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ILSVRC_evaluate_bbox.m
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ILSVRC_evaluate_bbox.m
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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,:);
%bbox = 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]);