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 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")
