From 5dfba7459128c789479cb1edd613c9687fcd9a7a Mon Sep 17 00:00:00 2001 From: Andy Zeng Date: Tue, 30 Aug 2016 14:52:17 -0400 Subject: [PATCH] fix pre-scanned object models for objects with no depth; add LICENSE --- LICENSE | 22 ++ evaluation/benchmark.m | 225 ++++++++++++------ .../pose_estimation/src/getObjectHypothesis.m | 63 ++--- .../src/pose_estimation/src/getObjectPose.m | 171 +++++++------ .../src/getObjectPoseNoDepth.m | 48 ++-- .../src/models/objects/centerObject.m | 2 +- .../models/objects/dasani_water_bottle.ply | Bin 747479 -> 3192150 bytes .../objects/rolodex_jumbo_pencil_cup.ply | Bin 275 -> 4453725 bytes .../src/pose_estimation/src/serviceCallback.m | 9 +- {visualization => vis-utils}/dispObjPose.m | 0 .../getColorPalette.m | 0 {visualization => vis-utils}/parseLabels.m | 0 {visualization => vis-utils}/splitToteIm.m | 0 {visualization => vis-utils}/visObjLabels.m | 0 {visualization => vis-utils}/visRepImage.m | 0 visualization/visualizeResults.m | 159 ------------- 16 files changed, 318 insertions(+), 381 deletions(-) create mode 100644 LICENSE rename {visualization => vis-utils}/dispObjPose.m (100%) rename {visualization => vis-utils}/getColorPalette.m (100%) rename {visualization => vis-utils}/parseLabels.m (100%) rename {visualization => vis-utils}/splitToteIm.m (100%) rename {visualization => vis-utils}/visObjLabels.m (100%) rename {visualization => vis-utils}/visRepImage.m (100%) delete mode 100644 visualization/visualizeResults.m diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..0b01721 --- /dev/null +++ b/LICENSE @@ -0,0 +1,22 @@ +Copyright (c) 2016, The Trustees of Princeton University +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/evaluation/benchmark.m b/evaluation/benchmark.m index 9a71c39..adb5dc6 100644 --- a/evaluation/benchmark.m +++ b/evaluation/benchmark.m @@ -2,7 +2,7 @@ % % Start ROS services marvin_convnet and pose_estimation before running this % script (and realsense_camera if necessary) -% Set marvin_convnet to mode 2 to read RGB-D data from disk +% Set marvin_convnet to _service_mode:=2 to read RGB-D data from disk % Run in same Matlab instance as where ROS service pose_estimation was started % % --------------------------------------------------------- @@ -17,107 +17,176 @@ % User configurations (change me) tmpDataPath = '/home/andyz/apc/toolbox/data/tmp'; % Temporary directory used by marvin_convnet, where all RGB-D images and detection masks are saved benchmarkPath = '/home/andyz/apc/toolbox/data/benchmark'; % Benchmark directory +modelsPath = '/home/andyz/apc/toolbox/ros-packages/catkin_ws/src/pose_estimation/src/models/objects'; % Directory holding pre-scanned object models rgbdUtilsPath = '/home/andyz/apc/toolbox/rgbd-utils'; % Directory of RGB-D toolbox utilities +visUtilsPath = '/home/andyz/apc/toolbox/vis-utils'; % Directory of visualization utilities resultVisPath = 'results'; % Directory to save visualizations of pose estimation results % Load benchmark scenes and labels sceneList = {}; +sceneNames = {}; fid = fopen(fullfile(benchmarkPath,'info.txt')); while ~feof(fid) - scenePath = fullfile(benchmarkPath,fscanf(fid,'%s,')); + sceneName = fscanf(fid,'%s,'); + scenePath = fullfile(benchmarkPath,sceneName); if ~feof(fid) sceneList{length(sceneList)+1} = scenePath(1:(end-1)); + sceneNames{length(sceneNames)+1} = sceneName; end end fclose(fid); % Add paths and create directories addpath(genpath(rgbdUtilsPath)); +addpath(genpath(visUtilsPath)); if ~exist(tmpDataPath,'file') mkdir(tmpDataPath); end if ~exist(resultVisPath,'file') mkdir(resultVisPath); end + +% Get set of colors for visualizations +colorPalette = getColorPalette(); % Loop through benchmark scenes for sceneIdx = 1:length(sceneList) - scenePath = sceneList{sceneIdx}; - fprintf(sprintf('[Test] %s\n',scenePath)); - - % Remove all files in temporary folder used by marvin_convnet - delete(fullfile(tmpDataPath,'*')); - - % Copy current scene to temporary folder used by marvin_convnet - copyfile(fullfile(scenePath,'*'),tmpDataPath); - - % Load scene - sceneData = loadScene(scenePath); - numFrames = length(sceneData.colorFrames); - - % Call marvin_convnet to do 2D object segmentation for each RGB-D frame - fprintf(' [Segmentation] Frame '); - binIds = 'ABCDEFGHIJKL'; - binNum = strfind(binIds,sceneData.binId)-1; - for frameIdx = 0:(numFrames-1) - fprintf('%d ',frameIdx); - [client,reqMsg] = rossvcclient('/marvin_convnet'); - reqMsg.BinId = binNum; - reqMsg.ObjectNames = sceneData.objects; - reqMsg.FrameId = frameIdx; - response = call(client,reqMsg); - end - fprintf('\n'); - - % Call apc_posest to do object pose estimation for the sequence - [client,reqMsg] = rossvcclient('/pose_estimation'); - reqMsg.SceneFiles = tmpDataPath; - if ~isempty(strfind(scenePath,'office')) - reqMsg.CalibrationFiles = '/home/andyz/apc/toolbox/data/benchmark/office/calibration'; - elseif ~isempty(strfind(scenePath,'warehouse')) - reqMsg.CalibrationFiles = '/home/andyz/apc/toolbox/data/benchmark/warehouse/calibration'; - end - reqMsg.DoCalibration = true; - response = call(client,reqMsg); - -% % Copy result and visualization files to results folder -% resultFiles = dir(fullfile(tmpDataPath,'*.result.txt')); -% resultImages = dir(fullfile(tmpDataPath,'*.result.png')); -% visFiles = dir('*.ply'); -% visObjNames = {}; -% visObjScores = {}; -% for resultIdx = 1:length(resultFiles) -% currResultFile = resultFiles(resultIdx).name; -% currResultFilenameDotIdx = strfind(currResultFile,'.'); -% currObjName = currResultFile(1:(currResultFilenameDotIdx(1)-1)); -% currResultFileMatrix = dlmread(fullfile(tmpDataPath,currResultFile)); -% currObjScore = currResultFileMatrix(length(currResultFileMatrix)); -% visObjNames{length(visObjNames)+1} = currObjName; -% visObjScores{length(visObjScores)+1} = currObjScore; -% % copyfile(fullfile(tmpPath,resultFiles(resultIdx).name),fullfile(resultPath,[testDir(seqIdx).name,'.',resultFiles(resultIdx).name])); -% end -% for resultIdx = 1:length(resultImages) -% currResultFile = resultImages(resultIdx).name; -% visResultImage = imread(fullfile(tmpDataPath,currResultFile)); -% [sortVals,sortIdx] = sortrows(cell2mat(visObjScores)',-1); -% visResultImage = insertText(visResultImage,[10 10],'Confidence : Object ID','TextColor','white','BoxColor','black'); -% textY = 32; -% for objIdx = 1:length(visObjNames) -% if ~isempty(strfind(currResultFile,visObjNames{sortIdx(objIdx)}))%resultIdx == sortIdx(objIdx) -% visResultImage = insertText(visResultImage,[10 textY],sprintf('%f : %s',visObjScores{sortIdx(objIdx)},visObjNames{sortIdx(objIdx)}),'TextColor','black','BoxColor','green'); -% else -% visResultImage = insertText(visResultImage,[10 textY],sprintf('%f : %s',visObjScores{sortIdx(objIdx)},visObjNames{sortIdx(objIdx)}),'TextColor','white','BoxColor','black'); -% end -% textY = textY + 22; -% end -% imwrite(visResultImage,fullfile(resultPath,strcat(testDir(sceneIdx).name,'.',resultImages(resultIdx).name))); -% % copyfile(fullfile(tmpPath,resultImages(resultIdx).name),fullfile(resultPath,[testDir(seqIdx).name,'.',resultImages(resultIdx).name])); -% end -% % copyfile(fullfile(tmpPath,'frame-000007.color.png'),fullfile(resultPath,[testDir(seqIdx).name,'.','frame-000007.color.png'])); -% for visFileIdx = 1:length(visFiles) -% movefile(visFiles(visFileIdx).name,fullfile(resultPath,[testDir(sceneIdx).name,'.',visFiles(visFileIdx).name])); -% end - + scenePath = sceneList{sceneIdx}; + fprintf(sprintf('[Test] %s\n',scenePath)); + + % Remove all files in temporary folder used by marvin_convnet + if exist(fullfile(tmpDataPath,'raw'),'file') + rmdir(fullfile(tmpDataPath,'raw'),'s'); + end + if exist(fullfile(tmpDataPath,'results'),'file') + rmdir(fullfile(tmpDataPath,'results'),'s'); + end + delete(fullfile(tmpDataPath,'*')); + + % Copy current scene to temporary folder used by marvin_convnet + copyfile(fullfile(scenePath,'*'),tmpDataPath); + + % Load scene + sceneData = loadScene(scenePath); + numFrames = length(sceneData.colorFrames); + + % Call marvin_convnet to do 2D object segmentation for each RGB-D frame + fprintf(' [Segmentation] Frame '); + binIds = 'ABCDEFGHIJKL'; + binNum = strfind(binIds,sceneData.binId)-1; + for frameIdx = 0:(numFrames-1) + fprintf('%d ',frameIdx); + [client,reqMsg] = rossvcclient('/marvin_convnet'); + reqMsg.BinId = binNum; + reqMsg.ObjectNames = sceneData.objects; + reqMsg.FrameId = frameIdx; + response = call(client,reqMsg); + end + fprintf('\n'); + + % Call pose_estimation to do 6D object pose estimation for the sequence + [client,reqMsg] = rossvcclient('/pose_estimation'); + reqMsg.SceneFiles = tmpDataPath; + if ~isempty(strfind(scenePath,'office')) + reqMsg.CalibrationFiles = '/home/andyz/apc/toolbox/data/benchmark/office/calibration'; + elseif ~isempty(strfind(scenePath,'warehouse')) + reqMsg.CalibrationFiles = '/home/andyz/apc/toolbox/data/benchmark/warehouse/calibration'; + end + reqMsg.DoCalibration = true; + respMsg= call(client,reqMsg); + + % Load results (predicted 6D object poses) + resultFiles = dir(fullfile(tmpDataPath,'results/*.result.txt')); + results = cell(1,length(resultFiles)); + confScores = []; + for resultIdx = 1:length(resultFiles) + tmpResultFile = resultFiles(resultIdx).name; + tmpResultFilenameDotIdx = strfind(tmpResultFile,'.'); + tmpResult.objName = tmpResultFile(1:(tmpResultFilenameDotIdx(1)-1)); + tmpResult.objNum = str2double(tmpResultFile((tmpResultFilenameDotIdx(1)+1):(tmpResultFilenameDotIdx(2)-1))); + tmpResult.objPoseWorld = eye(4); + tmpResult.objPoseWorld(1:3,4) = dlmread(fullfile(tmpDataPath,'results',tmpResultFile),'\t',[1,0,1,2])'; + objPoseRotQuat = dlmread(fullfile(tmpDataPath,'results',tmpResultFile),'\t',[4,0,4,3]); + tmpResult.objPoseWorld(1:3,1:3) = quat2rotm([objPoseRotQuat(4),objPoseRotQuat(1:3)]); + tmpResult.confScore = dlmread(fullfile(tmpDataPath,'results',tmpResultFile),'\t',[28,0,28,0]); + confScores = [confScores;tmpResult.confScore]; + results{resultIdx} = tmpResult; + end + + % Sort results by confidence scores + [~,sortIdx] = sortrows(confScores,-1); + + % Get random colors per object in the scene + randColorIdx = randperm(length(colorPalette),length(results)); + + % Create canvases for visualization + if strcmp(sceneData.env,'shelf') + canvas = sceneData.colorFrames{8}; + canvasSeg = uint8(ones(size(canvas))*255); + canvasPose = canvas; + else + canvas1 = sceneData.colorFrames{5}; + canvas2 = sceneData.colorFrames{14}; + canvasSeg1 = uint8(ones(size(canvas1))*255); + canvasPose1 = canvas1; + canvasSeg2 = uint8(ones(size(canvas2))*255); + canvasPose2 = canvas2; + end + + % Loop through each object and visualize predicted object pose + canvasPose = insertText(canvasPose,[10 10],'Confidence : Object','Font','LucidaSansDemiBold','FontSize',16,'TextColor','white','BoxColor','black'); + textPosY = 38; + for resultIdx = 1:length(resultFiles) + currResult = results{sortIdx(resultIdx)}; + objColor = colorPalette{randColorIdx(resultIdx)}; + + % Load pre-scanned object point cloud + objPointCloud = pcread(fullfile(modelsPath,sprintf('%s.ply',currResult.objName))); + + + % Draw projected object model and bounding box + if strcmp(sceneData.env,'shelf') + [canvasSeg,canvasPose] = dispObjPose(canvasSeg, ... + canvasPose, ... + sceneData.colorFrames{8}, ... + sceneData.depthFrames{8}, ... + sceneData.extCam2World{8}, ... + sceneData.colorK, ... + objPointCloud, ... + currResult.objPoseWorld, ... + objColor); + else + [canvasSeg1,canvasPose1] = dispObjPose(canvasSeg1, ... + canvasPose1, ... + sceneData.colorFrames{5}, ... + sceneData.depthFrames{5}, ... + sceneData.extCam2World{5}, ... + sceneData.colorK, ... + objPointCloud, ... + currResult.objPoseWorld, ... + objColor); + [canvasSeg2,canvasPose2] = dispObjPose(canvasSeg2, ... + canvasPose2, ... + sceneData.colorFrames{14}, ... + sceneData.depthFrames{14}, ... + sceneData.extCam2World{14}, ... + sceneData.colorK, ... + objPointCloud, ... + currResult.objPoseWorld, ... + objColor); + canvasSeg = [canvasSeg1;canvasSeg2]; + canvasPose = [canvasPose1;canvasPose2]; + end + + canvasPose = insertText(canvasPose,[10 textPosY],sprintf(' %f : %s',currResult.confScore,currResult.objName),'Font','LucidaSansDemiBold','FontSize',16,'TextColor',objColor,'BoxColor','black'); + textPosY = textPosY + 28; + end + + % Save visualization + visFileName = sceneNames{sceneIdx}; + visFileName = visFileName(1:(end-1)); + visFileName = strrep(visFileName,'/','_'); + imwrite(canvasPose,fullfile(resultVisPath,sprintf('%s.png',visFileName))); end diff --git a/ros-packages/catkin_ws/src/pose_estimation/src/getObjectHypothesis.m b/ros-packages/catkin_ws/src/pose_estimation/src/getObjectHypothesis.m index c895150..8a1339e 100644 --- a/ros-packages/catkin_ws/src/pose_estimation/src/getObjectHypothesis.m +++ b/ros-packages/catkin_ws/src/pose_estimation/src/getObjectHypothesis.m @@ -1,12 +1,12 @@ -function objectHypothesis = getObjectHypothesis(RtPCA,latentPCA,surfaceCentroid,surfaceRange,finalRt,finalScore,dataPath,objName,instanceIdx) +function objectHypothesis = getObjectHypothesis(surfPCAPoseWorld,latentPCA,surfCentroid,surfRangeWorld,predObjPoseWorld,predObjConfScore,scenePath,objName,instanceIdx) % Save object pose to ROS message poseTrans = rosmessage('geometry_msgs/Point'); -poseTrans.X = finalRt(1,4); -poseTrans.Y = finalRt(2,4); -poseTrans.Z = finalRt(3,4); +poseTrans.X = predObjPoseWorld(1,4); +poseTrans.Y = predObjPoseWorld(2,4); +poseTrans.Z = predObjPoseWorld(3,4); poseRot = rosmessage('geometry_msgs/Quaternion'); -poseQuat = rotm2quat(finalRt(1:3,1:3)); +poseQuat = rotm2quat(predObjPoseWorld(1:3,1:3)); poseRot.X = poseQuat(2); poseRot.Y = poseQuat(3); poseRot.Z = poseQuat(4); @@ -17,11 +17,11 @@ % Save PCA to ROS message pcaTrans = rosmessage('geometry_msgs/Point'); -pcaTrans.X = RtPCA(1,4); -pcaTrans.Y = RtPCA(2,4); -pcaTrans.Z = RtPCA(3,4); +pcaTrans.X = surfPCAPoseWorld(1,4); +pcaTrans.Y = surfPCAPoseWorld(2,4); +pcaTrans.Z = surfPCAPoseWorld(3,4); pcaRot = rosmessage('geometry_msgs/Quaternion'); -pcaQuat = rotm2quat(RtPCA(1:3,1:3)); +pcaQuat = rotm2quat(surfPCAPoseWorld(1:3,1:3)); pcaRot.X = pcaQuat(2); pcaRot.Y = pcaQuat(3); pcaRot.Z = pcaQuat(4); @@ -32,15 +32,15 @@ % Save PCA variances to ROS message pcaLatent = rosmessage('geometry_msgs/Point'); -pcaLatent.X = latentPCA(1)*50; -pcaLatent.Y = latentPCA(2)*50; -pcaLatent.Z = latentPCA(3)*50; +pcaLatent.X = latentPCA(1); +pcaLatent.Y = latentPCA(2); +pcaLatent.Z = latentPCA(3); % Save surface mean to ROS message surfaceMean = rosmessage('geometry_msgs/Point'); -surfaceMean.X = surfaceCentroid(1); -surfaceMean.Y = surfaceCentroid(2); -surfaceMean.Z = surfaceCentroid(3); +surfaceMean.X = surfCentroid(1); +surfaceMean.Y = surfCentroid(2); +surfaceMean.Z = surfCentroid(3); % Prepare ROS message objectHypothesis = rosmessage('pose_estimation/ObjectHypothesis'); @@ -49,23 +49,26 @@ objectHypothesis.Pca = pcaMsg; objectHypothesis.Latent = pcaLatent; objectHypothesis.Mean = surfaceMean; -objectHypothesis.RangeX = surfaceRange(1,:); -objectHypothesis.RangeY = surfaceRange(2,:); -objectHypothesis.RangeZ = surfaceRange(3,:); -objectHypothesis.Score = finalScore; +objectHypothesis.RangeX = surfRangeWorld(1,:); +objectHypothesis.RangeY = surfRangeWorld(2,:); +objectHypothesis.RangeZ = surfRangeWorld(3,:); +objectHypothesis.Score = predObjConfScore; % Save ROS message to file -fid = fopen(fullfile(dataPath,strcat(objName,sprintf('.%d.result.txt',instanceIdx))),'w'); -fprintf(fid,'%f\n%f\n%f\n',finalRt(1,4),finalRt(2,4),finalRt(3,4)); -fprintf(fid,'%f\n%f\n%f\n%f\n',poseQuat(2),poseQuat(3),poseQuat(4),poseQuat(1)); -fprintf(fid,'%f\n%f\n%f\n',RtPCA(1,4),RtPCA(2,4),RtPCA(3,4)); -fprintf(fid,'%f\n%f\n%f\n%f\n',pcaQuat(2),pcaQuat(3),pcaQuat(4),pcaQuat(1)); -fprintf(fid,'%f\n%f\n%f\n',latentPCA(1)*50,latentPCA(2)*50,latentPCA(3)*50); -fprintf(fid,'%f\n%f\n%f\n',surfaceCentroid(1),surfaceCentroid(2),surfaceCentroid(3)); -fprintf(fid,'%f\n%f\n',surfaceRange(1,1),surfaceRange(1,2)); -fprintf(fid,'%f\n%f\n',surfaceRange(2,1),surfaceRange(2,2)); -fprintf(fid,'%f\n%f\n',surfaceRange(3,1),surfaceRange(3,2)); -fprintf(fid,'%f\n',finalScore); +if ~exist(fullfile(scenePath,'results'),'file') + mkdir(fullfile(scenePath,'results')); +end +fid = fopen(fullfile(scenePath,'results',strcat(objName,sprintf('.%d.result.txt',instanceIdx))),'w'); +fprintf(fid,'# Predicted object pose translation (x,y,z in world coordinates)\n%15.8e\t %15.8e\t %15.8e\t\n\n',predObjPoseWorld(1,4),predObjPoseWorld(2,4),predObjPoseWorld(3,4)); +fprintf(fid,'# Predicted object pose rotation (x,y,z,w object-to-world quaternion)\n%15.8e\t %15.8e\t %15.8e\t %15.8e\t\n\n',poseQuat(2),poseQuat(3),poseQuat(4),poseQuat(1)); +fprintf(fid,'# Segmented object point cloud median (x,y,z in world coordinates)\n%15.8e\t %15.8e\t %15.8e\t\n\n',surfPCAPoseWorld(1,4),surfPCAPoseWorld(2,4),surfPCAPoseWorld(3,4)); +fprintf(fid,'# Segmented object point cloud PCA rotation (x,y,z,w object-to-world quaternion)\n%15.8e\t %15.8e\t %15.8e\t %15.8e\t\n\n',pcaQuat(2),pcaQuat(3),pcaQuat(4),pcaQuat(1)); +fprintf(fid,'# Segmented object point cloud PCA variance (in PC directions)\n%15.8e\t %15.8e\t %15.8e\t\n\n',latentPCA(1),latentPCA(2),latentPCA(3)); +fprintf(fid,'# Segmented object point cloud centroid/mean (x,y,z in world coordinates)\n%15.8e\t %15.8e\t %15.8e\t\n\n',surfCentroid(1),surfCentroid(2),surfCentroid(3)); +fprintf(fid,'# Segmented object point cloud x-range in world coordinates (bounding box in x-direction)\n%15.8e\t %15.8e\t\n\n',surfRangeWorld(1,1),surfRangeWorld(1,2)); +fprintf(fid,'# Segmented object point cloud y-range in world coordinates (bounding box in y-direction)\n%15.8e\t %15.8e\t\n\n',surfRangeWorld(2,1),surfRangeWorld(2,2)); +fprintf(fid,'# Segmented object point cloud z-range in world coordinates (bounding box in z-direction)\n%15.8e\t %15.8e\t\n\n',surfRangeWorld(3,1),surfRangeWorld(3,2)); +fprintf(fid,'# Prediction confidence score\n%.17g\n',predObjConfScore); fclose(fid); end diff --git a/ros-packages/catkin_ws/src/pose_estimation/src/getObjectPose.m b/ros-packages/catkin_ws/src/pose_estimation/src/getObjectPose.m index 1b2775c..c33f5c3 100644 --- a/ros-packages/catkin_ws/src/pose_estimation/src/getObjectPose.m +++ b/ros-packages/catkin_ws/src/pose_estimation/src/getObjectPose.m @@ -1,4 +1,4 @@ -function objectHypotheses = getObjectPose(scenePath,sceneData,scenePointCloud,backgroundPointCloud,extBin2Bg,objName,objModel,objNum) +function objHypotheses = getObjectPose(scenePath,sceneData,scenePointCloud,backgroundPointCloud,extBin2Bg,objName,objModel,objNum) global visPath; global savePointCloudVis; @@ -9,7 +9,7 @@ % Parameters for both shelf and tote scenario gridStep = 0.002; % grid size for downsampling point clouds icpWorstRejRatio = 0.9; % ratio of outlier points to ignore during ICP -objectHypotheses = []; +objHypotheses = []; % Parameters specific to shelf or tote scenario if strcmp(sceneData.env,'tote') @@ -29,28 +29,29 @@ [objSegmPts,objSegmConf] = getSegmentedPointCloud(sceneData,frames,objMasks,segmConfMaps); % Handle objects without depth -sceneData.extBin2World = inv(sceneData.extBin2World); -if (strcmp(objName,'dasani_water_bottle') && binID ~= -1) || strcmp(objName,'rolodex_jumbo_pencil_cup') || strcmp(objName,'platinum_pets_dog_bowl') - [finalRt,surfaceCentroid,surfaceRange] = getObjectPoseNoDepth(visPath,objSegmPts,objName,binID,frames,sceneData,objMasks,savePointClouds); - finalRt = sceneData.extBin2World * finalRt; - finalScore = mean(objSegmConf); +if strcmp(objName,'dasani_water_bottle') && strcmp(sceneData.env,'shelf') || ... + strcmp(objName,'rolodex_jumbo_pencil_cup') || ... + strcmp(objName,'platinum_pets_dog_bowl') + [predObjPoseBin,surfCentroid,surfRangeWorld] = getObjectPoseNoDepth(visPath,objSegmPts,objName,frames,sceneData,objMasks); + predObjPoseWorld = sceneData.extBin2World * predObjPoseBin; + predObjConfScore = mean(objSegmConf); for instanceIdx = 1:objNum - if savePointClouds - surfaceAxisPtsX = [surfaceRange(1,1):0.001:surfaceRange(1,2)]; - surfaceAxisPtsX = [surfaceAxisPtsX;repmat(surfaceCentroid(2),1,size(surfaceAxisPtsX,2));repmat(surfaceCentroid(3),1,size(surfaceAxisPtsX,2))]; - surfaceAxisPtsY = [surfaceRange(2,1):0.001:surfaceRange(2,2)]; - surfaceAxisPtsY = [repmat(surfaceCentroid(1),1,size(surfaceAxisPtsY,2));surfaceAxisPtsY;repmat(surfaceCentroid(3),1,size(surfaceAxisPtsY,2))]; - surfaceAxisPtsZ = [surfaceRange(3,1):0.001:surfaceRange(3,2)]; - surfaceAxisPtsZ = [repmat(surfaceCentroid(1),1,size(surfaceAxisPtsZ,2));repmat(surfaceCentroid(2),1,size(surfaceAxisPtsZ,2));surfaceAxisPtsZ]; + if savePointCloudVis + surfaceAxisPtsX = [surfRangeWorld(1,1):0.001:surfRangeWorld(1,2)]; + surfaceAxisPtsX = [surfaceAxisPtsX;repmat(surfCentroid(2),1,size(surfaceAxisPtsX,2));repmat(surfCentroid(3),1,size(surfaceAxisPtsX,2))]; + surfaceAxisPtsY = [surfRangeWorld(2,1):0.001:surfRangeWorld(2,2)]; + surfaceAxisPtsY = [repmat(surfCentroid(1),1,size(surfaceAxisPtsY,2));surfaceAxisPtsY;repmat(surfCentroid(3),1,size(surfaceAxisPtsY,2))]; + surfaceAxisPtsZ = [surfRangeWorld(3,1):0.001:surfRangeWorld(3,2)]; + surfaceAxisPtsZ = [repmat(surfCentroid(1),1,size(surfaceAxisPtsZ,2));repmat(surfCentroid(2),1,size(surfaceAxisPtsZ,2));surfaceAxisPtsZ]; pcwrite(pointCloud([surfaceAxisPtsX,surfaceAxisPtsY,surfaceAxisPtsZ]','Color',[repmat([255,0,0],size(surfaceAxisPtsX,2),1);repmat([0,255,0],size(surfaceAxisPtsY,2),1);repmat([0,0,255],size(surfaceAxisPtsZ,2),1)]),fullfile(visPath,sprintf('vis.objSurf.%s.%d',objName,instanceIdx)),'PLYFormat','binary'); end - surfaceCentroidWorld = sceneData.extBin2World(1:3,1:3) * surfaceCentroid + repmat(sceneData.extBin2World(1:3,4),1,size(surfaceCentroid,2)); - surfaceRangeWorld = sceneData.extBin2World(1:3,1:3) * surfaceRange + repmat(sceneData.extBin2World(1:3,4),1,size(surfaceRange,2)); - if saveResultImages - visualizeResults(finalRt,[0 0 0],surfaceCentroidWorld,surfaceRangeWorld,finalRt,finalScore,dataPath,objName,instanceIdx,binID,sceneData,[],objMasks,objModel.Location'); + surfCentroidWorld = sceneData.extBin2World(1:3,1:3) * surfCentroid + repmat(sceneData.extBin2World(1:3,4),1,size(surfCentroid,2)); + surfRangeWorld = sceneData.extBin2World(1:3,1:3) * surfRangeWorld + repmat(sceneData.extBin2World(1:3,4),1,size(surfRangeWorld,2)); + if saveResultImageVis + visualizeResults(predObjPoseWorld,[0 0 0],surfCentroidWorld,surfRangeWorld,predObjPoseWorld,predObjConfScore,scenePath,objName,instanceIdx,sceneData,[],objMasks,objModel.Location'); end - currObjectHypothesis = getObjectHypothesis(finalRt,[0 0 0],surfaceCentroidWorld,surfaceRangeWorld,finalRt,finalScore,dataPath,objName,instanceIdx); - objectHypotheses = [objectHypotheses currObjectHypothesis]; + currObjHypothesis = getObjectHypothesis(predObjPoseWorld,[0 0 0],surfCentroidWorld,surfRangeWorld,predObjPoseWorld,predObjConfScore,scenePath,objName,instanceIdx); + objHypotheses = [objHypotheses,currObjHypothesis]; end return; end @@ -85,10 +86,10 @@ % If object not found, return dummy pose if size(objSegmPts,2) < 200 - fprintf('%s: 0.000000\n',objName); + fprintf(' [Pose Estimation] %s: 0.000000\n',objName); for instanceIdx = 1:objNum - currObjectHypothesis = getEmptyObjectHypothesis(scenePath,objName,instanceIdx); - objectHypotheses = [objectHypotheses,currObjectHypothesis]; + currObjHypothesis = getEmptyObjectHypothesis(scenePath,objName,instanceIdx); + objHypotheses = [objHypotheses,currObjHypothesis]; end return; end @@ -115,113 +116,112 @@ objModelPts = objModelCloud.Location'; % Remove outliers from the segmented point cloud using PCA - currObjSegPts = instancePts{instanceIdx}; + currObjSegmPts = instancePts{instanceIdx}; if ~strcmp(objName,'barkely_hide_bones') if saveResultImageVis - pcwrite(pointCloud(currObjSegPts'),fullfile(visPath,sprintf('vis.objObs.%s.%d',objName,instanceIdx)),'PLYFormat','binary'); + pcwrite(pointCloud(currObjSegmPts'),fullfile(visPath,sprintf('vis.objObs.%s.%d',objName,instanceIdx)),'PLYFormat','binary'); end try - currObjSegPts = denoisePointCloud(currObjSegPts); + currObjSegmPts = denoisePointCloud(currObjSegmPts); end if saveResultImageVis - pcwrite(pointCloud(currObjSegPts'),fullfile(visPath,sprintf('vis.objCut.%s.%d',objName,instanceIdx)),'PLYFormat','binary'); + pcwrite(pointCloud(currObjSegmPts'),fullfile(visPath,sprintf('vis.objCut.%s.%d',objName,instanceIdx)),'PLYFormat','binary'); end end - fullCurrObjSegPts = currObjSegPts; + fullCurrObjSegmPts = currObjSegmPts; % Print segmentation confidence - finalScore = mean(instanceConf{instanceIdx}); + predObjConfScore = mean(instanceConf{instanceIdx}); if strcmp(objName,'dasani_water_bottle') && binID == -1 - finalScore = finalScore/4; + predObjConfScore = predObjConfScore/4; end - if size(currObjSegPts,2) < 100 % If object not found - fprintf('%s: 0.000000\n',objName); - currObjectHypothesis = getEmptyObjectHypothesis(dataPath,objName,instanceIdx); - objectHypotheses = [objectHypotheses currObjectHypothesis]; + if size(currObjSegmPts,2) < 100 % If object not found + fprintf(' [Pose Estimation] %s: 0.000000\n',objName); + currObjHypothesis = getEmptyObjectHypothesis(dataPath,objName,instanceIdx); + objHypotheses = [objHypotheses currObjHypothesis]; continue; else - fprintf('%s: %f\n',objName,finalScore); + fprintf(' [Pose Estimation] %s: %f\n',objName,predObjConfScore); end % Downsample segmented point cloud to same resolution as object model - currObjSegCloud = pointCloud(currObjSegPts'); + currObjSegCloud = pointCloud(currObjSegmPts'); currObjSegCloud = pcdownsample(currObjSegCloud,'gridAverage',gridStep); currObjSegCloud = pcdenoise(currObjSegCloud,'NumNeighbors',4); if size(currObjSegCloud.Location',2) < 200 % If object not found - currObjectHypothesis = getEmptyObjectHypothesis(dataPath,objName,instanceIdx); - objectHypotheses = [objectHypotheses currObjectHypothesis]; + currObjHypothesis = getEmptyObjectHypothesis(dataPath,objName,instanceIdx); + objHypotheses = [objHypotheses currObjHypothesis]; continue; end % Compute surface mean - surfaceCentroid = mean(currObjSegPts,2); - currObjSegPtsRangeX = currObjSegPts(:,find(((currObjSegPts(2,:)>(surfaceCentroid(2)-0.005)) & (currObjSegPts(2,:)<(surfaceCentroid(2)+0.005)) & ... - (currObjSegPts(3,:)>(surfaceCentroid(3)-0.005)) & (currObjSegPts(3,:)<(surfaceCentroid(3)+0.005))))); - currObjSegPtsRangeY = currObjSegPts(:,find(((currObjSegPts(1,:)>(surfaceCentroid(1)-0.005)) & (currObjSegPts(1,:)<(surfaceCentroid(1)+0.005)) & ... - (currObjSegPts(3,:)>(surfaceCentroid(3)-0.005)) & (currObjSegPts(3,:)<(surfaceCentroid(3)+0.005))))); - currObjSegPtsRangeZ = currObjSegPts(:,find(((currObjSegPts(2,:)>(surfaceCentroid(2)-0.005)) & (currObjSegPts(2,:)<(surfaceCentroid(2)+0.005)) & ... - (currObjSegPts(1,:)>(surfaceCentroid(1)-0.005)) & (currObjSegPts(1,:)<(surfaceCentroid(1)+0.005))))); + surfCentroid = mean(currObjSegmPts,2); + currObjSegPtsRangeX = currObjSegmPts(:,find(((currObjSegmPts(2,:)>(surfCentroid(2)-0.005)) & (currObjSegmPts(2,:)<(surfCentroid(2)+0.005)) & ... + (currObjSegmPts(3,:)>(surfCentroid(3)-0.005)) & (currObjSegmPts(3,:)<(surfCentroid(3)+0.005))))); + currObjSegPtsRangeY = currObjSegmPts(:,find(((currObjSegmPts(1,:)>(surfCentroid(1)-0.005)) & (currObjSegmPts(1,:)<(surfCentroid(1)+0.005)) & ... + (currObjSegmPts(3,:)>(surfCentroid(3)-0.005)) & (currObjSegmPts(3,:)<(surfCentroid(3)+0.005))))); + currObjSegPtsRangeZ = currObjSegmPts(:,find(((currObjSegmPts(2,:)>(surfCentroid(2)-0.005)) & (currObjSegmPts(2,:)<(surfCentroid(2)+0.005)) & ... + (currObjSegmPts(1,:)>(surfCentroid(1)-0.005)) & (currObjSegmPts(1,:)<(surfCentroid(1)+0.005))))); if isempty(currObjSegPtsRangeX) - currObjSegPtsRangeX = currObjSegPts; + currObjSegPtsRangeX = currObjSegmPts; end if isempty(currObjSegPtsRangeY) - currObjSegPtsRangeY = currObjSegPts; + currObjSegPtsRangeY = currObjSegmPts; end if isempty(currObjSegPtsRangeZ) - currObjSegPtsRangeZ = currObjSegPts; + currObjSegPtsRangeZ = currObjSegmPts; end - surfaceRangeX = [min(currObjSegPtsRangeX(1,:)),max(currObjSegPtsRangeX(1,:))]; - surfaceRangeY = [min(currObjSegPtsRangeY(2,:)),max(currObjSegPtsRangeY(2,:))]; - surfaceRangeZ = [min(currObjSegPtsRangeZ(3,:)),max(currObjSegPtsRangeZ(3,:))]; - surfaceRange = [surfaceRangeX;surfaceRangeY;surfaceRangeZ]; + surfRangeX = [min(currObjSegPtsRangeX(1,:)),max(currObjSegPtsRangeX(1,:))]; + surfRangeY = [min(currObjSegPtsRangeY(2,:)),max(currObjSegPtsRangeY(2,:))]; + surfRangeZ = [min(currObjSegPtsRangeZ(3,:)),max(currObjSegPtsRangeZ(3,:))]; + surfRangeWorld = [surfRangeX;surfRangeY;surfRangeZ]; % Visualize surface centroid and surface ranges if savePointCloudVis - surfaceAxisPtsX = [surfaceRangeX(1):0.001:surfaceRangeX(2)]; - surfaceAxisPtsX = [surfaceAxisPtsX;repmat(surfaceCentroid(2),1,size(surfaceAxisPtsX,2));repmat(surfaceCentroid(3),1,size(surfaceAxisPtsX,2))]; - surfaceAxisPtsY = [surfaceRangeY(1):0.001:surfaceRangeY(2)]; - surfaceAxisPtsY = [repmat(surfaceCentroid(1),1,size(surfaceAxisPtsY,2));surfaceAxisPtsY;repmat(surfaceCentroid(3),1,size(surfaceAxisPtsY,2))]; - surfaceAxisPtsZ = [surfaceRangeZ(1):0.001:surfaceRangeZ(2)]; - surfaceAxisPtsZ = [repmat(surfaceCentroid(1),1,size(surfaceAxisPtsZ,2));repmat(surfaceCentroid(2),1,size(surfaceAxisPtsZ,2));surfaceAxisPtsZ]; + surfaceAxisPtsX = [surfRangeX(1):0.001:surfRangeX(2)]; + surfaceAxisPtsX = [surfaceAxisPtsX;repmat(surfCentroid(2),1,size(surfaceAxisPtsX,2));repmat(surfCentroid(3),1,size(surfaceAxisPtsX,2))]; + surfaceAxisPtsY = [surfRangeY(1):0.001:surfRangeY(2)]; + surfaceAxisPtsY = [repmat(surfCentroid(1),1,size(surfaceAxisPtsY,2));surfaceAxisPtsY;repmat(surfCentroid(3),1,size(surfaceAxisPtsY,2))]; + surfaceAxisPtsZ = [surfRangeZ(1):0.001:surfRangeZ(2)]; + surfaceAxisPtsZ = [repmat(surfCentroid(1),1,size(surfaceAxisPtsZ,2));repmat(surfCentroid(2),1,size(surfaceAxisPtsZ,2));surfaceAxisPtsZ]; pcwrite(pointCloud([surfaceAxisPtsX,surfaceAxisPtsY,surfaceAxisPtsZ]','Color',[repmat([255,0,0],size(surfaceAxisPtsX,2),1);repmat([0,255,0],size(surfaceAxisPtsY,2),1);repmat([0,0,255],size(surfaceAxisPtsZ,2),1)]),fullfile(visPath,sprintf('vis.objSurf.%s.%d',objName,instanceIdx)),'PLYFormat','binary'); end % Convert surface centroid to world coordinates - surfaceCentroid = sceneData.extBin2World(1:3,1:3) * surfaceCentroid + repmat(sceneData.extBin2World(1:3,4),1,size(surfaceCentroid,2)); - surfaceRange = sceneData.extBin2World(1:3,1:3) * surfaceRange + repmat(sceneData.extBin2World(1:3,4),1,size(surfaceRange,2)); + surfCentroid = sceneData.extBin2World(1:3,1:3) * surfCentroid + repmat(sceneData.extBin2World(1:3,4),1,size(surfCentroid,2)); + surfRangeWorld = sceneData.extBin2World(1:3,1:3) * surfRangeWorld + repmat(sceneData.extBin2World(1:3,4),1,size(surfRangeWorld,2)); - % Recompute PCA Rt over denoised and downsampled segmented point cloud - currObjSegPts = currObjSegCloud.Location'; - [coeffPCA,scorePCA,latentPCA] = pca(currObjSegPts'); + % Recompute PCA pose over denoised and downsampled segmented point cloud + currObjSegmPts = currObjSegCloud.Location'; + [coeffPCA,scorePCA,latentPCA] = pca(currObjSegmPts'); if size(latentPCA,1) < 3 latentPCA = [latentPCA;0]; end coeffPCA = [coeffPCA(:,1),coeffPCA(:,2),cross(coeffPCA(:,1),coeffPCA(:,2))]; % Follow righthand rule - initRtPCA = [[coeffPCA median(currObjSegPts,2)]; 0 0 0 1]; -% testPCA = [[coeffPCAoutlier median(currObjSegPts,2)]; 0 0 0 1]; + surfPCAPoseBin = [[coeffPCA median(currObjSegmPts,2)]; 0 0 0 1]; if savePointCloudVis axisPts = [[[0:0.001:latentPCA(1)*50]; zeros(2,size([0:0.001:latentPCA(1)*50],2))],[zeros(1,size([0:0.001:latentPCA(2)*50],2)); [0:0.001:latentPCA(2)*50]; zeros(1,size([0:0.001:latentPCA(2)*50],2))],[zeros(2,size([0:0.001:latentPCA(3)*50],2)); [0:0.001:latentPCA(3)*50]]]; axisColors = uint8([repmat([255; 0; 0],1,size([0:0.001:latentPCA(1)*50],2)),repmat([0; 255; 0],1,size([0:0.001:latentPCA(2)*50],2)),repmat([0; 0; 255],1,size([0:0.001:latentPCA(3)*50],2))]); - tmpAxisPts = initRtPCA(1:3,1:3) * axisPts + repmat(initRtPCA(1:3,4),1,size(axisPts,2)); + tmpAxisPts = surfPCAPoseBin(1:3,1:3) * axisPts + repmat(surfPCAPoseBin(1:3,4),1,size(axisPts,2)); pcwrite(pointCloud(tmpAxisPts','Color',axisColors'),fullfile(visPath,sprintf('vis.objPost.%s.%d',objName,instanceIdx)),'PLYFormat','binary'); end - RtPCA = sceneData.extBin2World * initRtPCA; + surfPCAPoseWorld = sceneData.extBin2World * surfPCAPoseBin; % If object is deformable, return PCA as pose if strcmp(objName,'cherokee_easy_tee_shirt') || strcmp(objName,'kyjen_squeakin_eggs_plush_puppies') || strcmp(objName,'womens_knit_gloves') || strcmp(objName,'cloud_b_plush_bear') - finalRt = RtPCA; + predObjPoseWorld = surfPCAPoseWorld; if saveResultImageVis - visualizeResults(RtPCA,latentPCA,surfaceCentroid,surfaceRange,finalRt,finalScore,dataPath,objName,instanceIdx,binID,sceneData,fullCurrObjSegPts,objMasks,objModel.Location'); + visualizeResults(surfPCAPoseWorld,latentPCA,surfCentroid,surfRangeWorld,predObjPoseWorld,predObjConfScore,dataPath,objName,instanceIdx,sceneData,fullCurrObjSegmPts,objMasks,objModel.Location'); end - currObjectHypothesis = getObjectHypothesis(RtPCA,latentPCA,surfaceCentroid,surfaceRange,finalRt,finalScore,dataPath,objName,instanceIdx); - objectHypotheses = [objectHypotheses currObjectHypothesis]; + currObjHypothesis = getObjectHypothesis(surfPCAPoseWorld,latentPCA,surfCentroid,surfRangeWorld,predObjPoseWorld,predObjConfScore,dataPath,objName,instanceIdx); + objHypotheses = [objHypotheses,currObjHypothesis]; continue; end % Push objects back prior to ICP pushBackVal = max([objModel.XLimits(2),objModel.YLimits(2),objModel.ZLimits(2)]); - initRtPCA(1:3,4) = initRtPCA(1:3,4) + pushBackAxis.*pushBackVal; + surfPCAPoseBin(1:3,4) = surfPCAPoseBin(1:3,4) + pushBackAxis.*pushBackVal; % Attempt 6 different rotations using PCA directions % possibleRotations = cell(1,6); @@ -232,7 +232,7 @@ % possibleRotations{5} = vrrotvec2mat([0 1 0 pi/2]); % possibleRotations{6} = vrrotvec2mat([0,1,0,3*pi/2]); bestRotScore = inf; - bestRt = initRtPCA; + bestRt = surfPCAPoseBin; % for rotIdx = 1:length(possibleRotations) % tmpRt = initRtPCA * [possibleRotations{rotIdx} [0;0;0]; [0,0,0,1]]; % tmpObjModelPts = tmpRt(1:3,1:3) * objModelPts + repmat(tmpRt(1:3,4),1,size(objModelPts,2)); @@ -250,19 +250,19 @@ % end % initRtPCA = bestRt; if strcmp(sceneData.env,'shelf') && (strcmp(objName,'creativity_chenille_stems') || strcmp(objName,'dr_browns_bottle_brush') || strcmp(objName,'peva_shower_curtain_liner') || strcmp(objName,'kleenex_paper_towels')) - initRtPCA(1:3,1:3) = eye(3); + surfPCAPoseBin(1:3,1:3) = eye(3); end % Apply rigid transform computed prior to ICP - tmpObjModelPts = initRtPCA(1:3,1:3) * objModelPts + repmat(initRtPCA(1:3,4),1,size(objModelPts,2)); + tmpObjModelPts = surfPCAPoseBin(1:3,1:3) * objModelPts + repmat(surfPCAPoseBin(1:3,4),1,size(objModelPts,2)); if savePointCloudVis pcwrite(pointCloud(tmpObjModelPts','Color',repmat(uint8([0 0 255]),size(tmpObjModelPts,2),1)),fullfile(visPath,sprintf('vis.objPre.%s.%d',objName,instanceIdx)),'PLYFormat','binary'); end % Perform ICP to align segmented point cloud to object model - if size(currObjSegPts,2) > 3 + if size(currObjSegmPts,2) > 3 tmpObjModelCloud = pointCloud(tmpObjModelPts'); - objSegCloud = pointCloud(currObjSegPts'); + objSegCloud = pointCloud(currObjSegmPts'); [tform,movingReg,icpRmse] = pcregrigidGPU(objSegCloud,tmpObjModelCloud,'InlierRatio',icpWorstRejRatio,'MaxIterations',200,'Tolerance',[0.0001 0.0009],'Verbose',false,'Extrapolate',true); icpRt1 = inv(tform.T'); tmpObjModelPts = tmpObjModelCloud.Location'; @@ -274,16 +274,11 @@ else icpRt = eye(4); end - finalRt = icpRt * initRtPCA; - - tmpObjModelPts = finalRt(1:3,1:3) * objModelPts + repmat(finalRt(1:3,4),1,size(objModelPts,2)); + predObjPoseBin = icpRt * surfPCAPoseBin; if savePointCloudVis + tmpObjModelPts = predObjPoseBin(1:3,1:3) * objModelPts + repmat(predObjPoseBin(1:3,4),1,size(objModelPts,2)); pcwrite(pointCloud(tmpObjModelPts','Color',repmat(uint8([0 255 0]),size(tmpObjModelPts,2),1)),fullfile(visPath,sprintf('vis.objPost.%s.%d',objName,instanceIdx)),'PLYFormat','binary'); end - -% plot3(bgCloud.Location(:,1),bgCloud.Location(:,2),bgCloud.Location(:,3),'.r'); -% hold on; axis equal; grid on; xlabel('x'); ylabel('y'); zlabel('z'); hold off; -% pcshow(bgCloud) % Save pose as a rigid transform from model to world space @@ -298,12 +293,12 @@ % continue; % end - finalRt = sceneData.extBin2World * finalRt; + predObjPoseWorld = sceneData.extBin2World * predObjPoseBin; if saveResultImageVis - visualizeResults(RtPCA,latentPCA,surfaceCentroid,surfaceRange,finalRt,finalScore,scenePath,objName,instanceIdx,sceneData,fullCurrObjSegPts,objMasks,objModel.Location'); + visualizeResults(surfPCAPoseWorld,latentPCA,surfCentroid,surfRangeWorld,predObjPoseWorld,predObjConfScore,scenePath,objName,instanceIdx,sceneData,fullCurrObjSegmPts,objMasks,objModel.Location'); end - currObjectHypothesis = getObjectHypothesis(RtPCA,latentPCA,surfaceCentroid,surfaceRange,finalRt,finalScore,scenePath,objName,instanceIdx); - objectHypotheses = [objectHypotheses,currObjectHypothesis]; + currObjHypothesis = getObjectHypothesis(surfPCAPoseWorld,latentPCA,surfCentroid,surfRangeWorld,predObjPoseWorld,predObjConfScore,scenePath,objName,instanceIdx); + objHypotheses = [objHypotheses,currObjHypothesis]; end end diff --git a/ros-packages/catkin_ws/src/pose_estimation/src/getObjectPoseNoDepth.m b/ros-packages/catkin_ws/src/pose_estimation/src/getObjectPoseNoDepth.m index 071d74d..b4a00ea 100644 --- a/ros-packages/catkin_ws/src/pose_estimation/src/getObjectPoseNoDepth.m +++ b/ros-packages/catkin_ws/src/pose_estimation/src/getObjectPoseNoDepth.m @@ -1,7 +1,9 @@ -function [objRt,surfaceCentroid,surfaceRange] = getObjectPoseNoDepth(visPath,objSegPts,objName,binID,frames,data,objMasks,savePointClouds) +function [predObjPoseBin,surfCentroid,surfRange] = getObjectPoseNoDepth(visPath,objSegmPts,objName,frames,sceneData,objMasks) + +global savePointCloudVis; % Discretize bin into 3D grid -if binID ~= -1 +if strcmp(sceneData.env,'shelf') [binGridX,binGridY,binGridZ] = ndgrid(-0.05:0.01:0.40,-0.14:0.01:0.17,-0.04:0.01:0.20); gridOccupancyThreshold = floor(0.5*length(frames)); else @@ -14,12 +16,12 @@ binGridOccupancy = zeros(size(binGridX(:))); for frameIdx = frames currObjMask = objMasks{frameIdx}; - binGridPtsWorld = data.extBin2World(1:3,1:3) * binGridPts + repmat(data.extBin2World(1:3,4),1,size(binGridPts,2)); - currExtWorld2Cam = inv(data.extCam2World{frameIdx}); + binGridPtsWorld = sceneData.extBin2World(1:3,1:3) * binGridPts + repmat(sceneData.extBin2World(1:3,4),1,size(binGridPts,2)); + currExtWorld2Cam = inv(sceneData.extCam2World{frameIdx}); binGridPtsCam = currExtWorld2Cam(1:3,1:3) * binGridPtsWorld + repmat(currExtWorld2Cam(1:3,4),1,size(binGridPtsWorld,2)); binGridPtsPix = binGridPtsCam; - binGridPtsPix(1,:) = round((binGridPtsPix(1,:).*data.K(1,1))./binGridPtsPix(3,:)+data.K(1,3)); - binGridPtsPix(2,:) = round((binGridPtsPix(2,:).*data.K(2,2))./binGridPtsPix(3,:)+data.K(2,3)); + binGridPtsPix(1,:) = round((binGridPtsPix(1,:).*sceneData.colorK(1,1))./binGridPtsPix(3,:)+sceneData.colorK(1,3)); + binGridPtsPix(2,:) = round((binGridPtsPix(2,:).*sceneData.colorK(2,2))./binGridPtsPix(3,:)+sceneData.colorK(2,3)); pixWithinImage = find((binGridPtsPix(1,:) <= 640) & (binGridPtsPix(1,:) > 0) & (binGridPtsPix(2,:) <= 480) & (binGridPtsPix(2,:) > 0)); % binGridPtsPix = binGridPtsPix(:,pixWithinImage); % binGridPts = binGridPts(:,pixWithinImage); @@ -37,7 +39,7 @@ -objRt = eye(4); +predObjPoseBin = eye(4); bestGuessPtsInd = find(binGridOccupancy > gridOccupancyThreshold); if length(bestGuessPtsInd) < 10 @@ -47,29 +49,29 @@ instanceIdx = 1; objPts = sortrows(objPts',1); objCentroid = median(objPts); -objRt(1:3,4) = objCentroid'; +predObjPoseBin(1:3,4) = objCentroid'; -if binID ~= -1 +if strcmp(sceneData.env,'shelf') if strcmp(objName,'dasani_water_bottle') if objCentroid(1) < 0.20 % If on incline if objCentroid(3) + 0.03 > 0.06 - objRt(1:3,1:3) = [0 0 1; -1 0 0; 0 -1 0]'; + predObjPoseBin(1:3,1:3) = [0 0 1; -1 0 0; 0 -1 0]'; fprintf('Standing up on front\n'); else - objRt(1:3,1:3) = [-1 0 0; 0 0 1; 0 1 0]'; + predObjPoseBin(1:3,1:3) = [-1 0 0; 0 0 1; 0 1 0]'; fprintf('Lying pointing front on front\n'); end else if objCentroid(3) + 0.04 > 0.06 - objRt(1:3,1:3) = [0 0 1; -1 0 0; 0 -1 0]'; + predObjPoseBin(1:3,1:3) = [0 0 1; -1 0 0; 0 -1 0]'; fprintf('Standing up on back\n'); else ptsAlongCentroidX = find(objPts(:,1) == max(objPts(:,1))); if max(objPts(ptsAlongCentroidX,2))-min(objPts(ptsAlongCentroidX,2)) > 0.10 - objRt(1:3,1:3) = [0 -1 0; 0 0 1; -1 0 0]'; + predObjPoseBin(1:3,1:3) = [0 -1 0; 0 0 1; -1 0 0]'; fprintf('Lying on back sideways\n'); else - objRt(1:3,1:3) = [-1 0 0; 0 0 1; 0 1 0]'; + predObjPoseBin(1:3,1:3) = [-1 0 0; 0 0 1; 0 1 0]'; fprintf('Lying on back pointing forward\n'); end end @@ -80,36 +82,36 @@ else if strcmp(objName,'rolodex_jumbo_pencil_cup') - objCamPts = objSegPts; + objCamPts = objSegmPts; objCamPts = objCamPts(:,find((abs(objCamPts(1,:) - objCentroid(1)) < 0.04) & (abs(objCamPts(2,:) - objCentroid(2)) < 0.04))); numPtsAroundCentroid = size(objCamPts,2); objCamPts = objCamPts(:,find(objCamPts(3,:) > 0.08 & objCamPts(3,:) < 0.15)); ptsElevatedRatio = size(objCamPts,2)/numPtsAroundCentroid; if ptsElevatedRatio > 0.5 topPts = mean(objCamPts,2); - objRt(1:3,1:3) = [0 0 -1; -1 0 0; 0 -1 0]'; + predObjPoseBin(1:3,1:3) = [0 0 -1; -1 0 0; 0 -1 0]'; end end end -surfaceCentroid = objRt(1:3,4); -if strcmp(objName,'rolodex_jumbo_pencil_cup') && binID == -1 && exist('topPts','var') - surfaceRange = [surfaceCentroid(1)-0.05,surfaceCentroid(1)+0.05;surfaceCentroid(2)-0.05,surfaceCentroid(2)+0.05;surfaceCentroid(3)-0.05,topPts(3)]; +surfCentroid = predObjPoseBin(1:3,4); +if strcmp(objName,'rolodex_jumbo_pencil_cup') && strcmp(sceneData.env,'tote') && exist('topPts','var') + surfRange = [surfCentroid(1)-0.05,surfCentroid(1)+0.05;surfCentroid(2)-0.05,surfCentroid(2)+0.05;surfCentroid(3)-0.05,topPts(3)]; else - surfaceRange = [surfaceCentroid(1)-0.05,surfaceCentroid(1)+0.05;surfaceCentroid(2)-0.05,surfaceCentroid(2)+0.05;surfaceCentroid(3)-0.05,surfaceCentroid(3)+0.05]; + surfRange = [surfCentroid(1)-0.05,surfCentroid(1)+0.05;surfCentroid(2)-0.05,surfCentroid(2)+0.05;surfCentroid(3)-0.05,surfCentroid(3)+0.05]; end % Enforce dog bowl (which has no depth) to always point upward if strcmp(objName,'platinum_pets_dog_bowl') - objRt(1:3,1:3) = [-1 0 0; 0 1 0; 0 0 -1]; + predObjPoseBin(1:3,1:3) = [-1 0 0; 0 1 0; 0 0 -1]; end -if savePointClouds +if savePointCloudVis pcwrite(pointCloud(objPts,'Color',repmat(uint8([0 255 0]),size(objPts,1),1)),fullfile(visPath,sprintf('vis.objDots.%s.%d',objName,instanceIdx)),'PLYFormat','binary'); axisPts = [[[0:0.001:0.099]; zeros(2,100)],[zeros(1,100); [0:0.001:0.099]; zeros(1,100)],[zeros(2,100); [0:0.001:0.099]]]; axisColors = uint8([repmat([255; 0; 0],1,100),repmat([0; 255; 0],1,100),repmat([0; 0; 255],1,100)]); - tmpAxisPts = objRt(1:3,1:3) * axisPts + repmat(objRt(1:3,4),1,size(axisPts,2)); + tmpAxisPts = predObjPoseBin(1:3,1:3) * axisPts + repmat(predObjPoseBin(1:3,4),1,size(axisPts,2)); % pcwrite(pointCloud(tmpAxisPts','Color',axisColors'),fullfile(visPath,sprintf('vis.objPost.%s.%d',objName,instanceIdx)),'PLYFormat','binary'); end diff --git a/ros-packages/catkin_ws/src/pose_estimation/src/models/objects/centerObject.m b/ros-packages/catkin_ws/src/pose_estimation/src/models/objects/centerObject.m index cc94933..477c6be 100644 --- a/ros-packages/catkin_ws/src/pose_estimation/src/models/objects/centerObject.m +++ b/ros-packages/catkin_ws/src/pose_estimation/src/models/objects/centerObject.m @@ -2,7 +2,7 @@ % Load object % objCloud = offLoader('data/models/book_joke.off'); -objName = 'woods_extension_cord'; +objName = 'dasani_water_bottle'; objCloud = pcread(sprintf('%s.ply',objName)); % % Downsample object diff --git a/ros-packages/catkin_ws/src/pose_estimation/src/models/objects/dasani_water_bottle.ply b/ros-packages/catkin_ws/src/pose_estimation/src/models/objects/dasani_water_bottle.ply index 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