def main(*R):
""" Main function
"""
# Show intial progress bar
IJ.showProgress(0, 50)
IJ.showStatus("Heterogeneous Z Correction")
# Retrieve the values R (with default values) from previous attempts
Rstr = "[(-0.05977, 83.3, 78.73),(-0.05976, 41.65, 39.36)]"
R = eval(Prefs.get("HetZCorr.R", Rstr))
# Load current image and get infos
imp = IJ.getImage()
stk = imp.getStack()
(sx, sy, sz) = stackSize(stk)
cal = imp.getCalibration()
# Get unique values
val = getUniqueValues(stk)
# Get R from dialog
R = showDialogR(R, val)
# Generate optical model
correction = generateModel(stk, R, val)
imp.setCalibration(cal)
#Show model image
correction.show()
correction.setSlice(sz)
IJ.run(correction, "Enhance Contrast", "saturated=0.35")
IJ.run(correction, "Fire", "")
def generateModel(stk, R, val):
""" Generation optical model
"""
(sx, sy, sz) = stackSize(stk)
# Initialize correction output and temporary OPL
cor = IJ.createImage("Correction", "32-bit black", sx, sy, sz)
corstk = cor.getStack()
OPL = 0 * [len(val)]
OPL = [0 for i in range(len(val))]
lastOPL = OPL
for xx in range(sx):
for yy in range(sy):
sub = stk.getVoxels(xx, yy, 0, 1, 1, sz, [])
for zz in range(sz):
for ii in range(len(val)):
if sub[zz] == val[ii]:
if zz >= 1:
OPL[ii] = lastOPL[ii] + 1
else:
OPL[ii] = 1
else:
if zz >= 1:
OPL[ii] = lastOPL[ii]
else:
OPL[ii] = 0
lastOPL[ii] = OPL[ii]
vv = f(OPL, R)
corstk.setVoxel(xx, yy, zz, vv)
IJ.showProgress(xx, sx)
return cor
def download_from_url(download_url,
target_dir,
download_file=None,
download_msg=None):
'''
download file in temporary location
move to target_dir
clean up download
'''
from ij import IJ
print(download_url)
# open url and set up using header information
u = urllib2.urlopen(download_url)
headers = u.info()
download_size = int(headers['Content-Length'])
print(u)
print(headers)
if download_file == None:
if headers.has_key('Content-Disposition'):
download_file = re.sub(".*filename=", "",
headers['Content-Disposition'])
else:
IJ.error(
"No filename specified for download and none in http header!")
if download_msg == None:
download_msg = 'Downloading: %s' % (download_file)
tf = tempfile.NamedTemporaryFile(suffix=download_file, delete=False)
print 'Downloading ' + download_url + ' to ' + tf.name
print "Download size should be %d" % (download_size)
dest_file = os.path.join(target_dir, download_file)
print 'Destination location %s' % (dest_file)
# Now for the download
block_size = 100000
if download_size > block_size:
bytes_read = 0
while bytes_read < download_size:
IJ.showStatus("%s (%.1f/%.1f Mb)" % (download_msg,
(bytes_read / 1000000.0),
(download_size / 1000000.0)))
IJ.showProgress(bytes_read, download_size)
tf.file.write(u.read(block_size))
bytes_read += block_size
IJ.showProgress(1.0)
else:
tf.file.write(u.read())
u.close()
tf.file.close()
print('Downloaded file has size %d') % (os.path.getsize(tf.name))
tf.close()
shutil.move(tf.name, dest_file)
IJ.showStatus('Cleaning up!')
def get_drift_matrix(self):
'''Returns a NxN matrix that represents the drift of all images with each other.
'''
full_progress = len(self.images)**2
for i in range(len(self.images)):
for j in range(len(self.images)):
IJ.showProgress((i * len(self.images) + j) / full_progress)
self.drift_matrix[i][j] = self.get_drift(i, j)
IJ.showProgress(1.0)
return self.drift_matrix
def rot_around_x(input_stack): """do rotation around x axis""" output_slices = input_stack.getHeight(); output_width = input_stack.getWidth(); output_height = input_stack.getSize(); output_stack = ImageStack(output_width, output_height); for yidx in range(input_stack.getHeight()): IJ.showProgress(float(yidx)/output_slices); output_stack.addSlice(FloatProcessor(output_width, output_height, input_stack.getVoxels(0, yidx, 0, output_width, 1, output_height, []))); IJ.showProgress(1.0); return output_stack;
def concatenateImagePlus(files, outfile):
"""Concatenate images contained in files and save in outfile"""
options = ImporterOptions()
options.setId(files[0])
options.setVirtual(1)
options.setOpenAllSeries(1)
options.setQuiet(1)
images = BF.openImagePlus(options)
imageG = images[0]
nrPositions = len(images)
options.setOpenAllSeries(0)
nslices = imageG.getNSlices()
nframes = len(files)
nchannels = imageG.getNChannels()
luts = imageG.getLuts()
for i in range(0, nrPositions):
concatImgPlus = IJ.createHyperStack(
"ConcatFile", imageG.getWidth(), imageG.getHeight(),
imageG.getNChannels(), imageG.getNSlices(),
len(files), imageG.getBitDepth())
concatStack = ImageStack(imageG.getWidth(), imageG.getHeight())
IJ.showStatus("Concatenating files")
for file_ in files:
try:
print '...', basename(file_)
options.setSeriesOn(i, 1)
options.setId(file_)
image = BF.openImagePlus(options)[0]
imageStack = image.getImageStack()
sliceNr = imageStack.getSize()
for j in range(1, sliceNr+1):
concatStack.addSlice(imageStack.getProcessor(j))
image.close()
options.setSeriesOn(i, 0)
except Exception, e:
IJ.log("ERROR")
IJ.log(file_ + str(e))
raise
IJ.showProgress(files.index(file_), len(files))
concatImgPlus.setStack(concatStack, nchannels, nslices, nframes)
concatImgPlus.setCalibration(image.getCalibration())
concatImgPlus.setOpenAsHyperStack(True)
concatImgPlus.setLuts(luts)
concatImgPlus.close()
IJ.saveAs(concatImgPlus, "Tiff", outfile)
def get_drift_vector(self):
'''Returns a vector of length N that represents the drift of all images with the first one.
'''
full_progress = len(self.images) - 1
self.drift_vector[0] = (0., 0.)
for i in range(1, len(self.images)):
IJ.showProgress(i / full_progress)
self.drift_vector[i] = self.get_drift(i - 1, i)
self.drift_vector[i] = tuple([a + b for a, b in zip(self.drift_vector[i - 1],
self.drift_vector[i]
)])
IJ.showProgress(1.0)
return self.drift_vector
def run_script(imp, close, far, cutoff):
stack = imp.getImageStack()
fin_stack = ImageStack(imp.width, imp.height)
# iterate each slice in the stack
for i in range(1, stack.getSize() + 1):
# get progress
IJ.showProgress(i, stack.getSize() + 1)
# image processor converted to float to avoid byte problems
ip = stack.getProcessor(i).convertToFloat()
# pixels points to the array of floats
pixels = ip.getPixels()
# initial subtraction
stddev = initial_subtract(pixels, imp.width, imp.height)
mask = [255] * (imp.width * imp.height)
# mask borders of image dependent on settings
if far:
fringe_mask(mask, imp.width, 2)
elif close:
fringe_mask(mask, imp.width, 1)
else:
pass
# update mask
if close:
init_mask(pixels, mask)
close_subtract(pixels, mask, imp.width, cutoff, stddev)
if far:
init_mask(pixels, mask)
far_subtract(pixels, mask, imp.width, cutoff, stddev)
if close or far:
apply_mask(pixels, mask)
fin_stack.addSlice(None,
FloatProcessor(imp.width, imp.height, pixels, None))
# create new image from final stack
imp_fin = ImagePlus(imp.title[:-4] + "_background-subtracted.tif",
fin_stack)
# keep the same image calibration
imp_fin.setCalibration(imp.getCalibration().copy())
imp_fin.show()
IJ.showProgress(1) # show progess bar
def create_images(self, count=3, mode='simple'):
'''Creates N images to test the drift correction.
:param count: the number of images to create (default: 3)
:param mode: the type of images to create (options: 'simple', 'complex'; default: 'simple')
'''
IJ.showProgress(0)
if not len(self.images) == 0:
self.images = []
if mode == 'simple':
self._create_simple_images(count)
elif mode == 'complex':
self._create_complex_images(count)
else:
IJ.showProgress(1)
def concatenateImagePlus(files, outfile):
"""concatenate images contained in files and save in outfile"""
'''
if len(files) == 1:
IJ.log(files[0] + " has only one time point! Nothing to concatenate!")
return
'''
options = ImporterOptions()
options.setId(files[0])
options.setVirtual(1)
options.setOpenAllSeries(1)
options.setQuiet(1)
images = BF.openImagePlus(options)
imageG = images[0]
nrPositions = len(images)
options.setOpenAllSeries(0)
for i in range(0, nrPositions):
concatImgPlus = IJ.createHyperStack("ConcatFile", imageG.getWidth(), imageG.getHeight(), imageG.getNChannels(), imageG.getNSlices(), len(files), imageG.getBitDepth())
concatStack = ImageStack(imageG.getWidth(), imageG.getHeight())
IJ.showStatus("Concatenating files")
for file in files:
try:
IJ.log(" Add file " + file)
options.setSeriesOn(i,1)
options.setId(file)
image = BF.openImagePlus(options)[0]
imageStack = image.getImageStack()
sliceNr = imageStack.getSize()
for j in range(1, sliceNr+1):
concatStack.addSlice(imageStack.getProcessor(j))
image.close()
options.setSeriesOn(i,0)
except:
traceback.print_exc()
IJ.log(file + " failed to concatenate!")
IJ.showProgress(files.index(file), len(files))
concatImgPlus.setStack(concatStack)
concatImgPlus.setCalibration(image.getCalibration())
if len(images) > 1:
outfileP = addPositionName(i+1,outfile)
IJ.saveAs(concatImgPlus, "Tiff", outfileP)
else:
IJ.saveAs(concatImgPlus, "Tiff", outfile)
concatImgPlus.close()
def _create_simple_images(self, count):
width, height = self.dim_simple
ref = IJ.createImage('Ref', '32-bit black', width, height, 1)
self._create_rect_and_noise(ref, self.roi_simple)
self.images.append(ref)
IJ.showProgress(1 / count)
from random import randint
for i in range(2, count + 1):
roi_off = list(self.roi_simple[:])
roi_off[0] = randint(0.5 * self.roi_simple[0], 1.5 * self.roi_simple[0])
roi_off[1] = randint(0.5 * self.roi_simple[0], 1.5 * self.roi_simple[0])
imp = IJ.createImage('Img', '32-bit black', width, height, 1)
self._create_rect_and_noise(imp, roi_off)
imp.setTitle('%d,%d' % (roi_off[0] - self.roi_simple[0],
roi_off[1] - self.roi_simple[1]
))
self.images.append(imp)
IJ.showProgress(i / count)
def download_and_untar_url(download_url,target_dir,untarfun,download_file=None,
download_msg=None):
'''
download file in temporary location
untar to target_dir using untarfun
clean up download
'''
# open url and set up using header information
u = urllib2.urlopen(download_url)
headers = u.info()
download_size = int(headers['Content-Length'])
if download_file == None:
if headers.has_key('Content-Disposition'):
download_file = re.sub(".*filename=","",headers['Content-Disposition'])
else:
myErr("No filename specified for download and none in http header!")
if download_msg==None:
download_msg='Downloading: %s' % (download_file)
tf=tempfile.NamedTemporaryFile(suffix=download_file)
print 'Downloading '+download_url+' to '+ tf.name
print "Download size should be %d" % (download_size)
from ij import IJ
# Now for the download
block_size=100000
if download_size>block_size:
bytes_read=0
while bytes_read<download_size:
IJ.showStatus("%s (%.1f/%.1f Mb)" %
(download_msg,(bytes_read/1000000.0),(download_size/1000000.0)))
IJ.showProgress(bytes_read,download_size)
tf.file.write(u.read(block_size))
bytes_read+=block_size
IJ.showProgress(1.0)
else:
tf.file.write(u.read())
u.close()
tf.file.close()
print ('Downloaded file has size %d')%(os.path.getsize(tf.name))
untarfun(tf.name,target_dir)
IJ.showStatus('Cleaning up!')
tf.close()
def _create_complex_images(self, count):
source = IJ.openImage('http://imagej.nih.gov/ij/images/NileBend.jpg')
IJ.run(source, '32-bit', '')
ref = self._crop_and_noise(source, self.roi_complex)
ref.setTitle('Ref')
self.images.append(ref)
IJ.showProgress(1 / count)
from random import randint
for i in range(2, count + 1):
roi_off = list(self.roi_complex[:])
roi_off[0] = randint(0.5 * self.roi_complex[0], 1.5 * self.roi_complex[0])
roi_off[1] = randint(0.5 * self.roi_complex[1], 1.5 * self.roi_complex[1])
imp = self._crop_and_noise(source, roi_off)
imp.setTitle('%d,%d' % (self.roi_complex[0] - roi_off[0],
self.roi_complex[1] - roi_off[1]
))
self.images.append(imp)
IJ.showProgress(i / count)
source.close()
def __init__(self, pre1, pre2, post):
'''
Preparing the algorithem by passing thre images.
pre1 and pre2 are the pre-edge images used to estimate the power law background.
post is the image, the estimated background signal is subtracted from.
'''
self.pre1_imp = pre1
self.pre2_imp = pre2
self.post_imp = post
eloss_pattern = re.compile(r'(\d+(?:[\.,]\d+)?)eV')
self.eloss_pre1 = float(eloss_pattern.search(pre1.getTitle()).group(1))
self.eloss_pre2 = float(eloss_pattern.search(pre2.getTitle()).group(1))
self.eloss_post = float(eloss_pattern.search(post.getTitle()).group(1))
print('Energy-losses extracted form image titles: %deV, %deV and %deV'
% (self.eloss_pre1, self.eloss_pre2, self.eloss_post))
# There are 5 repetitive called functions where _update_progress() is called.
self.full_progress = 5 * post.getWidth() * post.getHeight() - 1
self.progress = 0
IJ.showProgress(0, self.full_progress)
def stack_median_filter(imp, radius_um=5.0):
"""perform 2D median filtering slicewise on a stack"""
t1 = datetime.now();
stack = imp.getStack();
title = imp.getTitle();
n_z = imp.getNSlices();
progress_inc = 1 if n_z/20 < 1 else n_z/20;
filt = RankFilters();
for idx in range(n_z):
#print("Median filtering stack, {} % complete...".format(100 * round(float(zidx)/n_z, 3)));
ip = stack.getProcessor(idx+1);
filt.rank(ip, radius_um, RankFilters.MEDIAN);
IJ.showProgress(float(idx)/n_z);
if idx%progress_inc==0:
IJ.showStatus("Median filtering stack in 2D: {}/{}".format(idx, n_z));
imp.updateAndDraw();
imp.setTitle("Median filtered (r={}) {}".format(radius_um, imp.getTitle()).replace(".tif", ""));
t2 = datetime.now();
IJ.showProgress(1.0);
print("Median filtering took {} s".format((t2-t1).total_seconds()));
return imp;
def compute_frame_translations(imp, channel, frames = None):
""" imp contains a hyper virtual stack, and we want to compute
the X,Y,Z translation between every time point in it
using the given preferred channel. """
if frames is None:
frames = range(1, imp.getNFrames()+1)
t1_vs = extract_frame(imp, frames[0], channel)
shifts = []
# store the first shift: between t1 and t2
shifts.append(Point3i(0, 0, 0))
# append the rest:
IJ.showProgress(0)
i = 1
for t in frames[1:]:
IJ.log("Load frame " + str(t) + " up to " + str(frames[-1]))
t2_vs = extract_frame(imp, t, channel)
IJ.log("Compute difference " + str(t) + " to " + str(frames[0]))
shift = compute_stitch(ImagePlus("1", t1_vs), ImagePlus("2", t2_vs))
shifts.append(shift)
t1_vs = t2_vs
IJ.showProgress(i / float(imp.getNFrames()))
i += 1
IJ.showProgress(1)
return shifts, frames
def compute_and_update_frame_translations_dt(imp, dt, options, shifts = None):
""" imp contains a hyper virtual stack, and we want to compute
the X,Y,Z translation between every t and t+dt time points in it
using the given preferred channel.
if shifts were already determined at other (lower) dt
they will be used and updated.
"""
nt = imp.getNFrames()
# get roi (could be None)
roi = imp.getRoi()
#if roi:
# print "ROI is at", roi.getBounds()
# init shifts
if shifts == None:
shifts = []
for t in range(nt):
shifts.append(Point3f(0,0,0))
# compute shifts
IJ.showProgress(0)
max_shifts = options['max_shifts']
for t in range(dt, nt+dt, dt):
if t > nt-1: # together with above range till nt+dt this ensures that the last data points are not missed out
t = nt-1 # nt-1 is the last shift (0-based)
IJ.log(" between frames "+str(t-dt+1)+" and "+str(t+1))
# get (cropped and processed) image at t-dt
roi1 = shift_roi(imp, roi, shifts[t-dt])
imp1 = extract_frame_process_roi(imp, t+1-dt, roi1, options)
# get (cropped and processed) image at t-dt
roi2 = shift_roi(imp, roi, shifts[t])
imp2 = extract_frame_process_roi(imp, t+1, roi2, options)
#if roi:
# print "ROI at frame",t-dt+1,"is",roi1.getBounds()
# print "ROI at frame",t+1,"is",roi2.getBounds()
# compute shift
local_new_shift = compute_shift(imp2, imp1)
limit_shifts_to_maximal_shifts(local_new_shift, max_shifts)
if roi: # total shift is shift of rois plus measured drift
#print "correcting measured drift of",local_new_shift,"for roi shift:",shift_between_rois(roi2, roi1)
local_new_shift = add_Point3f(local_new_shift, shift_between_rois(roi2, roi1))
# determine the shift that we knew alrady
local_shift = subtract_Point3f(shifts[t],shifts[t-dt])
# compute difference between new and old measurement (which come from different dt)
add_shift = subtract_Point3f(local_new_shift,local_shift)
#print "++ old shift between %s and %s: dx=%s, dy=%s, dz=%s" % (int(t-dt+1),int(t+1),local_shift.x,local_shift.y,local_shift.z)
#print "++ add shift between %s and %s: dx=%s, dy=%s, dz=%s" % (int(t-dt+1),int(t+1),add_shift.x,add_shift.y,add_shift.z)
# update shifts from t-dt to the end (assuming that the measured local shift will presist till the end)
for i,tt in enumerate(range(t-dt,nt)):
# for i>dt below expression basically is a linear drift predicition for the frames at tt>t
# this is only important for predicting the best shift of the ROI
# the drifts for i>dt will be corrected by the next measurements
shifts[tt].x += 1.0*i/dt * add_shift.x
shifts[tt].y += 1.0*i/dt * add_shift.y
shifts[tt].z += 1.0*i/dt * add_shift.z
#print "updated shift till frame",tt+1,"is",shifts[tt].x,shifts[tt].y,shifts[tt].z
IJ.showProgress(1.0*t/(nt+1))
IJ.showProgress(1)
return shifts
def get_result(self):
'''Returns the ImagePlus objects that represent the elemental map,
the SNR and the maps of the parameters r and ln(a).
If the calculation of results was not done before, ths method invokes the calculation.
'''
if not self.sigs:
self.calc_map()
lnA_imp = ImagePlus('Map of parameter ln(a)',
FloatProcessor(self.post_imp.getWidth(),
self.post_imp.getHeight(),
array(self.lnAs, 'f')
)
)
r_imp = ImagePlus('Map of parameter r',
FloatProcessor(self.post_imp.getWidth(),
self.post_imp.getHeight(),
array(self.rs, 'f')
)
)
sig_imp = ImagePlus('Elemental map %deV - %s' % (self.eloss_post,
self.post_imp.getShortTitle()),
FloatProcessor(self.post_imp.getWidth(),
self.post_imp.getHeight(),
array(self.sigs, 'f')
)
)
if not self.snrs:
self.calc_snr()
snr_imp = ImagePlus('SNR %deV - %s' % (self.eloss_post,
self.post_imp.getShortTitle()),
FloatProcessor(self.post_imp.getWidth(),
self.post_imp.getHeight(),
array(self.snrs, 'f')
)
)
IJ.showProgress(1.0)
return sig_imp, snr_imp, r_imp, lnA_imp
def depthmap(stack):
# Takes a single channel z stack.
width = stack.getWidth()
height = stack.getHeight()
# Loop through slices in stack.
size = stack.getSize()
outstack = ImageStack()
IJ.log("size: {}".format(size))
for z in range(1, size):
# Calculate maxfilter.
imslice = stack.getPixels(z)
imslice = [i for i in imslice]
imslice = FloatProcessor(width, height, imslice)
imslice = maxfilter(imslice)
outstack.addSlice(imslice)
IJ.showProgress(1.0*z/size)
# Return output stack.
return outstack
def robust_convertStackToGrayXbit(imp, x=8, normalise=False):
"""simplified from https://github.com/imagej/imagej1/blob/master/ij/process/StackConverter.java,
avoiding complications of scaling based on LUT taken from the current frame. Assumes conversion
from grayscale image"""
if x!=8 and x!=16 and x!=32:
raise NotImplementedError("can't convert to the specified bit depth");
if imp.getBitDepth()==x:
return imp;
if normalise:
stats = StackStatistics(imp);
offset = stats.min;
maxtomin = stats.max - stats.min;
IJ.run(imp, "Subtract...", "value={} stack".format(offset));
IJ.run(imp, "Multiply...", "value={} stack".format(float(2**x - 1)/maxtomin));
current_slice = imp.getCurrentSlice();
stack = imp.getStack();
out_stack = ImageStack(imp.getWidth(), imp.getHeight());
n_z = imp.getNSlices();
progress_inc = 1 if n_z/20 < 1 else n_z/20;
for idx in range(n_z):
# always use 1 as old stack index since we remove a slice at each iteration
label = stack.getSliceLabel(1);
ip = stack.getProcessor(1);
stack.deleteSlice(1);
if x==8:
out_stack.addSlice(label, ip.convertToByte(False));
elif x==16:
out_stack.addSlice(label, ip.convertToShort(False));
elif x==32:
out_stack.addSlice(label, ip.convertToFloat());
IJ.showProgress(float(idx)/n_z);
if idx%progress_inc==0:
IJ.showStatus("Converting stack to {}-bits: {}/{}".format(x, idx, n_z));
imp.setStack(out_stack);
IJ.showProgress(1.0);
imp.setSlice(current_slice);
return imp;
def compute_and_update_frame_translations_dt(imp, channel, dt, process, shifts = None):
""" imp contains a hyper virtual stack, and we want to compute
the X,Y,Z translation between every t and t+dt time points in it
using the given preferred channel.
if shifts were already determined at other (lower) dt
they will be used and updated.
"""
nt = imp.getNFrames()
# get roi (could be None)
roi = imp.getRoi()
if roi:
print "ROI is at", roi.getBounds()
# init shifts
if shifts == None:
shifts = []
for t in range(nt):
shifts.append(Point3f(0,0,0))
# compute shifts
IJ.showProgress(0)
for t in range(dt, nt+dt, dt):
if t > nt-1: # together with above range till nt+dt this ensures that the last data points are not missed out
t = nt-1 # nt-1 is the last shift (0-based)
IJ.log(" between frames "+str(t-dt+1)+" and "+str(t+1))
# get (cropped and processed) image at t-dt
roi1 = shift_roi(imp, roi, shifts[t-dt])
imp1 = extract_frame_process_roi(imp, t+1-dt, channel, process, roi1)
# get (cropped and processed) image at t-dt
roi2 = shift_roi(imp, roi, shifts[t])
imp2 = extract_frame_process_roi(imp, t+1, channel, process, roi2)
if roi:
print "ROI at frame",t-dt+1,"is",roi1.getBounds()
print "ROI at frame",t+1,"is",roi2.getBounds()
# compute shift
local_new_shift = compute_stitch(imp2, imp1)
if roi: # total shift is shift of rois plus measured drift
print "correcting measured drift of",local_new_shift,"for roi shift:",shift_between_rois(roi2, roi1)
local_new_shift = add_Point3f(local_new_shift, shift_between_rois(roi2, roi1))
# determine the shift that we knew alrady
local_shift = subtract_Point3f(shifts[t],shifts[t-dt])
# compute difference between new and old measurement (which come from different dt)
add_shift = subtract_Point3f(local_new_shift,local_shift)
print "++ old shift between %s and %s: dx=%s, dy=%s, dz=%s" % (int(t-dt+1),int(t+1),local_shift.x,local_shift.y,local_shift.z)
print "++ add shift between %s and %s: dx=%s, dy=%s, dz=%s" % (int(t-dt+1),int(t+1),add_shift.x,add_shift.y,add_shift.z)
# update shifts from t-dt to the end (assuming that the measured local shift will presist till the end)
for i,tt in enumerate(range(t-dt,nt)):
# for i>dt below expression basically is a linear drift predicition for the frames at tt>t
# this is only important for predicting the best shift of the ROI
# the drifts for i>dt will be corrected by the next measurements
shifts[tt].x += 1.0*i/dt * add_shift.x
shifts[tt].y += 1.0*i/dt * add_shift.y
shifts[tt].z += 1.0*i/dt * add_shift.z
print "updated shift till frame",tt+1,"is",shifts[tt].x,shifts[tt].y,shifts[tt].z
IJ.showProgress(1.0*t/(nt+1))
IJ.showProgress(1)
return shifts
def compute_frame_translations(imp, channel):
""" imp contains a hyper virtual stack, and we want to compute
the X,Y,Z translation between every time point in it
using the given preferred channel. """
t1_vs = extract_frame(imp, 1, channel)
shifts = []
# store the first shift: between t1 and t2
shifts.append(Point3i(0, 0, 0))
# append the rest:
IJ.showProgress(0)
i = 1
for t in range(2, imp.getNFrames()+1):
t2_vs = extract_frame(imp, t, channel)
shift = compute_stitch(ImagePlus("1", t1_vs), ImagePlus("2", t2_vs))
shifts.append(shift)
t1_vs = t2_vs
IJ.showProgress(i / float(imp.getNFrames()))
i += 1
IJ.showProgress(1)
return shifts
IJ.log("stack dimension error!")
#next step: measure the mean of each frame
means = []
if isFrames:
size = imp.getNFrames()
else:
size = imp.getNSlices()
for i in xrange(1, size+1):
#get imageprocessor for slice
ip = stack.getProcessor(i)
#show progress!
IJ.showProgress(i, size+1)
#find the mean using the getMean function, then append it to the list
mean = getMean(ip, imp)
means.append(mean)
IJ.showProgress(1)
IJ.resetMinAndMax()
#set up the variables for plotting and then plot!
x = xrange(1, size + 1)
y = means
plot = Plot("Illumination intensity stability (" + path.basename(stackpath) + ")", "Frame", "Mean frame intensity", [], [])
plot.setLineWidth(1)
def __track(self, imgName) :
"""
Tracks all the cells in ONE stack.
"""
tobool = [self.__subback, self.__manthresh, self.__optionAngle, self.__optionNewCells, self.__optionTimelapse]
self.__subback, self.__manthresh, self.__optionAngle, self.__optionNewCells, self.__optionTimelapse = map(bool, tobool)
os.makedirs(self.__pathdir, mode=0777)
imp=self.__dictImages[imgName]
IJ.run(imp, "Set Scale...", "distance=0 known=0 pixel=1 unit=pixel")
self.__maxLife=imp.getImageStackSize()
tempdict={}
self.__dict[imgName]=tempdict
# We calculate the rois in the first image.
RoisA = self.__calRois(imp,1)
# we add the rois found in the first image in the dictionary of the cells.
for i in range(len(RoisA)) :
t="%04i" % (i)
oldname=RoisA[i].getName()
#newname=oldname+"->cell"+t
newname="cell"+t
cellule = Bacteria_Cell(newname)
RoisA[i].setName(newname)
tempdict[newname]=cellule
cellule.setRoi(RoisA[i],0)
cellule.setSlideEnd(imp.getImageStackSize())
cellule.setlistTimes(self.__dictTimeStack[imgName])
# we look at all pairs of images at t and t+1 in the stack, and we search for connections between ROIs at t and t+1.
tempstacksize=imp.getImageStackSize()
for i in range(2,tempstacksize+1) :
IJ.showProgress(i, tempstacksize)
liens=[]
news=[]
losts=[]
RoisA=[cellule.getRoi(i-2) for cellule in self.__dict[imgName].values() if isinstance(cellule.getRoi(i-2),Roi) ]
RoisB = self.__calRois(imp,i)
# link returns 3 lists of tuples : one of rois that correspond, one of new rois at a given slide, and one of lost rois at a given slide.
outlink = link(imp, i-1, i, RoisA,RoisB, self.__distparam, self.__distmethod, self.__optionAngle, self.__nbdigits, self.__optionNewCells)
liens=outlink[0]
news=outlink[1]
losts=outlink[2]
# we update the tab of rois for the cells for which we found a new ROI in another slide.
lastindex=0
for lien in liens :
celltemp=self.__getCell(imgName, lien[0], i-2)
celltemp.setRoi(lien[1],i-1)
tempname=str(celltemp.getName())
endname=str.rsplit(tempname, "cell", 1)[1]
lien[1].setName(lien[1].getName()+"cell"+str(endname))
if int(endname)>lastindex : lastindex=int(endname)
# we create new cells and add them to the dictionary
count=lastindex+1
for new in news :
t="%04i" % (count)
#new[1].setName(new[1].getName()+"->cell"+t)
new[1].setName("cell"+t)
celltemp = Bacteria_Cell("cell"+t)
tempdict[celltemp.name]=celltemp
for j in range(i-1) :
celltemp.setRoi("NOT HERE YET",j)
celltemp.setRoi(new[1],i-1)
celltemp.setSlideInit(i)
celltemp.setSlideEnd(imp.getImageStackSize())
cellule.setlistTimes(self.__dictTimeStack[imgName])
count=count+1
# we complete the tab of rois of the cells that dispear in a slide.
for lost in losts :
celltemp=self.__getCell(imgName, lost[1], i-2)
if celltemp is None : continue
celltemp.setSlideEnd(i-1)
for j in range(i-1,imp.getImageStackSize()) :
celltemp.setRoi("LOST",j)
#if self.__optionSave == True :
self.__SaveStack(imgName,imp)
self.__subback, self.__manthresh, self.__optionAngle, self.__optionNewCells, self.__optionTimelapse = map(bool, tobool)
def process_imgdir( directory, close_after ) :
print( 'Processing: ' + directory )
original_image_filename = directory + '/uniform.tif'
probabilities_image_filename = directory + '/probabilities.tif'
cube_rois_filename = directory + '/CubeROIs.csv'
cubes = []
f = open(cube_rois_filename)
lines = f.readlines()
for line in lines[1:]:
#els = [ int(el)-1 for el in line.strip().split(',') ]
els = [ int(el) for el in line.strip().split(',') ]
cubes += [ { 'idx': els[0], 'class': els[1], 'x1':min(els[2],els[5]), 'y1':min(els[3],els[6]), 'z1':min(els[4],els[7]), 'x2':max(els[5],els[2]), 'y2':max(els[6],els[3]), 'z2':max(els[4],els[7]) } ]
f.close()
print( 'Read ' + str(len(cubes)) + ' cubes from file' )
#for cube in cubes:
#for cube_idx in range( len( cubes ) ):
for cube_idx in [0]:
cube = cubes[cube_idx]
print( cube )
all_edt_histograms['name'] += [ directory.split( '/' )[-1] + '_' + str(cube_idx) ]
all_skeleton_edt_histograms['name'] += [ directory.split( '/' )[-1] + '_' + str(cube_idx) ]
cube_basename = directory + '/cube_' + str(cube_idx) + '_'
# Check if we have a parameters file, and evaluate it if so, otherwise make one with defaults
param_filename = cube_basename + 'parameters.py'
# Make a default to start with
no_params_set = False
if not os.path.isfile( param_filename ):
f = open( param_filename, 'w' )
f.write( 'mesh_thresh=200\n' )
f.write( 'interior_coords=[ (1,1,1) ]\n' )
f.close()
no_params_set = True
if os.path.isfile( param_filename ):
f = open( param_filename )
lines = f.readlines()
for line in lines:
if 'mesh_thresh' in line:
mesh_thresh = eval(line.split('=')[1])
if 'interior_coords' in line:
interior_coords = eval(line.split('=')[1])
load_uniform = True
if load_uniform:
#open uniform
uniform = IJ.openImage( original_image_filename )
#crop uniform
uniform.setRoi( cube['x1'], cube['y1'], cube['x2'], cube['y2'] )
IJ.run( uniform, "Crop", "")
IJ.run( uniform, "Make Substack...", "slices=" + str(cube['z1']) + '-' + str(cube['z2']) )
uniform.close()
uniform = IJ.getImage()
IJ.saveAsTiff( uniform, cube_basename + 'raw.tif' )
#save max_projection of uniform
IJ.run(uniform, "Z Project...", "projection=[Max Intensity]")
uniform_mp = IJ.getImage()
IJ.run(uniform_mp, "Enhance Contrast", "saturated=0.35")
IJ.saveAsTiff( uniform_mp, cube_basename + 'raw_maxproj.tif' )
#open probabilites
probabilities = IJ.openImage( probabilities_image_filename )
#crop probabilites
probabilities.setRoi( cube['x1'], cube['y1'], cube['x2'], cube['y2'] )
IJ.run( probabilities, "Crop", "")
IJ.run( probabilities, "Make Substack...", "slices=" + str(cube['z1']) + '-' + str(cube['z2']) )
time.sleep( 5 )
probabilities.close()
probabilities = IJ.getImage()
#IJ.saveAsTiff( probabilities, cube_basename + 'probabilities.tif' )
time.sleep( 5 )
#save max_projection of probabilites
IJ.run(probabilities, "Z Project...", "projection=[Max Intensity]")
probabilities_mp = IJ.getImage()
IJ.run(probabilities_mp, "Enhance Contrast", "saturated=0.35")
IJ.saveAsTiff( probabilities_mp, cube_basename + 'probabilities_maxproj.tif' )
time.sleep( 5 )
# blur probabilities to help smooth the isosurfaces
blur_radius = 2
IJ.run( probabilities, "Gaussian Blur 3D...", 'x=' + str(blur_radius) + ' y=' + str(blur_radius) + ' z=' + str(blur_radius) )
#threshold probabilities
#IJ.run(probabilities, "Enhance Contrast", "saturated=0.35")
IJ.run( probabilities, "8-bit", "")
#IJ.saveAsTiff( probabilities, cube_basename + 'probabilities.tif' )
#for k in range( probabilities.getNSlices() ):
# probabilities.setSlice(k+1)
# probabilities.getProcessor().setThreshold( mesh_thresh - 1, mesh_thresh + 1, 0 )
#IJ.run( probabilities, 'Convert to Mask', 'method=Default background=Dark black')
#time.sleep( 5 )
IJ.saveAsTiff( probabilities, cube_basename + 'probabilities.tif' )
time.sleep( 5 )
#mesh binary_probabilities
#IJ.run( "3D Viewer", "")
threedType = 2
threedName = 'cube_' + str(cube_idx)
IJ.runPlugIn( "ij3d.ImageJ3DViewer", probabilities.getTitle() )
univ = Image3DUniverse.universes.get(0)
univ.addContent( probabilities, Color3f(1,1,1), threedName, mesh_thresh, [True, False, False], 2, threedType )
ImageJ3DViewer.select( threedName )
ImageJ3DViewer.exportContent( 'wavefront', cube_basename + 'mesh.obj' )
#smooth mesh
c = univ.getSelected()
n = c.getContent()
ctm = n.getMesh()
fim = customnode.FullInfoMesh( ctm.getMesh() )
ec = customnode.EdgeContraction( fim, False )
initial_num_verts = ec.getVertexCount()
num_to_remove = int( initial_num_verts * 0.1 )
#v = InteractiveMeshDecimation.simplify( ec, num_to_remove )
enable_smoothing = False
if enable_smoothing:
part = num_to_remove / 10
last = num_to_remove % 10
ret = 0
for i in range(10):
IJ.showProgress(i + 1, 10)
ret = ec.removeNext(part)
if (last != 0):
ret = ec.removeNext(last)
IJ.showProgress(1)
ctm.setMesh( fim.getMesh() )
ImageJ3DViewer.exportContent( 'wavefront', cube_basename + 'smooth_mesh.obj' )
# 3d viewer screenshot
screenshot_3d = univ.takeSnapshot()
IJ.saveAsTiff( screenshot_3d, cube_basename + 'mesh_screenshot.tif' )
#voxelize mesh
voxelizer = InteractiveMeshVoxelization()
voxelizer.voxelize( ctm, probabilities.getWidth(), probabilities.getHeight(), probabilities.getStackSize() )
#voxelization = WindowManager.getImage( ctm.getName() + '_voxelization' )
voxelization = WindowManager.getImage( 'null_voxelization' )
voxelization.setCalibration( probabilities.getCalibration().copy() )
#manually 3D fill vasculature, fill with thresholdable-color
#save selected vasculature
#call('process3d.Flood_Fill.fill', x,y,z)
if not no_params_set:
fill_color = 100
Flood_Fill.fill( voxelization, interior_coords[0][0], interior_coords[0][1], interior_coords[0][2], fill_color )
# Threshold to extract
#IJ.setAutoThreshold( voxelization, 'Default dark' )
#Prefs.blackBackground = true
#IJ.run( voxelization, 'Convert to Mask', 'method=Default background=Dark black')
for k in range( voxelization.getNSlices() ):
voxelization.setSlice(k+1)
voxelization.getProcessor().setThreshold( fill_color - 1, fill_color + 1, 0 )
IJ.run( voxelization, 'Convert to Mask', 'method=Default background=Dark black')
IJ.saveAsTiff( voxelization, cube_basename + 'voxelization.tif' )
# Calculate and record volume HERE
IJ.run(voxelization, "Z Project...", "projection=[Max Intensity]")
voxelization_mp = IJ.getImage()
IJ.run(voxelization_mp, "Enhance Contrast", "saturated=0.35")
IJ.saveAsTiff( voxelization_mp, cube_basename + 'voxelization_maxproj.tif' )
# Calculate EDT
IJ.run( voxelization, "Exact Euclidean Distance Transform (3D)", "" )
edt = WindowManager.getImage( 'EDT' )
IJ.run( edt, 'Fire', '' )
# Get the histogram data in an array
hist_nBins = int( ( hist_max - hist_min ) / hist_step )
#edt_stats = edt.getStatistics( Measurements.MEDIAN, hist_nBins, hist_min, hist_max )
edt_stats = StackStatistics( edt, hist_nBins, hist_min, hist_max )
hist_data = edt_stats.getHistogram()
hist_binLabels = [ ( hist_min + hist_step * el ) for el in range( hist_nBins ) ]
max_radius = 20
IJ.run( edt, 'Histogram', 'bins=' + str(hist_nBins) + ' x_min=' + str(hist_min) + ' x_max=' + str(hist_max) + ' y_max=Auto stack' )
edt_histogram = WindowManager.getImage( 'Histogram of EDT' )
IJ.saveAsTiff( edt_histogram, cube_basename + 'edt_histogram.tif' )
x_unit_scale = uniform.getCalibration().getX(1)
y_unit_scale = uniform.getCalibration().getY(1)
z_unit_scale = uniform.getCalibration().getZ(1)
f = open( cube_basename + 'edt_histogram.csv', 'w' )
for k in range( len( hist_data ) ):
f.write( str(hist_binLabels[k]) + '\t' + str(hist_data[k]*x_unit_scale*y_unit_scale*z_unit_scale) + '\n' )
#f.write( str(hist_binLabels[k]) + '\t' + str(hist_data[k]) + '\n' )
all_edt_histograms[hist_binLabels[k]] += [ hist_data[k] ]
f.close()
# Handling skeletons
IJ.run( voxelization, "Skeletonize (2D/3D)", "")
skeleton = voxelization # For simplicity later, but note that voxelization has been mutated
IJ.run(skeleton, "32-bit", "")
IJ.run(skeleton, "Calculator Plus", 'i1=' + str(skeleton.getTitle()) + ' i2=' + str(skeleton.getTitle()) + ' operation=[Scale: i2 = i1 x k1 + k2] k1=0.003921568627 k2=0' )
IJ.run(skeleton, "Z Project...", "projection=[Max Intensity]")
skeleton_mp = IJ.getImage()
IJ.run(skeleton_mp, "Enhance Contrast", "saturated=0.35")
IJ.saveAsTiff( skeleton_mp, cube_basename + 'skeleton_maxproj.tif' )
ic = ImageCalculator()
skeleton_edt = ic.run("Multiply 32-bit stack", edt, skeleton)
IJ.run( skeleton_edt, 'Fire', '' )
# Get the histogram data in an array
hist_nBins = int( ( hist_max - hist_min ) / hist_step )
#skeleton_edt_stats = edt.getStatistics( Measurements.MEDIAN, hist_nBins, hist_min, hist_max )
skeleton_edt_stats = StackStatistics( edt, hist_nBins, hist_min, hist_max )
hist_data = skeleton_edt_stats.getHistogram()
hist_binLabels = [ ( hist_min + hist_step * el ) for el in range( hist_nBins ) ]
IJ.run( skeleton_edt, 'Histogram', 'bins=' + str(hist_nBins) + ' x_min=' + str(hist_min) + ' x_max=' + str(hist_max) + ' y_max=Auto stack' )
skeleton_edt_histogram = WindowManager.getImage( 'Histogram of EDT' )
IJ.saveAsTiff( skeleton_edt_histogram, cube_basename + 'skeleton_edt_histogram.tif' )
unit_scale = uniform.getCalibration().getX(1)
f = open( cube_basename + 'skeleton_edt_histogram.csv', 'w' )
for k in range( len( hist_data ) ):
f.write( str(hist_binLabels[k]) + '\t' + str(hist_data[k] * unit_scale) + '\n' )
all_skeleton_edt_histograms[hist_binLabels[k]] += [ hist_data[k] * unit_scale ]
f.close()
else:
IJ.saveAsTiff( voxelization, cube_basename + 'voxelization.tif' )
# Free up resources
if close_after:
ImageJ3DViewer.close()
IJ.run( 'Close All', '' )
IJ.freeMemory()
def regBf(fn=None, imp=None, refId=None):
"""
Register a time series stack to a specified reference slice,
from a file (imported by BioFormat) or a stack ImagePlus.
Returns a registered ImagePlus.
The stack must have only 1 z layer.
refId is in the format of [int channel, int slice, int frame]
If no refId is supplied, will use the first slice [1,1,1]
Note: since TurboReg is used for registeration, there will be
temporary opened image windows.
"""
## Prepare the right ImagePlus
if imp is None:
if fn is None:
od = OpenDialog("Choose a file", None)
filename = od.getFileName()
if filename is None:
print "User canceled the dialog!"
return
else:
directory = od.getDirectory()
filepath = directory + filename
print "Selected file path:", filepath
else:
if os.path.exists(fn) and os.path.isfile(fn):
filepath = fn
else:
print "File does not exist!"
return
imps = BF.openImagePlus(filepath)
imp = imps[0]
if imp is None:
print "Cannot load file!"
return
else:
if fn is not None:
print "File or ImagePlus? Cannot load both."
return
width = imp.getWidth()
height = imp.getHeight()
# C
nChannels = imp.getNChannels()
# Z
nSlices = imp.getNSlices()
# T
nFrames = imp.getNFrames()
# pixel size
calibration = imp.getCalibration()
# Only supoort one z layer
if nSlices != 1:
print "Only support 1 slice at Z dimension."
return
# set registration reference slice
if refId is None:
refC = 1
refZ = 1
refT = 1
else:
refC = refId[0]
refZ = refId[1]
refT = refId[2]
if (refC not in range(1, nChannels+1) or
refZ not in range(1, nSlices+1) or
refT not in range(1, nFrames+1) ):
print "Invalid reference image!"
return
stack = imp.getImageStack()
registeredStack = ImageStack(width, height, nChannels*nFrames*nSlices)
# setup windows, these are needed by TurboReg
tmpip = FloatProcessor(width, height)
refWin = ImageWindow(ImagePlus("ref", tmpip))
bounds = refWin.getBounds()
# refWin.setVisible(False)
toRegWin = ImageWindow(ImagePlus("toReg", tmpip))
toRegWin.setLocation(bounds.width + bounds.x, bounds.y)
# toRegWin.setVisible(False)
toTransformWin = ImageWindow(ImagePlus("toTransform", tmpip))
toTransformWin.setLocation(2 * bounds.width + bounds.x, bounds.y)
# toTransformWin.setVisible(False)
# get reference image
refImp = ImagePlus("ref", stack.getProcessor(imp.getStackIndex(refC, refZ, refT)))
refWin.setImage(refImp)
tr = TurboReg_()
for t in xrange(1, nFrames+1):
IJ.showProgress(t-1, nFrames)
# print "t ", t
# do TurboReg on reference channel
toRegId = imp.getStackIndex(refC, refZ, t)
toRegImp = ImagePlus("toReg", stack.getProcessor(toRegId))
toRegWin.setImage(toRegImp)
regArg = "-align " +\
"-window " + toRegImp.getTitle() + " " +\
"0 0 " + str(width - 1) + " " + str(height - 1) + " " +\
"-window " + refImp.getTitle() + " " +\
"0 0 " + str(width - 1) + " " + str(height - 1) + " " +\
"-rigidBody " +\
str(width / 2) + " " + str(height / 2) + " " +\
str(width / 2) + " " + str(height / 2) + " " +\
"0 " + str(height / 2) + " " +\
"0 " + str(height / 2) + " " +\
str(width - 1) + " " + str(height / 2) + " " +\
str(width - 1) + " " + str(height / 2) + " " +\
"-hideOutput"
tr = TurboReg_()
tr.run(regArg)
registeredImp = tr.getTransformedImage()
sourcePoints = tr.getSourcePoints()
targetPoints = tr.getTargetPoints()
registeredStack.setProcessor(registeredImp.getProcessor(), toRegId)
# toRegImp.flush()
# apply transformation on other channels
for c in xrange(1, nChannels+1):
# print "c ", c
if c == refC:
continue
toTransformId = imp.getStackIndex(c, 1, t)
toTransformImp = ImagePlus("toTransform", stack.getProcessor(toTransformId))
toTransformWin.setImage(toTransformImp)
transformArg = "-transform " +\
"-window " + toTransformImp.getTitle() + " " +\
str(width) + " " + str(height) + " " +\
"-rigidBody " +\
str(sourcePoints[0][0]) + " " +\
str(sourcePoints[0][1]) + " " +\
str(targetPoints[0][0]) + " " +\
str(targetPoints[0][1]) + " " +\
str(sourcePoints[1][0]) + " " +\
str(sourcePoints[1][1]) + " " +\
str(targetPoints[1][0]) + " " +\
str(targetPoints[1][1]) + " " +\
str(sourcePoints[2][0]) + " " +\
str(sourcePoints[2][1]) + " " +\
str(targetPoints[2][0]) + " " +\
str(targetPoints[2][1]) + " " +\
"-hideOutput"
tr = TurboReg_()
tr.run(transformArg)
registeredStack.setProcessor(tr.getTransformedImage().getProcessor(), toTransformId)
# toTransformImp.flush()
sourcePoints = None
targetPoints = None
IJ.showProgress(t, nFrames)
IJ.showStatus("Frames registered: " + str(t) + "/" + str(nFrames))
refWin.close()
toRegWin.close()
toTransformWin.close()
imp2 = ImagePlus("reg_"+imp.getTitle(), registeredStack)
imp2.setCalibration(imp.getCalibration().copy())
imp2.setDimensions(nChannels, nSlices, nFrames)
# print "type ", imp.getType()
# print "type ", imp2.getType()
# print nChannels, " ", nSlices, " ", nFrames
# print registeredStack.getSize()
for key in imp.getProperties().stringPropertyNames():
imp2.setProperty(key, imp.getProperty(key))
# comp = CompositeImage(imp2, CompositeImage.COLOR)
# comp.show()
# imp2 = imp.clone()
# imp2.setStack(registeredStack)
# imp2.setTitle("reg"+imp.getTitle())
# imp2.show()
# imp.show()
return imp2
#for cell in listfilescells :
f1 = open(rootdir+now+"-R1-MT.txt", "w")
tab="\t"
f1.write("cell"+tab+"maxFrames"+tab+"maxcumul"+tab+"nrevs"+"\n")
for cle in listcellname :
rm.runCommand("reset")
#cle = cell.rsplit("/", 1)[1][:-len(".cell")]
#cles.append(cle)
rm.runCommand("Open", dictRois[cle])
rm.runCommand("Show None")
RawroisArray=rm.getRoisAsArray()
if len(RawroisArray)< minLife : continue
roisArray=[RawroisArray[i] for i in range(0,len(RawroisArray), subs)]
IJ.showStatus(cle)
IJ.showProgress(listcellname.index(cle), len(listcellname))
dxA=[]
dyA=[]
dxB=[]
dyB=[]
dA=[]
dB=[]
sensA = 1
sensB = -1
nrev = 0
color=colors.pop(0)
colorA=color.brighter()
colorB=color.darker()
# Script main body
#==============================================================================
options = getOptions()
if options is not None:
width = options.width
height = options.height
x0 = width / 2.0
sigma_x = width * options.sigma_x
y0 = height / 2.0
sigma_y = height * options.sigma_y
pixels = zeros('f', width * height)
pixels_index = MapPositionToIndex(width)
val_sum = 0
for y in range(height):
IJ.showProgress(y, height-1)
for x in range(width):
val = Gaussian2D(x0, y0, x, y, sigma_x, sigma_y)
val_sum += val
pixels[pixels_index.at(x, y)] = val
for i in range(len(pixels)):
IJ.showProgress(i, len(pixels) - 1)
pixels[i] = pixels[i] / val
pixels_fp = FloatProcessor(width, height, pixels, None)
imp = ImagePlus('2D Gaussian', pixels_fp)
imp.show()
def main(imp, tolerance, window_radius, pixel_size_nm, out_dir):
# set output dir
out_dir = str(out_dir)
# Find maxima
excludeOnEdge = True
polygon = MaximumFinder().getMaxima(imp.getProcessor(), tolerance, excludeOnEdge)
roi = PointRoi(polygon)
# get RoiManager
rm = RoiManager.getInstance();
if rm is None:
rm = RoiManager()
# Check if output table is writable
out_table_fn = os.path.join(out_dir, "fwhm_values.txt")
try:
file_handle = open(out_table_fn, 'w')
except IOError:
IJ.showMessage("Output file '' not writeable. Check if file is open in Excel...")
sys.exit(0)
# iterate and write output
with file_handle as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=["id", "peak_id", "x_pos", "y_pos",
"type", "fwhm", "fwhm_nm", "r2_GoF",
"avg_fwhm", "area_profile", "area_gauss"], delimiter="\t", lineterminator='\n')
writer.writeheader()
id_ = 0
# over all peaks
for i, p in list(enumerate(roi)):
IJ.showProgress(i, roi.getNCounters() +1)
# Horizontal
lroi = Line(p.x+0.5-window_radius, p.y+0.5,
p.x+0.5+window_radius, p.y+0.5)
output = fit_gauss(lroi, imp, p, i, id_, "H", rm)
writer.writerow(output)
id_+=1
# Vertical
lroi = Line(p.x+0.5, p.y+0.5-window_radius,
p.x+0.5, p.y+0.5+window_radius)
output = fit_gauss(lroi, imp, p, i, id_, "V", rm)
writer.writerow(output)
id_+=1
# Diagonal 1
lroi = Line(p.x+0.5-MN*window_radius, p.y+0.5+MN*window_radius,
p.x+0.5+MN*window_radius, p.y+0.5-MN*window_radius)
output = fit_gauss(lroi, imp, p, i, id_, "D1", rm)
writer.writerow(output)
id_+=1
# Diagonal 2
lroi = Line(p.x+0.5-MN*window_radius, p.y+0.5-MN*window_radius,
p.x+0.5+MN*window_radius, p.y+0.5+MN*window_radius)
output = fit_gauss(lroi, imp, p, i, id_, "D2", rm)
writer.writerow(output)
id_+=1
IJ.showProgress(1)
rm.runCommand("Deselect"); # deselect ROIs to save them all
rm.runCommand("Save", os.path.join(out_dir, "fwhm_fiji_rois.zip"))
IJ.showMessage("FWHM on Spots: Done")
def main_interactive():
"""The main routine for running interactively."""
log.info('Running in interactive mode.')
(base, fname) = ui_get_input_file()
if (base is None):
return
log.warn("Parsing project file: %s" % (base + fname))
IJ.showStatus("Parsing experiment file...")
mosaics = fv.FluoViewMosaic(join(base, fname), runparser=False)
IJ.showStatus("Parsing mosaics...")
progress = 0.0
count = len(mosaics.mosaictrees)
step = 1.0 / count
for subtree in mosaics.mosaictrees:
IJ.showProgress(progress)
mosaics.add_mosaic(subtree)
progress += step
IJ.showProgress(progress)
IJ.showStatus("Parsed %i mosaics." % len(mosaics))
dialog = GenericDialog('FluoView OIF / OIB Stitcher')
if len(mosaics) == 0:
msg = ("Couldn't find any (valid) mosaics in the project file.\n"
" \n"
"Please make sure to have all files available!\n"
" \n"
"Will stop now.\n")
log.warn(msg)
dialog.addMessage(msg)
dialog.showDialog()
return
msg = "------------------------ EXPORT OPTIONS ------------------------"
dialog.addMessage(msg)
formats = ["OME-TIFF", "ICS/IDS"]
dialog.addChoice("Export Format", formats, formats[0])
dialog.addCheckbox("separate files by Z slices (OME-TIFF only)?", False)
msg = "------------------------ EXPORT OPTIONS ------------------------"
dialog.addMessage(msg)
dialog.addMessage("")
dialog.addMessage("")
msg = gen_mosaic_details(mosaics)
log.warn(msg)
msg += "\n \nPress [OK] to write tile configuration files\n"
msg += "and continue with running the stitcher."
dialog.addMessage(msg)
dialog.showDialog()
opts = {}
if dialog.getNextChoice() == 'ICS/IDS':
opts['export_format'] = '".ids"'
else:
opts['export_format'] = '".ome.tif"'
if dialog.getNextBoolean() == True:
opts['split_z_slices'] = 'true'
code = imagej.gen_stitching_macro_code(mosaics, 'templates/stitching',
path=base, tplpath=imcftpl, opts=opts)
log.warn("============= generated macro code =============")
log.warn(flatten(code))
log.warn("============= end of generated macro code =============")
if dialog.wasOKed():
log.warn('Writing stitching macro.')
imagej.write_stitching_macro(code, fname='stitch_all.ijm', dname=base)
log.warn('Writing tile configuration files.')
imagej.write_all_tile_configs(mosaics, fixsep=True)
log.warn('Launching stitching macro.')
IJ.runMacro(flatten(code))
for layer in layers:
vector_data.addAll(
cast_collection(layer.getDisplayables(VectorData, False, True),
VectorData, True))
vector_data.addAll(
cast_collection(layerset.getZDisplayables(VectorData, True),
VectorData, True))
# Propagate before or propagate after
# TO DO
# Apply transforms to patches
progress = 0
for mesh, layer in zip(meshes, layers):
Utils.log("Applying transforms to patches...")
IJ.showProgress(0, len(layers))
mlt = MovingLeastSquaresTransform2()
mlt.setModel(AffineModel2D)
mlt.setAlpha(2.0)
mlt.setMatches(mesh.getVA().keySet())
# ElasticLayerAlignment uses newer concurrent methods for this
pool = Executors.newFixedThreadPool(MAX_NUM_THREADS)
patch_transforms = []
patches = layer.getDisplayables(Patch)
for patch in patches:
pt = PatchTransform(patch, mlt.copy())
patch_transforms.append(pt)
futures = pool.invokeAll(patch_transforms)
for future in futures:
A speed test example fixed
Modifications
Date Who Ver What
---------- --- ------ -------------------------------------------------
2017-04-02 JRM 0.1.00 Initial test with 4096. Original example did not
import functions. 11.594sec on jrmFastMac
"""
from org.python.core import codecs
codecs.setDefaultEncoding('utf-8')
from java.lang import System
from ij import IJ, ImagePlus
from ij.process import FloatProcessor
from math import sqrt
t0 = System.currentTimeMillis()
size = 4096
ip = FloatProcessor(size,size)
for y in range(size):
IJ.showProgress(y,size-1)
for x in range(size):
dx=x-size/2; dy=y-size/2
d = sqrt(dx*dx+dy*dy)
ip.setf(x,y,-d)
time = str((System.currentTimeMillis()-t0)/1000.0)+" seconds"
ImagePlus(time,ip).show()
IJ.run("Red/Green");
def show_progress(cur, final):
"""Wrapper to update the progress bar and issue a log message."""
# ij.IJ.showProgress is adding 1 to the value given as first parameter...
log.info("Progress: %s / %s (%s)", cur + 1, final, (1.0 + cur) / final)
IJ.showProgress(cur, final)
print basic_info
## Outputs each stitched z plane as a separate file
iReader = ImageReader()
iReader.setId(parentLSMFilePath)
for z in range(max_coords[2]+basic_info[4]):
## for z in range(50,51):
IJ.showStatus("z: "+str(z+1)+" of "+str(max_coords[2]+basic_info[4]))
chIps = []
resImages = []
for ch in range(basic_info[0]):
chIps.append(ByteProcessor(max_coords[0]+scale_info[2],max_coords[1]+scale_info[2]))
for ch in range(basic_info[0]):
resImages.append(ImagePlus("ch"+str(ch+1),chIps[ch]))
for se in range(basic_info[1]):
IJ.showProgress(se,basic_info[1])
if z >= coords_upscaled[se][2] and z <= coords_upscaled[se][2]+basic_info[4]-1:
iReader.setSeries(se)
for ch in range(basic_info[0]):
byteArray = iReader.openBytes((z-coords_upscaled[se][2])*basic_info[0]+ch)
testIp = ByteProcessor(scale_info[2],scale_info[2],byteArray)
testImage = ImagePlus("tester",testIp)
Image_stamper.stampStack(testImage,resImages[ch],coords_upscaled[se][0],coords_upscaled[se][1],0)
activeIp = chIps[ch]
testImage.close()
for ch in range(len(resImages)):
IJ.saveAsTiff(resImages[ch],parentLSMFilePath+"_tiles/v_img/img_z_"+str(z+1)+"_c_"+str(ch+1)+".tif")
#outPlaneImage = RGBStackMerge.mergeChannels(resImages,False)
#IJ.saveAsTiff(outPlaneImage,parentLSMFilePath+"_tiles/v_img/img_z_"+str(z+1)+".tif")
def processMovie(root, files, outfile):
"""Concatenate images and write ome.tiff file.
If image contains already multiple time points just copy the image"""
files.sort()
options = ImporterOptions()
options.setId(files[0])
options.setVirtual(1)
image = BF.openImagePlus(options)
image = image[0]
if image.getNFrames() > 1:
msg = ("%s Contains multiple time points. Can only concatenate"
" single time points!" %files[0])
raise RuntimeError(msg)
image.close()
reader = ImageReader()
reader.setMetadataStore(MetadataTools.createOMEXMLMetadata())
reader.setId(files[0])
timeInfo = []
omeOut = reader.getMetadataStore()
omeOut = setUpXml(omeOut, image, files)
reader.close()
image.close()
itime = 0
for fileName in files:
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(fileName)
timeInfo.append(getTimePoint(reader, omeMeta))
nrImages = reader.getImageCount()
for i in range(0, reader.getImageCount()):
try:
dT = round(timeInfo[files.index(fileName)]-timeInfo[0],2)
except:
dT = (timeInfo[files.index(fileName)]-timeInfo[0]).seconds
omeOut.setPlaneDeltaT(dT, 0, i + itime*nrImages)
omeOut.setPlanePositionX(omeOut.getPlanePositionX(0,i), 0, i + itime*nrImages)
omeOut.setPlanePositionY(omeOut.getPlanePositionY(0,i), 0, i + itime*nrImages)
omeOut.setPlanePositionZ(omeOut.getPlanePositionZ(0,i), 0, i + itime*nrImages)
omeOut.setPlaneTheC(omeOut.getPlaneTheC(0,i), 0, i + itime*nrImages)
omeOut.setPlaneTheT(omeOut.getPlaneTheT(0,i), 0, i + itime*nrImages)
omeOut.setPlaneTheZ(omeOut.getPlaneTheZ(0,i), 0, i + itime*nrImages)
itime = itime + 1
reader.close()
IJ.showProgress(files.index(fileName), len(files))
try:
omeOut.setPixelsTimeIncrement(float(dT/(len(files)-1)), 0)
except:
omeOut.setPixelsTimeIncrement(0, 0)
if len(files) <= 1:
raise RuntimeError('Found only one file. Nothing to concatenate')
outfile = concatenateImagePlus(files, outfile)
filein = RandomAccessInputStream(outfile)
fileout = RandomAccessOutputStream(outfile)
saver = TiffSaver(fileout, outfile)
saver.overwriteComment(filein, omeOut.dumpXML())
fileout.close()
filein.close()
def register_hyperstack_subpixel(imp, channel, shifts, target_folder, virtual):
""" Takes the imp, determines the x,y,z drift for each pair of time points, using the preferred given channel,
and outputs as a hyperstack.
The shifted image is computed using TransformJ allowing for sub-pixel shifts using interpolation.
This is quite a bit slower than just shifting the image by full pixels as done in above function register_hyperstack().
However it significantly improves the result by removing pixel jitter.
"""
# Compute bounds of the new volume,
# which accounts for all translations:
minx, miny, minz, maxx, maxy, maxz = compute_min_max(shifts)
# Make shifts relative to new canvas dimensions
# so that the min values become 0,0,0
for shift in shifts:
shift.x -= minx
shift.y -= miny
shift.z -= minz
# new canvas dimensions:
width = int(imp.width + maxx - minx)
height = int(maxy - miny + imp.height)
slices = int(maxz - minz + imp.getNSlices())
print "New dimensions:", width, height, slices
# prepare stack for final results
stack = imp.getStack()
if virtual is True:
names = []
else:
registeredstack = ImageStack(width, height, imp.getProcessor().getColorModel())
# prepare empty slice for padding
empty = imp.getProcessor().createProcessor(width, height)
IJ.showProgress(0)
for frame in range(1, imp.getNFrames()+1):
IJ.showProgress(frame / float(imp.getNFrames()+1))
fr = "t" + zero_pad(frame, len(str(imp.getNFrames()))) # for saving files in a virtual stack
# init
shift = shifts[frame-1]
tmpstack = ImageStack(width, height, imp.getProcessor().getColorModel())
print "frame",frame,"correcting drift",-shift.x-minx,-shift.y-miny,-shift.z-minz
IJ.log(" frame "+str(frame)+" correcting drift "+str(round(-shift.x-minx,2))+","+str(round(-shift.y-miny,2))+","+str(round(-shift.z-minz,2)))
# for doing the same with imglib2 i would have to put the channel loop
# to the outside and translate each individual channel as long as i don't figure out
# to two wrap a composite imglib2 image into an imp
# Add all slices of this frame
stack = imp.getStack()
for s in range(1, imp.getNSlices()+1):
for ch in range(1, imp.getNChannels()+1):
ip = stack.getProcessor(imp.getStackIndex(ch, s, frame))
ip2 = ip.createProcessor(width, height) # potentially larger
ip2.insert(ip, 0, 0)
tmpstack.addSlice("", ip2)
# Pad the end (in z) of this frame
for s in range(imp.getNSlices(), slices):
for ch in range(1, imp.getNChannels()+1):
tmpstack.addSlice("", empty)
# Set correct dimensions of this frame
# ..it is important *not* to set the calibration as translation should be in pixels units
imp_tmpstack = ImagePlus("registered time points", tmpstack)
imp_tmpstack.setProperty("Info", imp.getProperty("Info"))
imp_tmpstack.setDimensions(imp.getNChannels(), slices, 1)
imp_tmpstack.setOpenAsHyperStack(True)
# subpixel translation
imp_translated = translate_using_imagescience(imp_tmpstack, shift.x, shift.y, shift.z)
#imp_translated = translate_using_imglib2(imp_tmpstack, shift.x, shift.y, shift.z)
imp_translated.setProperty("Info", imp.getProperty("Info"))
imp_translated.setDimensions(imp.getNChannels(), slices, 1)
imp_translated.setOpenAsHyperStack(True)
# Add translated frame to final time-series
stack = imp_translated.getStack()
for s in range(1, imp_translated.getNSlices()+1):
ss = "_z" + zero_pad(s, len(str(slices)))
for ch in range(1, imp_translated.getNChannels()+1):
ip = stack.getProcessor(imp_translated.getStackIndex(ch, s, 1))
if virtual is True:
name = fr + ss + "_c" + zero_pad(ch, len(str(imp.getNChannels()))) +".tif"
names.append(name)
currentslice = ImagePlus("", ip)
currentslice.setCalibration(imp.getCalibration().copy())
currentslice.setProperty("Info", imp.getProperty("Info"));
FileSaver(currentslice).saveAsTiff(target_folder + "/" + name)
else:
registeredstack.addSlice("", ip)
IJ.showProgress(1)
if virtual is True:
# Create virtual hyper stack with the result
registeredstack = VirtualStack(width, height, None, target_folder)
for name in names:
registeredstack.addSlice(name)
registeredstack_imp = ImagePlus("registered time points", registeredstack)
registeredstack_imp.setDimensions(imp.getNChannels(), slices, imp.getNFrames())
registeredstack_imp.setCalibration(imp.getCalibration().copy())
registeredstack_imp.setOpenAsHyperStack(True)
else:
registeredstack_imp = ImagePlus("registered time points", registeredstack)
registeredstack_imp.setCalibration(imp.getCalibration().copy())
registeredstack_imp.setProperty("Info", imp.getProperty("Info"))
registeredstack_imp.setDimensions(imp.getNChannels(), slices, imp.getNFrames())
registeredstack_imp.setOpenAsHyperStack(True)
if 1 == registeredstack_imp.getNChannels():
return registeredstack_imp
#IJ.log("\nHyperstack dimensions: time frames:" + str(registeredstack_imp.getNFrames()) + ", slices: " + str(registeredstack_imp.getNSlices()) + ", channels: " + str(registeredstack_imp.getNChannels()))
# Else, as composite
mode = CompositeImage.COLOR;
if isinstance(imp, CompositeImage):
mode = imp.getMode()
else:
return registeredstack_imp
return CompositeImage(registeredstack_imp, mode)
def _update_progress(self):
'''Raises the progress by one and updates the progress bar.
'''
self.progress += 1
IJ.showProgress(self.progress, self.full_progress)
saveFilePath = saveFileDir + saveFileName
savefilehandler = open(saveFilePath,"w")
waitDialog = WaitForUserDialog("Use freeform tool to outline the piece of tissue")
waitDialog.show()
roi = theImage.getRoi()
if (roi is not None):
print type(roi)
thePolygon = roi.getPolygon()
boundRect = thePolygon.getBounds()
for i in range(boundRect.x,boundRect.x+boundRect.width):
pos_pixels = 0
tot_pixels = 0
IJ.showProgress(i-boundRect.x,boundRect.width)
for j in range(boundRect.y,boundRect.y+boundRect.height):
if thePolygon.contains(i,j):
value = ip.getPixelValue(i,j)
tot_pixels = tot_pixels + 1
if (value > 128):
pos_pixels = pos_pixels + 1
if tot_pixels > 0:
pos_fraction = pos_pixels / float(tot_pixels)
else:
pos_fraction = 0
str_out = str(i) + "," + str(pos_pixels) + "," + str(tot_pixels) + "," + str(pos_fraction) + "\n"
savefilehandler.write(str_out)
savefilehandler.close()
def register_hyperstack_subpixel(imp, channel, shifts, target_folder, virtual):
""" Takes the imp, determines the x,y,z drift for each pair of time points, using the preferred given channel,
and outputs as a hyperstack.
The shifted image is computed using TransformJ allowing for sub-pixel shifts using interpolation.
This is quite a bit slower than just shifting the image by full pixels as done in above function register_hyperstack().
However it significantly improves the result by removing pixel jitter.
"""
# Compute bounds of the new volume,
# which accounts for all translations:
minx, miny, minz, maxx, maxy, maxz = compute_min_max(shifts)
# Make shifts relative to new canvas dimensions
# so that the min values become 0,0,0
for shift in shifts:
shift.x -= minx
shift.y -= miny
shift.z -= minz
# new canvas dimensions:
width = int(imp.width + maxx - minx)
height = int(maxy - miny + imp.height)
slices = int(maxz - minz + imp.getNSlices())
print "New dimensions:", width, height, slices
# prepare stack for final results
stack = imp.getStack()
if virtual is True:
names = []
else:
registeredstack = ImageStack(width, height, imp.getProcessor().getColorModel())
# prepare empty slice for padding
empty = imp.getProcessor().createProcessor(width, height)
IJ.showProgress(0)
# get raw data as stack
stack = imp.getStack()
# loop across frames
for frame in range(1, imp.getNFrames()+1):
IJ.showProgress(frame / float(imp.getNFrames()+1))
fr = "t" + zero_pad(frame, len(str(imp.getNFrames()))) # for saving files in a virtual stack
# get and report current shift
shift = shifts[frame-1]
print "frame",frame,"correcting drift",-shift.x-minx,-shift.y-miny,-shift.z-minz
IJ.log(" frame "+str(frame)+" correcting drift "+str(round(-shift.x-minx,2))+","+str(round(-shift.y-miny,2))+","+str(round(-shift.z-minz,2)))
# loop across channels
for ch in range(1, imp.getNChannels()+1):
tmpstack = ImageStack(width, height, imp.getProcessor().getColorModel())
# get all slices of this channel and frame
for s in range(1, imp.getNSlices()+1):
ip = stack.getProcessor(imp.getStackIndex(ch, s, frame))
ip2 = ip.createProcessor(width, height) # potentially larger
ip2.insert(ip, 0, 0)
tmpstack.addSlice("", ip2)
# Pad the end (in z) of this channel and frame
for s in range(imp.getNSlices(), slices):
tmpstack.addSlice("", empty)
# subpixel translation
imp_tmpstack = ImagePlus("", tmpstack)
imp_translated = translate_single_stack_using_imglib2(imp_tmpstack, shift.x, shift.y, shift.z)
# add translated stack to final time-series
translated_stack = imp_translated.getStack()
for s in range(1, translated_stack.getSize()+1):
ss = "_z" + zero_pad(s, len(str(slices)))
ip = translated_stack.getProcessor(s).duplicate() # duplicate is important as otherwise it will only be a reference that can change its content
if virtual is True:
name = fr + ss + "_c" + zero_pad(ch, len(str(imp.getNChannels()))) +".tif"
names.append(name)
currentslice = ImagePlus("", ip)
currentslice.setCalibration(imp.getCalibration().copy())
currentslice.setProperty("Info", imp.getProperty("Info"));
FileSaver(currentslice).saveAsTiff(target_folder + "/" + name)
else:
registeredstack.addSlice("", ip)
IJ.showProgress(1)
if virtual is True:
# Create virtual hyper stack
registeredstack = VirtualStack(width, height, None, target_folder)
for name in names:
registeredstack.addSlice(name)
registeredstack_imp = ImagePlus("registered time points", registeredstack)
registeredstack_imp.setCalibration(imp.getCalibration().copy())
registeredstack_imp.setProperty("Info", imp.getProperty("Info"))
registeredstack_imp = HyperStackConverter.toHyperStack(registeredstack_imp, imp.getNChannels(), slices, imp.getNFrames(), "xyzct", "Composite");
return registeredstack_imp
y_start = 0
if y_stop > bin*height:
y_stop = bin*height
start_time = time.time()
title = image_in.getShortTitle()
bit_depth = image_in.getBitDepth()
options = NewImage.FILL_BLACK
image_out = NewImage.createImage(title+'_corrected', width, height, 1, bit_depth, options)
pixels_out = image_out.getProcessor().getPixels()
image_noI = NewImage.createImage(title+'_corrected_noI', width, height, 1, bit_depth, options)
pixels_noI = image_noI.getProcessor().getPixels()
for x2 in range(y_start, y_stop):
if x2%bin != 0:
continue
IJ.showProgress(x2 - y_start, y_stop - y_start)
start_time_row = time.time()
for x1 in range(0, bin*width):
if x1%bin != 0:
continue
pixels_out[x2/bin*width+x1/bin] = get_corrected_intensity(x1, x2)
pixels_noI[x1/bin + x2/bin*width] = pixels_in[int(round(calc_y1(x1, x2)/bin)) + int(round(calc_y2(x1, x2)/bin))*width]
print "Calculation of row", x2/bin, ":", int(round(time.time() - start_time_row)),"s"
duration = time.time() - start_time
print 'This calculation took',int(round(duration)),'s (',int(round(width*height/duration)),'px/s )'
IJ.showProgress(1.0)
image_out.show()
image_noI.show()
def compute_and_update_frame_translations_dt(imp, channel, method, bg_level, dt, process, roiz, shifts = None):
""" imp contains a hyper virtual stack, and we want to compute
the X,Y,Z translation between every t and t+dt time points in it
using the given preferred channel.
if shifts were already determined at other (lower) dt
they will be used and updated.
"""
nt = imp.getNFrames()
# get roi (could be None)
roi = imp.getRoi()
# init shifts
if shifts == None:
shifts = []
for t in range(nt):
shifts.append(Point3f(0,0,0))
# compute shifts
IJ.showProgress(0)
for t in range(dt, nt+dt, dt):
if t > nt-1: # together with above range till nt+dt this ensures that the last data points are not missed out
t = nt-1 # nt-1 is the last shift (0-based)
IJ.log(" between frames "+str(t-dt+1)+" and "+str(t+1))
# get (cropped and processed) image at t-dt
start_time = time.time()
roi1, roiz1 = shift_roi(imp, roi, roiz, shifts[t-dt])
imp1 = extract_frame_process_roi(imp, t+1-dt, channel, process, roi1, roiz1)
# get (cropped and processed) image at t
roi2, roiz2 = shift_roi(imp, roi, roiz, shifts[t])
imp2 = extract_frame_process_roi(imp, t+1, channel, process, roi2, roiz2)
print(" prepared images in [s]: "+str(round(time.time()-start_time,3)))
if roi:
print " ROI at frame",t-dt+1,"is", roi1.getBounds().x, roi1.getBounds().y, roiz1.z
print " ROI at frame",t+1,"is", roi2.getBounds().x, roi2.getBounds().y, roiz2.z
# compute shift
start_time = time.time()
if (method == 1):
local_new_shift = compute_shift_using_phasecorrelation(imp1, imp2, bg_level)
elif (method == 2):
local_new_shift = compute_shift_using_center_of_mass(imp1, imp2, bg_level)
print(" computed shift in [s]: "+str(round(time.time()-start_time,3)))
if roi: # total shift is shift of rois plus measured drift
print " measured additional shift of",local_new_shift,"on top of roi shift:",shift_between_rois(roi2, roiz2, roi1, roiz1)
local_new_shift = add_Point3f(local_new_shift, shift_between_rois(roi2, roiz2, roi1, roiz1))
print(" total local shift: "+str(round(local_new_shift.x,3))+" "+str(round(local_new_shift.y,3))+" "+str(round(local_new_shift.z,3)))
# determine the shift that we knew alrady
local_shift = subtract_Point3f(shifts[t],shifts[t-dt])
# compute difference between new and old measurement (which come from different dt)
add_shift = subtract_Point3f(local_new_shift,local_shift)
#print "++ old shift between %s and %s: dx=%s, dy=%s, dz=%s" % (int(t-dt+1),int(t+1),local_shift.x,local_shift.y,local_shift.z)
#print "++ add shift between %s and %s: dx=%s, dy=%s, dz=%s" % (int(t-dt+1),int(t+1),add_shift.x,add_shift.y,add_shift.z)
# update shifts from t-dt to the end (assuming that the measured local shift will presist till the end)
for i,tt in enumerate(range(t-dt,nt)):
# for i>dt below expression basically is a linear drift predicition for the frames at tt>t
# this is only important for predicting the best shift of the ROI
# the drifts for i>dt will be corrected by the next measurements
shifts[tt].x += 1.0*i/dt * add_shift.x
shifts[tt].y += 1.0*i/dt * add_shift.y
shifts[tt].z += 1.0*i/dt * add_shift.z
# print "updated shift till frame",tt+1,"is",shifts[tt].x,shifts[tt].y,shifts[tt].z
IJ.showProgress(1.0*t/(nt+1))
IJ.showProgress(1)
return shifts
def process_time_points(root, files, outdir):
'''Concatenate images and write ome.tiff file. If image contains already multiple time points just copy the image'''
concat = 1
files.sort()
options = ImporterOptions()
options.setId(files[0])
options.setVirtual(1)
image = BF.openImagePlus(options)
image = image[0]
if image.getNFrames() > 1:
IJ.log(files[0] + " Contains multiple time points. Can only concatenate single time points! Don't do anything!")
image.close()
return
width = image.getWidth()
height = image.getHeight()
for patt in pattern:
outName = re.match(patt, os.path.basename(files[0]))
if outName is None:
continue
if outdir is None:
outfile = os.path.join(root, outName.group(1) + '.ome.tif')
else:
outfile = os.path.join(outdir, outName.group(1) + '.ome.tif')
reader = ImageReader()
reader.setMetadataStore(MetadataTools.createOMEXMLMetadata())
reader.setId(files[0])
timeInfo = []
omeOut = reader.getMetadataStore()
omeOut = setUpXml(omeOut, image, files)
reader.close()
image.close()
IJ.log ('Concatenates ' + os.path.join(root, outName.group(1) + '.ome.tif'))
itime = 0
try:
for ifile, fileName in enumerate(files):
print fileName
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(fileName)
#print omeMeta.getPlaneDeltaT(0,0)
#print omeMeta.getPixelsTimeIncrement(0)
if fileName.endswith('.czi'):
if ifile == 0:
T0 = omeMeta.getPlaneDeltaT(0,0).value()
dT = omeMeta.getPlaneDeltaT(0,0).value() - T0
unit = omeMeta.getPlaneDeltaT(0,0).unit()
else:
timeInfo.append(getTimePoint(reader, omeMeta))
unit = omeMeta.getPixelsTimeIncrement(0).unit()
try:
dT = round(timeInfo[files.index(fileName)]-timeInfo[0],2)
except:
dT = (timeInfo[files.index(fileName)]-timeInfo[0]).seconds
nrImages = reader.getImageCount()
for i in range(0, reader.getImageCount()):
try:
omeOut.setPlaneDeltaT(dT, 0, i + itime*nrImages)
except TypeError:
omeOut.setPlaneDeltaT(Time(dT, unit),0, i + itime*nrImages)
omeOut.setPlanePositionX(omeOut.getPlanePositionX(0,i), 0, i + itime*nrImages)
omeOut.setPlanePositionY(omeOut.getPlanePositionY(0,i), 0, i + itime*nrImages)
omeOut.setPlanePositionZ(omeOut.getPlanePositionZ(0,i), 0, i + itime*nrImages)
omeOut.setPlaneTheC(omeOut.getPlaneTheC(0,i), 0, i + itime*nrImages)
omeOut.setPlaneTheT(NonNegativeInteger(itime), 0, i + itime*nrImages)
omeOut.setPlaneTheZ(omeOut.getPlaneTheZ(0,i), 0, i + itime*nrImages)
itime = itime + 1
reader.close()
IJ.showProgress(files.index(fileName), len(files))
try:
incr = float(dT/(len(files)-1))
except:
incr = 0
try:
omeOut.setPixelsTimeIncrement(incr, 0)
except TypeError:
#new Bioformats >5.1.x
omeOut.setPixelsTimeIncrement(Time(incr, unit),0)
outfile = concatenateImagePlus(files, outfile)
if outfile is not None:
filein = RandomAccessInputStream(outfile)
fileout = RandomAccessOutputStream(outfile)
saver = TiffSaver(fileout, outfile)
saver.overwriteComment(filein,omeOut.dumpXML())
fileout.close()
filein.close()
except:
traceback.print_exc()
finally:
#close all possible open files
try:
reader.close()
except:
pass
try:
filein.close()
except:
pass
try:
fileout.close()
except:
def register_hyperstack_subpixel(imp, channel, shifts, target_folder, virtual):
""" Takes the imp, determines the x,y,z drift for each pair of time points, using the preferred given channel,
and outputs as a hyperstack.
The shifted image is computed using TransformJ allowing for sub-pixel shifts using interpolation.
This is quite a bit slower than just shifting the image by full pixels as done in above function register_hyperstack().
However it significantly improves the result by removing pixel jitter.
"""
# Compute bounds of the new volume,
# which accounts for all translations:
minx, miny, minz, maxx, maxy, maxz = compute_min_max(shifts)
# Make shifts relative to new canvas dimensions
# so that the min values become 0,0,0
for shift in shifts:
shift.x -= minx
shift.y -= miny
shift.z -= minz
# new canvas dimensions:
width = int(imp.width + maxx - minx)
height = int(maxy - miny + imp.height)
slices = int(maxz - minz + imp.getNSlices())
#print "New dimensions:", width, height, slices
# prepare stack for final results
stack = imp.getStack()
if virtual is True:
names = []
else:
registeredstack = ImageStack(width, height, imp.getProcessor().getColorModel())
# prepare empty slice for padding
empty = imp.getProcessor().createProcessor(width, height)
IJ.showProgress(0)
# get raw data as stack
stack = imp.getStack()
# loop across frames
for frame in range(1, imp.getNFrames()+1):
IJ.showProgress(frame / float(imp.getNFrames()+1))
fr = "t" + zero_pad(frame, len(str(imp.getNFrames()))) # for saving files in a virtual stack
# get and report current shift
shift = shifts[frame-1]
#print "frame",frame,"correcting drift",-shift.x-minx,-shift.y-miny,-shift.z-minz
IJ.log(" frame "+str(frame)+" correcting drift "+str(round(-shift.x-minx,2))+","+str(round(-shift.y-miny,2))+","+str(round(-shift.z-minz,2)))
# loop across channels
for ch in range(1, imp.getNChannels()+1):
tmpstack = ImageStack(width, height, imp.getProcessor().getColorModel())
# get all slices of this channel and frame
for s in range(1, imp.getNSlices()+1):
ip = stack.getProcessor(imp.getStackIndex(ch, s, frame))
ip2 = ip.createProcessor(width, height) # potentially larger
ip2.insert(ip, 0, 0)
tmpstack.addSlice("", ip2)
# Pad the end (in z) of this channel and frame
for s in range(imp.getNSlices(), slices):
tmpstack.addSlice("", empty)
# subpixel translation
imp_tmpstack = ImagePlus("", tmpstack)
imp_translated = translate_single_stack_using_imglib2(imp_tmpstack, shift.x, shift.y, shift.z)
# add translated stack to final time-series
translated_stack = imp_translated.getStack()
for s in range(1, translated_stack.getSize()+1):
ss = "_z" + zero_pad(s, len(str(slices)))
ip = translated_stack.getProcessor(s).duplicate() # duplicate is important as otherwise it will only be a reference that can change its content
if virtual is True:
name = fr + ss + "_c" + zero_pad(ch, len(str(imp.getNChannels()))) +".tif"
names.append(name)
currentslice = ImagePlus("", ip)
currentslice.setCalibration(imp.getCalibration().copy())
currentslice.setProperty("Info", imp.getProperty("Info"));
FileSaver(currentslice).saveAsTiff(target_folder + "/" + name)
else:
registeredstack.addSlice("", ip)
IJ.showProgress(1)
if virtual is True:
# Create virtual hyper stack
registeredstack = VirtualStack(width, height, None, target_folder)
for name in names:
registeredstack.addSlice(name)
registeredstack_imp = ImagePlus("registered time points", registeredstack)
registeredstack_imp.setCalibration(imp.getCalibration().copy())
registeredstack_imp.setProperty("Info", imp.getProperty("Info"))
registeredstack_imp = HyperStackConverter.toHyperStack(registeredstack_imp, imp.getNChannels(), slices, imp.getNFrames(), "xyzct", "Composite");
return registeredstack_imp
def process(self, tree): toFiles(tree, self.targetFolder) IJ.showProgress(self.ai.incrementAndGet(), self.total)
def __settings(self, imgName) :
"""
Lets the user to choose different measures to make, and displays it following the choice of the user.
"""
try : dico=self.__dictCells[imgName]
except KeyError :
try : dico=self.__dictCells[imgName[:-4]]
except KeyError : return False
else : imgName=imgName[:-4]
dico=self.__dictCells[imgName]
for cellname in dico.keys() :
self.__dictMeasures[dico[cellname]]={}
# Represents the datas on a diagram
def diagrambuttonPressed(event) :
IJ.showMessage("Push 'Auto' button each time you want to see the diagram")
x1=10
y1=20
x2=100
y2=50
x3=60
y3=30
xr=10
yr=20
wr=20
hr=20
rect=Rectangle(xr,yr,wr,hr)
#img=IJ.getImage()
#nbslices=self.__img.getImageStackSize()
nbslices=self.__maxLife
IJ.run("Hyperstack...", "title=Diagram type=32-bit display=Color width="+str(x2+(nbslices+1)*x3)+" height="+str(y2+y3*len(dico))+" channels=1 slices="+str(len(self.__measures))+" frames=1")
im=IJ.getImage()
ip=im.getProcessor()
for i in range(len(self.__measures)) :
indiceligne=0
maxvalue=0
minvalue=1000000
im.setPosition(1,i+1,1)
for cellname in self.__listcellname :
indiceligne+=1
for indicecolonne in range(1,nbslices+1):
rect.setLocation(x2+indicecolonne*x3+int(x3/6),(y1+indiceligne*y3-int(y3/2)))
# we create at the first iteration a dictionary with the rectangles (for a future use)
if i==0 :
self.__gridrectangle[(indiceligne,indicecolonne)]=Rectangle(rect)
im.setRoi(rect)
ipr=im.getProcessor()
# we find the min and max values of the datas for a measure given.
if self.__dictMeasures[dico[cellname]][self.__measures[i]][indicecolonne-1]>maxvalue :
maxvalue=self.__dictMeasures[dico[cellname]][self.__measures[i]][indicecolonne-1]
if self.__dictMeasures[dico[cellname]][self.__measures[i]][indicecolonne-1]<minvalue :
minvalue=self.__dictMeasures[dico[cellname]][self.__measures[i]][indicecolonne-1]
# we fill the rectangle with the value of the measure
ipr.setValue(self.__dictMeasures[dico[cellname]][self.__measures[i]][indicecolonne-1])
ipr.fill()
# we write the names and the n of slices on the image with the maxvalue.
ip.setValue(maxvalue)
ip.moveTo(x1,y1)
ip.drawString(self.__measures[i])
for j in range(1,nbslices+1) :
ip.moveTo(x2+j*x3,y1)
ip.drawString("Slice "+str(j))
j=0
for cellname in self.__listcellname :
ip.moveTo(x1,y2+j*y3)
ip.drawString(cellname)
j+=1
im.killRoi()
im=IJ.run(im,"Fire","")
IJ.run("Brightness/Contrast...", "")
#im.setMinAndMax(minvalue,maxvalue)
#im.updateImage()
#we add a mouse listener in order to be able to show the roi corresponding to a rectangle when the user clicks on it.
listener = ML()
listener.name=imgName
for imp in map(WindowManager.getImage, WindowManager.getIDList()):
if imp.getTitle().startswith("Diagram") :
win = imp.getWindow()
if win is None:
continue
win.getCanvas().addMouseListener(listener)
# Represents the datas on a series of graphs.
def graphbuttonPressed(event) :
colors=[]
#img=IJ.getImage()
#nbslices=self.__img.getImageStackSize()
nbslices=self.__maxLife
acell=dico.values()[0]
if self.__useTime :
x = acell.getListTimes()
namex="Time sec"
else :
x = range(1,nbslices+1)
namex = "Frame"
maxx=max(x)
minx=min(x)
#x=[i for i in range(1,nbslices+1)]
font=Font("new", Font.BOLD, 14)
tempname = WindowManager.getUniqueName(self.__img.getShortTitle())
for i in range(len(self.__measures)) :
#print "i", i, self.__measures[i]
yarray=[]
flag=True
miny=10000000000
maxy=-1000000000
#we find the min and max values in order to set the scale.
for cellname in self.__listcellname :
colors.append(dico[cellname].getColor())
yarray.append(self.__dictMeasures[dico[cellname]][self.__measures[i]])
#for meas in self.__dictMeasures[dico[cellname]][self.__measures[i]] :
for meas in yarray[-1] :
if (meas<miny) and (Double.isNaN(meas)==False) :
miny=meas
if max(yarray[-1])>maxy : maxy=max(yarray[-1])
miny-=0.1*miny
maxy+=0.1*maxy
count=0.05
for j in range(len(yarray)) :
if j==0 :
if len(self.__measures)>1 :
plot=Plot("Plots-"+str(self.__measures[i]),namex,str(self.__measures[i]),x,yarray[j])
else :
plot=Plot("Plot-"+tempname,namex,str(self.__measures[i]),x,yarray[j])
plot.setLimits(minx,maxx,miny,maxy)
plot.setColor(colors[j])
plot.changeFont(font)
plot.addLabel(0.05, count, self.__listcellname[j])
else :
plot.setColor(colors[j])
plot.setLineWidth(3)
plot.addPoints(x,yarray[j],Plot.LINE)
plot.addLabel(0.05, count, self.__listcellname[j])
count+=0.05
plot.setColor(colors[0])
plot.show()
if len(self.__measures)>1 :
IJ.run("Images to Stack", "name="+tempname+"-plots title=Plots- use")
#def histbuttonPressed(event) :
#
# pass
# Represents the values in a tab.
def tabbuttonPressed(event) :
tab="\t"
headings=[]
measures=[]
#img=IJ.getImage()
#for i in range(self.__img.getImageStackSize()+1) :
for i in range(self.__maxLife+1) :
headings.append("Slice "+str(i))
headings[0]=WindowManager.getUniqueName(self.__img.getShortTitle())
#for m in self.__measurescompl :
for m in self.__dictMeasures[dico[self.__listcellname[0]]].keys() :
headstring=""
for head in headings:
headstring+=head+tab
tw=TextWindow(self.__listfiles[0]+"-"+m,headstring,"",800,600)
tp=tw.getTextPanel()
#for cellname in dico.keys() :
for cellname in self.__listcellname :
line=[]
line=[str(meas)+tab for meas in self.__dictMeasures[dico[cellname]][m]]
line.insert(0, cellname+tab)
linestr=""
for s in line: linestr+=s
tp.appendLine(linestr)
tp.updateDisplay()
if self.__measuresparambool_global[0] :
tw=TextWindow("Latency","cell\tLatency", "",800,600)
tp=tw.getTextPanel()
for i in range(len(self.__listcellname)) :
#if latencies[i][0] : line=self.__listcellname[i]+"\t"+str(latencies[i][1])
#else : line=self.__listcellname[i]+"\t"+"NaN"
line=self.__listcellname[i]+"\t"+str(latencies[i][1])
tp.appendLine(line)
tp.updateDisplay()
def helpbuttonPressed(event) :
IJ.showMessage("TO DO")
def newsetPressed(event) :
gd0.dispose()
self.__settings()
def alignbuttonPressed(event) :
IJ.showMessage("TO DO")
def mergebuttonPressed(event) :
IJ.showMessage("TO DO")
def saveResults() :
#if len(self.__listcellname) == 0 :
nbslices=self.__maxLife
acell=dico.values()[0]
if self.__useTime :
x = acell.getListTimes()
namex="Time_sec"
else :
x = range(1,nbslices+1)
namex = "Frame"
if not path.exists(self.__rootpath+"Results"+os.path.sep) : os.makedirs(self.__rootpath+os.path.sep+"Results"+os.path.sep, mode=0777)
tab="\t"
nl="\n"
measures=[]
headstring=""
#if self.__savemode : mode = "a"
#else : mode ="w"
mode = "a"
#for i in range(1, self.__maxLife+1) :headstring += "Slice_"+str(i)+tab
for i in range(self.__maxLife) :headstring += str(x[i])+tab
#for m in self.__measurescompl :
for m in self.__dictMeasures[dico[self.__listcellname[0]]].keys() :
f = open(self.__rootpath+"Results"+os.path.sep+m+".txt", mode)
#f.write(m+nl)
f.write(imgName+"-"+self.__time+"-"+m+"-"+namex+tab+headstring+nl)
if len(self.__listcellname) == 0 : f.write("no cells")
else :
for cellname in self.__listcellname :
linestr=cellname+tab
for measure in self.__dictMeasures[dico[cellname]][m] :
#print m, cellname, measure
linestr += str(measure)+tab
linestr+=nl
f.write(linestr)
f.close()
if self.__measuresparambool_global[0] :
m = "Latency"
f = open(self.__rootpath+"Results"+os.path.sep+m+".txt", mode)
f.write(imgName+"-"+self.__time+"-"+m+nl)
for i in range(len(self.__listcellname)) :
#if latencies[i][0] : line=self.__listcellname[i]+"\t"+str(latencies[i][1])
#else : line=self.__listcellname[i]+"\t"+"NaN"
line=self.__listcellname[i]+"\t"+str(latencies[i][1])
line+=nl
f.write(line)
f.close()
#
# ----------- main measures dialog -------------------------
#
# Allows the user to choose the measures to make, etc..
measureslabels_indep=["MaxFeret","MinFeret","AngleFeret","XFeret","YFeret","Area","Angle","Major","Minor","Solidity","AR","Round","Circ","XC","YC","FerCoord","FerAxis","MidAxis"]
measureslabels_dep=["Mean","StdDev","IntDen","Kurt","Skew","XM","YM","Fprofil","MidProfil","NFoci","ListFoci","ListAreaFoci","ListPeaksFoci","ListMeanFoci"]
measureslabels_global=["Latency", "velocity", "cumulatedDist"]
measureslabels_dep_tabonly=set(["MidAxis","FerCoord","FerAxis","Fprofil","MidProfil","ListFoci","ListAreaFoci","ListPeaksFoci","ListMeanFoci"])
ens_measures_global=set(measureslabels_global)
ens_measures_indep=set(measureslabels_indep)
ens_measures_dep=set(measureslabels_dep)
measureslabels=[]
for label in measureslabels_indep :
measureslabels.append(label)
for label in measureslabels_dep :
measureslabels.append(label)
#self.__defaultmeasures=[False for i in range(len(measureslabels))]
#self.__defaultmeasures_global=[False for i in range(len(measureslabels_global))]
gdmeasures=NonBlockingGenericDialog("MeasuresChoice")
gdmeasures.setFont(Font("Courrier", 1, 10))
gdmeasures.addMessage("******* TIME SETTINGS *******")
gdmeasures.addCheckbox("Only starting at begining :", self.__onlystart) # 1 only start
gdmeasures.addNumericField("Minimal Lifetime : ",self.__minLife,0)
gdmeasures.addNumericField("Maximal Lifetime : ",self.__maxLife,0)
#gdmeasures.addNumericField("Maximal Lifetime : ",self.__img.getImageStackSize(),0)
gdmeasures.addCheckbox("x axis in seconds", self.__useTime) # 2 use time
gdmeasures.addMessage("")
gdmeasures.addMessage("")
gdmeasures.addMessage("Choose the measures to make on the cells : ")
gdmeasures.addMessage("******* TIME MEASURES *******")
gdmeasures.addCheckboxGroup(4,8,measureslabels,self.__defaultmeasures)
gdmeasures.addMessage("")
gdmeasures.addMessage("******* GLOBAL MEASURES *******")
gdmeasures.addMessage("PLEASE : If you have selected movement parameters you MUST to select XC and YC !")
gdmeasures.addCheckboxGroup(3,1,measureslabels_global,self.__defaultmeasures_global)
gdmeasures.addNumericField("Noise value for maxima finder: ",self.__noise,0)
gdmeasures.addMessage("")
gdmeasures.addMessage("******* OPTIONS *******")
gdmeasures.addCheckbox("Select the cells in next dialog ?", self.__onlyselect) # 3 only select
gdmeasures.addCheckbox("Save results to text files ?", self.__savetables) # 4 save files
#gdmeasures.addCheckbox("Append mode ?", self.__savemode) # 5 append mode
gdmeasures.addCheckbox("Analyse in batch mode ?", self.__batchanalyse) # 6 analysis batch mode
gdmeasures.addCheckbox("Update overlay ?", self.__updateoverlay) # 7 update overlay
gdmeasures.addMessage("")
gdmeasures.addMessage("")
help_panel=Panel()
helpbutton=Button("HELP")
helpbutton.actionPerformed = helpbuttonPressed
help_panel.add(helpbutton)
gdmeasures.addPanel(help_panel)
gdmeasures.hideCancelButton()
if not self.__batchanalyse :
gdmeasures.showDialog()
self.__onlystart=gdmeasures.getNextBoolean() # 1 only start
self.__minLife=gdmeasures.getNextNumber()
self.__maxLife=gdmeasures.getNextNumber()
self.__useTime=gdmeasures.getNextBoolean() # 2 use time
self.__measuresparambool=[]
self.__measuresparambool_global=[]
for i in range(len(measureslabels)) :
self.__measuresparambool.append(gdmeasures.getNextBoolean())
self.__defaultmeasures[i]=self.__measuresparambool[-1]
for i in range(len(measureslabels_global)) :
self.__measuresparambool_global.append(gdmeasures.getNextBoolean())
self.__defaultmeasures_global[i] = self.__measuresparambool_global[i]
self.__noise=gdmeasures.getNextNumber()
self.__onlyselect=gdmeasures.getNextBoolean() # 3 only select
self.__savetables = gdmeasures.getNextBoolean() # 4 save files
#self.__savemode = gdmeasures.getNextBoolean() # 5 append mode
self.__batchanalyse = gdmeasures.getNextBoolean() # 6 analyse mode
self.__updateoverlay = gdmeasures.getNextBoolean() # 7 update overlay
# we update a list of all cells that have a lifetime corresponding to what the user chose.
if len (self.__allcells) == 0 :
for cellname in dico.keys() :
if dico[cellname].getLifeTime()>=self.__minLife : #and dico[cellname].getLifeTime()<=self.__maxLife :
if self.__onlystart :
if dico[cellname].getSlideInit()<2 : self.__allcells.append(cellname)
else : self.__allcells.append(cellname)
if self.__noise == 0 : self.__noise = None
if self.__batchanalyse : self.__onlyselect = False
if self.__onlyselect :
try :
self.__gw
except AttributeError :
if not path.exists(self.__pathdir+"Selected-Cells"+os.path.sep) : os.makedirs(self.__pathdir+os.path.sep+"Selected-Cells"+os.path.sep, mode=0777)
self.__gw = CellsSelection()
self.__gw.setTitle(imgName)
self.__gw.run(self.__allcells, self.__pathdir+"ROIs"+os.path.sep)
self.__gw.show()
self.__gw.setSelected(self.__allcells)
while not self.__gw.oked and self.__gw.isShowing() :
self.__gw.setLabel("Validate selection with OK !!")
self.__listcellname = list(self.__gw.getSelected())
self.__gw.resetok()
self.__gw.setLabel("...")
self.__gw.hide()
else :
if self.__gw.getTitle() == imgName :
self.__gw.show()
self.__gw.setSelected(self.__listcellname)
self.__listcellname[:]=[]
while not self.__gw.oked and self.__gw.isShowing() :
self.__gw.setLabel("Validate selection with OK !!")
self.__listcellname = list(self.__gw.getSelected())
self.__gw.resetok()
self.__gw.setLabel("...")
self.__gw.hide()
else :
self.__gw.dispose()
if not path.exists(self.__pathdir+"Selected-Cells"+os.path.sep) : os.makedirs(self.__pathdir+os.path.sep+"Selected-Cells"+os.path.sep, mode=0777)
self.__gw = CellsSelection()
self.__gw.setTitle(imgName)
self.__gw.run(self.__allcells, self.__pathdir+"ROIs"+os.path.sep)
self.__gw.show()
self.__gw.setSelected(self.__allcells)
self.__listcellname[:]=[]
while not self.__gw.oked and self.__gw.isShowing() :
self.__gw.setLabel("Validate selection with OK !!")
self.__listcellname = list(self.__gw.getSelected())
self.__gw.resetok()
self.__gw.setLabel("...")
self.__gw.hide()
filestodelet=glob.glob(self.__pathdir+"Selected-Cells"+os.path.sep+"*.cell")
for f in filestodelet : os.remove(f)
for cell in self.__listcellname :
sourcestr = self.__pathdir+"Cells"+os.path.sep+cell+".cell"
deststr = self.__pathdir+"Selected-Cells"+os.path.sep+cell+".cell"
#os.system("copy "+sourcestr+", "+deststr)
#shutil.copy(self.__pathdir+"Cells"+os.path.sep+cell+".cell",self.__pathdir+"Selected-Cells"+os.path.sep+cell+".cell")
shutil.copy(sourcestr,deststr)
self.__dictNcells[imgName] = len(self.__listcellname)
else :
self.__listcellname = list(self.__allcells)
self.__dictNcells[imgName] = len(self.__listcellname)
if len(self.__listcellname) == 0 :
self.__dictNcells[imgName] = 0
return False
self.__img.hide()
# we make the measures.
for i in range(len(measureslabels)) :
IJ.showProgress(i, len(measureslabels))
if self.__measuresparambool[i]==True :
self.__measurescompl.append(measureslabels[i])
if (measureslabels[i] in measureslabels_dep_tabonly)==False :
self.__measures.append(measureslabels[i])
if (i<18) and (measureslabels[i] in ens_measures_indep) :
self.__measureAll(self.__img,measureslabels[i],False, imgName, self.__noise)
ens_measures_indep.discard(measureslabels[i])
if i>=18 :
self.__measureAll(self.__img,measureslabels[i],True, imgName, self.__noise)
if self.__measuresparambool_global[0] : # calculate latency
latencies=[]
for i in range(len(self.__listcellname)) :
IJ.showProgress(i, len(self.__listcellname))
latencies.append(self.latencie(self.__listcellname[i], self.__img, imgName, self.__useTime))
if self.__measuresparambool_global[1] : # calculate velocity
self.__measures.append("velocity")
#velocities=[]
for i in range(len(self.__listcellname)) :
IJ.showProgress(i, len(self.__listcellname))
self.__measureVelocity(self.__img,imgName)
if self.__measuresparambool_global[2] : # calculate cumulatedDistance
self.__measures.append("cumulatedDist")
#velocities=[]
for i in range(len(self.__listcellname)) :
IJ.showProgress(i, len(self.__listcellname))
self.__measurecumulDist(self.__img,imgName)
self.__img.show()
self.__img.getProcessor().resetThreshold()
if self.__updateoverlay :
if self.__img.getOverlay() is not None : self.__img.getOverlay().clear()
outputrois=[]
cellnames=[]
self.__img.hide()
for cellname in self.__listcellname :
for r in dico[cellname].getListRoi():
if isinstance(r,Roi) :
pos=r.getPosition()
#print "MC overlay", cellname, r.getName(), pos
#r.setPosition(0)
#overlay.add(r)
outputrois.append(r)
if "cell" in r.getName() : cellnames.append(r.getName())
else : cellnames.append(str(pos)+"-"+cellname)
#print cellnames[-1]
rm = RoiManager.getInstance()
if (rm==None): rm = RoiManager()
rm.show()
self.__img.show()
IJ.selectWindow(self.__img.getTitle())
rm.runCommand("reset")
for i in range(len(outputrois)) :
outputrois[i].setName(cellnames[i])
rm.addRoi(outputrois[i])
rm.select(rm.getCount()-1)
rm.runCommand("Rename", cellnames[i])
IJ.run("Show Overlay", "")
rm.runCommand("UseNames", "true")
rm.runCommand("Associate", "true")
IJ.run(self.__img, "Labels...", "color=red font=12 show use")
IJ.run(self.__img, "From ROI Manager", "")
rm.runCommand("Show None")
rm.runCommand("Show All")
# ----------- batch analyse ------------------------
if self.__batchanalyse :
if self.__savetables : saveResults()
self.__dictMeasures.clear()
self.__allcells[:]=[]
self.__measurescompl[:]=[]
self.__measures[:]=[]
return False
# ---------- display methodes dialog ----------------
# Allows the user to choose how to see the results of the measures.
gd0=NonBlockingGenericDialog("Display")
gd0.addMessage("How do you want to see the results ?")
panel0=Panel()
diagrambutton=Button("Diagram")
diagrambutton.actionPerformed = diagrambuttonPressed
panel0.add(diagrambutton)
graphbutton=Button("Graph")
graphbutton.actionPerformed = graphbuttonPressed
panel0.add(graphbutton)
tabbutton=Button("Tab")
tabbutton.actionPerformed = tabbuttonPressed
panel0.add(tabbutton)
gd0.addPanel(panel0)
gd0.addCheckbox("Analyse next stack ?", self.__nextstack)
gd0.hideCancelButton()
gd0.showDialog()
self.__nextstack = gd0.getNextBoolean()
# ---------- save tables ---------------------------
if self.__savetables : saveResults()
# --------- re-start analysis -------------------
self.__dictMeasures.clear()
#self.__listcellname[:]=[]
self.__allcells[:]=[]
self.__measurescompl[:]=[]
self.__measures[:]=[]
if self.__nextstack : return False
else : return True
def regBf(fn=None, imp=None, refId=None):
"""
Register a time series stack to a specified reference slice,
from a file (imported by BioFormat) or a stack ImagePlus.
Returns a registered ImagePlus.
The stack must have only 1 z layer.
refId is in the format of [int channel, int slice, int frame]
If no refId is supplied, will use the first slice [1,1,1]
Note: since TurboReg is used for registeration, there will be
temporary opened image windows.
"""
## Prepare the right ImagePlus
if imp is None:
if fn is None:
od = OpenDialog("Choose a file", None)
filename = od.getFileName()
if filename is None:
print "User canceled the dialog!"
return
else:
directory = od.getDirectory()
filepath = directory + filename
print "Selected file path:", filepath
else:
if os.path.exists(fn) and os.path.isfile(fn):
filepath = fn
else:
print "File does not exist!"
return
imps = BF.openImagePlus(filepath)
imp = imps[0]
if imp is None:
print "Cannot load file!"
return
else:
if fn is not None:
print "File or ImagePlus? Cannot load both."
return
width = imp.getWidth()
height = imp.getHeight()
# C
nChannels = imp.getNChannels()
# Z
nSlices = imp.getNSlices()
# T
nFrames = imp.getNFrames()
# pixel size
calibration = imp.getCalibration()
# Only supoort one z layer
if nSlices != 1:
print "Only support 1 slice at Z dimension."
return
# set registration reference slice
if refId is None:
refC = 1
refZ = 1
refT = 1
else:
refC = refId[0]
refZ = refId[1]
refT = refId[2]
if (refC not in range(1, nChannels + 1)
or refZ not in range(1, nSlices + 1)
or refT not in range(1, nFrames + 1)):
print "Invalid reference image!"
return
stack = imp.getImageStack()
registeredStack = ImageStack(width, height, nChannels * nFrames * nSlices)
# setup windows, these are needed by TurboReg
tmpip = FloatProcessor(width, height)
refWin = ImageWindow(ImagePlus("ref", tmpip))
bounds = refWin.getBounds()
# refWin.setVisible(False)
toRegWin = ImageWindow(ImagePlus("toReg", tmpip))
toRegWin.setLocation(bounds.width + bounds.x, bounds.y)
# toRegWin.setVisible(False)
toTransformWin = ImageWindow(ImagePlus("toTransform", tmpip))
toTransformWin.setLocation(2 * bounds.width + bounds.x, bounds.y)
# toTransformWin.setVisible(False)
# get reference image
refImp = ImagePlus("ref",
stack.getProcessor(imp.getStackIndex(refC, refZ, refT)))
refWin.setImage(refImp)
tr = TurboReg_()
for t in xrange(1, nFrames + 1):
IJ.showProgress(t - 1, nFrames)
# print "t ", t
# do TurboReg on reference channel
toRegId = imp.getStackIndex(refC, refZ, t)
toRegImp = ImagePlus("toReg", stack.getProcessor(toRegId))
toRegWin.setImage(toRegImp)
regArg = "-align " +\
"-window " + toRegImp.getTitle() + " " +\
"0 0 " + str(width - 1) + " " + str(height - 1) + " " +\
"-window " + refImp.getTitle() + " " +\
"0 0 " + str(width - 1) + " " + str(height - 1) + " " +\
"-rigidBody " +\
str(width / 2) + " " + str(height / 2) + " " +\
str(width / 2) + " " + str(height / 2) + " " +\
"0 " + str(height / 2) + " " +\
"0 " + str(height / 2) + " " +\
str(width - 1) + " " + str(height / 2) + " " +\
str(width - 1) + " " + str(height / 2) + " " +\
"-hideOutput"
tr = TurboReg_()
tr.run(regArg)
registeredImp = tr.getTransformedImage()
sourcePoints = tr.getSourcePoints()
targetPoints = tr.getTargetPoints()
registeredStack.setProcessor(registeredImp.getProcessor(), toRegId)
# toRegImp.flush()
# apply transformation on other channels
for c in xrange(1, nChannels + 1):
# print "c ", c
if c == refC:
continue
toTransformId = imp.getStackIndex(c, 1, t)
toTransformImp = ImagePlus("toTransform",
stack.getProcessor(toTransformId))
toTransformWin.setImage(toTransformImp)
transformArg = "-transform " +\
"-window " + toTransformImp.getTitle() + " " +\
str(width) + " " + str(height) + " " +\
"-rigidBody " +\
str(sourcePoints[0][0]) + " " +\
str(sourcePoints[0][1]) + " " +\
str(targetPoints[0][0]) + " " +\
str(targetPoints[0][1]) + " " +\
str(sourcePoints[1][0]) + " " +\
str(sourcePoints[1][1]) + " " +\
str(targetPoints[1][0]) + " " +\
str(targetPoints[1][1]) + " " +\
str(sourcePoints[2][0]) + " " +\
str(sourcePoints[2][1]) + " " +\
str(targetPoints[2][0]) + " " +\
str(targetPoints[2][1]) + " " +\
"-hideOutput"
tr = TurboReg_()
tr.run(transformArg)
registeredStack.setProcessor(
tr.getTransformedImage().getProcessor(), toTransformId)
# toTransformImp.flush()
sourcePoints = None
targetPoints = None
IJ.showProgress(t, nFrames)
IJ.showStatus("Frames registered: " + str(t) + "/" + str(nFrames))
refWin.close()
toRegWin.close()
toTransformWin.close()
imp2 = ImagePlus("reg_" + imp.getTitle(), registeredStack)
imp2.setCalibration(imp.getCalibration().copy())
imp2.setDimensions(nChannels, nSlices, nFrames)
# print "type ", imp.getType()
# print "type ", imp2.getType()
# print nChannels, " ", nSlices, " ", nFrames
# print registeredStack.getSize()
for key in imp.getProperties().stringPropertyNames():
imp2.setProperty(key, imp.getProperty(key))
# comp = CompositeImage(imp2, CompositeImage.COLOR)
# comp.show()
# imp2 = imp.clone()
# imp2.setStack(registeredStack)
# imp2.setTitle("reg"+imp.getTitle())
# imp2.show()
# imp.show()
return imp2
# progress bar
from ij import IJ
imp = IJ.getImage()
stack = imp.getImageStack()
for i in xrange(1, stack.getSize() + 1):
# Report Progress
IJ.showProgress(i, stack.getSize() + 1)
# 뭐라도 하기
ip = stack.getProcessor(i)
# 완료
IJ.showProgress(1)
meanI = 0 dim = 0 print 'Applying top hat filter...' for s in range( 0, slices ): ip = stack.getProcessor(s+1) # ip, double radius, boolean createBackground, boolean lightBackground, boolean useParaboloid, # boolean doPresmooth, boolean correctCorners TOPHAT().rollingBallBackground( ip, 20, False, False, False, False, True ) for x in range( 0, imp.getWidth() ): for y in range( 0, imp.getHeight() ): val = ip.get( x, y ) if val > 500: meanI = meanI + val dim = dim + 1 IJ.showProgress(s+1, slices) #dim = imp.getWidth()*imp.getHeight()*slices intens = float(meanI)/float(dim) print 'Applying thresholding with intensity =', intens newImp = convertToBinaryMask( imp, int(intens) ) #newImp.show() # TODO #zp = ZProjector(newImp) #zp.setMethod( ZProjector.MAX_METHOD ) #zp.doProjection() #newMIPImp = zp.getProjection() #newMIPImp.show() #ipMIP = newMIPImp.getProcessor() #EDM().toEDM( ipMIP.convertToByte(True) )
