Open cyxcyx559 opened 2 years ago
for your convenience, I show you the part of codes, which can be downloaded from above links
function [pck_table, auc_table] = mpii_compute_3d_pck(error_data, joint_groups, output_base_path)
%Input
%error_data is a struct array of type mpii_3d_error
%The struct zcarries information about the name of the method as well as an
%nj x 1 x nf matrix with the joint errors.
%joint_groups is an ng x 2 cell, where ng is the number of groups. It
%carries the name of the group as well as the indices of the joints that
%belong to the group.
%If the error_data array has multiple inputs, there are additional
%comparative AUC plots output per joint in addition to the individual ones.
ng = size(joint_groups,1);
pck_curve_array = cell(length(error_data), ng+1); %Contains the PCK results per joint group, per error_data cell
pck_array = cell(length(error_data), ng+1); %Contains the AUC results per joint group
auc_array = cell(length(error_data), ng+1); %Contains the AUC results per joint group
%thresh = 0:5:200;
thresh = 0:5:150;
pck_thresh = 150;
for i = 1:length(error_data)
joint_count = 0;
nf = size(error_data(i).error,3);
for j = 1:ng
for ti =1:length(thresh)
t = thresh(ti);
pck_curve_array{i,j} = [pck_curve_array{i,j}, sum(sum(error_data(i).error(joint_groups{j,2},1,:) < t, 3),1) / (length(joint_groups{j,2}) *nf)];
end
joint_count = joint_count + length(joint_groups{j,2});
if(isempty(pck_curve_array{i,ng+1}))
pck_curve_array{i,ng+1} = pck_curve_array{i,j} * length(joint_groups{j,2});
else
pck_curve_array{i,ng+1} = pck_curve_array{i,ng+1} + pck_curve_array{i,j} * length(joint_groups{j,2});
end
auc_array{i,j} = 100* sum(pck_curve_array{i,j}(:))/ length(thresh);
pck_array{i,j} = 100* sum(sum(error_data(i).error(joint_groups{j,2},1,:) < pck_thresh, 3),1) / (length(joint_groups{j,2}) *nf);
if(isempty(pck_array{i,ng+1}))
pck_array{i,ng+1} = pck_array{i,j} * length(joint_groups{j,2});
else
pck_array{i,ng+1} = pck_array{i,ng+1} + pck_array{i,j} * length(joint_groups{j,2});
end
end
pck_array{i,ng+1} = pck_array{i,ng+1} / joint_count;
pck_curve_array{i,ng+1} = pck_curve_array{i,ng+1} / joint_count;
auc_array{i,ng+1} = 100* sum(pck_curve_array{i,ng+1}(:))/ length(thresh);
end
pck_table = cell(length(error_data)+1, ng+2);
pck_table{1,ng+2} = 'Total';
for i = 1:length(error_data)
pck_table{1+i,1} = error_data(i).method;
end
for i = 1:ng
pck_table{1,i+1} = joint_groups{i,1};
end
auc_table = pck_table;
auc_table(2:end,2:end) = auc_array;
pck_table(2:end,2:end) = pck_array;
if(~isempty(output_base_path))
%Generate and save plots to output_path
%First generate individual plots from each row of the pck_curve_array
colormap default;
for i = 1:length(error_data)
all_plot = [];
for j = 1:ng+1
figure(1);
cla;
plot(thresh,pck_curve_array{i,j},'LineWidth',2);
all_plot = [all_plot; pck_curve_array{i,j}];
axis([0 150 0 1]);
title([pck_table{1,j+1} ' PCK150mm']);
output_dir = [output_base_path filesep error_data(i).method];
if(exist(output_dir,'dir') ~= 7)
mkdir(output_dir);
end
saveas(gcf,[output_dir filesep pck_table{1,j+1}], 'fig');
saveas(gcf,[output_dir filesep pck_table{1,j+1}], 'svg');
saveas(gcf,[output_dir filesep pck_table{1,j+1}], 'png');
end
figure(2);
cla;
plot(thresh,all_plot,'LineWidth',2);
axis([0 150 0 1]);
hold off;
legend(pck_table(1,2:end));
saveas(gcf,[output_dir filesep 'All'], 'fig');
saveas(gcf,[output_dir filesep 'All'], 'svg');
saveas(gcf,[output_dir filesep 'All'], 'png');
end
end
end
%Then group the plots by methods
``
Hi, thank u for the good work and for sharing the code!I want to set some evaluation metrics to verify the accuracy of the algorithm. Does the mean per-vertex position error (MPVPE) mentioned in your article refer to per-vertex errow (PVE) and what is the principle of its calculation? Thank you again for your help!
------------------ 原始邮件 ------------------ 发件人: "hongsukchoi/3DCrowdNet_RELEASE" @.>; 发送时间: 2022年6月15日(星期三) 晚上11:19 @.>; 抄送: ". @.**@.>; 主题: Re: [hongsukchoi/3DCrowdNet_RELEASE] How do you calculate 3dpck? (Issue #11)
for your convenience, I show you the code, which can be downloaded from above links function [pck_table, auc_table] = mpii_compute_3d_pck(error_data, joint_groups, output_base_path) %Input %error_data is a struct array of type mpii_3d_error %The struct zcarries information about the name of the method as well as an %nj x 1 x nf matrix with the joint errors. %joint_groups is an ng x 2 cell, where ng is the number of groups. It %carries the name of the group as well as the indices of the joints that %belong to the group. %If the error_data array has multiple inputs, there are additional %comparative AUC plots output per joint in addition to the individual ones. ng = size(joint_groups,1); pck_curve_array = cell(length(error_data), ng+1); %Contains the PCK results per joint group, per error_data cell pck_array = cell(length(error_data), ng+1); %Contains the AUC results per joint group auc_array = cell(length(error_data), ng+1); %Contains the AUC results per joint group %thresh = 0:5:200; thresh = 0:5:150; pck_thresh = 150; for i = 1:length(error_data) joint_count = 0; nf = size(error_data(i).error,3); for j = 1:ng for ti =1:length(thresh) t = thresh(ti); pck_curve_array{i,j} = [pck_curve_array{i,j}, sum(sum(error_data(i).error(joint_groups{j,2},1,:) < t, 3),1) / (length(joint_groups{j,2}) nf)]; end joint_count = joint_count + length(joint_groups{j,2}); if(isempty(pck_curve_array{i,ng+1})) pck_curve_array{i,ng+1} = pck_curve_array{i,j} length(joint_groups{j,2}); else pck_curve_array{i,ng+1} = pck_curve_array{i,ng+1} + pck_curve_array{i,j} length(joint_groups{j,2}); end auc_array{i,j} = 100 sum(pck_curve_array{i,j}(:))/ length(thresh); pck_array{i,j} = 100 sum(sum(error_data(i).error(joint_groups{j,2},1,:) < pck_thresh, 3),1) / (length(joint_groups{j,2}) nf); if(isempty(pck_array{i,ng+1})) pck_array{i,ng+1} = pck_array{i,j} length(joint_groups{j,2}); else pck_array{i,ng+1} = pck_array{i,ng+1} + pck_array{i,j} length(joint_groups{j,2}); end end pck_array{i,ng+1} = pck_array{i,ng+1} / joint_count; pck_curve_array{i,ng+1} = pck_curve_array{i,ng+1} / joint_count; auc_array{i,ng+1} = 100* sum(pck_curve_array{i,ng+1}(:))/ length(thresh); end pck_table = cell(length(error_data)+1, ng+2); pck_table{1,ng+2} = 'Total'; for i = 1:length(error_data) pck_table{1+i,1} = error_data(i).method; end for i = 1:ng pck_table{1,i+1} = joint_groups{i,1}; end auc_table = pck_table; auc_table(2:end,2:end) = auc_array; pck_table(2:end,2:end) = pck_array; if(~isempty(output_base_path)) %Generate and save plots to output_path %First generate individual plots from each row of the pck_curve_array colormap default; for i = 1:length(error_data) all_plot = []; for j = 1:ng+1 figure(1); cla; plot(thresh,pck_curve_array{i,j},'LineWidth',2); all_plot = [all_plot; pck_curve_array{i,j}]; axis([0 150 0 1]); title([pck_table{1,j+1} ' PCK150mm']); output_dir = [output_base_path filesep error_data(i).method]; if(exist(output_dir,'dir') ~= 7) mkdir(output_dir); end saveas(gcf,[output_dir filesep pck_table{1,j+1}], 'fig'); saveas(gcf,[output_dir filesep pck_table{1,j+1}], 'svg'); saveas(gcf,[output_dir filesep pck_table{1,j+1}], 'png'); end figure(2); cla; plot(thresh,all_plot,'LineWidth',2); axis([0 150 0 1]); hold off; legend(pck_table(1,2:end)); saveas(gcf,[output_dir filesep 'All'], 'fig'); saveas(gcf,[output_dir filesep 'All'], 'svg'); saveas(gcf,[output_dir filesep 'All'], 'png'); end end end %Then group the plots by methods ``
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edit. 2022.07.06 I accidentally wrote MPVPE as MPJPE.. MPVPE = PVE. Personally I like MPVPE, since it is consistent expression with MPJPE.
Yes, MPJPE = PVE. Personally I like MPJPE, since it is consistent expression with MPJPE. It is just calculating the euclidean distance between vertices. Metric is milimeters. Actually MPVPE code is provide in this repo
Hi, did you reproduce the result on MuPoTs?
@hongsukchoi @xljh0520 have you reproduced the result on MuPoTs?
No, I didn't achieve the annotations.
There is no place in the code to calculate 3dpck, can you describe in detail how pck is calculated?