def __init__(self, parameters):
# Member variables.
self.background = None # Current estimate of the image background.
self.image = None # The original image.
self.iterations = parameters.iterations # Maximum number of cycles of peak finding, fitting and subtraction to perform.
self.margin = PeakFinderFitter.margin # Size of the unanalyzed "edge" around the image.
self.neighborhood = PeakFinder.unconverged_dist * parameters.sigma # Radius for marking neighbors as unconverged.
self.new_peak_radius = PeakFinder.new_peak_dist # Minimum allowed distance between new peaks and current peaks.
self.parameters = parameters # Keep access to the parameters object.
self.peak_locations = None # Initial peak locations, as explained below.
self.peak_mask = None # Mask for limiting peak identification to a particular AOI.
self.sigma = parameters.sigma # Peak sigma (in pixels).
self.taken = None # Spots in the image where a peak has already been added.
self.threshold = parameters.threshold # Peak minimum threshold (height, in camera units).
self.z_value = 0.0 # The starting z value to use for peak fitting.
# Radius (in pixels) over which the maxima is maximal.
if hasattr(parameters, "find_max_radius"):
self.find_max_radius = float(parameters.find_max_radius)
else:
self.find_max_radius = 5
#
# This is for is you already know where your want fitting to happen, as
# for example in a bead calibration movie and you just want to use the
# approximate locations as inputs for fitting.
#
# peak_locations is a text file with the peak x, y, height and background
# values as white spaced columns (x and y positions are in pixels as
# determined using visualizer).
#
# 1.0 2.0 1000.0 100.0
# 10.0 5.0 2000.0 200.0
# ...
#
if hasattr(parameters, "peak_locations"):
print "Using peak starting locations specified in", parameters.peak_locations
# Only do one cycle of peak finding as we'll always return the same locations.
if (self.iterations != 1):
print "WARNING: setting number of iterations to 1!"
self.iterations = 1
# Load peak x,y locations.
peak_locs = numpy.loadtxt(parameters.peak_locations, ndmin = 2)
print peak_locs.shape
# Create peak array.
self.peak_locations = numpy.zeros((peak_locs.shape[0],
util_c.getNResultsPar()))
self.peak_locations[:,util_c.getXCenterIndex()] = peak_locs[:,1] + self.margin
self.peak_locations[:,util_c.getYCenterIndex()] = peak_locs[:,0] + self.margin
self.peak_locations[:,util_c.getHeightIndex()] = peak_locs[:,2]
self.peak_locations[:,util_c.getBackgroundIndex()] = peak_locs[:,3]
self.peak_locations[:,util_c.getXWidthIndex()] = numpy.ones(peak_locs.shape[0]) * self.sigma
self.peak_locations[:,util_c.getYWidthIndex()] = numpy.ones(peak_locs.shape[0]) * self.sigma
def analyzeImage(self, new_image, save_residual=False, verbose=False):
[image, residual] = self.newImage(new_image)
self.peak_finder.newImage(image)
self.peak_fitter.newImage(image)
if save_residual:
resid_dax = daxwriter.DaxWriter("residual.dax", residual.shape[0],
residual.shape[1])
peaks = False
for i in range(self.iterations):
if save_residual:
resid_dax.addFrame(residual)
[found_new_peaks,
peaks] = self.peak_finder.findPeaks(residual, peaks)
if (type(peaks) == type(numpy.array([]))):
[peaks, residual] = self.peak_fitter.fitPeaks(peaks)
residual = self.peak_finder.subtractBackground(residual)
if verbose:
if (type(peaks) == type(numpy.array([]))):
print " peaks:", i, found_new_peaks, peaks.shape[0]
else:
print " peaks:", i, found_new_peaks, "NA"
if not found_new_peaks:
break
if save_residual:
resid_dax.addFrame(residual)
resid_dax.close()
if (type(peaks) == type(numpy.array([]))):
peaks[:, util_c.getXCenterIndex()] -= float(self.margin)
peaks[:, util_c.getYCenterIndex()] -= float(self.margin)
return [peaks, residual]
def analyzeImage(self, new_image, save_residual = False, verbose = False):
[image, residual] = self.newImage(new_image)
self.peak_finder.newImage(image)
self.peak_fitter.newImage(image)
if save_residual:
resid_dax = daxwriter.DaxWriter("residual.dax",
residual.shape[0],
residual.shape[1])
peaks = False
for i in range(self.iterations):
if save_residual:
resid_dax.addFrame(residual)
[found_new_peaks, peaks] = self.peak_finder.findPeaks(residual, peaks)
if (type(peaks) == type(numpy.array([]))):
[peaks, residual] = self.peak_fitter.fitPeaks(peaks)
residual = self.peak_finder.subtractBackground(residual)
if verbose:
if (type(peaks) == type(numpy.array([]))):
print " peaks:", i, found_new_peaks, peaks.shape[0]
else:
print " peaks:", i, found_new_peaks, "NA"
if not found_new_peaks:
break
if save_residual:
resid_dax.addFrame(residual)
resid_dax.close()
if (type(peaks) == type(numpy.array([]))):
peaks[:,util_c.getXCenterIndex()] -= float(self.margin)
peaks[:,util_c.getYCenterIndex()] -= float(self.margin)
return [peaks, residual]
def __init__(self, parameters):
# Member variables.
self.background = None # Current estimate of the image background.
self.image = None # The original image.
self.iterations = parameters.iterations # Maximum number of cycles of peak finding, fitting and subtraction to perform.
self.margin = PeakFinderFitter.margin # Size of the unanalyzed "edge" around the image.
self.neighborhood = PeakFinder.unconverged_dist * parameters.sigma # Radius for marking neighbors as unconverged.
self.new_peak_radius = PeakFinder.new_peak_dist # Minimum allowed distance between new peaks and current peaks.
self.parameters = parameters # Keep access to the parameters object.
self.peak_locations = None # Initial peak locations, as explained below.
self.peak_mask = None # Mask for limiting peak identification to a particular AOI.
self.sigma = parameters.sigma # Peak sigma (in pixels).
self.taken = None # Spots in the image where a peak has already been added.
self.threshold = parameters.threshold # Peak minimum threshold (height, in camera units).
self.z_value = 0.0 # The starting z value to use for peak fitting.
# Radius (in pixels) over which the maxima is maximal.
if hasattr(parameters, "find_max_radius"):
self.find_max_radius = float(parameters.find_max_radius)
else:
self.find_max_radius = 5
#
# This is for is you already know where your want fitting to happen, as
# for example in a bead calibration movie and you just want to use the
# approximate locations as inputs for fitting.
#
# peak_locations is a text file with the peak x, y, height and background
# values as white spaced columns (x and y positions are in pixels as
# determined using visualizer).
#
# 1.0 2.0 1000.0 100.0
# 10.0 5.0 2000.0 200.0
# ...
#
if hasattr(parameters, "peak_locations"):
print "Using peak starting locations specified in", parameters.peak_locations
# Only do one cycle of peak finding as we'll always return the same locations.
if (self.iterations != 1):
print "WARNING: setting number of iterations to 1!"
self.iterations = 1
# Load peak x,y locations.
peak_locs = numpy.loadtxt(parameters.peak_locations, ndmin=2)
print peak_locs.shape
# Create peak array.
self.peak_locations = numpy.zeros(
(peak_locs.shape[0], util_c.getNResultsPar()))
self.peak_locations[:, util_c.getXCenterIndex(
)] = peak_locs[:, 1] + self.margin
self.peak_locations[:, util_c.getYCenterIndex(
)] = peak_locs[:, 0] + self.margin
self.peak_locations[:, util_c.getHeightIndex()] = peak_locs[:, 2]
self.peak_locations[:, util_c.getBackgroundIndex()] = peak_locs[:,
3]
self.peak_locations[:, util_c.getXWidthIndex()] = numpy.ones(
peak_locs.shape[0]) * self.sigma
self.peak_locations[:, util_c.getYWidthIndex()] = numpy.ones(
peak_locs.shape[0]) * self.sigma