def Dialog(imp): dpi = 300 # a4 width in inches defaultWidth = 11.69 defaultHeight = defaultWidth / ratio defaultAspectRatio = 1.41 if imp: gd = GenericDialogPlus("Cover Maker") gd.addMessage("Input Options") gd.addFileField("Select image database", "", 20) gd.addMessage("Cover Maker Options") gd.addNumericField("tile width", 12, 0) gd.addNumericField("tile height", 9, 0) gd.showDialog() if gd.wasCanceled(): print "User canceled dialog!" return databasepath = gd.getNextString() tilewidth = gd.getNextNumber() tileheight = gd.getNextNumber() return databasepath, imp.getWidth(), imp.getHeight(), int( tilewidth), int(tileheight) else: IJ.showMessage("You should have at least one image open.")
def get_user_params(event): """ Allows user to select file and user parameters Parameters ---------- event : Event Waits for get_user_params_JB JButton to be pressed Returns ------- dict Dict containing filename, number of Gaussians to fit, % of dataset to import, whether to specify grey value limits, minimum grey value and maximum grey value to consider (optional) """ # Open user dialog gui = GenericDialogPlus("Define user parameters") gui.addFileField("Image filename", "Select image file") gui.addNumericField("Number of Gaussians to fit: ", 2, 0) gui.addNumericField("Percentage of dataset to import: ", 15, 0) gui.addCheckbox("Specify grey value limits?", False) gui.addNumericField("Min grey value (optional): ", 0, 0) gui.addNumericField("Max grey value (optional): ", 255, 0) gui.showDialog() # Extract user parameters if gui.wasOKed(): user_params = {} # empty dict user_params['img_fname'] = str(gui.getNextString()) user_params['n_gaussians'] = int(gui.getNextNumber()) user_params['pct_stack_import'] = float(gui.getNextNumber()) user_params['specify_gv'] = gui.getNextBoolean() user_params['min_gv'] = float(gui.getNextNumber()) user_params['max_gv'] = float(gui.getNextNumber()) # Create results directory results_dir = os.path.splitext(user_params['img_fname'])[0] + "_results" if os.path.isdir(results_dir) == False: os.mkdir(results_dir) print("Results directory: {}".format(results_dir)) # Write user parameters to text file user_params_fname = os.path.join(results_dir, "Users_Params.csv") with open(user_params_fname, "wb") as f: w = csv.DictWriter(f, user_params.keys()) w.writeheader() w.writerow(user_params) # Write directory to look for user params to text file in main/ temp_user_dir = os.path.join(os.path.dirname(__file__), "temp_user_dir.txt") if os.path.isfile(temp_user_dir) == True: os.remove(temp_user_dir) # delete temp_user_dir.txt if present f = open(temp_user_dir, "w") f.write(user_params_fname) f.close() return user_params
def dialog(imp2, labelColorBarImp): gd = GenericDialogPlus("ROI labeller") categories=11 #placeholder slider variables so the class can be initiated slider1=0 slider2=0 slider3=0 slider4=0 test=previewLabelerAndListeners(imp2, slider1,slider2,slider3,slider4, gd) for i in range(1,categories): gd.addButton("label "+str(i), test) gd.addImage(labelColorBarImp) #imp7.close() - causes an error as image needed for the dialog gd.addButton("Set top",test) gd.addButton("Whole stack",test) gd.addButton("Set bottom", test) gd.addSlider("Top",1, imp2.getStackSize(), 1) gd.addSlider("Bottom",1, imp2.getStackSize(),imp2.getStackSize()) gd.addSlider("Minimum ROI size", 0, 9999, 0, 1) gd.addSlider("Maximum ROI size", 1, 10000, 10000, 1) slider1=gd.getSliders().get(0) slider2=gd.getSliders().get(1) test.slider1=slider1 test.slider2=slider2 slider3=gd.getSliders().get(2) slider4=gd.getSliders().get(3) test.slider3=slider3 test.slider4=slider4 slider1.addAdjustmentListener(test) slider2.addAdjustmentListener(test) slider3.addAdjustmentListener(test) slider4.addAdjustmentListener(test) gd.addChoice("Apply labeling to:", ["(Sub)stack", "Slice"], "(Sub)stack") gd.setLayout(GridLayout(0,2)) gd.setModal(False) buttons=gd.getButtons() gd.showDialog() while ((not gd.wasCanceled()) and not (gd.wasOKed())): Thread.sleep(50) return test
def getOptions(): gui = GenericDialogPlus("Options for Moving TIFF files") gui.addMessage("Select the number of imput files to be used per stitched image") gui.addSlider("", 2, 10, 2) gui.addFileField("Select csv file", "") gui.addMessage("Add key values and corresponding genotype, when finished, leave fields empty") gui.addStringField("Key :", prefs.get(None, "key1", "p")) gui.addToSameRow() gui.addStringField("Genotype :", prefs.get(None, "genotype1", "PROM-1_HA")) gui.addStringField("Key :", prefs.get(None, "key2", "p")) gui.addToSameRow() gui.addStringField("Genotype :", prefs.get(None, "genotype2", "PROM-1_HA")) gui.addStringField("Key :", prefs.get(None, "key3", "p")) gui.addToSameRow() gui.addStringField("Genotype :", prefs.get(None, "genotype3", "PROM-1_HA")) gui.addStringField("Key :", prefs.get(None, "key4", "p")) gui.addToSameRow() gui.addStringField("Genotype :", prefs.get(None, "genotype4", "PROM-1_HA")) gui.addStringField("Key :", prefs.get(None, "key5", "p")) gui.addToSameRow() gui.addStringField("Genotype :", prefs.get(None, "genotype5", "PROM-1_HA")) gui.showDialog() if gui.wasOKed(): tiffs_per_image = int(gui.getNextNumber()) csv_file = str(gui.getNextString()) genotypes = {} for i in range(5): key = str(gui.getNextString()) value = str(gui.getNextString()) if key != "": genotypes[key] = value else: return if len(genotypes) > 0: a = list(genotypes.items())[0] prefs.put(None, "key1", a[0]) prefs.put(None, "genotype1", a[1]) if len(genotypes) > 1: a = list(genotypes.items())[1] prefs.put(None, "key2", a[0]) prefs.put(None, "genotype2", a[1]) if len(genotypes) > 2: a = list(genotypes.items())[2] prefs.put(None, "key3", a[0]) prefs.put(None, "genotype3", a[1]) if len(genotypes) > 3: a = list(genotypes.items())[3] prefs.put(None, "key4", a[0]) prefs.put(None, "genotype4", a[1]) if len(genotypes) > 4: a = list(genotypes.items())[4] prefs.put(None, "key5", a[0]) prefs.put(None, "genotype5", a[1]) return tiffs_per_image, csv_file, genotypes
def DialogGenerate(imageBaseDir, summary): dpi = 300 defaultAspectRatio = 1.33 defaultTileWidth = 15 defaultOriginalWidth = 150 defaultOriginalHeight = 113 defaultTileHeight = round(defaultTileWidth / defaultAspectRatio) gd = GenericDialogPlus("Cover Maker") gd.addMessage("Prepare Image database") gd.addDirectoryField("Select base directory containing images", imageBaseDir, 20) gd.addMessage(summary) gd.addNumericField("Aspect ratio", defaultAspectRatio, 2) gd.addNumericField("Original width", defaultOriginalWidth, 0) gd.addNumericField("Original height", defaultOriginalHeight, 0) gd.addNumericField("minimal tile width", defaultTileWidth, 0) gd.addNumericField("maximal tile width", defaultTileWidth, 0) gd.addNumericField("minimal tile height", defaultTileHeight, 0) gd.addNumericField("maximal tile height", defaultTileHeight, 0) fields = gd.getNumericFields() aspRatio = fields.get(0) minw = fields.get(3) maxw = fields.get(4) minh = fields.get(5) maxh = fields.get(6) # resolution and size listener textListener = RatioToDim(aspRatio, minw, maxw, minh, maxh) aspRatio.addTextListener(textListener) minw.addTextListener(textListener) maxw.addTextListener(textListener) gd.showDialog() if gd.wasCanceled(): print "User canceled dialog!" return imageBaseDir = gd.getNextString() aspectRatio = gd.getNextNumber() majorWidth = gd.getNextNumber() majorHeight = gd.getNextNumber() mintilewidth = gd.getNextNumber() maxtilewidth = gd.getNextNumber() return int(mintilewidth), int(maxtilewidth), imageBaseDir, float( aspectRatio), int(majorWidth), int(majorHeight)
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 optionsDialog(): dialog = GenericDialogPlus("Automated Weka Percentages") dialog.addDirectoryField("Image Directory", "") dialog.addFileField("Training Model", "") dialog.addStringField("Output Subdirectory", "output", 20) dialog.addStringField("Probability Threshold", "0.75", 20) dialog.showDialog() # Check if canceled if dialog.wasCanceled(): return None textVals = [x.text for x in dialog.getStringFields()] #boolVals = [x.getState() for x in dialog.getCheckboxes()] return textVals
def openMainDialogBox(): #od = OpenDialog("Selectionner un fichier") #folder = od.getDirectory() #IJ.log(folder); #filename = od.getFileName() #intérêt de récupérer le nom du fichier -> récupérer l'extension #extension = od.getFileName().split('.').pop() #Array.pop(). Pratique pour faire une fonction getExtension() #IJ.log(folder+filename) # Create an instance of GenericDialogPlus mainDialogBox = GenericDialogPlus("Restack Tiff deconvolved images from Huygens") mainDialogBox.addMessage("Ne fonctionnera correctement que si les noms des fichiers images de sortie de Huygens ont ete laisses intacts. Ne pas les modifier.") #mainDialogBox.addButton("Ouvrir image", imageSelectionListener()) mainDialogBox.addMessage("------------------------------------------") mainDialogBox.addDirectoryField("Choisir un repertoire-cible", "None") mainDialogBox.addMessage("------------------------------------------") mainDialogBox.addDirectoryField("Choisir un repertoire pour deposer les piles d'images", "None") mainDialogBox.addMessage("------------------------------------------") #Select File Type choixType = ["1 fichier par canal (NOM_FICHIER_chXX.tif)", "1 fichier par canal + temps (NOM_FICHIER_tXX_chXX.tif)", "1 fichier par canal et par profondeur (NOM_FICHIER_zXX_chXX.tif)", "1 fichier par canal et par profondeur + temps (NOM_FICHIER_tXX_zXX_chXX.tif)"] selectionType = choixType[0] mainDialogBox.addChoice("Selectionner type de fichiers",choixType,selectionType) mainDialogBox.addMessage("------------------------------------------") choixDisplayMode = ["Color", "Greyscale", "Composite"] selectionDisplayModeDefaut = choixDisplayMode[0] mainDialogBox.addChoice("Color Display Mode",choixDisplayMode,selectionDisplayModeDefaut) mainDialogBox.addMessage("------------------------------------------") #Affichage de la boîte de dialogue mainDialogBox.showDialog(); #Récupération choix folder = mainDialogBox.getNextString() save_folder = mainDialogBox.getNextString() vecteurChoix=mainDialogBox.getChoices() selectionTypeFichier = vecteurChoix[0] valeurSelectionTypeFichier = str(selectionTypeFichier.getSelectedItem()) selectionDisplayMode = vecteurChoix[1] valeurSelectionDisplayMode = str(selectionDisplayMode.getSelectedItem()) if mainDialogBox.wasCanceled() == True: print("Canceled, Values set to None") folder = None save_folder = None valeurSelectionTypeFichier = None valeurSelectionDisplayMode = None return folder, save_folder, valeurSelectionTypeFichier, valeurSelectionDisplayMode
def DialogAnalyze(): dpi = 300 defaultAspectRatio = 1.41 gd = GenericDialogPlus("Cover Maker") gd.addMessage("Prepare Image database") gd.addDirectoryField("Select base directory containing images", "", 20) gd.showDialog() if gd.wasCanceled(): print "User canceled dialog!" return imageBaseDir = gd.getNextString() return imageBaseDir
def optionsDialog(): dialog = GenericDialogPlus("Cilia Sizes") dialog.addDirectoryField("Image Directory", DEFAULT_DIR) dialog.addFileField("Training Model", DEFAULT_TRAIN) dialog.addStringField("Output Subdirectory", "output", 20) dialog.addStringField("Confocal Channel", "1", 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 get_parameters(p): gd = GenericDialogPlus("Please enter parameters") for k in p['expose_to_gui']['value']: if p[k]['type'] == 'boolean': gd.addCheckbox(k, p[k]['value']) elif p[k]['type'] == 'folder': gd.addDirectoryField(k, p[k]['value'], 100) elif p[k]['type'] == 'file': gd.addFileField(k, p[k]['value'], 100) elif p[k]['type'] == 'string': if p[k]['choices']: gd.addChoice(k, p[k]['choices'], p[k]['value']) else: gd.addStringField(k, p[k]['value']) elif p[k]['type'] == 'int': if p[k]['choices']: gd.addChoice(k, p[k]['choices'], p[k]['value']) else: gd.addNumericField(k, p[k]['value'], 0) elif p[k]['type'] == 'float': gd.addNumericField(k, p[k]['value'], 2) gd.showDialog() if gd.wasCanceled(): return for k in p['expose_to_gui']['value']: if p[k]['type'] == 'boolean': p[k]['value'] = gd.getNextBoolean() elif p[k]['type'] == 'folder' or p[k]['type'] == 'file': p[k]['value'] = gd.getNextString() elif p[k]['type'] == 'string': if p[k]['choices']: p[k]['value'] = gd.getNextChoice() else: p[k]['value'] = gd.getNextString() elif p[k]['type'] == 'int': if p[k]['choices']: p[k]['value'] = int(gd.getNextChoice()) else: p[k]['value'] = int(gd.getNextNumber()) elif p[k]['type'] == 'float': p[k]['value'] = gd.getNextNumber() return p
def SaveDialog(imp): dpi = 300 # a4 width in inches defaultWidth = 11.69 defaultHeight = defaultWidth / ratio defaultAspectRatio = 1.41 if imp: gd = GenericDialogPlus("Cover Maker") gd.addMessage("Saving options") gd.addNumericField("resolution (dpi)", dpi, 0) gd.addNumericField("width (pixels)", defaultWidth * dpi, 0) gd.addNumericField("height (pixels)", defaultHeight * dpi, 0) gd.addNumericField("width (inches)", defaultWidth, 2) gd.addNumericField("height (inches)", defaultHeight, 2) gd.addFileField("Select Originals database", "", 20) fields = gd.getNumericFields() resField = fields.get(0) widthPixels = fields.get(1) heightPixels = fields.get(2) widthInches = fields.get(3) heightInches = fields.get(4) # resolution and size listener textListener = ResolutionListener(resField, widthPixels, heightPixels, widthInches, heightInches) resField.addTextListener(textListener) widthInches.addTextListener(textListener) heightInches.addTextListener(textListener) gd.showDialog() if gd.wasCanceled(): print "User canceled dialog!" return newres = gd.getNextNumber() newwidth = gd.getNextNumber() newheight = gd.getNextNumber() originalspath = gd.getNextString() return int(newwidth), int(newheight), newres, originalspath else: IJ.showMessage("You should have at least one image open.")
def optionsDialog(): dialog = GenericDialogPlus("Automated Size Analysis") dialog.addDirectoryField("Image Directory", "") dialog.addStringField("Output Subdirectory", "output", 20) dialog.addStringField("Minimum Pixel Size", "50000", 20) dialog.addStringField("Minimum Roundness", "0.4", 20) dialog.addStringField("Gaussian Blur", "6", 20) dialog.addStringField("Rotation Steps", "3", 20) dialog.addStringField("Thresholds", ",".join(THRESHOLDS), 20) dialog.addCheckbox("Rotate Images", True) dialog.addCheckbox("Multiple Thresholds", True) dialog.showDialog() # Check if canceled if dialog.wasCanceled(): return None textVals = [x.text for x in dialog.getStringFields()] boolVals = [x.getState() for x in dialog.getCheckboxes()] return textVals + boolVals
def getImgRoiDir(): gui = GenericDialogPlus( "Select directories to process images and ROI sets to apply") default_dir = OpenDialog.getDefaultDirectory() gui.addDirectoryField("Choose a iamge folder to be processed", default_dir) gui.addDirectoryField("Choose a ROI set folder to apply", default_dir) gui.showDialog() if gui.wasOKed(): img_dir = gui.getNextString() if not img_dir.endswith(os.sep): img_dir = img_dir + os.sep roi_dir = gui.getNextString() if not roi_dir.endswith(os.sep): roi_dir = roi_dir + os.sep return img_dir, roi_dir
def selectionDialog(categories,labelColorBarImp): gd = GenericDialogPlus("ROI labeller -image picking") imps = WM.getImageTitles() nonimages=WM.getNonImageTitles() gd.addChoice("Image to quantify", imps, imps[0]) try: gd.addChoice("FRETENTATOR results table", nonimages, nonimages[0]) fail=0 except: gd.addMessage("No results table open") fail=1 gd.addImage(labelColorBarImp) for i in range(categories): gd.addStringField("Label "+str(i) +" name:", "Label "+str(i)) gd.addChoice("Quantify an open image or add labels to open results table?", ["Image", "Results table"], "Results table") #quantImp= IJ.getImage(gd.getNextChoice()) gd.setModal(False) gd.showDialog() while ((not gd.wasCanceled()) and not (gd.wasOKed())): Thread.sleep(50) names=dict() for i in range(categories): names[i]=str(gd.getNextString()) imageName=gd.getNextChoice() if fail==0: resultsName=gd.getNextChoice() imageOrTable=gd.getNextChoice() else: imageOrTable="Image" resultsName=0 return names, imageName, resultsName, imageOrTable
from ij.gui import GenericDialog from fiji.util.gui import GenericDialogPlus import os, shutil gdp = GenericDialogPlus("File Mover") file_type = gdp.getNextString() gdp.addStringField("File Extension ", '.tif', 5) x = gdp.addDirectoryField("Source: ", " ") gdp.addDirectoryField("Destination: ", " ") gdp.showDialog() if gdp.wasOKed(): file_type = gdp.getNextString().strip() source_path = gdp.getNextString() dest_path = gdp.getNextString() print(file_type) print("source " + source_path) print("dest " + dest_path) else: exit() moved = 'Moved Images' path = os.path.join(dest_path, moved) try: dest = os.mkdir(path) except OSError as error: print("Error: Moved Images is already present") exit() def move(): for root, dirs, files in os.walk((os.path.normpath(source_path)),
# configure initial scaling step calib = imp.getCalibration(); scaleX = calib.pixelWidth / calib.pixelDepth * zoom; scaleY = calib.pixelHeight / calib.pixelDepth * zoom; scaleZ = 1.0 * zoom; # initialize state input = None; formerT = None; resultCylinderMaxProjection = None; resultMaxProjection = None; spots = None; circles = None; blobs = None; # build up user interface gdp = GenericDialogPlus("Spot detection workflow"); gdp.addMessage("Noise and background subtraction (DoG)"); gdp.addCheckbox("Do noise and background subtraction ", formerDoNoiseAndBackgroundRemoval); gdp.addSlider("Sigma 1 (in 0.1 pixel)", 0, 100, formerSigma1); gdp.addSlider("Sigma 2 (in 0.1 pixel)", 0, 100, formerSigma2); gdp.addMessage("Rigid transform"); gdp.addCheckbox("Do rigid transformation", formerDoRigidTransform); gdp.addSlider("Translation X (in pixel)", -100, 100, formerTranslationX); gdp.addSlider("Translation Y (in pixel)", -100, 100, formerTranslationY); gdp.addSlider("Translation Z (in pixel)", -100, 100, formerTranslationZ); gdp.addSlider("Rotation X (in degrees)", -180, 180, formerRotationX); gdp.addSlider("Rotation Y (in degrees)", -180, 180, formerRotationY); gdp.addSlider("Rotation Z (in degrees)", -180, 180, formerRotationZ); gdp.addMessage("Spot detection") gdp.addCheckbox("Do spot detection", formerDoSpotDetection);
from ij import IJ, ImagePlus from ij.plugin import ImageCalculator from ij.process import ImageProcessor from ij.process import FloatProcessor from fiji.util.gui import GenericDialogPlus #Input parameters gd = GenericDialogPlus("Input Parameters") gd.addDirectoryOrFileField("Select composite color image, 32-bit", "") gd.addNumericField("Pixel size", 4, 0) # show 3 decimals gd.addNumericField("Background color", 150, 0) # show 3 decimals gd.showDialog() directory_w = gd.getNextString() px = int(gd.getNextNumber()) bc = int(gd.getNextNumber()) #path to RGB image IJ.open(str(directory_w)) imp = IJ.getImage() IJ.run("8-bit") IJ.run("32-bit") n_slicesa = imp.getStack().getSize() L = [] for i in range(1, n_slicesa + 1): imp.setSlice(i) n = imp.getProcessor().getPixels() n2 = [int(val) for val in n] L.append(n2) imp.changes = False
#This creates a sparse approximation w of a signal X provided a dictionary D. #||X-Dw|| #Requires mpv2 package: http://www.ux.uis.no/~karlsk/dle/mpv2-class.zip #Improvements to come in the next version from ij import IJ, ImagePlus from ij.plugin import ImageCalculator from ij.process import ImageProcessor from ij.process import FloatProcessor from fiji.util.gui import GenericDialogPlus from mpv2 import MatchingPursuit as MP, JamaMatrix as Matrix, SymmetricMatrix as SM #Input parameters gd = GenericDialogPlus("Sparse Approximation, Input Parameters") gd.addDirectoryOrFileField("Select dictionary", "") gd.addChoice("Greedy algorithm type", ["MP", "OMP", "ORMP"], "OMP") gd.addNumericField("Number of non-zero elements", 3, 0) # show 3 decimals gd.showDialog() directory_w = gd.getNextString() t_w = int(gd.getNextNumber()) Rt = gd.getNextChoice() imp2 = IJ.getImage() IJ.run("32-bit") imp2 = IJ.getImage() #path to lighting directions IJ.run("Text Image... ", "open=" + str(directory_w)) imp = IJ.getImage() imp.setTitle("Dictionary Atoms")
ypos_def = default_values['ypos_def'] file_def = default_values['file_def'] # Dumps values into a dictionary json_object = json.dumps(default_values, indent=4) dest = IJ.getDirectory("image") os.chdir(dest) json_selected = os.path.join(dest, json_selected) json_selected = json_selected+'/' # Writing to sample.json with open(json_selected, "w") as outfile: outfile.write(json_object) # sets up a generic dialog box to start the script. dest = IJ.getDirectory("image") gdp = GenericDialogPlus("Transport Assay - "+version) gdp.addDirectoryField("Image Folder:", dest, 40) gdp.addStringField("Processed Image Folder:", ProFolder_def, 40) gdp.addStringField("Transport Output:", Quant_def, 40) gdp.addStringField("Mean_Max Output:", Quant_MM_def, 40) gdp.addMessage("------------------------------------------------------------", italicFont) gdp.addChoice("Transport Channel:", channels_available, tran_def) gdp.addToSameRow() gdp.addStringField("File Type: ", file_def, 5) gdp.addChoice(" Golgi Channel:", channels_available, golgi_def) gdp.addToSameRow() gdp.addStringField("Processed File Extension: ", ext_def, 5) gdp.addMessage(" ", italicFont) gdp.addToSameRow() gdp.addMessage(" ", italicFont) gdp.addChoice(" Zoom: ", zoom, zoom_def)
def previewDialog(imp): gd = GenericDialogPlus("FRETENATOR") #create a list of the channels in the provided imagePlus types = [] for i in xrange(1, imp.getNChannels()+1): types.append(str(i)) gd.addMessage("""Rowe, J. H, Rizza, A., Jones A. M. (2022) Quantifying phytohormones in vivo with FRET biosensors and the FRETENATOR analysis toolset Methods in Molecular Biology Rowe, JH., Grangé-Guermente, M., Exposito-Rodriguez, M.,Wimalasekera, R., Lenz, M., Shetty, K., Cutler, S., Jones, AM., Next-generation ABACUS biosensors reveal cellular ABA dynamics driving root growth at low aerial humidity """) #user can pick which channel to base the segmentation on if len(types)>2: gd.addChoice("Channel number to use for segmentation", types, types[2]) gd.addChoice("Channel number to use for donor", types, types[0]) gd.addChoice("Channel number to use for acceptor (FRET)", types, types[1]) gd.addChoice("Channel number to use for acceptor", types, types[2]) #print('YAY') else: gd.addChoice("Channel number to use for segmentation", types, types[-1]) gd.addChoice("Channel number to use for donor", types, types[0]) gd.addChoice("Channel number to use for acceptor (FRET)", types, types[-2]) gd.addChoice("Channel number to use for acceptor", types, types[-1]) methods=["Otsu","Default", "Huang", "Intermodes", "IsoData", "IJ_IsoData", "Li", "MaxEntropy", "Mean", "MinError", "Minimum", "Moments", "Percentile", "RenyiEntropy", "Shanbhag", "Triangle", "Yen"] gd.addChoice("Autosegmentation method", methods, methods[0]) intensities=["254", "4094", "65534"] gd.addChoice("Max Intensity", intensities, intensities[-1]) gd.addSlider("Small DoG sigma", 0.5, 10, 0.8, 0.1) gd.addSlider("Large DoG sigma", 0.5, 20, 4 ,0.1) gd.addCheckbox("TopHat background subtraction? (Slower, but better) ", False) gd.addSlider("TopHat sigma", 5, 20, 8 ,0.1) gd.setModal(False) gd.addCheckbox("Manually set threshold? ", False) gd.addSlider("Manual threshold", 10, 65534, 2000, 1) dilationOptions=["0", "1", "2","3", "4", "5", "6"] gd.addChoice("Dilation?", dilationOptions, "0") gd.addCheckbox("Size exclusion of ROI? ", False) gd.addSlider("Minimum ROI size", 0, 9999, 20, 1) gd.addSlider("Maximum ROI size", 1, 10000, 10000, 1) gd.addCheckbox("Create nearest point projection with outlines (SLOW)? ", True) gd.addCheckbox("Watershed object splitting? ", True) gd.showDialog() cal = imp.getCalibration() pixelAspect=(cal.pixelDepth/cal.pixelWidth) originalTitle=imp1.getTitle() choices=gd.getChoices() sliders=gd.getSliders() checkboxes=gd.getCheckboxes() segmentChannel=int(choices.get(0).getSelectedItem()) donorChannel=int(choices.get(1).getSelectedItem()) acceptorChannel=int(choices.get(2).getSelectedItem()) acceptorChannel2=int(choices.get(3).getSelectedItem()) thresholdMethod=choices.get(4).getSelectedItem() maxIntensity=int(choices.get(5).getSelectedItem()) gaussianSigma=sliders.get(0).getValue()/10.0 largeDoGSigma = gd.sliders.get(1).getValue()/10.0 topHat=gd.checkboxes.get(0).getState() topHatSigma=gd.sliders.get(2).getValue()/10.0 manualSegment = gd.checkboxes.get(1).getState() manualThreshold=gd.sliders.get(3).getValue() dilation=int(choices.get(6).getSelectedItem()) sizeExclude=gd.checkboxes.get(2).getState() minSize = gd.sliders.get(4).getValue() maxSize = gd.sliders.get(5).getValue() watershed = gd.checkboxes.get(4).getState() #print dir(gd.sliders.get(5)) #print maxSize segmentChannelOld=segmentChannel thresholdMethodOld=thresholdMethod maxIntensityOld=maxIntensity gaussianSigmaOld=gaussianSigma largeDoGSigmaOld= largeDoGSigma topHatOld=topHat topHatSigmaOld=topHatSigma manualSegmentOld= manualSegment manualThresholdOld=manualThreshold dilationOld=dilation sizeExcludeOld=sizeExclude minSizeOld=minSize maxSizeOld=maxSize watershedOld=watershed clij2.clear() segmentImp=extractChannel(imp1, segmentChannel, 0) try: gfx1=clij2.push(segmentImp) gfx2=clij2.create(gfx1) gfx3=clij2.create(gfx1) gfx4=clij2.create(gfx1) gfx5=clij2.create(gfx1) gfx7=clij2.create([imp.getWidth(), imp.getHeight()]) except: try: Thread.sleep(500) print("Succeeded to sending to graphics card on the second time...") gfx1=clij2.push(segmentImp) gfx2=clij2.create(gfx1) gfx3=clij2.create(gfx1) gfx4=clij2.create(gfx1) gfx5=clij2.create(gfx1) gfx7=clij2.create([imp.getWidth(), imp.getHeight()]) except: errorDialog("""Could not send image to graphics card, it may be too large! Easy solutions: Try processing as 8-bit, cropping or scaling the image, or select a different CLIJ2 GPU. This issue is often intermittent, so trying again may also work! See the "Big Images on x graphics cards' notes at: https://clij2.github.io/clij2-docs/troubleshooting for more solutions """ + str(clij2.reportMemory()) ) gfx1,gfx2,gfx3,gfx4,gfx5 = segment(gfx1,gfx2,gfx3,gfx4,gfx5, gaussianSigma, thresholdMethod,maxIntensity, largeDoGSigma, pixelAspect, originalTitle, topHat, topHatSigma , manualSegment, manualThreshold, dilation,sizeExclude, minSize, maxSize, watershed) clij2.maximumZProjection(gfx5, gfx7) labelPrevImp= clij2.pull(gfx7) IJ.setMinAndMax(labelPrevImp, 0,clij2.getMaximumOfAllPixels(gfx7)) labelPrevImp.setTitle("Preview segmentation") labelPrevImp.show() IJ.run("glasbey_inverted") while ((not gd.wasCanceled()) and not (gd.wasOKed())): segmentChannel=int(choices.get(0).getSelectedItem()) donorChannel=int(choices.get(1).getSelectedItem()) acceptorChannel=int(choices.get(2).getSelectedItem()) acceptorChannel2=int(choices.get(3).getSelectedItem()) thresholdMethod=choices.get(4).getSelectedItem() maxIntensity=int(choices.get(5).getSelectedItem()) gaussianSigma=sliders.get(0).getValue()/10.0 largeDoGSigma = gd.sliders.get(1).getValue()/10.0 topHat=gd.checkboxes.get(0).getState() topHatSigma=gd.sliders.get(2).getValue()/10.0 manualSegment = gd.checkboxes.get(1).getState() manualThreshold = gd.sliders.get(3).getValue() dilation=int(choices.get(6).getSelectedItem()) sizeExclude=gd.checkboxes.get(2).getState() minSize = gd.sliders.get(4).getValue() maxSize = gd.sliders.get(5).getValue() watershed = gd.checkboxes.get(4).getState() if (segmentChannelOld !=segmentChannel or thresholdMethodOld !=thresholdMethod or maxIntensityOld !=maxIntensity or gaussianSigmaOld !=gaussianSigma or largeDoGSigmaOld != largeDoGSigma or topHatOld !=topHat or topHatSigmaOld !=topHatSigma or manualSegmentOld != manualSegment or manualThresholdOld !=manualThreshold or dilation != dilationOld or sizeExcludeOld!=sizeExclude or minSizeOld!=minSize or maxSizeOld!=maxSize or watershedOld!=watershed ): if minSizeOld!=minSize: if minSize>=maxSize: maxSize=minSize+1 gd.sliders.get(5).setValue(maxSize) if maxSizeOld!=maxSize: if minSize>=maxSize: minSize=maxSize-1 gd.sliders.get(4).setValue(minSize) if segmentChannelOld!=segmentChannel: clij2.clear() segmentImp=extractChannel(imp1, segmentChannel, 0) gfx1=clij2.push(segmentImp) gfx2=clij2.create(gfx1) gfx3=clij2.create(gfx1) gfx4=clij2.create(gfx1) gfx5=clij2.create(gfx1) gfx7=clij2.create([imp.getWidth(), imp.getHeight()]) gfx1,gfx2,gfx3,gfx4,gfx5 = segment(gfx1,gfx2,gfx3,gfx4,gfx5, gaussianSigma, thresholdMethod,maxIntensity, largeDoGSigma, pixelAspect, originalTitle, topHat,topHatSigma, manualSegment, manualThreshold, dilation,sizeExclude, minSize, maxSize, watershed) clij2.maximumZProjection(gfx5, gfx7) labelPrevImp.close() labelPrevImp= clij2.pull(gfx7) IJ.setMinAndMax(labelPrevImp, 0,clij2.getMaximumOfAllPixels(gfx7)) labelPrevImp.setTitle("Preview segmentation") labelPrevImp.show() IJ.run("glasbey_inverted") segmentChannelOld=segmentChannel thresholdMethodOld=thresholdMethod maxIntensityOld=maxIntensity gaussianSigmaOld=gaussianSigma largeDoGSigmaOld = largeDoGSigma topHatOld=topHat topHatSigmaOld=topHatSigma manualSegmentOld= manualSegment manualThresholdOld=manualThreshold dilationOld=dilation sizeExcludeOld=sizeExclude minSizeOld=minSize maxSizeOld=maxSize watershedOld=watershed Thread.sleep(200) labelPrevImp.close() makeNearProj = gd.checkboxes.get(3).getState() return segmentChannel, donorChannel, acceptorChannel, acceptorChannel2, thresholdMethod, maxIntensity, gaussianSigma, largeDoGSigma, topHat, topHatSigma, manualSegment, manualThreshold, makeNearProj, dilation, sizeExclude, minSize, maxSize, watershed
from fiji.util.gui import GenericDialogPlus from ij.gui import GenericDialog import os, shutil import uuid import sys gdp=GenericDialogPlus("File Randomizer") file_type = gdp.getNextString() gdp.addStringField("File Extension ", '.tif',5) x=gdp.addDirectoryField("Source:", " ") gdp.addDirectoryField("Destination:", "(optional)") gdp.addCheckbox("Copy & Move",False) gdp.showDialog() if gdp.wasOKed(): file_type = gdp.getNextString().strip() source_path = gdp.getNextString().strip() dest_path = gdp.getNextString().strip() copymove=gdp.getNextBoolean() if dest_path == '(optional)': dest_path = source_path else: exit() ##makes a new directory in the destination directory, called randomized. key='key.csv' r_key= os.path.join(dest_path,key) if copymove ==True: randomized = 'randomized' path = os.path.join(dest_path,randomized)
cmtkgui.movefile(frompath, target_dir) print("Cleaning up!") try: shutil.rmtree(td) except OSError: print("Failed to remove temporary directory: " + td + "\n") pass download_urls = cmtkgui.downloads() # download_urls=['http://www.nitrc.org/frs/download.php/4814/CMTK-2.2.3-CYGWIN-i686.tar.gz', 'http://www.nitrc.org/frs/download.php/4812/CMTK-2.2.3-Linux-x86_64.tar.gz', 'http://www.nitrc.org/frs/download.php/4820/CMTK-2.2.3-MacOSX-10.4-i686.tar.gz', 'http://www.nitrc.org/frs/download.php/4822/CMTK-2.2.3-MacOSX-10.5-x86_64.tar.gz', 'http://www.nitrc.org/frs/download.php/4824/CMTK-2.2.3-MacOSX-10.6-x86_64.tar.gz', 'http://www.nitrc.org/frs/download.php/4604/CMTK-2.2.1-CYGWIN-i686.tar.gz', 'http://www.nitrc.org/frs/download.php/4596/CMTK-2.2.1-Linux-x86_64.tar.gz', 'http://www.nitrc.org/frs/download.php/4608/CMTK-2.2.1-MacOSX-10.4-i686.tar.gz', 'http://www.nitrc.org/frs/download.php/4610/CMTK-2.2.1-MacOSX-10.5-x86_64.tar.gz', 'http://www.nitrc.org/frs/download.php/4611/CMTK-2.2.1-MacOSX-10.6-x86_64.tar.gz'] download_files = map(os.path.basename, download_urls) download_dict = dict(zip(download_files, download_urls)) print cmtkgui.install_dir() gd = GenericDialogPlus('Install CMTK') gd.addMessage('Currently installed CMTK version: ' + cmtkgui.installed_version()) recommended_file = cmtkgui.recommended_file(download_files) if recommended_file is None: recommended_file = download_files[0] gd.addChoice("Download file", download_files, recommended_file) gd.addMessage('By downloading this file you agree to the following (if you ' + 'do not agree to this, please press "Cancel" below):') gd.addMessage( 'Core CMTK code is licensed under the GPLv3.\nBundled software' + ' may be licensed under different terms - see licences/ directory for details' ) gd.showDialog() if gd.wasOKed(): download_file = gd.getNextChoice()
def errorDialog(message): gd = GenericDialogPlus("Error") gd.addMessage(message) gd.showDialog() return
session = None channel = None DIALOG = True if (DIALOG): # ----------------- DIALOG TO FILL GLOBAL PARAMETERS ----- gui = GenericDialog("Parameters") gui.addStringField("NGROK Server address :", "0.tcp.ngrok.io") gui.addStringField("PORT :", "") gui.showDialog() HOST = gui.getNextString() PORT = int(gui.getNextString()) gui = GenericDialogPlus("Parameters") gui.addFileField("Select a model file in Keras format (*.h5) : ", "") gui.showDialog() if gui.wasOKed(): LOCAL_MODEL_FILENAME = gui.getNextString() gui = GenericDialogPlus("Parameters") gui.addFileField("Select a python script file to upload on the server (*.py) : ", "") gui.showDialog() if gui.wasOKed(): LOCAL_PREDICTION_SCRIPT = gui.getNextString() jsch=JSch() # SSH Connection to DeepLearning server
import sys import os import csv from fiji.util.gui import GenericDialogPlus from loci.formats import ChannelSeparator from ij.io import Opener import json import time boldFont = Font("SansSerif", Font.BOLD, 13) fret_fura = [] fret_fura.append("C1/C2 (FURA)") fret_fura.append("C2/C1 (FRET)") dest = IJ.getDirectory("image") bck_path = 'optional' gdp = GenericDialogPlus("FRET Assay, Version 3.4") gdp.addDirectoryField("Output Location:", dest, 40) gdp.addStringField("Processed Folder:", 'Processed_FRET', 40) gdp.addStringField("FRET Outfile:", 'FRET_Outfile.csv', 40) gdp.addStringField("Selection Radius:", '3', 10) gdp.addStringField("Image interval (sec):", '4', 10) gdp.addStringField("ZeroDivisionErorVal:", 'NA', 10) gdp.addRadioButtonGroup("", fret_fura, 1, 2, "C1/C2 (FURA)") #gdp.addCheckbox("Set Background to value:", False) gdp.addFileField("background file:", bck_path, 40) gdp.addToSameRow() gdp.addStringField("", '0', 5) gdp.addCheckbox("Apply Gaussian blur? Input sigma value:", False) gdp.addToSameRow() gdp.addStringField("", '4', 5)
def previewDialog(imp): gd = GenericDialogPlus("Nuclear segmentation and quantification v1.01") #create a list of the channels in the provided imagePlus types = [] for i in xrange(1, imp.getNChannels() + 1): types.append(str(i)) #user can pick which channel to base the segmentation on gd.addChoice("Channel number to use for segmentation", types, types[0]) gd.addChoice("Channel to quantify", types, types[0]) methods = [ "Otsu", "Default", "Huang", "Intermodes", "IsoData", "IJ_IsoData", "Li", "MaxEntropy", "Mean", "MinError", "Minimum", "Moments", "Percentile", "RenyiEntropy", "Shanbhag", "Triangle", "Yen" ] gd.addChoice("Autosegmentation method", methods, methods[0]) intensities = ["254", "4094", "65534"] gd.addChoice("Max Intensity", intensities, intensities[-1]) gd.addSlider("Small DoG sigma", 0.5, 10, 1, 0.1) gd.addSlider("Large DoG sigma", 0.5, 20, 5, 0.1) gd.addCheckbox("TopHat background subtraction? (Slower, but better) ", True) gd.addSlider("TopHat sigma", 5, 20, 8, 0.1) gd.setModal(False) gd.addCheckbox("Manually set threshold? ", False) gd.addSlider("Manual threshold", 10, 65534, 2000, 1) gd.hideCancelButton() gd.showDialog() cal = imp.getCalibration() pixelAspect = (cal.pixelDepth / cal.pixelWidth) originalTitle = imp1.getTitle() choices = gd.getChoices() print choices sliders = gd.getSliders() checkboxes = gd.getCheckboxes() segmentChannel = int(choices.get(0).getSelectedItem()) quantChannel = int(choices.get(1).getSelectedItem()) thresholdMethod = choices.get(2).getSelectedItem() maxIntensity = int(choices.get(3).getSelectedItem()) gaussianSigma = sliders.get(0).getValue() / 10.0 largeDoGSigma = gd.sliders.get(1).getValue() / 10.0 topHat = gd.checkboxes.get(0).getState() topHatSigma = gd.sliders.get(2).getValue() / 10.0 manualSegment = gd.checkboxes.get(1).getState() manualThreshold = gd.sliders.get(3).getValue() segmentChannelOld = segmentChannel thresholdMethodOld = thresholdMethod maxIntensityOld = maxIntensity gaussianSigmaOld = gaussianSigma largeDoGSigmaOld = largeDoGSigma topHatOld = topHat topHatSigmaOld = topHatSigma manualSegmentOld = manualSegment manualThresholdOld = manualThreshold clij2.clear() segmentImp = extractChannel(imp1, segmentChannel, 0) try: gfx1 = clij2.push(segmentImp) gfx2 = clij2.create(gfx1) gfx3 = clij2.create(gfx1) gfx4 = clij2.create(gfx1) gfx5 = clij2.create(gfx1) gfx7 = clij2.create([imp.getWidth(), imp.getHeight()]) except: try: Thread.sleep(500) IJ.log( "Succeeded to sending to graphics card on the second time...") gfx1 = clij2.push(segmentImp) gfx2 = clij2.create(gfx1) gfx3 = clij2.create(gfx1) gfx4 = clij2.create(gfx1) gfx5 = clij2.create(gfx1) gfx7 = clij2.create([imp.getWidth(), imp.getHeight()]) except: errorDialog( """Could not send image to graphics card, it may be too large! Easy solutions: Try processing as 8-bit, cropping or scaling the image, or select a different CLIJ2 GPU. This issue is often intermittent, so trying again may also work! See the "Big Images on x graphics cards' notes at: https://clij2.github.io/clij2-docs/troubleshooting for more solutions """ + str(clij2.reportMemory())) gfx1, gfx2, gfx3, gfx4, gfx5 = segment(gfx1, gfx2, gfx3, gfx4, gfx5, gaussianSigma, thresholdMethod, maxIntensity, largeDoGSigma, pixelAspect, originalTitle, topHat, topHatSigma, manualSegment, manualThreshold) clij2.maximumZProjection(gfx5, gfx7) labelPrevImp = clij2.pull(gfx7) IJ.setMinAndMax(labelPrevImp, 0, clij2.getMaximumOfAllPixels(gfx7)) labelPrevImp.setTitle("Preview segmentation") labelPrevImp.show() IJ.run("glasbey_inverted") while ((not gd.wasCanceled()) and not (gd.wasOKed())): segmentChannel = int(choices.get(0).getSelectedItem()) quantChannel = int(choices.get(1).getSelectedItem()) thresholdMethod = choices.get(2).getSelectedItem() maxIntensity = int(choices.get(3).getSelectedItem()) gaussianSigma = sliders.get(0).getValue() / 10.0 largeDoGSigma = gd.sliders.get(1).getValue() / 10.0 topHat = gd.checkboxes.get(0).getState() topHatSigma = gd.sliders.get(2).getValue() / 10.0 manualSegment = gd.checkboxes.get(1).getState() manualThreshold = gd.sliders.get(3).getValue() if (segmentChannelOld != segmentChannel or thresholdMethodOld != thresholdMethod or maxIntensityOld != maxIntensity or gaussianSigmaOld != gaussianSigma or largeDoGSigmaOld != largeDoGSigma or topHatOld != topHat or topHatSigmaOld != topHatSigma or manualSegmentOld != manualSegment or manualThresholdOld != manualThreshold): if segmentChannelOld != segmentChannel: clij2.clear() segmentImp = extractChannel(imp1, segmentChannel, 0) gfx1 = clij2.push(segmentImp) gfx2 = clij2.create(gfx1) gfx3 = clij2.create(gfx1) gfx4 = clij2.create(gfx1) gfx5 = clij2.create(gfx1) gfx7 = clij2.create([imp.getWidth(), imp.getHeight()]) gfx1, gfx2, gfx3, gfx4, gfx5 = segment( gfx1, gfx2, gfx3, gfx4, gfx5, gaussianSigma, thresholdMethod, maxIntensity, largeDoGSigma, pixelAspect, originalTitle, topHat, topHatSigma, manualSegment, manualThreshold) clij2.maximumZProjection(gfx5, gfx7) labelPrevImp.close() labelPrevImp = clij2.pull(gfx7) IJ.setMinAndMax(labelPrevImp, 0, clij2.getMaximumOfAllPixels(gfx7)) labelPrevImp.setTitle("Preview segmentation") labelPrevImp.show() IJ.run("glasbey_inverted") segmentChannelOld = segmentChannel thresholdMethodOld = thresholdMethod maxIntensityOld = maxIntensity gaussianSigmaOld = gaussianSigma largeDoGSigmaOld = largeDoGSigma topHatOld = topHat topHatSigmaOld = topHatSigma manualSegmentOld = manualSegment manualThresholdOld = manualThreshold Thread.sleep(200) labelPrevImp.close() return segmentChannel, quantChannel, thresholdMethod, maxIntensity, gaussianSigma, largeDoGSigma, topHat, topHatSigma, manualSegment, manualThreshold
# define some variables # EM = electron microscopic image # rLM = real light microscopic image (ground truth) with chromatin channel # pLM = predictet light microscopic image # c1LM, c2LM, c3LM ... = channels of interest (COI) # define input (java.io.File to string) EMfilepath = EMfilepath.toString() rLMfilepath = rLMfilepath.toString() workdir = workdir.toString() # check if workdir is empty if len(os.listdir(workdir)) != 0: gd = GenericDialogPlus("Options") gd.addMessage("Working directory must be empty!") gd.addMessage( "Please delete all files in the working directory, or choose another working directory and restart the Deep CLEM Plugin." ) gd.showDialog() if len(os.listdir(workdir)) != 0: sys.exit() # create a couple of folder in workdir # for registration registration_inputdir = os.path.join(workdir, "registration_input") os.mkdir(registration_inputdir) registration_outputdir = os.path.join(workdir, "registration_output")
Then each template is searched in the target image. This yield as set of correlation maps Minima/maxima in the correlation maps are detected, followed by Non-Maxima Supression when several object are explected in the target image - matchTemplate Method limited to normalised method to have correlation map in range 0-1 : easier to apply a treshold. The search region can be limited to a rectangular ROI, that is drawn on the image/stack before execution of the plugin. Requirements: - IJ-OpenCV update site ''' #import time #@PrefService prefs from fiji.util.gui import GenericDialogPlus ## Create GUI Win = GenericDialogPlus("Multiple Template Matching") Win.addImageChoice("Template", prefs.get("Template", "Choice")) Win.addImageChoice("Image", prefs.get("Image", "Choice")) Win.addCheckbox("Flip_template_vertically", prefs.getInt("FlipV", False)) Win.addCheckbox("Flip_template_horizontally", prefs.getInt("FlipH", False)) Win.addStringField("Rotate template by ..(comma-separated)", prefs.get("Angles", "")) Win.addChoice("Matching_method", [ "Normalised Square Difference", "Normalised cross-correlation", "Normalised 0-mean cross-correlation" ], prefs.get("Method", "Normalised 0-mean cross-correlation")) Win.addNumericField("Number_of_templates expected", prefs.getInt("N_hit", 1), 0) Win.addMessage("If more than 1 template expected :") Win.addNumericField("Score_Threshold [0-1]", prefs.getFloat("Score_Threshold", 0.5), 2)
#Bring up roimanager rm = RoiManager().getInstance() #Set some defaults IJ.run( "Set Measurements...", "area integrated area_fraction limit display scientific redirect=None decimal=3" ) IJ.setForegroundColor(0, 0, 0) IJ.setBackgroundColor(255, 255, 255) #User Input Dialoge analyses = ["Counts", "ATP"] yn = ["YES", "NO"] gdp = GenericDialogPlus("Ex Vivo Granule Analysis") gdp.addMessage("Choose a UNIQUE directory to save analysis files") gdp.addDirectoryOrFileField("Output Directory", "D:/Samantha/") gdp.addMessage("IMPORTANT: Files are tracked based on input order") gdp.addMessage("Select input files (.tiff)...") gdp.addFileField("Wildtype", "D:/Samantha/test/VLEwt.tif") gdp.addFileField("DIC", "D:/Samantha/test/dic.tif") gdp.addFileField("Mutant", "D:/Samantha/test/ddmut.tif") gdp.addFileField("Background (DAPI)", "D:/Samantha/test/bg.tif") gdp.addChoice("Select analysis type", analyses, "Counts") gdp.addMessage("Choose variable conditions:") gdp.addNumericField( "Minimum Threshold. Choose value (0-1) as a percent of maximum threshold", 0.20, 2) gdp.addNumericField("Minimum granule size (pixel^2)", 20, 0) gdp.addNumericField(