/
fluo_count.py
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/
fluo_count.py
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# Copyright 2017, Anthony Westbrook <anthony.westbrook@unh.edu>, University of New Hampshire
# To install this script as a FIJI macro, perform the following steps:
#
# 1. From the "Plugins" menu, select "Install..."
# 2. Select this script (fluo_count.py)
# 3. Accept the default installation directory
# 4. Restart FIJI
# 5. The plugin will be available to run from the "Plugins" menu at the bottom
from ij import IJ, WindowManager
from ij.measure import ResultsTable
from fiji.util.gui import GenericDialogPlus
from trainableSegmentation import WekaSegmentation
import math
import sys
import os
HEADER = "File\tProbability\tCell\tArea\tPerimeter\tCircularity\tAspectRatio\tRoundness\tSolidity"
RESULTS_FILE = "results.csv"
def setupMeasurements():
options = "area perimeter shape roundness"
IJ.run("Set Measurements...", options)
def optionsDialog():
dialog = GenericDialogPlus("Fluorescent Cell Counting")
dialog.addDirectoryField("Image Directory", "")
dialog.addFileField("Training Model", "")
dialog.addStringField("Output Subdirectory", "output", 20)
dialog.addStringField("Probability Threshold", "0.67", 20)
dialog.addStringField("Minimum Pixel Size", "2", 20)
dialog.showDialog()
# Check if canceled
if dialog.wasCanceled(): return None
textVals = [x.text for x in dialog.getStringFields()]
return textVals
def prepareImage(passDir, passFile):
# Attempt to open as image, exit if not
fullPath = os.path.join(passDir, passFile)
retImage = IJ.openImage(fullPath)
if not retImage: return None
retImage.show()
return retImage
def finalizeImage(passImage):
passImage.changes = False
passImage.close()
def analyzeImage(passImage, passModel, passProbability, passPixels, passOutput):
retResults = list()
# Apply WEKA training model to image
wekaSeg = WekaSegmentation(passImage)
wekaSeg.loadClassifier(passModel)
wekaSeg.applyClassifier(True)
# Extract first slice of probability map
wekaImg = wekaSeg.getClassifiedImage();
wekaImg.show()
IJ.selectWindow("Probability maps")
IJ.setSlice(1)
IJ.run("Duplicate...", "title=temp")
# Apply threshold and save
IJ.setThreshold(passProbability, 1, "Black & White")
fileParts = passImage.getTitle().split(".")
IJ.save(os.path.join(passOutput, "{0}-probmap.png".format(fileParts[0], '.'.join(fileParts[1:]))))
# Perform particle analysis and save
IJ.run("Analyze Particles...", "size={0}-Infinity show=Outlines pixel clear".format(passPixels))
IJ.selectWindow("Drawing of temp")
IJ.save(os.path.join(passOutput, "{0}-particles.png".format(fileParts[0], '.'.join(fileParts[1:]))))
# Get measurements (skip final row, this will correspond to legend)
tableResults = ResultsTable.getResultsTable()
for rowIdx in range(tableResults.size() - 1):
retResults.append(tableResults.getRowAsString(rowIdx).split())
# Close interim windows
IJ.run("Close")
IJ.selectWindow("temp")
IJ.run("Close")
IJ.selectWindow("Probability maps")
IJ.run("Close")
return retResults
def processImages(passOptions):
optImageDir = passOptions[0]
optModel = passOptions[1]
optOutput = passOptions[2]
optProbability = 1.0 - float(passOptions[3])
optPixels = int(passOptions[4])
retResults = dict()
# Iterate through all images in chosen directory
root, dirs, files = next(os.walk(optImageDir))
for curFile in files:
# Prepare image
curImage = prepareImage(root, curFile)
if not curImage: continue
# Analyze image
retResults[curFile] = analyzeImage(curImage, optModel, optProbability, optPixels, os.path.join(options[0], optOutput))
# Close image
finalizeImage(curImage)
return retResults
def writeResults(passResults, passOutput, passProbability):
# Create results TSV
with open(passOutput, "w") as fileHandle:
fileHandle.write("{0}\n".format(HEADER))
for image in passResults:
for row in passResults[image]:
fieldText = "\t".join(map(str, row))
fileHandle.write("{0}\t{1}\t{2}\n".format(image, passProbability, fieldText))
# Setup measurements to record in CSV
setupMeasurements()
# Present user definable options, then process
options = optionsDialog()
if options:
# Prepare output directory
if not os.path.exists(os.path.join(options[0], options[2])):
os.makedirs(os.path.join(options[0], options[2]))
# Analyze images
results = processImages(options)
writeResults(results, os.path.join(options[0], options[2], RESULTS_FILE), options[3])
IJ.showMessage("Analysis Complete!")