New script for data analysis.
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Data-Analyzer/fourierAnalysis.m
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477
Data-Analyzer/fourierAnalysis.m
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%% Parameters
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groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axis_Camera/in_situ_absorption", "/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", "/images/Vertical_Axis_Camera/in_situ_absorption"];
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folderPath = "C:/Users/Karthik/Documents/GitRepositories/Calculations/Data-Analyzer/";
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run = '0013';
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folderPath = strcat(folderPath, run);
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cam = 5;
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angle = 0;
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center = [1285, 2105];
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span = [200, 200];
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fraction = [0.1, 0.1];
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pixel_size = 5.86e-6;
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removeFringes = false;
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%% Compute OD image, rotate and extract ROI for analysis
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% Get a list of all files in the folder with the desired file name pattern.
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filePattern = fullfile(folderPath, '*.h5');
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files = dir(filePattern);
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refimages = zeros(span(1) + 1, span(2) + 1, length(files));
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absimages = zeros(span(1) + 1, span(2) + 1, length(files));
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for k = 1 : length(files)
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baseFileName = files(k).name;
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fullFileName = fullfile(files(k).folder, baseFileName);
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fprintf(1, 'Now reading %s\n', fullFileName);
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atm_img = im2double(imrotate(h5read(fullFileName, append(groupList(cam), "/atoms")), angle));
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bkg_img = im2double(imrotate(h5read(fullFileName, append(groupList(cam), "/background")), angle));
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dark_img = im2double(imrotate(h5read(fullFileName, append(groupList(cam), "/dark")), angle));
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refimages(:,:,k) = subtractBackgroundOffset(cropODImage(bkg_img, center, span), fraction)';
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absimages(:,:,k) = subtractBackgroundOffset(cropODImage(calculateODImage(atm_img, bkg_img, dark_img), center, span), fraction)';
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end
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% Fringe removal
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if removeFringes
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optrefimages = removefringesInImage(absimages, refimages);
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absimages_fringe_removed = absimages(:, :, :) - optrefimages(:, :, :);
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nimgs = size(absimages_fringe_removed,3);
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od_imgs = cell(1, nimgs);
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for i = 1:nimgs
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od_imgs{i} = absimages_fringe_removed(:, :, i);
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end
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else
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nimgs = size(absimages(:, :, :),3);
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od_imgs = cell(1, nimgs);
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for i = 1:nimgs
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od_imgs{i} = absimages(:, :, i);
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end
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end
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%% Display Images
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figure(1)
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clf
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set(gcf,'Position',[50 50 950 750])
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% Calculate the x and y limits for the cropped image
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y_min = center(1) - span(2) / 2;
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y_max = center(1) + span(2) / 2;
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x_min = center(2) - span(1) / 2;
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x_max = center(2) + span(1) / 2;
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% Generate x and y arrays representing the original coordinates for each pixel
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x_range = linspace(x_min, x_max, span(1));
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y_range = linspace(y_min, y_max, span(2));
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% Display the cropped image
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for k = 1 : length(od_imgs)
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imagesc(x_range, y_range, od_imgs{k})
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axis equal tight;
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hcb = colorbar;
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hL = ylabel(hcb, 'Optical Density', 'FontSize', 16);
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set(hL,'Rotation',-90);
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colormap jet;
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set(gca,'CLim',[0 3.0]);
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set(gca,'YDir','normal')
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set(gca, 'YTick', linspace(y_min, y_max, 5)); % Define y ticks
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set(gca, 'YTickLabel', flip(linspace(y_min, y_max, 5))); % Flip only the labels
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xlabel('Horizontal', 'Interpreter', 'tex','FontSize',16);
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ylabel('Vertical', 'Interpreter', 'tex','FontSize',16);
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drawnow
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pause(0.5)
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end
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%% Get rotation angles
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theta_values = zeros(1, length(files));
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% Get information about the '/globals' group
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for k = 1 : length(files)
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baseFileName = files(k).name;
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fullFileName = fullfile(files(k).folder, baseFileName);
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info = h5info(fullFileName, '/globals');
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for i = 1:length(info.Attributes)
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if strcmp(info.Attributes(i).Name, 'rot_mag_fin_pol_angle')
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theta_values(k) = 180 - info.Attributes(i).Value;
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end
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end
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end
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%% Run Fourier analysis over images
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fft_imgs = cell(1, nimgs);
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% Create VideoWriter object for movie
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videoFile = VideoWriter('Single_Shot_FFT.avi', 'Motion JPEG AVI');
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videoFile.FrameRate = 2; % Set the frame rate (frames per second)
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open(videoFile); % Open the video file to write
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% Display the cropped image
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for k = 1 : length(od_imgs)
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IMG = od_imgs{k};
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[IMGFFT, IMGBIN] = computeFourierTransform(IMG);
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figure(2);
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clf
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set(gcf,'Position',[50 50 1500 550])
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set(gca,'FontSize',16,'Box','On','Linewidth',2);
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t = tiledlayout(1, 3, 'TileSpacing', 'compact', 'Padding', 'compact'); % 1x2 grid
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% Calculate the x and y limits for the cropped image
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y_min = center(1) - span(2) / 2;
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y_max = center(1) + span(2) / 2;
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x_min = center(2) - span(1) / 2;
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x_max = center(2) + span(1) / 2;
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% Generate x and y arrays representing the original coordinates for each pixel
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x_range = linspace(x_min, x_max, span(1));
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y_range = linspace(y_min, y_max, span(2));
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% Display the cropped image
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nexttile
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imagesc(x_range, y_range, IMG)
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% Define normalized positions (relative to axis limits)
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x_offset = 0.025; % 5% offset from the edges
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y_offset = 0.025; % 5% offset from the edges
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% Top-right corner (normalized axis coordinates)
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text(1 - x_offset, 1 - y_offset, ['Angle: ', num2str(theta_values(k), '%.1f')], ...
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'Color', 'white', 'FontWeight', 'bold', 'Interpreter', 'tex', 'FontSize', 20, 'Units', 'normalized', 'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
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axis equal tight;
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hcb = colorbar;
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hL = ylabel(hcb, 'Optical Density', 'FontSize', 16);
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set(hL,'Rotation',-90);
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set(gca,'YDir','normal')
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set(gca, 'YTick', linspace(y_min, y_max, 5)); % Define y ticks
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set(gca, 'YTickLabel', flip(linspace(y_min, y_max, 5))); % Flip only the labels
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xlabel('X', 'Interpreter', 'tex','FontSize',16);
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ylabel('Y', 'Interpreter', 'tex','FontSize',16);
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nexttile
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imagesc(x_range, y_range, IMGBIN)
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axis equal tight;
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hcb = colorbar;
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set(gca,'YDir','normal')
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set(gca, 'YTick', linspace(y_min, y_max, 5)); % Define y ticks
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set(gca, 'YTickLabel', flip(linspace(y_min, y_max, 5))); % Flip only the labels
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xlabel('X', 'Interpreter', 'tex','FontSize',16);
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ylabel('Y', 'Interpreter', 'tex','FontSize',16);
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title('Denoised - Masked - Binarized','FontSize',16);
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nexttile
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[rows, cols] = size(IMGFFT);
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zoom_size = 50; % Zoomed-in region around center
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mid_x = floor(cols/2);
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mid_y = floor(rows/2);
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zoomedIMGFFT = IMGFFT(mid_y-zoom_size:mid_y+zoom_size, mid_x-zoom_size:mid_x+zoom_size);
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fft_imgs{k} = zoomedIMGFFT;
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imagesc(log(1 + zoomedIMGFFT));
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% Define normalized positions (relative to axis limits)
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x_offset = 0.025; % 5% offset from the edges
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y_offset = 0.025; % 5% offset from the edges
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% Top-right corner (normalized axis coordinates)
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text(1 - x_offset, 1 - y_offset, ['Angle: ', num2str(theta_values(k), '%.1f')], ...
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'Color', 'white', 'FontWeight', 'bold', 'Interpreter', 'tex', 'FontSize', 20, 'Units', 'normalized', 'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
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axis equal tight;
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hcb = colorbar;
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set(gca,'YDir','normal')
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xlabel('X', 'Interpreter', 'tex','FontSize',16);
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ylabel('Y', 'Interpreter', 'tex','FontSize',16);
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title('Fourier Power Spectrum','FontSize',16);
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drawnow
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pause(0.5)
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% Capture the current frame and write it to the video
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frame = getframe(gcf); % Capture the current figure as a frame
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writeVideo(videoFile, frame); % Write the frame to the video
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end
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% Close the video file
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close(videoFile);
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%% Averaged FFT
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% Assuming od_imgs is a cell array of size 4*n
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n = length(fft_imgs) / 4; % Calculate n
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fft_imgs_avg = cell(1, n); % Initialize the new cell array to hold the averaged images
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for i = 1:n
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% Take the 4 corresponding images from od_imgs
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img1 = fft_imgs{4*i-3}; % 1st image in the group
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img2 = fft_imgs{4*i-2}; % 2nd image in the group
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img3 = fft_imgs{4*i-1}; % 3rd image in the group
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img4 = fft_imgs{4*i}; % 4th image in the group
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% Compute the average of these 4 images
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avg_img = (img1 + img2 + img3 + img4) / 4;
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% Store the averaged image in the new cell array
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fft_imgs_avg{i} = avg_img;
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end
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% Create VideoWriter object for movie
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videoFile = VideoWriter('Averaged_FFT.avi', 'Motion JPEG AVI');
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videoFile.FrameRate = 2; % Set the frame rate (frames per second)
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open(videoFile); % Open the video file to write
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figure(3)
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clf
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set(gcf,'Position',[50 50 950 750])
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% Display the cropped image
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for k = 1 : length(fft_imgs_avg)
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imagesc(log(1 + fft_imgs_avg{k}));
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% Define normalized positions (relative to axis limits)
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x_offset = 0.025; % 5% offset from the edges
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y_offset = 0.025; % 5% offset from the edges
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% Top-right corner (normalized axis coordinates)
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text(1 - x_offset, 1 - y_offset, ['Angle: ', num2str(theta_values(k), '%.1f')], ...
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'Color', 'white', 'FontWeight', 'bold', 'Interpreter', 'tex', 'FontSize', 20, 'Units', 'normalized', 'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
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axis equal tight;
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hcb = colorbar;
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set(gca,'YDir','normal')
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xlabel('X', 'Interpreter', 'tex','FontSize',16);
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ylabel('Y', 'Interpreter', 'tex','FontSize',16);
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title('Averaged Fourier Power Spectrum','FontSize',16);
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drawnow
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pause(0.5)
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% Capture the current frame and write it to the video
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frame = getframe(gcf); % Capture the current figure as a frame
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writeVideo(videoFile, frame); % Write the frame to the video
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end
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% Close the video file
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close(videoFile);
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%% Helper Functions
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function [IMGFFT, IMGBIN] = computeFourierTransform(I)
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% computeFourierSpectrum - Computes the 2D Fourier power spectrum
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% of binarized and enhanced lattice image features, with optional central mask.
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%
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% Inputs:
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% I - Grayscale or RGB image matrix
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%
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% Output:
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% F_mag - 2D Fourier power spectrum (shifted)
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% Preprocessing: Denoise
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I_filt = imgaussfilt(I, 1); % adjust sigma as needed
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% Elliptical mask parameters
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[rows, cols] = size(I_filt);
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[X, Y] = meshgrid(1:cols, 1:rows);
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cx = cols / 2;
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cy = rows / 2;
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% Shifted coordinates
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x = X - cx;
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y = Y - cy;
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% Ellipse semi-axes
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rx = 0.4 * cols;
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ry = 0.2 * rows;
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% Rotation angle in degrees -> radians
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theta_deg = 30; % Adjust as needed
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theta = deg2rad(theta_deg);
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% Rotated ellipse equation
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cos_t = cos(theta);
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sin_t = sin(theta);
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x_rot = (x * cos_t + y * sin_t);
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y_rot = (-x * sin_t + y * cos_t);
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ellipseMask = (x_rot.^2) / rx^2 + (y_rot.^2) / ry^2 <= 1;
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% Apply cutout mask
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I_masked = I_filt .* ellipseMask;
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% Apply global intensity threshold mask
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intensity_thresh = 0.8;
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intensity_mask = I_masked > intensity_thresh;
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I_masked = I_masked .* intensity_mask;
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% Adaptive binarization
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IMGBIN = imbinarize(I_masked, 'adaptive', 'Sensitivity', 0.0);
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% Compute 2D Fourier Transform
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F = fft2(double(I));
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IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
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% Define the radius for the circular region to exclude
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region_radius = 4; % Adjust the radius as needed
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% Create a circular mask
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[~, center_idx] = max(IMGFFT(:));
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[cx, cy] = ind2sub(size(IMGFFT), center_idx);
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% Equation for a circle (centered at cx, cy)
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center_region = (X - cx).^2 + (Y - cy).^2 <= region_radius^2;
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% Define a scaling factor for the central region (e.g., reduce amplitude by 90%)
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scaling_factor = 0.1; % Scale center region by 10%
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% Apply the scaling factor to the center region
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IMGFFT(center_region) = IMGFFT(center_region) * scaling_factor;
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end
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function ret = getBkgOffsetFromCorners(img, x_fraction, y_fraction)
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% image must be a 2D numerical array
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[dim1, dim2] = size(img);
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s1 = img(1:round(dim1 * y_fraction), 1:round(dim2 * x_fraction));
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s2 = img(1:round(dim1 * y_fraction), round(dim2 - dim2 * x_fraction):dim2);
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s3 = img(round(dim1 - dim1 * y_fraction):dim1, 1:round(dim2 * x_fraction));
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s4 = img(round(dim1 - dim1 * y_fraction):dim1, round(dim2 - dim2 * x_fraction):dim2);
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ret = mean([mean(s1(:)), mean(s2(:)), mean(s3(:)), mean(s4(:))]);
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end
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function ret = subtractBackgroundOffset(img, fraction)
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% Remove the background from the image.
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% :param dataArray: The image
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% :type dataArray: xarray DataArray
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% :param x_fraction: The fraction of the pixels used in x axis
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% :type x_fraction: float
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% :param y_fraction: The fraction of the pixels used in y axis
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% :type y_fraction: float
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% :return: The image after removing background
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% :rtype: xarray DataArray
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x_fraction = fraction(1);
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y_fraction = fraction(2);
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offset = getBkgOffsetFromCorners(img, x_fraction, y_fraction);
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ret = img - offset;
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end
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function ret = cropODImage(img, center, span)
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% Crop the image according to the region of interest (ROI).
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% :param dataSet: The images
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% :type dataSet: xarray DataArray or DataSet
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% :param center: The center of region of interest (ROI)
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% :type center: tuple
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% :param span: The span of region of interest (ROI)
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% :type span: tuple
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% :return: The cropped images
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% :rtype: xarray DataArray or DataSet
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x_start = floor(center(1) - span(1) / 2);
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x_end = floor(center(1) + span(1) / 2);
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y_start = floor(center(2) - span(2) / 2);
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y_end = floor(center(2) + span(2) / 2);
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ret = img(y_start:y_end, x_start:x_end);
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end
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function ret = calculateODImage(imageAtom, imageBackground, imageDark)
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% Calculate the OD image for absorption imaging.
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% :param imageAtom: The image with atoms
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% :type imageAtom: numpy array
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% :param imageBackground: The image without atoms
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% :type imageBackground: numpy array
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% :param imageDark: The image without light
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% :type imageDark: numpy array
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% :return: The OD images
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% :rtype: numpy array
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numerator = imageBackground - imageDark;
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denominator = imageAtom - imageDark;
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numerator(numerator == 0) = 1;
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denominator(denominator == 0) = 1;
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ret = -log(double(abs(denominator ./ numerator)));
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if numel(ret) == 1
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ret = ret(1);
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end
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end
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function [optrefimages] = removefringesInImage(absimages, refimages, bgmask)
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% removefringesInImage - Fringe removal and noise reduction from absorption images.
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% Creates an optimal reference image for each absorption image in a set as
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% a linear combination of reference images, with coefficients chosen to
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% minimize the least-squares residuals between each absorption image and
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% the optimal reference image. The coefficients are obtained by solving a
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% linear set of equations using matrix inverse by LU decomposition.
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%
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% Application of the algorithm is described in C. F. Ockeloen et al, Improved
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% detection of small atom numbers through image processing, arXiv:1007.2136 (2010).
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%
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% Syntax:
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% [optrefimages] = removefringesInImage(absimages,refimages,bgmask);
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%
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% Required inputs:
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% absimages - Absorption image data,
|
||||
% typically 16 bit grayscale images
|
||||
% refimages - Raw reference image data
|
||||
% absimages and refimages are both cell arrays containing
|
||||
% 2D array data. The number of refimages can differ from the
|
||||
% number of absimages.
|
||||
%
|
||||
% Optional inputs:
|
||||
% bgmask - Array specifying background region used,
|
||||
% 1=background, 0=data. Defaults to all ones.
|
||||
% Outputs:
|
||||
% optrefimages - Cell array of optimal reference images,
|
||||
% equal in size to absimages.
|
||||
%
|
||||
|
||||
% Dependencies: none
|
||||
%
|
||||
% Authors: Shannon Whitlock, Caspar Ockeloen
|
||||
% Reference: C. F. Ockeloen, A. F. Tauschinsky, R. J. C. Spreeuw, and
|
||||
% S. Whitlock, Improved detection of small atom numbers through
|
||||
% image processing, arXiv:1007.2136
|
||||
% Email:
|
||||
% May 2009; Last revision: 11 August 2010
|
||||
|
||||
% Process inputs
|
||||
|
||||
% Set variables, and flatten absorption and reference images
|
||||
nimgs = size(absimages,3);
|
||||
nimgsR = size(refimages,3);
|
||||
xdim = size(absimages(:,:,1),2);
|
||||
ydim = size(absimages(:,:,1),1);
|
||||
|
||||
R = single(reshape(refimages,xdim*ydim,nimgsR));
|
||||
A = single(reshape(absimages,xdim*ydim,nimgs));
|
||||
optrefimages=zeros(size(absimages)); % preallocate
|
||||
|
||||
if not(exist('bgmask','var')); bgmask=ones(ydim,xdim); end
|
||||
k = find(bgmask(:)==1); % Index k specifying background region
|
||||
|
||||
% Ensure there are no duplicate reference images
|
||||
% R=unique(R','rows')'; % comment this line if you run out of memory
|
||||
|
||||
% Decompose B = R*R' using singular value or LU decomposition
|
||||
[L,U,p] = lu(R(k,:)'*R(k,:),'vector'); % LU decomposition
|
||||
|
||||
for j=1:nimgs
|
||||
b=R(k,:)'*A(k,j);
|
||||
% Obtain coefficients c which minimise least-square residuals
|
||||
lower.LT = true; upper.UT = true;
|
||||
c = linsolve(U,linsolve(L,b(p,:),lower),upper);
|
||||
|
||||
% Compute optimised reference image
|
||||
optrefimages(:,:,j)=reshape(R*c,[ydim xdim]);
|
||||
end
|
||||
end
|
||||
Loading…
Reference in New Issue
Block a user