133 lines
5.0 KiB
Matlab
133 lines
5.0 KiB
Matlab
function [ responses ] = PatchResponseSVM_multi_modal( patches, patch_experts, visibilities, normalisationOptions, clmParameters, window_size)
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%PATCHRESPONSESVM Summary of this function goes here
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% Detailed explanation goes here
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patchSize = normalisationOptions.patchSize;
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responses = cell(size(patches, 1), 1);
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empty = zeros(window_size(1)-patchSize(1)+1, window_size(2)-patchSize(2)+1);
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% prepare the patches through either turning them to gradients or
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if(clmParameters.use_multi_modal)
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patches_to_use = cell(numel(clmParameters.multi_modal_types),1);
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for t=1:numel(clmParameters.multi_modal_types)
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if(strcmp(clmParameters.multi_modal_types{t}, 'reg'))
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patches_reg = patches;
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if(normalisationOptions.zscore)
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meanCurr = mean(patches, 2);
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stdCurr = std(double(patches), 0, 2);
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stdCurr(stdCurr == 0) = 1;
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patches_reg = bsxfun(@minus, patches, meanCurr);
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patches_reg = bsxfun(@rdivide, patches_reg, stdCurr);
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end
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patches_to_use{t} = patches_reg;
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elseif(strcmp(clmParameters.multi_modal_types{t}, 'grad'))
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v = [1];
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h = [-1 0 1];
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grad_patches = zeros(size(patches));
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for i = 1:numel(patches(:,1))
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if visibilities(i)
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currSample = reshape(patches(i,:), window_size(1), window_size(2));
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edgeX = conv2(conv2(currSample, v, 'same'), h, 'same');
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edgeY = conv2(conv2(currSample, v', 'same'), h', 'same');
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grad = edgeX.^2 + edgeY.^2;
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grad(1,:) = 0;
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grad(:,1) = 0;
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grad(end,:) = 0;
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grad(:,end) = 0;
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grad_patches(i,:) = reshape(grad, window_size(1) * window_size(1),1);
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end
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end
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patches_to_use{t} = grad_patches;
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end
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end
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else
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patches_reg = patches;
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if(normalisationOptions.zscore)
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if(normalisationOptions.ignoreInvalidInMeanStd)
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% invalid data represented with 0, ignore it when computing
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% mean and standard deviation (useful for depth)
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for i = 1:size(patches,1)
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mask = patches(i,:) ~= 0;
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meanCurr = mean(patches(i,mask));
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stdCurr = std(patches(i,mask));
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patches(i,mask) = patches(i, mask) - meanCurr;
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if(stdCurr ~= 0)
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patches(i, mask) = patches(i, mask) ./ meanCurr;
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end
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patches(i, ~mask) = normalisationOptions.setIllegalToPost;
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end
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patches_reg = patches;
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else
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meanCurr = mean(patches, 2);
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stdCurr = std(double(patches), 0, 2);
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stdCurr(stdCurr == 0) = 1;
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patches_reg = bsxfun(@minus, patches, meanCurr);
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patches_reg = bsxfun(@rdivide, patches_reg, stdCurr);
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end
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end
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patches_to_use = {patches_reg};
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end
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for i = 1:numel(patches(:,1))
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responses{i} = empty;
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if visibilities(i)
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% responses{i} = ones(size(empty));
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colNorm = normalisationOptions.useNormalisedCrossCorr == 1;
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for p=1:numel(patches_to_use)
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smallRegionVec = patches_to_use{p}(i,:);
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smallRegion = reshape(smallRegionVec, window_size(1), window_size(2));
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% get the patch response
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response = SVMresponse(smallRegion, patch_experts{i}(p).w, colNorm, patchSize);
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response = (exp(-(patch_experts{i}(p).scaling*response+patch_experts{i}(p).bias))+1).^-1;
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if(p==1)
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responses{i} = response;
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else
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responses{i} = responses{i} .* response;
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end
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end
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normOp = (sum(responses{i}(:)));
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if(normOp ~= 0)
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responses{i} = responses{i} ./ normOp;
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end
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end
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end
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end
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function response = SVMresponse(region, patchExpert, normalise_x_corr,patchSize)
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if(normalise_x_corr)
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% the much faster mex version
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[response] = normxcorr2_mex(patchExpert, region);
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response = response(patchSize(1):end-patchSize(1)+1,patchSize(2):end-patchSize(2)+1);
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else
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% this assumes that the patch is already normed
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template = rot90(patchExpert,2);
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response = conv2(region, template, 'valid');
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end
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end
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