def findROI(self, image):
"""
Finds the ROI.
"""
assert (image.flags['C_CONTIGUOUS']), "Image is not C contiguous!"
assert (
image.dtype == numpy.float64), "Images is not numpy.float64 type."
self.mxf.resetTaken()
# Create convolution object, if we have not already done this.
if self.fg_filter is None:
fg_psf = fitting.gaussianPSF(image.shape, self.sigma)
self.fg_filter = matchedFilterC.MatchedFilter(fg_psf)
# Find peaks.
smoothed_image = self.fg_filter.convolve(image)
[x, y, z, h] = self.mxf.findMaxima([smoothed_image], want_height=True)
# No peaks found check
if (x.size == 0):
return [0, 0, False]
# Slice out ROI.
max_index = numpy.argmax(h)
mx = int(round(x[max_index])) + 1
my = int(round(y[max_index])) + 1
rs = self.roi_size
roi = image[my - rs:my + rs, mx - rs:mx + rs]
roi -= numpy.min(roi)
return [mx, my, roi]
def findFitPeak(self, image):
"""
Returns the 2D gaussian fit to the brightest peak in the image.
"""
# Convert image and add offset, this is to keep the MLE fitter from
# overfitting the background and/or taking logs of zero.
#
image = numpy.ascontiguousarray(image, dtype = numpy.float64)
image += self.offset
self.mxf.resetTaken()
# Create the peak fitter object, if we have not already done this.
#
if self.mfit is None:
background = numpy.zeros(image.shape)
# Create convolution filter.
fg_psf = fitting.gaussianPSF(image.shape, self.sigma)
self.fg_filter = matchedFilterC.MatchedFilter(fg_psf)
# Create fitter.
self.mfit = daoFitC.MultiFitter2DFixed(roi_size = self.roi_size)
#self.mfit = daoFitC.MultiFitter2D()
#self.mfit = daoFitC.MultiFitter3D()
#self.mfit.default_tol = 1.0e-3
self.mfit.initializeC(image)
self.mfit.newImage(image)
self.mfit.newBackground(background)
else:
self.mfit.newImage(image)
# Find peaks.
smoothed_image = self.fg_filter.convolve(image)
[x, y, z, h] = self.mxf.findMaxima([smoothed_image], want_height = True)
# No peaks found check
if(x.size == 0):
return [0, 0, False]
max_index = numpy.argmax(h)
peaks = {"x" : numpy.array([x[max_index]]),
"y" : numpy.array([y[max_index]]),
"z" : numpy.array([z[max_index]]),
"sigma" : numpy.array([self.sigma])}
# Pass peaks to fitter & fit.
self.mfit.newPeaks(peaks, "finder")
self.mfit.doFit(max_iterations = 50)
# Check for fit convergence.
if (self.mfit.getUnconverged() == 0):
# Return peak location.
x = self.mfit.getPeakProperty("x")
y = self.mfit.getPeakProperty("y")
return [y[0], x[0], True]
else:
return [0, 0, False]
def findFitPeak(self, image):
"""
Returns the 2D gaussian fit to the brightest peak in the image.
"""
# Convert image and add offset, this is to keep the MLE fitter from
# overfitting the background and/or taking logs of zero.
#
image = numpy.ascontiguousarray(image, dtype = numpy.float64)
image += self.offset
self.mxf.resetTaken()
# Create the peak fitter object, if we have not already done this.
#
if self.mfit is None:
background = numpy.zeros(image.shape)
# Create convolution filter.
fg_psf = fitting.gaussianPSF(image.shape, self.sigma)
self.fg_filter = matchedFilterC.MatchedFilter(fg_psf)
# Create fitter.
self.mfit = daoFitC.MultiFitter2DFixed(roi_size = self.roi_size)
#self.mfit = daoFitC.MultiFitter2D()
#self.mfit = daoFitC.MultiFitter3D()
#self.mfit.default_tol = 1.0e-3
self.mfit.initializeC(image)
self.mfit.newImage(image)
self.mfit.newBackground(background)
else:
self.mfit.newImage(image)
# Find peaks.
smoothed_image = self.fg_filter.convolve(image)
[x, y, z, h] = self.mxf.findMaxima([smoothed_image], want_height = True)
# No peaks found check
if(x.size == 0):
return [0, 0, False]
max_index = numpy.argmax(h)
peaks = {"x" : numpy.array([x[max_index]]),
"y" : numpy.array([y[max_index]]),
"z" : numpy.array([z[max_index]]),
"sigma" : numpy.array([self.sigma])}
# Pass peaks to fitter & fit.
self.mfit.newPeaks(peaks, "finder")
self.mfit.doFit(max_iterations = 50)
# Check for fit convergence.
if (self.mfit.getUnconverged() == 0):
# Return peak location.
x = self.mfit.getPeakProperty("x")
y = self.mfit.getPeakProperty("y")
return [y[0], x[0], True]
else:
return [0, 0, False]
def setVariances(self, variances):
"""
setVariances() customized for gaussian PSFs.
"""
# Make sure that the number of (sCMOS) variance arrays
# matches the number of image planes.
#
assert (len(variances) == self.n_channels)
# Pad variances to correct size.
#
temp = []
for variance in variances:
temp.append(fitting.padArray(variance, self.margin))
variances = temp
# Create "foreground" and "variance" filters. There is
# only one z value here.
#
# These are stored in a list indexed by z value, then by
# channel / plane. So self.mfilters[1][2] is the filter
# for z value 1, plane 2.
#
self.mfilters.append([])
self.vfilters.append([])
psf_norm = fitting.gaussianPSF(
variances[0].shape, self.parameters.getAttr("foreground_sigma"))
var_norm = psf_norm * psf_norm
for i in range(self.n_channels):
self.mfilters[0].append(
matchedFilterC.MatchedFilter(
psf_norm,
fftw_estimate=self.parameters.getAttr("fftw_estimate"),
memoize=True,
max_diff=1.0e-3))
self.vfilters[0].append(
matchedFilterC.MatchedFilter(
var_norm,
fftw_estimate=self.parameters.getAttr("fftw_estimate"),
memoize=True,
max_diff=1.0e-3))
# Save a pictures of the PSFs for debugging purposes.
if self.check_mode:
print("psf max", numpy.max(psf))
filename = "psf_z0.0_c{1:d}.tif".format(j)
tifffile.imsave(filename, psf.astype(numpy.float32))
# This handles the rest of the initialization.
#
super(MPPeakFinderDao, self).setVariances(variances)
return variances
def setVariances(self, variances):
"""
setVariances() customized for gaussian PSFs.
"""
# Make sure that the number of (sCMOS) variance arrays
# matches the number of image planes.
#
assert(len(variances) == self.n_channels)
# Pad variances to correct size.
#
temp = []
for variance in variances:
temp.append(fitting.padArray(variance, self.margin))
variances = temp
# Create "foreground" and "variance" filters. There is
# only one z value here.
#
# These are stored in a list indexed by z value, then by
# channel / plane. So self.mfilters[1][2] is the filter
# for z value 1, plane 2.
#
self.mfilters.append([])
self.vfilters.append([])
psf_norm = fitting.gaussianPSF(variances[0].shape, self.parameters.getAttr("foreground_sigma"))
var_norm = psf_norm * psf_norm
for i in range(self.n_channels):
self.mfilters[0].append(matchedFilterC.MatchedFilter(psf_norm,
memoize = True,
max_diff = 1.0e-3))
self.vfilters[0].append(matchedFilterC.MatchedFilter(var_norm,
memoize = True,
max_diff = 1.0e-3))
# Save a pictures of the PSFs for debugging purposes.
if self.check_mode:
print("psf max", numpy.max(psf))
filename = "psf_z0.0_c{1:d}.tif".format(j)
tifffile.imsave(filename, psf.astype(numpy.float32))
# This handles the rest of the initialization.
#
super(MPPeakFinderDao, self).setVariances(variances)
return variances
def findFitPeak(self, image):
"""
Returns the optimal alignment (based on the correlation score) between
a Gaussian and the brightest peak in the image.
"""
image = numpy.ascontiguousarray(image, dtype=numpy.float64)
self.mxf.resetTaken()
# Create convolution object, if we have not already done this.
if self.fg_filter is None:
fg_psf = fitting.gaussianPSF(image.shape, self.sigma)
self.fg_filter = matchedFilterC.MatchedFilter(fg_psf)
# Find peaks.
smoothed_image = self.fg_filter.convolve(image)
[x, y, z, h] = self.mxf.findMaxima([smoothed_image], want_height=True)
# No peaks found check
if (x.size == 0):
return [0, 0, False]
# Slice out ROI.
max_index = numpy.argmax(h)
mx = int(round(x[max_index])) + 1
my = int(round(y[max_index])) + 1
rs = self.roi_size
roi = image[my - rs:my + rs, mx - rs:mx + rs]
roi -= numpy.min(roi)
# Pass to aligner and find optimal offset.
self.c2dg.setImage(roi)
[disp, success, fun, status] = self.c2dg.maximize()
if (success) or (status == 2):
return [my + disp[0] - 0.5, mx + disp[1] - 0.5, True]
else:
return [0, 0, False]
def setVariances(self, variances):
"""
We initialize the following here because at __init__ we
don't know how big the images are. This is called after
the analysis specific version pads the variances and
creates the mfilters[] and the vfilters[] class members.
Note the assumption that every frame in all the movies
is the same size.
"""
# Create mask to limit peak finding to a user defined sub-region of the image.
#
self.peak_mask = fitting.peakMask(variances[0].shape, self.parameters,
self.margin)
# Create matched filter for background. There is one of these for
# each imaging plane for the benefit of memoization.
#
for i in range(self.n_channels):
bg_psf = fitting.gaussianPSF(
variances[0].shape,
self.parameters.getAttr("background_sigma"))
self.bg_filters.append(
matchedFilterC.MatchedFilter(bg_psf,
memoize=True,
max_diff=1.0e-3))
# Process variance arrays now as they don't change from frame
# to frame.
#
# This initializes the self.variances array with a list
# of lists with the same organization as foreground and
# psf / variance filters.
#
# Use variance filter. I now think this is correct as this is
# also what we are doing with the image background term. In
# the case of the image background we are estimating the
# variance under the assumption that it is Poisson so the
# mean of the background is the variance. With the cameras
# we know what the variance is because we measured it. Now
# we need to weight it properly given the PSF filter that
# we are applying to the foreground.
#
# Iterate over z values.
#
for i in range(len(self.mfilters)):
variance = numpy.zeros(variances[0].shape)
# Iterate over channels / planes.
for j in range(len(self.mfilters[i])):
# Convolve variance with the appropriate variance filter.
conv_var = self.vfilters[i][j].convolve(variances[j])
# Transform variance to the channel 0 frame.
if self.atrans[j] is None:
variance += conv_var
else:
variance += self.atrans[j].transform(conv_var)
self.variances.append(variance)
# Save results if needed for debugging purposes.
if self.check_mode:
with tifffile.TiffWriter("camera_variances.tif") as tf:
for var in self.variances:
tf.save(var.astype(numpy.float32))
def setVariances(self, variances):
"""
We initialize the following here because at __init__ we
don't know how big the images are. This is called after
the analysis specific version pads the variances and
creates the mfilters[] and the vfilters[] class members.
Note the assumption that every frame in all the movies
is the same size.
"""
# Create mask to limit peak finding to a user defined sub-region of the image.
#
self.peak_mask = fitting.peakMask(variances[0].shape, self.parameters, self.margin)
# Create matched filter for background. There is one of these for
# each imaging plane for the benefit of memoization.
#
for i in range(self.n_channels):
bg_psf = fitting.gaussianPSF(variances[0].shape, self.parameters.getAttr("background_sigma"))
self.bg_filters.append(matchedFilterC.MatchedFilter(bg_psf,
memoize = True,
max_diff = 1.0e-3))
# Process variance arrays now as they don't change from frame
# to frame.
#
# This initializes the self.variances array with a list
# of lists with the same organization as foreground and
# psf / variance filters.
#
# Use variance filter. I now think this is correct as this is
# also what we are doing with the image background term. In
# the case of the image background we are estimating the
# variance under the assumption that it is Poisson so the
# mean of the background is the variance. With the cameras
# we know what the variance is because we measured it. Now
# we need to weight it properly given the PSF filter that
# we are applying to the foreground.
#
# Iterate over z values.
#
for i in range(len(self.mfilters)):
variance = numpy.zeros(variances[0].shape)
# Iterate over channels / planes.
for j in range(len(self.mfilters[i])):
# Convolve variance with the appropriate variance filter.
conv_var = self.vfilters[i][j].convolve(variances[j])
# Transform variance to the channel 0 frame.
if self.atrans[j] is None:
variance += conv_var
else:
variance += self.atrans[j].transform(conv_var)
self.variances.append(variance)
# Save results if needed for debugging purposes.
if self.check_mode:
with tifffile.TiffWriter("camera_variances.tif") as tf:
for var in self.variances:
tf.save(var.astype(numpy.float32))
