def iterateFit(fit_func, fit_data):
if (type(fit_data.peaks) == type(numpy.array([]))):
# Calculate "regularized" image.
regularized_image = fit_data.regularizer.regularizeImage(fit_data.image)
#regularized_image = fit_data.regularizer.normalizeImage(fit_data.image)
#regularized_image = fit_data.image
# Fit to update peak locations.
result = fit_func(regularized_image,
fit_data.peaks,
scmos_cal = fit_data.lg_scmos_cal)
fit_peaks = multi_c.getGoodPeaks(result[0],
0.9*fit_data.threshold,
0.5*fit_data.sigma)
# Remove peaks that are too close to each other & refit.
fit_peaks = util_c.removeClosePeaks(fit_peaks, fit_data.sigma, fit_data.neighborhood)
result = fit_func(regularized_image,
fit_peaks,
scmos_cal = fit_data.lg_scmos_cal)
fit_peaks = multi_c.getGoodPeaks(result[0],
0.9*fit_data.threshold,
0.5*fit_data.sigma)
fit_data.peaks = fit_peaks
# FIXME:
# For multi-peak finding to work correctly we should subtract the
# peaks out of the actual image, not the regularized image, which
# is what this is.
fit_data.residual = result[1]
def fitPeaks(self, peaks):
# Adjust to z starting position.
z_index = utilC.getZCenterIndex()
peaks[:,z_index] = peaks[:,z_index] * float(self.sfitter.getSize())
if False:
print "Before fitting"
for i in range(5):
print " ", peaks[i,0], peaks[i,1], peaks[i,3], peaks[i,5], peaks[i,6], peaks[i,7]
print ""
# Fit to update peak locations.
self.sfitter.doFit(peaks)
fit_peaks = self.sfitter.getGoodPeaks(min_height = 0.9 * self.fit_threshold)
# Remove peaks that are too close to each other & refit.
fit_peaks = utilC.removeClosePeaks(fit_peaks, self.fit_sigma, self.fit_neighborhood)
# Redo the fit for the remaining peaks.
self.sfitter.doFit(fit_peaks)
fit_peaks = self.sfitter.getGoodPeaks(min_height = 0.9*self.fit_threshold)
residual = self.sfitter.getResidual()
if False:
print "After fitting"
for i in range(5):
print " ", fit_peaks[i,0], fit_peaks[i,1], fit_peaks[i,3], fit_peaks[i,5], fit_peaks[i,6], fit_peaks[i,7]
print ""
return [fit_peaks, residual]
def fitPeaks(self, peaks):
# Fit to update peak locations.
self.sfitter.doFit(peaks)
fit_peaks = self.sfitter.getGoodPeaks(min_height = 0.9 * self.threshold,
verbose = False)
# Remove peaks that are too close to each other & refit.
fit_peaks = utilC.removeClosePeaks(fit_peaks, self.sigma, self.neighborhood)
# Redo the fit for the remaining peaks.
self.sfitter.doFit(fit_peaks)
fit_peaks = self.sfitter.getGoodPeaks(min_height = 0.9*self.threshold,
verbose = False)
residual = self.sfitter.getResidual()
return [fit_peaks, residual]
def iterateFit(fit_func, fit_data):
if (type(fit_data.peaks) == type(numpy.array([]))):
# Fit to update peak locations.
result = fit_func(fit_data.image, fit_data.peaks)
fit_peaks = multi_c.getGoodPeaks(result[0],
0.9*fit_data.threshold,
0.5*fit_data.sigma)
# Remove peaks that are too close to each other & refit.
fit_peaks = util_c.removeClosePeaks(fit_peaks, fit_data.sigma, fit_data.neighborhood)
result = fit_func(fit_data.image, fit_peaks)
fit_peaks = multi_c.getGoodPeaks(result[0],
0.9*fit_data.threshold,
0.5*fit_data.sigma)
fit_data.peaks = fit_peaks
fit_data.residual = result[1]
def peakFinder(self, no_bg_image):
all_new_peaks = None
save_convolution = False
if save_convolution:
tif = tifffile.TiffWriter("image.tif")
tif.save(self.image.astype(numpy.uint16) + 100)
tif.save(no_bg_image.astype(numpy.uint16) + 100)
tif.save(self.background.astype(numpy.uint16) + 100)
#
# Find peaks in image convolved with the PSF at different z values.
#
for i in range(len(self.mfilter)):
height_rescale = self.height_rescale[i]
mfilter = self.mfilter[i]
taken = self.taken[i]
z_value = self.z_value[i]
# Smooth image with gaussian filter.
smooth_image = mfilter.convolve(no_bg_image)
if save_convolution:
tif.save(smooth_image.astype(numpy.uint16) + 100)
# Mask the image so that peaks are only found in the AOI.
masked_image = smooth_image * self.peak_mask
# Identify local maxima in the masked image.
[new_peaks, taken] = utilC.findLocalMaxima(masked_image,
taken,
self.cur_threshold,
self.find_max_radius,
self.margin)
#
# Fill in initial values for peak height, background and sigma.
#
# FIXME: We just add the smoothed image and the background together
# as a hack so that we can still use the initializePeaks()
# function.
#
new_peaks = utilC.initializePeaks(new_peaks, # The new peaks.
smooth_image + self.background, # Smooth image + background.
self.background, # The current estimate of the background.
self.sigma, # The starting sigma value.
z_value) # The starting z value.
# Correct initial peak heights.
h_index = utilC.getHeightIndex()
new_peaks[:,h_index] = new_peaks[:,h_index] * height_rescale
if all_new_peaks is None:
all_new_peaks = new_peaks
else:
all_new_peaks = numpy.append(all_new_peaks, new_peaks, axis = 0)
if save_convolution:
tif.close()
#
# Remove the dimmer of two peaks with similar x,y values but different z values.
#
if (len(self.mfilter) > 1):
if False:
print "before", all_new_peaks.shape
for i in range(all_new_peaks.shape[0]):
print all_new_peaks[i,:]
print ""
all_new_peaks = utilC.removeClosePeaks(all_new_peaks,
self.find_max_radius,
self.find_max_radius)
if False:
print "after", all_new_peaks.shape
for i in range(all_new_peaks.shape[0]):
print all_new_peaks[i,:]
print ""
return all_new_peaks
