OPTIMISE_ALL_WARPS_MDOEL:
Code written by Katherine Smith, 2003
GENERAL
[param_list, warped_points, warped_image, score] =
optimise_all_warps_model(ref_image, ref_points, unwarped_image,
unwarped_points, spline_type, placement_method, n_points, verbose,
do_plot, subplot_fig, pane,
image_width, number_of_images, verbose_score)
INPUT/S
-X:
X
OUTPUT/S
-X:
X
PENDING WORK
KNOWN BUG/S
COMMENT/S
RELATED FUNCTION/S
ABOUT
-Created: November 23rd, 2003
-Last update: January 23rd, 2004
-Revision: 0.1.5
-Author: R. S. Schestowitz, University of Manchester
==============================================================0001 function [param_list, warped_points, warped_image, score] = optimise_all_warps_model(ref_image, ref_points, unwarped_image, unwarped_points, spline_type, placement_method, n_points, verbose, do_plot, subplot_fig, pane, column, image_width, number_of_images, verbose_score, model_evaluation_method, n_modes, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising, precision_required); %use_parameters, parameters) 0002 % OPTIMISE_ALL_WARPS_MDOEL: 0003 % 0004 % Code written by Katherine Smith, 2003 0005 % 0006 % GENERAL 0007 % 0008 % [param_list, warped_points, warped_image, score] = 0009 % optimise_all_warps_model(ref_image, ref_points, unwarped_image, 0010 % unwarped_points, spline_type, placement_method, n_points, verbose, 0011 % do_plot, subplot_fig, pane, 0012 % image_width, number_of_images, verbose_score) 0013 % 0014 % INPUT/S 0015 % 0016 % -X: 0017 % X 0018 % 0019 % OUTPUT/S 0020 % 0021 % -X: 0022 % X 0023 % 0024 % PENDING WORK 0025 % 0026 % 0027 % 0028 % KNOWN BUG/S 0029 % 0030 % 0031 % 0032 % COMMENT/S 0033 % 0034 % 0035 % 0036 % RELATED FUNCTION/S 0037 % 0038 % 0039 % 0040 % ABOUT 0041 % 0042 % -Created: November 23rd, 2003 0043 % -Last update: January 23rd, 2004 0044 % -Revision: 0.1.5 0045 % -Author: R. S. Schestowitz, University of Manchester 0046 % ============================================================== 0047 0048 0049 % figure(3); 0050 % hold on 0051 % plot(unwarped_image) 0052 % hold off ** Image is dynamic 0053 [grid, steps] = setup_grid(2, unwarped_image, ref_points, placement_method, n_points); 0054 0055 % POSSIBLE OPTIMISATION: where no randomisation is applied for grid 0056 % selection, e.g. 'overlap grid' and 'grid', the grid needs to be set up ONCE, not every 0057 % iteration. A test for this needs to be put outside this function to avoid 0058 % unnecessary computations. 0059 0060 if (do_plot), 0061 figure(subplot_fig); 0062 subplot(number_of_images, column ,pane , 'replace'); 0063 plot(unwarped_image); 0064 axis([0 image_width 0 1]); 0065 drawnow; 0066 end 0067 0068 warp_params.green = 'biharmCPS'; 0069 0070 0071 % added 16/03/04 0072 % if (~use_parameters), 0073 % param_list.d = [0,0,0,0,0]; 0074 % else 0075 % for i=1:size(grid), 0076 % param_list.d(i) = parameters.d(i); 0077 % end 0078 % end 0079 % if (use_parameters), 0080 % parameters.d 0081 % end 0082 0083 0084 % 16/03/04:param_list = []; 0085 % also remove 2 last arguments of this function 0086 0087 param_list = []; 0088 start_image = unwarped_image; 0089 start_points = unwarped_points; 0090 0091 %last_score = msd(ref_image, unwarped_image); 0092 0093 0094 if (strcmp(spline_type,'single_point')), 0095 0096 0097 % steps 0098 % grid 0099 % size(grid) 0100 0101 0102 last_score = eval_model_cps_warp(zeros(size(grid)), grid, steps, unwarped_image, unwarped_points, ref_image, ref_points, n_modes, start_points, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising); 0103 % last_score 0104 0105 % if (~use_parameters), 0106 0107 0108 [params, score] = fminsearch(@eval_model_cps_warp, zeros(size(grid),1), optimset('Display', verbose, 'TolX', precision_required) , grid, steps, start_image, start_points, ref_image, ref_points, n_modes, start_points, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising); 0109 0110 0111 % 'MaxIter', 1000 0112 % str2num([['1e-'],[num2str(ceil(rand*100))]]) 0113 % score 0114 % params=0.1; 0115 % [score1,start_points] = eval_model_cps_warp(params, grid, steps, start_image, start_points, ref_image, ref_points, n_modes, start_points, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising); 0116 % score1 0117 % params=0.2; 0118 % [score2,start_points] = eval_model_cps_warp(params, grid, steps, start_image, start_points, ref_image, ref_points, n_modes, start_points, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising); 0119 % score2 0120 % params=0.2; 0121 % [score2,start_points] = eval_model_cps_warp(params, grid, steps, start_image, start_points, ref_image, ref_points, n_modes, start_points, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising); 0122 % score2 0123 % params=0.3; 0124 % [score3,start_points] = eval_model_cps_warp(params, grid, steps, start_image, start_points, ref_image, ref_points, n_modes, start_points, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising); 0125 % score3 0126 % params=0.4; 0127 % [score4,start_points] = eval_model_cps_warp(params, grid, steps, start_image, start_points, ref_image, ref_points, n_modes, start_points, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising); 0128 % score4 0129 % params=0.5; 0130 % [score5,start_points] = eval_model_cps_warp(params, grid, steps, start_image, start_points, ref_image, ref_points, n_modes, start_points, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising); 0131 % score5 0132 % else 0133 % [params, score] = fminsearch(@eval_model_cps_warp,parameters.d,optimset('Display',verbose),grid,steps,start_image, start_points, ref_image, ref_points, n_modes, start_points, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept); 0134 % end 0135 % try randn(size(grid),1) 0136 param_list(size(param_list,1)+1).d = params; 0137 [dummy,start_points] = eval_model_cps_warp(params, grid, steps, start_image, start_points, ref_image, ref_points, n_modes, start_points, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising); 0138 start_image = interp1(unwarped_points, unwarped_image, start_points, 'linear',0); 0139 0140 0141 0142 0143 elseif (strcmp(spline_type,'multi_point')), 0144 warped_grid = grid; 0145 last_score = eval_model_multi_warp(zeros(size(grid)), grid, unwarped_image, unwarped_points, ref_image, ref_points, n_modes, start_points, warp_params, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising); 0146 [params, score] = fminsearch(@eval_model_multi_warp, zeros(size(grid)), optimset('Display', verbose, 'TolX', precision_required), grid, unwarped_image, unwarped_points, ref_image, ref_points, n_modes, start_points, warp_params, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising); 0147 param_list(size(param_list,1)+1,:).d = params; 0148 warped_grid = grid + params; 0149 start_points = nrr_trans_1d(start_points, grid, warped_grid, warp_params,[]); 0150 start_image = interp1(unwarped_points,unwarped_image,start_points, 'linear',0); 0151 0152 0153 else 0154 error('Unknown spline type'); 0155 end 0156 0157 0158 0159 0160 if (verbose_score == 1), 0161 disp(['Initial score: ',num2str(last_score),' Final score: ', num2str(score), ' Difference: ', num2str(last_score - score)]); 0162 end 0163 0164 warped_points = start_points; 0165 warped_image = start_image; 0166 0167 if (do_plot), 0168 figure(subplot_fig); 0169 subplot(number_of_images, column , pane, 'replace'); 0170 plot(warped_image); 0171 axis([0 image_width 0 1]); 0172 title(num2str(score)); 0173 drawnow; 0174 end