forked from zhoubolei/CAM
-
Notifications
You must be signed in to change notification settings - Fork 0
/
ILSVRC_evaluate_bbox.m
153 lines (119 loc) · 6.17 KB
/
ILSVRC_evaluate_bbox.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
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]);