/
Profiler.m
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Profiler.m
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classdef Profiler < handle
properties(Transient=true)
end
properties % objects
streak@radon.Streak; % point back to the streak that contains this profiler
fitter@radon.Fitter;
end
properties % inputs/outputs
image;
im_offset; % what we need to translate coordinates in the cutout to the real
expT = 30; % default value
image_filt;
image_rot;
image_crop;
trunc_mask; % area of the filtered image that need to be truncated
star_mask; % area of the filtered image that has stars in it
trunc_mask_rot;
star_mask_rot;
trunc_mask_crop;
star_mask_crop;
% image_bin;
width;
bin_size;
good_fitness;
filter_width;
filter_snr;
filter_loss;
filter_edge;
filter_delta;
edgyness;
edge_map;
edge_norm;
angle_correction;
amplitudes;
amp_lowpass;
offsets;
spreads;
line_start;
line_end;
variability;
brightest_value;
brightest_number;
brightest_ratio;
lateral_profile;
lateral_skewness;
lateral_spread;
glint_power;
runtime;
% these are all going away soon...
fit_period;
fit_amplitude;
fit_slope;
fit_residual;
type = '';
end
properties % switches/controls
use_angle_cut = 1;
use_star_mask = 1;
use_trunc_mask = 1;
max_length = 500;
max_filter_delta = 0.5;
max_filter_loss = 0.1;
min_snr = 50;
nsigma_width = 10;
angle_step = 0.5;
angle_range = 6.0;
lowpass_freq = 0.08;
debug_bit = 1;
show_bit = 0;
end
properties(Dependent=true)
l1; % shortcut property for "line_start" (also, defaults to 1)
l2; % shortcut property for "line_end" (also, defaults to size of cutout)
end
properties(Hidden=true)
lateral_profile_regular;
lateral_profile_filter;
lateral_profile_overfilter;
fit_options;
fit_type;
version = 1.00;
end
methods % constructor
function obj = Profiler(varargin)
if ~isempty(varargin) && isa(varargin{1}, 'radon.Profiler')
if obj.debug_bit, fprintf('Profiler copy-constructor v%4.2f\n', obj.version); end
obj = util.oop.full_copy(varargin{1});
elseif ~isempty(varargin) && isa(varargin{1}, 'radon.Streak')
% if obj.debug_bit, fprintf('Profiler streak constructor v%4.2f\n', obj.version); end
obj.streak = varargin{1};
obj.initialize;
else
if obj.debug_bit, fprintf('Profiler constructor v%4.2f\n', obj.version); end
obj.initialize;
end
end
end
methods % reset/clear
function initialize(obj)
obj.reset;
end
function reset(obj)
obj.clear;
end
function clear(obj)
obj.image = [];
obj.image_rot = [];
obj.image_crop = [];
obj.image_filt = [];
obj.width = [];
obj.bin_size = [];
obj.good_fitness = [];
obj.filter_width = [];
obj.filter_snr = [];
obj.filter_loss = [];
obj.filter_edge = [];
obj.edgyness = [];
obj.angle_correction = [];
obj.amplitudes = [];
obj.offsets = [];
obj.spreads =[];
obj.line_start = [];
obj.line_end = [];
obj.glint_power = [];
obj.variability = []; % maybe depricated soon
obj.brightest_value = [];
obj.brightest_number = [];
obj.brightest_ratio = [];
obj.lateral_profile = [];
obj.lateral_skewness = [];
obj.lateral_spread = [];
obj.fit_options = [];
obj.fit_type = [];
% not sure if we need these...
obj.fit_period = [];
obj.fit_amplitude = [];
obj.fit_slope = [];
obj.fit_residual = [];
obj.lateral_profile_regular = [];
obj.lateral_profile_filter = [];
end
end
methods % getters
function val = get.l1(obj)
if isempty(obj.line_start)
val = 1;
else
val = obj.line_start;
end
end
function val = get.l2(obj)
if isempty(obj.line_end)
val = size(obj.image);
else
val = obj.line_end;
end
end
end
methods % setters
end
methods % calculations
function input(obj, varargin)
input = util.text.InputVars;
input.use_ordered_numeric = 1;
input.input_var('image', []);
input.input_var('offset', []);
input.input_var('show_bit', 0);
input.scan_vars(varargin);
if isempty(obj.streak)
error('Profiler must have a Streak object to run!');
end
t= tic;
if isempty(input.image)
if ~isempty(obj.streak.original_image)
input.image = obj.streak.original_image;
else
input.image = obj.streak.image;
input.offset = [0 0];
end
end
if isempty(input.offset)
if all(size(input.image)==size(obj.streak.original_image))
input.offset = obj.streak.offset_original;
else
input.offset = [0 0];
end
end
if isempty(input.image)
error('Streak does not have an original image. Please input the original image...');
end
obj.clear;
cleanup = onCleanup(@() obj.finishup(t));
[obj.image, obj.im_offset] = obj.streak.getCutout('width', obj.streak.radon_dx.*4, 'image', input.image, 'offset', input.offset);
% obj.image = util.img.pad2size(obj.image, 2*ceil(size(obj.image)/2)); % round image dimensions to even sizes
obj.im_offset = obj.im_offset - obj.streak.offset_original;
obj.width = round(obj.streak.psf_sigma*2*obj.nsigma_width+1);
obj.bin_size = round(obj.streak.psf_sigma*2); % do we even need this anymore?
if obj.bin_size<1, obj.bin_size = 1; end
% test number 1: length of streak from FRT (don't continue
% testing very long streaks aka LEO satellites)
if obj.streak.L>obj.max_length
% need to do anything else?
return;
end
% obj.calcFiltering; % best PSF sigma to filter with
% obj.calcBestAngle; % find the best rotation angle around the FRT output
% obj.calcLength; % find the real length of the streak
% obj.calcBestAngle; % find the best rotation angle around the FRT output
% obj.calcLength; % find the real length of the streak
if obj.use_angle_cut && abs(mod(obj.streak.th,90))<1
return;
end
obj.rotateCrop;
obj.calcFiltering;
if obj.filter_delta>obj.max_filter_delta || obj.filter_edge || obj.filter_loss>obj.max_filter_loss
return;
end
obj.calcFit(input.show_bit);
obj.rotateCrop;
obj.calcAmpOffsetSpread; % find the amplitudes, offsets and spreads of the streak
% obj.calcFiltering;
obj.calcVariability;
obj.calcLateralSpread;
obj.calcBrightest;
obj.calcEdgyness;
% obj.calcPeriodicity; % test 6: find the best periodicity in the signal
% obj.fitLine;
% obj.correctEdges;
end
function finishup(obj, t)
obj.runtime = toc(t);
end
function calcFiltering(obj)
import util.stat.sum2;
import util.stat.max2;
filter_range = -3.5:0.1:3.5;
sig = filter_range + obj.streak.psf_sigma;
sig = sig(sig>=0.3);
I = obj.image_crop;
SNR = zeros(1,length(sig));
for ii = 1:length(sig)
P = util.img.gaussian2(sig(ii), 'norm', 2);
If = filter2(P,I);
% If = util.img.truncate(If, obj.streak.threshold, obj.streak.noise_var);
% line = sum(If(:,obj.line_start:obj.line_end),2)./sqrt(obj.streak.noise_var.*(obj.line_end-obj.line_start).*sum2(P));
% [SNR(ii), ~] = max(line);
SNR(ii) = sum(max(If(:,obj.line_start:obj.line_end)),2, 'omitnan')./sqrt(obj.streak.noise_var.*(obj.line_end-obj.line_start).*sum2(P));
end
[~,idx1] = min(abs(sig-obj.streak.psf_sigma_effective)); % the closest value to the "effective PSF" sigma
expected_snr = SNR(idx1);
obj.filter_width = sig;
obj.filter_snr = SNR;
[mx, idx2] = max(SNR); % the value with the most S/N
obj.filter_loss = abs(mx-expected_snr)./mx;
% obj.filter_loss = abs(mx-expected_snr);
obj.filter_delta = sig(idx2)-sig(idx1);
if idx2==1 || idx2==length(SNR)
obj.filter_edge = 1;
else
obj.filter_edge = 0;
end
end
function calcFit(obj, show_bit)
import util.stat.sum2;
obj.image_filt = filter2(obj.streak.psf, obj.image);
if obj.use_trunc_mask
obj.trunc_mask = obj.image_filt./sqrt(obj.streak.noise_var.*sum2(obj.streak.psf))>obj.streak.threshold;
else
obj.trunc_mask = false(size(obj.image_filt));
end
if obj.use_star_mask
obj.star_mask = imdilate(obj.image_filt./sqrt(obj.streak.noise_var.*sum2(obj.streak.psf))>obj.streak.threshold*2, ones(9));
else
obj.trunc_mask = false(size(obj.image_filt));
end
% remove the mean (without stars)
I = obj.image;
I(obj.star_mask) = NaN;
I = I - util.stat.median2(I);
I(isnan(I)) = 0;
obj.image = I;
I_sigma_clipped = obj.image;
% mask = obj.image>sqrt(obj.streak.noise_var);
% neighborhood = true(3);
% mask = imerode(mask, neighborhood);
% mask = imdilate(mask, neighborhood);
% I_sigma_clipped(~mask) = 0;
obj.fitter = radon.Fitter;
obj.fitter.show_bit = show_bit;
obj.fitter.initial_x1 = obj.streak.x1_frt - obj.im_offset(2);
obj.fitter.initial_x2 = obj.streak.x2_frt - obj.im_offset(2);
obj.fitter.initial_y1 = obj.streak.y1_frt - obj.im_offset(1);
obj.fitter.initial_y2 = obj.streak.y2_frt - obj.im_offset(1);
obj.fitter.amplitude_normalization = obj.streak.I_frt;
obj.fitter.psf_sigma = obj.streak.psf_sigma;
obj.fitter.variance = obj.streak.noise_var;
obj.fitter.run(I_sigma_clipped);
% fprintf('FITTER: x1= %g | x2= %g | y1= %g | y2= %g\n', obj.fitter.x1, obj.fitter.x2, obj.fitter.y1, obj.fitter.y2);
% fprintf('STREAK: x1= %g | x2= %g | y1= %g | y2= %g\n', obj.streak.x1, obj.streak.x2, obj.streak.y1, obj.streak.y2);
obj.angle_correction = obj.streak.th - obj.fitter.angle;
obj.streak.x1_fit = obj.fitter.x1 + obj.im_offset(2);
obj.streak.x2_fit = obj.fitter.x2 + obj.im_offset(2);
obj.streak.y1_fit = obj.fitter.y1 + obj.im_offset(1);
obj.streak.y2_fit = obj.fitter.y2 + obj.im_offset(1);
obj.streak.I_fit = obj.fitter.amplitude;
obj.streak.snr_fit = obj.streak.I_fit./sqrt(2.*sqrt(pi).*obj.streak.psf_sigma.*obj.streak.noise_var./(obj.streak.L));
obj.good_fitness = obj.fitter.chi2./obj.fitter.dof;
if obj.checkFitGood % check the fit is even remotely successfull...
obj.streak.x1 = obj.streak.x1_fit;
obj.streak.x2 = obj.streak.x2_fit;
obj.streak.y1 = obj.streak.y1_fit;
obj.streak.y2 = obj.streak.y2_fit;
obj.streak.th = obj.fitter.angle;
obj.streak.L = sqrt((obj.streak.x2-obj.streak.x1).^2 + (obj.streak.y2-obj.streak.y1).^2);
obj.streak.I = obj.streak.I_fit;
obj.streak.snr = obj.streak.snr_fit;
end
end
function rotateCrop(obj) % make a rotated image and masks
import util.stat.sum2;
obj.image_rot = imrotate(obj.image, obj.streak.th, 'bilinear','crop');
norm = sqrt(obj.streak.noise_var.*sum2(obj.streak.psf));
obj.trunc_mask_rot = filter2(obj.streak.psf, obj.image_rot)./norm>obj.streak.threshold;
obj.star_mask_rot = imdilate(filter2(obj.streak.psf, obj.image_rot)./norm>obj.streak.threshold*2, ones(9));
mid = floor(util.vec.imsize(obj.image)/2)+1;
% obj.line_start = floor((obj.fitter.x1-mid(2)).*cosd(obj.fitter.angle) + (obj.fitter.y1-mid(1)).*sind(obj.fitter.angle) + mid(2));
obj.line_start = floor((obj.streak.x1-obj.im_offset(2)-mid(2)).*cosd(obj.streak.th) + (obj.streak.y1-obj.im_offset(1)-mid(1)).*sind(obj.streak.th) + mid(2));
if obj.line_start<1
obj.line_start = 1;
end
% obj.line_end = ceil((obj.fitter.x2-mid(2)).*cosd(obj.fitter.angle) + (obj.fitter.y2-mid(1)).*sind(obj.fitter.angle) + mid(2));
obj.line_end = ceil((obj.streak.x2-obj.im_offset(2)-mid(2)).*cosd(obj.streak.th) + (obj.streak.y2-obj.im_offset(1)-mid(1)).*sind(obj.streak.th) + mid(2));
if obj.line_end<obj.line_start
temp = obj.line_end;
obj.line_end = obj.line_start;
obj.line_start = temp;
end
if obj.line_end>size(obj.image,2)
obj.line_end = size(obj.image,2);
end
% make a cropped image and masks
% [~, pos] = max(sum(obj.image_rot,2));
pos = round([obj.streak.midpoint_y, obj.streak.midpoint_x] - obj.im_offset);
rel_pos = pos - mid;
rel_pos = rel_pos*[cosd(obj.streak.th) sind(obj.streak.th); -sind(obj.streak.th) cosd(obj.streak.th)];
pos = round(rel_pos + mid);
top = pos(1) - floor(obj.width/2-1);
bottom = pos(1) + floor(obj.width/2+1);
if top<1
top = 1;
bottom = obj.width;
elseif bottom>size(obj.image_rot,1)
top = size(obj.image_rot,1)-obj.width+1;
bottom = size(obj.image_rot,1);
end
obj.image_crop = obj.image_rot(top:bottom,:);
obj.trunc_mask_crop = obj.trunc_mask_rot(top:bottom,:);
obj.star_mask_crop = obj.star_mask_rot(top:bottom,:);
end
function val = checkFitGood(obj)
val = true;
val = val && (obj.fitter.chi2/obj.fitter.dof < 3);
L = sqrt((obj.streak.x2_fit-obj.streak.x1_fit).^2+(obj.streak.y2_fit-obj.streak.y1_fit).^2);
val = val && L<2*max(util.vec.imsize(obj.image)) && L>5;
val = val && obj.fitter.chi2>0;
val = val && obj.streak.snr_fit>0;
end
function calcBestAngle(obj) % not used
import util.stat.sum2;
angles = -obj.angle_range:obj.angle_step:obj.angle_range;
score = zeros(length(angles),1);
pos = zeros(length(angles),1);
obj.image_filt = filter2(obj.streak.psf, obj.image);
obj.trunc_mask = obj.image_filt./sqrt(obj.streak.noise_var.*sum2(obj.streak.psf))>obj.streak.threshold;
obj.star_mask = imdilate(obj.image_filt./sqrt(obj.streak.noise_var.*sum2(obj.streak.psf))>obj.streak.threshold*2, ones(9));
% remove the mean (without stars)
I = obj.image;
I(obj.star_mask) = NaN;
I = I - util.stat.median2(I);
I(isnan(I)) = 0;
obj.image = I;
I = obj.image_filt;
I(obj.trunc_mask) = obj.streak.threshold.*sqrt(obj.streak.noise_var.*sum2(obj.streak.psf));
I(obj.star_mask) = 0;
for ii = 1:length(angles)
Ir = imrotate(I, angles(ii)+obj.streak.th, 'bilinear','crop');
[score(ii), pos(ii)] = max(sum(Ir(:,obj.l1:obj.l2),2));
if obj.show_bit
plot(sum(Ir,2));
hold on;
xlabel(['angle= ' num2str(angles(ii)) ' | score= ' num2str(score(ii))]);
ax=gca;
ax.YLim = [0 1.5.*max(score(1))];
pause(0.5);
end
end
if obj.show_bit
hold off;
plot(angles, score);
pause(2);
end
[~,idx] = max(score);
pos = round(median(pos)); % where the streak on the y axis
obj.angle_correction = angles(idx);
% make a rotated, then a cropped image
obj.image_rot = imrotate(obj.image, obj.streak.th+obj.angle_correction, 'bilinear','crop');
obj.trunc_mask_rot = filter2(obj.streak.psf, obj.image_rot)./sqrt(obj.streak.noise_var)>obj.streak.threshold;
obj.star_mask_rot = imdilate(filter2(obj.streak.psf, obj.image_rot)./sqrt(obj.streak.noise_var)>obj.streak.threshold*2, ones(9));
top = pos - floor(obj.width/2-1);
if top<1, top = 1; end
bottom = pos + floor(obj.width/2+1);
if bottom>size(obj.image_rot,1), bottom = size(obj.image_rot,1); end
obj.image_crop = obj.image_rot(top:bottom,:);
obj.trunc_mask_crop = obj.trunc_mask_rot(top:bottom,:);
obj.star_mask_crop = obj.star_mask_rot(top:bottom,:);
end
function calcLength(obj) % not used
% if we want to used some kind of binning before length estimates
% obj.image_bin = filter2(ones(1,obj.bin_size), obj.image_crop);
% obj.image_bin = obj.image_bin(:,1:obj.bin_size:end);
% I = filter2(sum(obj.streak.psf,2), obj.image_bin); % binned along the streak, matched-filtered in the lateral direction...
% obj.image_filt = filter2(obj.streak.psf, obj.image_rot);
I = obj.image_crop;
If = filter2(obj.streak.psf, I);
If(obj.trunc_mask_crop) = obj.streak.threshold.*sqrt(obj.streak.noise_var);
If(obj.star_mask_crop) = 0;
N = size(I,2);
best_start = 1;
best_end = N;
best_snr = 0;
for ii = 1:N % we can improve this a little bit using smart additions (is this slowing us down?)
for jj = ii:N
noise = sqrt(jj-ii+1); % how many pixels in the line
signal = sum(max(If(:, ii:jj)), 'omitnan');
snr = signal./noise;
if snr>best_snr
best_snr = snr;
best_start = ii;
best_end = jj;
end
end
end
% obj.line_start = best_start.*obj.bin_size;
% obj.line_end = best_end.*obj.bin_size;
obj.line_start = best_start;
obj.line_end = best_end;
end
function calcAmpOffsetSpread(obj)
I = obj.image_crop;
I(obj.star_mask_crop) = 0;
for ii = 1:size(obj.image_crop,2)
slice = I(:,ii);
y = (1:length(slice))'-floor(length(slice)/2)-1;
obj.amplitudes(ii) = max(slice);
slice(slice<0) = 0;
S = sum(slice);
if S==0, S = 1; end % this shouldn't happen but if it does, I prefer to get offsets and spreads equal zero than Inf
obj.offsets(ii) = sum(slice.*y, 'omitnan')./S;
obj.spreads(ii) = sqrt(sum(slice.*(y-obj.offsets(ii)).^2, 'omitnan')./S);
end
pixel_width = mean(obj.spreads(obj.line_start:obj.line_end));
% new_value = 2*pixel_width./obj.streak.psf_sigma;
if pixel_width>obj.streak.subtract_psf_widths
obj.streak.subtract_psf_widths = pixel_width;
end
end
function calcEdgyness(obj)
I = obj.image_crop;
k = [1, 2, 1; 0, 0, 0; -1, -2, -1]; % sobel operator in Y
If = filter2(k, I);
% If2 = filter2(k',I);
% If = sqrt(If.^2+If2.^2);
% Sf = max(abs(sum(If(:,obj.line_start:obj.line_end),2)));
% S = max(sum(I(:,obj.line_start:obj.line_end),2));
obj.edge_norm = median(max(I(:,obj.line_start:obj.line_end), [], 1)); % average peak brightness
% obj.edgyness = Sf./S;
% obj.edgyness = obj.edgyness.*sqrt(obj.streak.psf_sigma);
obj.edge_map = abs(If./obj.edge_norm);
obj.edgyness = median(max(obj.edge_map(:, obj.line_start:obj.line_end),[],1));
% obj.edgyness = obj.edgyness./20; % empirical normalization
obj.edgyness = obj.edgyness.*sqrt(obj.streak.psf_sigma);
end
function calcVariability(obj)
if obj.line_start+5>obj.line_end
obj.variability = 0;
obj.glint_power = 0;
disp('streak found is too short for variability / glint calculation!');
return;
end
a = obj.amplitudes(obj.line_start+2:obj.line_end-2);
v = mean(a) + obj.streak.noise_var; % background and source noise together (assuming GAIN=1)
b = a./sqrt(v); % normalize to unity noise
obj.variability = std(b);
% obj.amp_lowpass = obj.lowpass(obj.amplitudes);
obj.amp_lowpass = obj.lowpass(a);
% obj.amp_lowpass = obj.lowpass(obj.amplitudes);
% obj.amp_lowpass = obj.amp_lowpass(obj.line_start:obj.line_end);
obj.glint_power = std(obj.amp_lowpass)./sqrt(median(abs(obj.amp_lowpass)));
end
function calcLateralSpread(obj)
% I = filter2(obj.streak.psf, obj.image_crop);
% I(obj.trunc_mask_crop) = obj.streak.threshold.*sqrt(obj.streak.noise_var);
I = obj.image_crop(:,obj.line_start:obj.line_end);
slice = sum(I,2);
slice(slice<0) = 0;
y = (1:length(slice))'-floor(length(slice)/2)-1;
m1 = sum(slice.*y)./sum(slice);
% m1 = 0;
m2 = sum(slice.*(y-m1).^2)./sum(slice);
m3 = sum(slice.*(y-m1).^3)./sum(slice);
obj.lateral_spread = sqrt(m2)./obj.streak.psf_sigma;
% obj.lateral_skewness = m3./m2.^(3/2)./obj.streak.psf_sigma;
obj.lateral_skewness = m3./obj.streak.psf_sigma;
obj.lateral_profile = slice;
end
function calcBrightest(obj)
obj.brightest_value = util.stat.max2(obj.image_crop);
obj.brightest_number = nnz(obj.image_crop==obj.brightest_value);
obj.brightest_ratio = obj.brightest_number./obj.streak.L;
end
function calcPeriodicity(obj) % not used
values = obj.amplitudes(obj.line_start:obj.line_end)'./sqrt(obj.streak.noise_var);
times = linspace(0,obj.expT,length(values))';
dt = times(2)-times(1);
func = @(p) util.stat.lscov_sinus(times, values, 1./p);
% p_found = fminsearch(func,3);
periods = 5*dt:dt:obj.expT*10;
res = zeros(length(periods),1);
for ii = 1:length(periods)
res(ii) = func(periods(ii));
end
[min_res, idx] = min(res);
p_found = periods(idx);
if obj.show_bit
plot(periods, res);
xlabel('period [seconds]');
ylabel('residuals');
title(['best period= ' num2str(p_found) ' | res= ' num2str(min_res)]);
pause(2);
end
[residual, pars, model_data] = func(p_found);
if obj.show_bit
plot(times,values, '.', times, model_data);
legend({'data', 'fit'});
xlabel('time [sec]');
ylabel('brightness [normalized]');
title(['period= ' num2str(p_found) ' | res= ' num2str(residual)]);
pause(2);
end
obj.fit_period = p_found;
obj.fit_amplitude = sqrt((pars(3).^2+pars(4).^2)*obj.streak.noise_var);
obj.fit_slope = pars(2);
obj.fit_residual = residual;
end
function fitLine(obj) % not used
obj.fit_type = fittype('c + a*exp(-0.5*(x-b)^2/sig^2)', 'Problem', 'sig', 'Independent', 'x', 'Coefficients', {'a', 'b', 'c'});
obj.fit_options = fitoptions('Method', 'NonLinearLeastSquares', 'Robust', 'Bisquare', 'Display', 'off', ...
'Lower', [0,-obj.width*1/4,-Inf], 'Upper', [Inf, obj.width*1/4, Inf], 'StartPoint', [1,0,0]);
I = obj.image_bin;
N = size(I,2);
obj.amplitudes = zeros(1,N);
obj.offsets = zeros(1,N);
x = (1:obj.width)'-obj.width/2;
for ii = 1:N
fr = fit(x, I(:,ii), obj.fit_type, obj.fit_options, 'problem', obj.streak.psf_sigma);
obj.amplitudes(ii) = fr.a;
obj.offsets(ii) = fr.b;
if obj.show_bit
plot(fr, x, I(:,ii));
util.plot.inner_title(['ii= ' num2str(ii)], 'Position', 'Bottom');
drawnow;
end
end
end
function calcFiltering2(obj) % to be depricated
import util.stat.sum2;
import util.stat.max2;
filter_range = -3.5:0.1:3.5;
sig = filter_range + obj.streak.psf_sigma;
sig = sig(sig>=0.3);
RV = radon.frt(ones(size(obj.image)).*obj.streak.noise_var); % radon space variance (for normalization)
I = obj.image;
I(obj.star_mask) = NaN;
I = I - util.stat.median2(I);
I(isnan(I)) = 0;
obj.image = I;
SNR = zeros(1,length(sig));
for ii = 1:length(sig)
P = util.img.gaussian2(sig(ii), 'norm', 2);
If = filter2(P,I);
% If(If./sqrt(obj.streak.noise_var)>obj.streak.threshold) = obj.streak.threshold.*sqrt(obj.streak.noise_var);
If = util.img.truncate(If, obj.streak.threshold, obj.streak.noise_var);
if obj.show_bit
util.plot.show(If, 'bias', 0);
pause(0.5);
end
R = radon.frt(If, 'transpose', 0);
RT = radon.frt(If, 'transpose', 1);
SNR(ii) = max(max2(RT./sqrt(RV)), max2(R./sqrt(RV)))./sqrt(sum2(P));
if sig(ii)==obj.streak.psf_sigma
expected_snr = SNR(ii);
end
end
obj.filter_width = sig;
obj.filter_snr = SNR;
[mx, idx] = max(SNR);
obj.filter_loss = abs(mx-expected_snr)./mx;
if idx==1 || idx==length(SNR)
obj.filter_edge = 1;
else
obj.filter_edge = 0;
end
if obj.show_bit
plot(sig, SNR);
xlabel('PSF sigma');
ylabel('relative SNR');
pause(2);
end
end
function calcFiltering3(obj) % to be depricated!
import util.stat.sum2;
% filter the image using
if isempty(obj.streak.original_image) && obj.streak.was_convolved
P = obj.streak.psf;
obj.image_filt = obj.image_rot; % this means the streak only has the processed, filtered, image
P_over = util.img.gaussian2(3.*obj.streak.psf_sigma, 'norm', 2);
obj.image_overfilt = filter2(P_over, obj.image_rot); % use slightly sharper PSF for overfiltering
else
P = obj.streak.psf;
obj.image_filt = filter2(P, obj.image_rot);
P_over = util.img.gaussian2(4.*obj.streak.psf_sigma, 'norm', 2);
obj.image_overfilt = filter2(P_over, obj.image_rot);
end
% correction = sqrt(obj.streak.L.*obj.streak.noise_var.*max(abs(cosd(obj.streak.th)), abs(sind(obj.streak.th))));
correction = sqrt(obj.streak.L.*obj.streak.noise_var);
obj.lateral_profile_filter = sum(obj.image_filt,2)./sqrt(sum2(P))./correction;
obj.streak.filtered_snr = max(obj.lateral_profile_filter);
obj.lateral_profile_overfilter = sum(obj.image_overfilt,2)./sqrt(sum2(P_over))./correction;
obj.streak.overfiltered_snr = max(obj.lateral_profile_overfilter);
if isempty(obj.streak.original_image) && obj.streak.was_convolved
obj.lateral_profile_regular = obj.lateral_profile_filter;
obj.streak.unfiltered_snr = obj.streak.filtered_snr;
else
obj.lateral_profile_regular = sum(obj.image_rot,2)./correction;
obj.streak.unfiltered_snr = max(obj.lateral_profile_regular);
end
if obj.show_bit
plot(obj.lateral_profile_regular);
hold on;
plot(obj.lateral_profile_filter);
plot(obj.lateral_profile_overfilter);
hold off;
legend({'Non-filtered', 'Filtered', 'Over-filtered'});
% xlabel(sprintf('REG: mx= %4.2f | std= %4.2f --- FILT: mx= %4.2f | std= %4.2f', ...
% score_regular.*std_regular, std_regular, score_filter.*std_filter, std_filter));
pause(2);
end
end
function [score, sd] = calcLateralProfileScore(obj, prof) % to be depricated! this gives a score that is much too low for the filtered image (noise is overestimated...)
prof = util.vec.torow(prof);
[mx, idx] = max(prof);
prof_noise = [];
if idx>obj.width
prof_noise = [prof_noise prof(1:idx-obj.width)];
end
if idx<=length(prof)-obj.width
prof_noise = [prof_noise prof(idx+obj.width:end)];
end
sd = std(prof_noise);
score = mx./sd;
end
function correctEdges(obj) % to be depricated!
if abs(obj.angle_correction)<obj.angle_range
obj.streak.th = obj.streak.th + obj.angle_correction;
end
L = obj.streak.L;
th = obj.streak.th;
S = length(obj.amplitudes);
mid_amp = floor(S/2)+1;
distance_start = mid_amp - obj.line_start;
distance_end = obj.line_end - mid_amp;
delta_start = distance_start - L/2;
delta_end = distance_end - L/2;
% if obj.streak.th<0
% delta_start = -(L/2 - distance_end);
% delta_end = -(L/2 - distance_start);
% end
obj.streak.x1 = (obj.streak.x1 - cosd(th).*delta_start);
obj.streak.y1 = (obj.streak.y1 - sind(th).*delta_start);
if delta_start>0
obj.streak.x1_subtraction = obj.streak.x1;
obj.streak.y1_subtraction = obj.streak.y1;
end
obj.streak.x2 = (obj.streak.x2 + cosd(th).*delta_end);