From 619aaa2db4fa75b04ca31501746f315e6181b9b9 Mon Sep 17 00:00:00 2001
From: John Lawson <j.m.lawson@soton.ac.uk>
Date: Tue, 13 Jun 2023 18:01:16 +0100
Subject: [PATCH] cleared up old files from calibration folder
---
calibration/automatic_filter.m | 63 ----
calibration/bounding_polygon.m | 34 ---
calibration/bounding_polyhedron.m | 42 ---
calibration/bulk_marker_search.m | 276 -----------------
calibration/bundle_calibration_fit.m | 185 ------------
calibration/calcmask.m | 107 -------
calibration/calibration_to_config.m | 135 ---------
calibration/calpts2mat.m | 82 -----
calibration/check_calibration.m | 120 --------
calibration/combine_calibrations.m | 309 -------------------
calibration/combine_markers.m | 316 --------------------
calibration/cubicfit_2d.m | 64 ----
calibration/cubicmap_2d.m | 7 -
calibration/dewarp_2d.m | 23 --
calibration/dewarp_pinhole_2d.m | 13 -
calibration/distortion_test.m | 158 ----------
calibration/dlt/dlt_maths.m | 59 ----
calibration/dlt/dlt_model.m | 40 ---
calibration/dlt/dlt_test.m | 157 ----------
calibration/dlt/randomorthogonal.m | 5 -
calibration/dlt/randortho.m | 6 -
calibration/find_calibration_markers.m | 195 ------------
calibration/find_equispaced_values.m | 135 ---------
calibration/find_intersection.m | 142 ---------
calibration/find_largest_volume.m | 50 ----
calibration/flip_calibration.m | 30 --
calibration/local_maxima2.m | 16 -
calibration/local_maxima3.m | 22 --
calibration/local_minima.m | 15 -
calibration/local_minima3.m | 22 --
calibration/log_find_markers.m | 69 -----
calibration/log_test.m | 179 -----------
calibration/make_dark.m | 92 ------
calibration/manual_sheet_thickness.m | 61 ----
calibration/p2d_dewarp.m | 9 -
calibration/plot_calibration_residuals.m | 57 ----
calibration/plot_dewarped.m | 102 -------
calibration/plot_dewarped_3d.m | 51 ----
calibration/plot_marker_search.m | 40 ---
calibration/plot_residuals.m | 16 -
calibration/plot_residuals_3d.m | 176 -----------
calibration/plot_traverse_plate_alignment.m | 60 ----
calibration/poly/polyfit2d3.m | 26 --
calibration/poly/polyfit2d4.m | 30 --
calibration/poly/polymap2d3.m | 31 --
calibration/poly/polymap2d4.m | 33 --
calibration/refraction_correction.m | 61 ----
calibration/search_markers.m | 46 ---
calibration/sheet_calibration.m | 280 -----------------
calibration/test_bounding_polygon.m | 19 --
50 files changed, 4266 deletions(-)
delete mode 100644 calibration/automatic_filter.m
delete mode 100644 calibration/bounding_polygon.m
delete mode 100644 calibration/bounding_polyhedron.m
delete mode 100644 calibration/bulk_marker_search.m
delete mode 100644 calibration/bundle_calibration_fit.m
delete mode 100644 calibration/calcmask.m
delete mode 100644 calibration/calibration_to_config.m
delete mode 100644 calibration/calpts2mat.m
delete mode 100644 calibration/check_calibration.m
delete mode 100644 calibration/combine_calibrations.m
delete mode 100644 calibration/combine_markers.m
delete mode 100644 calibration/cubicfit_2d.m
delete mode 100644 calibration/cubicmap_2d.m
delete mode 100644 calibration/dewarp_2d.m
delete mode 100644 calibration/dewarp_pinhole_2d.m
delete mode 100644 calibration/distortion_test.m
delete mode 100644 calibration/dlt/dlt_maths.m
delete mode 100644 calibration/dlt/dlt_model.m
delete mode 100644 calibration/dlt/dlt_test.m
delete mode 100644 calibration/dlt/randomorthogonal.m
delete mode 100644 calibration/dlt/randortho.m
delete mode 100644 calibration/find_calibration_markers.m
delete mode 100644 calibration/find_equispaced_values.m
delete mode 100644 calibration/find_intersection.m
delete mode 100644 calibration/find_largest_volume.m
delete mode 100644 calibration/flip_calibration.m
delete mode 100644 calibration/local_maxima2.m
delete mode 100644 calibration/local_maxima3.m
delete mode 100644 calibration/local_minima.m
delete mode 100644 calibration/local_minima3.m
delete mode 100644 calibration/log_find_markers.m
delete mode 100644 calibration/log_test.m
delete mode 100644 calibration/make_dark.m
delete mode 100644 calibration/manual_sheet_thickness.m
delete mode 100644 calibration/p2d_dewarp.m
delete mode 100644 calibration/plot_calibration_residuals.m
delete mode 100644 calibration/plot_dewarped.m
delete mode 100644 calibration/plot_dewarped_3d.m
delete mode 100644 calibration/plot_marker_search.m
delete mode 100644 calibration/plot_residuals.m
delete mode 100644 calibration/plot_residuals_3d.m
delete mode 100644 calibration/plot_traverse_plate_alignment.m
delete mode 100644 calibration/poly/polyfit2d3.m
delete mode 100644 calibration/poly/polyfit2d4.m
delete mode 100644 calibration/poly/polymap2d3.m
delete mode 100644 calibration/poly/polymap2d4.m
delete mode 100644 calibration/refraction_correction.m
delete mode 100644 calibration/search_markers.m
delete mode 100644 calibration/sheet_calibration.m
delete mode 100644 calibration/test_bounding_polygon.m
diff --git a/calibration/automatic_filter.m b/calibration/automatic_filter.m
deleted file mode 100644
index 1eebc87..0000000
--- a/calibration/automatic_filter.m
+++ /dev/null
@@ -1,63 +0,0 @@
-cd%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% automatic_filter.m
-%
-% utility to help find the best filter size for identifying markers
-% for a given image file, the Laplacian of Gaussian method for finding
-% markers will be applied and the centre three markers extracted
-% the filter scale can be adjusted to allow experimentation
-
-addpath([pwd '/../../mex/minmax/']);
-addpath([pwd '/../']);
-addpath([pwd '/pinhole/']);
-addpath([pwd '/../gmv/']);
-
-%% input parameters
-
-grid_dx = 2.54; % real units, mm
-grid_dy = 2.54;
-filter_type = 'disk';
-
-input_file = '/nfs/scratch23/lawson/K62/170203/calib/POS01.cam1.tif';
-
-%% load image
-[~,~,ext] = fileparts(input_file);
-if strcmpi(ext, '.gmv')
- % loading of .gmv movie files
- im = mean(double(read_gmv_matlab(input_file)), 3);
- im = fliplr(im);
-else
- % loading of regular image files
- im = double(imread(input_file));
- im = fliplr(im');
-end
-Ni = size(im, 1);
-Nj = size(im, 2);
-i_axis = 0 : Ni-1;
-j_axis = 0 : Nj-1;
-
-%% loop
-while true
- %% get filter scale
- s_resp = input('Enter filter scale in pixels or q to quit: ', 's');
- if strcmp(s_resp, 'q')
- break;
- end
- blob_scale = str2double(s_resp);
- if isnan(blob_scale)
- continue;
- end
-
- %% filter
- [ij_small,im_filt] = log_find_markers(i_axis, j_axis, im, blob_scale, 1000, filter_type);
- [ij_big,im_filt_big] = log_find_markers(i_axis, j_axis, im, 2*blob_scale, 3, filter_type);
-
- %% plot
- figure(1);
- clf;
- imagesc(i_axis, j_axis, im_filt');
- hold on;
- plot(ij_small(1,:), ij_small(2,:), 'r.');
- plot(ij_big(1,:), ij_big(2,:), 'ro');
- axis equal tight xy;
- colorbar;
-end
\ No newline at end of file
diff --git a/calibration/bounding_polygon.m b/calibration/bounding_polygon.m
deleted file mode 100644
index d3d96dc..0000000
--- a/calibration/bounding_polygon.m
+++ /dev/null
@@ -1,34 +0,0 @@
-function [A,b] = bounding_polygon(corners)
- % [A,b] = bounding_polygon(corners)
- % determine the coefficients of an inequality Ay <= b such that
- %
- % if Ay <= b
- % if polygon is convex:
- % y is inside polygon
- % else
- % y is inside the convex hull of the polygon
- % else
- % y is outside polygon
- %
-
- %% get convex hull
- idx = convhull(corners(1,:)', corners(2,:)');
- corners = corners(:,idx);
-
- %% get coefficients
- n_vert = size(corners,2)-1;
- A = zeros(n_vert,2);
- b = zeros(n_vert,1);
-
- for v = 1 : n_vert
- % get normal of edge from y0 -> y1
- y0 = corners(:, v);
- y1 = corners(:, v+1);
- y01 = y1 - y0;
- n = [y01(2); -y01(1)];
- % we are inside if (y - y0).n <= 0
- % i.e. n.y <= n.y0
- A(v,:)= n';
- b(v) = dot(n, y0);
- end
-end
\ No newline at end of file
diff --git a/calibration/bounding_polyhedron.m b/calibration/bounding_polyhedron.m
deleted file mode 100644
index 33e548c..0000000
--- a/calibration/bounding_polyhedron.m
+++ /dev/null
@@ -1,42 +0,0 @@
-function [A_hull,b_hull] = bounding_polyhedron(tri, vert)
- % [A,b] = bounding_polyhedron(tri,vert)
- %
- % determine the system of linear constraints
- % Ax <= b
- % that describe the inside of the convex polyhedron described by
- % tri and vert. That is, if x lies within the polyhedron, Ax<=b
- %
- % vert is a 3 x n_vert array of vertex coordinates
- % tri is a n_face x 3 array of indices, describing the triangular
- % faces of the polyhedron
-
- n_vert = size(vert, 2);
- n_face = size(tri, 1);
-
- %% determine surface normals
- % vertices of each triangular face
- A = vert(:, tri(:,1));
- B = vert(:, tri(:,2));
- C = vert(:, tri(:,3));
- D = mean(vert, 2);
- DA = bsxfun(@minus, A, D);
- % surface normal
- n = cross(B-A, C-A, 1);
- n = bsxfun(@rdivide, n, sqrt(sum(n.^2,1)));
- % sign of surface normal is outwards positive
- % D is inside polyhedron so (A-D).n >= 0
- S = sign(sum(DA.*n, 1));
- n = bsxfun(@times, S, n);
-
- %% equation of plane: x.n + d = 0
- d = -sum(n.*A, 1);
-
- %% determine linear constraints
- % each equation of plane has been chosen so that
- % n.x + d = 0
- % and if inside
- % n.x + d > 0
- % since n points outwards
- A_hull = n';
- b_hull = -d';
-end
\ No newline at end of file
diff --git a/calibration/bulk_marker_search.m b/calibration/bulk_marker_search.m
deleted file mode 100644
index 89b1d37..0000000
--- a/calibration/bulk_marker_search.m
+++ /dev/null
@@ -1,276 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% bulk_marker_search.m
-% find markers in images of calibration plates
-% then perform a 2D calibration to dewarp the calibration plate image
-
-addpath([pwd '/../mex/minmax/']);
-
-%% input parameters
-
-grid_dx = 10; % real units, mm
-grid_dy = 10;
-invert_image = 1; % invert image for black dots on white
-blur_image = 1; % gaussian blur image
-normalise_image = 1;
-xlim_zoom = 1024*2/4 + [-512 512]/1;
-ylim_zoom = 1024*2/4 + [-512 512]/1;
-
-output_dir = 'D:/bigtank-jul-2013/CAL-20-07-13/NEWSHEET/';
-input_file_list = { 'D:/bigtank-jul-2013/CAL-20-07-13/SHEET-01/PLATE/CAM1_000001.tif', ...
- 'D:/bigtank-jul-2013/CAL-20-07-13/SHEET-02/PLATE/CAM1_000001.tif', ...
- 'D:/bigtank-jul-2013/CAL-20-07-13/SHEET-03/PLATE/CAM1_000001.tif', ...
- 'D:/bigtank-jul-2013/CAL-20-07-13/SHEET-04/PLATE/CAM1_000001.tif', ...
- 'D:/bigtank-jul-2013/CAL-20-07-13/SHEET-05/PLATE/CAM1_000001.tif', ...
- 'D:/bigtank-jul-2013/CAL-20-07-13/SHEET-06/PLATE/CAM1_000001.tif', ...
- 'D:/bigtank-jul-2013/CAL-20-07-13/SHEET-07/PLATE/CAM1_000001.tif', ...
- 'D:/bigtank-jul-2013/CAL-20-07-13/SHEET-08/PLATE/CAM1_000001.tif', ...
- 'D:/bigtank-jul-2013/CAL-20-07-13/SHEET-09/PLATE/CAM1_000001.tif', ...
- 'D:/bigtank-jul-2013/CAL-20-07-13/SHEET-10/PLATE/CAM1_000001.tif'};
-plate_origin = [-24 0
- -20 0
- -16 0
- -12 0
- -8 0
- -4 0
- 0 0
- 4 0
- 8 0
- 12 0]';
-camera_index = 1;
-dot_half_window = [24 24];
-calib_idx_offset = 0;
-origin_pos = [0 0];
-
-camera_clim = [0 1];
-
-%% generate calibration for each image
-
-nCalibrations = length(input_file_list);
-
-calib_idx = 1;
-
-while calib_idx <= nCalibrations
- %% update user of status
- input_file = input_file_list{calib_idx};
- output_file = sprintf('%s/CAM%d-%02d.mat', output_dir, camera_index, calib_idx+calib_idx_offset);
- fprintf('Image %02d of %02d:\n', calib_idx, nCalibrations);
-
- % check if calibration already exists
- if exist(output_file, 'file')
- sInput = input('Calibration already exists - overwrite? y to overwrite: ', 's');
- if ~strcmpi(sInput, 'y')
- calib_idx = calib_idx + 1;
- continue;
- end
- end
- % check if image input exists
- if ~exist(input_file, 'file')
- fprintf(' - could not find image file\n');
- end
-
- %% load image, request position
- im = imread(input_file)';
- im = double(im);
- im = fliplr(im);
- Nx = size(im, 1);
- Ny = size(im, 2);
-
- %% create filtered image for processing
- im_filt = im;
- if invert_image
- im_filt = max(im(:))-im;
- end
- if blur_image
- G = fspecial('gaussian', [5 5]);
- im_filt = filter2(G, im_filt);
- end
- if normalise_image
- [minv, maxv] = minmaxfiltnd(single(im_filt), dot_half_window*2+1);
- im_filt = (im_filt - minv) ./ max(32, maxv-minv);
- end
-
- %% display image
- figure(gcf);
- hold off;
- imagesc(im_filt');
- axis equal tight xy;
- colormap gray;
- set(gca,'Clim', camera_clim);
-
- %% get center, right and top marker position
- bDone = false;
- while ~bDone
- %% get marker position from user
- fprintf('Please identify the origin, right and top markers:\n');
- xlim(xlim_zoom);
- ylim(ylim_zoom);
- [i_coord,j_coord] = ginput(3);
- axis tight;
-
- %% find barycenter of centre marker
- dot_img_i = round(i_coord(1)) + (-dot_half_window(1) : dot_half_window(1));
- dot_img_j = round(j_coord(1)) + (-dot_half_window(2) : dot_half_window(2));
- [imat,jmat] = ndgrid(dot_img_i, dot_img_j);
- dot_img = im_filt(dot_img_i, dot_img_j);
- ij_center(1) = sum(dot_img(:) .* imat(:)) / sum(dot_img(:));
- ij_center(2) = sum(dot_img(:) .* jmat(:)) / sum(dot_img(:));
-
- ij_right = [i_coord(2) j_coord(2)];
- ij_top = [i_coord(3) j_coord(3)];
-
- %% ask user if they want to keep it
- hold on;
- plot_markers = plot( ij_center(1), ij_center(2), 'bs', ...
- ij_right(1), ij_right(2), 'ro', ...
- ij_top(1), ij_top(2), 'gd');
- %legend('Origin', 'Right', 'Top', 'Location', 'SouthOutside');
-
- sDone = input('Enter y if you would like to try again: ', 's');
- bDone = true;
- if strcmpi(sDone,'y')
- bDone = false;
- end
- delete(plot_markers);
- end
-
- %% find marker positions
- [ij_markers, xy_markers, onx_idx, ony_idx] = find_calibration_markers(im_filt, ij_center, ij_right, ij_top, dot_half_window);
- on_x_axis = false(1,size(ij_markers,2));
- on_y_axis = false(1,size(ij_markers,2));
- on_x_axis(onx_idx) = true;
- on_y_axis(ony_idx) = true;
- xy_markers(1,:) = xy_markers(1,:) * grid_dx + origin_pos(1);
- xy_markers(2,:) = xy_markers(2,:) * grid_dy + origin_pos(2);
-
- %% find positions more accurately using barycentres
- Nmarkers = size(ij_markers,2);
- for marker_idx = 1 : Nmarkers
- dot_img_i = round(ij_markers(1,marker_idx)) + (-dot_half_window(1) : dot_half_window(1));
- dot_img_j = round(ij_markers(2,marker_idx)) + (-dot_half_window(2) : dot_half_window(2));
- dot_img_i = dot_img_i(dot_img_i > 1 & dot_img_i < Nx);
- dot_img_j = dot_img_j(dot_img_j > 1 & dot_img_j < Ny);
- dot_img = im_filt(dot_img_i, dot_img_j);
- [imat,jmat] = ndgrid(dot_img_i, dot_img_j);
- ij_markers(1,marker_idx) = sum(dot_img(:).*imat(:)) / sum(dot_img(:));
- ij_markers(2,marker_idx) = sum(dot_img(:).*jmat(:)) / sum(dot_img(:));
- end
-
- %% throw away any markers too near edges
- bad_markers = ij_markers(1,:) < 10 | ij_markers(1,:) > (Nx-10) ...
- | ij_markers(2,:) < 10 | ij_markers(2,:) > (Ny-10);
- xy_markers = xy_markers(:, ~bad_markers);
- ij_markers = ij_markers(:, ~bad_markers);
- on_x_axis = on_x_axis(~bad_markers);
- on_y_axis = on_y_axis(~bad_markers);
-
- %% plot x/y coords of each marker
- n_markers = size(ij_markers, 2);
-
- for idx = 1 : n_markers
- str = sprintf('(%0.0f,%0.0f)', xy_markers(1,idx), xy_markers(2,idx));
- text( 'Position', ij_markers(:,idx) + [-20; -20], ...
- 'String', str, ...
- 'FontSize', 8, ...
- 'Color', 'red');
- end
-
- %% query user for markers to delete
- bDone = false;
- while ~bDone
- figure(gcf);
- plot_markers = plot( ij_markers(1,:), ij_markers(2,:), 'r+', ...
- ij_markers(1,on_x_axis), ij_markers(2,on_x_axis), 'go', ...
- ij_markers(1,on_y_axis), ij_markers(2,on_y_axis), 'gd', ...
- ij_center(1), ij_center(2), 'bs');
- %legend('Markers', 'X axis', 'Y axis', 'Center');
-
- bDone = true;
- sDelete = input('Would you like to delete a marker? y to delete: ', 's');
- if strcmp(sDelete, 'y')
- bDone = false;
- fprintf('Click near the marker you''d like to delete\n');
- [i_delete,j_delete] = ginput(1);
- [idx, distance] = knnsearch(ij_markers', [i_delete,j_delete], 'K', 1);
- if distance > 10
- fprintf('You didn''t click anywhere near a marker!\n');
- continue;
- end
- keep = (1 : size(ij_markers,2)) ~= idx;
- ij_markers = ij_markers(:, keep);
- xy_markers = xy_markers(:, keep);
- on_x_axis = on_x_axis(keep);
- on_y_axis = on_y_axis(keep);
-
- delete(plot_markers);
- end
- end
-
- %% create calibration and dewarp
- xy_to_i_coeffs = cubicfit_2d(xy_markers(1,:), xy_markers(2,:), ij_markers(1,:), 1);
- xy_to_j_coeffs = cubicfit_2d(xy_markers(1,:), xy_markers(2,:), ij_markers(2,:), 1);
- ij_to_x_coeffs = cubicfit_2d(ij_markers(1,:), ij_markers(2,:), xy_markers(1,:), 1);
- ij_to_y_coeffs = cubicfit_2d(ij_markers(1,:), ij_markers(2,:), xy_markers(2,:), 1);
-
- i_fit = cubicmap_2d(xy_markers(1,:), xy_markers(2,:), xy_to_i_coeffs);
- j_fit = cubicmap_2d(xy_markers(1,:), xy_markers(2,:), xy_to_j_coeffs);
- i_error = ij_markers(1,:) - i_fit;
- j_error = ij_markers(2,:) - j_fit;
- px_error = sqrt(i_error.^2 + j_error.^2);
- rms_error = sqrt(mean(px_error.^2));
-
- fprintf('RMS fit error: %0.2f px\n', rms_error);
-
- % work out x and y axes
- [imat,jmat] = ndgrid(1 : 32 : Nx, 1 : 32 : Ny);
- xmat = cubicmap_2d(imat, jmat, ij_to_x_coeffs);
- ymat = cubicmap_2d(imat, jmat, ij_to_y_coeffs);
- x_lim = fix([min(xmat(:)), max(xmat(:))]);
- y_lim = fix([min(ymat(:)), max(ymat(:))]);
- x_axis = linspace(x_lim(1), x_lim(2), Nx);
- y_axis = linspace(y_lim(1), y_lim(2), Ny);
- i_axis = 1 : Nx;
- j_axis = 1 : Ny;
- % calculate dewarping
- [xmat,ymat] = ndgrid(x_axis, y_axis);
- imat = cubicmap_2d(xmat, ymat, xy_to_i_coeffs);
- jmat = cubicmap_2d(xmat, ymat, xy_to_j_coeffs);
- im_xy = interp2(i_axis, j_axis, im', imat, jmat, 'linear');
- hold off;
- imagesc(x_axis, y_axis, im_xy');
- xlim(x_lim);
- ylim(y_lim);
- colormap gray;
- axis xy equal tight;
- hold on;
- set(gca,'Clim', camera_clim);
- for x = (round(x_lim(1)/grid_dx) : round(x_lim(end)/grid_dx)) * grid_dx
- plot([x x], [y_axis(1) y_axis(end)], 'r-');
- end
- for y = (round(y_lim(1)/grid_dy) : round(y_lim(end)/grid_dy)) * grid_dy
- plot([x_axis(1) x_axis(end)], [y y], 'r-');
- end
-
- %% query user to save calibration or repeat
-
- sInput = input('Would you like to repeat the calibration? y to repeat: ', 's');
- if strcmpi(sInput, 'y')
- continue; % continue without incrementing the counter
- end
-
- %% save output
- fprintf('Saving ... ');
- fstruct = struct('source_image', im, ...
- 'dewarped_image', im_xy, ...
- 'plate_pos', plate_origin(:, calib_idx), ...
- 'xy_to_i_coeffs', xy_to_i_coeffs, 'xy_to_j_coeffs', xy_to_j_coeffs, ...
- 'ij_to_x_coeffs', ij_to_x_coeffs, 'ij_to_y_coeffs', ij_to_y_coeffs, ...
- 'i_axis', i_axis, 'j_axis', j_axis, ...
- 'x_axis', x_axis, 'y_axis', y_axis, 'x_lim', x_lim, 'y_lim', y_lim, ...
- 'grid_dx', grid_dx, 'grid_dy', grid_dy, ...
- 'ij_markers', ij_markers, 'xy_markers', xy_markers, ...
- 'ij_center', ij_center, 'ij_right', ij_right, 'ij_top', ij_top, ...
- 'camera_index', camera_index);
- save(output_file, '-struct', 'fstruct');
- fprintf('done\n');
- calib_idx = calib_idx + 1;
-end
\ No newline at end of file
diff --git a/calibration/bundle_calibration_fit.m b/calibration/bundle_calibration_fit.m
deleted file mode 100644
index b04c8a3..0000000
--- a/calibration/bundle_calibration_fit.m
+++ /dev/null
@@ -1,185 +0,0 @@
-function new_setup = bundle_calibration_fit(setup)
- % perform a bundle calibration for a set of cameras and scene
- % information provided in setup
-
- %% extract basic information
- n_cameras = setup.n_cameras;
- n_views = setup.n_views;
-
- %% initialise setup with a regular pinhole fit
- fprintf('initial pinhole fit ... ');
- s_cam_calib = pinhole_camera_fit(setup);
- for c = 1 : n_cameras
- setup.camera(c).calib = s_cam_calib(c);
- end
- fprintf('done\n');
-
- %% initialise perturbation model
- setup.omega = eye(3);
- setup.gl_plate_dx = zeros(3, n_views);
-
- %% fill arrays for least squares fitting
- % uv_data should be n_total x 4 array of [u, v, cam, view]
- % ij_data should be n_total x 2 array of [i, j] position of markers
- n_total = 0;
- uv_data = [];
- ij_data = [];
- for c = 1 : n_cameras
- camera = setup.camera(c);
- for v = 1 : n_views
- %% extract marker position in plate coord sys and image
- % xdata contains (u,v) coord of marker on plate
- % as well as the index of the plate position and camera
- % that the projection of this marker in image space corresponds to
- uv_mark = camera.view(v).uv_markers;
- ij_mark = camera.view(v).ij_markers;
- n_mark = size(uv_mark,2);
-
- uv_mark = [uv_mark; c*ones(1,n_mark); v*ones(1,n_mark)];
- uv_data = [uv_data; uv_mark'];
- ij_data = [ij_data; ij_mark'];
- n_total = n_total + n_mark;
- end
- end
-
- %% least squares fitting operation
- fprintf('doing bundle fit ...');
- X0 = pack_model(setup);
- NX = length(X0);
- opts = optimset('MaxFunEvals', NX*10000, 'MaxIter', 10000, 'Display', 'on');
- X = lsqcurvefit(@(X,xdata) bundle_model(X,xdata,setup), X0, uv_data, ij_data, [], [], opts);
- new_setup = unpack_model(X, setup);
- fprintf('done\n');
-end
-
-function ij_selected = bundle_model(perturb_vec, uv_mark, setup)
- % given a perturbation (packed into perturbation vector)
- % project markers with perturbed model
- setup = unpack_model(perturb_vec, setup);
-
- %% perturbed position and orientation of plate
- gl_plate_pos = setup.gl_plate_pos + setup.gl_plate_dx;
- pl_to_gl = setup.omega * setup.pl_to_gl;
-
- %% project markers
- n_cameras = setup.n_cameras;
- n_markers = size(uv_mark, 1);
- cam = uv_mark(:, 3);
- view = uv_mark(:, 4);
- % gl_mark and pl_mark are 3 x n_markers matrices
- pl_mark = [uv_mark(:, 1:2), zeros(n_markers,1)]';
- gl_mark = pl_to_gl*pl_mark + gl_plate_pos(:, view);
-
- % project markers in *every* camera
- ij_mark = zeros(2, n_markers, n_cameras);
- for c = 1 : n_cameras
- ij_mark(:,:,c) = pinhole_model(gl_mark, setup.camera(c).calib);
- end
-
- % return only the appropriate projections, according to uv_mark(:,3)
- ij_selected = zeros(n_markers, 2);
- ij_selected(:,1) = ij_mark(sub2ind([2 n_markers n_cameras], 1*ones(n_markers,1), (1:n_markers)', cam));
- ij_selected(:,2) = ij_mark(sub2ind([2 n_markers n_cameras], 2*ones(n_markers,1), (1:n_markers)', cam));
-end
-
-function X = pack_model(setup)
- % function to pack a representation of the internal
- % camera and scene model into a vector for least squares optimisation
- % routine
-
- n_cameras = setup.n_cameras;
- n_views = setup.n_views;
-
- %% camera model
- X_cam = zeros(15, n_cameras);
- for c = 1 : n_cameras
- calib = setup.camera(c).calib;
- G = calib.G;
- R = calib.R;
- r_vec = vrrotmat2vec(R);
- r_vec = r_vec(1:3) * r_vec(4);
- xc = calib.xc;
- kr = calib.kr;
- ic = calib.ic;
-
- i_axis = setup.camera(c).i_axis;
- j_axis = setup.camera(c).j_axis;
- diag_px = sqrt((i_axis(end)-i_axis(1))^2 + (j_axis(end)-j_axis(1))^2);
-
- X_cam(1,c) = G(1,1);
- X_cam(2,c) = G(1,2);
- X_cam(3,c) = G(1,3) / diag_px;
- X_cam(4,c) = G(2,2);
- X_cam(5,c) = G(2,3) / diag_px;
- X_cam(6:8,c) = r_vec(:);
- X_cam(9:11,c) = xc(:);
- % dewarping parameters are normalised to dimensionless quantities,
- % scaled by sensor size in px
- X_cam(12:13,c) = [kr(1)*diag_px^2; kr(2)*diag_px^4];
- X_cam(14:15,c) = ic(:) / diag_px;
- end
-
- %% plate perturbation model
- % plate perturbation consists of a displacement and rotation
- X_plate = setup.gl_plate_dx([1 3], :);
- omega_vec = vrrotmat2vec(setup.omega);
- X_rot = omega_vec(1:3) * omega_vec(4);
-
- X = [X_cam(:); X_plate(:); X_rot(:)]';
-end
-
-function setup = unpack_model(X, setup)
- % extract the perturbation model specified in X
- % and update setup structure
- n_views = setup.n_views;
- n_cameras = setup.n_cameras;
-
- %% extract
- off = 1;
- X_cam = reshape(X(off : off-1 + n_cameras*15), [15 n_cameras]);
- off = off + n_cameras*15;
- X_plate = reshape(X(off : off-1 + n_views*2), [2 n_views]);
- off = off + n_views*2;
- X_rot = X(off : off + 2);
-
- %% update camera parameters
- for c = 1 : n_cameras
- i_axis = setup.camera(c).i_axis;
- j_axis = setup.camera(c).j_axis;
- diag_px = sqrt((i_axis(end)-i_axis(1))^2 + (j_axis(end)-j_axis(1))^2);
-
- % intrinsic parameters
- fx = X_cam(1,c);
- tau = X_cam(2,c);
- ox = X_cam(3,c) * diag_px;
- fy = X_cam(4,c);
- oy = X_cam(5,c) * diag_px;
- G = [fx tau ox;
- 0 fy oy;
- 0 0 1];
- % rotation
- r_vec = X_cam(6:8,c);
- R = vrrotvec2mat([r_vec(:)/(eps+norm(r_vec)); norm(r_vec)]);
-
- % position
- xc = X_cam(9:11,c);
-
- % dewarping parameters are normalised to dimensionless quantities,
- % scaled by sensor size in px
- kr = [X_cam(12,c)/diag_px^2; X_cam(13,c)/diag_px^4]; % undo normalisation
- ic = X_cam(14:15,c) * diag_px;
-
- % full projection model
- P = G*R*[eye(3), -xc(:)];
-
- calib = struct('P', P, 'G', G, 'R', R, 'xc', xc, 'kr', kr, 'ic', ic);
- setup.camera(c).calib = calib;
- end
-
- %% update plate position parameters
- omega_vec = [X_rot(:)/(eps+norm(X_rot)); norm(X_rot)];
- setup.omega = vrrotvec2mat(omega_vec);
- setup.gl_plate_dx = [X_plate(1,:); zeros(1,n_views); X_plate(2,:)];
-end
-
-
diff --git a/calibration/calcmask.m b/calibration/calcmask.m
deleted file mode 100644
index 3f2f086..0000000
--- a/calibration/calcmask.m
+++ /dev/null
@@ -1,107 +0,0 @@
-function mask = calcmask(camset, c0)
- % mask = calcmask(camset, c)
- %
- % for each pixel in camera c of camset, determine the number
- % of other cameras its epipolar ray is visible in
-
- %% get things
- n_cameras= length(camset);
- cam0 = camset(c0);
- Ni = length(cam0.i_axis);
- Nj = length(cam0.j_axis);
- Npx = Ni*Nj;
-
- %% dewarp
- [im,jm] = ndgrid(cam0.i_axis, cam0.j_axis);
- ui_pos = inverse_radial_distortion([im(:),jm(:)]', cam0.calib.ic, cam0.calib.kr);
-
- %% get equation of ray
- % we write the equation of ray as
- % x = x0 + s*e_s
- ytilde = [ui_pos; ones(1,Npx)];
- GR = cam0.calib.G * cam0.calib.R;
- e_ray = GR \ ytilde;
- x0_ray = cam0.calib.xc;
-
- %% determine mask
- mask = zeros(Ni, Nj);
- for c1 = 1 : n_cameras
- %% skip if same camera
- if c0 == c1
- continue;
- end
-
- %% find linear inequality for bounds test
- % the test for whether a point y is within the bounds of image space
- % of this camera is described as an inequality Ay <= b. If satisfied,
- % the point is within bounds, if not, it is outside bounds. we
- % construct this inequality based on borders of image in undistorted
- % image space
- cam1 = camset(c1);
- i_axis = cam1.i_axis(:);
- j_axis = cam1.j_axis(:);
- Ni1 = length(i_axis);
- Nj1 = length(j_axis);
- ir = [1 : floor(Ni1/4) : Ni1, Ni1];
- jr = [1 : floor(Nj1/4) : Nj1, Nj1];
- i_corners = [i_axis(ir([1 1 1 1 1]));
- i_axis(ir );
- i_axis(ir([5 5 5 5 5]));
- i_axis(ir(end:-1:1 ))];
- j_corners = [j_axis(jr );
- j_axis(jr([5 5 5 5 5]));
- j_axis(jr(end:-1:1 ));
- j_axis(jr([1 1 1 1 1]))];
- di_corners = [i_corners'; j_corners'];
- ui_corners = inverse_radial_distortion(di_corners, cam1.calib.ic, cam1.calib.kr);
- [A,b] = bounding_polygon(ui_corners);
-
- %% get equation of ray in image coordinates
- % we want an equation of the form
- % y = y0 + s * n
- % that describes the equation of each ray from camera 0 in the
- % (undistorted) image space of camera 1
-
- GR1 = cam1.calib.G * cam1.calib.R;
- z0 = GR1(1:2,:)*(x0_ray - cam1.calib.xc);
- zprime = GR1(1:2,:)*e_ray;
- lambda0 = GR1( 3,:)*(x0_ray - cam1.calib.xc);
- lprime = GR1( 3,:)*e_ray;
-
- y0 = bsxfun(@rdivide, zprime, lprime);
- e_n = bsxfun(@minus, z0, lambda0*y0);
- e_n = bsxfun(@rdivide, e_n, sqrt(sum(e_n.^2,1))); % normalise
-
- %% determine existence of solutions for inequality
- % subsituting in y = y0 + s*e_n
- % into our inequality Ay <= b
- % yields the inequality
- % L*s <= R
- % where L = A*e_n and R = b - A*y0
- % if this inequality has solutions for any s, then there is at least
- % one point on the ray which is visible in camera 1
-
- L = A*e_n;
- R = bsxfun(@minus, b, A*y0);
- gamma_lim = R ./ L;
- is_ub = L >= 0;
- is_lb = L < 0;
-
- % find smallest upper bound
- gamma_ub = gamma_lim;
- gamma_ub(is_lb) = inf; % ignore all those lower bounds
- gamma_ub = min(gamma_ub, [], 1);
-
- % find largest lower bound
- gamma_lb = gamma_lim;
- gamma_lb(is_ub) = -inf; % ignore all the upper bounds
- gamma_lb = max(gamma_lb, [], 1);
-
- b_visible = gamma_lb <= gamma_ub;
-
- %% add to sum
- b_visible = reshape(b_visible, [Ni Nj]);
- mask = mask + b_visible;
- end
-end
-
\ No newline at end of file
diff --git a/calibration/calibration_to_config.m b/calibration/calibration_to_config.m
deleted file mode 100644
index 307ea70..0000000
--- a/calibration/calibration_to_config.m
+++ /dev/null
@@ -1,135 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% based on a camera calibration performed in MATLAB using John Lawson's
-% code, create a corresponding .cfg file for use with Haitao Xu's PTV code
-%
-
-addpath([pwd '/../calibration-mpids/']);
-
-%% input parameters
-calib_dir = '/data/cluster/160323/';
-calib_file_mat = [calib_dir '/camera-model-160323.mat'];
-calib_file_cfg = [calib_dir '/PTVSetup_160323.cfg'];
-points_file_pat = [calib_dir '/calibpointspos.cam%d.dat'];
-
-pixel_pitch_mm = [10E-3 10E-3];
-
-%% load calibration
-fs = importdata(calib_file_mat);
-n_cameras = fs.n_cameras;
-camera = fs.camera;
-
-%% calculate Tsai camera calibration
-fprintf('Calculating Tsai calibration ... ');
-
-for c = 1 : n_cameras
- %% load parameters
- i_axis = camera(c).i_axis;
- j_axis = camera(c).j_axis;
- Ni = length(i_axis);
- Nj = length(j_axis);
- cparams = struct('Npixh', Nj, 'Npixw', Ni, ...
- 'hpix', pixel_pitch_mm(1), ...
- 'wpix', pixel_pitch_mm(2));
-
- %% load markers and calculate residuals
- gl_markers = camera(c).calib.gl_markers;
- ij_markers = camera(c).calib.ij_markers;
- ij_markers_proj = pinhole_model(gl_markers, camera(c).calib, true);
- model_res = ij_markers - ij_markers_proj;
-
- %% generate Tsai calibration
- % Tsai calibration uses a different coordinate system for image plane
- % in our calibration, (i,j) corresponds to x,y coordinate, as measured
- % from the bottom left corner. ij the Tsai calibration, (i,j)
- % coordinates start from the top left corner and move down and across.
- ij_markers_perm = [ij_markers(1,:);
- Nj-1 - ij_markers(2,:)];
-
- [tsai_calib, tsai_res] = calib_Tsai(ij_markers_perm', gl_markers', cparams);
- tsai_res = tsai_res';
- camera(c).tsai_calib = tsai_calib;
-
- %% save calibpointspos file
- points_file = sprintf(points_file_pat, c - 1);
- fid = fopen(points_file, 'w');
- fprintf(fid, '# pimg_x (pix)\t pimg_y (pix) \t x (mm) \t y (mm) \t z (mm) \t Np \t sum(x) \t sum(y) \t sum(I) \n');
- fprintf(fid, '%12.7f\t%12.7f\t%12.7f\t%12.7f\t%12.7f\t0\n', [ij_markers_perm; gl_markers]);
- fclose(fid);
-
- %% generate statistics of error
- model_rms = rms(model_res, 2);
- tsai_rms = rms(tsai_res, 2);
-
- %% report on error
- fprintf('Camera %d\n', c);
- fprintf(' Regular: %0.3f %0.3f\n', model_rms);
- fprintf(' Tsai: %0.3f %0.3f\n', tsai_rms);
-end
-fprintf('done\n');
-
-%% write config file
-fprintf('Saving to file ... ');
-
-% Now write the configuration file
-fid = fopen(calib_file_cfg, 'w');
-fprintf(fid, '# PTV experiment configuration file\n');
-fprintf(fid, '\n %d\t# ncams\n\n', n_cameras);
-for c = 1 : n_cameras
- %% load info
- tsai_calib = camera(c).tsai_calib;
- i_axis = camera(c).i_axis;
- j_axis = camera(c).j_axis;
- Ni = length(i_axis);
- Nj = length(j_axis);
-
- %% write to file
- fprintf(fid, '######## cam %d ########\n\n', c - 1);
- fprintf(fid, '%d\t\t\t# Npix_x\n', Ni);
- fprintf(fid, '%d\t\t\t# Npix_y\n', Nj);
- fprintf(fid, '%11.8f\t\t\t# pixsize_x (mm)\n', pixel_pitch_mm(1));
- fprintf(fid, '%11.8f\t\t# pixsize_y (mm)\n', pixel_pitch_mm(1));
- fprintf(fid, '%12.8f\t\t# effective focal length (mm)\n', tsai_calib.f_eff);
- % Note the sign change for k1, because its meaning is different in calib_Tsai and the stereomatching code
- fprintf(fid, '%15.8e\t# radial distortion k1 (1/pixel)\n', -tsai_calib.k1);
- fprintf(fid, '%15.8e\t# tangential distortion p1 (1/pixel)\n', tsai_calib.p1);
- fprintf(fid, '%15.8e\t# tangential distortion p2 (1/pixel)\n', tsai_calib.p2);
- fprintf(fid, '0.0\t\t# x0\n');
- fprintf(fid, '0.0\t\t# y0\n');
- % Parameters for camera movement correction
- fprintf(fid, '0.0\t\t# x0_offset (pixel)\n');
- fprintf(fid, '0.0\t\t# y0_offset (pixel)\n');
- fprintf(fid, '1.0\t\t# x_rot (cos(theta))\n');
- fprintf(fid, '0.0\t\t# y_rot (sin(theta))\n');
- fprintf(fid, '# rotation matrix R\n');
- fprintf(fid, '%12.8f\t%12.8f\t%12.8f\n', (tsai_calib.R)');
- fprintf(fid, '# translation vector T\n');
- fprintf(fid, '%15.8f\n', tsai_calib.T);
- fprintf(fid, '# inverse rotation matrix Rinv\n');
- fprintf(fid, '%12.8f\t%12.8f\t%12.8f\n', (tsai_calib.Rinv)');
- fprintf(fid, '# inverse translation vector Tinv\n');
- fprintf(fid, '%15.8f\n', tsai_calib.Tinv);
- fprintf(fid, '# rms distance between particle centers found on image plane and their projections\n');
- fprintf(fid, '# %15.8f\t\t# err_x\n', tsai_calib.err_x);
- fprintf(fid, '# %15.8f\t\t# err_y\n', tsai_calib.err_y);
- fprintf(fid, '# %15.8f\t\t# err_t\n', tsai_calib.err_t);
- fprintf(fid, '\n');
-end
-% laser beam parameters
-fprintf(fid, '##### laser beam #####\n');
-fprintf(fid, '\n');
-fprintf(fid, '0\t\t# finite volume illumination\n');
-fprintf(fid, '-50\t\t# illum_xmin\n');
-fprintf(fid, '+50\t\t# illum_xmax\n');
-fprintf(fid, '-50\t\t# illum_ymin\n');
-fprintf(fid, '+50\t\t# illum_ymax\n');
-fprintf(fid, '-50\t\t# illum_zmin\n');
-fprintf(fid, '+50\t\t# illum_zmax\n');
-fprintf(fid, '\n');
-% 3D matching parameters
-fprintf(fid, '#### thresholds for 3D matching ####\n');
-fprintf(fid, '\n');
-fprintf(fid, '3.0\t\t# mindist_pix (pixel)\n');
-fprintf(fid, '1.0\t\t# maxdist_3D (mm)\n');
-fclose(fid);
-
-fprintf('done\n');
\ No newline at end of file
diff --git a/calibration/calpts2mat.m b/calibration/calpts2mat.m
deleted file mode 100644
index e8a089f..0000000
--- a/calibration/calpts2mat.m
+++ /dev/null
@@ -1,82 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% load a text file from Jan/Holly's calibration
-% containing indices/image coordinates of calibration points
-% and convert this to the common .MAT file format
-
-addpath([pwd '/pinhole/']);
-
-%% input parameters
-calib_dir = '/nfs/scratch21/lawson/zugspitze/calib/';
-mat_file_pat = '%s/CAM%d-%02d.mat';
-calpts_file_pat = '%s/CalibPts%d.txt';
-
-camera_index = 3;
-camera_id = camera_index - 1;
-grid_dx = 0.5; % real units, mm
-grid_dy = 0.5;
-
-img_width = 1280;
-img_height = 800;
-i_axis = 0 : 1 : img_width - 1;
-j_axis = 0 : 1 : img_height - 1;
-
-DO_DEBUG_PLOT = true;
-
-%% load calibration points file
-fname = sprintf(calpts_file_pat, calib_dir, camera_id);
-C = textread(fname);
-gl_markers = C(:, 1:3)';
-ij_markers = C(:, [5 4])';
-
-%% get plate pos from z
-plate_pos = unique(gl_markers(3,:))';
-n_views = length(plate_pos);
-plate_pos = [zeros(n_views,2), plate_pos];
-
-%% iterate over planes
-for calib_idx = 1 : n_views
- %% load subset belonging to this plane
- rng = gl_markers(3,:) == plate_pos(calib_idx, 3);
- xy_mark = gl_markers(1:2, rng);
- ij_mark = ij_markers(:, rng);
- gl_plate_pos = plate_pos(calib_idx,:);
-
- %% fit pinhole
- [P2D, ij_res] = p2d_fit(xy_mark, ij_mark);
- ij_rms = rms(ij_res, 2);
-
- %% axes and limits
- x_lim = [min(xy_mark(1,:)), max(xy_mark(1,:))];
- y_lim = [min(xy_mark(2,:)), max(xy_mark(2,:))];
- Nx = img_width;
- Ny = img_height;
- x_axis = linspace(x_lim(1), x_lim(2), Nx);
- y_axis = linspace(y_lim(1), y_lim(2), Ny);
-
- %% plot
- if DO_DEBUG_PLOT
- figure(1);
- clf;
- plot3(xy_mark(1,:), xy_mark(2,:), ij_mark(1,:), 'b.');
- hold on;
- plot3(xy_mark(1,:), xy_mark(2,:), ij_mark(2,:), 'r.');
- end
-
- %% save
- output_file = sprintf(mat_file_pat, calib_dir, camera_index, calib_idx);
- fprintf('Saving to %s... ', output_file);
- fstruct = struct('plate_pos', gl_plate_pos, ...
- 'P', P2D, ...
- 'i_axis', i_axis, 'j_axis', j_axis, ...
- 'x_axis', x_axis, 'y_axis', y_axis, 'x_lim', x_lim, 'y_lim', y_lim, ...
- 'grid_dx', grid_dx, 'grid_dy', grid_dy, ...
- 'ij_markers', ij_mark, 'xy_markers', xy_mark, ...
- 'camera_index', camera_index, ...
- 'id', camera_id);
- save(output_file, '-struct', 'fstruct');
- fprintf('done\n');
-
- %% report
- fprintf('2D fit plane %d:\n', calib_idx);
- fprintf(' RMS: %0.3f %0.3f px\n', ij_rms);
-end
\ No newline at end of file
diff --git a/calibration/check_calibration.m b/calibration/check_calibration.m
deleted file mode 100644
index ccc4b0b..0000000
--- a/calibration/check_calibration.m
+++ /dev/null
@@ -1,120 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%
-% check calibration by loading and dewarping particle images
-%
-
-addpath([pwd '/../reconstruction/']);
-
-%% input parameters
-
-omega_y_mrad = -40;
-
-% image to dewarp and plot
-cam_index = 2;
-figure_index = 100*cam_index;
-sheet_index_range = 20;%[11:14];
-volume_index = 1;
-series_index = 1;
-
-n_sheets = length(sheet_index_range);
-
-% calibration
-calibration_file = 'F:/bigtank-jul-2013/CAL-06-08-13/scanning-setup.mat';
-
-% image processing options
-DO_IMAGE_FLIPUPDOWN = 0;
-DO_IMAGE_FLIPLEFTRIGHT = 0;
-DO_DARK_IMAGE = 1;
-image_clim = [0 4];
-
-% path of files to reconstruct
-source_img_dir = 'F:/bigtank-jul-2013/MEAS-06-08-13/SCAN-05/';
-dark_img_dir = 'F:/bigtank-jul-2013/DARK/MEAS-06-08-13/SCAN-05/';
-source_img_file_pat = 'CAMID%d-series-%02d-volume-%02d-frame-%02d.raw12';
-dark_img_file_pat = 'CAMID%d-frame-%02d.raw12';
-
-source_file_name_fun = @(camid,series,volume,frame) sprintf(['%s/' source_img_file_pat], source_img_dir, camid, series, volume, frame);
-dark_file_name_fun = @(camid,frame) sprintf(['%s/' dark_img_file_pat], dark_img_dir, camid, frame);
-
-%% load the calibration data
-fprintf('------------------------------------------------------------------\n');
-fprintf('CALIBRATION:\n');
-
-fstruct = importdata(calibration_file);
-% camera id
-camera_id = fstruct.camera_id(cam_index);
-% coordinate system and axes
-volume_x = fstruct.X;
-volume_y = fstruct.Y;
-volume_z = fstruct.Z;
-volume_Lx = volume_x(end) - volume_x(1);
-volume_Ly = volume_y(end) - volume_y(1);
-volume_Lz = volume_z(end) - volume_z(1);
-volume_Nx = length(volume_x);
-volume_Ny = length(volume_y);
-volume_Nz = length(volume_z);
-% camera mapping functions
-sCamCalib = fstruct.camera_calibration(cam_index);
-re_plane_coeffs = fstruct.re_plane_coeffs;
-% image coordinate system
-im_i = fstruct.i;
-im_j = fstruct.j;
-
-
-%% modify plane normal
-
-re_plane_normal = re_plane_coeffs(:, 1:3);
-Omega = vrrotvec2mat([0 1 0 omega_y_mrad/1000]);
-re_plane_normal = (Omega*re_plane_normal')';
-re_plane_coeffs(:,1:3) = re_plane_normal;
-
-%% read the images from file
-fprintf(' --- loading images ... ');
-source_file_names = arrayfun(source_file_name_fun, ones(1,n_sheets)*camera_id, ...
- ones(1,n_sheets)*series_index, ...
- ones(1,n_sheets)*volume_index, ...
- sheet_index_range, 'UniformOutput', false);
-[imgvol, fail] = load_image_array(source_file_names, DO_IMAGE_FLIPUPDOWN, DO_IMAGE_FLIPLEFTRIGHT);
-
-if DO_DARK_IMAGE
- dark_file_names = arrayfun( dark_file_name_fun, ones(1, n_sheets)*camera_id, ...
- sheet_index_range, 'UniformOutput', false);
- darkvol = load_image_array(dark_file_names, DO_IMAGE_FLIPUPDOWN, DO_IMAGE_FLIPLEFTRIGHT);
- % subtract dark images
- imgvol = max(imgvol - darkvol, 0);
-end
-
-fprintf('done\n');
-
-%% dewarp each image
-% back-projection onto planes with regular x and y
-% (z coordinate determined by plane of laser sheet)
-imgvol_dewarped = zeros(volume_Nx, volume_Ny, n_sheets);
-
-[xmat, ymat] = ndgrid(volume_x, volume_y);
-
-for it = 1 : n_sheets
- %% calculate z coords on this plane and get image coords
- sheet_index = sheet_index_range(it);
- M = re_plane_coeffs(sheet_index, :);
- zmat = -(M(4) + M(1)*xmat + M(2)*ymat) / M(3);
- xyzmat = [xmat(:) ymat(:) zmat(:)]';
- ij = pinhole_model(xyzmat, sCamCalib);
-
- %% load image and interpolate
- im = imgvol(:, :, it);
- im_int = interp2(im_i, im_j, im', ij(1,:), ij(2,:), 'linear', nan);
- im_int = reshape(im_int, [volume_Nx volume_Ny]);
- imgvol_dewarped(:,:,it) = im_int;
-end
-
-%% display
-
-for it = 1 : n_sheets
- figure(figure_index + sheet_index_range(it));
- hold off;
- im = imgvol_dewarped(:,:,it);
- imagesc(volume_x, volume_y, im' / rms(im(:)));
- axis equal tight xy;
- set(gca,'clim',image_clim);
-end
diff --git a/calibration/combine_calibrations.m b/calibration/combine_calibrations.m
deleted file mode 100644
index 493c4dc..0000000
--- a/calibration/combine_calibrations.m
+++ /dev/null
@@ -1,309 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% combine_c.m
-%
-% create a pinhole camera calibration for one or more cameras
-% from a set of views of the same calibration plate in different positions
-% a compensation is made for error in calibration plate position and
-% orientation
-
-addpath([pwd '/pinhole/']);
-
-%% input parameters
-
-calib_dir = 'D:/bigtank-jul-2013/CAL-20-07-13/NEWSHEET/';
-cam_model_file = [calib_dir '/camera-model-280415.mat'];
-sheet_model_file = [calib_dir '/sheet-model-280415.mat'];
-setup_file = [calib_dir '/scanning-setup-280415.mat'];
-poly_order = 5;
-
-%% load camera calibration
-
-fstruct = importdata(cam_model_file);
-n_cameras = fstruct.n_cameras;
-camera_setup = fstruct.camera;
-im_i = camera_setup(1).i_axis;
-im_j = camera_setup(1).j_axis;
-
-%% load laser sheet calibration
-
-fstruct = importdata(sheet_model_file);
-n_sheets = fstruct.n_sheets;
-gl_eZ_mat = fstruct.gl_sheet_normal;
-gl_xs_mat = fstruct.gl_sheet_pos;
-gl_on_sheet = fstruct.gl_on_sheet;
-w_vec = fstruct.sheet_width;
-
-%% define orientation of [X,Y,Z] coordinate system
-
-% orentation of reconstruction coordinate system in global system
-gl_eZ = mean(gl_eZ_mat, 1);
-gl_eX = cross([0 1 0]', gl_eZ);
-gl_eX = gl_eX / norm(gl_eX);
-gl_eY = cross(gl_eZ, gl_eX);
-
-% transfer matrices for gl->re and re->gl orientation
-re_to_gl = [gl_eX(:), gl_eY(:), gl_eZ(:)];
-gl_to_re = re_to_gl^-1;
-
-% origin of reconstruction coordinate system
-gl_X0 = [0 0 0]'; % origin in gl coordinates
-re_X0 = gl_to_re * gl_X0; % origin in re
-re_eZ_mat = (gl_to_re * gl_eZ_mat')';
-re_xs_mat = (gl_to_re * (gl_xs_mat - ones(n_sheets,1)*gl_X0(:)')')';
-
-%% calculate the plane coefficients
-% these are a set of 4 coefficients per plane, M(1:4)
-% all points [X,Y,Z] on this plane satisfy XM(1) + YM(2) + ZM(3) + M(4) = 0
-
-re_plane_coeffs = zeros(n_sheets, 4);
-
-for sheet_idx = 1:n_sheets
- % get equation of plane dot(M,X) = 1
- gl_sheet_xs = gl_xs_mat(sheet_idx, :)';
- gl_sheet_en = gl_eZ_mat(sheet_idx, :)';
- re_sheet_xs = gl_to_re * (gl_sheet_xs - gl_X0);
- re_sheet_en = gl_to_re * gl_sheet_en;
- re_sheet_en = re_sheet_en / norm(re_sheet_en);
- % save to matrix
- re_plane_coeffs(sheet_idx,1:3) = re_sheet_en;
- re_plane_coeffs(sheet_idx,4) = -dot(re_sheet_xs, re_sheet_en);
-end
-
-% smooth out errors by replacing measured value with fitted value
-k = 0 : n_sheets - 1;
-re_plane_coeffs_o = re_plane_coeffs;
-for i = 1:4
- p = polyfit(k, re_plane_coeffs(:,i)', poly_order);
- re_plane_coeffs(:,i) = polyval(p, k);
-end
-% fix coefficients so M(1:3) is a unit vector
-for i = 1 : n_sheets
- en = re_plane_coeffs(i,1:3);
- d = re_plane_coeffs(i,4);
- re_plane_coeffs(i,:) = [en d] / norm(en);
-end
-
-sheet_spacing = polyfit(k, re_plane_coeffs(:,4)', 1);
-sheet_spacing = abs(sheet_spacing(1));
-
-%% convert camera calibration to reconstruction coordinate system
-% incorporates shift of origin and rotation
-
-for cam_idx = 1 : n_cameras
- gl_pinhole_model = camera_setup(cam_idx).calib;
- gl_G = gl_pinhole_model.G;
- gl_R = gl_pinhole_model.R;
- gl_xc = gl_pinhole_model.xc;
- re_R = gl_R * re_to_gl;
- re_xc = gl_to_re * (gl_xc(:) - gl_X0(:));
- re_P = gl_G * re_R * [eye(3) -re_xc];
-
- re_pinhole_model = gl_pinhole_model;
- re_pinhole_model.P = re_P;
- re_pinhole_model.R = re_R;
- re_pinhole_model.xc = re_xc;
- camera_setup(cam_idx).calib = re_pinhole_model;
- camera_setup(cam_idx).gl_calib= gl_pinhole_model;
-end
-
-%% calculate axes for reconstruction volume
-% with knowledge of each camera's pinhole model
-% and the laser sheet setup
-% we can define axes for the reconstruction volume
-
-X_lim_mat = zeros(n_cameras, 2);
-Y_lim_mat = zeros(n_cameras, 2);
-Z_lim_mat = zeros(n_cameras, 2);
-
-ilim = [im_i(3) im_i(end-2)];
-jlim = [im_j(3) im_j(end-2)];
-
-% report on what we are about to do
-fprintf('------------------------------------------------------------------\n');
-fprintf('CAMERA CALIBRATION\n');
-fprintf('Number of cameras: %d\n', n_cameras);
-
-for cam_idx = 1 : n_cameras
- %% find the extent of the visible volume
- % the largest possible volume, bound by first and last laser sheets
- % which projects onto the space between ilim(1) < i < ilim(2)
- % and jlim(1) < j < lim(2)
- M1 = re_plane_coeffs(1, :);
- Mend = re_plane_coeffs(end, :);
- camera_calib = camera_setup(cam_idx).calib;
- P = camera_calib.P;
- [XLim,YLim,ZLim] = find_largest_volume(P, ilim, jlim, M1, Mend);
- % add a little extra bit of space in reconstruction around edges in z
- % direction
- ZLim = ZLim + sheet_spacing*[-2 2]';
- % save
- X_lim_mat(cam_idx,:)= XLim;
- Y_lim_mat(cam_idx,:)= YLim;
- Z_lim_mat(cam_idx,:)= ZLim;
-
- %% report for this camera
- fprintf('Camera %d, ID = %d\n', cam_idx, camera_setup(cam_idx).id);
- fprintf('X limits: %0.1f to %0.1f\n', XLim(1), XLim(2));
- fprintf('Y limits: %0.1f to %0.1f\n', YLim(1), YLim(2));
- fprintf('Z limits: %0.1f to %0.1f\n', ZLim(1), ZLim(2));
- fprintf('Camera position: %0.2f %0.2f %0.2f\n', camera_calib.xc);
- fprintf('Internal camera parameters:\n');
- disp(camera_calib.G);
-end
-
-%% define a coordinate system visible to all cameras
-X_lim = [max(X_lim_mat(:,1)) min(X_lim_mat(:,2))];
-Y_lim = [max(Y_lim_mat(:,1)) min(Y_lim_mat(:,2))];
-Z_lim = [max(Z_lim_mat(:,1)) min(Z_lim_mat(:,2))];
-% fix resolution
-re_dY = (Y_lim(2) - Y_lim(1)) / (jlim(2) - jlim(1));
-re_dX = re_dY;
-re_dZ = re_dX;
-re_X = X_lim(1) : re_dX : X_lim(2);
-re_Y = Y_lim(1) : re_dY : Y_lim(2);
-re_Z = Z_lim(1) : re_dZ : Z_lim(2);
-
-fprintf('------------------------------------------------------------------\n');
-fprintf('Combined calibration:\n');
-fprintf('X limits: %0.1f to %0.1f (%d vox)\n', X_lim(1), X_lim(2), length(re_X));
-fprintf('Y limits: %0.1f to %0.1f (%d vox)\n', Y_lim(1), Y_lim(2), length(re_Y));
-fprintf('Z limits %0.1f to %0.1f (%d vox)\n', Z_lim(1), Z_lim(2), length(re_Z));
-fprintf('voxel size: %0.2f x %0.2f x %0.2f\n', re_dX, re_dY, re_dZ);
-
-%% save to file
-
-fprintf('------------------------------------------------------------------\n');
-fprintf('Multiple camera calibration complete\n');
-fprintf('Saving to file ... ');
-
-fstruct = struct('n_sheets', n_sheets, ...
- 'gl_sheet_normal', gl_eZ_mat, 're_sheet_normal', re_eZ_mat, ...
- 'gl_sheet_pos', gl_xs_mat, 're_sheet_pos', re_xs_mat, ...
- 're_plane_coeffs', re_plane_coeffs, ...
- 'sheet_width', w_vec, ...
- 'gl_X0', gl_X0, ...
- 'gl_to_re', gl_to_re, ...
- 'i', im_i, 'j', im_j, ...
- 'X', re_X, 'Y', re_Y, 'Z', re_Z, ...
- 'n_cameras', n_cameras, ...
- 'camera_calibration', [camera_setup.calib], ...
- 'camera_id', [camera_setup.id]);
-save(setup_file, '-struct', 'fstruct');
-fprintf('done\n');
-
-%% plot residuals of fit for sheet coefficients
-figure(1);
-clf;
-plot(k, re_plane_coeffs_o(:,1) - re_plane_coeffs(:,1), 'r.', ...
- k, re_plane_coeffs_o(:,2) - re_plane_coeffs(:,2), 'g.', ...
- k, re_plane_coeffs_o(:,3) - re_plane_coeffs(:,3), 'b.');
-xlabel('sheet index');
-ylabel('coefficient residual (unit vectors)');
-title('residuals for polynomial fit of laser sheet normal');
-
-figure(2);
-plot(k, (re_plane_coeffs_o(:,4) - re_plane_coeffs(:,4))/re_dX, 'k.', ...
- [0 n_sheets-1], [1 1]*+0.01 * sheet_spacing / re_dX, 'k--', ...
- [0 n_sheets-1], [1 1]*-0.01 * sheet_spacing / re_dX, 'k--');
-ylim(sheet_spacing * [-1 1]*0.1 / re_dX);
-xlabel('sheet index');
-ylabel('coefficient residual (normal distance) / vx');
-title('residuals for polynomial fit of laser sheet position');
-
-%% plot projection of reconstruction volume in each view
-
-figure(9);
-clf;
-for cam_idx = 1 : n_cameras
- %% create figure
- subplot(1,n_cameras,cam_idx);
- cla;
-
- %% plot corners
- camera_calib = camera_setup(cam_idx).calib;
- corners_xyz = [X_lim([1 1 1 1 2 2 2 2]);
- Y_lim([1 1 2 2 1 1 2 2]);
- Z_lim([1 2 1 2 1 2 1 2])];
- corners_ij = pinhole_model(corners_xyz, camera_calib);
- plot( corners_ij(1,[1 2 3 4 1]), corners_ij(2, [1 2 3 4 1]), 'k.-', ...
- corners_ij(1,[5 6 7 8 5]), corners_ij(2, [5 6 7 8 5]), 'k.-', ...
- corners_ij(1,[1 3 7 5 1]), corners_ij(2, [1 3 7 5 1]), 'k.-', ...
- corners_ij(1,[2 4 8 6 2]), corners_ij(2, [2 4 8 6 2]), 'k.-');
- % plot without pinhole model
- hold on
- camera_calib.kr = [0 0];
- camera_calib.ic = [512 512];
- corners_ij = pinhole_model(corners_xyz, camera_calib);
- plot( corners_ij(1,[1 2 3 4 1]), corners_ij(2, [1 2 3 4 1]), 'r.-', ...
- corners_ij(1,[5 6 7 8 5]), corners_ij(2, [5 6 7 8 5]), 'r.-', ...
- corners_ij(1,[1 3 7 5 1]), corners_ij(2, [1 3 7 5 1]), 'r.-', ...
- corners_ij(1,[2 4 8 6 2]), corners_ij(2, [2 4 8 6 2]), 'r.-');
- %% make pretty
- axis equal tight xy;
- xlim(im_i([1 end]));
- ylim(im_j([1 end]));
- xlabel('i');
- ylabel('j');
- title(sprintf('Camera %d, ID %d', cam_idx, camera_setup(cam_idx).id));
-end
-
-
-%% plot in reconstruction coordinate system
-
-figure(10);
-clf;
-hold off;
-
-% plot planes
-M1 = re_plane_coeffs(1,:);
-Mend = re_plane_coeffs(end,:);
-x_corners = [X_lim(1) X_lim(1) X_lim(2) X_lim(2) X_lim(1)];
-y_corners = [Y_lim(1) Y_lim(2) Y_lim(2) Y_lim(1) Y_lim(1)];
-% first plane
-z_corners = -(M1(1)*x_corners + M1(2)*y_corners + M1(4)) / M1(3);
-P1 = patch(x_corners, y_corners, z_corners, 'g');
-
-% last plane
-z_corners = -(Mend(1)*x_corners + Mend(2)*y_corners + Mend(4)) / Mend(3);
-Pend = patch(x_corners, y_corners, z_corners, 'g');
-set([P1 Pend], 'FaceAlpha', 0.5, 'edgealpha', 1);
-
-hold on;
-
-% plot viewing frustum of each camera
-ilim = [im_i(1) im_i(end)];
-jlim = [im_j(1) im_j(end)];
-i_corners = [ilim(1) ilim(1) ilim(2) ilim(2)];
-j_corners = [jlim(1) jlim(2) jlim(2) jlim(1)];
-ij_corners = [i_corners; j_corners];
-
-str_colour = {'r','k','b'};
-
-for cam_idx = 1 : n_cameras
- calib = camera_setup(cam_idx).calib;
- Gc = calib.G;
- Rc = calib.R;
- xc = calib.xc;
- ic = calib.ic;
- kr = calib.kr;
-
- % convert dewarped image coordinates to pinhole image coordinates
- ij_undist= inverse_radial_distortion(ij_corners, ic, kr);
- for corner = 1 : 4
- ytilde= [ij_undist(:, corner); 1];
- e_ray = Rc^-1 * Gc^-1 * ytilde;
- e_ray = e_ray / norm(e_ray);
-
- x_far = xc + e_ray * norm(xc) * 1.2;
- x_near= xc;
-
- plot3([x_near(1) x_far(1)], ...
- [x_near(2) x_far(2)], ...
- [x_near(3) x_far(3)], '-', 'Color', str_colour{cam_idx});
- end
-end
-
-axis equal;% tight xy;
-xlim([-100 100]);
-ylim([-100 100]);
-zlim([-100 100]);
\ No newline at end of file
diff --git a/calibration/combine_markers.m b/calibration/combine_markers.m
deleted file mode 100644
index fa31d1c..0000000
--- a/calibration/combine_markers.m
+++ /dev/null
@@ -1,316 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% combine_markers.m
-%
-% combine calibration images of the same markers from a single camera
-%
-addpath([pwd '/../../piv2d/']);
-addpath([pwd '/pinhole/']);
-
-%% input parameters
-
-calibration_dir = '/nfs/data/lawson/160715/calib/';
-calib_infile_pat = 'CAM2-%02d.mat';
-calib_outfile_pat = 'C-CAM2-%02d.mat';
-calib_file_range = 1 : 16;
-peak_mag_threshold= 0.5;
-
-marker_size = [0.25 0.25]; % mm, interpreted as half-size
-marker_min_bright = 50; % minimum brightness of marker image
-
-%% load calibration files
-
-x_lim = [inf -inf];
-y_lim = [inf -inf];
-n_calibrations = length(calib_file_range);
-
-fprintf('------------------------------------------------------------------\n');
-fprintf('Loading %d calibration files ... ', n_calibrations);
-
-for file_idx = calib_file_range
- file_name = sprintf([calibration_dir '/' calib_infile_pat], file_idx);
- fstruct = importdata(file_name);
-
- x_lim = [min([fstruct.x_lim(1) x_lim(1)]) max([fstruct.x_lim(2) x_lim(2)])];
- y_lim = [min([fstruct.y_lim(1) y_lim(1)]) max([fstruct.y_lim(2) y_lim(2)])];
-end
-
-grid_dx = fstruct.grid_dx;
-grid_dy = fstruct.grid_dy;
-Nx = size(fstruct.source_image, 1);
-Ny = size(fstruct.source_image, 2);
-
-fprintf('done\n');
-
-%% resample images onto common grid
-
-fprintf('Resampling images onto common grid ... ');
-
-x = linspace(x_lim(1), x_lim(2), Nx);
-y = linspace(y_lim(1), y_lim(2), Ny);
-dx = x(2) - x(1);
-dy = y(2) - y(1);
-[xmat, ymat] = ndgrid(x, y);
-
-dewarped_image_mat= zeros(n_calibrations, Nx, Ny);
-for calib_idx = 1 : n_calibrations
- file_idx = calib_file_range(calib_idx);
- file_name = sprintf([calibration_dir '/' calib_infile_pat], file_idx);
- fstruct = importdata(file_name);
-
- source_image = fstruct.source_image;
- xytilde = [xmat(:) ymat(:) ones(Nx*Ny,1)]';
- ijtilde = fstruct.P * xytilde;
- imat = reshape(ijtilde(1,:) ./ ijtilde(3,:), [Nx Ny]);
- jmat = reshape(ijtilde(2,:) ./ ijtilde(3,:), [Nx Ny]);
- i_axis = fstruct.i_axis;
- j_axis = fstruct.j_axis;
- dewarped_image = interp2(i_axis, j_axis, source_image', imat, jmat, 'linear', nan);
-
- dewarped_image_mat(calib_idx, :, :) = dewarped_image;
-end
-
-fprintf('done\n');
-
-%% identify which markers are in which images
-
-x_lim_grid = fix(x_lim/grid_dx) * grid_dx;
-y_lim_grid = fix(y_lim/grid_dy) * grid_dy;
-x_grid = x_lim_grid(1) : grid_dx : x_lim_grid(2);
-y_grid = y_lim_grid(1) : grid_dy : y_lim_grid(2);
-Nx_grid = length(x_grid);
-Ny_grid = length(y_grid);
-
-[x_grid_mat, y_grid_mat] = ndgrid(x_grid, y_grid);
-n_shared_views = zeros(Nx_grid, Ny_grid);
-b_marker_visible = false(Nx_grid, Ny_grid, n_calibrations);
-
-fprintf('Searching for common markers ... ');
-
-for ix = 1 : Nx_grid
- for iy = 1 : Ny_grid
- %% find which images contain this marker
- % extrapolated values in images are nans
- % so markers we cannot see have nans near them
- marker_is_visible = false(1, n_calibrations);
- xrange = abs(x - x_grid(ix)) < marker_size(1);
- yrange = abs(y - y_grid(iy)) < marker_size(2);
- for calib_idx = 1 : n_calibrations
- dewarped_image = dewarped_image_mat(calib_idx, :, :);
- dewarped_image = squeeze(dewarped_image);
- marker_image = dewarped_image(xrange,yrange);
- marker_brightness = mean(marker_image(:));
- marker_is_visible(calib_idx) = ...
- all(~isnan(marker_image(:))) && marker_brightness > marker_min_bright;
- end
- n_shared_views(ix,iy) = sum(marker_is_visible);
- b_marker_visible(ix, iy, :) = marker_is_visible;
- end
-end
-
-fprintf('done\n');
-
-%% report on what markers are common
-b_all_common = n_shared_views == n_calibrations;
-b_some_common = n_shared_views >= 2;
-n_all_common = sum(b_all_common(:));
-n_some_common = sum(b_some_common(:));
-
-fprintf('Found %d markers common across all views\n', n_all_common);
-fprintf('Found %d markers in at least 2 views\n', n_some_common);
-
-sum_of_all_images = mean(dewarped_image_mat, 1);
-sum_of_all_images = squeeze(sum_of_all_images);
-
-figure(1);
-hold off;
-imagesc(x, y, sum_of_all_images');
-hold on;
-plot(x_grid_mat(b_all_common ), y_grid_mat(b_all_common ), 'go');
-plot(x_grid_mat(b_some_common), y_grid_mat(b_some_common), 'rx');
-axis equal tight xy;
-colormap gray;
-xlim(x_lim);
-ylim(y_lim);
-title('Average of all images');
-xlabel('x / mm');
-ylabel('y / mm');
-
-sInput = input('Do you want to check markers? (y): ', 's');
-if strcmp(sInput, 'y')
- for calib_idx = 1 : n_calibrations
- dewarped_image = squeeze(dewarped_image_mat(calib_idx, :, :));
- marker_visible = squeeze(b_marker_visible(:,:,calib_idx));
-
- figure(1);
- hold off;
- imagesc(x, y, dewarped_image');
- axis equal tight xy;
- colormap gray;
- xlim(x_lim);
- ylim(y_lim);
- hold on;
- plot(x_grid_mat(marker_visible), y_grid_mat(marker_visible), 'go');
-
- fprintf('Image %d of %d\n', calib_idx, n_calibrations);
- pause();
- end
-end
-
-%% cross-correlate to find offset
-
-fprintf('Finding optimal shift of markers ... ');
-
-marker_shift_x = zeros(Nx_grid, Ny_grid, n_calibrations);
-marker_shift_y = zeros(Nx_grid, Ny_grid, n_calibrations);
-
-for ix = 1 : Nx_grid
- for iy = 1 : Ny_grid
- %% can't make a correction if there are <2 views
- if n_shared_views(ix, iy) < 2
- continue;
- end
-
- %% get image of marker in first view
- i_shared_views = find(b_marker_visible(ix, iy, :));
- xrange = abs(x - x_grid(ix)) < marker_size(1);
- yrange = abs(y - y_grid(iy)) < marker_size(2);
- first_view_idx = i_shared_views(1);
- first_view_img = dewarped_image_mat(first_view_idx, xrange, yrange);
- first_view_img = squeeze(first_view_img);
- first_view_img = first_view_img - mean(first_view_img(:));
- first_view_e = sum(first_view_img(:).^2);
-
- %% cross correlate to find offset
- for iv = 2 : n_shared_views(ix, iy)
- other_view_idx = i_shared_views(iv);
- other_view_img = dewarped_image_mat(other_view_idx, xrange, yrange);
- other_view_img = squeeze(other_view_img);
- other_view_img = other_view_img - mean(other_view_img(:));
- other_view_e = sum(other_view_img(:).^2);
-
- xx = xcorr2(first_view_img, other_view_img);
- xx = xx / sqrt(first_view_e * other_view_e);
- peak_mag = max(xx(:));
- peak_loc = PIV_2d_peaklocate(xx);
-
- % check for sufficient correlation
- if peak_mag < peak_mag_threshold
- fprintf('bad match marker (%0.1f,%0.2f) in view %d\n', x_grid(ix), y_grid(iy), other_view_idx);
- marker_shift= [0 0];
- b_marker_visible(ix, iy, iv) = false;
- else
- marker_shift= (peak_loc(:)' - size(first_view_img)) .* [dx dy];
- end
-
- marker_shift_x(ix, iy, other_view_idx) = -marker_shift(1);
- marker_shift_y(ix, iy, other_view_idx) = -marker_shift(2);
- end
-
- % update which views share an image of this marker
- i_shared_views = find(b_marker_visible(ix, iy, :));
-
- %% average out shifts so no image is preferred
- average_shift_x = mean(marker_shift_x(ix, iy, i_shared_views));
- average_shift_y = mean(marker_shift_y(ix, iy, i_shared_views));
- marker_shift_x(ix, iy, i_shared_views) = marker_shift_x(ix, iy, i_shared_views) - average_shift_x;
- marker_shift_y(ix, iy, i_shared_views) = marker_shift_y(ix, iy, i_shared_views) - average_shift_y;
- end
-end
-
-fprintf('done\n');
-
-%% re-calibrate and re-dewarp
-
-fprintf('Recomputing calibration ... \n');
-
-rewarped_image_mat = dewarped_image_mat;
-
-for calib_idx = 1 : n_calibrations
- %% report
- fprintf(' - calibration %2d of %2d ... ', calib_idx, n_calibrations);
-
- %% find markers on this image
- b_visible_markers = b_marker_visible(:, :, calib_idx);
- marker_x = x_grid_mat + marker_shift_x(:, :, calib_idx);
- marker_y = y_grid_mat + marker_shift_y(:, :, calib_idx);
- marker_x_old = marker_x(b_visible_markers);
- marker_y_old = marker_y(b_visible_markers);
- marker_x_new = x_grid_mat(b_visible_markers);
- marker_y_new = y_grid_mat(b_visible_markers);
- n_visible_markers = sum(b_visible_markers(:));
-
- %% get updated i/j coordinates of marker positions
- file_idx = calib_file_range(calib_idx);
- file_name = sprintf([calibration_dir '/' calib_infile_pat], file_idx);
- fstruct = importdata(file_name);
- [marker_i,marker_j] = pinholemap_2d(marker_x_old(:), marker_y_old(:), fstruct.P);
-
- %% recalibrate
- marker_ij_tilde = [marker_i(:) marker_j(:) ones(n_visible_markers,1)]';
- marker_xy_tilde = [marker_x_new(:) marker_y_new(:) ones(n_visible_markers,1)]';
- P2D_new = dlt33(marker_ij_tilde, marker_xy_tilde);
-
- %% re-dewarp image
- im = fstruct.source_image;
- i_axis = fstruct.i_axis;
- j_axis = fstruct.j_axis;
-
- [imat,jmat] = pinholemap_2d(xmat, ymat, P2D_new);
- rewarped_img = interp2(i_axis, j_axis, im', imat, jmat, 'linear', nan);
- rewarped_image_mat(calib_idx, :, :) = rewarped_img;
-
- %% save
- fstruct.P = P2D_new;
- fstruct.ij_markers = [marker_i(:) marker_j(:)]';
- fstruct.xy_markers = [marker_x_new(:) marker_y_new(:)]';
- fstruct.x_axis = x;
- fstruct.y_axis = y;
- fstruct.dewarped_image = rewarped_img;
-
- output_file = sprintf([calibration_dir '/' calib_outfile_pat], file_idx);
- save(output_file, '-struct', 'fstruct');
-
- fprintf('done\n');
-end
-
-fprintf('Done\n');
-
-%% compare dewarped and re-dewarped images
-
-for img_idx = 1 : n_calibrations
- discrepancy_x = squeeze( marker_shift_x(:,:, img_idx) );
- discrepancy_y = squeeze( marker_shift_y(:,:, img_idx) );
- marker_x = x_grid_mat + discrepancy_x;
- marker_y = y_grid_mat + discrepancy_y;
- dewarped_img = squeeze(dewarped_image_mat(img_idx, :, :));
- rewarped_img = squeeze(rewarped_image_mat(img_idx, :, :));
-
- figure(2);
- %{
- subplot(1, 2, 1);
- hold off;
- imagesc(x, y, dewarped_img');
- hold on;
- plot(marker_x(:), marker_y(:), 'rx');
- axis xy equal tight;
- grid on;
- xlim(x_lim);
- ylim(y_lim);
- %xlim([-25 25]);
- %ylim([-25 25]);
- title(sprintf('dewarped %d', img_idx));
-
-
- subplot(1, 2, 2);
- %}
- hold off;
- imagesc(x, y, rewarped_img');
- colormap jet;
- grid on;
- axis xy equal tight;
- xlim(x_lim);
- ylim(y_lim);
- title(sprintf('rewarped %d', img_idx));
-
- pause();
-end
diff --git a/calibration/cubicfit_2d.m b/calibration/cubicfit_2d.m
deleted file mode 100644
index bd24206..0000000
--- a/calibration/cubicfit_2d.m
+++ /dev/null
@@ -1,64 +0,0 @@
-function C = cubicfit_2d(i, j, X, Niter)
- % fits coefficients C to observations of X(i,j) to create a model of X
- % according to
- % X* = C(1) + C(2)*i + C(3)*i.^2 + C(4)*i.^3 + ...
- % + C(5)*j + C(6)*j.^2 + C(7)*j.^3 + ...
- % + C(8)*i.*j + C(9)*i.^2.*j + C(10)*i.*j.^2;
- %{
- ii = i.^2;
- iii = i.^3;
- jj = j.^2;
- jjj = j.^3;
- ij = i.*j;
- iij = i.^2.*i;
- ijj = i.*j.^2;
- Np = numel(i);
- %}
- myfun = @(X,xdata) cubicmap_2d(xdata(:,1), xdata(:,2), X);
- C0 = zeros(1,10);
- opts = optimset('MaxFunEvals', 100000, 'MaxIter', 5000, 'Display', 'off');
- [C,~,res] = lsqcurvefit(myfun, C0, [i(:) j(:)], X(:), [], [], opts);
- if nargin > 3
- for iter = 2 : Niter
- % cut out the chaff
- thresh = prctile(res, 97);
- ok = res < thresh;
- i = i(ok);
- j = j(ok);
- X = X(ok);
- % repeat
- [C,~,res]= lsqcurvefit(myfun, C0, [i(:) j(:)], X(:), [], [], opts);
- end
- end
-
-
- %{
- A = [ones(Np,1) i(:) ii(:) iii(:) j(:) jj(:) jjj(:) ij(:) iij(:) ijj(:)];
- b = X(:);
- [C,~,res] = lsqlin(A, b, [], []);
-
- % iteratively remove outliers
- if nargin > 3
- for iter = 2 : Niter
- % cut out the chaff
- thresh = prctile(res, 97);
- ok = res < thresh;
- i = i(ok);
- ii = ii(ok);
- iii = iii(ok);
- j = j(ok);
- jj = jj(ok);
- jjj = jjj(ok);
- ij = ij(ok);
- iij = iij(ok);
- ijj = ijj(ok);
- X = X(ok);
- Np = numel(i);
- % repeat
- A = [ones(Np,1) i(:) ii(:) iii(:) j(:) jj(:) jjj(:) ij(:) iij(:) ijj(:)];
- b = X(:);
- [C,~,res]= lsqlin(A, b, [], []);
- end
- end
- %}
-end
\ No newline at end of file
diff --git a/calibration/cubicmap_2d.m b/calibration/cubicmap_2d.m
deleted file mode 100644
index 77edbde..0000000
--- a/calibration/cubicmap_2d.m
+++ /dev/null
@@ -1,7 +0,0 @@
-function X = cubicmap_2d(i, j, C)
- % maps [i,j] onto X = X(i,j) using coefficients provided in C
- % coefficients derived from cubicfit_2d
- X = C(1) + C(2)*i + C(3)*i.^2 + C(4)*i.^3 + ...
- + C(5)*j + C(6)*j.^2 + C(7)*j.^3 + ...
- + C(8)*i.*j + C(9)*i.^2.*j + C(10)*i.*j.^2;
-end
\ No newline at end of file
diff --git a/calibration/dewarp_2d.m b/calibration/dewarp_2d.m
deleted file mode 100644
index 184366f..0000000
--- a/calibration/dewarp_2d.m
+++ /dev/null
@@ -1,23 +0,0 @@
-function dewarped_img = dewarp_2d(raw_img, xy_to_i, xy_to_j, x, y)
- % reinterpolates raw_img onto grid [x,y] from [i,j] using the dewarping coefficients
- % xy_to_i and xy_to_j
-
- Nx = length(x);
- Ny = length(y);
- [xmat, ymat] = ndgrid(x, y);
- imat = cubicmap_2d(xmat, ymat, xy_to_i);
- jmat = cubicmap_2d(xmat, ymat, xy_to_j);
-
- ivec = 1 : size(raw_img, 1);
- jvec = 1 : size(raw_img, 2);
- dewarped_img = interp2(ivec, jvec, double(raw_img'), imat, jmat, 'linear', 0);
-
- % lanczos interpolation is faster than matlab's interp2 and better
- % quality. kernel size is specified by ksize. 18 is good.
- %{
- ksize = 10;
- xi = [imat(:)-1, jmat(:)-1]';
- dewarped_img = interp2lanczos(single(raw_img), single(xi), ksize);
- dewarped_img = reshape(dewarped_img, Nx, Ny);
- %}
-end
\ No newline at end of file
diff --git a/calibration/dewarp_pinhole_2d.m b/calibration/dewarp_pinhole_2d.m
deleted file mode 100644
index f521baa..0000000
--- a/calibration/dewarp_pinhole_2d.m
+++ /dev/null
@@ -1,13 +0,0 @@
-function im_uv = dewarp_pinhole_2d(im, P2D, u_axis, v_axis)
- Nu = length(u_axis);
- Nv = length(v_axis);
- [umat, vmat] = ndgrid(u_axis, v_axis);
- uv_tilde = [umat(:) vmat(:) ones(Nu*Nv,1)]';
- ij_tilde = P2D * uv_tilde;
- lambda = ij_tilde(3, :);
- imat = reshape(ij_tilde(1,:) ./ lambda, [Nu Nv]);
- jmat = reshape(ij_tilde(2,:) ./ lambda, [Nu Nv]);
- i_axis = 1 : size(im, 1);
- j_axis = 1 : size(im, 2);
- im_uv = interp2(i_axis, j_axis, im', imat, jmat, 'linear');
-end
\ No newline at end of file
diff --git a/calibration/distortion_test.m b/calibration/distortion_test.m
deleted file mode 100644
index af10171..0000000
--- a/calibration/distortion_test.m
+++ /dev/null
@@ -1,158 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% fit calibration data obtained from pinhole camera model fit
-% to a higher order camera model
-%
-% we follow Machioane et al. 2016 to extract a transfer function that
-% maps image space coordinates to rays through object space
-%
-% 1) obtain 2D transfer function for each calibration plane
-% 2) trace rays back for each pixel, based on intersection
-% with each calibration plane
-% 3) fit a transfer function to map rays to
-
-addpath([pwd '/pinhole']);
-addpath([pwd '/poly']);
-
-%% input parameters
-
-calib_dir = '/nfs/data/lawson/160715/calib/';
-cam_calib_file_pat = [calib_dir '/CAM%d-%02d.mat'];
-cam_model_file = [calib_dir '/camera-model-original.mat'];
-
-cam_index_range = [1 2 3];
-cam_view_range = 1 : 16;
-n_views = length(cam_view_range);
-n_cameras = length(cam_index_range);
-n_iterations = 2;
-
-DO_DEBUG_PLOT = false;
-
-%% load model
-setup = importdata(camera_model_file);
-
-% camera calibration
-n_cameras = setup.n_cameras;
-n_views = setup.n_views;
-
-% plate pose
-gl_plate_pos = setup.gl_plate_pos;
-pl_to_gl_mat = repmat(setup.pl_to_gl, [1 1 n_views]);
-camera_model = setup.camera;
-gl_to_re = eye(3);
-gl_origin = setup.gl_origin;
-r_sphere = min(setup.r_sphere);
-n_views = setup.n_views;
-
-%% obtain 2D transfer function for each plane
-fprintf('Fitting 2D transfer function for each plate in each camera\n');
-fprintf('This gives best case scenario for how advanced model will reduce error\n');
-
-quiver_scale = 10;
-for c = 1 : n_cameras
- %% initialise
- camera = setup.camera(c);
- i_axis = camera.i_axis;
- j_axis = camera.j_axis;
- ij_res_poly = [];
- ij_res_pin = [];
-
- gl_mark_ary = [];
- ij_mark_ary = [];
- sh_mark_ary = [];
-
- %% loop over views
- for v = 1 : n_views
- %% get plate, image and world coordinates of markers
- pl_to_gl = pl_to_gl_mat(:,:,v);
- gl_pos = gl_plate_pos(:,v);
- uv_mark = camera.view(v).uv_markers;
- n_mark = size(uv_mark, 2);
- gl_mark = bsxfun(@plus, gl_pos, pl_to_gl*[uv_mark; zeros(1,n_mark)]);
- ij_mark = camera.view(v).ij_markers;
-
- %% get sphere coordinates of markers
- R = camera.calib.R;
- xc = camera.calib.xc;
- eu_mark = R * bsxfun(@minus, gl_mark, xc);
- eu_len = sqrt(sum(eu_mark.^2,1));
- eu_mark = bsxfun(@rdivide, eu_mark, eu_len);
- theta_mark = acos(eu_mark(3,:));
- phi_mark = atan2(eu_mark(2,:), eu_mark(1,:));
-
- sh_mark = [theta_mark; phi_mark];
-
- ij_mark_ary = [ij_mark_ary, ij_mark];
- gl_mark_ary = [gl_mark_ary, gl_mark];
- sh_mark_ary = [sh_mark_ary, sh_mark];
-
- %% create transfer function
- P_i2p = polyfit2d3(ij_mark, uv_mark);
- P_p2i = polyfit2d3(uv_mark, ij_mark);
- camera.view(v).P_i2p = P_i2p;
- camera.view(v).P_p2i = P_p2i;
-
- %% compare transfer function to pinhole model
- uv_res = polymap2d3(P_i2p, ij_mark) - uv_mark;
- ij_res_poly_view = polymap2d3(P_p2i, uv_mark) - ij_mark;
- ij_res_pin_view = pinhole_model(gl_mark, camera.calib, true) - ij_mark;
-
- ij_res_poly = [ij_res_poly, ij_res_poly_view];
- ij_res_pin = [ij_res_pin, ij_res_pin_view];
-
- %% debug plot
- if DO_DEBUG_PLOT
- %% residuals
- figure(1);
- clf;
- quiversc(ij_mark(1,:), ij_mark(2,:), ...
- ij_res_poly_view(1,:), ij_res_poly_view(2,:), quiver_scale, 'k-');
- hold on;
- quiversc(ij_mark(1,:), ij_mark(2,:), ...
- ij_res_pin_view(1,:), ij_res_pin_view(2,:), quiver_scale, 'r-');
- axis equal xy;
- xlim(i_axis([1 end]));
- ylim(j_axis([1 end]));
- %%
- pause();
- end
- end
-
- %% polynomial map, undistorted image -> image
- ij_proj_ary = pinhole_model(gl_mark_ary, camera.calib, false);
- ij_res_ary = ij_proj_ary - ij_mark_ary;
- P_u2d = polyfit2d4(ij_proj_ary, ij_mark_ary);
- P_d2u = polyfit2d4(ij_mark_ary, ij_proj_ary);
-
- ij_dist_ary = polymap2d4(P_u2d, ij_proj_ary);
- ij_res_pp = ij_dist_ary - ij_mark_ary;
-
- %% polynomial map, spherical -> image
- P_s2i = polyfit2d3(sh_mark_ary, ij_mark_ary);
- P_i2s = polyfit2d3(ij_mark_ary, sh_mark_ary);
- camera.calib.P_s2i = P_s2i;
- camera.calib.P_i2s = P_i2s;
-
- %% mapping
- figure(2);
- quiversc(ij_mark_ary(1,:), ij_mark_ary(2,:), ...
- ij_res_pp(1,:), ij_res_pp(2,:), 2, 'k-');
- axis equal tight xy;
- xlim(i_axis([1 end]));
- ylim(j_axis([1 end]));
-
- %% save
- setup.camera(c) = camera;
-
- %% report differences
- ij_rms_poly = rms(ij_res_poly, 2);
- ij_rms_pin = rms(ij_res_pin, 2);
- ij_rms_pp = rms(ij_res_pp, 2);
- ij_max_poly = max(abs(ij_res_poly), [], 2);
- ij_max_pin = max(abs(ij_res_pin ), [], 2);
- ij_max_pp = max(abs(ij_res_pp ), [], 2);
- fprintf('Residuals in camera %d\n', c);
- fprintf(' polynomial: %0.3f %0.3f px rms %0.3f %0.3f max\n', ij_rms_poly, ij_max_poly);
- fprintf(' pinhole+dist: %0.3f %0.3f px rms %0.3f %0.3f max\n', ij_rms_pp , ij_max_pp);
- fprintf(' pinhole+rad: %0.3f %0.3f px rms %0.3f %0.3f max\n', ij_rms_pin , ij_max_pin);
-end
-
diff --git a/calibration/dlt/dlt_maths.m b/calibration/dlt/dlt_maths.m
deleted file mode 100644
index 8d89c27..0000000
--- a/calibration/dlt/dlt_maths.m
+++ /dev/null
@@ -1,59 +0,0 @@
-%% work out maths for parallel DLT
-% we have a problem where
-% y1_k ~ T1 * x_k
-% y2_k ~ T2 * x_k
-% but we don't know x_k, or T1 or T2
-%
-% so this can be written as
-% a_k * y1_k = T1 * x_k
-% b_k * y2_k = T2 * x_k
-% for k = 1 .. N equations
-%
-% if we choose an antisymmetric matrix Hi, then
-% a_k * y1_k' * Hi * y2_k = y1_k' * Hi * T1 * x_k
-% = 0
-% b_k * y2_k' * Hj * y2_k = y2_k' * Hj * T2 * x_k
-% = 0
-
-%% set up T1 and T2
-
-% we need two random orthogonal matrices
-Omega1 = randortho(3);
-Omega2 = randortho(3);
-% and random displacements xc1 and xc2
-xc1 = randn(3,1) * 100 + [0 1000 0]';
-xc2 = randn(3,1) * 100 + [1000 0 0]';
-T1 = Omega1 * [eye(3) -xc1];
-T2 = Omega2 * [eye(3) -xc2];
-T = [T1; T2];
-
-%% set up observations of xk, y1k and y2k
-
-N = 1000;
-xk = randn(3,N) * 100;
-xk_tilde = [xk; ones(1,N)];
-y1k = T1 * xk;
-y2k = T2 * xk;
-zk = [y1k; y2k];
-
-%% generate initial guess of xk, Omega1/2, xc1 and xc2
-
-xk_guess = xk;
-SNR = 100;
-for k = 1 : N
- xk_guess(:,k) = xk(:,k) + randn(3,1)*norm(xk(:,k))/SNR;
-end
-
-omega1 = vrrotmat2vec(Omega1);
-omega2 = vrrotmat2vec(Omega2);
-omega1 = omega1(1:3) * omega1(4);
-omega2 = omega2(1:3) * omega2(4);
-omega1_guess= omega1 + randn(3,1) * norm(omega1) / SNR;
-omega2_guess= omega2 + randn(3,1) * norm(omega2) / SNR;
-xc1_guess = xc1 + norm(xc1)*randn(3,1)/SNR;
-xc2_guess = xc2 + norm(xc2)*randn(3,1)/SNR;
-
-
-
-%% generate initial guess for model
-
diff --git a/calibration/dlt/dlt_model.m b/calibration/dlt/dlt_model.m
deleted file mode 100644
index 21053d0..0000000
--- a/calibration/dlt/dlt_model.m
+++ /dev/null
@@ -1,40 +0,0 @@
-function residuals = dlt_model(params, y1k, y2k, xc1, xc2)
- %% extract model parameters
- omega1 = params(1:3);
- omega2 = params(4:6);
- delta = [ 0 0 0]';%params(7:9);
- xc1_prime= xc1(:) + delta(:);
- xc2_prime= xc2(:) + delta(:);
-
- % get rotation matrices
- theta1 = norm(omega1);
- theta2 = norm(omega2);
- omega1 = [omega1/theta1; theta1];
- omega2 = [omega2/theta2; theta2];
- if theta1 < 1E-6; omega1 = [1 0 0 0]; end
- if theta2 < 1E-6; omega2 = [1 0 0 0]; end
- Omega1 = vrrotvec2mat(omega1);
- Omega2 = vrrotvec2mat(omega2);
-
- % get T1 and T2
- T1 = Omega1 * [eye(3), -xc1_prime];
- T2 = Omega2 * [eye(3), -xc2_prime];
-
- % get model of xk
- N = (length(params) - 6) / 3;
- xk = reshape(params(7:end), [3 N]);
-
- %% produce model result
- H1 = [0 0 0; 0 0 -1; 0 1 0];
- H2 = [0 0 1; 0 0 0; -1 0 0];
- H3 = [0 -1 0; 1 0 0; 0 0 0];
- residuals= zeros(6*N, 1);
- for k = 1 : N
- residuals((k-1)*6 + 1) = y1k(:,k)' * H1 * T1 * [xk(:,k); 1];
- residuals((k-1)*6 + 2) = y1k(:,k)' * H2 * T1 * [xk(:,k); 1];
- residuals((k-1)*6 + 3) = y1k(:,k)' * H3 * T1 * [xk(:,k); 1];
- residuals((k-1)*6 + 4) = y2k(:,k)' * H1 * T2 * [xk(:,k); 1];
- residuals((k-1)*6 + 5) = y2k(:,k)' * H2 * T2 * [xk(:,k); 1];
- residuals((k-1)*6 + 6) = y2k(:,k)' * H3 * T2 * [xk(:,k); 1];
- end
-end
\ No newline at end of file
diff --git a/calibration/dlt/dlt_test.m b/calibration/dlt/dlt_test.m
deleted file mode 100644
index dad8d02..0000000
--- a/calibration/dlt/dlt_test.m
+++ /dev/null
@@ -1,157 +0,0 @@
-%% test solution of parallel DLT
-% we have a problem where
-% y1_k ~ T1 * x_k
-% y2_k ~ T2 * x_k
-% but we don't know x_k, or T1 or T2
-%
-% so this can be written as
-% a_k * y1_k = T1 * x_k
-% b_k * y2_k = T2 * x_k
-% for k = 1 .. N equations
-%
-% if we choose an antisymmetric matrix Hi, then
-% a_k * y1_k' * Hi * y2_k = y1_k' * Hi * T1 * x_k
-% = 0
-% b_k * y2_k' * Hj * y2_k = y2_k' * Hj * T2 * x_k
-% = 0
-%
-% solve using a non-linear solver
-% to find x_k, T1 and T2
-clear;
-clc;
-
-%% set up T1 and T2
-
-% we need two random orthogonal matrices
-Omega1 = randortho(3);
-Omega2 = randortho(3);
-% and random displacements xc1 and xc2
-xc1 = randn(3,1) * 100 + [0 1000 0]';
-xc2 = randn(3,1) * 100 + [1000 0 0]';
-delta = [0 0 0]';%randn(3,1) * 10;
-xc1_prime = xc1 + delta;
-xc2_prime = xc2 + delta;
-T1 = Omega1 * [eye(3), -xc1_prime];
-T2 = Omega2 * [eye(3), -xc2_prime];
-T = [T1; T2];
-
-%% set up observations of xk, y1k and y2k
-
-N = 100;
-xk = randn(3,N) * 100;
-xk_tilde = [xk; ones(1,N)];
-y1k = T1 * xk_tilde;
-y2k = T2 * xk_tilde;
-% apply arbitrary scaling a_k and b_k
-y1k_norm = y1k;
-y2k_norm = y2k;
-for k = 1 : N
- y1k_norm(:,k) = y1k(:,k) / norm(y1k(:,k));
- y2k_norm(:,k) = y2k(:,k) / norm(y2k(:,k));
-end
-
-zk = [y1k; y2k];
-
-%% generate initial guess of xk, Omega1/2, xc1 and xc2
-
-xk_guess = xk;
-SNR = 100;
-for k = 1 : N
- xk_guess(:,k) = xk(:,k) + randn(3,1)*norm(xk(:,k))/SNR;
-end
-
-omega1 = vrrotmat2vec(Omega1);
-omega2 = vrrotmat2vec(Omega2);
-omega1 = omega1(1:3)' * omega1(4);
-omega2 = omega2(1:3)' * omega2(4);
-omega1_guess= omega1 + randn(3,1) * norm(omega1) / SNR;
-omega2_guess= omega2 + randn(3,1) * norm(omega2) / SNR;
-delta_guess = [0 0 0]';
-
-%% solve model
-
-X0 = [omega1_guess;
- omega2_guess;
- xk_guess(:)];
-fun = @(X) dlt_model(X, y1k, y2k, xc1, xc2);
-
-fprintf('Solving ... ');
-opts = optimset('TolFun', 1E-8, 'Display', 'on');
-[X_sol,n,res,flag] = lsqnonlin(fun, X0, [], [], opts);
-fprintf('done\n');
-fprintf('Solver exited with flag %d\n', flag);
-fprintf('RMS residual: %0.3e\n', sqrt(mean(res.^2)));
-fprintf('Norm of residuals: %0.3e\n', norm(res));
-
-%% extract solution
-
-omega1_sol = X_sol(1:3);
-omega2_sol = X_sol(4:6);
-delta_sol = [0 0 0]';
-xk_sol = reshape(X_sol(7:end), [3 N]);
-
-Omega1_sol = vrrotvec2mat([omega1_sol/norm(omega1_sol); norm(omega1_sol)]);
-Omega2_sol = vrrotvec2mat([omega2_sol/norm(omega2_sol); norm(omega2_sol)]);
-
-fprintf('|omega1 error| / |omega1| %0.3f %%\n', norm(omega1_sol-omega1)/norm(omega1)*100);
-fprintf('|omega2 error| / |omega2| %0.3f %%\n', norm(omega2_sol-omega2)/norm(omega2)*100);
-fprintf('|delta error| / |delta| %0.3f %%\n', norm(delta_sol-delta)/norm(delta)*100);
-
-figure(1);
-hold off;
-plot3(xk(1,:), xk(2,:), xk(3,:), 'b.');
-hold on;
-plot3(xk_sol(1,:), xk_sol(2,:), xk_sol(3,:), 'ro');
-axis equal
-title('Points in object space and their model solutions');
-
-%% check back-projected points line up
-T1_sol = Omega1_sol * [eye(3), -(xc1+delta_sol)];
-T2_sol = Omega2_sol * [eye(3), -(xc2+delta_sol)];
-y1k_sol = T1_sol * [xk_sol; ones(1,N)];
-y2k_sol = T2_sol * [xk_sol; ones(1,N)];
-% normalise
-for k = 1 : N
- y1k_sol(:,k) = y1k_sol(:,k) / norm(y1k_sol(:,k));
- y2k_sol(:,k) = y2k_sol(:,k) / norm(y2k_sol(:,k));
-end
-
-% compare unit vectors
-figure(2);
-hold off;
-plot3(y1k_norm(1,:), y1k_norm(2,:), y1k_norm(3,:), 'b.');
-hold on;
-plot3(y1k_sol(1,:) , y1k_sol(2,:) , y1k_sol(3,:) , 'bo');
-
-plot3(y2k_norm(1,:), y2k_norm(2,:), y2k_norm(3,:), 'r.');
-plot3(y2k_sol(1,:) , y2k_sol(2,:) , y2k_sol(3,:) , 'ro');
-
-xlim([-1 1]);
-ylim([-1 1]);
-zlim([-1 1]);
-%{
-
-%% check for rotation and translation
-
-% find best fit xk_sol = M * xk + x0
-% then find nearest orthogonal M
-M1 = lsqlin([xk' ones(N,1)], xk_sol(1,:)', [], []);
-M2 = lsqlin([xk' ones(N,1)], xk_sol(2,:)', [], []);
-M3 = lsqlin([xk' ones(N,1)], xk_sol(3,:)', [], []);
-M = [M1(1:3)'; M2(1:3)'; M3(1:3)'];
-d = [M1(4) M2(4) M3(4)]';
-
-figure(2);
-%[U,D,V] = svd(M);
-%M = U*V';
-M_xk = M * xk;
-M_xk(1,:)= M_xk(1,:) + M1(4);
-M_xk(2,:)= M_xk(2,:) + M2(4);
-M_xk(3,:)= M_xk(3,:) + M3(4);
-
-figure(2);
-hold off;
-plot3(M_xk(1,:), M_xk(2,:), M_xk(3,:), 'b.');
-hold on;
-plot3(xk_sol(1,:), xk_sol(2,:), xk_sol(3,:), 'r.');
-%}
\ No newline at end of file
diff --git a/calibration/dlt/randomorthogonal.m b/calibration/dlt/randomorthogonal.m
deleted file mode 100644
index cfb5359..0000000
--- a/calibration/dlt/randomorthogonal.m
+++ /dev/null
@@ -1,5 +0,0 @@
-function Q = randomorthogonal(N)
- S = randn(N);
- [Q,D] = eig(S+S');
- Q = Q * det(Q);
-end
\ No newline at end of file
diff --git a/calibration/dlt/randortho.m b/calibration/dlt/randortho.m
deleted file mode 100644
index a31c3d0..0000000
--- a/calibration/dlt/randortho.m
+++ /dev/null
@@ -1,6 +0,0 @@
-function Q = randortho(N)
- % create a random, orthogonal NxN matrix
- S = randn(N);
- [Q,D] = eig(S+S');
- Q = Q * det(Q);
-end
\ No newline at end of file
diff --git a/calibration/find_calibration_markers.m b/calibration/find_calibration_markers.m
deleted file mode 100644
index de47e97..0000000
--- a/calibration/find_calibration_markers.m
+++ /dev/null
@@ -1,195 +0,0 @@
-function [ij_markers, xy_markers, on_x_axis, on_y_axis] = find_calibration_markers(img, ij_center, ij_right, ij_top, dot_half_size)
- % find_markers(img, ij_center, ij_top, ij_right)
- %
- % find markers on a regular calibration grid in the image img
- % x_markers is returned, which contains the [i,j] coordinates (in image
- % space) of each marker identified. in addition, also return the [x,y]
- % coordinates of each marker. these are, by definition, integer values
- %
- % to start the search, the user must provide the [i,j] coordinates of
- % ij_center - the calibration marker at the center of the coordinate
- % system
- % ij_top - the calibration marker immediately above the center marker
- % ij_right - the calibration marker immediately to the right of the
- % center marker
-
- %% put data in right format
- ij_center = ij_center(:);
- ij_top = ij_top(:);
- ij_right = ij_right(:);
-
- %% get size of image
- Nx = size(img, 1);
- Ny = size(img, 2);
-
- thresh = 0.6; % minimum correlation coefficient required for match
-
- %% get dot img
- dot_size = dot_half_size * 2 + 1;
- dot_img_i = round(ij_center(1)) + (-dot_half_size(1) : dot_half_size(1));
- dot_img_j = round(ij_center(2)) + (-dot_half_size(2) : dot_half_size(2));
- if dot_img_i(1) < 1 || dot_img_i(end) > Nx || dot_img_j(1) < 1 dot_img_j(end) > Ny
- error('Center dot is too close to edge of image');
- end
- dot_img = img(dot_img_i, dot_img_j);
-
- %% convolve image and find indices of maxima
- dot_img = dot_img - mean(dot_img(:));
- % subtract sliding average so DC component of x-correlation windows is
- % zero
- img_sliding_av = filter2(ones(size(dot_img)), img) / numel(dot_img);
- img = img - img_sliding_av;
- % cross-correlate
- convolved = filter2(dot_img, img);
- % normalise by image energy
- image_energy = filter2(ones(size(dot_img)), img.^2);
- dot_energy = sum(dot_img(:).^2);
- correl = convolved ./ (sqrt(image_energy) * sqrt(dot_energy));
- %correl = correl / correl(round(ij_center(1)), round(ij_center(2)));
-
- correl_left = inf([Nx Ny]);
- correl_right = inf([Nx Ny]);
- correl_above = inf([Nx Ny]);
- correl_below = inf([Nx Ny]);
-
- correl_left(1:Nx-2, 2:Ny-1) = correl(2:Nx-1, 2:Ny-1);
- correl_right(3:Nx, 2:Ny-1) = correl(2:Nx-1, 2:Ny-1);
- correl_above(2:Nx-1, 1:Ny-2) = correl(2:Nx-1, 2:Ny-1);
- correl_below(2:Nx-1, 3:Ny) = correl(2:Nx-1, 2:Ny-1);
-
- bw_markers = correl > correl_left ...
- & correl > correl_right ...
- & correl > correl_below ...
- & correl > correl_above ...
- & correl > thresh;
- %figure(2);
- %imagesc(correl' > thresh);
- %axis equal tight xy;
- %% identify likely markers which are on the grid
- ivec = 1 : Nx;
- jvec = 1 : Ny;
- [imat,jmat] = ndgrid(ivec, jvec);
-
- Nmarkers = sum(bw_markers(:));
- ij_markers = zeros(2, Nmarkers);
- ij_markers(1,:)= imat(bw_markers);
- ij_markers(2,:)= jmat(bw_markers);
-
- % find the position of the top/right markers more accurately
- ij_right = ij_markers(:, knnsearch(ij_markers', ij_right', 'K', 1));
- ij_top = ij_markers(:, knnsearch(ij_markers', ij_top', 'K', 1));
-
- % search for dots on the i/j axes
- dx = norm(ij_center - ij_right);
- dy = norm(ij_center - ij_top);
- e_x = (ij_right - ij_center) / dx;
- e_y = (ij_top - ij_center) / dy;
- % angle tolerance, in radians, that must be satisfied in order for a
- % point to be considered on the x or y axes. angle is taken between the
- % x/y axis and the line between the center and the point under
- % consideration
- delta_theta_x = 0.5 * dy / Nx;
- delta_theta_y = 0.5 * dx / Ny;
-
- ij_rel_markers = ij_markers - ij_center * ones(1,Nmarkers);
- ij_rel_length = sqrt(sum(ij_rel_markers.^2,1));
- [~,center_idx] = min(ij_rel_length);
- e_x_mat = e_x * ones(1,Nmarkers);
- e_y_mat = e_y * ones(1,Nmarkers);
- cos_theta_x = dot(ij_rel_markers, e_x_mat, 1) ./ ij_rel_length;
- cos_theta_y = dot(ij_rel_markers, e_y_mat, 1) ./ ij_rel_length;
-
- % get indices of points on x and y axes
- % include the center marker in this
- on_x_axis = find(abs(cos_theta_x) > cos(delta_theta_x));
- on_y_axis = find(abs(cos_theta_y) > cos(delta_theta_y));
- on_x_axis = sort(unique([on_x_axis, center_idx]));
- on_y_axis = sort(unique([on_y_axis, center_idx]));
- Nonx = length(on_x_axis);
- Nony = length(on_y_axis);
-
- %% eliminate markers which are not at the right spacing
- % if distance is more than a percentage threshold out, reject
- x_on_x = dot(ij_rel_markers(:,on_x_axis), e_x*ones(1,Nonx), 1);
- y_on_y = dot(ij_rel_markers(:,on_y_axis), e_y*ones(1,Nony), 1);
- [x_valid,x_ind]= find_equispaced_values(x_on_x, dx);
- [y_valid,y_ind]= find_equispaced_values(y_on_y, dy);
- % truncate markers which are not equispaced
- on_x_axis = on_x_axis(x_valid);
- on_y_axis = on_y_axis(y_valid);
- Nonx = length(on_x_axis);
- Nony = length(on_y_axis);
- % identify which coordinate each marker corresponds to on x/y axes
- [~,x_center] = min(abs(x_on_x(x_valid)));
- [~,y_center] = min(abs(y_on_y(y_valid)));
- x_axis = x_ind - x_ind(x_center);
- y_axis = y_ind - y_ind(y_center);
-
- %% improve estimate of e_x and e_y
- px = polyfit(ij_markers(1,on_x_axis), ij_markers(2,on_x_axis), 1);
- py = polyfit(ij_markers(2,on_y_axis), ij_markers(1,on_y_axis), 1);
- e_x = [1; px(1)] / sqrt(1 + px(1)^2);
- e_y = [py(1); 1] / sqrt(1 + py(1)^2);
-
- %% identify markers which lie on the grid
- b_on_grid = false(1, Nmarkers);
- b_on_grid(center_idx)= true;
- b_on_grid(on_x_axis) = true;
- b_on_grid(on_y_axis) = true;
- xy_markers = zeros(2, Nmarkers);
- xy_markers(1,on_x_axis) = x_axis;
- xy_markers(2,on_y_axis) = y_axis;
- % for each point on x-axis, search up/down for points // to y axis
- for xidx = 1 : Nonx
- % identify markers on the axis with x = const, // to y axis
- marker_idx = on_x_axis(xidx);
- ij_rel_markers = ij_markers - ij_markers(:,marker_idx)*ones(1, Nmarkers);
- ij_rel_length = sqrt(sum(ij_rel_markers.^2,1));
- cos_theta_y = dot(ij_rel_markers, e_y_mat, 1) ./ ij_rel_length;
- on_this_axis = abs(cos_theta_y) > cos(delta_theta_y);
- b_on_grid = b_on_grid | on_this_axis;
- % save the x/y coord of these markers
- Nonthis = sum(on_this_axis);
- y_component = dot(ij_rel_markers(:,on_this_axis), e_y*ones(1,Nonthis), 1);
- xy_markers(1,on_this_axis) = x_axis(xidx);
- xy_markers(2,on_this_axis) = round(y_component/dy);
- end
-
- %% delete markers are not on grid or accidentally have duplicates
- b_on_x_axis = false(1, Nmarkers);
- b_on_x_axis(on_x_axis) = true;
- b_on_y_axis = false(1, Nmarkers);
- b_on_y_axis(on_y_axis) = true;
-
- ij_markers = ij_markers(:, b_on_grid);
- xy_markers = xy_markers(:, b_on_grid);
- b_on_x_axis = b_on_x_axis(b_on_grid);
- b_on_y_axis = b_on_y_axis(b_on_grid);
- % get rid of markers which are repeated more than once
- [~,IA] = unique(xy_markers', 'rows');
- repeats = setxor(IA, 1 : size(xy_markers,2));
- [xy_markers,IA]= setxor(xy_markers', xy_markers(:, repeats)', 'rows');
- xy_markers = xy_markers';
- ij_markers = ij_markers(:, IA);
- b_on_x_axis = b_on_x_axis(IA);
- b_on_y_axis = b_on_y_axis(IA);
- on_x_axis = find(b_on_x_axis);
- on_y_axis = find(b_on_y_axis);
- idx_center = find(ij_markers(1,:)==ij_center(1) & ij_markers(2,:)== ij_center(2));
-
- %% plot things
- %{
- hold off;
- imagesc(img'); axis xy;
- hold on;
- plot(ij_markers(1,:), ij_markers(2,:), 'r+');
- axis equal tight;
- colormap gray;
- plot(ij_markers(1,on_x_axis), ij_markers(2,on_x_axis), 'go');
- plot(ij_markers(1,on_y_axis), ij_markers(2,on_y_axis), 'gd');
- plot(ij_markers(1,idx_center), ij_markers(2,idx_center), 'bs');
- colormap gray;
- %}
-end
-
-
diff --git a/calibration/find_equispaced_values.m b/calibration/find_equispaced_values.m
deleted file mode 100644
index 22861dd..0000000
--- a/calibration/find_equispaced_values.m
+++ /dev/null
@@ -1,135 +0,0 @@
-function [equispaced, index] = find_equispaced_values(x, dx_guess)
- % for a set of values x
- % which are approximately equispaced, but with some invalid points added
- % that are not equispaced, try to find the points which are equally
- % spaced apart
-
- %% sort x ascending
- [x,idx] = sort(x, 'ascend');
- [~,reverse] = sort(idx, 'ascend');
- Nx = length(x);
-
- %% identify potential collisions
- % place values into bins which are a fraction of initial guess dx
- % if values are repeated approximately dx apart, then there should only
- % be one per bin
-
- bin_width = 0.8 * dx_guess;
- bin_index = round(x / bin_width);
- bin_centres = bin_width * (min(bin_index) : max(bin_index));
- n_bins = length(bin_centres);
- n_in_bin = zeros(1, n_bins); % number of elements in this bin
- idx_in_bin = cell(1, n_bins); % index of elements in x belonging to this bin
- possible_repeat= false(1, Nx);
- for i = 1 : n_bins
- centre_distance= abs(x - bin_centres(i));
- in_this_bin = centre_distance < bin_width / 2;
- n_in_bin(i) = sum(in_this_bin);
- idx_in_bin{i} = find(in_this_bin);
- if n_in_bin(i) > 1
- possible_repeat(in_this_bin) = true;
- end
- end
-
- %% find typical spacing of values not likely to be collisions
- dx_vec = x(2:end) - x(1:end-1);
- dx_vec = dx_vec(:);
- dx_vec = ([dx_vec; dx_vec(end)] + [dx_vec(1); dx_vec]) / 2; % mean of left and right spacing
- x_filtered = x(~possible_repeat);
- dx_filtered = dx_vec(~possible_repeat);
- dx_meas = median(dx_filtered);
-
- %% preliminary search
- % filter the data into a non-contiguous set of bins
- % which are spaced dx_meas apart, but are narrow
- % and find the offset that maximises the number of items identified
- %
- % ----x----x----x----x---- 0 fall into bins
- % ----x----x----x----x---- 0
- % ----x----x----x----x---- 0
- % ----x----x----x----x---- 1
- % ----x----x----x----x---- 4
- % * * * ** * *
- bin_spacing = dx_meas;
- bin_width = 0.15 * bin_spacing; % treated as half-width
- bin_offset = (-dx_meas/2) : bin_width/4 : (dx_meas/2);
- Nbins = length(bin_offset);
- Nvalid = zeros(1, Nbins);
- for iter = 1 : Nbins
- x_offset = x_filtered - bin_offset(iter);
- in_a_bin = abs(x_offset - round(x_offset/bin_spacing)*bin_spacing) < bin_width;
- Nvalid(iter)= sum(in_a_bin(:));
- end
- [~,best] = max(Nvalid);
- best_bin_offset= bin_offset(best);
-
- % identify values classified as equispaced
- % ignoring repeat values
- x_offset = x - bin_offset(best);
- bin_index = round(x_offset / bin_spacing);
- bin_centres = bin_spacing * (min(bin_index) : max(bin_index));
- n_bins = length(bin_centres);
- n_in_bin = zeros(1, n_bins);
- equispaced = false(1, Nx);
- for i = 1 : n_bins
- centre_distance= abs(x_offset - bin_centres(i));
- [d,closest] = min(centre_distance);
- if d < bin_width
- equispaced(closest) = true;
- end
- end
-
- % select only one contiguous block of equispaced points
- [labelled,N] = bwlabeln(equispaced);
- blocksize = zeros(1,N);
- for i = 1 : N
- blocksize(i) = sum(labelled == i);
- end
- [~,biggest_block] = max(blocksize);
- equispaced = equispaced & labelled == biggest_block;
-
- %% apply less conservative search
- % first search is usually too conservative, because of error in dx_meas
- % however, it gives us a good base for the next step
- % starting from the left, decide if a point in x belongs in the
- % equispaced set, because it's approximately dx_meas away from the last
- % point in that set
- bin_width = 0.15 * dx_meas;
- for candidate = 2 : Nx
- last_good = find(equispaced & ((1:Nx) < candidate), 1, 'last');
- if isempty(last_good)
- continue;
- end
- dx = x(candidate) - x(last_good);
- if abs(dx - round(dx/dx_meas)*dx_meas) < bin_width && abs(round(dx/dx_meas)) >= 1
- equispaced(candidate) = true;
- end
- end
- % apply same search from right to left
- for candidate = Nx-1 : -1 : 1
- first_good = find(equispaced & ((1:Nx) > candidate), 1, 'first');
- if isempty(first_good)
- continue;
- end
- dx = x(first_good) - x(candidate);
- if abs(dx - round(dx/dx_meas)*dx_meas) < bin_width && abs(round(dx/dx_meas)) >= 1
- equispaced(candidate) = true;
- end
- end
-
- %% index the markers
- Nvalid = sum(equispaced(:));
- i_valid = find(equispaced);
- index = zeros(1,Nvalid);
- for it = 2 : Nvalid
- dx = x(i_valid(it)) - x(i_valid(it-1));
- index(it) = index(it-1) + round(dx/dx_meas);
- end
- index_full = nan(1, Nx);
- index_full(equispaced) = index;
-
- %% undo the sorting
- equispaced = equispaced(reverse);
- index_full = index_full(reverse);
- index = index_full(equispaced);
-end
\ No newline at end of file
diff --git a/calibration/find_intersection.m b/calibration/find_intersection.m
deleted file mode 100644
index 9241f2c..0000000
--- a/calibration/find_intersection.m
+++ /dev/null
@@ -1,142 +0,0 @@
-function [m, c, width, line_u, line_v, bSuccess] = find_intersection(u, v, sheet_img, max_thickness, v_min, v_max, grid_du, grid_dv)
- % find_intersection(image)
- %
- % finds the line of intersection of a laser sheet and a calibration
- % plate in the image provided, in the format provided by readimx
- %
- % the image should have been dewarped in Davis first
- %
- % returns the line of intersection u = m*v + c in the image coordinate
- % system [u,v]
- % as well as the e^-2 thickness w of the laser sheet, as it is observed when
- % projected onto the calibration plate
- %
- % a gaussian profile of intensity for the laser sheet is assumed
- %
- % additionally, it is possible to specify:
- % max_thickness - the maximum thickness the laser sheet should be
- % v_min - minimum v to search for the intersection
- % v_max - maximum v to search for the intersection
-
- %% load coordinate system
- search_width = 10;
- grid_du = 10;
- grid_dv = 10;
- Nu = length(u);
- Nv = length(v);
-
- %% filter image
- G = fspecial('gaussian', [7 7], 2);
- sheet_img_filt = filter2(G, sheet_img);
-
- %% identify points on sheet
- % find maximum intensity along rows
- ok_range = v > v_min & v < v_max;
- line_v = v;
- line_u = zeros(1, Nv);
- bSuccess = false;
- for j = 1:Nv
- [m,i_maximum] = max(sheet_img_filt(:, j));
- if m > 500
- bSuccess = true;
- end
- line_u(j) = u(i_maximum);
- end
-
- if(~bSuccess)
- m = nan();
- c = nan();
- width = nan();
- return;
- end
-
- % find equation of fit
- line_coeffs = polyfit(line_v(ok_range), line_u(ok_range), 1);
- m = line_coeffs(1);
- c = line_coeffs(2);
-
- %% check whether fit is any good
- % if it's not, don't bother continuing
- fit_error = line_u(ok_range) - polyval(line_coeffs, line_v(ok_range));
- std_error = sqrt(prctile(fit_error.^2,66));
- max_error = abs(u(2) - u(1)) * 10;
- if std_error > max_error
- m = nan();
- c = nan();
- width = nan();
- bSuccess = false;
- return;
- end
- bSuccess = true;
-
- %% improve fit and measure e^-2 width
- % hack to avoid measurements across calibration markers
- v_min = round(v_min/grid_dv) * grid_dv;
- v_max = round(v_max/grid_dv) * grid_dv;
- v_min = max(min(v), v_min);
- v_max = min(max(v), v_max);
- line_v = linspace(v_min, v_max, Nrows);
- for row = 1 : Nrows
- rem = line_v(row) - round(line_v(row)/grid_dv)*grid_dv;
- if abs(rem) < 0.15 * grid_dv
- if rem < 0
- line_v(row) = (round(line_v(row)/grid_dv)-0.15) * grid_dv;
- else
- line_v(row) = (round(line_v(row)/grid_dv)+0.15) * grid_dv;
- end
- end
- end
- % v must be at integer pixel location
- row_idx = round(interp1(v, 1 : Nv, line_v));
- row_idx = min(max(row_idx,1),Nv);
- line_v = v(row_idx);
- line_u = zeros(1, Nrows);
- line_width = zeros(1, Nrows);
-
- for row = 1 : Nrows
- j = row_idx(row);
- u_line = m*v(j) + c;
- nearby = abs(u' - u_line) < search_width/2;
- dc_level = mean(sheet_img(:, j));
- thresh = max(sheet_img(nearby,j) - dc_level) * exp(-2);
- lb = find(nearby & (sheet_img(:,j)-dc_level) > thresh, 1, 'first');
- ub = find(nearby & (sheet_img(:,j)-dc_level) > thresh, 1, 'last');
- nearby = lb : ub;
- u_nearby = u(nearby);
- sig_nearby = sheet_img(nearby, j) + 1;
-
- % fit a cubic polynomial,
- % then around the maximum, evaluate coefficients for a quadratic
- % polynomial in that vicinity.
- %{
- p = polyfit(u_nearby(:), log(sig_nearby(:)), 3);
- p_prime = [3*p(1) 2*p(2) p(3)];
- extrema = roots(p_prime);
- if isreal(extrema)
- % identify which extrema is the maximum, find taylor series
- % expansion around it
- p_2prime = [6*p(1) 2*p(2)];
- extrema_type = polyval(p_2prime, extrema);
- x0 = extrema(extrema_type < 0);
- f_x0 = polyval(p, x0);
- fp_x0 = polyval(p_prime, x0);
- fpp_x0 = polyval(p_2prime, x0);
- p = [(fpp_x0/2), (fp_x0 - fpp_x0*x0), (f_x0 - fp_x0*x0 + fpp_x0*x0^2/2)];
- else
- % the fit doesn't have a local maximum/minimum,
- % i.e. we get imaginary roots. resort to a quadratic fit
- p = polyfit(u_nearby(:), log(sig_nearby(:)), 2);
- end
- %}
- p = polyfit(u_nearby(:), log(sig_nearby(:)), 2);
-
- line_u(row) = -p(2) / (2*p(1));
- line_width(row)= sqrt(-8 / p(1));
- end
-
- ok = ~imag(line_width) & ~isnan(line_width);
- width = mean(line_width(ok));
- line_coeffs = polyfit(line_v(ok), line_u(ok), 1);
- m = line_coeffs(1);
- c = line_coeffs(2);
-end
diff --git a/calibration/find_largest_volume.m b/calibration/find_largest_volume.m
deleted file mode 100644
index fc66240..0000000
--- a/calibration/find_largest_volume.m
+++ /dev/null
@@ -1,50 +0,0 @@
-function [XLim, YLim, ZLim] = find_largest_volume(P, ilim, jlim, M1, M2)
- % for the projection matrix P
- % which maps
- % [i j 1]' ~ P[x y z 1]'
- %
- % find the largest volume
- % xlim(1) <= x <= xlim(2)
- % ylim(1) <= y <= ylim(2)
- % zlim(1) <= z <= zlim(2)
- %
- % which is bound by planes represented by M1 and M2
- % points on a plane satisfy M(1:3)*x + M(4) = 0
- %
- % which may be projected onto [i,j] and still fit within
- % ilim(1) < i < ilim(2)
- % jlim(1) < j < jlim(2)
- %
- %
-
- % M1 and M2 are equ
- P1 = [P; M1];
- P2 = [P; M2];
- P1i = P1^-1;
- P2i = P2^-1;
-
- % corners on image plane
- i_corners = [ilim(1) ilim(1) ilim(2) ilim(2)];
- j_corners = [jlim(1) jlim(2) jlim(2) jlim(1)];
- ij10 = [i_corners(:) j_corners(:) ones(4,1) zeros(4,1)];
-
- % corners on plane 1
- lambda_inv = ij10 * P1i(4,:)';
- x_corners = ij10 * P1i(1,:)' ./ lambda_inv;
- y_corners = ij10 * P1i(2,:)' ./ lambda_inv;
- z_corners = ij10 * P1i(3,:)' ./ lambda_inv;
-
- % corners on plane 2
- lambda_inv = ij10 * P2i(4,:)';
- x_corners = [x_corners; ij10 * P2i(1,:)' ./ lambda_inv];
- y_corners = [y_corners; ij10 * P2i(2,:)' ./ lambda_inv];
- z_corners = [z_corners; ij10 * P2i(3,:)' ./ lambda_inv];
-
- % use this simple trick to work out largest box that fits
- XLim = sort(x_corners, 'ascend');
- YLim = sort(y_corners, 'ascend');
- ZLim = sort(z_corners, 'ascend');
- XLim = XLim([4 5]);
- YLim = YLim([4 5]);
- ZLim = ZLim([4 5]);
-end
\ No newline at end of file
diff --git a/calibration/flip_calibration.m b/calibration/flip_calibration.m
deleted file mode 100644
index 7f1ac81..0000000
--- a/calibration/flip_calibration.m
+++ /dev/null
@@ -1,30 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% flip_calibration.m
-%
-% flip the axes of a camera calibration
-
-%% input parameters
-FLIP_X = false;
-FLIP_Y = true;
-calib_dir = '/nfs/data/lawson/iso60/160726/calib/';
-calib_file_in = [calib_dir 'camera-model-original.mat'];
-calib_file_out = [calib_dir 'camera-model-flip.mat'];
-
-%% load
-fprintf('Loading %s\n', calib_file_in);
-setup = importdata(calib_file_in);
-
-% quick fix of camera model
-for c = 1 : setup.n_cameras
- if FLIP_X
- setup.camera(c).i_axis = setup.camera(c).i_axis(end:-1:1);
- end
- if FLIP_Y
- setup.camera(c).j_axis = setup.camera(c).j_axis(end:-1:1);
- end
-end
-
-%% save
-fprintf('Saving %s\n', calib_file_out);
-save(calib_file_out, '-struct', 'setup');
-fprintf('done\n');
\ No newline at end of file
diff --git a/calibration/local_maxima2.m b/calibration/local_maxima2.m
deleted file mode 100644
index 2f3b5e3..0000000
--- a/calibration/local_maxima2.m
+++ /dev/null
@@ -1,16 +0,0 @@
-function locmin = local_maxima2(im)
- sz = size(im);
- rx = 2 : sz(1)-1;
- ry = 2 : sz(2)-1;
- locmin = false(sz);
- locmin(rx,ry) = true;
-
- for i = -1 : 1
- for j = -1 : 1
- if i == j
- continue;
- end
- locmin(rx,ry) = locmin(rx,ry) & im(rx,ry) > im(rx+i,ry+j);
- end
- end
-end
\ No newline at end of file
diff --git a/calibration/local_maxima3.m b/calibration/local_maxima3.m
deleted file mode 100644
index 16bebfa..0000000
--- a/calibration/local_maxima3.m
+++ /dev/null
@@ -1,22 +0,0 @@
-function locmin = local_minima3(X)
- % return bool array of points that are local minima in the array x
- sz = size(X);
- rx = 2 : sz(1) - 1;
- ry = 2 : sz(2) - 1;
- rz = 2 : sz(3) - 1;
-
- locmin = true(sz-2);
-
- for i = -1 : 1
- for j = -1 : 1
- for k = -1 : 1
- if i == j && j == k
- continue;
- end
-
- locmin = locmin & X(rx,ry,rz) > X(rx+i,ry+j,rz+k);
- end
- end
- end
- locmin = padarray(locmin, [1 1 1], false, 'both');
-end
diff --git a/calibration/local_minima.m b/calibration/local_minima.m
deleted file mode 100644
index cde435b..0000000
--- a/calibration/local_minima.m
+++ /dev/null
@@ -1,15 +0,0 @@
-function locmin = local_minima(im)
- sz = size(im);
- locmin = true(sz);
- rx = 2 : sz(1)-1;
- ry = 2 : sz(2)-1;
-
- for i = -1 : 1
- for j = -1 : 1
- if i == j
- continue;
- end
- locmin(rx,ry) = locmin(rx,ry) & im(rx,ry) < im(rx+i,ry+j);
- end
- end
-end
\ No newline at end of file
diff --git a/calibration/local_minima3.m b/calibration/local_minima3.m
deleted file mode 100644
index f90b259..0000000
--- a/calibration/local_minima3.m
+++ /dev/null
@@ -1,22 +0,0 @@
-function locmin = local_minima3(X)
- % return bool array of points that are local minima in the array x
- sz = size(X);
- rx = 2 : sz(1) - 1;
- ry = 2 : sz(2) - 1;
- rz = 2 : sz(3) - 1;
-
- locmin = true(sz-2);
-
- for i = -1 : 1
- for j = -1 : 1
- for k = -1 : 1
- if i == j && j == k
- continue;
- end
-
- locmin = locmin & X(rx,ry,rz) < X(rx+i,ry+j,rz+k);
- end
- end
- end
- locmin = padarray(locmin, [1 1 1], false, 'both');
-end
diff --git a/calibration/log_find_markers.m b/calibration/log_find_markers.m
deleted file mode 100644
index 4ad8c50..0000000
--- a/calibration/log_find_markers.m
+++ /dev/null
@@ -1,69 +0,0 @@
-function [ij_mark, im_filt] = log_find_markers(i_axis, j_axis, im, scale, n_max, filter_type)
- % use Laplacian of Gaussian filter to find markers in image im
- % at lengthscale scale.
- %
- % returns n_max locations where the LoG filter is a local maximum
- % ordered by magnitude of the peak
- %
- % local quadratic peak finding is applied to get accurate locations of
- % maxima
-
- %% define filter
- if nargin < 6
- filter_type = 'log';
- end
- if strcmp(filter_type, 'log')
- % default laplacian of Gaussian filter
- kernel_size = round(scale*[1 1]*6);
- kernel_size = 2*floor(kernel_size/2) + 1; % always use odd kernel
- G = fspecial('log', kernel_size, scale);
- elseif strcmp(filter_type, 'disk')
- % disk filter
- G = -fspecial('disk', round(scale));
- kernel_size = size(G);
- end
- im_filt = single(conv2(im, G, 'same'));
-
- %% identify local maxima
- % grid
- Ni = length(i_axis);
- Nj = length(j_axis);
- [imat,jmat] = ndgrid(i_axis, j_axis);
- % search for local maximum in neighbourhood of kernel
- im_filt_max = maxfiltnd(im_filt, kernel_size); % sliding maximum filter over kernel_size area
- local_max = im_filt == im_filt_max;
- peak_mag = im_filt(local_max);
- % identify locations of local maxima
- [pli,plj] = find(local_max);
- ij_mark = [imat(local_max), jmat(local_max)];
- % sort by size of peak
- [~,reidx] = sort(peak_mag, 'descend');
- ij_mark = ij_mark(reidx, :);
- plij = [pli(reidx), plj(reidx)];
-
- % exclude those near edges
- edge = round(scale);
- i_lim = sort(i_axis([edge+1 end-edge]), 'ascend');
- j_lim = sort(j_axis([edge+1 end-edge]), 'ascend');
- near_edge = ij_mark(:,1) < i_lim(1) | ij_mark(:,1) > i_lim(2) ...
- | ij_mark(:,2) < j_lim(1) | ij_mark(:,2) > j_lim(2);
- ij_mark = ij_mark(~near_edge,:);
- plij = plij(~near_edge, :);
-
- %% recover n_max largest local maxima
- n_mark = min(n_max, size(ij_mark,1));
- ij_mark = ij_mark(1:n_mark, :)';
- plij = plij(1:n_mark,:);
-
- %% apply local quadratic peak finding
- rng = (-edge : edge);
- di = (i_axis(end) - i_axis(1))/(Ni-1);
- dj = (j_axis(end) - j_axis(1))/(Nj-1);
- for n = 1 : n_mark
- p_x = polyfit(rng*di, im_filt(plij(n,1) + rng, plij(n,2))', 2);
- p_y = polyfit(rng*dj, im_filt(plij(n,1), plij(n,2) + rng) , 2);
- ij_mark(1,n) = ij_mark(1,n) - p_x(2) / (2*p_x(1));
- ij_mark(2,n) = ij_mark(2,n) - p_y(2) / (2*p_y(1));
- end
-end
-
\ No newline at end of file
diff --git a/calibration/log_test.m b/calibration/log_test.m
deleted file mode 100644
index cf3bb99..0000000
--- a/calibration/log_test.m
+++ /dev/null
@@ -1,179 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% test laplacian of Gaussian method for automatic identification of markers
-% in image
-
-
-blob_scale = 3;
-grid_tol = 0.2;
-n_iter = 3;
-grid_size_x = 21; % must be odd #
-grid_size_y = 21; % must be odd #
-img_file = '/data/LowRe-2013/calib/CAMERA-01/CAM1_000001.tif';
-
-%% load image
-im = imread(img_file)';
-im = double(im);
-im = fliplr(im);
-sz = size(im);
-Ni = sz(1);
-Nj = sz(2);
-i_axis = 1 : sz(1);
-j_axis = 1 : sz(2);
-[imat,jmat] = ndgrid(i_axis,j_axis);
-
-%% filter image at twice blob scale, get 3 big markers
-[ij_big,im_filt] = log_find_markers(i_axis, j_axis, im, 2*blob_scale, 3);
-
-% identify top, right and center markers
-% calculate unit vectors corresponding to each edge
-normals = [(ij_big(:,3) - ij_big(:,2))' / norm(ij_big(:,3) - ij_big(:,2));
- (ij_big(:,3) - ij_big(:,1))' / norm(ij_big(:,3) - ij_big(:,1));
- (ij_big(:,2) - ij_big(:,1))' / norm(ij_big(:,2) - ij_big(:,1))];
-% if n_12 is nearly parallel to y |n_12.y| ~= 1
-% then vertex 3 is the right hand vertex. use this trick to find right hand
-% vertex
-[~,i_right] = max(abs(normals(:,2)));
-% remaining vertices are top & center. top is higher up in image than
-% center
-i_tb = setdiff(1:3, i_right);
-if ij_big(2, i_tb(1)) > ij_big(2, i_tb(2))
- i_top = i_tb(1);
- i_center = i_tb(2);
-else
- i_top = i_tb(2);
- i_center = i_tb(1);
-end
-
-ij_center = ij_big(:,i_center);
-ij_top = ij_big(:,i_top);
-ij_right = ij_big(:,i_right);
-ij_big = [ij_center, ij_right, ij_top];
-xy_big = [[0;0], [1;0], [0;1]];
-
-% calculate included area of triangle
-tri_area_px = abs(ij_big(1,1)*(ij_big(2,2) - ij_big(2,3)) ...
- + ij_big(1,2)*(ij_big(2,3) - ij_big(2,1)) ...
- + ij_big(1,3)*(ij_big(2,1) - ij_big(2,2))) / 2;
-tri_area_mm = 0.5;
-n_markers_est = floor(Ni * Nj / tri_area_px / 2);
-search_tolerance = 0.05 * norm(ij_top - ij_right);
-scale_px_to_mm = sqrt(tri_area_px / tri_area_mm);
-
-%% debug: plot identified markers
-figure(1);
-clf;
-imagesc(i_axis, j_axis, im_filt');
-hold on;
-plot(ij_center(1), ij_center(2), 'ro');
-plot(ij_top(1), ij_top(2), 'go');
-plot(ij_right(1), ij_right(2), 'bo');
-legend('center', 'top', 'right');
-%plot(ij_markers(1,:), ij_markers(2,:), 'k.');
-
-axis equal tight xy;
-colormap gray;
-xlim(i_axis([1 end]));
-ylim(j_axis([1 end]));
-
-%% filter image at blob scale, find ordinary markers
-[ij_ordinary,im_filt]= log_find_markers(i_axis, j_axis, im, blob_scale, n_markers_est);
-% exclude those near cent025er, top + right markers
-d_center = sqrt(sum(bsxfun(@minus, ij_ordinary, ij_center).^2, 1));
-d_right = sqrt(sum(bsxfun(@minus, ij_ordinary, ij_right ).^2, 1));
-d_top = sqrt(sum(bsxfun(@minus, ij_ordinary, ij_top ).^2, 1));
-too_near = d_center < search_tolerance*4 ...
- | d_right < search_tolerance*4 ...
- | d_top < search_tolerance*4;
-ij_ordinary = ij_ordinary(:, ~too_near);
-% combine set with location of top, center and right markers
-ij_markers = [ij_center, ij_right, ij_top, ij_ordinary];
-
-%% determine affine transformation, select extra markers
-A_xy_to_ij = [ij_right-ij_center, ij_top-ij_center];
-xy_markers = A_xy_to_ij \ bsxfun(@minus, ij_markers, ij_center);
-
-% choose a set of nearby markers
-x_near = -1 : 1;
-y_near = x_near;
-[xm_near,ym_near] = ndgrid(x_near, y_near);
-xy_near = [xm_near(:), ym_near(:)]';
-
-% find matching markers and calcuate pinhole model
-[xy_near,ij_near] = search_markers(xy_near, ij_markers, search_tolerance, 'affine', A_xy_to_ij, ij_center);
-n_near = size(xy_near, 2);
-[P2D, ij_res] = p2d_fit(xy_near, ij_near);
-
-%% iteratively refine model, search for more markers
-max_grid_size = max(grid_size_x, grid_size_y);
-for grid_size = 3 : 2 : max_grid_size
- %% choose a set of nearby markers to search for
- search_size_x = min(grid_size, grid_size_x);
- search_size_y = min(grid_size, grid_size_y);
- x_near = -(search_size_x-1)/2 : +(search_size_x-1)/2;
- y_near = -(search_size_y-1)/2 : +(search_size_y-1)/2;
- [xm_near,ym_near] = ndgrid(x_near, y_near);
- xy_near = [xm_near(:), ym_near(:)]';
-
- %% find matching markers and refine pinhole model
- [xy_near,ij_near,ij_pred] = search_markers(xy_near, ij_markers, search_tolerance, 'pinhole', P2D);
- n_near = size(xy_near, 2);
- [P2D, ij_res] = p2d_fit(xy_near, ij_near);
-end
-
-%% report statistics of residuals
-fprintf('found %d markers\n', n_near);
-fprintf('RMS residual:\n');
-fprintf(' x %0.3f px\n', sqrt(mean(ij_res(1,:).^2)));
-fprintf(' y %0.3f px\n', sqrt(mean(ij_res(2,:).^2)));
-fprintf('Largest residual:\n');
-fprintf(' x %0.3f px\n', max(abs(ij_res(1,:))));
-fprintf(' y %0.3f px\n', max(abs(ij_res(2,:))));
-
-%% dewarp with pinhole model
-x_lim = [min(xy_near(1,:)), max(xy_near(1,:))];
-y_lim = [min(xy_near(2,:)), max(xy_near(2,:))];
-Nx = round((x_lim(2)-x_lim(1))*scale_px_to_mm);
-Ny = round((y_lim(2)-y_lim(1))*scale_px_to_mm);
-x_axis = linspace(x_lim(1), x_lim(2), Nx);
-y_axis = linspace(y_lim(1), y_lim(2), Ny);
-[imat_dewarped,jmat_dewarped] = p2d_dewarp(P2D, x_axis, y_axis);
-im_dewarped = interp2(i_axis, j_axis, im', imat_dewarped, jmat_dewarped);
-
-xy_near_true = p2d_map_im2obj(P2D, ij_near);
-xy_big = p2d_map_im2obj(P2D, ij_big);
-xy_center = xy_big(:,1);
-xy_right = xy_big(:,2);
-xy_top = xy_big(:,3);
-
-%% plot
-figure(2);
-clf;
-imagesc(x_axis, y_axis, im_dewarped');
-% plot markers
-hold on;
-plot(xy_near_true(1,:), xy_near_true(2,:), 'r.');
-viscircles(xy_center', 0.1, 'DrawBackgroundCircle', true, 'EdgeColor', 'red');
-viscircles(xy_right' , 0.1, 'DrawBackgroundCircle', true, 'EdgeColor', 'blue');
-viscircles(xy_top' , 0.1, 'DrawBackgroundCircle', true, 'EdgeColor', 'green');
-
-axis equal tight xy;
-xlim(x_lim);
-ylim(y_lim);
-colormap gray;
-grid on;
-set(gca, 'xtick', x_lim(1) : x_lim(2));
-set(gca, 'ytick', y_lim(1) : y_lim(2));
-%{
-%plot(ij_center(1), ij_center(2), 'rx');
-%plot(ij_top(1), ij_top(2), 'gx');
-%plot(ij_right(1), ij_right(2), 'kx');
-%plot(ij_markers(1,:), ij_markers(2,:), 'k.');
-plot(ij_near(1,:), ij_near(2,:), 'rx');
-viscircles(ij_pred', search_tolerance*ones(n_near,1));
-for n = 1 : n_near
- text(ij_near(1,n)+20, ij_near(2,n), sprintf('%d,%d', xy_near(:,n)));
-end
-axis equal tight xy;
-xlim(i_axis([1 end]));
-ylim(j_axis([1 end]));
-%}
\ No newline at end of file
diff --git a/calibration/make_dark.m b/calibration/make_dark.m
deleted file mode 100644
index c72494c..0000000
--- a/calibration/make_dark.m
+++ /dev/null
@@ -1,92 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% generate a dark image based on particle tracking movies
-
-addpath([pwd '/../gmv']);
-
-%% input parameters
-root_dir = '/scratch21/lawson/iso960/';
-gmv_pat = '%s/gmv/series_%04d.cam%d.gmv';
-dark_pat = '%s/calib/dark_160803.cam%d.gmv';
-
-cam_idx = 0;
-series_range = 0 : 40 : 2999;
-frame_skip = 35;
-c_lim = [0 128];
-
-%% load first image
-gmv_name = sprintf(gmv_pat,root_dir,series_range(1),cam_idx);
-[dark_img,header] = read_gmv(gmv_name, 0, 1);
-threshold = floor(header.threshold / 256);
-dark_img = ones(size(dark_img), 'uint16') * threshold;
-n_frames = header.n_frames;
-
-%% iterate over series
-n_series = length(series_range);
-for is = 1 : n_series
- %% report
- series_idx = series_range(is);
- gmv_name = sprintf(gmv_pat,root_dir,series_idx,cam_idx);
- fprintf('Series %4d: %s\n', series_idx, gmv_name);
-
- %% load frames
- for f = 0 : frame_skip : n_frames-1
- frm = read_gmv(gmv_name, f, 1);
- frm = uint16(frm);
- nz = frm ~= 0;
- dark_img(nz) = min(dark_img(nz), frm(nz));
- end
-end
-
-%% save dark image
-dark_name = sprintf(dark_pat, root_dir, cam_idx);
-fprintf('Saving to %s\n', dark_name);
-b_write = false;
-if exist(dark_name, 'file')
- str = input('File already exists. Overwrite? y/n ', 's');
- if strcmp(str, 'y')
- b_write = true;
- end
-else
- b_write = true;
-end
-
-if b_write
- save_gmv(dark_name, dark_img, header);
-end
-fprintf('done\n');
-
-%% plot
-
-figure(1);
-imagesc(dark_img');
-ax = gca;
-axis equal tight ij;
-set(gca, 'clim', [0 threshold]);
-colormap(gray);
-colorbar;
-title(sprintf('Dark image of camera %d', cam_idx));
-
-%% plot last frame with dark correction
-frm_corr = frm - dark_img;
-figure(2);
-imagesc(frm_corr');
-ax(2)=gca;
-axis equal tight ij;
-set(gca, 'clim', c_lim);
-colormap(1-gray);
-colorbar;
-title(sprintf('Corrected image from camera %d', cam_idx));
-
-%% plot last frame without dark correction
-figure(3);
-imagesc(frm');
-ax(3)=gca;
-axis equal tight ij;
-set(gca, 'clim', c_lim);
-colormap(1-gray);
-colorbar;
-title(sprintf('Uncorrected image from camera %d', cam_idx));
-
-%% link axes
-linkaxes(ax, 'xy');
-
diff --git a/calibration/manual_sheet_thickness.m b/calibration/manual_sheet_thickness.m
deleted file mode 100644
index d027111..0000000
--- a/calibration/manual_sheet_thickness.m
+++ /dev/null
@@ -1,61 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% manually measure laser sheet thickness
-
-im_file = '/nfs/scratch23/lawson/K62/170119/sheet/sheet_thickness_9213.tif';
-
-%% load image
-im = imread(im_file);
-im = im(:,:,1);
-im = double(im);
-
-%% plot
-figure(1);
-clf;
-ax = [0,0];
-ax(2) = subplot(1,2,2);
-ax(1) = subplot(1,2,1);
-
-imagesc(im);
-axis equal tight ij;
-
-%% get points
-fprintf('Select 2 points\n');
-ij_points = ginput(2);
-ij_start = round(ij_points(1,:));
-ij_end = round(ij_points(2,:));
-ij_end = [ij_end(1), ij_start(2)];
-ij_points = [ij_start; ij_end];
-
-%% get brightness and fit
-i_axis = ij_start(1) : ij_end(1);
-j_axis = ij_start(2) * ones(size(i_axis));
-line_profile= im(ij_start(2), i_axis);
-
-ft = fittype('A*exp( -((x-x0)/s).^2 ) + C', 'indep', 'x', 'coefficients', {'A', 'x0', 'C', 's'});
-
-% initial guess
-A0 = max(line_profile) - min(line_profile);
-C0 = min(line_profile);
-x0 = mean(i_axis);
-s0 = max(i_axis) - min(i_axis);
-
-P0 = [A0, x0, C0, s0];
-fitfun = fit(i_axis(:), line_profile(:), ft, 'StartPoint', P0);
-
-sigma = fitfun.s;
-width = 2*sqrt(2)*sigma;
-
-%% report
-fprintf('Measured at y = %d px\n', ij_start(2));
-fprintf('Thickness: %0.1f px\n', sigma);
-fprintf('e^-2 thickness: %0.1f px\n', width);
-
-%% plot points
-hold(ax(1), 'on');
-
-plot(ax(1), ij_points(:,1), ij_points(:,2), 'rx-');
-
-hold(ax(2), 'off');
-plot(ax(2), i_axis, line_profile, 'kx');
-hold(ax(2), 'on');
-plot(ax(2), i_axis, fitfun(i_axis), 'k-');
\ No newline at end of file
diff --git a/calibration/p2d_dewarp.m b/calibration/p2d_dewarp.m
deleted file mode 100644
index 7cac550..0000000
--- a/calibration/p2d_dewarp.m
+++ /dev/null
@@ -1,9 +0,0 @@
-function [imat,jmat] = p2d_dewarp(P2D, x_axis, y_axis)
- Nx = length(x_axis);
- Ny = length(y_axis);
- [xmat, ymat] = ndgrid(x_axis, y_axis);
- xy = [xmat(:), ymat(:)]';
- ij = p2d_map_obj2im(P2D, xy);
- imat = reshape(ij(1,:), [Nx Ny]);
- jmat = reshape(ij(2,:), [Nx Ny]);
-end
\ No newline at end of file
diff --git a/calibration/plot_calibration_residuals.m b/calibration/plot_calibration_residuals.m
deleted file mode 100644
index 2378ee9..0000000
--- a/calibration/plot_calibration_residuals.m
+++ /dev/null
@@ -1,57 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% plot residuals in calibration procedure, comparing projected points
-% according to model and observed points
-
-addpath([pwd '/../calibration-mpids/']);
-
-%% input parameters
-calib_dir = '/nfs/scratch21/lawson/161019/calib/';
-calib_file_mat = [calib_dir '/camera-model-original.mat'];
-font_size = 20;
-pixel_pitch_mm = [10E-3 10E-3];
-
-%% load calibration
-fs = importdata(calib_file_mat);
-n_cameras = fs.n_cameras;
-camera = fs.camera;
-
-%% iterate over cameras
-for c = 1 : n_cameras
- %% load parameters
- i_axis = camera(c).i_axis;
- j_axis = camera(c).j_axis;
- Ni = length(i_axis);
- Nj = length(j_axis);
-
- %% load markers and calculate residuals
- gl_markers = camera(c).calib.gl_markers;
- ij_markers = camera(c).calib.ij_markers;
- ij_markers_proj = pinhole_model(gl_markers, camera(c).calib, true);
- ij_markers_res = ij_markers - ij_markers_proj;
-
- %% Tsai calibration
- cparams = struct('Npixh', Nj, 'Npixw', Ni, ...
- 'hpix', pixel_pitch_mm(1), ...
- 'wpix', pixel_pitch_mm(2));
- [tsai_calib, tsai_res] = calib_Tsai(ij_markers', gl_markers', cparams);
- tsai_res = -tsai_res';
-
- %% plot
- title_str = sprintf('Camera %d', c);
-
- figure(c);
- clf;
- quiver(ij_markers(1,:), ij_markers(2,:), ...
- ij_markers_res(1,:), ij_markers_res(2,:), 'b');
- %hold on;
- %quiver(ij_markers(1,:), ij_markers(2,:), ...
- % tsai_res(1,:), tsai_res(2,:), 'r');
- axis equal tight xy;
- xlim(i_axis([1 end]));
- ylim(j_axis([1 end]));
-
- % make pretty
- title(title_str, 'interpreter', 'latex', 'fontsize', font_size);
- xlabel('$i$ / px', 'interpreter', 'latex', 'fontsize', font_size);
- ylabel('$j$ / px', 'interpreter', 'latex', 'fontsize', font_size);
-end
\ No newline at end of file
diff --git a/calibration/plot_dewarped.m b/calibration/plot_dewarped.m
deleted file mode 100644
index 07e6716..0000000
--- a/calibration/plot_dewarped.m
+++ /dev/null
@@ -1,102 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% load a raw image from file and dewarp it with a 2D dewarping specified
-% in the provided calibration file
-
-%% input parameters
-
-img_file = '/data/cluster/160309/9219-003.TIF';
-calib_file = '/data/cluster/160309/CAM1-01.mat';
-x0 = [7, -2];
-
-%% load calibration
-fs = importdata(calib_file);
-P2D = fs.P;
-i_axis = fs.i_axis;
-j_axis = fs.j_axis;
-x_axis = fs.x_axis;
-y_axis = fs.y_axis;
-Ni = length(i_axis);
-Nj = length(j_axis);
-grid_dx = fs.grid_dx;
-grid_dy = fs.grid_dy;
-
-%% load image and dewarp
-[xmat,ymat] = ndgrid(x_axis, y_axis);
-[imat_xy,jmat_xy] = p2d_dewarp(P2D, x_axis, y_axis);
-im_ij = double(fliplr(imread(img_file)'));
-im_xy = interp2(i_axis, j_axis, im_ij', imat_xy, jmat_xy, 'linear', 0);
-
-G = fspecial('gaussian', [33 33], 3);
-im_filt = filter2(G, im_xy);
-
-%% cut a small subregion out
-x_prime = x_axis - x0(1);
-y_prime = y_axis - x0(2);
-xrange = abs(x_prime) < 30;
-yrange = abs(y_prime) < 30;
-im_cut = im_filt(xrange,yrange);
-x_cut = x_prime(xrange);
-y_cut = y_prime(yrange);
-[xmc, ymc] = ndgrid(x_cut, y_cut);
-
-%% plot
-figure(1);
-S = surf(x_cut, y_cut, im_cut'); set(S, 'edgecolor', 'none');
-axis equal tight xy;
-grid on;
-colormap jet;
-
-%% fit gaussian
-fitfun = fittype('A*exp(-((x-x0).^2 + (y-y0).^2)/s^2)', ...
- 'dependent', 'z', ...
- 'indep', {'x','y'}, ...
- 'coeff', {'A', 'x0', 'y0', 's'});
-
-[fm,good] = fit([xmc(:),ymc(:)], im_cut(:), fitfun, 'StartPoint', startpoint);
-im_model = fm(xmc, ymc);
-im_res = im_cut - im_model;
-
-disp(fm);
-
-%% plot solution
-figure(2);
-clf;
-
-subplot(1,2,1);
-cla;
-hold off;
-imagesc(x_cut, y_cut, im_cut');
-hold on;
-[C,h] = contour(x_cut, y_cut, im_model'/fm.A, linspace(0, 1, 11));
-clabel(C,h);
-
-axis equal tight xy;
-grid on;
-colormap jet;
-colorbar;
-
-subplot(1,2,2);
-imagesc(x_cut, y_cut, im_res');
-axis equal tight xy;
-grid on;
-colormap jet;
-colorbar;
-
-%% get points and draw
-%{
-xy_circ = ginput();
-%%
-xy_orig = mean(xy_circ, 1);
-xy_dist = sqrt(sum(bsxfun(@minus,xy_circ, xy_orig).^2, 2));
-radius = mean(xy_dist);
-
-theta = linspace(0,2*pi);
-cost = cos(theta);
-sint = sin(theta);
-
-hold on;
-plot(xy_circ(:,1), xy_circ(:,2), 'rx');
-plot(xy_orig(1), xy_orig(2), 'ro');
-plot(xy_orig(1)+cost*radius, xy_orig(2)+sint*radius, 'r-');
-fprintf('radius = %0.3f mm\n', radius);
-%}
\ No newline at end of file
diff --git a/calibration/plot_dewarped_3d.m b/calibration/plot_dewarped_3d.m
deleted file mode 100644
index df88480..0000000
--- a/calibration/plot_dewarped_3d.m
+++ /dev/null
@@ -1,51 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% load a raw image from file and dewarp it with a 2D dewarping specified
-% in the provided calibration file
-
-%% input parameters
-model_file = '/scratch23/lawson/K62/170118/calib/camera-model-original.mat';
-calib_file = '/scratch23/lawson/K62/170118/calib/CAM2-05.mat';
-
-%% load calibration
-model = importdata(model_file);
-fs = importdata(calib_file);
-cam_idx = fs.camera_index;
-cam = model.camera(cam_idx);
-
-i_axis = fs.i_axis;
-j_axis = fs.j_axis;
-Ni = length(i_axis);
-Nj = length(j_axis);
-
-x_axis = fs.x_axis*2;
-y_axis = fs.y_axis*2;
-gl_plate = fs.plate_pos;
-Nx = length(x_axis);
-Ny = length(y_axis);
-im_ij = fs.source_image;
-grid_dx = fs.grid_dx;
-grid_dy = fs.grid_dy;
-
-%% load image and dewarp
-[xmat,ymat,zmat] = ndgrid(x_axis, y_axis, gl_plate(3));
-gl_mat = [xmat(:) ymat(:) zmat(:)]';
-ij_mat = pinhole_model(gl_mat, cam.calib, true);
-imat_xy = reshape(ij_mat(1,:), [Nx Ny]);
-jmat_xy = reshape(ij_mat(2,:), [Nx Ny]);
-
-im_xy = interp2(i_axis, j_axis, im_ij', imat_xy, jmat_xy, 'linear', 0);
-
-G = fspecial('gaussian', [33 33], 3);
-im_filt = filter2(G, im_xy);
-
-%% plot
-figure(1);
-clf;
-
-xtick = min(x_axis) : grid_dx : max(x_axis);
-ytick = min(y_axis) : grid_dy : max(y_axis);
-
-imagesc(x_axis, y_axis, im_xy');
-axis equal tight xy;
-set(gca, 'xtick', xtick, 'ytick', ytick);
-grid on;
\ No newline at end of file
diff --git a/calibration/plot_marker_search.m b/calibration/plot_marker_search.m
deleted file mode 100644
index 3511ceb..0000000
--- a/calibration/plot_marker_search.m
+++ /dev/null
@@ -1,40 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% make a simple plot of the markers identified by search
-% for a given calibration
-addpath([pwd '/../misc']);
-calib_file = 'D:\bigtank-jul-2013\CAL-20-07-13\CAMERA\CAM1-11.mat';
-
-%% load
-fs = importdata(calib_file);
-i_axis = fs.i_axis;
-j_axis = fs.j_axis;
-ij_markers = fs.ij_markers;
-ij_center = fs.ij_center;
-ij_right = fs.ij_right;
-ij_top = fs.ij_top;
-uv_marker = fs.xy_markers;
-im_source = fs.source_image;
-
-%% plot
-draw_arrow = @(x,y,varargin) quiver( x(1),y(1),x(2)-x(1),y(2)-y(1),0, varargin{:} );
-
-
-figure(1);
-clf;
-imagesc(i_axis, j_axis, im_source');
-axis equal tight xy;
-colormap gray;
-
-hold on;
-plot(ij_markers(1,:), ij_markers(2,:), 'rx', 'markersize', 4);
-plot(ij_center(1), ij_center(2), 'ro', 'MarkerSize', 6);
-plot(ij_right(1), ij_right(2), 'bo', 'MarkerSize', 6);
-plot(ij_top(1), ij_top(2), 'go', 'MarkerSize', 6);
-
-%clim([0 1024]);
-set(gca, 'xtick', []);
-set(gca, 'ytick', []);
-%annotation('arrow', [ij_center(1) ij_right(1)], [ij_center(2) ij_right(2)], '-', 'Color', [0 0 1]);
-%annotation('arrow', [ij_center(1) ij_top(1)], [ij_center(2) ij_top(2)], '-', 'Color', [0 1 0]);
-
-
diff --git a/calibration/plot_residuals.m b/calibration/plot_residuals.m
deleted file mode 100644
index c2410d2..0000000
--- a/calibration/plot_residuals.m
+++ /dev/null
@@ -1,16 +0,0 @@
-file_name_pat = '/data/050216/calib/CAM1-%02d.mat';
-
-for n = 1 : 11
- file_name = sprintf(file_name_pat, n);
- load(file_name);
-
- ij_pred = p2d_map_obj2im(P, xy_markers);
- res = ij_pred-ij_markers;
-
- figure(1);
- clf;
- quiver(ij_markers(1,:),ij_markers(2,:), res(1,:),res(2,:), 'b-');
- axis equal tight xy;
-
- pause();
-end
\ No newline at end of file
diff --git a/calibration/plot_residuals_3d.m b/calibration/plot_residuals_3d.m
deleted file mode 100644
index 4610e2f..0000000
--- a/calibration/plot_residuals_3d.m
+++ /dev/null
@@ -1,176 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% plot residuals of calibration in 3D
-
-addpath([pwd '/pinhole/']);
-
-%% input parameters
-calib_file_pat = '/scratch23/lawson/K62/170118/calib/CAM%d-%02d.mat';
-camera_model_file = '/scratch23/lawson/K62/170118/calib/camera-model-original.mat';
-font_size = 20;
-DO_DEBUG_PLOT = false;
-
-%% load model
-setup = importdata(camera_model_file);
-
-% camera calibration
-n_cameras = setup.n_cameras;
-n_views = setup.n_views;
-
-% plate pose
-gl_plate_pos = setup.gl_plate_pos;
-pl_to_gl_mat = repmat(setup.pl_to_gl, [1 1 n_views]);
-camera_model = setup.camera;
-gl_to_re = eye(3);
-gl_origin = setup.gl_origin;
-r_sphere = min(setup.r_sphere);
-n_views = setup.n_views;
-
-%% calculate coordinates of inscribed sphere
-theta = linspace(0, pi, 21);
-phi = linspace(0, 2*pi, 41);
-[tmat,pmat] = ndgrid(theta,phi);
-en_sphere = [sin(tmat(:)').*cos(pmat(:)');
- sin(tmat(:)').*sin(pmat(:)');
- cos(tmat(:)')];
-gl_sphere = bsxfun(@plus, r_sphere * en_sphere, gl_origin);
-
-%% triangulate markers from plate
-gl_markers_ref = [];
-gl_markers_tri = [];
-ij_res_ary = cell(1, n_cameras);
-
-for v = 1 : n_views
- %% load markers
- fprintf('View %d\n', v);
- ij_markers = cell(n_cameras, 1);
- uv_markers = cell(n_cameras, 1);
- n_markers = zeros(n_cameras, 1);
- for c = 1 : n_cameras
- fname = sprintf(calib_file_pat, c, v);
- fs = importdata(fname);
- ij_markers{c} = fs.ij_markers;
- uv_markers{c} = fs.xy_markers;
- n_markers(c) = size(fs.ij_markers,2);
-
- fprintf(' c%d: %d markers\n', c, n_markers(c));
- end
-
- %% identify common markers
- uv_common = uv_markers{1};
- for c = 2 : n_cameras
-
- uv_common = intersect(uv_common.', uv_markers{c}.', 'rows').';
- end
- n_common = size(uv_common, 2);
- ij_common = zeros(2, n_common, n_cameras);
- for c = 1 : n_cameras
- [~, ia, ib] = intersect(uv_markers{c}.', uv_common.', 'rows');
- ij_common(:,:,c) = ij_markers{c}(:, ia);
- end
- fprintf(' %d common markers\n', n_common);
-
- %% global coords of common markers
- pl_to_gl = pl_to_gl_mat(:,:,v);
- gl_plate = gl_plate_pos(:,v);
- gl_common = mtimesx(pl_to_gl, [uv_common; zeros(1,n_common)]);
- gl_common = bsxfun(@plus, gl_plate, gl_common);
-
- %% triangulate
- [gl_tri, gl_res, ij_res] = triangulate(setup.camera, ij_common, true);
-
- %% add to array
- gl_markers_tri = [gl_markers_tri, gl_tri];
- gl_markers_ref = [gl_markers_ref, gl_common];
- for c = 1 : n_cameras
- ij_res_ary{c} = [ij_res_ary{c}; ij_res(:,c)];
- end
-
- %% plot common markers
- style = {'r.', 'g.', 'b.'};
- if DO_DEBUG_PLOT
- figure(1);
- clf;
- plot(uv_common(1,:), uv_common(2,:), 'ko');
- hold on;
- for c = 1 : n_cameras
- plot(uv_markers{c}(1,:), uv_markers{c}(2,:), style{c});
- end
- axis equal tight xy;
- make_plot_pretty('$u$ [mm]', '$v$ [mm]', '', 20);
- end
-end
-ij_res_ary = cell2mat(ij_res_ary);
-
-%% calculate disparity
-gl_disp = gl_markers_tri - gl_markers_ref;
-ij_rms = rms(ij_res_ary, 1);
-ij_max = max(ij_res_ary, [], 1);
-gl_rms = rms(gl_disp, 2);
-gl_max = max(abs(gl_disp), [], 2);
-gl_mean = mean(gl_disp, 2);
-
-%% report
-fprintf('Disparity after triangulation:\n');
-fprintf(' c%d: %0.3f px rms %0.3f px max\n', [1:n_cameras; ij_rms; ij_max]);
-fprintf(' x: %6.3f um rms %6.3f um max %+6.3f um mean\n', 1000*gl_rms(1), 1000*gl_max(1), 1000*gl_mean(1));
-fprintf(' y: %6.3f um rms %6.3f um max %+6.3f um mean\n', 1000*gl_rms(2), 1000*gl_max(2), 1000*gl_mean(2));
-fprintf(' z: %6.3f um rms %6.3f um max %+6.3f um mean\n', 1000*gl_rms(3), 1000*gl_max(3), 1000*gl_mean(3));
-
-%% plot markers and their triangulation
-quiver_scale = 40;
-figure(2);
-clf;
-quiver3sc( gl_markers_ref(1,:), gl_markers_ref(2,:), gl_markers_ref(3,:), ...
- gl_disp(1,:), gl_disp(2,:), gl_disp(3,:), quiver_scale, 'k-');
-hold on;
-plot3(gl_sphere(1,:), gl_sphere(2,:), gl_sphere(3,:), 'b.');
-
-axis equal tight;
-xlabel('$x$ [mm]', 'interpreter', 'latex', 'fontsize', font_size);
-ylabel('$y$ [mm]', 'interpreter', 'latex', 'fontsize', font_size);
-zlabel('$z$ [mm]', 'interpreter', 'latex', 'fontsize', font_size);
-
-%% triangulate markers using calibration
-error('stop');
-
-gl_ref = [];
-gl_meas = [];
-ij_meas = [];
-
-for v = 1 : n_views
- %% get plate, image and world coordinates of markers
- pl_to_gl = pl_to_gl_mat(:,:,v);
- gl_pos = gl_plate_pos(:,v);
- uv_mark = camera.view(v).uv_markers;
- n_mark = size(uv_mark, 2);
- gl_mark = bsxfun(@plus, gl_pos, pl_to_gl*[uv_mark; zeros(1,n_mark)]);
- ij_mark = camera.view(v).ij_markers;
-
- %% calculate equation of plane
- n = pl_to_gl(:,3);
- d = -dot(gl_pos, n);
- M = [n; d];
-
- %% back-project
- gl_proj = back_project_1c(camera.calib, M, ij_mark, true);
-
- %% save
- gl_ref = [gl_ref , gl_mark];
- gl_meas = [gl_meas, gl_proj];
- ij_meas = [ij_meas, ij_mark];
-end
-
-% calculate disparity
-gl_disp = gl_meas - gl_ref;
-
-%% plot
-quiver_scale = 10;
-figure(1);
-clf;
-quiver3sc( gl_ref(1,:) , gl_ref(2,:) , gl_ref(3,:) , ...
- gl_disp(1,:), gl_disp(2,:), gl_disp(3,:), quiver_scale, 'k-');
-axis equal tight xy;
-
-xlabel('x / mm');
-ylabel('y / mm');
-zlabel('z / mm');
diff --git a/calibration/plot_traverse_plate_alignment.m b/calibration/plot_traverse_plate_alignment.m
deleted file mode 100644
index d883b5f..0000000
--- a/calibration/plot_traverse_plate_alignment.m
+++ /dev/null
@@ -1,60 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% make a pretty plot for thesis of the traverse/plate alignment
-addpath([pwd '/../misc/']);
-trav_x = [-30
--25
--20
--15
--10
--5
-0
-5
-10
-15
-20
-25
-30];
-trav_z1 = [-0.17
--0.17
--0.18
--0.19
--0.19
--0.21
--0.23
--0.25
--0.26
--0.27
--0.26
--0.26
--0.28];
-trav_z2 = [-0.2
--0.19
--0.16
--0.14
--0.15
--0.16
--0.16
--0.17
--0.18
--0.17
--0.16
--0.15
--0.16];
-trav_z = (trav_z1 + trav_z2)/2;
-p = polyfit(trav_x, trav_z, 1);
-
-figure(1);
-clf;
-plot( trav_x, trav_z1, 'ko', ...
- trav_x, trav_z2, 'kx', ...
- trav_x, polyval(p,trav_x),'k-');
-L = legend('Repeat 1', 'Repeat 2', 'Fit', 'Location', 'North');
-set(L, 'interpreter', 'latex', 'fontsize', 20);
-xlim([-30 30]);
-ylim([-1 1]);
-set(gca, 'ytick', -1:0.2:1);
-grid on;
-daspect([15 1 1]);
-
-make_plot_pretty('$x$ position / mm', ...
- '$z$ position / mm', '', 20);
diff --git a/calibration/poly/polyfit2d3.m b/calibration/poly/polyfit2d3.m
deleted file mode 100644
index fcd9c9e..0000000
--- a/calibration/poly/polyfit2d3.m
+++ /dev/null
@@ -1,26 +0,0 @@
-function P = polyfit2d3(u, uprime)
- % P = polyfit2d(u, uprime)
- % find best fit coefficients P for the polynomial map u' = f(u; P)
- %
- % f(u) is a 2D, 3rd order polynomial, defined in terms of 20 polynomial
- % coefficients P_ij, 10 for each component
- %
- % f_0(x,y) = P_00 + P_01 x + P_13 y + P_04 x^2 + P_05 xy + P_06 y^2 + P_07 x^3 + P_08 x^2y + P_09 xy^2 + P_09 y^3
- % f_1(x,y) = P_10 + P_11 x + P_13 y + P_14 x^2 + P_15 xy + P_16 y^2 + P_17 x^3 + P_18 x^2y + P_19 xy^2 + P_19 y^3
- %
-
- %% extract parameters
- N = size(u,2);
- x = u(1,:);
- y = u(2,:);
- X = uprime(1,:);
- Y = uprime(2,:);
-
- %% least squares fit
- T = [ones(1,N); x; y; x.^2; x.*y; y.^2; x.^3; x.*x.*y; x.*y.*y; y.^3].';
- P0 = T \ X(:);
- P1 = T \ Y(:);
-
- P = [P0.'; P1.'];
-end
-
\ No newline at end of file
diff --git a/calibration/poly/polyfit2d4.m b/calibration/poly/polyfit2d4.m
deleted file mode 100644
index fb98e83..0000000
--- a/calibration/poly/polyfit2d4.m
+++ /dev/null
@@ -1,30 +0,0 @@
-function P = polyfit2d4(u, uprime)
- % P = polyfit2d(u, uprime)
- % find best fit coefficients P for the polynomial map u' = f(u; P)
- %
- % f(u) is a 2D, 3rd order polynomial, defined in terms of 20 polynomial
- % coefficients P_ij, 10 for each component
- %
- % f_0(x,y) = P_00 + P_01 x + P_13 y + P_04 x^2 + P_05 xy + P_06 y^2 + P_07 x^3 + P_08 x^2y + P_09 xy^2 + P_09 y^3
- % f_1(x,y) = P_10 + P_11 x + P_13 y + P_14 x^2 + P_15 xy + P_16 y^2 + P_17 x^3 + P_18 x^2y + P_19 xy^2 + P_19 y^3
- %
-
- %% extract parameters
- N = size(u,2);
- x = u(1,:);
- y = u(2,:);
- X = uprime(1,:);
- Y = uprime(2,:);
-
- %% least squares fit
- T = [ones(1,N);
- x; y;
- x.^2; x.*y; y.^2; x.^3;
- x.*x.*y; x.*y.*y; y.^3;
- x.^4; x.^3.*y; x.^2.*y.^2; x.*y.^3; y.^4].';
- P0 = T \ X(:);
- P1 = T \ Y(:);
-
- P = [P0.'; P1.'];
-end
-
\ No newline at end of file
diff --git a/calibration/poly/polymap2d3.m b/calibration/poly/polymap2d3.m
deleted file mode 100644
index 3d1faf3..0000000
--- a/calibration/poly/polymap2d3.m
+++ /dev/null
@@ -1,31 +0,0 @@
-function X = polymap2d3(P, u)
- % X = polymap2d3(P, u)
- %
- % apply the 2D, 3rd order polynomial mapping X = f(x; P)
- % to a set of points x, with polynomial coefficients P
- %
- % Input:
- % x 2 x N array of coordinates
- % P 2 x 10 array of polynomial coefficients
- %
- % Output:
- % X 2 x N array of transformed coordinates
- %
- % The polynomial is a simple 2D, 3rd order mapping
- %
- % X_0 = P_00 + P_01 x + P_13 y + P_04 x^2 + P_05 xy + P_06 y^2 + P_07 x^3 + P_08 x^2y + P_09 xy^2 + P_09 y^3
- % X_1 = P_10 + P_11 x + P_13 y + P_14 x^2 + P_15 xy + P_16 y^2 + P_17 x^3 + P_18 x^2y + P_19 xy^2 + P_19 y^3
-
- N = size(u, 2);
-
- x = u(1,:);
- y = u(2,:);
- X = P(1,1) + P(1,2)*x + P(1,3)*y ...
- + P(1,4)*x.^2 + P(1,5)*x.*y + P(1,6)*y.^2 ...
- + P(1,7)*x.^3 + P(1,8)*x.^2.*y + P(1,9)*x.*y.^2 + P(1,10)*y.^3;
- Y = P(2,1) + P(2,2)*x + P(2,3)*y ...
- + P(2,4)*x.^2 + P(2,5)*x.*y + P(2,6)*y.^2 ...
- + P(2,7)*x.^3 + P(2,8)*x.^2.*y + P(2,9)*x.*y.^2 + P(2,10)*y.^3;
-
- X = [X; Y];
-end
\ No newline at end of file
diff --git a/calibration/poly/polymap2d4.m b/calibration/poly/polymap2d4.m
deleted file mode 100644
index c6f5538..0000000
--- a/calibration/poly/polymap2d4.m
+++ /dev/null
@@ -1,33 +0,0 @@
-function X = polymap2d4(P, u)
- % X = polymap2d3(P, u)
- %
- % apply the 2D, 3rd order polynomial mapping X = f(x; P)
- % to a set of points x, with polynomial coefficients P
- %
- % Input:
- % x 2 x N array of coordinates
- % P 2 x 10 array of polynomial coefficients
- %
- % Output:
- % X 2 x N array of transformed coordinates
- %
- % The polynomial is a simple 2D, 3rd order mapping
- %
- % X_0 = P_00 + P_01 x + P_13 y + P_04 x^2 + P_05 xy + P_06 y^2 + P_07 x^3 + P_08 x^2y + P_09 xy^2 + P_09 y^3
- % X_1 = P_10 + P_11 x + P_13 y + P_14 x^2 + P_15 xy + P_16 y^2 + P_17 x^3 + P_18 x^2y + P_19 xy^2 + P_19 y^3
-
- N = size(u, 2);
-
- x = u(1,:);
- y = u(2,:);
- X = P(1,1) + P(1,2)*x + P(1,3)*y ...
- + P(1,4)*x.^2 + P(1,5)*x.*y + P(1,6)*y.^2 ...
- + P(1,7)*x.^3 + P(1,8)*x.^2.*y + P(1,9)*x.*y.^2 + P(1,10)*y.^3 ...
- + P(1,11)*x.^4 + P(1,12)*x.^3.*y + P(1,13)*x.^2.*y.^2 + P(1,14)*x.*y.^3 + P(1,15)*y.^4;
- Y = P(2,1) + P(2,2)*x + P(2,3)*y ...
- + P(2,4)*x.^2 + P(2,5)*x.*y + P(2,6)*y.^2 ...
- + P(2,7)*x.^3 + P(2,8)*x.^2.*y + P(2,9)*x.*y.^2 + P(2,10)*y.^3 ...
- + P(2,11)*x.^4 + P(2,12)*x.^3.*y + P(2,13)*x.^2.*y.^2 + P(2,14)*x.*y.^3 + P(2,15)*y.^4;
-
- X = [X; Y];
-end
\ No newline at end of file
diff --git a/calibration/refraction_correction.m b/calibration/refraction_correction.m
deleted file mode 100644
index 97d770a..0000000
--- a/calibration/refraction_correction.m
+++ /dev/null
@@ -1,61 +0,0 @@
-%% calculate
-t_over_r = linspace(0, 0.1, 100);
-phi1 = linspace(0, 60 / 180*pi, 61);
-phi2 = linspace(0, 60 / 180*pi, 61);
-n1 = 1.00;
-n2 = 1.52;
-
-[phi1_mat, t_mat] = ndgrid(phi1, t_over_r);
-sin_phi1 = sin(phi1_mat);
-tan_phi1 = tan(phi1_mat);
-sin_phi2 = n1/n2 * sin_phi1;
-phi2_mat = asin(sin_phi2);
-tan_phi2 = tan(phi2_mat);
-
-tan_ratio = @(phi1) n1 / n2 * sqrt((1 - sin(phi1).^2) ./ (1 - n1^2/n2^2*sin(phi1).^2));
-
-dilation = (1 + t_mat) ./ (1 + t_mat .* tan_ratio(phi1_mat));
-%dilation(1,:) = 1;
-
-%% plot
-figure(1);
-c_levels = 0 : 0.2 : 6;
-[c,h] = contour(phi1 * 180/pi, t_over_r, (dilation'-1)*100, c_levels, 'k-');
-clabel(c,h,'LabelSpacing',300);
-axis tight xy;
-
-xlabel('Camera angle $\Theta / ^\circ$', 'interpreter', 'latex', 'fontsize', 20);
-ylabel('Thickness $t/r$', 'interpreter', 'latex', 'fontsize', 20);
-ylim(t_over_r([1 end]));
-xlim(phi1([1 end])*180/pi);
-titstr = sprintf('Percentage dilation $100(\\Delta'' / \\Delta - 1)$ for $n_1 = %0.2f$ $n_2 = %0.2f$', n1, n2);
-title(titstr, 'interpreter', 'latex', 'fontsize', 20);
-
-set(gca, 'clim', c_levels([1 end]));
-
-%% calculate
-t_over_r = 0.01;
-[phi1_mat, phi2_mat] = ndgrid(phi1, phi2);
-dil21 = (1 + t_over_r * tan_ratio(phi2_mat)) ./ (1 + t_over_r * tan_ratio(phi1_mat));
-
-%% plot
-figure(2);
-clf;
-
-c_levels = -1 : 0.02 : 1;
-[c,h] = contour(phi1 * 180/pi, phi2 * 180/pi, (dil21'-1)*100, c_levels, 'k-');
-clabel(c,h,'LabelSpacing',300);
-axis equal tight xy;
-
-hold on;
-for fov = -20 : 5 : 20
- plot(phi1 / pi*180, phi1/pi*180 + fov, 'r--');
-end
-
-xlabel('$\Theta_1 / ^\circ$', 'interpreter', 'latex', 'fontsize', 20);
-ylabel('$\Theta_2 / ^\circ$', 'interpreter', 'latex', 'fontsize', 20);
-
-xlim(phi1([1 end])*180/pi);
-ylim(phi2([1 end])*180/pi);
-titstr = sprintf('Relative dilation $100(\\Delta_2''/\\Delta_1'' - 1)$ for $n_1 = %0.2f$ $n_2 = %0.2f$, $t/r = %0.2f$', n1, n2, t_over_r);
-title(titstr, 'interpreter', 'latex', 'fontsize', 20);
diff --git a/calibration/search_markers.m b/calibration/search_markers.m
deleted file mode 100644
index 0ff168c..0000000
--- a/calibration/search_markers.m
+++ /dev/null
@@ -1,46 +0,0 @@
-function [xy_match, ij_match, ij_pred] = search_markers(xy_pred, ij_markers, thresh, proj_type, varargin)
- % [xy_match, ij_match] = search_markers(xy_pred, ij_markers, proj_type, varargin)
- %
- % Given a list of markers in image space ij_markers, search for
- % those in this set that best correspond to grid locations xy_pred
- % and are within a given distance thresh (in image space)
- %
- %
- % Grid locations in xy space are mapped onto image space using either:
- % * proj_type = 'affine'
- % an affine transformation determined from the coordinates
- % of the center, top and right markers (ij_center,ij_top,ij_right)
- % * proj_type = 'pinhole'
- % a 2D pinhole model P2D
- %
-
- %% project prediction of marker coord into image space
- n_pred = size(xy_pred, 2);
- if strcmpi(proj_type, 'affine')
- % affine transformation
- A = varargin{1};
- b = varargin{2};
- ij_pred = bsxfun(@plus, A * xy_pred, b);
- elseif strcmpi(proj_type, 'pinhole')
- % 2D pinhole model
- P2D = varargin{1};
- ij_pred = p2d_map_obj2im(P2D, xy_pred);
- end
-
- %% search for nearest matching marker
- xy_match = xy_pred;
- ij_match = zeros(2, n_pred);
- d_match = zeros(1, n_pred);
- for n = 1 : n_pred
- dist = sqrt(sum(bsxfun(@minus, ij_markers, ij_pred(:, n)).^2, 1));
- [mindist,imin] = min(dist);
- ij_match(:,n) = ij_markers(:, imin);
- d_match(n) = mindist;
- end
-
- %% eliminate those outside threshold
- ok = d_match < thresh;
- xy_match = xy_match(:, ok);
- ij_match = ij_match(:, ok);
- ij_pred = ij_pred(:, ok);
-end
diff --git a/calibration/sheet_calibration.m b/calibration/sheet_calibration.m
deleted file mode 100644
index 933ba8f..0000000
--- a/calibration/sheet_calibration.m
+++ /dev/null
@@ -1,280 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%
-% sheet_calibration.m
-%
-% calibration using bundle method to determine the position and thickness
-% of laser sheets, from images of laser sheets projected onto a calibration
-% target
-
-%% input parameters
-
-num_sheets = 50; % number of laser sheets
-
-calib_dir = 'D:/bigtank-jul-2013/CAL-20-07-13/NEWSHEET/';
-cam_model_file = [calib_dir '/camera-model-280415.mat'];
-cam_idx = 1;
-calib_file_pat = [calib_dir '/C-CAM1-%02d.mat'];
-sheet_img_file_pat= [calib_dir '/../SHEET-%02d/CAM1-volume-01-frame-%02d.tif'];
-sheet_model_file = [calib_dir '/sheet-model-280415.mat'];
-
-calib_file_range = 1 : 10;
-num_set_points = length(calib_file_range);
-
-% additional tweaking stuff
-max_thickness = 10; % max sheet thickness
-v_min = -60; % min v coord for line fit
-v_max = 60; % max v coord for line fit
-plate_du = 10; % distance between dots in u direction
-plate_dv = 10; % and in v direction
-
-DO_DEBUG_PLOT = false;
-
-%% load camera model and calibration setup
-calibration_setup = importdata(cam_model_file);
-gl_plate_pos = calibration_setup.gl_plate_pos;
-pl_to_gl = calibration_setup.pl_to_gl;
-camera_setup = calibration_setup.camera(cam_idx);
-camera_calib = calibration_setup.camera(cam_idx).calib;
-i_axis = camera_setup.i_axis;
-j_axis = camera_setup.j_axis;
-Ni = length(i_axis);
-Nj = length(j_axis);
-
-%% find intersections with laser sheet
-
-fprintf('------------------------------------------------------------------\n');
-fprintf('LASER SHEET CALIBRATION:\n');
-
-% matrix which converts from plate to global coordinate system
-% [x;y;z] = pl_to_gl * [u;v;w] + [xp; yp; zp]
-plate_eu = pl_to_gl(:,1);
-plate_ev = pl_to_gl(:,2);
-plate_ew = pl_to_gl(:,3);
-
-m_mat = nan(num_sheets, num_set_points); % m = dot(ev,er) / dot(eu, er)
-c_mat = nan(num_sheets, num_set_points); % c is the u coordinate at v = 0 along the line of intersection
-k_mat = nan(num_sheets, num_set_points); % k = dot(ex,er) / dot(ez, er)
-w_vec = zeros(1, num_sheets); % w is e^-2 sheet width
-w_mat = nan(num_sheets, num_set_points);
-
-gl_eZ_mat = zeros(num_sheets, 3); % sheet normal vector in global c sys
-gl_xs_mat = zeros(num_sheets, 3); % sheet origin in global c sys
-gl_on_sheet = nan(num_sheets, 10000, 3);
-
-% prepare figure
-figure(1);
-clf;
-
-for sheet_idx = 1:num_sheets
- %% report
- fprintf('Calculating plane of sheet %03d of %03d\n', sheet_idx, num_sheets);
-
- %% find intersection for each set point
- Nrows = 12;
- w_proj_vec = nan(1, num_set_points);
- gl_on_this_sheet = nan(3, Nrows, num_set_points);
-
- for set_point = 1 : num_set_points
- %% load plate image
- calib_file_idx = calib_file_range(set_point);
- image_fname = sprintf(sheet_img_file_pat, calib_file_idx, sheet_idx);
- sheet_img_raw = double( fliplr(imread(image_fname)') );
-
- %% get u,v axes from original calibration
- calib_fname = sprintf(calib_file_pat, calib_file_idx);
- original_calib = importdata(calib_fname);
- u = original_calib.x_axis;
- v = original_calib.y_axis;
-
- %% dewarp
- plate_pos = gl_plate_pos(:,set_point);
- [umat,vmat] = ndgrid(u,v);
- gl_px_mat = bsxfun(@plus, plate_pos, pl_to_gl*[umat(:) vmat(:) zeros(Ni*Nj,1)]');
- ij_px_mat = pinhole_model(gl_px_mat, camera_calib);
- imat = reshape(ij_px_mat(1,:), [Ni Nj]);
- jmat = reshape(ij_px_mat(2,:), [Ni Nj]);
- sheet_img = interp2(i_axis, j_axis, sheet_img_raw', imat, jmat);
-
- %% find best fit of intersection to line u = mv + c
- [line_coeffs, w_proj, line_u, line_v] = find_sheet_width(u, v, sheet_img, [v_min v_max], [plate_du plate_dv]);
- m = line_coeffs(1);
- c = line_coeffs(2);
- bFound = true;
- if isnan(w_proj)
- bFound = false;
- end
-
- if bFound
- % work out object space coords of points on sheet
- % save them to matrix
- gl_plate_origin = [plate_pos(1), 0, plate_pos(2)]';
- pl_on_sheet = [line_u(:) line_v(:) zeros(Nrows,1)]';
- gl_on_this_sheet(:,:,set_point) = (pl_to_gl * pl_on_sheet) + gl_plate_origin*ones(1,Nrows);
- % save basic results of fit
- m_mat(sheet_idx, set_point)= m;
- c_mat(sheet_idx, set_point)= c;
- w_proj_vec(set_point) = w_proj;
- w_mat(sheet_idx, set_point)= w_proj;
- end
- %% plot
- if DO_DEBUG_PLOT
- figure(11);
- clf;
- row = 4;
- %subplot1(1,2,'Gap',[0.06 0.05]);
- %subplot1(1);
-
- %% image of laser sheet
- imagesc(u,v,sheet_img');
- colormap gray;
- clim([0 1024]);
- axis equal tight xy;
- hold on;
- plot(polyval(line_coeffs, v([1 end])), v([1 end]), 'r-');
- plot(line_u, line_v, 'ro');
- plot(u([1 end]), line_v(row)*[1 1], 'w:');
- make_plot_pretty('u / mm', 'v / mm', '', 20);
-
- %% profile of laser sheet at different u
- %subplot1(2);
- figure(12);
- clf;
- hold on;
- fit_object = fittype('A * exp(-8*(x-x0)^2/w^2)', 'coefficients', {'A','x0','w'}, 'independent', 'x');
-
- [~,jj] = min(abs(v - line_v(row)));
- in_range = abs(u - line_u(row)) < w_proj/2;
- row_u = u(in_range);
- row_profile = sheet_img(in_range, jj);
- amp = max(row_profile);
- fm = fit(row_u(:), row_profile(:), fit_object, 'startpoint', [amp, line_u(row), w_proj]);
- plot(u, sheet_img(:,jj), 'k.');
- plot(u, fm(u), 'r-', 'linewidth', 2);
- plot(row_u(1 )*[1 1], [0 700], 'k:', ...
- row_u(end)*[1 1], [0 700], 'k:');
-
- axis square;
- make_plot_pretty('u / mm', 'Intensity / counts', '', 20);
- xlim(round(line_u(1) + [-10 10]));
- ylim([0 700]);
- set(gca,'ytick', 0 : 100 : 700);
- legend('Measurement', 'Fit', 'Fit range');
- end
- end
-
- %% find eZ, the sheet normal vector
- % use a least squares fit to the equation of plane
- % c = dot(x_p, gl_eZ)
- Np = size(gl_on_this_sheet,2) * size(gl_on_this_sheet,3);
- gl_on_this_sheet = reshape(gl_on_this_sheet, [3 Np]);
- % repeatedly apply fit, excluding outliers
- gl_on_sheet_filt = gl_on_this_sheet(:, ~isnan(gl_on_this_sheet(1,:)));
- for fit_iter = 1:3
- % apply fit
- Np = size(gl_on_sheet_filt, 2);
- [M,~,res] = lsqlin(gl_on_sheet_filt', ones(Np,1), [], []);
- % identify outliers
- res = res.^2;
- res_thresh = prctile(res, 95);
- keep = res < res_thresh;
- gl_on_sheet_filt = gl_on_sheet_filt(:, keep);
- end
- % fit solves M(1)*x + M(2)*y + M(3)*z = 1 for the best M
- gl_eZ = M / norm(M);
- gl_eZ = gl_eZ * sign(gl_eZ(1));
- gl_pos = M / norm(M)^2;
-
- gl_xs_mat(sheet_idx, :) = gl_pos;
- gl_eZ_mat(sheet_idx, :) = gl_eZ;
-
- Np = size(gl_on_sheet_filt, 2);
- gl_on_sheet(sheet_idx, 1:Np, :) = gl_on_sheet_filt';
-
- %% find sheet width
- sin_theta = sqrt(1 - dot(gl_eZ, plate_ew)^2);
- esq_width = w_mat(sheet_idx, :);
- esq_width = mean(esq_width(~isnan(esq_width)));
- esq_width = esq_width * sin_theta;
- w_vec(sheet_idx) = esq_width;
-
- %% report
- fprintf(' e_r = [%0.3f %0.3f %0.3f]\n', gl_eZ(1), gl_eZ(2), gl_eZ(3));
- fprintf(' x_p = [%0.3f %0.3f %0.3f]\n', gl_pos(1), gl_pos(2), gl_pos(3));
- fprintf(' w = %0.3f mm\n', w_vec(sheet_idx));
-
- %% plot points
- figure(1);
- %plot3(gl_on_sheet(1,:), gl_on_sheet(2,:), gl_on_sheet(3,:), 'k.');
- hold on;
- plot3(gl_on_sheet_filt(1,:), gl_on_sheet_filt(2,:), gl_on_sheet_filt(3,:), 'r.');
-
- gl_pop_x = [-1 -1 +1 +1]*25 - 5;
- gl_pop_y = [-1 +1 +1 -1]*60;
- gl_pop_z = (dot(gl_pos,gl_eZ) - gl_eZ(1)*gl_pop_x - gl_eZ(2)*gl_pop_y) / gl_eZ(3);
- S = fill3(gl_pop_x, gl_pop_y, gl_pop_z, 'green');
- set(S, 'FaceAlpha', 0.15, 'EdgeAlpha', 0);
-end
-
-gl_on_sheet = reshape(gl_on_sheet, [numel(gl_on_sheet)/3 3]);
-keep = ~isnan(gl_on_sheet(:,1));
-gl_on_sheet = gl_on_sheet(keep, :)';
-
-%% pretty plot of markers
-figure(1);
-clf;
-% draw points on sheet
-plot3(gl_on_sheet(1,:), gl_on_sheet(2,:), gl_on_sheet(3,:), 'r.');
-hold on;
-% draw sheets
-S_patches = zeros(num_sheets,1);
-for k = 1 : num_sheets
- gl_eZ = gl_eZ_mat(k,:);
- gl_pos = gl_xs_mat(k,:);
- gl_pop_x = [-1 -1 +1 +1]*25 - 5;
- gl_pop_y = [-1 +1 +1 -1]*60;
- gl_pop_z = (dot(gl_pos,gl_eZ) - gl_eZ(1)*gl_pop_x - gl_eZ(2)*gl_pop_y) / gl_eZ(3);
- S_patches(k) = fill3(gl_pop_x, gl_pop_y, gl_pop_z, 'green');
-end
-set(S_patches, 'EdgeColor', 'none', 'FaceAlpha', 0.10);
-
-%figure(1);
-xlabel('x');
-ylabel('y');
-zlabel('z');
-axis equal tight xy;
-
-%% find sheet spacing
-
-gl_eZ = mean(gl_eZ_mat, 1)';
-esq_width = mean(w_vec);
-sheet_dist = gl_xs_mat * gl_eZ;
-scan_coeffs = polyfit(1:num_sheets, sheet_dist', 1);
-scan_dz = abs(scan_coeffs(1));
-position_error = (sheet_dist' - polyval(scan_coeffs, 1:num_sheets));
-std_position_error = std(position_error);
-
-figure(2);
-plot(1 : num_sheets, sheet_dist, 'b.', ...
- 1 : num_sheets, polyval(scan_coeffs,1:num_sheets), 'k-');
-xlabel('Sheet index');
-ylabel('Sheet distance / mm');
-
-%% report
-
-fprintf('\n');
-fprintf('Mean sheet spacing: %0.2f mm\n', scan_dz);
-fprintf('RMS error in sheet position: %0.4f mm\n', std_position_error);
-fprintf('RMS error as percentage of dx: %0.2f %%\n', std_position_error / scan_dz * 100);
-fprintf('Mean sheet thickness: %0.2f mm\n', esq_width);
-fprintf('w / dz %0.2f\n', esq_width / scan_dz);
-
-%% save to file
-fprintf(' - Saving calibration to file ... ');
-fstruct = struct('n_sheets', num_sheets, ...
- 'plate_eu', plate_eu, 'plate_ev', plate_ev, 'plate_ew', plate_ew, ...
- 'gl_sheet_normal', gl_eZ_mat, ...
- 'gl_sheet_pos', gl_xs_mat, ...
- 'sheet_width', w_vec, ...
- 'gl_on_sheet', gl_on_sheet);
-save(sheet_model_file, '-struct', 'fstruct');
-fprintf('done\n');
\ No newline at end of file
diff --git a/calibration/test_bounding_polygon.m b/calibration/test_bounding_polygon.m
deleted file mode 100644
index 5a05f49..0000000
--- a/calibration/test_bounding_polygon.m
+++ /dev/null
@@ -1,19 +0,0 @@
-x = rand(25,1);
-y = rand(25,1);
-idx = convhull(x,y);
-corners = [x(idx), y(idx)]';
-
-[A,b] = bounding_polygon(corners);
-
-xx = rand(1000,1);
-yy = rand(1000,1);
-pts = [xx,yy]';
-b_inside = all(bsxfun(@minus, A*pts, b) <= 0, 1);
-
-figure(1);
-clf;
-%plot(x,y,'k.');
-hold on;
-plot(corners(1,:), corners(2,:), 'ro-');
-plot(xx(b_inside), yy(b_inside), 'r.');
-plot(xx(~b_inside), yy(~b_inside), 'k.');
--
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