（转载请注明作者和出处 楼燚(yì)航的blog ：http://www.cnblogs.com/louyihang loves baiyan/ 未经允许请勿用于商业用途）
由于目前DPM模型训练的代码没有C++版本，至少我没看见opencv ccv conrib等一些库中都没有看到相关训练的部分倒都是有detector的部分），大部分人都是基于Matlab来做训练的，放到wnidows下用一些别的DPM的库或者自己C++实现，那么这些模型的文件类型大多都是XML的，网上现成的都比较混乱，看到opencv contrib库中的作者在他的另一个Git库中放了这个转换文件，没有公开说明。这个转换的文件可以达到目的，亲测可用。
function mat2opencvxml(fname_in, fname_out)
% Convert DPM (2007) model to cascade model and
% save in OpenCV file storage format (.xml)
% mat2opencvxml(fname_in, fname_out)
%
% e.g., mat2opencvxml('./INRIA/inriaperson_final.mat', 'inriaperson_cascade_cv.xml'
%
% Arguments
% fname_in File name of the DPM VOC 2007 model
% fname_out File name of the OpenCV file storage model (.xml)
% load VOC2007 DPM model
load(fname_in);
thresh = model.thresh;
pca = 5;
csc_model = cascade_model(model, '2007', pca, thresh);
num_feat = 32;
rootfilters = [];
for i = 1:length(csc_model.rootfilters)
rootfilters{i} = csc_model.rootfilters{i}.w;
end
partfilters = [];
for i = 1:length(csc_model.partfilters)
partfilters{i} = csc_model.partfilters{i}.w;
end
for c = 1:csc_model.numcomponents
ridx{c} = csc_model.components{c}.rootindex;
oidx{c} = csc_model.components{c}.offsetindex;
root{c} = csc_model.rootfilters{ridx{c}}.w;
root_pca{c} = csc_model.rootfilters{ridx{c}}.wpca;
offset{c} = csc_model.offsets{oidx{c}}.w;
loc{c} = csc_model.loc{c}.w;
rsize{c} = [size(root{c},1) size(root{c},2)];
numparts{c} = length(csc_model.components{c}.parts);
for j = 1:numparts{c}
pidx{c,j} = csc_model.components{c}.parts{j}.partindex;
didx{c,j} = csc_model.components{c}.parts{j}.defindex;
part{c,j} = csc_model.partfilters{pidx{c,j}}.w;
part_pca{c,j} = csc_model.partfilters{pidx{c,j}}.wpca;
psize{c,j} = [size(part{c,j},1) size(part{c,j},2)];
end
end
maxsizex = ceil(csc_model.maxsize(2));
maxsizey = ceil(csc_model.maxsize(1));
pca_rows = size(csc_model.pca_coeff, 1);
pca_cols = size(csc_model.pca_coeff, 2);
f = fopen(fname_out, 'wb');
fprintf(f, '\n');
fprintf(f, '\n');
fprintf(f, '%d \n', csc_model.sbin);
fprintf(f, '%d \n', csc_model.numcomponents);
fprintf(f, '%d \n', num_feat);
fprintf(f, '%d \n', csc_model.interval);
fprintf(f, '%d \n', maxsizex);
fprintf(f, '%d \n', maxsizey);
%the pca coeff
fprintf(f, '\n');
fprintf(f, '\t%d \n', pca_rows);
fprintf(f, '\t%d \n', pca_cols);
fprintf(f, '\td \n');
fprintf(f, '\t\n');
for i=1:pca_rows
fprintf(f, '\t');
for j=1:pca_cols
fprintf(f, '%f ', csc_model.pca_coeff(i, j));
end
fprintf(f, '\n');
end
fprintf(f, '\t\n');
fprintf(f, ' \n');
fprintf(f, '%d \n', pca_cols);
fprintf(f, '%.16f \n', csc_model.thresh);
fprintf(f, '\n');
for c = 1:csc_model.numcomponents
fprintf(f, '%f ', offset{c});
end
fprintf(f, '\n \n');
fprintf(f, '\n');
for c = 1:csc_model.numcomponents
rootfilter = root{c};
rows = size(rootfilter,1);
cols = size(rootfilter,2);
depth = size(rootfilter,3);
fprintf(f, '\t<_ type_id="opencv-matrix">\n');
fprintf(f, '\t%d \n', rows);
fprintf(f, '\t%d \n', cols*depth);
fprintf(f, '\td \n');
fprintf(f, '\t\n');
for i=1:rows
fprintf(f, '\t');
for j=1:cols
for k=1:depth
fprintf(f, '%f ', rootfilter(i, j, k));
end
end
fprintf(f, '\n');
end
fprintf(f, '\t\n');
fprintf(f, '\t\n');
end
fprintf(f, ' \n');
fprintf(f, '\n');
for c = 1:csc_model.numcomponents
rootfilter_pca = root_pca{c};
rows = size(rootfilter_pca,1);
cols = size(rootfilter_pca,2);
depth = size(rootfilter_pca,3);
fprintf(f, '\t<_ type_id="opencv-matrix">\n');
fprintf(f, '\t%d \n', rows);
fprintf(f, '\t%d \n', cols*depth);
fprintf(f, '\td \n');
fprintf(f, '\t\n');
for i=1:rows
fprintf(f, '\t');
for j=1:cols
for k=1:depth
fprintf(f, '%f ', rootfilter_pca(i, j, k));
end
end
fprintf(f, '\n');
end
fprintf(f, '\t\n');
fprintf(f, '\t\n');
end
fprintf(f, ' \n');
fprintf(f, '\n');
for c = 1:csc_model.numcomponents
for p=1:numparts{c}
partfilter = part{c,p};
rows = size(partfilter,1);
cols = size(partfilter,2);
depth = size(partfilter,3);
fprintf(f, '\t<_ type_id="opencv-matrix">\n');
fprintf(f, '\t%d \n', rows);
fprintf(f, '\t%d \n', cols*depth);
fprintf(f, '\td \n');
fprintf(f, '\t\n');
for i=1:rows
fprintf(f, '\t');
for j=1:cols
for k=1:depth
fprintf(f, '%f ', partfilter(i, j, k));
end
end
fprintf(f, '\n');
end
fprintf(f, '\t\n');
fprintf(f, '\t\n');
end
end
fprintf(f, ' \n');
fprintf(f, '\n');
for c = 1:csc_model.numcomponents
for p=1:numparts{c}
partfilter = part_pca{c,p};
rows = size(partfilter,1);
cols = size(partfilter,2);
depth = size(partfilter,3);
fprintf(f, '\t<_ type_id="opencv-matrix">\n');
fprintf(f, '\t%d \n', rows);
fprintf(f, '\t%d \n', cols*depth);
fprintf(f, '\td \n');
fprintf(f, '\t\n');
for i=1:rows
fprintf(f, '\t');
for j=1:cols
for k=1:depth
fprintf(f, '%f ', partfilter(i, j, k));
end
end
fprintf(f, '\n');
end
fprintf(f, '\t\n');
fprintf(f, '\t\n');
end
end
fprintf(f, ' \n');
fprintf(f, '\n');
for c = 1:csc_model.numcomponents
fprintf(f, '\t<_>\n');
fprintf(f, '\t');
t = csc_model.cascade.t{ridx{c}};
for j=1:length(t)
fprintf(f, '%f ', t(j));
end
fprintf(f, '\n\t\n');
end
fprintf(f, ' \n');
fprintf(f, '\n');
for c = 1:csc_model.numcomponents
for p=1:numparts{c}
fprintf(f, '\t<_>\n');
fprintf(f, '\t');
anchor = csc_model.defs{didx{c,p}}.anchor;
for j=1:length(anchor)
fprintf(f, '%f ', anchor(j));
end
fprintf(f, '\n\t\n');
end
end
fprintf(f, ' \n');
fprintf(f, '\n');
for c = 1:csc_model.numcomponents
for p=1:numparts{c}
fprintf(f, '\t<_>\n');
fprintf(f, '\t');
def = csc_model.defs{didx{c,p}}.w;
for j=1:length(def)
fprintf(f, '%f ', def(j));
end
fprintf(f, '\n\t\n');
end
end
fprintf(f, ' \n');
fprintf(f, '\n');
for c = 1:csc_model.numcomponents
fprintf(f, '%f ', numparts{c});
end
fprintf(f, ' \n');
fprintf(f, '\n');
for c = 1:csc_model.numcomponents
fprintf(f, '\t<_>\n');
fprintf(f, '\t');
order = csc_model.cascade.order{c};
for i=1:length(order)
fprintf(f, '%f ', order(i));
end
fprintf(f, '\n\t\n');
end
fprintf(f, ' \n');
fprintf(f, '\n');
for c = 1:csc_model.numcomponents
fprintf(f, '\t<_>\n');
fprintf(f, '\t');
loc_w = loc{c};
for i=1:length(loc_w)
fprintf(f, '%f ', loc_w(i));
end
fprintf(f, '\n\t\n');
end
fprintf(f, ' \n');
fprintf(f, ' ');
fclose(f);