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REGISTER_2D_IMAGES: Genrates examples of 2-D images and warps them
GENERAL
[status] = register_2d_images(number_of_images, image_width,
image_height, position, width, height, width_variation, height_variation)
INPUT/S
-number_of_images:
The number of images to generate.
-image_width:
The width of the 2-D images to be created.
-image_height:
The width of the 2-D images to be created.
-width:
The width of the object in the image.
-width_variation:
The width variation of the object in the image.
-position:
The horizontal position of the object in the image.
-height:
The height of the object in the image.
-height_variation:
The height variation of the object in the image.
OUTPUT/S
-status
Status on the success of the process.
PENDING WORK
KNOWN BUG/S
-
COMMENT/S
RELATED FUNCTION/S
MAKE_1D_IMAGES, MAKE_1D_BUMP, REGISTER_2D_IMAGES
ABOUT
-Created: January 6th, 2003
-Last update: January 6th, 2003
-Revision: 0.0.1
-Author: R. S. Schestowitz, University of Manchester
==============================================================0001 function [status] = register_2d_images(number_of_images, image_width, image_height, position, width, height, width_variation, height_variation) 0002 % ==============================================================. 0003 % REGISTER_2D_IMAGES: Genrates examples of 2-D images and warps them 0004 % 0005 % GENERAL 0006 % 0007 % [status] = register_2d_images(number_of_images, image_width, 0008 % image_height, position, width, height, width_variation, height_variation) 0009 % 0010 % INPUT/S 0011 % 0012 % -number_of_images: 0013 % The number of images to generate. 0014 % 0015 % -image_width: 0016 % The width of the 2-D images to be created. 0017 % 0018 % -image_height: 0019 % The width of the 2-D images to be created. 0020 % 0021 % -width: 0022 % The width of the object in the image. 0023 % 0024 % -width_variation: 0025 % The width variation of the object in the image. 0026 % 0027 % -position: 0028 % The horizontal position of the object in the image. 0029 % 0030 % -height: 0031 % The height of the object in the image. 0032 % 0033 % -height_variation: 0034 % The height variation of the object in the image. 0035 % 0036 % OUTPUT/S 0037 % 0038 % -status 0039 % Status on the success of the process. 0040 % 0041 % PENDING WORK 0042 % 0043 % 0044 % KNOWN BUG/S 0045 % 0046 % - 0047 % 0048 % COMMENT/S 0049 % 0050 % 0051 % RELATED FUNCTION/S 0052 % 0053 % MAKE_1D_IMAGES, MAKE_1D_BUMP, REGISTER_2D_IMAGES 0054 % 0055 % ABOUT 0056 % 0057 % -Created: January 6th, 2003 0058 % -Last update: January 6th, 2003 0059 % -Revision: 0.0.1 0060 % -Author: R. S. Schestowitz, University of Manchester 0061 % ============================================================== 0062 0063 0064 warp_flag = 1; 0065 % should the images be warped (else only displayed) 0066 verbose_mode = 1; 0067 % internally controlled parameter 0068 debugging_mode = 1; 0069 % does extra tests 0070 0071 subplot_fig = figure; 0072 H = figure(subplot_fig); 0073 0074 for i=1:number_of_images, 0075 if (verbose_mode == 1) 0076 disp(['generating image #',num2str(i),' of ',num2str(number_of_images)]); 0077 end 0078 [index,current_image_vectors,current_point_vectors]=make_2d_bump(1, image_width, image_height, position, width, height, width_variation, height_variation); 0079 if (debugging_mode == 1) % view it if debugging 0080 size(current_image_vectors) 0081 index 0082 end 0083 subplot(number_of_images+4,2,i); 0084 hold on; 0085 title(['Generated image #',num2str(i)]); 0086 imshow(current_image_vectors); 0087 image_vectors(:,:,i) = current_image_vectors; 0088 point_vectors(:,:,i) = current_point_vectors; 0089 hold off; 0090 end 0091 0092 if( warp_flag == 1), 0093 number_of_iterations = 1; 0094 % how many iterations of the optimisation/warping to run 0095 0096 reference_points_vector = point_vectors(:,:,1); 0097 reference_image_vector = image_vectors(:,:,1); 0098 % set reference to be the first image 0099 if (debugging_mode == 1) % view it if debugging 0100 subplot(number_of_images+4,2,number_of_images+2); 0101 hold on; 0102 title('Reference image'); 0103 imshow(reference_image_vector); 0104 hold off; 0105 end 0106 0107 warped_images = image_vectors; 0108 warped_points = point_vectors; % copy these values as warped variables are dynamic 0109 0110 for n=1:number_of_iterations, 0111 % images are now warped one by one with respect to reference image 0112 for i=1:number_of_images, 0113 if (verbose_mode == 1) 0114 disp(['Warping image #',num2str(i),' of ',num2str(number_of_images)]); 0115 end 0116 current_image_vector_to_warp = warped_images(:,:,i); 0117 current_point_vector_to_warp = warped_points(:,:,i); 0118 0119 % warp image using one objective function or another - currently msd_opt_together 0120 0121 0122 scores_mi(i) = mi(reference_image_vector, warped_images(:,:,i),10); 0123 scores_smsd(i) = sum(msd(reference_image_vector, warped_images(:,:,i))); 0124 % get similarity measures 0125 subplot(number_of_images+4,2,number_of_images+3); 0126 hold on; 0127 title('MI scores'); 0128 plot(scores_mi,'*'); 0129 hold off; 0130 0131 subplot(number_of_images+4,2,number_of_images+4); 0132 hold on; 0133 title('MSD scores'); 0134 plot(scores_smsd,'*'); 0135 hold off; 0136 end 0137 0138 warp_params.green = 'biharmCPS'; 0139 param_list = []; 0140 %%%% The following are the ref and images: warped_images, reference_image_vector 0141 [grid, steps] = setup_grid(2, unwarped_image, ref_points, placement_method, n_points); 0142 % do this knowt-point selection in 2-D. see the brain equivalent which 0143 % picks up points around the skull. in the above, steps are the points 0144 % in 1-D - see GRID_TEST 0145 % now with knotpoints chosen in 2-D space.... 0146 warped_grid = grid; 0147 % copy these knot-points. grid should be called knot-points or something similar. Maybe a 2-d grid 0148 [params, score] = fminsearch(@eval_msd_multi_warp,zeros(size(grid)),optimset('Display',verbose,'TolX',1e-10),grid, unwarped_image, unwarped_points, ref_image, ref_points, 20, warp_params); 0149 % find good paramters to achieve good similarity to the reference image 0150 param_list(size(param_list,1)+1,:).d = params; 0151 % copy the parameters of the warp. Meaning of the notation above is still unknown. 0152 warped_grid = grid + params; 0153 % append parameters to the grip. Perhaps parameters are offests to the grid 0154 start_points = nrr_trans_1d(start_points, grid, warped_grid, warp_params,[]); 0155 % non-rigid transformation applied 0156 start_image = interp1(unwarped_points,unwarped_image,start_points, 'linear',0); 0157 % interpolate the image 0158 warped_points = start_points; 0159 warped_image = start_image; 0160 % copy some values 0161 end 0162 end 0163 0164 0165 status = 0; 0166 % report okay status 0167 % if problems have occurred earlier on in this function, set this flag to 1. 0168 0169 0170 0171 % The following is taken from the MATLAB logo function and can be used to 0172 % show the 2D set as a plain with startling lighting effects. 0173 % 0174 % L = point_vectors(:,:,1); 0175 % axes('CameraPosition', [-193.4013 -265.1546 220.4819],... 0176 % 'CameraTarget',[26 26 10], ... 0177 % 'CameraUpVector',[0 0 1], ... 0178 % 'CameraViewAngle',9.5, ... 0179 % 'DataAspectRatio', [1 1 .9],... 0180 % 'Position',[0 0 1 1], ... 0181 % 'Visible','off', ... 0182 % 'XLim',[1 51], ... 0183 % 'YLim',[1 51], ... 0184 % 'ZLim',[-13 40]); 0185 % s = surface(L, ... 0186 % 'EdgeColor','none', ... 0187 % 'FaceColor',[0.8 0.2 0.2], ... 0188 % 'FaceLighting','phong', ... 0189 % 'AmbientStrength',0.3, ... 0190 % 'DiffuseStrength',0.6, ... 0191 % 'Clipping','off',... 0192 % 'BackFaceLighting','lit', ... 0193 % 'SpecularStrength',1.1, ... 0194 % 'SpecularColorReflectance',1, ... 0195 % 'SpecularExponent',7); 0196 % l1 = light('Position',[40 100 20], ... 0197 % 'Style','local', ... 0198 % 'Color',[0 0.7 0.7]); 0199 % l2 = light('Position',[.5 -1 .4], ... 0200 % 'Color',[1 1 0]);