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 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
# 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.writerow(l)
# save rois
roim.runCommand("Deselect")
roim.runCommand("Save", roimsave)
# close both
roim.close()
img.close()
def generate_background_rois(input_mask_imp,
params,
membrane_edges,
dilations=5,
threshold_method=None,
membrane_imp=None):
"""automatically identify background region based on auto-thresholded image, existing membrane edges and position of midpoint anchor"""
if input_mask_imp is None and membrane_imp is not None:
segmentation_imp = Duplicator().run(membrane_imp)
# do thresholding using either previous method if threhsold_method is None or using (less conservative?) threshold method
if (threshold_method is None
or not (threshold_method in params.listThresholdMethods())):
mask_imp = make_and_clean_binary(segmentation_imp,
params.threshold_method)
else:
mask_imp = make_and_clean_binary(segmentation_imp,
threshold_method)
segmentation_imp.close()
else:
input_mask_imp.killRoi()
mask_imp = Duplicator().run(input_mask_imp)
rois = []
IJ.setForegroundColor(0, 0, 0)
roim = RoiManager(True)
rt = ResultsTable()
for fridx in range(mask_imp.getNFrames()):
mask_imp.setT(fridx + 1)
# add extra bit to binary mask from loaded membrane in case user refined edges...
# flip midpoint anchor across the line joining the two extremes of the membrane,
# and fill in the triangle made by this new point and those extremes
poly = membrane_edges[fridx].getPolygon()
l1 = (poly.xpoints[0], poly.ypoints[0])
l2 = (poly.xpoints[-1], poly.ypoints[-1])
M = (0.5 * (l1[0] + l2[0]), 0.5 * (l1[1] + l2[1]))
Mp1 = (params.manual_anchor_midpoint[0][0] - M[0],
params.manual_anchor_midpoint[0][1] - M[1])
p2 = (M[0] - Mp1[0], M[1] - Mp1[1])
new_poly_x = list(poly.xpoints)
new_poly_x.append(p2[0])
new_poly_y = list(poly.ypoints)
new_poly_y.append(p2[1])
mask_imp.setRoi(PolygonRoi(new_poly_x, new_poly_y, PolygonRoi.POLYGON))
IJ.run(mask_imp, "Fill", "slice")
mask_imp.killRoi()
# now dilate the masked image and identify the unmasked region closest to the midpoint anchor
ip = mask_imp.getProcessor()
dilations = 5
for d in range(dilations):
ip.dilate()
ip.invert()
mask_imp.setProcessor(ip)
mxsz = mask_imp.getWidth() * mask_imp.getHeight()
pa = ParticleAnalyzer(
ParticleAnalyzer.ADD_TO_MANAGER | ParticleAnalyzer.SHOW_PROGRESS,
ParticleAnalyzer.CENTROID, rt, 0, mxsz)
pa.setRoiManager(roim)
pa.analyze(mask_imp)
ds_to_anchor = [
math.sqrt((x - params.manual_anchor_midpoint[0][0])**2 +
(y - params.manual_anchor_midpoint[0][1])**2)
for x, y in zip(
rt.getColumn(rt.getColumnIndex("X")).tolist(),
rt.getColumn(rt.getColumnIndex("Y")).tolist())
]
if len(ds_to_anchor) > 0:
roi = roim.getRoi(ds_to_anchor.index(min(ds_to_anchor)))
rois.append(roi)
else:
rois.append(None)
roim.reset()
rt.reset()
roim.close()
mask_imp.close()
return rois
# Some of the parameters we configure below need to have
# a reference to the model at creation. So we create an
# empty model now.
model = Model();
# Send all messages to ImageJ log window.
model.setLogger(Logger.IJ_LOGGER);
model.setPhysicalUnits(Calib.getUnit(), Calib.getTimeUnit());
#------------------------
# Prepare settings object
#------------------------
settings = Settings();
rm.select(imp,idx);
actual_roi = rm.getRoi(idx);
imp.setRoi(actual_roi);
settings.setFrom(imp);
settings.tstart = Initial_frames;
settings.tend = imp.getNFrames() - Final_frames -1;
#settings.roi = rm.getSelectedRoisAsArray();
# Configure detector - We use the Strings for the keys
settings.detectorFactory = LogDetectorFactory();
settings.detectorSettings = {
'DO_SUBPIXEL_LOCALIZATION' : Subpixel_localization,
'RADIUS' : Spot_radius,
'TARGET_CHANNEL' : 1,
'THRESHOLD' : Intensity_threshold,
'DO_MEDIAN_FILTERING' : Median_filtering,
};
def Overlayer(org_size, dirs):
""" Overlays ROIs with appropriate color,
saves to .tif and animates aligned images to .gif """
# Get colors.
Colors, Colors_old = colorlist()
# Get ROImanager.
rm = RoiManager().getInstance()
rois = rm.getCount()
# Overlays ROI on aligned images, converts to 8-bit (for gif).
for root, directories, filenames in os.walk(dirs["Composites_Aligned"]):
for filename in filenames:
imp = IJ.openImage(os.path.join(root, filename))
converter = ImageConverter(imp)
converter.setDoScaling(True)
converter.convertToGray8()
# Lookup table and local contrast enhancement for vizualisation.
IJ.run(imp, "Rainbow RGB", "")
IJ.run(imp, "Enhance Local Contrast (CLAHE)",
"blocksize=127 histogram=256 maximum=3 mask=*None*")
for roi in range(rois):
roi_obj = rm.getRoi(roi)
roi_obj.setStrokeWidth(2)
if roi < 19:
roi_obj.setStrokeColor(Color(*Colors[roi][0:3]))
else:
roi_obj.setStrokeColor(eval(Colors_old[roi]))
rm.moveRoisToOverlay(imp)
IJ.saveAs(imp, "Tiff", os.path.join(dirs["Overlays"], filename))
# Opens overlaid images, saves as tiff stack.
overlay_stack = IJ.run("Image Sequence...", "open="+dirs["Overlays"]+
" number=3040 starting=0 increment=1 scale=300 file=.tif sort")
# Takes care of spaces in titles.
tiftitle = Title.replace(" ", "_")
tiftitle = tiftitle.replace(".", "_")
# Gets dimensions for scalebar.
imp = WindowManager.getImage("Overlays")
dimensions = imp.getDimensions()
size = dimensions[0] + dimensions[1]
microns = org_size / size
# Sets scale and writes scale-bar, flattens overlays.
IJ.run(imp, "Set Scale...", "distance=1 known="
+str(microns)+" pixel=1 unit=micron")
IJ.run(imp, "Scale Bar...",
"width=10 height=4 font=14 color=Yellow background=None location=[Lower Right] bold overlay")
IJ.run(imp, "Flatten", "stack")
IJ.saveAs(imp, "Tiff", os.path.join(dirs["Gifs"], tiftitle))
# Animates tiff stack from directoy.
for root, directories, filenames in os.walk(dirs["Gifs"]):
for filename in filenames:
if tiftitle in filename and filename.endswith(".tif"):
# set=xx parameter controls gif speed.
# for additional parameters run with macro recorder.
try:
print "Animating gif..."
imp = WindowManager.getImage(tiftitle + ".tif")
gif = IJ.run("Animated Gif ... ",
"set=200 number=0 filename="
+ os.path.join(dirs["Gifs"], tiftitle + ".gif"))
except Exception, e:
print str(e)
print "gif animated."
def tethered_cell(image_path, frame_number=100, frame_rate=100.0, CCW=1):
"""
parameter setting; frame rate (frame/sec)
CCW = 1 : the motor rotation direction and the cell rotation direction on the image are same
CCW = -1: the motor rotation direction and the cell rotation direction on the image are different
"""
opener = Opener()
imp = opener.openImage(image_path)
image_slice_number = imp.getNSlices()
rm = RoiManager().getInstance()
if image_slice_number < frame_number: # too short movie
IJ.log('Number of frame of the movie is fewer than the number of frame that you selected')
return False
# create result directory
result_path = image_path + '_tethered_cell_result'
if os.path.lexists(result_path) is False:
os.mkdir(result_path)
#z projection; standard deviation, tethered cell shorws circle
IJ.run(imp, 'Subtract Background...', 'rolling=5 light stack')
IJ.run(imp, 'Median...', 'radius=2 stack')
IJ.run(imp, 'Z Project...', 'stop=500 projection=[Standard Deviation]')
zimp = IJ.getImage()
IJ.saveAs(zimp, 'bmp', os.path.join(result_path,'STD_DEV.bmp'))
# pick up tethered cell
IJ.setAutoThreshold(zimp, 'MaxEntropy dark')
IJ.run(zimp, 'Convert to Mask', '')
IJ.run('Set Measurements...', "area centroid bounding shape feret's limit redirect=None decimal=3")
IJ.run(zimp, 'Analyze Particles...', 'size=30-Infinity circularity=0.88-1.00 show=Nothing display exclude clear include')
zrt = ResultsTable.getResultsTable()
IJ.saveAs('Results', os.path.join(result_path,'RoiInfo.csv'))
#tcX and tcY are xy coordinates of tethered cell, tcdia is outer diameter of rotating tethered cell
#add ROI into stack image
for i in range(zrt.getCounter()):
tcX = zrt.getValue('X', i)
tcY = zrt.getValue('Y', i)
tcdia = zrt.getValue('Feret', i)
rm.add(imp, OvalRoi(tcX - tcdia/2.0, tcY - tcdia/2.0, tcdia + 1, tcdia + 1), i)
#calculate rotation speed by ellipse fitting
IJ.setAutoThreshold(imp, 'Li')
for roi_number in range(rm.getCount()):
t = []
XM = []
YM = []
theta = []
rotation_speed = []
area = []
imp.setRoi(rm.getRoi(roi_number))
cropped_imp = Duplicator().run(imp)
IJ.run('Set Measurements...', 'area mean center fit limit redirect=None decimal=3')
rm.select(roi_number)
rt = rm.multiMeasure(imp)
# check cell is present while analysis. Don't a cell gose anywhare?
for i in range(frame_number):
area.append(rt.getValue('Area1', i))
if 0 in area:
continue
for i in range(frame_number):
t.append((1/frame_rate)*i)
XM.append(rt.getValue('XM1', i))
YM.append(rt.getValue('YM1', i))
theta.append(rt.getValue('Angle1', i)/180.0*math.pi) # convert to radian
if i == 0:
rotation_speed.append(0)
else:
# phase treatment, theta should be -pi ~ pi
temp_rotation_speed = [theta[i] - theta[i-1],
theta[i] - theta[i-1] + math.pi,
theta[i] - theta[i-1] - math.pi,
theta[i] - theta[i-1] + 2*math.pi,
theta[i] - theta[i-1] - 2*math.pi]
temp_rotation_speed = sorted(temp_rotation_speed, key = lambda x :abs(x) )[0]
rotation_speed.append(CCW*temp_rotation_speed/(2.0*math.pi)*frame_rate)
# write csv
# earch columns indicate 1:index, 2:time(sec), 3:X-coordinate of center of mass(pixel), 4:Y-coordinate of center of mass (pixel), 5:Angle(Radian), 6:Rotation Speed(Hz)
with open(os.path.join(result_path,'Roi' + str(roi_number) + '.csv'), 'w') as f:
writer = csv.writer(f)
writer.writerow(['Index', 'time(s)', 'X', 'Y', 'Angle(rad)', 'Rotation Speed(Hz)'])
for i in range(len(t)):
writer.writerow([i, t[i], XM[i], YM[i], theta[i], rotation_speed[i]])
# plot x-y, t-x, t-y, t-rotation speed, save plot as bmp
plotRotation(roi_number, result_path, t, XM, YM, rotation_speed)
IJ.saveAs(cropped_imp, 'tiff', os.path.join(result_path,'Roi' + str(roi_number) + '.tiff'))
rt.reset()
# get analysis date and time
dt = datetime.datetime.today()
dtstr = dt.strftime('%Y-%m-%d %H:%M:%S')
# wtite analysis setting
with open(os.path.join(result_path,'analysis_setting.csv'), 'w') as f:
writer = csv.writer(f)
writer.writerow(['Analysis Date','frame number','frame rate','CCW direction', 'Method','Auto threshold', 'Subtruct Background', 'Median filter'])
writer.writerow([dtstr, frame_number, frame_rate, CCW, 'Ellipse', 'Li', '5.0', '2'])
# save roi
if rm.getCount() != 0:
rm.runCommand('Save', os.path.join(result_path, 'Roi.zip'))
zimp.close()
imp.close()
rm.close()
zrt.reset()
