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