this error and thanks please ?

[Mahran55]

[collinswakholi]

@Mahran55, could you please give me more details? Mine is running fine

[Mahran55]

clear all close all addpath(genpath(fileparts(which(mfilename))))

%% Program arguments dataset = 'bb'; % images initials calib = 'calib_12'; cleanSparse = 1;% no clean = 0 dense = 1;

% select for dense cloud no_sample = length(dir(['images/' dataset '*.png'])); no_choice = 12; % how many images for making dense reconstruction drp = fix(1:(no_sample/no_choice):no_sample); % fix, round, floor

threads = 1; %% Reading image dataset disp(['Running pipeline for dataset "' dataset '"']) imsDir = dir(['ims/' dataset '*']); disp(['Dataset contains ' num2str(length(imsDir)) ' images']) imsNames = {imsDir.name}; imsNo = length(imsNames); Cim = cell(1,imsNo); for i = 1:length(imsNames) Cim{i} = imread(imsNames{i}); % [~,Cim{i}] = createMask_wm(Cim{i});%%%% createMask for ginseng imshow(Cim{i}); pause(0.5) end

load(calib) % camera parameters (K) disp('Calibration matrix: '), disp(K) d=[0,0]; if exist('d','var') disp('Radial distortion parameters: '), disp(d) Cim = UndistortImages(Cim,K,d); end if threads ~= 1 && isempty(gcp('nocreate')), parpool(threads); end

%% Feature extraction and matching selec = [1 0 0 1 1];%change based on %[Surf, kaze, fast, brisk, sift] combinaton is okay % example [1 1 0 1 0] ==> combination of surf, kaze, brisk features % selec = [0 1 0 0 1] % selec = [1 1 0 0 1] % selec = [0 1 1 0 0] CfeatPts = cell(1,imsNo); for i = 1:imsNo disp(['Feature points estimation: image ' num2str(i) ' of ' num2str(imsNo)]) % CfeatPts{i} = EstimateFeaturePoints(Cim{i},'KAZE'); % change CfeatPts{i} = EstimateFeaturePoints_lalit(Cim{i},selec); end

Ccorrs = cell(1,imsNo-1); CfeatPtsPlusOne = CfeatPts(2:end);

parfor i = 1:imsNo-1 disp(['Feature matching: pair ' num2str(i) ' of ' num2str(imsNo-1)]) mtds = {'Exhaustive',...%mtds{1,1} 'Approximate'};%mtds{1,2} tr = 0.45;% range 0--1 Ccorrs{i} = MatchFeaturePoints(CfeatPts{i}, CfeatPtsPlusOne{i},mtds{1,2},tr); end

%% Correspondence filtering (outlier rejection from matched features) CcorrsIn = cell(1,imsNo-1); CF = cell(1,imsNo-1); parfor i = 1:imsNo-1 disp(['Correspondence filtering: pair ' num2str(i) ' of ' num2str(imsNo-1)]) [CF{i}, Cinliers] = RANSAC(num2cell(Ccorrs{i},1),... @EstimateFundamentalMatrix, 8, @SampsonDistance, 8); CcorrsIn{i} = cell2mat(Cinliers); end

%% Fundamental Matrix estimation (unnormalized to normalized image using SVD) parfor i = 1:imsNo-1 disp(['Fundamental Matrix estimation: pair ' num2str(i) ' of ' num2str(imsNo-1)]) CF(i) = EstimateFundamentalMatrix(num2cell(CcorrsIn{i},1)); % CF is the fundamental matrix end

%% Background Filtering CcorrsInFil = cell(1,imsNo-1); parfor i = 1:imsNo-1 disp(['Background Filtering: pair ' num2str(i) ' of ' num2str(imsNo-1)]) CcorrsInFil{i} = FilterBackgroundFromCorrs(KCANONICAL_POSE,... KEstimateRealPose(K'CF{i}K, NormalizeCorrs(CcorrsIn{i},K)), CcorrsIn{i}); CF(i) = EstimateFundamentalMatrix(num2cell(CcorrsInFil{i},1)); end

%% Essential Matrix from Fundamental Matrix CE = cell(1,imsNo-1); % CE is the essential matrix CcorrsNormInFil = cell(1,imsNo-1); for i = 1:imsNo-1 disp(['Essential Matrix from Fundamental Matrix: '... num2str(i) ' of ' num2str(imsNo-1)]) CE{i} = K'CF{i}K/norm(K'CF{i}K)*sqrt(2)/2; CcorrsNormInFil{i} = NormalizeCorrs(CcorrsInFil{i}, K); end

%% Structure from Motion disp('Pose estimation: default first pair') CP = cell(1,imsNo); CP{1} = CANONICAL_POSE; CP{2} = EstimateRealPose(CE{1}, CcorrsNormInFil{1}); LOCALBA_OCCUR_PER1 = 8; LOCALBA_OCCUR_PER2 = 10;

for i = 2:imsNo-1 disp(['Pose estimation: ' num2str(i+1) ' of ' num2str(imsNo)]) CP{i+1} = EstimateRealPose(CE{i}, CcorrsNormInFil{i}, CP{i}); TrackedCorrs = CascadeTrack({CcorrsNormInFil{i-1}, CcorrsNormInFil{i}}); TrackedCorrsIso = IsolateTransitiveCorrs(TrackedCorrs); disp(['Transitivity: ' num2str(size(TrackedCorrsIso,2))]) CP{i+1} = OptimizeTranslationBaseline(CP{i-1}, CP{i}, CP{i+1}, TrackedCorrsIso);

if ~mod(i-1, LOCALBA_OCCUR_PER1-2)
    disp('Local Bundle Adjustment 1...')
    disp(['Refine ' num2str(i-(LOCALBA_OCCUR_PER1-2)) '->' num2str(i+1)])
    C = IsolateTransitiveCorrs(CascadeTrack(...
        CcorrsNormInFil(i-(LOCALBA_OCCUR_PER1-2) : i)), 'displaySize');
    if size(C,2) > 1
        CPBA = BundleAdjustment(CP(i-(LOCALBA_OCCUR_PER1-2) : i+1), C);
        CP(i-(LOCALBA_OCCUR_PER1-2) : i+1) = CPBA;
    end
end
if ~mod(i-1, LOCALBA_OCCUR_PER2-2)
    disp('Local Bundle Adjustment 2...')
    disp(['Refine ' num2str(i-(LOCALBA_OCCUR_PER2-2)) '->' num2str(i+1)])
    C = IsolateTransitiveCorrs(CascadeTrack(...
        CcorrsNormInFil(i-(LOCALBA_OCCUR_PER2-2) : i)), 'displaySize');
    if size(C,2) > 1
        CPBA = BundleAdjustment(CP(i-(LOCALBA_OCCUR_PER2-2) : i+1), C);
        CP(i-(LOCALBA_OCCUR_PER2-2) : i+1) = CPBA;
    end
end

end

if imsNo > LOCALBA_OCCUR_PER1 disp(['Refine ' num2str((imsNo-1)-(LOCALBA_OCCUR_PER1-2)) '->' num2str(imsNo)]) C = IsolateTransitiveCorrs(CascadeTrack(... CcorrsNormInFil((imsNo-1)-(LOCALBA_OCCUR_PER1-2) : imsNo-1)), 'displaySize'); if size(C,2) > 1 CPBA = BundleAdjustment(CP((imsNo-1)-(LOCALBA_OCCUR_PER1-2) : imsNo), C); CP((imsNo-1)-(LOCALBA_OCCUR_PER1-2) : imsNo) = CPBA; end end if imsNo > LOCALBA_OCCUR_PER2 disp(['Refine ' num2str((imsNo-1)-(LOCALBA_OCCUR_PER2-2)) '->' num2str(imsNo)]) C = IsolateTransitiveCorrs(CascadeTrack(... CcorrsNormInFil((imsNo-1)-(LOCALBA_OCCUR_PER2-2) : imsNo-1)), 'displaySize'); if size(C,2) > 1 CPBA = BundleAdjustment(CP((imsNo-1)-(LOCALBA_OCCUR_PER2-2) : imsNo), C); CP((imsNo-1)-(LOCALBA_OCCUR_PER2-2) : imsNo) = CPBA; end end

Tracks = CascadeTrack(CcorrsNormInFil); X = TriangulateCascade(CP,Tracks); C = SampleColors(Tracks,Cim,K);

% denoise sparse ptcd if cleanSparse ==1 ptcd_sparse = pointCloud(X','Color', C'); minDistance = 0.03;% 0.03 [labels,numClusters] = pcsegdist(ptcd_sparse,minDistance);% cluster_No = 1; den_ptcd_sparse = selectPointColud (ptcd_sparse,labels,numClusters,cluster_No); X = den_ptcd_sparse.Location'; C = den_ptcd_sparse.Color'; end %% write sparse MakePly([dataset '-sparse.ply'], X, [], C) PlotSparse(CP,X,C);

%% Dense Matching CX = cell(1,length(drp)); CC = cell(1,length(drp)); CXSc = cell(1,length(drp)); CCSc = cell(1,length(drp)); % Parallel groundwork CimDrp = Cim(drp); CimDrpPlusOne = Cim(drp+1); CcorrsNormInFilDrp = CcorrsNormInFil(drp); CFDrp = CF(drp); CPDrp = CP(drp); CPDrpPlusOne = CP(drp+1);

zz = [-10.6,... %mu 4,... %window 7,... %zone gap 0.9,... %pm_tau 0.7]; %tau

for i = 1:length(drp) disp(['Dense Reconstruction: pair ' num2str(i) ' of ' num2str(length(drp))]) zRange = ExtractZRange(CANONICAL_POSE, DehomogenizeMat(... HomogenizeMat(CPDrpPlusOne{i})/HomogenizeMat(CPDrp{i})),... CcorrsNormInFilDrp{i}); [CX{i}, CC{i}, CXSc{i}, CCSc{i}] = RectifyAndDenseTriangulate(zz,CropBackground(... CimDrp{i},Unnormalize(CcorrsNormInFilDrp{i}(1:2,:),K),1.1),... CropBackground(CimDrpPlusOne{i},Unnormalize(CcorrsNormInFilDrp{i}(3:4,:),... K),1.1), CFDrp{i}, K, CPDrp{i}, CPDrpPlusOne{i}, zRange,'denoise'); %,'denoise' end

MakePly([dataset '-dense.ply'], cell2mat(CX), [], cell2mat(CC)) PlotDense(cell2mat(CX),cell2mat(CC)) % % % %% Remeshing CXFil = cell(1,length(drp)); CCFil = cell(1,length(drp)); CNFil = cell(1,length(drp)); for i = 1:length(drp) disp(['Computing normals: set ' num2str(i) ' of ' num2str(length(drp))]) [CNFil{i}, filInd] = ComputeNormalsAndFilter(CXSc{i}); CXFil{i} = CX{i}(:,filInd); CCFil{i} = CC{i}(:,filInd); end % disp('Remeshing') MakePly([dataset '-colored.ply'],... cell2mat(CXFil), cell2mat(CNFil), cell2mat(CCFil))

ptcd = pcread([dataset,'-colored.ply']); figure(2), pcshow(ptcd) delete(['out/',dataset,'-colored.ply']); % % minDistance = .2;% 0.03 [labels,numClusters] = pcsegdist(ptcd,minDistance);% cluster_No = 3; ptcd_selec = selectPointColud (ptcd,labels,numClusters,cluster_No);

pcwrite(ptcd_selec,['out/',dataset '-colored.ply'])

PoissonReconWrapper(['out/',dataset '-colored.ply'], ['out/', dataset '-cleaned.ply'], 8, feature('numcores'));%8

ptcd = pcread('ffa-cleaned.ply');%% preview pcshow(ptcd) % % % % fclose('all'); % % % %% volume computation % % ptcd = pcread('r-cleaned.ply');%% preview % % pcshow(ptcd) % % % % % % % dt = delaunayTriangulation(double(ptcd.Location)); % % dt = delaunayTriangulation(double(ptcd.Location)); % % [ch, Volume] = convexHull(dt); % % X = ch(:,1); % % Y = ch(:,2); % % Z = ch(:,3); % % % % trisurf(ch, X, Y, Z); % % % % [normals,curvature] = findPointNormals(ptcd.Location); % % % % pc = ptcd; % % pc.select('UniformSampling', 2); % % pc.plot('Color', 'r', 'MarkerSize', 5); % % pc.plotNormals('Color', 'y', 'Scale', 10); % % % % % trisurf(ch, dt.Points(:,1),dt.Points(:,2),dt.Points(:,3), 'FaceColor', 'cyan')

this is the main code

[Mahran55]

you need more informations ?

[collinswakholi]

Yeah, this is the main code. Did you do calibration of your camera?

[Mahran55]

yeah i did it so i do a new calibration ?

[collinswakholi]

Okay great, could you take for me a screenshot of your current working directory in matlab?

[collinswakholi]

Or you could send me your sample data so that I could try it myself - I want to understand the real problem

[Mahran55]

[Mahran55]

i send you the image in dataset and the 36 others images ?

[Mahran55]

if you like can i send you all the folder to your mail ?

[collinswakholi]

Send me a google drive link or wetransfer link

[Mahran55]

ok just a few minute and i will send you . thanks .

[Mahran55]

files are large :(

[collinswakholi]

Please try to use wetransfer.com, it can allow transfer of files up to 2GB

[Mahran55]