File size: 10,659 Bytes
7142654 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 | function [patches] = Load_Patch_Experts( col_patch_dir, col_patch_file, depth_patch_dir, depth_patch_file, clmParams)
%LOAD_PATCH_EXPERTS Summary of this function goes here
% Detailed explanation goes here
colourPatchFiles = dir([col_patch_dir col_patch_file]);
% load all of the pathes
for i=1:numel(colourPatchFiles)
load([col_patch_dir, colourPatchFiles(i).name]);
% determine patch type (slightly hacky but SVR patch experts don't
% CCNF ratio variable)
if(isfield(normalisationOptions, 'ccnf_ratio'))
patch = struct;
patch.centers = centers;
patch.trainingScale = trainingScale;
patch.visibilities = visiIndex;
patch.patch_experts = patch_experts.patch_experts;
patch.correlations = patch_experts.correlations;
patch.rms_errors = patch_experts.rms_errors;
patch.modalities = patch_experts.types;
patch.multi_modal_types = patch_experts.types;
patch.type = 'CCNF';
% Knowing what normalisation was performed during training is
% important for fitting
patch.normalisationOptionsCol = normalisationOptions;
% As the similarity inverses will depend on the window size
% and alphas and betas, but not actual data, precalculate
% them here
% create the similarity inverses
window_sizes = unique(clmParams.window_size(:));
for s=1:size(window_sizes,1)
for view=1:size(patch.patch_experts,1)
for lmk=1:size(patch.patch_experts,2)
if(visiIndex(view, lmk))
num_modalities = size(patch.patch_experts{view,lmk}.thetas,3);
num_hls = size(patch.patch_experts{view,lmk}.thetas,1);
patchSize = sqrt(size( patch.patch_experts{view,lmk}.thetas,2)-1);
patchSize = [patchSize, patchSize];
% normalisation so that patch expert can be
% applied using convolution
w = cell(num_hls, num_modalities);
norm_w = cell(num_hls, num_modalities);
for hl=1:num_hls
for p=1:num_modalities
w_c = patch.patch_experts{view,lmk}.thetas(hl, 2:end, p);
norm_w_c = norm(w_c);
w_c = w_c/norm(w_c);
w_c = reshape(w_c, patchSize);
w{hl,p} = w_c;
norm_w{hl,p} = norm_w_c;
end
end
patch.patch_experts{view,lmk}.w = w;
patch.patch_experts{view,lmk}.norm_w = norm_w;
similarities = {};
response_side_length = window_sizes(s) - 11 + 1;
for st=1:size(patch.patch_experts{view,lmk}.similarity_types, 1)
type_sim = patch.patch_experts{view,lmk}.similarity_types{st};
neighFn = @(x) similarity_neighbor_grid(x, response_side_length(1), type_sim);
similarities = [similarities; {neighFn}];
end
sparsities = {};
for st=1:size(patch.patch_experts{view,lmk}.sparsity_types, 1)
spFn = @(x) sparsity_grid(x, response_side_length(1), patch.patch_experts{view,lmk}.sparsity_types(st,1), patch.patch_experts{view,lmk}.sparsity_types(st,2));
sparsities = [sparsities; {spFn}];
end
region_length = response_side_length^2;
[ ~, ~, PrecalcQ2sFlat, ~ ] = CalculateSimilarities_sparsity( 1, {zeros(region_length,1)}, similarities, sparsities);
PrecalcQ2flat = PrecalcQ2sFlat{1};
SigmaInv = CalcSigmaCCNFflat(patch.patch_experts{view,lmk}.alphas, patch.patch_experts{view,lmk}.betas, region_length, PrecalcQ2flat, eye(region_length), zeros(region_length));
if(s == 1)
patch.patch_experts{view,lmk}.Sigma = {inv(SigmaInv)};
else
patch.patch_experts{view,lmk}.Sigma = cat(1, patch.patch_experts{view,lmk}.Sigma, {inv(SigmaInv)});
end
end
end
end
end
if(i==1)
patches = patch;
else
patches = [patches; patch];
end
else
% creating the struct
patch = struct;
patch.centers = centers;
patch.trainingScale = trainingScale;
patch.visibilities = visiIndex;
patch.type = 'SVR';
% if the normalisation options present in the loaded patch use
% them, if not we use default values
if(exist('normalisationOptions', 'var'))
patch.normalisationOptionsCol = normalisationOptions;
else
patch.normalisationOptionsCol = normalisationColour;
end
% default for depth uses normalised-cross-corr per each sample,
% rather than in a broad area
if(~isfield(patch.normalisationOptionsCol, 'useZeroMeanPerPatch'))
patch.normalisationOptionsCol.zscore = 0;
patch.normalisationOptionsCol.useNormalisedCrossCorr = 1;
patch.normalisationOptionsCol.useZeroMeanPerPatch = 1;
end
% Multi-modal section
patch.patch_experts = cell(size(visiIndex,1), size(visiIndex,2));
for view=1:size(visiIndex,1)
for landmark=1:size(visiIndex,2)
patch.patch_experts{view, landmark} = struct;
multi_modal_types = patch_experts.types;
for p=1:numel(patch_experts.types)
patch.patch_experts{view, landmark}(p).type = patch_experts.types(p);
patch.patch_experts{view, landmark}(p).correlations = patch_experts.correlations(p);
patch.patch_experts{view, landmark}(p).rms_errors = patch_experts.rms_errors(p);
patch.patch_experts{view,landmark}(p).scaling = patch_experts.patch_experts{p}(view, landmark, 1);
patch.patch_experts{view,landmark}(p).bias = patch_experts.patch_experts{p}(view, landmark, 2);
patch.patch_experts{view,landmark}(p).w = reshape(patch_experts.patch_experts{p}(view, landmark, 3:end),11,11);
end
end
end
patch.multi_modal_types = multi_modal_types;
if(i==1)
patches = patch;
else
patches = [patches; patch];
end
end
clear 'normalisationOptions, centers, trainingScale, visiIndex, correlations, rmsErrors';
end
if(~isempty(depth_patch_file))
depthPatchFiles = dir([depth_patch_dir depth_patch_file]);
% load all of the depth patches
for i=1:numel(depthPatchFiles)
load([depth_patch_dir, depthPatchFiles(i).name]);
% assuming that same view seen in depth and intensity
% if the normalisation options present in the loaded patch use
% them, if not we use default values
if(exist('normalisationOptions', 'var'))
patches(i).normalisationOptionsDepth = normalisationOptions;
else
patches(i).normalisationOptionsDepth = normalisationDepth;
end
% Multi-modal section
patches(i).patch_experts_depth = cell(size(visiIndex,1), size(visiIndex,2));
for view=1:size(visiIndex,1)
for landmark=1:size(visiIndex,2)
patches(i).patch_experts_depth{view, landmark} = struct;
if(exist('patch_m', 'var'))
multi_modal_types = patch_m.types;
for p=1:numel(patch_m.types)
patches(i).patch_experts_depth{view, landmark}(p).type = patch_m.types(p);
patches(i).patch_experts_depth{view, landmark}(p).correlations = patch_m.correlations(p);
patches(i).patch_experts_depth{view, landmark}(p).rms_errors = patch_m.rms_errors(p);
patches(i).patch_experts_depth{view,landmark}(p).scaling = patch_m.patch_experts{p}(view, landmark, 1);
patches(i).patch_experts_depth{view,landmark}(p).bias = patch_m.patch_experts{p}(view, landmark, 2);
patches(i).patch_experts_depth{view,landmark}(p).w = reshape(patch_m.patch_experts{p}(view, landmark, 3:end),11,11);
end
else
multi_modal_types = {'reg'};
% for backward compatibility
patches(i).patch_experts_depth{view, landmark}(1).type = {'reg'};
patches(i).patch_experts_depth{view, landmark}(1).correlations = correlations;
patches(i).patch_experts_depth{view, landmark}(1).rms_errors = rmsErrors;
patches(i).patch_experts_depth{view,landmark}(1).scaling = patchExperts(view, landmark, 1);
patches(i).patch_experts_depth{view,landmark}(1).bias = patchExperts(view, landmark, 2);
patches(i).patch_experts_depth{view,landmark}(1).w = reshape(patchExperts(view, landmark, 3:end), 11,11);
end
end
end
clear 'normalisationOptions, centers, trainingScale, visiIndex, correlations, rmsErrors';
end
end
end
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