["summed branch lengths stdevp"] = eztables.statistical.stdevp(summed_lengths)
branches = list(skel_result.getBranches())
output_parameters["network branches mean"] = eztables.statistical.average(branches)
output_parameters["network branches median"] = eztables.statistical.median(branches)
output_parameters["network branches stdevp"] = eztables.statistical.stdevp(branches)
# Create/append results to a ResultsTable...
status.showStatus("Display results...")
if "Mito Morphology" in list(WindowManager.getNonImageTitles()):
rt = WindowManager.getWindow("Mito Morphology").getTextPanel().getOrCreateResultsTable()
else:
rt = ResultsTable()
rt.incrementCounter()
for key in output_order:
rt.addValue(key, str(output_parameters[key]))
# Add user comments intelligently
if user_comment != None and user_comment != "":
if "=" in user_comment:
comments = user_comment.split(",")
for comment in comments:
rt.addValue(comment.split("=")[0], comment.split("=")[1])
else:
rt.addValue("Comment", user_comment)
rt.show("Mito Morphology")
# Create overlays on the original ImagePlus and display them if 2D...
if imp.getNSlices() == 1:
status.showStatus("Generate overlays...")
IJ.run(skeleton, "Green", "")
IJ.run(binary, "Magenta", "")
skeleton_ROI = ImageRoi(0,0,skeleton.getProcessor())
skeleton_ROI.setZeroTransparent(True)
skeleton_ROI.setOpacity(1.0)
binary_ROI = ImageRoi(0,0,binary.getProcessor())
binary_ROI.setZeroTransparent(True)
binary_ROI.setOpacity(0.25)
overlay = Overlay()
overlay.add(binary_ROI)
overlay.add(skeleton_ROI)
imp.setOverlay(overlay)
imp.updateAndDraw()
# Generate a 3D model if a stack
if imp.getNSlices() > 1:
univ = Image3DUniverse()
univ.show()
pixelWidth = imp_calibration.pixelWidth
pixelHeight = imp_calibration.pixelHeight
pixelDepth = imp_calibration.pixelDepth
# Add end points in yellow
end_points = skel_result.getListOfEndPoints()
end_point_list = []
for p in end_points:
end_point_list.append(Point3f(p.x * pixelWidth, p.y * pixelHeight, p.z * pixelDepth))
univ.addIcospheres(end_point_list, Color3f(255.0, 255.0, 0.0), 2, 1*pixelDepth, "endpoints")
# Add junctions in magenta
junctions = skel_result.getListOfJunctionVoxels()
junction_list = []
for p in junctions:
junction_list.append(Point3f(p.x * pixelWidth, p.y * pixelHeight, p.z * pixelDepth))
univ.addIcospheres(junction_list, Color3f(255.0, 0.0, 255.0), 2, 1*pixelDepth, "junctions")
# Add the lines in green
graphs = skel_result.getGraph()
for graph in range(len(graphs)):
edges = graphs[graph].getEdges()
for edge in range(len(edges)):
branch_points = []
for p in edges[edge].getSlabs():
branch_points.append(Point3f(p.x * pixelWidth, p.y * pixelHeight, p.z * pixelDepth))
univ.addLineMesh(branch_points, Color3f(0.0, 255.0, 0.0), "branch-%s-%s"%(graph, edge), True)
# Add the surface
univ.addMesh(binary)
univ.getContent("binary").setTransparency(0.5)
# Perform any postprocessing steps...
status.showStatus("Running postprocessing...")
if postprocessor_path != None:
if postprocessor_path.exists():
postprocessor_thread = scripts.run(postprocessor_path, True)
postprocessor_thread.get()
else:
pass
status.showStatus("Done analysis!")
def __addroi(self, event) :
if ( not self.__init) :
IJ.showMessage("", "please start a new stack")
return
if ( not self.__initDIA) :
IJ.showMessage("", "please select an image for DIA")
return
if ( not self.__initFLUO) :
IJ.showMessage("", "please select an image for FLUO")
return
twres = TextWindow("measures-"+self.__name, "label\tname\tsol\tarea\tcirc\tAR\tFeret\taxis\traf\tdMajor\tdFeret\tdArea", "", 300, 450)
tab="\t"
self.__widthl = self.__display2.getText()
IJ.selectWindow(self.__impF.getTitle())
self.__rm = RoiManager.getInstance()
if (self.__rm==None): self.__rm = RoiManager()
if self.__impF.getImageStackSize() > 1 :
roisarray =[(roi, self.__rm.getSliceNumber(roi.getName())) for roi in self.__rm.getRoisAsArray()]
else :
roisarray =[(roi, 1) for roi in self.__rm.getRoisAsArray()]
self.__rm.runCommand("reset")
#self.__rm.runCommand("Delete")
IJ.selectWindow(self.__impF.getTitle())
self.__maxraf=float(self.__display19.text)
self.__minraf=float(self.__display20.text)
count=1
for roielement in roisarray :
roi = roielement[0]
pos = roielement[1]
lab = self.__impF.getImageStack().getShortSliceLabel(pos)
if lab==None : lab=str(pos)
if self.__conEllipses :
IJ.selectWindow(self.__impF.getTitle())
self.__impF.setSlice(pos)
self.__impF.setRoi(roi)
self.__rm.runCommand("Add")
IJ.run(self.__impF, "Fit Ellipse", "")
ellipse=self.__impF.getRoi()
params = ellipse.getParams()
ferets = ellipse.getFeretValues()
imp2 = Duplicator().run(self.__impF,pos,pos)
IJ.run(imp2, "Rotate... ", "angle="+str(ferets[1])+" grid=0 interpolation=Bilinear enlarge slice")
temproi=Roi((imp2.getWidth()-ferets[0])/2.0,(imp2.getHeight()-ferets[2])/2.0,ferets[0],ferets[2])
imp2.setRoi(temproi)
imp3 = Duplicator().run(imp2,1,1)
ip3=imp3.getProcessor()
if int(self.__display5.text) < ip3.getWidth() < int(self.__display6.text) :
self.__iplist.append(ip3)
self.__display.text = self.__name + " cell " + str(len(self.__iplist))
fer=Line(params[0],params[1],params[2],params[3])
self.__cellsrois.append((fer, pos))
self.__labels.append(self.__isF.getShortSliceLabel(pos))
m=Morph(self.__impF, roi)
twres.append(lab+tab+str(roi.getName())+tab+str(m.Solidity)+tab+str(m.Area)+tab+str(m.Circ)+tab+str(m.AR)+tab+str(m.MaxFeret)+tab+str(fer.getLength())+tab+str(1)+tab+str(0)+tab+str(0)+tab+str(0))
self.__dictCells[count]=(str(roi.getName()), lab, roi)
count=count+1
continue
if roi.getType() in [6,7] :
self.__impF.setSlice(pos)
self.__impF.setRoi(roi)
self.__rm.runCommand("Add")
elif roi.getType() in [2,4] :
self.__impF.setSlice(pos)
self.__impF.setRoi(roi)
m=Morph(self.__impF, roi)
m.setMidParams(10, 2)
midroi=m.MidAxis
if midroi == None : continue
raf = m.MaxFeret/midroi.getLength()
if (self.__maxraf < raf) or (raf < self.__minraf) : continue
maxsol = float(self.__display7.text)
minsol = float(self.__display8.text)
maxarea = float(self.__display9.text)
minarea = float(self.__display10.text)
maxcirc = float(self.__display11.text)
mincirc = float(self.__display12.text)
maxar = float(self.__display13.text)
minar = float(self.__display14.text)
maxfer = float(self.__display15.text)
minfer = float(self.__display16.text)
maxmean = float(self.__display17.text)
minmean = float(self.__display18.text)
maxmferet = float(self.__display21.text)
minmferet = float(self.__display22.text)
testsol = (minsol<= m.Solidity <= maxsol)
testarea = (minarea<= m.Area <= maxarea)
testcirc = (mincirc<= m.Circ <= maxcirc)
testar = (minar<= m.AR <= maxar)
testfer = (minfer<= m.MaxFeret <= maxfer)
testmean = (minmean <= m.Mean <= maxmean)
testmferet = (minmferet <= m.MinFeret <= maxmferet)
#print minmferet , m.MinFeret , maxmferet
test = (testsol+testarea+testcirc+testar+testfer+testmean+testmferet)/7
if test :
fmaj, ffmx, fa =[],[],[]
for r in m.getMidSegments(10, 40, 0)[0] :
if r == None : continue
m2=Morph(self.__impF, r)
fmaj.append(m2.Major)
ffmx.append(m2.MaxFeret)
fa.append(m2.Area)
diffmajor, diffferet, diffarea = 0,0,0
if len(fa) > 4 :
medfmaj = self.listmean(fmaj[1:-1])
medffmx = self.listmean(ffmx[1:-1])
medfa = self.listmean(fa[1:-1])
diffmajor = (max(fmaj[1:-1])-medfmaj)/medfmaj
diffferet = (max(ffmx[1:-1])-medffmx)/medffmx
diffarea = (max(fa[1:-1])-medfa)/medfa
twres.append(lab+tab+str(roi.getName())+tab+str(m.Solidity)+tab+str(m.Area)+tab+str(m.Circ)+tab+str(m.AR)+tab+str(m.MaxFeret)+tab+str(midroi.getLength())+tab+str(m.MaxFeret/midroi.getLength())+tab+str(diffmajor)+tab+str(diffferet)+tab+str(diffarea))
#print lab+tab+str(roi.getName())+tab+str(m.Solidity)+tab+str(m.Area)+tab+str(m.Circ)+tab+str(m.AR)+tab+str(m.MaxFeret)+tab+str(midroi.getLength())+tab+str(m.MaxFeret/midroi.getLength())+tab+str(diffmajor)+tab+str(diffferet)+tab+str(diffarea)
self.__impF.setRoi(roi)
self.__rm.runCommand("Add")
self.__impF.killRoi()
self.__impF.setRoi(midroi)
#self.__dictCells[str(roi.getName())]=(str(roi.getName()), lab, roi)
self.__dictCells[count]=(str(roi.getName()), lab, roi)
count=count+1
else :
#print "test falls"
continue
else :
print "out loop"
continue
straightener = Straightener()
new_ip = straightener.straighten(self.__impF, midroi, int(self.__widthl))
if int(self.__display5.text) < new_ip.getWidth() < int(self.__display6.text) :
self.__iplist.append(new_ip.convertToShort(False))
self.__display.text = self.__name + " cell " + str(len(self.__iplist))
#print "add", roi.getName(), roi.getType()
self.__cellsrois.append((midroi, pos))
self.__labels.append(self.__isF.getShortSliceLabel(pos))
#roisarray=self.__rm.getRoisAsArray()
#self.__rm.runCommand("reset")
#self.__rm.runCommand("Delete")
self.__impD.killRoi()
self.__impF.killRoi()
IJ.selectWindow(self.__impD.getTitle())
display.createDisplay("log", data.create(ImgPlus(log)))
otsu=ops.run("threshold", ops.create( dimensions2D, BitType()), imgBgs, Otsu())
display.createDisplay("thresholded", data.create(ImgPlus(otsu)))
'''
#Utility.clearOutsideRoi(imp, clone)
IJ.run(imp, "Auto Local Threshold", "method=MidGrey radius=15 parameter_1=0 parameter_2=0 white");
IJ.run(imp, "Fill Holes", "");
IJ.run(imp, "Close-", "");
IJ.run(imp, "Watershed", "");
iplus.updateAndDraw()
# create a hidden roi manager
roim = RoiManager(True)
# count the particles
countParticles(iplus, roim, 10, 200, 0.5, 1.0)
[truecolor1.getProcessor().draw(roi) for roi in roim.getRoisAsArray()]
truecolor1.updateAndDraw()
#Prefs.blackBackground = False;
#IJ.run("Make Binary", "");
#IJ.run("LoG 3D");
#IJ.run("Duplicate...", "title="+"test")
#IJ.run("RGB Stack");
#IJ.run("Convert Stack to Images");
UFOV = Duplicator().run(original) # duplicate the original image, only the CFOV
UFOV.setTitle("UFOV")
UFOV.show()
CFOV_fraction = 0.75 # choose the fraction of the UFOV that defines the CFOV
IJ.run(original,"Scale... ", "x="+str(CFOV_fraction)+" y="+str(CFOV_fraction)+" centered") # rescale bounding box to get CFOV
CFOV = Duplicator().run(original) # duplicate the original image, only the CFOV
CFOV.setTitle("CFOV")
CFOV.show()
######### Nema process including Re-bin image to larger pixels ################################################################################
desired_pixel_width = getPixel() # 6.4 mm default, remember tolerance is +/-30%
current_pixel_width = CFOV.getCalibration().pixelWidth #get pixel width, 1.16 mm
shrink_factor = int(desired_pixel_width/current_pixel_width) # must be an integer
IJ.run(CFOV, "Bin...", "x="+str(shrink_factor)+" y="+str(shrink_factor)+" bin=Sum") # run the bin plugin
IJ.run(CFOV, "Convolve...", "text1=[1 2 1\n2 4 2\n1 2 1\n] normalize") # apply the nema filter
######## Analyse pixels
CFOVpixels = CFOV.getProcessor().convertToFloat().getPixels() # access processor, get float array of pixels
CFOVmean = sum(CFOVpixels) / len(CFOVpixels)
#pixels_above_threshold = filter(lambda pix: pix > 0.75*mean, pixels) # return a new array with values above zero, use anonymous function
CFOVunifiormity = getUniformity(min(CFOVpixels), max(CFOVpixels))
######## Print results
results_string = "The integral uniformity of the CFOV is: " +str(CFOVunifiormity)+ "% for pixel size of "+str(desired_pixel_width)+" mm"
IJ.log(results_string)
IJ.showMessage(results_string)
