def get_roi_manager(new=False):
"""
flexible ROI mgr handling, copied from Particles_From_Mask.py template in Fiji
if new = True, a new blank mgr is returned
if new = False (default) and the ROI manager is open, returns that mgr.
if new = False and the ROI manager is NOT open, creates a new one without throwing an error
"""
rm = RoiManager.getInstance()
if not rm:
rm = RoiManager()
if new:
rm.runCommand("Reset")
return rm
def mergeRois(components):
RoiManager()
roiManager = RoiManager.getRoiManager()
toBeDeleted = []
for component in components:
if len(component) > 1:
roiManager.setSelectedIndexes(component)
roiManager.runCommand("Combine")
roiManager.runCommand("Update")
toBeDeleted = toBeDeleted + component[1:]
if len(toBeDeleted) > 0:
roiManager.setSelectedIndexes(toBeDeleted)
roiManager.runCommand("Delete")
def generate_limit_labels(imp, limits, cb_fraction, params):
"""generate text ROIs in correct positions to label colorbar"""
rois = []
w = imp.getWidth()
h = imp.getHeight()
txt_h_px = float(h) / 15
txt_sz_pt = int(round((18.0 / 24.0) * txt_h_px))
for limit in limits:
roi = TextRoi(1, 1, str(round(limit, 3)),
Font("SansSerif", Font.ITALIC, txt_sz_pt))
roi.setJustification(TextRoi.RIGHT)
roi.setFillColor(Color.BLACK)
roi.setStrokeColor(Color.WHITE)
rois.append(roi)
roim = RoiManager(False)
roim.reset()
imp.show()
mbui.autoset_zoom(imp)
for fridx in range(1, imp.getNSlices() + 1):
imp.setPosition(fridx)
roi_uu = rois[1].clone()
xpos = w - cb_fraction * w - float(w) / 100 - rois[1].getFloatWidth()
roi_uu.setLocation(xpos, 1)
imp.setRoi(roi_uu)
roim.addRoi(roi_uu)
roi_ll = rois[0].clone()
roi_ll.setLocation(xpos, h - rois[0].getFloatHeight())
imp.setRoi(roi_ll)
roim.addRoi(roi_ll)
roim.runCommand("Show All")
FileSaver(imp).saveAsTiff(
os.path.join(params.output_path,
"overlaid curvature nudged labels.tif"))
# nudge positions
roim.reset()
imp.killRoi()
for fridx in range(1, imp.getNSlices() + 1):
imp.setPosition(fridx)
roi_uu = rois[1].clone()
xpos = w - cb_fraction * w - float(w) / 100
roi_uu.setLocation(xpos, 1)
imp.setRoi(roi_uu)
roim.addRoi(roi_uu)
roi_ll = rois[0].clone()
roi_ll.setLocation(xpos, h - rois[0].getFloatHeight())
imp.setRoi(roi_ll)
roim.addRoi(roi_ll)
roim.runCommand("Show All")
FileSaver(imp).saveAsTiff(
os.path.join(params.output_path, "overlaid curvature.tif"))
return imp
def createROI(xy_coord, diameter):
imp = WindowManager.getCurrentImage()
pixelWidth = imp.getCalibration().pixelWidth
pixelHeight = imp.getCalibration().pixelHeight
x_diameter = diameter / pixelWidth
y_diameter = diameter / pixelHeight
x_coord = xy_coord[0] / pixelWidth - (0.5 * x_diameter)
y_coord = xy_coord[1] / pixelHeight - (0.5 * y_diameter)
rm = RoiManager.getInstance()
if not rm:
rm = RoiManager()
roi = OvalRoi(x_coord, y_coord, x_diameter, y_diameter)
rm.addRoi(roi)
def generate_cell_rois(seg_binary_imp):
"""generate rois from which cell shape information will be gleaned"""
seg_binary_imp.killRoi()
mxsz = seg_binary_imp.width * seg_binary_imp.height
roim = RoiManager(False)
pa_options = ParticleAnalyzer.AREA | ParticleAnalyzer.PERIMETER | ParticleAnalyzer.SHAPE_DESCRIPTORS
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, pa_options, None,
1000, mxsz)
pa.setRoiManager(roim)
roim.reset()
pa.analyze(seg_binary_imp)
rois = roim.getRoisAsArray()
roim.reset()
roim.close()
return rois
def process(inputpath, outputpath):
imp = IJ.openImage(inputpath)
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
IJ.setThreshold(imp, t1, 255)
#imp.show()
#WaitForUserDialog("Title", "Look at image").show()
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Watershed", "")
# Counts and measures the area of particles and adds them to a table called areas. Also adds them to the ROI manager
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA,
table, 50, 9999999999999999, 0.2, 1.0)
pa.setHideOutputImage(True)
pa.analyze(imp)
imp.changes = False
imp.close()
areas = table.getColumn(0)
summary = {}
if areas:
summary['Image'] = filename
summary['Nuclei.count'] = len(areas)
summary['Area.Covered'] = sum(areas)
fieldnames = list(summary.keys())
with open(outputpath, 'a') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
extrasaction='ignore',
lineterminator='\n')
if os.path.getsize(outputDirectory + "/" + outputname + ".csv") < 1:
writer.writeheader()
writer.writerow(summary)
def save_qc_image(imp, rois, output_path):
"""save rois overlaid on imp to output_path"""
imp.killRoi()
if len(rois) > 0:
roim = RoiManager(False)
for roi in rois:
roim.addRoi(roi)
roim.runCommand("Show All with labels")
RGBStackConverter.convertToRGB(imp)
roim.moveRoisToOverlay(imp)
FileSaver(imp).saveAsTiff(output_path)
roim.runCommand("Show None")
roim.close()
else:
FileSaver(imp).saveAsTiff(output_path)
return
def f3_clic_SaveROIs(event):
RM = RoiManager() # we create an instance of the RoiManager class
rm = RM.getRoiManager()
pixels = gvars['eroded_pixels']
path_to_updated_ROIs = str(gvars['path_original_image'].replace(".tif", "") + "_Erosion_" +str(pixels)+ "px_" + "RoiSet.zip")
rm.runCommand("Save", path_to_updated_ROIs)
print("ROIs saved")
print pixels
#Save Outline Images
imp = gvars["workingImage"]
path_to_simple_outline = str(gvars['path_original_image'].replace(".tif", "") + "_Erosion_" +str(pixels)+ "px_" + "Outlines.jpg")
outlines_image = imp.flatten()
IJ.saveAs(outlines_image, "JPG", path_to_simple_outline)
JOptionPane.showMessageDialog(None, "ROIs saved to:\n%s\nOutline image saved to:\n%s" % (path_to_updated_ROIs, path_to_simple_outline))
def get_no_nuclei_fully_enclosed(roi, full_nuclei_imp, overlap_threshold=0.65):
"""for a given cell roi and ImagePlus with binary nuclei, return how many nuclei lie ENTIRELY within the cell"""
bbox = roi.getBounds()
full_nuclei_imp.setRoi(roi)
cropped_nuc_imp = full_nuclei_imp.crop()
roi.setLocation(0, 0)
cropped_nuc_imp.setRoi(roi)
cropped_nuc_imp.killRoi()
roim = RoiManager(False)
mxsz = cropped_nuc_imp.getWidth() * cropped_nuc_imp.getHeight()
pa = ParticleAnalyzer(
ParticleAnalyzer.ADD_TO_MANAGER, ParticleAnalyzer.AREA
| ParticleAnalyzer.SLICE | ParticleAnalyzer.CENTROID, None, 0, mxsz)
pa.setRoiManager(roim)
pa.analyze(cropped_nuc_imp)
cell_imp = IJ.createImage("Cell binary", cropped_nuc_imp.getWidth(),
cropped_nuc_imp.getHeight(), 1, 8)
IJ.run(cell_imp, "Select All", "")
IJ.run(cell_imp, "Set...", "value=0 slice")
cell_imp.setRoi(roi)
IJ.run(cell_imp, "Set...", "value=255 slice")
no_enclosed_nuclei = 0
for idx, nuc_roi in enumerate(roim.getRoisAsArray()):
test_imp = Duplicator().run(cell_imp)
test_imp.setRoi(nuc_roi)
IJ.run(test_imp, "Set...", "value=255 slice")
test_imp.killRoi()
IJ.run(test_imp, "Create Selection", "")
IJ.run(test_imp, "Make Inverse", "")
test_roi = test_imp.getRoi()
test_roi_stats = test_roi.getStatistics()
cell_roi_stats = roi.getStatistics()
nuc_roi_stats = nuc_roi.getStatistics()
if test_roi_stats.area < (
cell_roi_stats.area +
(1 - overlap_threshold) * nuc_roi_stats.area
): # i.e. if more than (100*overlap_threshold)% of nucleus is inside cell...
no_enclosed_nuclei += 1
test_imp.changes = False
test_imp.close()
roi.setLocation(bbox.getX(), bbox.getY())
cropped_nuc_imp.changes = False
cropped_nuc_imp.close()
cell_imp.changes = False
cell_imp.close()
return no_enclosed_nuclei
def RoiEroder(pixels):
RM = RoiManager() # we create an instance of the RoiManager class
rm = RM.getRoiManager() # "activate" the RoiManager otherwise it can behave strangely
rm.reset()
# Re-open temporary original ROIs
temp_roi_path = gvars['tempFile']
rm.runCommand("Open", temp_roi_path)
print "Temp ROIs OPENED"
for i in range(0, rm.getCount()):
roi = rm.getRoi(i)
new_roi = RoiEnlarger.enlarge(roi, -pixels) # Key to use this instead of the IJ.run("Enlarge... much faster!!
rm.setRoi(new_roi,i)
gvars['eroded_pixels'] = pixels # Store globally the amount of pixels used to later save them in the ROI file name
def roiprocess(imp, filename):
# get the ROI manager instance
rm = RoiManager.getInstance()
if rm is None:
rm = RoiManager()
rm.runCommand(imp, "Select All")
# rm.runCommand("Deselect"); # deselect ROIs to save them all
rm.runCommand(imp, 'Show All')
# define the path to save the rois as azip file
roisavelocation = os.path.splitext(filename)[0] + '_RoiSet.zip'
# log.info('ROISs saved: ' + roisavelocation)
# print('ROIs saved: ', roisavelocation)
rm.runCommand("Save", roisavelocation)
return roisavelocation
def analyzeTUB():
rm = RoiManager().getInstance()
#Set imageJ preference meassurements to "area"
IJ.run("Set Measurements...", "area display redirect=None decimal=3")
#Variable used for iteration
counter = 0
#Clear previous resuslts
IJ.run("Clear Results")
# Make library, to be iterated through. Room for improvement
lis_dic = [{'path': sub[i]} for i in range(len(sub))]
if not lis_dic:
sys.exit('Did you check the right box?')
# Calculate area of channel #1 (Calculation requires that area is chosen as measured value)
for i in lis_dic:
print i
for p in data[counter][::-1]:
print p
if '_' + channel1 + '_' in p:
imp1 = IJ.openImage(i['path'] + '/' + p)
IJ.setThreshold(imp1, lowth1, 255)
IJ.run(imp1, "Create Selection", "")
roi = rm.getRoi(imp1)
#rm.runCommand(imp1, 'Add')
IJ.run(imp1, "Measure", "")
if '_' + channel2 + '_' in p:
imp2 = IJ.openImage(i['path'] + '/' + p)
IJ.setThreshold(imp2, lowth2, 255)
rm.runCommand(imp1, 'Select')
IJ.run(imp2, "Analyze Particles...", "size=0-4000 summarize")
counter += 1
# Reset counter
counter = 0
IJ.renameResults(channel2)
def Weka_Segm(dirs):
""" Loads trained classifier and segments cells """
""" in aligned images according to training. """
# Define reference image for segmentation (default is timepoint000).
w_train = os.path.join(dirs["Composites_Aligned"], "Timepoint000.tif")
trainer = IJ.openImage(w_train)
weka = WekaSegmentation()
weka.setTrainingImage(trainer)
# Select classifier model.
weka.loadClassifier(str(classifier))
weka.applyClassifier(False)
segmentation = weka.getClassifiedImage()
segmentation.show()
# Convert image to 8bit
ImageConverter(segmentation).convertToRGB()
ImageConverter(segmentation).convertToGray8()
# Threshold segmentation to soma only.
hist = segmentation.getProcessor().getHistogram()
lowth = Auto_Threshold.IJDefault(hist)
segmentation.getProcessor().threshold(lowth)
segmentation.getProcessor().setThreshold(0, 0, ImageProcessor.NO_LUT_UPDATE)
segmentation.getProcessor().invert()
segmentation.show()
# Run Watershed Irregular Features plugin, with parameters.
IJ.run(segmentation, "Watershed Irregular Features",
"erosion=20 convexity_treshold=0 separator_size=0-Infinity")
# Make selection and add to RoiManager.
RoiManager()
rm = RoiManager.getInstance()
rm.runCommand("reset")
roi = ThresholdToSelection.run(segmentation)
segmentation.setRoi(roi)
rm.addRoi(roi)
rm.runCommand("Split")
def save_roi_set(imp=IJ.getImage()):
img_dir, name = get_file_info()
roi_dir = make_roi_dir(img_dir, name)
roi_path = make_roi_path(roi_dir, name)
Roi.setColor(Color.blue)
rm = RoiManager().getInstance()
rm.deselect()
rm.runCommand("Save", roi_path)
ol = imp.getOverlay()
if ol is None:
ol = Overlay()
for roi in rm.getRoisAsArray():
ol.add(roi)
imp.setOverlay(ol)
rm.runCommand("delete")
ol.setStrokeColor(Color.blue)
Roi.setColor(Color.yellow)
def manual_analysis(imp, file_name, output_folder):
"""perform analysis based on manually drawn cells"""
cal = imp.getCalibration()
channel_imps = ChannelSplitter.split(imp)
gfp_imp = channel_imps[0]
IJ.setTool("freehand")
proceed = False
roim = RoiManager()
roim.runCommand("Show all with labels")
dialog = NonBlockingGenericDialog("Perform manual segmentation")
dialog.setOKLabel("Proceed to next image...")
dialog.addMessage("Perform manual segmentation: ")
dialog.addMessage(
"Draw around cells and add to the region of interest manager (Ctrl+T)")
dialog.addMessage(
"You can see what you've added so far if you check \"show all\" on the ROI manager"
)
dialog.addMessage(
"Then press \"proceed to next image\" when all cells have been added")
dialog.showDialog()
if dialog.wasCanceled():
raise KeyboardInterrupt("Run canceled")
elif dialog.wasOKed():
rois = roim.getRoisAsArray()
roim.reset()
roim.close()
out_stats = generate_cell_shape_results(rois, gfp_imp, cal, file_name)
print("Number of cells identified = {}".format(len(out_stats)))
# save output
save_qc_image(
imp, rois, "{}_plus_overlay.tiff".format(
os.path.join(output_folder,
os.path.splitext(file_name)[0])))
save_cell_rois(rois, output_folder, os.path.splitext(file_name)[0])
imp.changes = False
imp.close()
save_output_csv(out_stats, output_folder)
return out_stats
return None
def AnalyzeParticle(IMP):
rm = RoiManager().getInstance2()
rt = ResultsTable()
#再現性確保のために最終的には実装
#IJ.run("Set Measurements...","area centroid fit redirect=None decimal=3")
#https://imagej.nih.gov/ij/developer/api/constant-values.html#ij.plugin.filter.ParticleAnalyzer.SHOW_RESULTS
#表示オプション無し、resultは全部選択
PA = ParticleAnalyzer(0 , 1043199 , rt, 10000, 300000, 0.2, 1.0)
PA.setRoiManager(rm)
PA.analyze(IMP)
#IJ.run(IMP, "Analyze Particles...", "display clear include add")
rm.runCommand("Save", "C:/Users/For Programming/Documents/Python Scripts/OutletHDD/aaa.zip")
rt.saveAs("C:/Users/For Programming/Documents/Python Scripts/OutletHDD/aaa.csv")
#最後に全ての結果をCloseする。
#写真を先に消さないとバグる。
IMP.close()
rm.close()
rt.reset()
def process(srcDir, dstDir, currentDir, fileName, keepDirectories):
print "Processing:"
ROIpath, V1S1RL = os.path.split(currentDir)
# Opening the image
print "Open image file", fileName
imp = IJ.openImage(os.path.join(currentDir, fileName))
imp.show()
print "Compute F/F0"
IJ.run(imp, "F div F0", "6")
imp.close()
V1S1RL = "RoiSet " + V1S1RL + ".zip"
# Put your processing commands here!
ROIPath = ROIpath + "/" + V1S1RL
print "Open ROIS", ROIPath
rm = RoiManager.getInstance()
if not rm:
rm = RoiManager()
rm.reset()
rm.runCommand("open", ROIPath)
rm.runCommand("Show All")
print "Multi Measure"
imp = IJ.getImage()
rt = rm.multiMeasure(imp)
imp.close()
imp = IJ.getImage()
imp.close()
# Saving the image
saveDir = currentDir.replace(srcDir, dstDir) if keepDirectories else dstDir
if not os.path.exists(saveDir):
os.makedirs(saveDir)
print "Saving to", saveDir
rt.save(os.path.join(saveDir, fileName + '.csv'))
# IJ.saveAs(imp, "Tiff", os.path.join(saveDir, fileName));
imp.close()
def main(parentpath, cellNo):
rootname = "cell" + str(cellNo)
roizipname = 'RoiSet_' + rootname + '.zip'
imagename = rootname + '_virus_median.tif'
#pp = '/Users/miura/Desktop/161122 ctrl croped and 16 frames/RoiSet_cell5.zip'
# unzipping http://stackoverflow.com/questions/3451111/unzipping-files-in-python
#pp = '/Users/miura/Desktop/161122 ctrl croped and 16 frames/RoiSet_cell5/0005-0419-0327.roi'
zippp = os.path.join(parentpath, roizipname)
rm = RoiManager(False)
rm.runCommand("Open", zippp)
cellroi = rm.getRoi(2)
if cellroi.getType() != 3:
print "ROI type mismatch! ... ABORT"
sys.exit()
#imagepath = '/Users/miura/Desktop/161122 ctrl croped and 16 frames/cell1_virus_median.tif'
imagepath = os.path.join(parentpath, imagename)
tracks, cellarea, postcounts, postcounts2, precounts, imp, reallength, secondFrameVirusCounts = core(cellroi, imagepath)
return tracks, cellarea, postcounts, postcounts2, precounts, imp, reallength, secondFrameVirusCounts
def runScript(nCols, nRows, circRad):
print('___Running Blobber___')
circXUpLeft = 33.
circYUpLeft = 915.
circXUpRight = 1033.
circYUpRight = 627.
circXLowLeft = 95.
circYLowLeft = 1125.
imp = IJ.getImage()
nSlices = imp.getNSlices()
print('The image has {} slices'.format(nSlices))
circXUpLeft, circYUpLeft = getEdgeCoord(circXUpLeft, circYUpLeft, circRad,
imp, 'Upper Left')
print('upper left x,y: {},{}'.format(circXUpLeft, circYUpLeft))
circXUpRight, circYUpRight = getEdgeCoord(circXUpRight, circYUpRight,
circRad, imp, 'Upper Right')
print('upper right x,y: {},{}'.format(circXUpRight, circYUpRight))
circXLowLeft, circYLowLeft = getEdgeCoord(circXLowLeft, circYLowLeft,
circRad, imp, 'Lower Left')
print('lower left x,y: {},{}'.format(circXLowLeft, circYLowLeft))
hOffsetY = (circYUpRight - circYUpLeft) / (nCols - 1)
hOffsetX = (circXUpRight - circXUpLeft) / (nCols - 1)
vOffsetY = (circYLowLeft - circYUpLeft) / (nRows - 1)
vOffsetX = (circXLowLeft - circXUpLeft) / (nRows - 1)
x, y = createArrayCoord(hOffsetX, hOffsetY, vOffsetX, vOffsetY, nCols,
nRows, circXUpLeft, circYUpLeft)
RM = RoiManager()
rm = RM.getRoiManager()
imp, rm = makeSelections(x, y, rm, imp, circRad)
imp, rm = measureSelections(imp, rm, nCols, nRows)
IJ.run(imp, "Select None", "")
def keep_largest_blob(imp):
"""remove all blobs other than the largest by area"""
rt = ResultsTable();
mxsz = imp.width * imp.height;
roim = RoiManager(False);
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, ParticleAnalyzer.AREA | ParticleAnalyzer.SLICE, rt, 0, mxsz);
pa.setRoiManager(roim);
for idx in range(1, imp.getImageStackSize()+1):
roim.reset();
rt.reset();
imp.setPosition(idx);
pa.analyze(imp);
rt_areas = rt.getColumn(rt.getColumnIndex("Area")).tolist();
mx_ind = rt_areas.index(max(rt_areas))
indices_to_remove = [a for a in range(0,len(rt_areas)) if a != mx_ind];
indices_to_remove.reverse();
for rem_idx in indices_to_remove:
roim.select(imp, rem_idx);
IJ.run(imp, "Set...", "value=0 slice");
imp.killRoi();
roim.reset();
roim.close();
return imp;
def analyze_homogeneity(image_title):
IJ.selectWindow(image_title)
raw_imp = IJ.getImage()
IJ.run(raw_imp, "Duplicate...", "title=Homogeneity duplicate")
IJ.selectWindow('Homogeneity')
hg_imp = IJ.getImage()
# Get a 2D image
if hg_imp.getNSlices() > 1:
IJ.run(hg_imp, "Z Project...", "projection=[Average Intensity]")
hg_imp.close()
IJ.selectWindow('MAX_Homogeneity')
hg_imp = IJ.getImage()
hg_imp.setTitle('Homogeneity')
# Blur and BG correct the image
IJ.run(hg_imp, 'Gaussian Blur...', 'sigma=' + str(HOMOGENEITY_RADIUS) + ' stack')
# Detect the spots
IJ.setAutoThreshold(hg_imp, HOMOGENEITY_THRESHOLD + " dark")
rm = RoiManager(True)
table = ResultsTable()
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES,
Measurements.AREA, # measurements
table, # Output table
0, # MinSize
500, # MaxSize
0.0, # minCirc
1.0) # maxCirc
pa.setHideOutputImage(True)
pa.analyze(hg_imp)
areas = table.getColumn(table.getHeadings().index('Area'))
median_areas = compute_median(areas)
st_dev_areas = compute_std_dev(areas, median_areas)
thresholds_areas = (median_areas - (2 * st_dev_areas), median_areas + (2 * st_dev_areas))
roi_measurements = {'integrated_density': [],
'max': [],
'area': []}
IJ.setForegroundColor(0, 0, 0)
for roi in rm.getRoisAsArray():
hg_imp.setRoi(roi)
if REMOVE_CROSS and hg_imp.getStatistics().AREA > thresholds_areas[1]:
rm.runCommand('Fill')
else:
roi_measurements['integrated_density'].append(hg_imp.getStatistics().INTEGRATED_DENSITY)
roi_measurements['max'].append(hg_imp.getStatistics().MIN_MAX)
roi_measurements['integrated_densities'].append(hg_imp.getStatistics().AREA)
rm.runCommand('Delete')
measuremnts = {'mean_integrated_density': compute_mean(roi_measurements['integrated_density']),
'median_integrated_density': compute_median(roi_measurements['integrated_density']),
'std_dev_integrated_density': compute_std_dev(roi_measurements['integrated_density']),
'mean_max': compute_mean(roi_measurements['max']),
'median_max': compute_median(roi_measurements['max']),
'std_dev_max': compute_std_dev(roi_measurements['max']),
'mean_area': compute_mean(roi_measurements['max']),
'median_area': compute_median(roi_measurements['max']),
'std_dev_area': compute_std_dev(roi_measurements['max']),
}
# generate homogeinity image
# calculate interpoint distance in pixels
nr_point_columns = int(sqrt(len(measuremnts['mean_max'])))
# TODO: This is a rough estimation that does not take into account margins or rectangular FOVs
inter_point_dist = hg_imp.getWidth() / nr_point_columns
IJ.run(hg_imp, "Maximum...", "radius="+(inter_point_dist*1.22))
# Normalize to 100
IJ.run(hg_imp, "Divide...", "value=" + max(roi_measurements['max'] / 100))
IJ.run(hg_imp, "Gaussian Blur...", "sigma=" + (inter_point_dist/2))
hg_imp.getProcessor.setMinAndMax(0, 255)
# Create a LUT based on a predefined threshold
red = zeros(256, 'b')
green = zeros(256, 'b')
blue = zeros(256, 'b')
acceptance_threshold = HOMOGENEITY_ACCEPTANCE_THRESHOLD * 256 / 100
for i in range(256):
red[i] = (i - acceptance_threshold)
green[i] = (i)
homogeneity_LUT = LUT(red, green, blue)
hg_imp.setLut(homogeneity_LUT)
return hg_imp, measuremnts
elif template.height > image.height or template.width > image.width:
raise Exception(
'The template is larger in width and/or height than the searched image'
)
### Initialise outputs ###
if show_table:
from ij.measure import ResultsTable
from utils import AddToTable
Table = ResultsTable().getResultsTable(
) # allows to append to an existing table
if add_roi:
from ij.plugin.frame import RoiManager
RM = RoiManager()
rm = RM.getInstance()
# Convert method string to the opencv corresponding index
Dico_Method = {
"Square difference": 0,
"Normalised Square Difference": 1,
"Cross-Correlation": 2,
"Normalised cross-correlation": 3,
"0-mean cross-correlation": 4,
"Normalised 0-mean cross-correlation": 5
}
Method = Dico_Method[method]
# Loop over the images in the stack (or directly process if unique)
ImageStack = image.getStack()
#convert to RGB
IC(imp).convertToRGB()
#show image
imp.show()
#Define ROI of whole image (basically)
imp.setRoi(1,1,478,479)
######OPTIONAL##############
IJ.run("Brightness/Contrast...")
IJ.run(imp, "Enhance Contrast", "saturated=.8")
#open and clear ROI manager
rm = RoiManager.getInstance()
if not rm:
rm = RoiManager()
rm.reset()
#If want to choose regions
#IJ.setTool("rectangle")
#waiting = WaitForUserDialog("Action required","Draw a single ROI with all puncta of interest inside! Then hit OK")
#waiting.show()
#set title variable of image
Title=imp.getTitle()
#make output as outpath and title
outdir=os.path.join(outdir,Title)
#run puncta analyzer
IJ.run(imp, "Puncta Analyzer", "condition=1 red green subtract save rolling=50 light");
#save results
IJ.selectWindow("Results")
def analyzeParticles(imp,
minsize,
maxsize,
mincirc,
maxcirc,
#filename='Test.czi',
addROIManager=False,
#headless=False,
exclude=True):
if GraphicsEnvironment.isHeadless():
print('Headless Mode detected. Do not use ROI Manager.')
addROIManager = False
if addROIManager:
# get the ROI manager instance
rm = RoiManager.getInstance()
if rm is None:
rm = RoiManager()
rm.runCommand("Associate", "true")
if not exclude:
options = PA.SHOW_ROI_MASKS \
+ PA.SHOW_RESULTS \
+ PA.DISPLAY_SUMMARY \
+ PA.ADD_TO_MANAGER \
+ PA.ADD_TO_OVERLAY \
if exclude:
options = PA.SHOW_ROI_MASKS \
+ PA.SHOW_RESULTS \
+ PA.DISPLAY_SUMMARY \
+ PA.ADD_TO_MANAGER \
+ PA.ADD_TO_OVERLAY \
+ PA.EXCLUDE_EDGE_PARTICLES
if not addROIManager:
if not exclude:
options = PA.SHOW_ROI_MASKS \
+ PA.SHOW_RESULTS \
+ PA.DISPLAY_SUMMARY \
+ PA.ADD_TO_OVERLAY \
if exclude:
options = PA.SHOW_ROI_MASKS \
+ PA.SHOW_RESULTS \
+ PA.DISPLAY_SUMMARY \
+ PA.ADD_TO_OVERLAY \
+ PA.EXCLUDE_EDGE_PARTICLES
measurements = PA.STACK_POSITION \
+ PA.LABELS \
+ PA.AREA \
+ PA.RECT \
+ PA.PERIMETER \
+ PA.SLICE \
+ PA.SHAPE_DESCRIPTORS \
+ PA.CENTER_OF_MASS \
+ PA.CENTROID
results = ResultsTable()
p = PA(options, measurements, results, minsize, maxsize, mincirc, maxcirc)
p.setHideOutputImage(True)
particlestack = ImageStack(imp.getWidth(), imp.getHeight())
for i in range(imp.getStackSize()):
imp.setSliceWithoutUpdate(i + 1)
ip = imp.getProcessor()
# convert to a mask for the particle analyzer
ip.invert()
# do the particle analysis
p.analyze(imp, ip)
mmap = p.getOutputImage()
# add the slide to the full stack
particlestack.addSlice(mmap.getProcessor())
return particlestack, results
IJ.run("Threshold...")
WaitForUserDialog("Title", "Adjust threshold for " + color[i]).show()
#Get the threshold you've used
summary[color[i] + "-threshold-used"] = ImageProcessor.getMinThreshold(channel.getProcessor())
#Threshold and watershed
IJ.run(channel, "Convert to Mask", "")
IJ.run(channel, "Watershed", "")
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
#Analyses particles: finds all the objects that match criteria
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER | ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES, Measurements.AREA, table, minimum_size, maximum_size, 0.1, 1.0)
pa.setHideOutputImage(True)
pa.analyze(channel)
if thresholdMode:
channel.show()
WaitForUserDialog("Title", "Look at threshold for" + color[i]).show()
#adds count to summary
def poreDetectionUV(inputImp, inputDataset, inputRoi, ops, data, display, detectionParameters):
title = inputImp.getTitle()
title=title.replace('UV', 'SD')
print title
#trueColorImp= WindowManager.getImage(title)
#print type( trueColorImp)
# calculate are of roi
stats=inputImp.getStatistics()
inputRoiArea=stats.area
print inputRoi
# get the bounding box of the active roi
inputRec = inputRoi.getBounds()
x1=long(inputRec.getX())
y1=long(inputRec.getY())
x2=x1+long(inputRec.getWidth())-1
y2=y1+long(inputRec.getHeight())-1
print x1
print y1
print x2
print y2
# crop the roi
interval=FinalInterval( array([x1, y1 ,0], 'l'), array([x2, y2, 2], 'l') )
cropped=ops.crop(interval, None, inputDataset.getImgPlus() )
datacropped=data.create(cropped)
display.createDisplay("cropped", datacropped)
croppedPlus=IJ.getImage()
duplicator=Duplicator()
substackMaker=SubstackMaker()
# duplicate the roi
duplicate=duplicator.run(croppedPlus)
#duplicate.show()
# convert duplicate of roi to HSB and get brightness
IJ.run(duplicate, "HSB Stack", "");
brightnessPlus=substackMaker.makeSubstack(duplicate, "3-3")
brightness=ImgPlus(ImageJFunctions.wrapByte(brightnessPlus))
brightnessPlus.setTitle("Brightness")
#brightnessPlus.show()
# make another duplicate, split channels and get red
duplicate=duplicator.run(croppedPlus)
channels=ChannelSplitter().split(duplicate)
redPlus=channels[0]
red=ImgPlus(ImageJFunctions.wrapByte(redPlus))
redPlus.show()
# convert to lab
IJ.run(croppedPlus, "Color Transformer", "colour=Lab")
IJ.selectWindow('Lab')
labPlus=IJ.getImage()
# get the A channel
APlus=substackMaker.makeSubstack(labPlus, "2-2")
APlus.setTitle('A')
APlus.show()
APlus.getProcessor().resetMinAndMax()
APlus.updateAndDraw()
AThresholded=threshold(APlus, -10, 50)
# get the B channel
BPlus=substackMaker.makeSubstack(labPlus, "3-3")
BPlus.setTitle('B')
BPlus.show()
BPlus.getProcessor().resetMinAndMax()
BPlus.updateAndDraw()
BThresholded=threshold(BPlus, -10, 50)
# AND the Athreshold and Bthreshold to get a map of the red pixels
ic = ImageCalculator();
redMask = ic.run("AND create", AThresholded, BThresholded);
IJ.run(redMask, "Divide...", "value=255");
#redMask.show()
labPlus.close()
# threshold the spots from the red channel
thresholdedred=SpotDetectionGray(red, data, display, ops, False)
display.createDisplay("thresholdedred", data.create(thresholdedred))
impthresholdedred = ImageJFunctions.wrap(thresholdedred, "wrapped")
# threshold the spots from the brightness channel
thresholded=SpotDetectionGray(brightness, data, display, ops, False)
display.createDisplay("thresholded", data.create(thresholded))
impthresholded=ImageJFunctions.wrap(thresholded, "wrapped")
# or the thresholding results from red and brightness channel
impthresholded = ic.run("OR create", impthresholded, impthresholdedred);
# convert to mask
Prefs.blackBackground = True
IJ.run(impthresholded, "Convert to Mask", "")
# clear the region outside the roi
clone=inputRoi.clone()
clone.setLocation(0,0)
Utility.clearOutsideRoi(impthresholded, clone)
# create a hidden roi manager
roim = RoiManager(True)
# count the particlesimp.getProcessor().setColor(Color.green)
countParticles(impthresholded, roim, detectionParameters.minSize, detectionParameters.maxSize, detectionParameters.minCircularity, detectionParameters.maxCircularity)
# define a function to determine the percentage of pixels that are foreground in a binary image
# inputs:
# imp: binary image, 0=background, 1=foreground
# roi: an roi
def isRed(imp, roi):
stats = imp.getStatistics()
if (stats.mean>detectionParameters.redPercentage): return True
else: return False
def notRed(imp, roi):
stats = imp.getStatistics()
if (stats.mean>detectionParameters.redPercentage): return False
else: return True
allList=[]
for roi in roim.getRoisAsArray():
allList.append(roi.clone())
# count particles that are red
redList=CountParticles.filterParticlesWithFunction(redMask, allList, isRed)
# count particles that are red
blueList=CountParticles.filterParticlesWithFunction(redMask, allList, notRed)
print "Total particles: "+str(len(allList))
print "Filtered particles: "+str(len(redList))
# for each roi add the offset such that the roi is positioned in the correct location for the
# original image
[roi.setLocation(roi.getXBase()+x1, roi.getYBase()+y1) for roi in allList]
# create an overlay and add the rois
overlay1=Overlay()
inputRoi.setStrokeColor(Color.green)
overlay1.add(inputRoi)
[CountParticles.addParticleToOverlay(roi, overlay1, Color.red) for roi in redList]
[CountParticles.addParticleToOverlay(roi, overlay1, Color.cyan) for roi in blueList]
def drawAllRoisOnImage(imp, mainRoi, redList, blueList):
imp.getProcessor().setColor(Color.green)
IJ.run(imp, "Line Width...", "line=3");
imp.getProcessor().draw(inputRoi)
imp.updateAndDraw()
IJ.run(imp, "Line Width...", "line=1");
[CountParticles.drawParticleOnImage(imp, roi, Color.magenta) for roi in redList]
[CountParticles.drawParticleOnImage(imp, roi, Color.green) for roi in blueList]
imp.updateAndDraw()
drawAllRoisOnImage(inputImp, inputRoi, redList, blueList)
#drawAllRoisOnImage(trueColorImp, inputRoi, redList, blueList)
# draw overlay
#inputImp.setOverlay(overlay1)
#inputImp.updateAndDraw()
statsdict=CountParticles.calculateParticleStats(APlus, BPlus, redMask, roim.getRoisAsArray())
print inputRoiArea
areas=statsdict['Areas']
poreArea=0
for area in areas:
poreArea=poreArea+area
ATotal=0
ALevels=statsdict['ALevel']
for A in ALevels:
ATotal=ATotal+A
AAverage=ATotal/len(ALevels)
BTotal=0
BLevels=statsdict['BLevel']
for B in BLevels:
BTotal=BTotal+B
BAverage=BTotal/len(BLevels)
redTotal=0
redPercentages=statsdict['redPercentage']
for red in redPercentages:
redTotal=redTotal+red
redAverage=redTotal/len(redPercentages)
pixwidth=inputImp.getCalibration().pixelWidth
inputRoiArea=inputRoiArea/(pixwidth*pixwidth)
print str(len(allList))+" "+str(len(redList))+" "+str(len(blueList))+" "+str(poreArea/inputRoiArea)+" "+str(redAverage)
# return
# Prevent further propagation of the key event:
keyEvent.consume()
# MAIN code
imp = IJ.getImage()
killIt = False
if imp.getHeight() == 1024: # half-chip size
hOffset = 512
elif imp.getHeight() == 2048: #full-chip image
hOffset = 0
else:
gdGoodBye = GenericDialog("Dimensions error")
gdGoodBye.addMessage(
'Image must be 2048x2048, or 2048x1024 pixels! \n I retire!')
gdGoodBye.showDialog()
killIt = True
if not killIt:
IJ.setTool(Toolbar.RECTANGLE)
manager = RoiManager.getInstance()
if manager is None:
manager = RoiManager()
reset()
listener = ML()
keyLis = ListenToKey()
win = imp.getWindow()
win.getCanvas().addMouseListener(listener)
win.getCanvas().addKeyListener(keyLis)
def process(subFolder, outputDirectory, filename):
imp = IJ.openImage(inputDirectory + subFolder + '/' +
rreplace(filename, "_ch00.tif", ".tif"))
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.setThreshold(imp, 2, 255)
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Remove Outliers...",
"radius=5" + " threshold=50" + " which=Dark")
IJ.run(imp, "Remove Outliers...",
"radius=5" + " threshold=50" + " which=Bright")
imp.getProcessor().invert()
rm = RoiManager(True)
imp.getProcessor().setThreshold(0, 0, ImageProcessor.NO_LUT_UPDATE)
boundroi = ThresholdToSelection.run(imp)
rm.addRoi(boundroi)
if not displayImages:
imp.changes = False
imp.close()
images = [None] * 5
intensities = [None] * 5
blobsarea = [None] * 5
blobsnuclei = [None] * 5
bigAreas = [None] * 5
for chan in channels:
v, x = chan
images[x] = IJ.openImage(inputDirectory + subFolder + '/' +
rreplace(filename, "_ch00.tif", "_ch0" +
str(x) + ".tif"))
imp = images[x]
for roi in rm.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.MEAN | Measurements.AREA)
intensities[x] = stats.mean
bigAreas[x] = stats.area
rm.close()
# Opens the ch00 image and sets default properties
imp = IJ.openImage(inputDirectory + subFolder + '/' + filename)
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
# Sets the threshold and watersheds. for more details on image processing, see https://imagej.nih.gov/ij/developer/api/ij/process/ImageProcessor.html
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.run(imp, "Remove Outliers...",
"radius=2" + " threshold=50" + " which=Dark")
IJ.run(imp, "Gaussian Blur...", "sigma=" + str(blur))
IJ.setThreshold(imp, lowerBounds[0], 255)
if displayImages:
imp.show()
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Watershed", "")
if not displayImages:
imp.changes = False
imp.close()
# Counts and measures the area of particles and adds them to a table called areas. Also adds them to the ROI manager
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA,
table, 15, 9999999999999999, 0.2, 1.0)
pa.setHideOutputImage(True)
#imp = impM
# imp.getProcessor().invert()
pa.analyze(imp)
areas = table.getColumn(0)
# This loop goes through the remaining channels for the other markers, by replacing the ch00 at the end with its corresponding channel
# It will save all the area fractions into a 2d array called areaFractionsArray
areaFractionsArray = [None] * 5
for chan in channels:
v, x = chan
# Opens each image and thresholds
imp = images[x]
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.setThreshold(imp, lowerBounds[x], 255)
if displayImages:
imp.show()
WaitForUserDialog("Title",
"Adjust Threshold for Marker " + v).show()
IJ.run(imp, "Convert to Mask", "")
# Measures the area fraction of the new image for each ROI from the ROI manager.
areaFractions = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA_FRACTION)
areaFractions.append(stats.areaFraction)
# Saves the results in areaFractionArray
areaFractionsArray[x] = areaFractions
roim.close()
for chan in channels:
v, x = chan
imp = images[x]
imp.deleteRoi()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA, table, 15, 9999999999999999,
0.2, 1.0)
pa.analyze(imp)
blobs = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA)
blobs.append(stats.area)
blobsarea[x] = sum(blobs)
blobsnuclei[x] = len(blobs)
if not displayImages:
imp.changes = False
imp.close()
roim.reset()
roim.close()
# Creates the summary dictionary which will correspond to a single row in the output csv, with each key being a column
summary = {}
summary['Image'] = filename
summary['Directory'] = subFolder
# Adds usual columns
summary['size-average'] = 0
summary['#nuclei'] = 0
summary['all-negative'] = 0
summary['too-big-(>' + str(tooBigThreshold) + ')'] = 0
summary['too-small-(<' + str(tooSmallThreshold) + ')'] = 0
# Creates the fieldnames variable needed to create the csv file at the end.
fieldnames = [
'Name', 'Directory', 'Image', 'size-average',
'too-big-(>' + str(tooBigThreshold) + ')',
'too-small-(<' + str(tooSmallThreshold) + ')', '#nuclei',
'all-negative'
]
# Adds the columns for each individual marker (ignoring Dapi since it was used to count nuclei)
summary["organoid-area"] = bigAreas[x]
fieldnames.append("organoid-area")
for chan in channels:
v, x = chan
summary[v + "-positive"] = 0
fieldnames.append(v + "-positive")
summary[v + "-intensity"] = intensities[x]
fieldnames.append(v + "-intensity")
summary[v + "-blobsarea"] = blobsarea[x]
fieldnames.append(v + "-blobsarea")
summary[v + "-blobsnuclei"] = blobsnuclei[x]
fieldnames.append(v + "-blobsnuclei")
# Adds the column for colocalization between first and second marker
if len(channels) > 2:
summary[channels[1][0] + '-' + channels[2][0] + '-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-positive')
# Adds the columns for colocalization between all three markers
if len(channels) > 3:
summary[channels[1][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[2][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[1][0] + '-' + channels[2][0] + '-' + channels[3][0] +
'-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[2][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-' +
channels[3][0] + '-positive')
# Loops through each particle and adds it to each field that it is True for.
areaCounter = 0
for z, area in enumerate(areas):
log.write(str(area))
log.write("\n")
if area > tooBigThreshold:
summary['too-big-(>' + str(tooBigThreshold) + ')'] += 1
elif area < tooSmallThreshold:
summary['too-small-(<' + str(tooSmallThreshold) + ')'] += 1
else:
summary['#nuclei'] += 1
areaCounter += area
temp = 0
for chan in channels:
v, x = chan
if areaFractionsArray[x][z] > areaFractionThreshold[
0]: #theres an error here im not sure why. i remember fixing it before
summary[chan[0] + '-positive'] += 1
if x != 0:
temp += 1
if temp == 0:
summary['all-negative'] += 1
if len(channels) > 2:
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
if areaFractionsArray[2][z] > areaFractionThreshold[2]:
summary[channels[1][0] + '-' + channels[2][0] +
'-positive'] += 1
if len(channels) > 3:
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
if areaFractionsArray[3][z] > areaFractionThreshold[3]:
summary[channels[1][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[2][z] > areaFractionThreshold[2]:
if areaFractionsArray[3][z] > areaFractionThreshold[3]:
summary[channels[2][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
summary[channels[1][0] + '-' + channels[2][0] +
'-' + channels[3][0] + '-positive'] += 1
# Calculate the average of the particles sizes
if float(summary['#nuclei']) > 0:
summary['size-average'] = round(areaCounter / summary['#nuclei'], 2)
# Opens and appends one line on the final csv file for the subfolder (remember that this is still inside the loop that goes through each image)
with open(outputDirectory + "/" + outputName + ".csv", 'a') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
extrasaction='ignore',
lineterminator='\n')
if os.path.getsize(outputDirectory + "/" + outputName + ".csv") < 1:
writer.writeheader()
writer.writerow(summary)
import os
import shutil
import csv
blinded_base = "/Users/nick/Desktop/em-round-2/calibrated-blinded"
data = "/Users/nick/Desktop/em-round-2/done"
img = WindowManager.getCurrentImage()
image_title = img.getShortTitle()
# make paths for moving when done.
full_img_path_orig = os.path.join(blinded_base, image_title + ".tif")
full_img_path_done = os.path.join(data, image_title + ".tif")
# get roi manager
roim = RoiManager().getRoiManager()
# make savepaths for csv and rois
roimsave = os.path.join(data, image_title + "_rois.zip")
csvsave = os.path.join(data, image_title + "_data.csv")
csv_ = [["image", "roi_name", "uncalibrated_length"]]
for roi, _ in enumerate(roim.getRoisAsArray()):
target = roim.getRoi(roi)
csv_.append([image_title, target.getName(), target.getLength()])
# write and save csv
with open(csvsave, "w") as c:
writer = csv.writer(c)
for l in csv_:
],
)
writer.writeheader()
writer.writerows(current)
print("wrote results to {}".format(out_path))
# imp = IJ.openImage(th_files[0])
# imp.show()
# get open image
imp = IJ.getImage()
image_name = imp.getShortTitle()
# get roi manager
roi_manager = RoiManager().getInstance()
all_rois = roi_manager.getRoisAsArray()
csv_name = make_path(out_dir, image_name, ".csv")
roi_name = make_path(out_dir, image_name, "_rois.zip")
old_name = make_path(target_dir, image_name, ".tif")
new_name = make_path(out_dir, image_name, ".tif")
current = []
for roi in all_rois:
res = get_stats(2, roi, imp)
current.append(res)
# write csv
write_csv(current, csv_name)
# save all rois
