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main_CrossVal_Velodyne_fruit_detection.m
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main_CrossVal_Velodyne_fruit_detection.m
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clc
clear
session=35;
directory='F:\Detecció Fruits 2017\velodyne_vent';
pcDirectory_txt=strcat(directory,'\data\AllTrees_pcloud\');
Groundtruth_Directory=strcat(directory,'\data\AllTrees_Groundtruth\');
models_Directory=strcat(directory,'\data\Trained_models\');
pcTreatDirectory=strcat(directory,'\data\Treated_pcloud\');
save_directory=strcat(directory,'\results\');
%Trials2eval={'E1_SV','E1_V','E1_VF','E2_SV','E2_V','E2_VF','E12_SV','E12_V','E12_VF'}; %E1_SV: Ensayo1 sin viento; E2_V:Ensayo 2 con viento; E12_VF: Ensayo 1+2 viento flojo.
%Trials2eval={'E1_SV','E1_VF','E1_V','E1_SV_VF','E1_SV_V','E1_SV_VF_V','E2_SV','E2_VF','E2_V','E2_SV_VF','E2_SV_V','E2_SV_VF_V','E12_SV','E12_VF','E12_V','E12_SV_VF','E12_SV_V','E12_SV_VF_V',...
% 'E1_SV_E','E1_VF_E','E1_V_E','E1_SV_VF_E','E1_SV_V_E','E1_SV_VF_V_E','E2_SV_E','E2_VF_E','E2_V_E','E2_SV_VF_E','E2_SV_V_E','E2_SV_VF_V_E','E12_SV_E','E12_VF_E','E12_V_E','E12_SV_VF_E','E12_SV_V_E','E12_SV_VF_V_E',...
% 'E1_SV_O','E1_VF_O','E1_V_O','E1_SV_VF_O','E1_SV_V_O','E1_SV_VF_V_O','E2_SV_O','E2_VF_O','E2_V_O','E2_SV_VF_O','E2_SV_V_O','E2_SV_VF_V_O','E12_SV_O','E12_VF_O','E12_V_O','E12_SV_VF_O','E12_SV_V_O','E12_SV_VF_V_O'};
%Trials2eval={'E1_SV','E1_SV_V','E1_SV_VF_V','E2_SV','E2_SV_V','E2_SV_VF_V'};
%Trials2eval={'E1_SV','E2_SV','E12_SV'};
%Trials2eval={'E1_SV'};
Trials2eval={'E1_SV','E1_SV_E','E1_SV_O'};
%Late_fusion_comb={[1,4],[2,5],[3,6],[1,2,4,5],[1,3,4,6],[1,2,3,4,5,6]}; %Trials2eval combinations that are want to be fused using late fusion.
Late_fusion_comb={};
[Trials , Trials2eval]= ListFiles_trial(pcDirectory_txt,Trials2eval,Late_fusion_comb);
save_dets=0; %save detections
save_all=0; %save all detections (after each step)
show3D=0;
train=1; %1 to train, 0 for test.
%% Algorithm parameters:
thresh=60;
Th=0;
NumNeigh=20;
K=15;
Eps=0.03;
technique_FP=2; % 1: thresholding ; 2: SVM
Split_technique=5; %3: thresholding; 5: SVM
%Training_features:
%2: nº of Points; 3-5: XYZ dimensions; 6: Volume using boundary points
%7-9: Principal values; 10-14: Intensity histogram from 60:8:100;
%15: Geometrical parameter; 16: Bounding box in catesian coordinates;
%17: Mean intensity; 18: max intensity; 19: Intensity standard distribution
Training_features=[2,6,7,8,9,10,11,12,13,14,15,17,18,19];
%Training_features=[6,10,11,12,13,14,15,7,8,9];
KernelFunctionFP='linear';
BoxConstrainFP=0.35;
StandardizeFP=true;
KernelFunctionCCwoa='linear';
BoxConstrainCCwoa=0.35;
StandardizeCCwoa=true;
KernelFunctionCCwmtoa='linear';
BoxConstrainCCwmtoa=0.35;
StandardizeCCwmtoa=true;
param_name={'Trials','thresh','Th','NumNeigh','K','Eps','technique_FP','Split_technique',...
'KernelFunctionFP','BoxConstrainFP','StandardizeFP',...
'KernelFunctionCCwoa','BoxConstrainCCwoa','StandardizeCCwoa',...
'KernelFunctionCCwmtoa','BoxConstrainCCwmtoa','StandardizeCCwmtoa'};
params={strcat(Trials2eval{:}),thresh,Th,NumNeigh,K,Eps,technique_FP,Split_technique,...
KernelFunctionFP,BoxConstrainFP,StandardizeFP,...
KernelFunctionCCwoa,BoxConstrainCCwoa,StandardizeCCwoa,...
KernelFunctionCCwmtoa,BoxConstrainCCwmtoa,StandardizeCCwmtoa};
fid = fopen(strcat(save_directory,'results.xlsx'), 'a');
fclose('all');
while (fid == -1)
errormsg = strcat('the file: ',save_directory,'results.xlsx', ' is open. please close it!');
waitfor(msgbox(errormsg,'Error'));
fid = fopen(strcat(save_directory,'results.xlsx'), 'a');
fclose('all');
end
xlswrite(strcat(save_directory,'results.xlsx'), param_name , strcat('Results_s',num2str(session)) , ['B1'] );
xlswrite(strcat(save_directory,'results.xlsx'), params , strcat('Results_s',num2str(session)) , ['B2'] );
%% Pre-processing: Thresholding + DbSCAN
if ~exist(pcTreatDirectory, 'dir')
mkdir(pcTreatDirectory);
end
tic;
for trialID=1:size(Trials,2)
for treeID=1:size(Trials(trialID).trees,2)
if exist(strcat(pcTreatDirectory,Trials(trialID).trees(treeID).treeID,'_',Trials2eval{trialID},'_treated_s',num2str(session),'.mat'),'file')
disp(strcat('Loading pre-procesado: ',Trials(trialID).trees(treeID).file));
t=toc;
load(strcat(pcTreatDirectory,Trials(trialID).trees(treeID).treeID,'_',Trials2eval{trialID},'_treated_s',num2str(session),'.mat'));
Trials(trialID).trees(treeID).ptCloud_th=ptCloud_th;
Trials(trialID).trees(treeID).class=class;
Trials(trialID).trees(treeID).CCfeatures=CCfeatures;
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
elseif strcmp(Trials(trialID).trees(treeID).file(1:4),'late')
ptCloud_all_xyz=[];
for fuseID=Trials(trialID).trees(treeID).fuseTrials
load(strcat(pcTreatDirectory,Trials(fuseID).trees(treeID).file(1:end-4),'_treated_s',num2str(session),'.mat'));
ptCloud_all_xyz=[ptCloud_all_xyz; ptCloud_th];
end
ptCloud_th=ptCloud_all_xyz;
%% Clustering dbscan
disp('Inicio Clustering dbscan...')
[class,type] = dbscan(ptCloud_th(:,1:3), K, Eps);
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
%% CC feature extraction
disp('Inicio feature extraction...')
CCfeatures=CCfeatureExtraction(ptCloud_th,class,Groundtruth_Directory,{Trials(trialID).trees(treeID).treeID});
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
Trials(trialID).trees(treeID).ptCloud_th=ptCloud_th;
Trials(trialID).trees(treeID).class=class;
Trials(trialID).trees(treeID).CCfeatures=CCfeatures;
save(strcat(pcTreatDirectory,Trials(trialID).trees(treeID).treeID,'_',Trials2eval{trialID},'_treated_s',num2str(session),'.mat'),'ptCloud_th','class','CCfeatures');
else
disp(strcat('Inicio pre-procesado: ',Trials(trialID).trees(treeID).file));
%% Point cloud reading
disp('Inicio Point cloud reading...')
tic;
% if ~strcmp(Trials(trialID).trees(treeID).file(1:4),'late')
[~,ptCloud_all_xyz]=pointCloudReading({Trials(trialID).trees(treeID).file(1:end-4)},pcDirectory_txt);
t=toc; disp(strcat(' realizado en:__', num2str(toc), ' seg.'))
% else
% ptCloud_all_xyz=[];
%
% for fuseID=Trials(trialID).trees(treeID).fuseTrials
% fileID=fopen(strcat(save_directory,'dets_s',num2str(session),'\dets',Trials(fuseID).trees(treeID).file(1:end-4),...
% '\all_det',Trials(fuseID).trees(treeID).file(1:end-4),'_s',num2str(session),'.txt'),'r');
% ptCloud_xyz=fscanf(fileID,'%f %f %f %f %f %f %f',[7 Inf])';
% fclose(fileID);
% ptCloud_all_xyz=[ptCloud_all_xyz;ptCloud_xyz(:,1:4)];
% end
% ptCloud_all_xyz(:,5:6)=ones([size(ptCloud_all_xyz,1) 2]);
% end
%% Point cloud thresholding
disp('Inicio Point cloud thresholding...')
ptCloud_all_xyz_thresh60=ptCloud_all_xyz(ptCloud_all_xyz(:,4)>thresh,:);
ptCloud_all_thresh60=pointCloud(ptCloud_all_xyz_thresh60(:,1:3));
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
%% Outliers removal
disp('Inicio Outlier removal...')
[ptCloud_all_thresh60_notOutliers,inlierIndices,~]=pcdenoise(ptCloud_all_thresh60,'NumNeighbors',NumNeigh,'Threshold',Th);
ptCloud_th=ptCloud_all_thresh60_notOutliers.Location;
ptCloud_th(:,4:5)=ptCloud_all_xyz_thresh60(inlierIndices,[4 6]);
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
%% Clustering dbscan
disp('Inicio Clustering dbscan...')
[class,type] = dbscan(ptCloud_th(:,1:3), K, Eps);
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
%% CC feature extraction
disp('Inicio feature extraction...')
CCfeatures=CCfeatureExtraction(ptCloud_th,class,Groundtruth_Directory,{Trials(trialID).trees(treeID).treeID});
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
Trials(trialID).trees(treeID).ptCloud_th=ptCloud_th;
Trials(trialID).trees(treeID).class=class;
Trials(trialID).trees(treeID).CCfeatures=CCfeatures;
% if ~strcmp(Trials(trialID).trees(treeID).file(1:4),'late')
save(strcat(pcTreatDirectory,Trials(trialID).trees(treeID).file(1:end-4),'_treated_s',num2str(session),'.mat'),'ptCloud_th','class','CCfeatures');
% end
end
end
end
%% Training and loading models for ClusterSplit and FP rmoval
if train
disp('Inicio Training...')
if ~exist(models_Directory, 'dir')
mkdir(models_Directory);
end
for trialID=1:size(Trials,2)
for treeID=1:size(Trials(trialID).trees,2)
treeID_comp=(1:size(Trials(trialID).trees,2));
treeID_comp(treeID_comp==treeID)=[];
CCfeatures_comp=[];
for Te=treeID_comp
CCfeatures_comp=[CCfeatures_comp ; Trials(trialID).trees(Te).CCfeatures];
end
Trials(trialID).trees(treeID).CCfeatures_comp=CCfeatures_comp;
[Trials(trialID).trees(treeID).SVMModelFP,Trials(trialID).trees(treeID).SVMModelCCwoa,Trials(trialID).trees(treeID).SVMModelCCwmtoa]=trainingCCwmtoaFP(...
CCfeatures_comp,Trials(trialID).trees(treeID).file,Training_features,...
StandardizeFP,KernelFunctionFP,BoxConstrainFP,...
StandardizeCCwoa,KernelFunctionCCwoa,BoxConstrainCCwoa,...
StandardizeCCwmtoa,KernelFunctionCCwmtoa,BoxConstrainCCwmtoa,models_Directory,session);
end
end
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
else
disp('Inicio Loading models...')
for trialID=1:size(Trials,2)
for treeID=1:size(Trials(trialID).trees,2)
Trials(trialID).trees(treeID).SVMModelFP=loadCompactModel(strcat(models_Directory,'SVMModelFP_',...
Trials(trialID).trees(treeID).file(1:end-4),'_s',...
num2str(session),'.mat'));
Trials(trialID).trees(treeID).SVMModelCCwoa=loadCompactModel(strcat(models_Directory,'SVMModelCCwoa_',...
Trials(trialID).trees(treeID).file(1:end-4),'_s',...
num2str(session),'.mat'));
Trials(trialID).trees(treeID).SVMModelCCwmtoa=loadCompactModel(strcat(models_Directory,'SVMModelCCwmtoa_',...
Trials(trialID).trees(treeID).file(1:end-4),'_s',...
num2str(session),'.mat'));
end
end
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
end
%% FP rmoval
%
% if technique_FP
% disp('Inicio FP removal...')
% for trialID=1:size(Trials,2)
% for treeID=1:size(Trials(trialID).trees,2)
% [Trials(trialID).trees(treeID).class,Trials(trialID).trees(treeID).CCfeatures_nonFP]=FP_removal(Trials(trialID).trees(treeID).SVMModelFP,...
% Trials(trialID).trees(treeID).CCfeatures,...
% Trials(trialID).trees(treeID).class,...
% technique_FP,Training_features,...
% Trials(trialID).trees(treeID).file(1:end-4));
% end
% end
% disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
% t=toc;
% else
for trialID=1:size(Trials,2)
for treeID=1:size(Trials(trialID).trees,2)
Trials(trialID).trees(treeID).CCfeatures_nonFP=Trials(trialID).trees(treeID).CCfeatures;
end
end
% end
t=toc;
%% CCwmtoa split
disp('Inicio CCwmtoa split...')
for trialID=1:size(Trials,2)
for treeID=1:size(Trials(trialID).trees,2)
% [CCwmtoa_K]=Split_predict(Split_technique,Trials(trialID).trees(treeID).SVMModelCCwoa,...
% Trials(trialID).trees(treeID).SVMModelCCwmtoa,...
% Trials(trialID).trees(treeID).CCfeatures_nonFP,...
% Trials(trialID).trees(treeID).ptCloud_th,...
% Trials(trialID).trees(treeID).class,Training_features);
[CCwmtoa_K]=Split_predict(Split_technique,Trials(1).trees(treeID).SVMModelCCwoa,...
Trials(1).trees(treeID).SVMModelCCwmtoa,...
Trials(trialID).trees(treeID).CCfeatures_nonFP,...
Trials(trialID).trees(treeID).ptCloud_th,...
Trials(trialID).trees(treeID).class,Training_features);
Trials(trialID).trees(treeID).class=CC_split(CCwmtoa_K,Trials(trialID).trees(treeID).ptCloud_th,Trials(trialID).trees(treeID).class);
end
end
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
%% CC feature extraction
%%%%%AQUÍ
for trialID=1:size(Trials,2)
for treeID=1:size(Trials(trialID).trees,2)
disp('Inicio feature extraction...')
Trials(trialID).trees(treeID).CCfeatures_splited=CCfeatureExtraction(Trials(trialID).trees(treeID).ptCloud_th,Trials(trialID).trees(treeID).class,Groundtruth_Directory,{Trials(trialID).trees(treeID).treeID});
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
end
end
%% Training and loading models splitted FP rmoval
if train
for trialID=1:size(Trials,2)
for treeID=1:size(Trials(trialID).trees,2)
treeID_comp=(1:size(Trials(trialID).trees,2));
treeID_comp(treeID_comp==treeID)=[];
CCfeatures_comp=[];
for Te=treeID_comp
CCfeatures_comp=[CCfeatures_comp ; Trials(trialID).trees(Te).CCfeatures_splited];
end
Trials(trialID).trees(treeID).CCfeatures_comp=CCfeatures_comp;
[Trials(trialID).trees(treeID).SVMModelFP_splited,~,~]=trainingCCwmtoaFP(...
CCfeatures_comp,Trials(trialID).trees(treeID).file,Training_features,...
StandardizeFP,KernelFunctionFP,BoxConstrainFP,0,0,0,0,0,0,models_Directory,session);
end
end
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
else
disp('Inicio Loading models...')
for trialID=1:size(Trials,2)
for treeID=1:size(Trials(trialID).trees,2)
Trials(trialID).trees(treeID).SVMModelFP_splited=loadCompactModel(strcat(models_Directory,'SVMModelFP_splited_',...
Trials(trialID).trees(treeID).file(1:end-4),'_s',...
num2str(session),'.mat'));
end
end
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
end
%% FP splited rmoval
if technique_FP
disp('Inicio FP removal...')
for trialID=1:size(Trials,2)
for treeID=1:size(Trials(trialID).trees,2)
[Trials(trialID).trees(treeID).class,Trials(trialID).trees(treeID).CCfeatures_splited_nonFP]=FP_removal(Trials(trialID).trees(treeID).SVMModelFP_splited,...
Trials(trialID).trees(treeID).CCfeatures_splited,...
Trials(trialID).trees(treeID).class,...
technique_FP,Training_features,...
Trials(trialID).trees(treeID).file(1:end-4));
end
end
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
else
for trialID=1:size(Trials,2)
for treeID=1:size(Trials(trialID).trees,2)
Trials(trialID).trees(treeID).CCfeatures_splited_nonFP=Trials(trialID).trees(treeID).CCfeatures_splited;
end
end
end
t=toc;
%% Detection assesment
pcID=1;
for trialID=1:size(Trials,2)
for treeID=1:size(Trials(trialID).trees,2)
disp(strcat('Inicio Detection assesment technique',num2str(Split_technique),'...'))
tree_index=(Trials(trialID).trees(treeID).class'>0);
apple_detectionsPC=pointCloud(Trials(trialID).trees(treeID).ptCloud_th(tree_index,1:3));
LCM=LabelClusterMatrix(apple_detectionsPC,Groundtruth_Directory,...
{Trials(trialID).trees(treeID).treeID},...
Trials(trialID).trees(treeID).class(tree_index));
[Trials(trialID).trees(treeID).LCM_LOC,Trials(trialID).trees(treeID).LCM_LOC_COL]=ClusterLabelAssignment(LCM);
[T,P,ClustersinLabel,LabelsDetected,...
TP,FP_LOC_COL,FP,Localization_success_C,...
Localization_success_L,FDR_LOC_COL,FDR,precision,...
recall,F1]=detection_assesment(LCM,Trials(trialID).trees(treeID).LCM_LOC_COL);
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
if ~strcmp(Trials(trialID).trees(treeID).file(1:4),'late')
results={Trials(trialID).trees(treeID).file(1:end-4),T,LabelsDetected,FP,Localization_success_L,FDR,...
TP,FP_LOC_COL,recall,FDR_LOC_COL,F1,toc,precision};
else
results={strcat(Trials(trialID).trees(treeID).treeID,Trials(trialID).trees(treeID).file,'_',num2str(Trials(trialID).trees(treeID).fuseTrials)),...
T,LabelsDetected,FP,Localization_success_L,FDR,...
TP,FP_LOC_COL,recall,FDR_LOC_COL,F1,toc,precision};
end
fid = fopen(strcat(save_directory,'results.xlsx'), 'a');
fclose('all');
while (fid == -1)
errormsg = strcat('the file: ',save_directory,'results.xlsx', ' is open. please close it!');
waitfor(msgbox(errormsg,'Error'));
fid = fopen(strcat(save_directory,'results.xlsx'), 'a');
fclose('all');
end
xlswrite(strcat(save_directory,'results.xlsx'), results , strcat('Results_s',num2str(session)) , ['B' num2str(pcID+4) ] );
allT(treeID,trialID)=T; %Ground Truth labels
allLabelsDetected(treeID,trialID)=LabelsDetected; %TPloc
allFP(treeID,trialID)=FP;%FPloc
allLocalization_success_L(treeID,trialID)=Localization_success_L;%Successloc
allFDR(treeID,trialID)=FDR;%FDRloc
allTP(treeID,trialID)=TP;%TPid
allFP_LOC_COL(treeID,trialID)=FP_LOC_COL;%FPid
allrecall(treeID,trialID)=recall;%Successid
allFDR_LOC_COL(treeID,trialID)=FDR_LOC_COL;%FDRid
allF1(treeID,trialID)=F1;
allprecision(treeID,trialID)=precision;
pcID=pcID+1;
end
end
%
% distributionHistograms_horizontal(Groundtruth_Directory,Trials);
% distributionHistograms(Groundtruth_Directory,Trials);
Loc_suc_trials=sum(allLabelsDetected)./(sum(allT));
Loc_FDR_trials=sum(allFP)./(sum(allLabelsDetected)+sum(allFP));
Id_suc_trials=sum(allTP)./(sum(allT));
Id_FDR_trials=sum(allFP_LOC_COL)./(sum(allTP)+sum(allFP_LOC_COL));
F1_trials=2*sum(allTP)./(sum(allTP)+sum(allFP_LOC_COL)+sum(allT));
F1_trials_not2_6=2*sum(allTP([1,3:5,7:11],:))./(sum(allTP([1,3:5,7:11],:))+sum(allFP_LOC_COL([1,3:5,7:11],:))+sum(allT([1,3:5,7:11],:)));
F1_trees=2*sum(allTP,2)./(sum(allTP,2)+sum(allFP_LOC_COL,2)+sum(allT,2));
disp(strcat('F1_trials=',num2str(F1_trials),' F1_trials_not2_6=', num2str(F1_trials_not2_6)));
%
% xlswrite(strcat(save_directory,'results.xlsx'), {'Loc_suc_trials','Loc_FDR_trials','Id_suc_trials','Id_FDR_trials','F1_trials','F1_trials_not2_6'} , strcat('Results_s',num2str(session)) , ['C' num2str(pcID+7) ] );
% xlswrite(strcat(save_directory,'results.xlsx'), Trials2eval' , strcat('Results_s',num2str(session)) , ['B' num2str(pcID+8) ] );
% xlswrite(strcat(save_directory,'results.xlsx'), Loc_suc_trials' , strcat('Results_s',num2str(session)) , ['C' num2str(pcID+8) ] );
% xlswrite(strcat(save_directory,'results.xlsx'), Loc_FDR_trials' , strcat('Results_s',num2str(session)) , ['D' num2str(pcID+8) ] );
% xlswrite(strcat(save_directory,'results.xlsx'), Id_suc_trials' , strcat('Results_s',num2str(session)) , ['E' num2str(pcID+8) ] );
% xlswrite(strcat(save_directory,'results.xlsx'), Id_FDR_trials' , strcat('Results_s',num2str(session)) , ['F' num2str(pcID+8) ] );
% xlswrite(strcat(save_directory,'results.xlsx'), F1_trials' , strcat('Results_s',num2str(session)) , ['G' num2str(pcID+8) ] );
% xlswrite(strcat(save_directory,'results.xlsx'), F1_trials_not2_6' , strcat('Results_s',num2str(session)) , ['H' num2str(pcID+8) ] );
%
% xlswrite(strcat(save_directory,'results.xlsx'), {'F1_trials'} , strcat('Results_s',num2str(session)) , ['C' num2str(pcID+8+size(Trials2eval,2)) ] );
% xlswrite(strcat(save_directory,'results.xlsx'), {'tree01','tree02','tree03','tree04','tree05',...
% 'tree06','tree07','tree08','tree09','tree10','tree11'}' , strcat('Results_s',num2str(session)) , ['B' num2str(pcID+9+size(Trials2eval,2)) ] );
% xlswrite(strcat(save_directory,'results.xlsx'), F1_trees , strcat('Results_s',num2str(session)) , ['C' num2str(pcID+9+size(Trials2eval,2)) ] );
%% compute predictions
[ab, predicted, error, RMSE, b_origin, predicted_origin, error_origin, RMSE_origin]=compute_predictions([139,106,139,137,94,131,119,145,139,136,159]',allTP+allFP_LOC_COL,Trials);
xlswrite(strcat(save_directory,'results.xlsx'), RMSE' , strcat('Results_s',num2str(session)) , ['I' num2str(pcID+8) ] );
xlswrite(strcat(save_directory,'results.xlsx'), RMSE_origin' , strcat('Results_s',num2str(session)) , ['J' num2str(pcID+8) ] );
%% Save_clusters
if save_dets
disp('Inicio Save...')
if save_all
if ~exist(strcat(save_directory,'dets_s',num2str(session)), 'dir')
mkdir(strcat(save_directory,'dets_s',num2str(session)));
end
%Saving after thresholding
dlmwrite(strcat(save_directory,'dets_s',num2str(session),'\',[Trials{:}],'_thresh_',num2str(thresh),'.txt'),ptCloud_all_xyz_thresh60,'precision','%.3f','delimiter','\t')
%Saving after outlier_removal
dlmwrite(strcat(save_directory,'dets_s',num2str(session),'\',[Trials{:}],'_thresh_',num2str(thresh),'denoise_Th_',num2str(Th),'NumNeigh_',num2str(NumNeigh),'.txt'),ptCloud_all_thresh60_notOutliers_xyz,'precision','%.3f','delimiter','\t')
%Saving after dbscan
color_class=colormap(lines(max(class_dbscan)));
if ~exist(strcat(save_directory,'dets_s',num2str(session),'\dets_dbscan',Trials{:}), 'dir')
mkdir(strcat(save_directory,'dets_s',num2str(session),'\dets_dbscan',Trials{:}));
end
for i=1:max(class_dbscan)
CCxyz=ptCloud_all_thresh60_notOutliers_xyz(class_dbscan==i,1:4);
CCi=[CCxyz,repmat(color_class(i,:)*255,size(CCxyz,1),1)];
dlmwrite(strcat(save_directory,'dets_s',num2str(session),'\dets_dbscan',Trials{:},'\det_dbscan',[Trials{:}],'_s',num2str(session),'_',num2str(i,'%03.f'),'.txt'),CCi,'precision','%.3f','delimiter','\t')
end
end
for trialID=1:size(Trials,2)
for treeID=1:size(Trials(trialID).trees,2)
if ~exist(strcat(save_directory,'dets_s',num2str(session),'\dets',Trials(trialID).trees(treeID).file(1:end-4)), 'dir')
mkdir(strcat(save_directory,'dets_s',num2str(session),'\dets',Trials(trialID).trees(treeID).file(1:end-4)));
end
CCall=[];
color_class=colormap(lines(max(Trials(trialID).trees(treeID).class)));
for i=1:max(class)
CCxyz=Trials(trialID).trees(treeID).ptCloud_th(Trials(trialID).trees(treeID).class==i,1:4);
if size(CCxyz,1)
CCi=[CCxyz,repmat(color_class(i,:)*255,size(CCxyz,1),1)];
CCall=[CCall;CCi];
dlmwrite(strcat(save_directory,'dets_s',num2str(session),'\dets',Trials(trialID).trees(treeID).file(1:end-4),'\det',Trials(trialID).trees(treeID).file(1:end-4),'_s',num2str(session),'_',num2str(i,'%03.f'),'.txt'),CCi,'precision','%.3f','delimiter','\t')
end
end
dlmwrite(strcat(save_directory,'dets_s',num2str(session),'\dets',Trials(trialID).trees(treeID).file(1:end-4),'\all_det',Trials(trialID).trees(treeID).file(1:end-4),'_s',num2str(session),'.txt'),CCall,'precision','%.3f','delimiter','\t')
disp(strcat(' realizado en:__', num2str(toc-t), ' seg.'))
t=toc;
end
end
end
%% plots
if show3D
figure;
pcshow(ptCloud_all_xyz(:,1:3),ptCloud_all_xyz(:,4)/100)
title('ptCloud\_all');
colormap jet
figure;
pcshow(ptCloud_all_xyz_thresh60(:,1:3),ptCloud_all_xyz_thresh60(:,4)/100)
title('ptCloud\_all\_thresh');
colormap jet
figure;
pcshow(ptCloud_all_thresh60_notOutliers_xyz(:,1:3),ptCloud_all_thresh60_notOutliers_xyz(:,4)/100)
title('ptCloud\_all\_thresh\_notoutliers');
colormap jet
figure;
pcshow(ptCloud_all_thresh60_notOutliers_xyz(class>0,1:3),class(class>0))
title('ptCloud\_all\_thresh\_notoutliers\_CC');
colormap lines
%Features 2D
colors='gbmkr';
markers= 'sd*x+';
feature_pair=[2 6 ; 2 17 ; 2 15 ; 6 17 ; 6 15 ; 17 15];
feature_pair_label={'Nº of Points','Cluster Volume [m^3]';...
'Nº of Points','Mean reflectance [%]';...
'Nº of Points','\Psi';...
'Cluster Volume [m^3]','Mean reflectance [%]';...
'Cluster Volume [m^3]','\Psi';...
'Mean reflectance [%]','\Psi'};
for j=1:size(feature_pair,1)
figure;
plot(CCfeatures(CCfeatures(:,1)==1,feature_pair(j,1)),CCfeatures(CCfeatures(:,1)==1,feature_pair(j,2)), [colors(1) markers(1)]);
hold on;
plot(CCfeatures(CCfeatures(:,1)==2,feature_pair(j,1)),CCfeatures(CCfeatures(:,1)==2,feature_pair(j,2)), [colors(2) markers(2)]);
hold on;
plot(CCfeatures(CCfeatures(:,1)==3,feature_pair(j,1)),CCfeatures(CCfeatures(:,1)==3,feature_pair(j,2)), [colors(3) markers(3)]);
hold on;
plot(CCfeatures(CCfeatures(:,1)>3,feature_pair(j,1)),CCfeatures(CCfeatures(:,1)>3,feature_pair(j,2)), [colors(4) markers(4)]);
legend('K = 1','K = 2','K = 3','K \geq 4');
title(strcat('Clusters features ','Eps:__', num2str(Eps),'NumNeigh:__', num2str(NumNeigh),'Th:__', num2str(Th)));
xlabel(feature_pair_label{j,1})
ylabel(feature_pair_label{j,2})
end
%Features splited 2D
colors='rb';
markers= 'xo';
feature_pair=[17 6 ; 15 6 ; 17 15];
feature_pair_label={'Mean reflectance [%]','Cluster Volume [m^3]';...
'\Psi','Cluster Volume [m^3]';...
'Mean reflectance [%]','\Psi'};
for j=1:size(feature_pair,1)
figure;
plot(CCfeatures_splited(CCfeatures_splited(:,1)==0,feature_pair(j,1)),CCfeatures_splited(CCfeatures_splited(:,1)==0,feature_pair(j,2)), [colors(1) markers(1)]);
hold on;
plot(CCfeatures_splited(CCfeatures_splited(:,1)>0,feature_pair(j,1)),CCfeatures_splited(CCfeatures_splited(:,1)>0,feature_pair(j,2)), [colors(2) markers(2)]);
legend('False Positive','True Positive');
%title(strcat('Clusters features ','Eps:__', num2str(Eps),'NumNeigh:__', num2str(NumNeigh),'Th:__', num2str(Th)));
xlabel(feature_pair_label{j,1})
ylabel(feature_pair_label{j,2})
end
line([65.25 65.25],[0.4 1],'color','black');
h=legend('False Positive','True Positive','$$V_{th\_FP}$$','$$\psi_{th\_FP}$$');
set(h,'Interpreter','latex')
%Features 3D
colors='rgbmkc';
markers= 'osd*x+';
figure;
for i=0:max(CCfeatures(:,1))
plot3(CCfeatures(CCfeatures(:,1)==i,2),CCfeatures(CCfeatures(:,1)==i,6),CCfeatures(CCfeatures(:,1)==i,15), [colors(i+1) markers(i+1)]);
hold on;
end
legend('0','1','2','3','4','5');
title(strcat('Clusters features ','Eps:__', num2str(Eps),'NumNeigh:__', num2str(NumNeigh),'Th:__', num2str(Th)));
xlabel('Nº of Points')
ylabel('Cluster Volume')
zlabel('Eigenvalues parameter')
figure;
for i=0:max(CCfeatures(:,1))
plot3(CCfeatures(CCfeatures(:,1)==i,2),CCfeatures(CCfeatures(:,1)==i,17),CCfeatures(CCfeatures(:,1)==i,15), [colors(i+1) markers(i+1)]);
hold on;
end
legend('0','1','2','3','4','5');
title(strcat('Clusters features ','Eps:__', num2str(Eps),'NumNeigh:__', num2str(NumNeigh),'Th:__', num2str(Th)));
xlabel('Nº of Points')
ylabel('Mean reflectivity')
zlabel('Eigenvalues parameter')
%Features splited
colors='rgbmkc';
markers= 'osd*x+';
figure;
for i=0:max(CCfeatures_splited(:,1))
plot3(CCfeatures_splited(CCfeatures_splited(:,1)==i,2),CCfeatures_splited(CCfeatures_splited(:,1)==i,6),CCfeatures_splited(CCfeatures_splited(:,1)==i,15), [colors(i+1) markers(i+1)]);
hold on;
end
legend('0','1','2','3','4','5');
title(strcat('Clusters features splited ','Eps:__', num2str(Eps),'NumNeigh:__', num2str(NumNeigh),'Th:__', num2str(Th)));
xlabel('Nº of Points')
ylabel('Cluster Volume')
zlabel('Eigenvalues parameter')
figure;
for i=0:max(CCfeatures_splited(:,1))
plot3(CCfeatures_splited(CCfeatures_splited(:,1)==i,2),CCfeatures_splited(CCfeatures_splited(:,1)==i,17),CCfeatures_splited(CCfeatures_splited(:,1)==i,15), [colors(i+1) markers(i+1)]);
hold on;
end
legend('0','1','2','3','4','5');
title(strcat('Clusters features splited ','Eps:__', num2str(Eps),'NumNeigh:__', num2str(NumNeigh),'Th:__', num2str(Th)));
xlabel('Nº of Points')
ylabel('Mean reflectivity')
zlabel('Eigenvalues parameter')
%Features nonFP
colors='rgbmkc';
markers= 'osd*x+';
figure;
for i=0:max(CCfeatures_splited_nonFP(:,1))
plot3(CCfeatures_splited_nonFP(CCfeatures_splited_nonFP(:,1)==i,2),CCfeatures_splited_nonFP(CCfeatures_splited_nonFP(:,1)==i,6),CCfeatures_splited_nonFP(CCfeatures_splited_nonFP(:,1)==i,15), [colors(i+1) markers(i+1)]);
hold on;
end
legend('0','1','2','3','4','5');
title(strcat('Clusters features nonFP ','Eps:__', num2str(Eps),'NumNeigh:__', num2str(NumNeigh),'Th:__', num2str(Th)));
xlabel('Nº of Points')
ylabel('Cluster Volume')
zlabel('Eigenvalues parameter')
figure;
for i=0:max(CCfeatures_splited_nonFP(:,1))
plot3(CCfeatures_splited_nonFP(CCfeatures_splited_nonFP(:,1)==i,2),CCfeatures_splited_nonFP(CCfeatures_splited_nonFP(:,1)==i,17),CCfeatures_splited_nonFP(CCfeatures_splited_nonFP(:,1)==i,15), [colors(i+1) markers(i+1)]);
hold on;
end
legend('0','1','2','3','4','5');
title(strcat('Clusters features splited ','Eps:__', num2str(Eps),'NumNeigh:__', num2str(NumNeigh),'Th:__', num2str(Th)));
xlabel('Nº of Points')
ylabel('Mean reflectivity')
zlabel('Eigenvalues parameter')
%Depth distribution histogram
rng 'default'
edges = -0.7:0.1:0.7;
center_of_labels=GroundTruthCenters(strcat(Groundtruth_Directory,Trials{:}));
h1=histcounts(center_of_labels(sum(LCM_LOC_COL,2)==1,1)-336312,edges);
h2=histcounts(center_of_labels(:,1)-336312,edges);
h=h1./h2;
h(isnan(h))=0;
figure
yyaxis left
bar(edges(1:end-1),[h1; h2]')
yyaxis right
plot(edges(1:end-1),h*100)
legend('TP','GroundTruth')
title('Depth distribution histogram');
%Height distribution histogram
rng 'default'
edges = 0:0.2:3.5;
center_of_labels=GroundTruthCenters(strcat(Groundtruth_Directory,Trials{:}));
Dist_z_min=min(center_of_labels(:,3));
h1=histcounts(center_of_labels(sum(LCM_LOC_COL,2)==1,3)-Dist_z_min+0.2,edges);
h2=histcounts(center_of_labels(:,3)-Dist_z_min+0.2,edges);
h=h1./h2;
h(isnan(h))=0;
figure
yyaxis left
bar(edges(1:end-1),[h1; h2]')
yyaxis right
plot(edges(1:end-1),h*100)
legend('TP','GroundTruth')
title('Height distribution histogram');
end
%%
disp(strcat('Final processado. Tiempo total procesado:__', num2str(toc), ' seg.'))
t=toc;
%end
figure;
bar(allLocalization_success_L);
title(strcat('Localization\_success\_s',num2str(session)));
figure;
bar(allFDR);
title(strcat('Localization\_FDR\_s',num2str(session)));
figure;
bar(allrecall);
title(strcat('Identification\_success or Recall\_s',num2str(session)));
figure;
bar(allFDR_LOC_COL);
title(strcat('Identification\_FDR\_s',num2str(session)));
figure;
bar(allF1);
title(strcat('F1\_s',num2str(session)));
figure;
bar(allprecision);
title(strcat('precision\_s',num2str(session)));