From af29c4f9013c46ac7e51dfb14573847dcc9565bc Mon Sep 17 00:00:00 2001 From: pdollar Date: Mon, 5 Sep 2016 20:29:06 -0700 Subject: [PATCH] MatlabAPI/CocoEval.m: adding keypoint evaluation code!!! (needs more testing) --- MatlabAPI/CocoEval.m | 76 ++++++++++++++++--- MatlabAPI/evalDemo.m | 16 ++-- ...ints_val2014_fakekeypoints100_results.json | 1 + results/val2014_fake_eval_res.txt | 48 ++++++++++++ 4 files changed, 122 insertions(+), 19 deletions(-) create mode 100644 results/person_keypoints_val2014_fakekeypoints100_results.json create mode 100644 results/val2014_fake_eval_res.txt diff --git a/MatlabAPI/CocoEval.m b/MatlabAPI/CocoEval.m index def78182..cd7c451e 100644 --- a/MatlabAPI/CocoEval.m +++ b/MatlabAPI/CocoEval.m @@ -20,7 +20,7 @@ % recThrs - [0:.01:1] R=101 recall thresholds for evaluation % areaRng - [...] A=4 object area ranges for evaluation % maxDets - [1 10 100] M=3 thresholds on max detections per image - % iouType - ['segm'] set iouType to 'segm' or 'bbox' + % iouType - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints' % useCats - [1] if true use category labels for evaluation % Note: iouType replaced the now DEPRECATED useSegm parameter. % Note: if useCats=0 category labels are ignored as in proposal scoring. @@ -83,17 +83,25 @@ end methods - function ev = CocoEval( cocoGt, cocoDt ) + function ev = CocoEval( cocoGt, cocoDt, iouType ) % Initialize CocoEval using coco APIs for gt and dt. if(nargin>0), ev.cocoGt = cocoGt; end if(nargin>1), ev.cocoDt = cocoDt; end if(nargin>0), ev.params.imgIds = sort(ev.cocoGt.getImgIds()); end if(nargin>0), ev.params.catIds = sort(ev.cocoGt.getCatIds()); end + if(nargin<3), iouType='segm'; end ev.params.iouThrs = .5:.05:.95; ev.params.recThrs = 0:.01:1; - ev.params.areaRng = [0 1e5; 0 32; 32 96; 96 1e5].^2; - ev.params.maxDets = [1 10 100]; - ev.params.iouType = 'segm'; + if( any(strcmp(iouType,{'bbox','segm'})) ) + ev.params.areaRng = [0 1e5; 0 32; 32 96; 96 1e5].^2; + ev.params.maxDets = [1 10 100]; + elseif( strcmp(iouType,'keypoints') ) + ev.params.areaRng = [0 1e5; 32 96; 96 1e5].^2; + ev.params.maxDets = 20; + else + error('unknown iouType: %s',iouType); + end + ev.params.iouType = iouType; ev.params.useCats = 1; end @@ -122,6 +130,9 @@ function evaluate( ev ) f='bbox'; if(isempty(dt)), [dt(:).(f)]=deal(); end if(~isfield(dt,f)), s=MaskApi.toBbox([dt.segmentation]); for d=1:nDt(i), dt(d).(f)=s(d,:); end; end + elseif( strcmp(p.iouType,'keypoints') ) + gtIg=[gt.ignore]|[gt.num_keypoints]==0; + for g=1:nGt(i), gt(g).ignore=gtIg(g); end else error('unknown iouType: %s',p.iouType); end @@ -186,10 +197,17 @@ function accumulate( ev ) function summarize( ev ) % Compute and display summary metrics for evaluation results. if(isempty(ev.eval)), error('Please run accumulate() first'); end - k=100; M={{1,':','all',k},{1,.50,'all',k}, {1,.75,'all',k},... - {1,':','small',k}, {1,':','medium',k}, {1,':','large',k},... - {0,':','all',1}, {0,':','all',10}, {0,':','all',k},... - {0,':','small',k}, {0,':','medium',k}, {0,':','large',k}}; + if( any(strcmp(ev.params.iouType,{'bbox','segm'})) ) + k=100; M={{1,':','all',k},{1,.50,'all',k}, {1,.75,'all',k},... + {1,':','small',k}, {1,':','medium',k}, {1,':','large',k},... + {0,':','all',1}, {0,':','all',10}, {0,':','all',k},... + {0,':','small',k}, {0,':','medium',k}, {0,':','large',k}}; + elseif( strcmp(ev.params.iouType,'keypoints') ) + k=20; M={{1,':','all',k},{1,.50,'all',k}, {1,.75,'all',k},... + {1,':','medium',k}, {1,':','large',k},... + {0,':','all',k},{0,.50,'all',k}, {0,.75,'all',k},... + {0,':','medium',k}, {0,':','large',k}}; + end k=length(M); ev.stats=zeros(1,k); for s=1:k, ev.stats(s)=summarize1(M{s}{:}); end @@ -349,10 +367,11 @@ function makeplot( rs, ps, outDir, nm ) if(D>p.maxDets), D=p.maxDets; dt=dt(1:D); end % compute iou between each dt and gt region iscrowd = uint8([gt.iscrowd]); - t=find(strcmp(p.iouType,{'segm','bbox'})); + t=find(strcmp(p.iouType,{'segm','bbox','keypoints'})); if(t==1), g=[gt.segmentation]; elseif(t==2), g=cat(1,gt.bbox); end if(t==1), d=[dt.segmentation]; elseif(t==2), d=cat(1,dt.bbox); end - ious=MaskApi.iou(d,g,iscrowd); + if(t<=2), ious=MaskApi.iou(d,g,iscrowd); else + ious=CocoEval.oks(gt,dt); end % attempt to match each (sorted) dt to each (sorted) gt gtm=zeros(T,G); gtIds=[gt.id]; gtIg=[gt.ignore]; dtm=zeros(T,D); dtIds=[dt.id]; dtIg=zeros(T,D); @@ -380,5 +399,40 @@ function makeplot( rs, ps, outDir, nm ) dtImgIds=ones(1,D)*p.imgIds; gtImgIds=ones(1,G)*p.imgIds; e = {dtIds,gtIds,dtImgIds,gtImgIds,dtm,gtm,[dt.score],dtIg,gtIg}; end + + function o = oks( gt, dt ) + % Compute Object Keypoint Similarity (OKS) between objects. + G=length(gt); D=length(dt); o=zeros(D,G); if(~D||~G), return; end + % sigmas hard-coded for person class, will need params eventually + sigmas=[.26 .25 .25 .35 .35 .79 .79 .72 .72 .62 ... + .62 1.07 1.07 .87 .87 .89 .89]/10; + vars=(sigmas*2).^2; k=length(sigmas); m=k*3; bb=cat(1,gt.bbox); + % create bounds for ignore regions (double the gt bbox) + x0=bb(:,1)-bb(:,3); x1=bb(:,1)+bb(:,3)*2; + y0=bb(:,2)-bb(:,4); y1=bb(:,2)+bb(:,4)*2; + % extract keypoint locations and visibility flags + gKp=cat(1,gt.keypoints); assert(size(gKp,2)==m); + dKp=cat(1,dt.keypoints); assert(size(dKp,2)==m); + xg=gKp(:,1:3:m); yg=gKp(:,2:3:m); vg=gKp(:,3:3:m); + xd=dKp(:,1:3:m); yd=dKp(:,2:3:m); + % compute oks between each detection and ground truth object + for d=1:D + for g=1:G + v=vg(g,:); x=xd(d,:); y=yd(d,:); k1=nnz(v); + if( k1>0 ) + % measure the per-keypoint distance if keypoints visible + dx=x-xg(g,:); dy=y-yg(g,:); + else + % measure minimum distance to keypoints in (x0,y0) & (x1,y1) + dx=max(0,x0(g,:)-x)+max(0,x-x1(g,:)); + dy=max(0,y0(g,:)-y)+max(0,y-y1(g,:)); + end + % use the distances to compute the oks + e=(dx.^2+dy.^2)./vars/gt(g).area/2; + if(k1>0), e=e(v>0); else k1=k; end + o(d,g)=sum(exp(-e))/k1; + end + end + end end end diff --git a/MatlabAPI/evalDemo.m b/MatlabAPI/evalDemo.m index b526738f..65628cf2 100644 --- a/MatlabAPI/evalDemo.m +++ b/MatlabAPI/evalDemo.m @@ -1,17 +1,18 @@ %% Demo demonstrating the algorithm result formats for COCO %% select results type for demo (either bbox or segm) -type = {'segm','bbox'}; type = type{1}; % specify type here +type = {'segm','bbox','keypoints'}; type = type{1}; % specify type here fprintf('Running demo for *%s* results.\n\n',type); %% initialize COCO ground truth api -dataDir='../'; dataType='val2014'; -annFile=sprintf('%s/annotations/instances_%s.json',dataDir,dataType); -if(~exist('cocoGt','var')), cocoGt=CocoApi(annFile); end +dataDir='../'; prefix='instances'; dataType='val2014'; +if(strcmp(type,'keypoints')), prefix='person_keypoints'; end +annFile=sprintf('%s/annotations/%s_%s.json',dataDir,prefix,dataType); +cocoGt=CocoApi(annFile); %% initialize COCO detections api -resFile='%s/results/instances_%s_fake%s100_results.json'; -resFile=sprintf(resFile,dataDir,dataType,type); +resFile='%s/results/%s_%s_fake%s100_results.json'; +resFile=sprintf(resFile,dataDir,prefix,dataType,type); cocoDt=cocoGt.loadRes(resFile); %% visialuze gt and dt side by side @@ -33,9 +34,8 @@ if(0), f=fopen(resFile,'w'); fwrite(f,gason(res)); fclose(f); end %% run COCO evaluation code (see CocoEval.m) -cocoEval=CocoEval(cocoGt,cocoDt); +cocoEval=CocoEval(cocoGt,cocoDt,type); cocoEval.params.imgIds=imgIds; -cocoEval.params.iouType=type; cocoEval.evaluate(); cocoEval.accumulate(); cocoEval.summarize(); diff --git a/results/person_keypoints_val2014_fakekeypoints100_results.json b/results/person_keypoints_val2014_fakekeypoints100_results.json new file mode 100644 index 00000000..42ce5e37 --- /dev/null +++ b/results/person_keypoints_val2014_fakekeypoints100_results.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/results/val2014_fake_eval_res.txt b/results/val2014_fake_eval_res.txt new file mode 100644 index 00000000..5c89ebf6 --- /dev/null +++ b/results/val2014_fake_eval_res.txt @@ -0,0 +1,48 @@ +------------------------------------------------------------------------------ +type=segm +Running per image evaluation... DONE (t=0.45s). +Accumulating evaluation results... DONE (t=0.08s). + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.320 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.562 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.299 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.387 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.310 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.327 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.268 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.415 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.417 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.469 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.377 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.381 + +------------------------------------------------------------------------------ +type=bbox +Running per image evaluation... DONE (t=0.34s). +Accumulating evaluation results... DONE (t=0.08s). + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.697 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.573 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.586 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.519 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.501 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.387 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.594 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.595 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.640 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.566 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.564 + +------------------------------------------------------------------------------ +type=keypoints +Running per image evaluation... DONE (t=0.06s). +Accumulating evaluation results... DONE (t=0.00s). + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.372 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.636 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.348 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.384 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.386 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.514 + Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.734 + Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.504 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.508 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.522