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- addpath('util/jsonlab/');
- addpath('src');
- addpath('util');
- addpath('util/ojwoodford-export_fig-5735e6d/');
- %addpath('/data/Repo/Realtime_Multi-Person_Pose_Estimation/training/dataset/COCO/coco/MatlabAPI');
- fid = fopen('../val2014_flist.2k.csv');
- data=textscan(fid,'%f %s','delimiter',',');
- fclose(fid);
- display(data);
- for i = 1:length(data{1})
- coco_val(i).file = data{2}{i};
- coco_val(i).image_id = data{1}(i);
- end
- orderCOCO = [1,0 7,9,11, 6,8,10, 13,15,17, 12,14,16, 3,2,5,4];
- mode = 1;
- param = config(mode);
- model = param.model(param.modelID);
- net = caffe.Net(model.deployFile, model.caffemodel, 'test');
- pred(length(coco_val)) = struct('annorect', [], 'candidates', []);
- % iterate all val images
- display(length(coco_val));
- for i = 1:length(coco_val)
- display(i);
- fn = strcat('/data/Realtime_Multi-Person_Pose_Estimation/training/dataset/COCO/images/', coco_val(i).file);
- display(coco_val(i).file);
- oriImg = imread(fn);
- scale0 = 368/size(oriImg, 1);
- twoLevel = 1;
- [final_score, ~] = applyModel(oriImg, param, net, scale0, 1, 1, 0, twoLevel);
- vis = 0;
- [candidates, subset] = connect56LineVec(oriImg, final_score, param, vis);
- point_cnt = 0;
- for ridxPred = 1:size(subset,1)
- point = struct([]);
- part_cnt = 0;
- for part = 1:18
- if part == 2
- continue;
- end
- index = subset(ridxPred,part);
- if(index >0)
- part_cnt = part_cnt +1;
- point(part_cnt).x = candidates(index,1);
- point(part_cnt).y = candidates(index,2);
- point(part_cnt).score = candidates(index,3);
- point(part_cnt).id = orderCOCO(part);
- end
- end
- point_cnt = point_cnt +1;
- pred(i).annorect(point_cnt).annopoints.point = point;
- %pred(i).annorect(point_cnt).annopoints.score = subset(ridxPred,end-1)/subset(ridxPred,end);
- pred(i).annorect(point_cnt).annopoints.score = subset(ridxPred,end-1);
- end
- pred(i).candidates = candidates;
- end
- %% convert the format
- json_for_coco_eval = struct('image_id', [], 'category_id', [], 'keypoints', [], 'score', []);
- count = 1;
- for j = 1:length(pred)
- for d = 1:length(pred(j).annorect)
- json_for_coco_eval(count).image_id = coco_val(j).image_id;
- json_for_coco_eval(count).category_id = 1;
- json_for_coco_eval(count).keypoints = zeros(3, 17);
- %length(pred(j).annorect(d).annopoints.point)
- for p = 1:length(pred(j).annorect(d).annopoints.point)
- point = pred(j).annorect(d).annopoints.point(p);
- json_for_coco_eval(count).keypoints(1, point.id) = point.x - 0.5;
- json_for_coco_eval(count).keypoints(2, point.id) = point.y - 0.5;
- json_for_coco_eval(count).keypoints(3, point.id) = 1;
- end
- json_for_coco_eval(count).keypoints = reshape(json_for_coco_eval(count).keypoints, [1 51]);
- json_for_coco_eval(count).score = pred(j).annorect(d).annopoints.score *length(pred(j).annorect(d).annopoints.point);
- count = count + 1;
- end
- end
- display(json_for_coco_eval);
- opt.FileName = 'result.json';
- opt.FloatFormat = '%.3f';
- savejson('', json_for_coco_eval, opt);
- %evalDemo(opt.FileName);
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