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project_demo.m
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project_demo.m
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function [cls_dets,gpBox,net,nnOpt,im_1,im_2] = project_demo(filename,class,GPU)
% Demo:
% Input:
% filename: input image. Default as 000005.jpg.
% class: class of target object. Default as 'person'.
% GPU: GPU mode or not (cuDNN). Default [1]. If cpu only then [].
% Output:
% cls_dets: bounding boxes yielded from RCNN+ box regression
% gpBox: boundingboxes after GP
% net: the Dagnn instance of neural network.
% nnOpt. Hyperparameters of nn.
% im_1. The yielded image with boxes drawn inside.
% PS: Each row of boxes are [x1 y1 x2 y2], i.e. the left-top and right-bot
% coordinates.
if (nargin<1)
filename = '000005.jpg';
class = {'person'};
GPU = [1];
elseif nargin<2
class = {'person'};
GPU = [1];
elseif nargin<3
GPU = [1];
end
[net,nnOpt] = getRCNN(class,GPU);
%% Generate edge boxes
[boxModel,boxOpt] = EB_Model();
tic;
imD = imread(filename);
boxes=edgeBoxesWrapper(imD,boxModel,boxOpt);
toc;
tic;
[cls_dets,labels,gpBox,gpLabel] = predImageSingle(net,boxes,nnOpt,imD,1);
toc;
figure;
if size(gpBox(gpLabel==1,:),1)<=0
class_name =' Nothing found';
else
class_name = class{1};
end
im_1 = bbox_draw(imD,gpBox(gpLabel==1,:));
title(sprintf('Detections for class ''%s''', class_name)) ;
figure;
im_2 = bbox_draw(imD,cls_dets(labels==1,:));
title(sprintf('No GP Detections for class ''%s''', class_name)) ;
end
%{
function [boxes]= edgeBoxesWrapper(imD,boxModel,boxOpt)
imdb_gtBox{revID}(imdb_gtLabel{revID}==CLS_PERSON,:)
%% Define the conventional formation of bounding box;
boxes=edgeBoxes(imD,boxModel,boxOpt);
boxes = floor(boxes(:,1:4));
boxes(:,3) = boxes(:,1)+boxes(:,3);
boxes(:,4) = boxes(:,2)+boxes(:,4);
end
%}
%{
function [net,nnOpt,net2] = getRCNN(class,GPU)
if nargin < 2
class = 'class';
GPU = 1;
end
nnOpt = getDaggModel(class,GPU);
% Load the network and put it in test mode.
net2 = load(nnOpt.modelPath) ;
net = dagnn.DagNN.loadobj(net2);
net.mode = 'test' ;
% Mark class and bounding box predictions as `precious` so they are
% not optimized away during evaluation.
net.vars(net.getVarIndex('cls_prob')).precious = 1 ;
net.vars(net.getVarIndex('bbox_pred')).precious = 1 ;
end
%}
%{
function [nnOpt] = getDaggModel(class,GPU)
if nargin < 2
class = 'class';
GPU = 1;
end
nnOpt.modelPath = '' ;
nnOpt.classes = {class} ;
nnOpt.gpu = [GPU] ;
nnOpt.confThreshold = 0.5 ;
nnOpt.nmsThreshold = 0.3 ;
nnOpt = vl_argparse(nnOpt, {}) ;
paths = {nnOpt.modelPath, ...
'./fast-rcnn-vgg16-dagnn.mat', ...
fullfile(vl_rootnn, 'data', 'models', 'fast-rcnn-vgg16-pascal07-dagnn.mat'), ...
fullfile(vl_rootnn, 'data', 'models-import', 'fast-rcnn-vgg16-pascal07-dagnn.mat')} ;
ok = min(find(cellfun(@(x)exist(x,'file'), paths))) ;
if isempty(ok)
fprintf('Downloading the Fast RCNN model ... this may take a while\n') ;
nnOpt.modelPath = fullfile(vl_rootnn, 'data', 'models', 'fast-rcnn-vgg16-pascal07-dagnn.mat') ;
mkdir(fileparts(nnOpt.modelPath)) ;
urlwrite('http://www.vlfeat.org/matconvnet/models/fast-rcnn-vgg16-pascal07-dagnn.mat', ...
nnOpt.modelPath) ;
else
nnOpt.modelPath = paths{ok} ;
end
end
%}
%{
function [boxModel,boxOpt] = EB_Model()
%% load pre-trained edge detection model and set opts (see edgesDemo.m)
boxModel=load('models/forest/modelBsds');
boxModel=boxModel.model;
boxModel.opts.multiscale=0;
boxModel.opts.sharpen=2;
boxModel.opts.nThreads=4;
%% set up opts for edgeBoxes (see edgeBoxes.m)
boxOpt = edgeBoxes();
boxOpt.alpha = .65; % step size of sliding window search
boxOpt.beta = .90; % nms threshold for object proposals
boxOpt.minScore = .02; % min score of boxes to detect
boxOpt.maxBoxes = 500; % max number of boxes to detect
boxOpt.gamma = 1.5;
boxOpt.eta=.9996;
end
%}
%{
function [res_Box,imo] = predImageSingle(net,boxes,nnOpt,imD)
% Load a test image and candidate bounding boxes.
opts = nnOpt;
if nargin<5
imD = imread( '000005.jpg');
end
%imD = imread(filename);
im = single(imD) ;
imo = im; % keep original image
%boxes = load('000004_boxes.mat') ;%Changed
boxes = single(boxes') + 1 ;
boxeso = boxes - 1; % keep original boxes
% Resize images and boxes to a size compatible with the network.
imageSize = size(im) ;
fullImageSize = net.meta.normalization.imageSize(1) ...
/ net.meta.normalization.cropSize ;
scale = max(fullImageSize ./ imageSize(1:2)) ;
im = imresize(im, scale, ...
net.meta.normalization.interpolation, ...
'antialiasing', false) ;
boxes = bsxfun(@times, boxes - 1, scale) + 1 ;
% Remove the average color from the input image.
imNorm = bsxfun(@minus, im, net.meta.normalization.averageImage) ;
% Convert boxes into ROIs by prepending the image index. There is only
% one image in this batch.
rois = [ones(1,size(boxes,2)) ; boxes] ;
% Evaluate network either on CPU or GPU.
if numel(opts.gpu) > 0
gpuDevice(opts.gpu) ;
imNorm = gpuArray(imNorm) ;
rois = gpuArray(rois) ;
net.move('gpu') ;
end
net.conserveMemory = true ;
net.eval({'data', imNorm, 'rois', rois});
% Extract class probabilities and bounding box refinements
%squeeze(gather(res(end).x))
probs = squeeze(gather(net.vars(net.getVarIndex('cls_prob')).value)) ;
deltas = squeeze(gather(net.vars(net.getVarIndex('bbox_pred')).value)) ;
% Visualize results for one class at a time
for i = 1:numel(opts.classes)
c = find(strcmp(opts.classes{i}, net.meta.classes.name)) ;
cprobs = probs(c,:) ;
cdeltas = deltas(4*(c-1)+(1:4),:)' ;
cboxes = bbox_transform_inv(boxeso', cdeltas);
cls_dets = [cboxes cprobs'] ;
keep = bbox_nms(cls_dets, opts.nmsThreshold) ;
cls_dets = cls_dets(keep, :) ;
sel_boxes = find(cls_dets(:,end) >= opts.confThreshold) ;
if 1
imo = bbox_draw(imo/255,cls_dets(sel_boxes,:));
end
title(sprintf('Detections for class ''%s''', opts.classes{i})) ;
fprintf('Detections for category ''%s'':\n', opts.classes{i});
for j=1:size(sel_boxes,1)
bbox_id = sel_boxes(j,1);
fprintf('\t(%.1f,%.1f)\t(%.1f,%.1f)\tprobability=%.6f\n', ...
cls_dets(bbox_id,1), cls_dets(bbox_id,2), ...
cls_dets(bbox_id,3), cls_dets(bbox_id,4), ...
cls_dets(bbox_id,end));
end
end
%res_Box = cls_dets(sel_boxes,:);
%[regBox]=GP_BoxReg(res_Box);
% Box = [];
Box = cls_dets(sel_boxes,:);
net.move('gpu') ;
net.conserveMemory = true ;
for jj = 1 :20
%gpuDevice([]);
[regBox]=GP_BoxReg(Box,jj);
if isempty(regBox)
continue;
end
regBox = single(regBox') + 1 ;
boxeso = regBox-1';
regBox = bsxfun(@times, regBox - 1, scale) + 1 ;
imNorm = bsxfun(@minus, im, net.meta.normalization.averageImage) ;
rois = [ones(1,size(regBox,2)) ; regBox] ;
if numel(opts.gpu) > 0
imNorm = gpuArray(imNorm) ;
rois = gpuArray(rois) ;
end
net.eval({'data', imNorm, 'rois', rois});
probs = squeeze(gather(net.vars(net.getVarIndex('cls_prob')).value)) ;
deltas = squeeze(gather(net.vars(net.getVarIndex('bbox_pred')).value)) ;
%reset(gpuDevice(opts.gpu));
for i = 1:numel(opts.classes)
c = find(strcmp(opts.classes{i}, net.meta.classes.name)) ;
cprobs = probs(c,:) ;
cdeltas = deltas(4*(c-1)+(1:4),:)' ;
cboxes = bbox_transform_inv(boxeso', cdeltas);
cls_dets = [cboxes cprobs'] ;
keep = bbox_nms(cls_dets, opts.nmsThreshold) ;
cls_dets = cls_dets(keep, :) ;
sel_boxes = find(cls_dets(:,end) >= opts.confThreshold) ;
end
Box = [Box;cls_dets(sel_boxes,:)];
end
keep = bbox_nms(Box, opts.nmsThreshold)
res_Box = Box(keep,:);
end
%}
%{
function min_func = getMin()
Solver_Timeout = 10;
minFunc_Method = 'lbfgs';
minFuncX_OPTS = struct();
minFuncX_OPTS.timeout = Solver_Timeout;
minFuncX_OPTS.Display = 'off';
minFuncX_OPTS.Method = minFunc_Method;
min_func = @(func,x0) minFuncX( func,x0, minFuncX_OPTS);
end
%}
%{
function [regBox]=GP_BoxReg(Box,Idx) %%Assume x1y1x2y2p
if (size(Box,1))<=0
regBox = [];
return;
end
min_func = getMin();
%% Convert Box
newBox = [Box(:,2) Box(:,1) Box(:,4) Box(:,3)];
fN = Box(:,5);
%%
[GPModel22] = InitModel();
PsiN1 = bbox_ltrb2param( newBox, 'yxhwl').'; %% Must transpose: column as bbox
latent_obj = @(z) sgp_negloglik( GPmodel(c), z, PsiN1, fN );
z0 = 0;
try
z_hat = min_func( latent_obj, z0);
catch
% warning( 'Optimization on z is failed' );
z_hat = 0;
end
expnz = exp(-z_hat);
if Idx> size(Box,1)
Idx = size(Box,1);
end
PsiN = PsiN1;
PsiN = PsiN.*expnz;
KN = sgp_cov( GPModel22, 0, PsiN ); %% Non-rescaled
fN_hat = max(fN);
psiNp1_0 = PsiN1(:,Idx);
search_obj = @(psiNp1) sgp_neg_acquisition_ei( GPModel22, ...
psiNp1, PsiN, fN, fN_hat, KN );
psiNp1_hat = min_func( search_obj, psiNp1_0 );
%PsiN = [PsiN psiNp1_hat]; %
%PsiN = PsiN/expnz;
%regBox = psiNp1_hat;Debug
%regBox = bbox_param2ltrb(PsiN','yxhwl'); %Transpose back in the input
regBox = bbox_param2ltrb(psiNp1_hat','yxhwl'); %Transpose back in the input
regBox = [regBox(:,2) regBox(:,1) regBox(:,4) regBox(:,3)];
end
%}
%{
function [GPModel22] = InitModel()
meanfunc = @meanConst;
hyp.mean = 0;
covfunc = @covSEard;
ell = 2.0; sf = 1.0;
likfunc = @likGauss; hyp.lik = log(0.1);
hyp.cov = log([ell ell ell ell sf]);
% hyp = minimize.....
GPModel22 = sgp_model_from_general(hyp);
end
%}
%{
function [net] = getRCNN_Net2(net2)
% Load the network and put it in test mode.
net = dagnn.DagNN.loadobj(net2);
net.mode = 'test' ;
% Mark class and bounding box predictions as `precious` so they are
% not optimized away during evaluation.
net.vars(net.getVarIndex('cls_prob')).precious = 1 ;
net.vars(net.getVarIndex('bbox_pred')).precious = 1 ;
end
%}