sustaining_gazes/matlab_version/fitting/PatchResponseSVM_multi_modal.m

133 lines
5 KiB
Mathematica
Raw Normal View History

2016-04-28 19:40:36 +00:00
function [ responses ] = PatchResponseSVM_multi_modal( patches, patch_experts, visibilities, normalisationOptions, clmParameters, window_size)
%PATCHRESPONSESVM Summary of this function goes here
% Detailed explanation goes here
patchSize = normalisationOptions.patchSize;
responses = cell(size(patches, 1), 1);
empty = zeros(window_size(1)-patchSize(1)+1, window_size(2)-patchSize(2)+1);
% prepare the patches through either turning them to gradients or
if(clmParameters.use_multi_modal)
patches_to_use = cell(numel(clmParameters.multi_modal_types),1);
for t=1:numel(clmParameters.multi_modal_types)
if(strcmp(clmParameters.multi_modal_types{t}, 'reg'))
patches_reg = patches;
if(normalisationOptions.zscore)
meanCurr = mean(patches, 2);
stdCurr = std(double(patches), 0, 2);
stdCurr(stdCurr == 0) = 1;
patches_reg = bsxfun(@minus, patches, meanCurr);
patches_reg = bsxfun(@rdivide, patches_reg, stdCurr);
end
patches_to_use{t} = patches_reg;
elseif(strcmp(clmParameters.multi_modal_types{t}, 'grad'))
v = [1];
h = [-1 0 1];
grad_patches = zeros(size(patches));
for i = 1:numel(patches(:,1))
if visibilities(i)
currSample = reshape(patches(i,:), window_size(1), window_size(2));
edgeX = conv2(conv2(currSample, v, 'same'), h, 'same');
edgeY = conv2(conv2(currSample, v', 'same'), h', 'same');
grad = edgeX.^2 + edgeY.^2;
grad(1,:) = 0;
grad(:,1) = 0;
grad(end,:) = 0;
grad(:,end) = 0;
grad_patches(i,:) = reshape(grad, window_size(1) * window_size(1),1);
end
end
patches_to_use{t} = grad_patches;
end
end
else
patches_reg = patches;
if(normalisationOptions.zscore)
if(normalisationOptions.ignoreInvalidInMeanStd)
% invalid data represented with 0, ignore it when computing
% mean and standard deviation (useful for depth)
for i = 1:size(patches,1)
mask = patches(i,:) ~= 0;
meanCurr = mean(patches(i,mask));
stdCurr = std(patches(i,mask));
patches(i,mask) = patches(i, mask) - meanCurr;
if(stdCurr ~= 0)
patches(i, mask) = patches(i, mask) ./ meanCurr;
end
patches(i, ~mask) = normalisationOptions.setIllegalToPost;
end
patches_reg = patches;
else
meanCurr = mean(patches, 2);
stdCurr = std(double(patches), 0, 2);
stdCurr(stdCurr == 0) = 1;
patches_reg = bsxfun(@minus, patches, meanCurr);
patches_reg = bsxfun(@rdivide, patches_reg, stdCurr);
end
end
patches_to_use = {patches_reg};
end
for i = 1:numel(patches(:,1))
responses{i} = empty;
if visibilities(i)
% responses{i} = ones(size(empty));
colNorm = normalisationOptions.useNormalisedCrossCorr == 1;
for p=1:numel(patches_to_use)
smallRegionVec = patches_to_use{p}(i,:);
smallRegion = reshape(smallRegionVec, window_size(1), window_size(2));
% get the patch response
response = SVMresponse(smallRegion, patch_experts{i}(p).w, colNorm, patchSize);
response = (exp(-(patch_experts{i}(p).scaling*response+patch_experts{i}(p).bias))+1).^-1;
if(p==1)
responses{i} = response;
else
responses{i} = responses{i} .* response;
end
end
normOp = (sum(responses{i}(:)));
if(normOp ~= 0)
responses{i} = responses{i} ./ normOp;
end
end
end
end
function response = SVMresponse(region, patchExpert, normalise_x_corr,patchSize)
if(normalise_x_corr)
% the much faster mex version
[response] = normxcorr2_mex(patchExpert, region);
response = response(patchSize(1):end-patchSize(1)+1,patchSize(2):end-patchSize(2)+1);
else
% this assumes that the patch is already normed
template = rot90(patchExpert,2);
response = conv2(region, template, 'valid');
end
end