def getSpots(imp, channel, detector_type, radius, threshold, overlay,
roi_type="large", roi_color=ColorRGB("blue")):
""" Performs the detection, adding spots to the image overlay
:imp: The image (ImagePlus) being analyzed
:channel: The target channel
:detector_type: A string describing the detector: "LoG" or "DoG"
:radius: Spot radius (NB: trackmate GUI accepts diameter)
:threshold: Quality cutoff value
:overlay: The image overlay to store spot (MultiPoint) ROIs
:roi_type: A string describing how spot ROIs should be displayed
:returns: The n. of detected spots
"""
settings = Settings()
settings.setFrom(imp)
settings.detectorFactory = (LogDetectorFactory() if "LoG" in detector_type
else DogDetectorFactory())
settings.detectorSettings = {
DK.KEY_DO_SUBPIXEL_LOCALIZATION: False,
DK.KEY_DO_MEDIAN_FILTERING: True,
DK.KEY_TARGET_CHANNEL: channel,
DK.KEY_RADIUS: radius,
DK.KEY_THRESHOLD: threshold,
}
trackmate = TrackMate(settings)
if not trackmate.execDetection():
lservice.error(str(trackmate.getErrorMessage()))
return 0
model = trackmate.model
spots = model.getSpots()
count = spots.getNSpots(False)
ch_id = "Spots Ch%d" % channel
if count > 0:
roi = None
cal = imp.getCalibration()
t_pos = imp.getT()
if (t_pos > 1):
lservice.warn("Only frame %d was considered..." % t_pos)
for spot in spots.iterable(False):
x = cal.getRawX(spot.getFeature(spot.POSITION_X))
y = cal.getRawY(spot.getFeature(spot.POSITION_Y))
z = spot.getFeature(spot.POSITION_Z)
if z == 0 or not cal.pixelDepth or cal.pixelDepth == 0:
z = 1
else:
z = int(z // cal.pixelDepth)
imp.setPosition(channel, z, t_pos)
if roi is None:
roi = PointRoi(int(x), int(y), imp)
else:
roi.addPoint(imp, x, y)
roi.setStrokeColor(colorRGBtoColor(roi_color))
if "large" in roi_type:
roi.setPointType(3)
roi.setSize(4)
else:
roi.setPointType(2)
roi.setSize(1)
overlay.add(roi, ch_id)
return count
def updatePointRoi(self):
# Surround with try/except to prevent blocking
# ImageJ's stack slice updater thread in case of error.
try:
# Update PointRoi
self.imp.killRoi()
points = self.nuclei[
self.imp.getSlice() -
1] # map 1-based slices to 0-based nuclei Z coords
if 0 == len(points):
IJ.log("No points for slice " + str(self.imp.getSlice()))
return
roi = PointRoi()
# Style: large, red dots
roi.setSize(4) # ranges 1-4
roi.setPointType(2) # 2 is a dot (filled circle)
roi.setFillColor(Color.red)
roi.setStrokeColor(Color.red)
# Add points
for point in points: # points are floats
roi.addPoint(self.imp, int(point[0]), int(point[1]))
self.imp.setRoi(roi)
except:
IJ.error(sys.exc_info())
# In the differece of gaussian peak detection, the img acts as the interval
# within which to look for peaks. The processing is done on the infinite imgE.
dog = DogDetection(imgE, img, calibration, sigmaSmaller, sigmaLarger,
extremaType, minPeakValue, normalizedMinPeakValue)
peaks = dog.getPeaks()
# Create a PointRoi from the DoG peaks, for visualization
roi = PointRoi(0, 0)
# A temporary array of integers, one per dimension the image has
p = zeros(img.numDimensions(), 'i')
# Load every peak as a point in the PointRoi
for peak in peaks:
# Read peak coordinates into an array of integers
peak.localize(p)
roi.addPoint(imp, p[0], p[1])
imp.setRoi(roi)
# Now, iterate each peak, defining a small interval centered at each peak,
# and measure the sum of total pixel intensity,
# and display the results in an ImageJ ResultTable.
table = ResultsTable()
for peak in peaks:
# Read peak coordinates into an array of integers
peak.localize(p)
# Define limits of the interval around the peak:
# (sigmaSmaller is half the radius of the embryo)
minC = [p[i] - sigmaSmaller for i in range(img.numDimensions())]
maxC = [p[i] + sigmaSmaller for i in range(img.numDimensions())]
# In the differece of gaussian peak detection, the img acts as the interval
# within which to look for peaks. The processing is done on the infinite imgE.
dog = DogDetection(imgE, img, calibration, sigmaSmaller, sigmaLarger,
extremaType, minPeakValue, normalizedMinPeakValue)
peaks = dog.getPeaks()
# Create a PointRoi from the DoG peaks, for visualization
roi = PointRoi(0, 0)
# A temporary array of integers, one per dimension the image has
p = zeros(img.numDimensions(), 'i')
# Load every peak as a point in the PointRoi
for peak in peaks:
# Read peak coordinates into an array of integers
peak.localize(p)
roi.addPoint(imp, p[0], p[1])
imp.setRoi(roi)
# Now, iterate each peak, defining a small interval centered at each peak,
# and measure the sum of total pixel intensity,
# and display the results in an ImageJ ResultTable.
table = ResultsTable()
for peak in peaks:
# Read peak coordinates into an array of integers
peak.localize(p)
# Define limits of the interval around the peak:
# (sigmaSmaller is half the radius of the embryo)
minC = [p[i] - sigmaSmaller for i in range(img.numDimensions())]
maxC = [p[i] + sigmaSmaller for i in range(img.numDimensions())]
# of each pair (PointMatch) of points. That is, to the point from
# the first image.
PointMatch.apply(inliers, model)
except NotEnoughDataPointsException, e:
print e
if modelFound:
# Store inlier pointmatches: the spatially coherent subset
roi1pm = PointRoi()
roi1pm.setName("matches in cut1")
roi2pm = PointRoi()
roi2pm.setName("matches in cut2")
for pm in inliers:
p1 = pm.getP1()
roi1pm.addPoint(p1.getL()[0], p1.getL()[1])
p2 = pm.getP2()
roi2pm.addPoint(p2.getL()[0], p2.getL()[1])
roi_manager.addRoi(roi1pm)
roi_manager.addRoi(roi2pm)
# Register images
# Transform the top-left and bottom-right corner of imp2
# (use applyInverse: the model describes imp1 -> imp2)
x0, y0 = model.applyInverse([0, 0])
x1, y1 = model.applyInverse([imp2.getWidth(), 0])
x2, y2 = model.applyInverse([0, imp2.getHeight()])
x3, y3 = model.applyInverse([imp2.getWidth(), imp2.getHeight()])
xtopleft = min(x0, x1, x2, x3)
ytopleft = min(y0, y1, y2, y3)
def process(srcDir, dstDir, currentDir, fileName, keepDirectories, Channel_1, Channel_2, radius_background, sigmaSmaller, sigmaLarger, minPeakValue, min_dist):
IJ.run("Close All", "")
# Opening the image
IJ.log("Open image file:" + fileName)
#imp = IJ.openImage(os.path.join(currentDir, fileName))
#imp = IJ.getImage()
imp = BF.openImagePlus(os.path.join(currentDir, fileName))
imp = imp[0]
# getDimensions(width, height, channels, slices, frames)
IJ.log("Computing Max Intensity Projection")
if imp.getDimensions()[3] > 1:
imp_max = ZProjector.run(imp,"max")
else:
imp_max = imp
ip1, ip2 = extract_channel(imp_max, Channel_1, Channel_2)
IJ.log("Substract background")
imp1, imp2 = back_substraction(ip1, ip2, radius_background)
IJ.log("Finding Peaks")
ip1_1, ip2_1, peaks_1, peaks_2 = find_peaks(imp1, imp2, sigmaSmaller, sigmaLarger, minPeakValue)
# Create a PointRoi from the DoG peaks, for visualization
roi_1 = PointRoi(0, 0)
roi_2 = PointRoi(0, 0)
roi_3 = PointRoi(0, 0)
roi_4 = PointRoi(0, 0)
# A temporary array of integers, one per dimension the image has
p_1 = zeros(ip1_1.numDimensions(), 'i')
p_2 = zeros(ip2_1.numDimensions(), 'i')
# Load every peak as a point in the PointRoi
for peak in peaks_1:
# Read peak coordinates into an array of integers
peak.localize(p_1)
roi_1.addPoint(imp1, p_1[0], p_1[1])
for peak in peaks_2:
# Read peak coordinates into an array of integers
peak.localize(p_2)
roi_2.addPoint(imp2, p_2[0], p_2[1])
# Chose minimum distance in pixel
#min_dist = 20
for peak_1 in peaks_1:
peak_1.localize(p_1)
for peak_2 in peaks_2:
peak_2.localize(p_2)
d1 = distance(p_1, p_2)
if d1 < min_dist:
roi_3.addPoint(imp1, p_2[0], p_2[1])
break
for peak_2 in peaks_2:
peak_2.localize(p_2)
for peak_1 in peaks_1:
peak_1.localize(p_1)
d2 = distance(p_2, p_1)
if d2 < min_dist:
roi_4.addPoint(imp1, p_2[0], p_2[1])
break
cal = imp.getCalibration()
min_distance = str(round((cal.pixelWidth * min_dist),1))
table = ResultsTable()
table.incrementCounter()
table.addValue("Numbers of Neuron Markers", roi_1.getCount(0))
table.addValue("Numbers of Glioma Markers", roi_2.getCount(0))
table.addValue("Numbers of Glioma within %s um of Neurons" %(min_distance), roi_3.getCount(0))
table.addValue("Numbers of Neurons within %s um of Glioma" %(min_distance), roi_4.getCount(0))
#table.show("Results Analysis")
saveDir = currentDir.replace(srcDir, dstDir) if keepDirectories else dstDir
if not os.path.exists(saveDir):
os.makedirs(saveDir)
IJ.log("Saving to" + saveDir)
table.save(os.path.join(saveDir, fileName + ".csv"))
IJ.selectWindow("Log")
IJ.saveAs("Text", os.path.join(saveDir, fileName + ".csv"));
def getSpots(imp,
channel,
detector_type,
radius,
threshold,
overlay,
roi_type="large",
roi_color=ColorRGB("blue")):
""" Performs the detection, adding spots to the image overlay
:imp: The image (ImagePlus) being analyzed
:channel: The target channel
:detector_type: A string describing the detector: "LoG" or "DoG"
:radius: Spot radius (NB: trackmate GUI accepts diameter)
:threshold: Quality cutoff value
:overlay: The image overlay to store spot (MultiPoint) ROIs
:roi_type: A string describing how spot ROIs should be displayed
:returns: The n. of detected spots
"""
settings = Settings()
settings.setFrom(imp)
settings.detectorFactory = (LogDetectorFactory() if "LoG" in detector_type
else DogDetectorFactory())
settings.detectorSettings = {
DK.KEY_DO_SUBPIXEL_LOCALIZATION: False,
DK.KEY_DO_MEDIAN_FILTERING: True,
DK.KEY_TARGET_CHANNEL: channel,
DK.KEY_RADIUS: radius,
DK.KEY_THRESHOLD: threshold,
}
trackmate = TrackMate(settings)
if not trackmate.execDetection():
lservice.error(str(trackmate.getErrorMessage()))
return 0
model = trackmate.model
spots = model.getSpots()
count = spots.getNSpots(False)
ch_id = "Spots Ch%d" % channel
if count > 0:
roi = None
cal = imp.getCalibration()
t_pos = imp.getT()
if (t_pos > 1):
lservice.warn("Only frame %d was considered..." % t_pos)
for spot in spots.iterable(False):
x = cal.getRawX(spot.getFeature(spot.POSITION_X))
y = cal.getRawY(spot.getFeature(spot.POSITION_Y))
z = spot.getFeature(spot.POSITION_Z)
if z == 0 or not cal.pixelDepth or cal.pixelDepth == 0:
z = 1
else:
z = int(z // cal.pixelDepth)
imp.setPosition(channel, z, t_pos)
if roi is None:
roi = PointRoi(int(x), int(y), imp)
else:
roi.addPoint(imp, x, y)
roi.setStrokeColor(colorRGBtoColor(roi_color))
if "large" in roi_type:
roi.setPointType(3)
roi.setSize(4)
else:
roi.setPointType(2)
roi.setSize(1)
overlay.add(roi, ch_id)
return count
def processOneImage(inputDir):
tmp = glob.glob(os.path.join(inputDir, "fibrone*"))
fibronectin = tmp[0]
tmp = glob.glob(os.path.join(inputDir, "nucleus*"))
nucleus = tmp[0]
tmp = glob.glob(os.path.join(inputDir, "actin*"))
actin = tmp[0]
# read sample name
head,tail = os.path.split(inputDir)
sample = tail.replace(".tif_Files","")
# original images
imp_fn_orig = IJ.openImage(fibronectin)
imp_nuc_orig = IJ.openImage(nucleus)
# work copies
imp_fn = imp_fn_orig.duplicate()
imp_nuc = imp_nuc_orig.duplicate()
IJ.run(imp_fn,"Set Scale...", "distance=1 known=1 pixel=1 unit=pixels")
IJ.run(imp_fn,"Gaussian Blur...","sigma=5")
IJ.run(imp_fn,"Make Binary","")
IJ.run(imp_nuc,"Set Scale...", "distance=1 known=1 pixel=1 unit=pixels")
IJ.run(imp_nuc,"Gaussian Blur...","sigma=5")
IJ.run(imp_nuc,"Make Binary","")
# get moments of the fibronectin image
moments_file = os.path.join(OUTPUT, sample + " moments.txt")
printMoments(fibronectin, moments_file)
moments = readMoments(moments_file)
print moments.m00
sys.exit()
# centroid of fibronectin anchor
centers = getParticleCenters(imp_fn)
cxfn = int(round(centers[0][0]))
cyfn = int(round(centers[1][0]))
fn_centroid_roi = PointRoi(cxfn,cyfn)
fn_centroid_roi.setDefaultMarkerSize("Large")
fn_centroid_roi.setStrokeColor(Color.CYAN)
# center of mass of nucleus
centers = getParticleCenters(imp_nuc)
cxnuc = int(round(centers[2][0]))
cynuc = int(round(centers[3][0]))
nuc_com_roi = PointRoi(cxnuc,cynuc)
nuc_com_roi.setDefaultMarkerSize("Large")
# skeletonize fibronectin anchor to find its orientation
IJ.run(imp_fn,"Skeletonize","")
skel = AnalyzeSkeleton_()
skel.setup("",imp_fn)
skelResult = skel.run(skel.NONE, False, True, None, True, True)
graph = skelResult.getGraph()
print len(graph)
print skelResult.getNumOfTrees()
# find the longest graph
graph = sorted(graph, key=lambda g: getGraphLength(g), reverse=True)
graph = graph[0]
edges = graph.getEdges()
# find longest edge, the main axis of the anchor
edges = sorted(edges, key=lambda edge: edge.getLength(), reverse=True)
#for e in edges:
# print e.getLength()
v1long = edges[0].getV1()
v2long = edges[0].getV2()
x1 = v1long.getPoints()[0].x
y1 = v1long.getPoints()[0].y
x2 = v2long.getPoints()[0].x
y2 = v2long.getPoints()[0].y
anchor_roi = PointRoi(x1,y1)
anchor_roi = anchor_roi.addPoint(x2,y2)
# find top and bottom vertices of the graph
vertices = graph.getVertices()
vertices = sorted(vertices, key=lambda vertex: vertex.getPoints()[0].y)
v1short = vertices[len(vertices)-1]
v2short = vertices[0]
x3 = v1short.getPoints()[0].x
y3 = v1short.getPoints()[0].y
x4 = v2short.getPoints()[0].x
y4 = v2short.getPoints()[0].y
anchor_roi = anchor_roi.addPoint(x3,y3)
anchor_roi = anchor_roi.addPoint(x4,y4)
# calculate angles
a1 = math.atan(abs(float(y2-y1)/float(x2-x1))) / math.pi * 360
a2 = math.atan(abs(float(x4-x3)/float(y4-y3))) / math.pi * 360
amean = float((a1+a2)/2)
dx = cxfn-cxnuc
print sample,cxfn,cyfn,cxnuc,cynuc,dx,math.cos(amean)*dx,x1,y1,x2,y2,x3,y3,x4,y4,a1,a2
# create composite
comp = ImagePlus("composite",imp_nuc_orig.getProcessor().convertToColorProcessor())
comp.getProcessor().setChannel(2,imp_fn_orig.getProcessor())
comp.getProcessor().setChannel(3,imp_fn.getProcessor())
comp.show()
comp.getProcessor().drawRoi(fn_centroid_roi)
comp.getProcessor().drawRoi(nuc_com_roi)
comp.getProcessor().drawRoi(anchor_roi)
comp.repaintWindow()
IJ.saveAsTiff(comp, os.path.join(OUTPUT,sample + ".tif"))
# In the differece of gaussian peak detection, the img acts as the interval
# within which to look for peaks. The processing is done on the infinite imgE.
dog = DogDetection(imgE, img, calibration, sigmaSmaller, sigmaLarger,
extremaType, minPeakValue, normalizedMinPeakValue)
peaks = dog.getSubpixelPeaks()
# Create a PointRoi from the DoG peaks, for visualization
roi = PointRoi(0, 0)
# A temporary array of integers, one per dimension the image has
p = zeros(img.numDimensions(), 'd')
# Load every peak as a point in the PointRoi
for peak in peaks:
# Read peak coordinates into an array of integers
peak.localize(p)
roi.addPoint(imp, int(p[0]), int(p[1]))
imp.setRoi(roi)
# Now, iterate each peak, defining a small interval centered at each peak,
# and measure the sum of total pixel intensity,
# and display the results in an ImageJ ResultTable.
table = ResultsTable()
for peak in peaks:
# Read peak coordinates into an array of integers
peak.localize(p)
# Define limits of the interval around the peak:
# (sigmaSmaller is half the radius of the embryo)
minC = [int(p[i] - sigmaSmaller) for i in xrange(img.numDimensions())]
maxC = [int(p[i] + sigmaSmaller) for i in xrange(img.numDimensions())]
def run():
IJ.run("Close All", "")
IJ.log("\\Clear")
IJ.log("Find_close_peaks")
imp = IJ.run("Bio-Formats Importer")
imp = IJ.getImage()
Channel_1, Channel_2, radius_background, sigmaSmaller, sigmaLarger, minPeakValue, min_dist = getOptions()
IJ.log("option used:" \
+ "\n" + "channel 1:" + str(Channel_1) \
+ "\n" + "channel 2:"+ str(Channel_2) \
+ "\n" + "Radius Background:"+ str(radius_background) \
+ "\n" + "Smaller Sigma:"+ str(sigmaSmaller) \
+ "\n" + "Larger Sigma:"+str(sigmaLarger) \
+ "\n" + "Min Peak Value:"+str(minPeakValue) \
+ "\n" + "Min dist between peaks:"+str(min_dist))
IJ.log("Computing Max Intensity Projection")
if imp.getDimensions()[3] > 1:
imp_max = ZProjector.run(imp,"max")
#imp_max = IJ.run("Z Project...", "projection=[Max Intensity]")
#imp_max = IJ.getImage()
else:
imp_max = imp
ip1, ip2 = extract_channel(imp_max, Channel_1, Channel_2)
imp1, imp2 = back_substraction(ip1, ip2, radius_background)
imp1.show()
imp2.show()
IJ.log("Finding Peaks")
ip1_1, ip2_1, peaks_1, peaks_2 = find_peaks(imp1, imp2, sigmaSmaller, sigmaLarger, minPeakValue)
# Create a PointRoi from the DoG peaks, for visualization
roi_1 = PointRoi(0, 0)
roi_2 = PointRoi(0, 0)
roi_3 = PointRoi(0, 0)
roi_4 = PointRoi(0, 0)
# A temporary array of integers, one per dimension the image has
p_1 = zeros(ip1_1.numDimensions(), 'i')
p_2 = zeros(ip2_1.numDimensions(), 'i')
# Load every peak as a point in the PointRoi
for peak in peaks_1:
# Read peak coordinates into an array of integers
peak.localize(p_1)
roi_1.addPoint(imp1, p_1[0], p_1[1])
for peak in peaks_2:
# Read peak coordinates into an array of integers
peak.localize(p_2)
roi_2.addPoint(imp2, p_2[0], p_2[1])
# Chose minimum distance in pixel
#min_dist = 20
for peak_1 in peaks_1:
peak_1.localize(p_1)
for peak_2 in peaks_2:
peak_2.localize(p_2)
d1 = distance(p_1, p_2)
if d1 < min_dist:
roi_3.addPoint(imp1, p_2[0], p_2[1])
break
for peak_2 in peaks_2:
peak_2.localize(p_2)
for peak_1 in peaks_1:
peak_1.localize(p_1)
d2 = distance(p_2, p_1)
if d2 < min_dist:
roi_4.addPoint(imp1, p_2[0], p_2[1])
break
rm = RoiManager.getInstance()
if not rm:
rm = RoiManager()
rm.reset()
rm.addRoi(roi_1)
rm.addRoi(roi_2)
rm.addRoi(roi_3)
rm.addRoi(roi_4)
rm.select(0)
rm.rename(0, "ROI neuron")
rm.runCommand("Set Color", "yellow")
rm.select(1)
rm.rename(1, "ROI glioma")
rm.runCommand("Set Color", "blue")
rm.select(2)
rm.rename(2, "ROI glioma touching neurons")
rm.runCommand("Set Color", "red")
rm.select(3)
rm.rename(3, "ROI neurons touching glioma")
rm.runCommand("Set Color", "green")
rm.runCommand(imp1, "Show All")
#Change distance to be in um
cal = imp.getCalibration()
min_distance = str(round((cal.pixelWidth * min_dist),1))
table = ResultsTable()
table.incrementCounter()
table.addValue("Numbers of Neuron Markers", roi_1.getCount(0))
table.addValue("Numbers of Glioma Markers", roi_2.getCount(0))
table.addValue("Numbers of Glioma within %s um of Neurons" %(min_distance), roi_3.getCount(0))
table.addValue("Numbers of Neurons within %s um of Glioma" %(min_distance), roi_4.getCount(0))
table.show("Results Analysis")
