datasetName = 'ILSVRCvalSet';

load('imagenet_toolkit/ILSVRC2014_devkit/evaluation/cache_groundtruth.mat');

load('imagenet_toolkit/ILSVRC2014_devkit/data/meta_clsloc.mat');

% download the toolkit at http://www.image-net.org/challenges/LSVRC/2014/

datasetPath = 'dataset/ILSVRC2012';

load([datasetPath '/imageListVal.mat']);

load('sizeImg_ILSVRC2014.mat');

% datasetName = 'ILSVRCtestSet';

% datasetPath = '/data/vision/torralba/deeplearning/imagenet_toolkit';

% load([datasetPath '/imageListTest.mat']);

nImgs = size(imageList,1);

ground_truth_file='imagenet_toolkit/ILSVRC2014_devkit/data/ILSVRC2014_clsloc_validation_ground_truth.txt';

gt_labels = dlmread(ground_truth_file);

categories_gt = [];

categoryIDMap = containers.Map();

for i=1:numel(synsets)

categories_gt{synsets(i).ILSVRC2014_ID,1} = synsets(i).words;

categories_gt{synsets(i).ILSVRC2014_ID,2} = synsets(i).WNID;

categoryIDMap(synsets(i).WNID) = i;

end

%% network to evaluate

% backpropa-heatmap

%netName = 'caffeNet_imagenet';

%netName = 'googlenetBVLC_imagenet';

%netName = 'VGG16_imagenet';

% CAM-based network

%netName = 'NIN';

%netName = 'CAM_imagenetCNNaveSumDeep';

%netName = 'CAM_googlenetBVLC_imagenet';% the direct output

netName = 'CAM_googlenetBVLCshrink_imagenet';

%netName = 'CAM_googlenetBVLCshrink_imagenet_maxpool';

%netName = 'CAM_VGG16_imagenet';

%netName = 'CAM_alexnet';

load('categoriesImageNet.mat');

visualizationPointer = 0;

topCategoryNum = 5;

predictionResult_bbox1 = zeros(nImgs, topCategoryNum*5);

predictionResult_bbox2 = zeros(nImgs, topCategoryNum*5);

predictionResult_bboxCombine = zeros(nImgs, topCategoryNum*5);

if matlabpool('size')==0

try

matlabpool

catch e

end

end

heatMapFolder = ['heatMap-' datasetName '-' netName];

bbox_threshold = [20, 100, 110];

curParaThreshold = [num2str(bbox_threshold(1)) ' ' num2str(bbox_threshold(2)) ' ' num2str(bbox_threshold(3))];

parfor i=1:size(imageList,1)

curImgIDX = i;

height_original = sizeFull_imageList(curImgIDX,1);%tmp.Height;

weight_original = sizeFull_imageList(curImgIDX,2);%tmp.Width;

[a b c] = fileparts(imageList{curImgIDX,1});

curPath_fullSizeImg = ['/data/vision/torralba/deeplearning/imagenet_toolkit/ILSVRC2012_img_val/' b c];

curMatFile = [heatMapFolder '/' b '.mat'];

[heatMapSet, value_category, IDX_category] = loadHeatMap( curMatFile);

curResult_bbox1 = [];

curResult_bbox2 = [];

curResult_bboxCombine = [];

for j=1:5

curHeatMapFile = [heatMapFolder '/top' num2str(j) '/' b '.jpg'];

curBBoxFile = [heatMapFolder '/top' num2str(j) '/' b '_default.txt'];

%curBBoxFileGraphcut = [heatMapFolder '/top' num2str(j) '/' b '_graphcut.txt'];

curCategory = categories{IDX_category(j),1};

%imwrite(curHeatMap, ['result_bbox/heatmap_tmp' b randString '.jpg']);

if ~exist(curBBoxFile)

%system(['/data/vision/torralba/deeplearning/package/bbox_hui/final ' curHeatMapFile ' ' curBBoxFile]);

system(['/data/vision/torralba/deeplearning/package/bbox_hui_new/./dt_box ' curHeatMapFile ' ' curParaThreshold ' ' curBBoxFile]);

end

curPredictCategory = categories{IDX_category(j),1};

curPredictCategoryID = categories{IDX_category(j),1}(1:9);

curPredictCategoryGTID = categoryIDMap(curPredictCategoryID);

boxData = dlmread(curBBoxFile);

boxData_formulate = [boxData(1:4:end)' boxData(2:4:end)' boxData(1:4:end)' boxData(3:4:end)' boxData(2:4:end)' boxData(4:4:end)'];

boxData_formulate = [min(boxData_formulate(:,1),boxData_formulate(:,3)),min(boxData_formulate(:,2),boxData_formulate(:,4)),max(boxData_formulate(:,1),boxData_formulate(:,3)),max(boxData_formulate(:,2),boxData_formulate(:,4))];

% try

% boxDataGraphcut = dlmread(curBBoxFileGraphcut);

% boxData_formulateGraphcut = [boxDataGraphcut(1:4:end)' boxDataGraphcut(2:4:end)' boxDataGraphcut(1:4:end)' boxDataGraphcut(3:4:end)' boxDataGraphcut(2:4:end)' boxDataGraphcut(4:4:end)'];

% catch exception

% boxDataGraphcut = dlmread(curBBoxFile);

% boxData_formulateGraphcut = [boxDataGraphcut(1:4:end)' boxDataGraphcut(2:4:end)' boxDataGraphcut(1:4:end)' boxDataGraphcut(3:4:end)' boxDataGraphcut(2:4:end)' boxDataGraphcut(4:4:end)'];

% boxData_formulateGraphcut = boxData_formulateGraphcut(1,:);

% end

bbox = boxData_formulate(1,:);

curPredictTuple = [curPredictCategoryGTID bbox(1) bbox(2) bbox(3) bbox(4)];

curResult_bbox1 = [curResult_bbox1 curPredictTuple];

curResult_bboxCombine = [curResult_bboxCombine curPredictTuple];

bbox = boxData_formulate(2,:);

�ox = boxData_formulateGraphcut(1,:);

curPredictTuple = [curPredictCategoryGTID bbox(1) bbox(2) bbox(3) bbox(4)];

curResult_bbox2 = [curResult_bbox2 curPredictTuple];

curResult_bboxCombine = [curResult_bboxCombine curPredictTuple];

if visualizationPointer == 1

curHeatMap = imread(curHeatMapFile);

curHeatMap = imresize(curHeatMap,[height_original weight_original]);

subplot(1,2,1),hold off, imshow(curPath_fullSizeImg);

hold on

curBox = boxData_formulate(1,:);

rectangle('Position',[curBox(1) curBox(2) curBox(3)-curBox(1) curBox(4)-curBox(2)],'EdgeColor',[1 0 0]);

subplot(1,2,2),imagesc(curHeatMap);

title(curCategory);

waitforbuttonpress

end

end

predictionResult_bbox1(i, :) = curResult_bbox1;

predictionResult_bbox2(i, :) = curResult_bbox2;

predictionResult_bboxCombine(i,:) = curResult_bboxCombine(1:topCategoryNum*5);

disp([netName ' processing ' b])

end

addpath('evaluation');

disp([netName '--------bbox1' ]);

[cls_error, clsloc_error] = simpleEvaluation(predictionResult_bbox1);

disp([(1:5)',clsloc_error,cls_error]);

disp([netName '--------bbox2' ]);

[cls_error, clsloc_error] = simpleEvaluation(predictionResult_bbox2);

disp([(1:5)',clsloc_error,cls_error]);

disp([netName '--------bboxCombine' ]);

[cls_error, clsloc_error] = simpleEvaluation(predictionResult_bboxCombine);

disp([(1:5)',clsloc_error,cls_error]);