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StaticFusion-imagesequenceassoc.cpp
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StaticFusion-imagesequenceassoc.cpp
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/***************************************************************************************
**StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments**
** ---------------------------------------- **
** **
** Copyright(c) 2018, Raluca Scona, Edinburgh Centre for Robotics **
** Copyright(c) 2015, Mariano Jaimez, University of Malaga & TU Munich **
** **
** This program is free software: you can redistribute it and/or modify **
** it under the terms of the GNU General Public License (version 3) as **
** published by the Free Software Foundation. **
** **
** This program is distributed in the hope that it will be useful, but **
** WITHOUT ANY WARRANTY; without even the implied warranty of **
** MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the **
** GNU General Public License for more details. **
** **
** You should have received a copy of the GNU General Public License **
** along with this program. If not, see <http://www.gnu.org/licenses/> **
** **
***************************************************************************************/
/*********************************************************************************
**Fast Odometry and Scene Flow from RGB-D Cameras based on Geometric Clustering **
**------------------------------------------------------------------------------**
** **
** Copyright(c) 2017, Mariano Jaimez Tarifa, University of Malaga & TU Munich **
** Copyright(c) 2017, Christian Kerl, TU Munich **
** Copyright(c) 2017, MAPIR group, University of Malaga **
** Copyright(c) 2017, Computer Vision group, TU Munich **
** **
** This program is free software: you can redistribute it and/or modify **
** it under the terms of the GNU General Public License (version 3) as **
** published by the Free Software Foundation. **
** **
** This program is distributed in the hope that it will be useful, but **
** WITHOUT ANY WARRANTY; without even the implied warranty of **
** MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the **
** GNU General Public License for more details. **
** **
** You should have received a copy of the GNU General Public License **
** along with this program. If not, see <http://www.gnu.org/licenses/>. **
** **
*********************************************************************************/
#include <string.h>
#include <StaticFusion.h>
#include <iostream>
#include <fstream>
int main(int argc, char* argv[])
{
if(argc<2) {
throw std::runtime_error("missing log file");
}
const std::string dir = argv[1];
const unsigned int res_factor = 2;
StaticFusion staticFusion(res_factor);
//Flags
staticFusion.use_motion_filter = true;
//Solver
staticFusion.ctf_levels = log2(staticFusion.cols/40) + 2;
staticFusion.max_iter_per_level = 3;
staticFusion.previous_speed_const_weight = 0.1f;
staticFusion.previous_speed_eig_weight = 2.f; //0.5f;
staticFusion.k_photometric_res = 0.15f;
staticFusion.irls_delta_threshold = 0.0015f;
staticFusion.max_iter_irls = 6;
staticFusion.lambda_reg = 0.35f; //0.4
staticFusion.lambda_prior = 0.5f; //0.5
staticFusion.kc_Cauchy = 0.5f; //0.5
staticFusion.kb = 1.5f; //1.5
staticFusion.kz = 1.5f;
bool denseModel = false;
bool modelInitialised = false;
//Set first image to load, decimation factor and the sequence dir
int im_count = 1;
const unsigned int decimation = 1;
std::vector<double> timestamps;
std::vector<std::string> filesDepth;
std::vector<std::string> filesColor;
const std::string assocFile = "/rgbd_assoc.txt";
staticFusion.loadAssoc(dir, assocFile, timestamps, filesDepth, filesColor);
if (filesDepth.empty() || filesColor.empty()) {
throw std::runtime_error("no image files");
}
cv::Mat weightedImage;
bool stop = false;
{
stop = im_count >= filesDepth.size();
staticFusion.loadImageFromSequenceAssoc(filesDepth[im_count], filesColor[im_count], res_factor);
staticFusion.depthPrediction.swap(staticFusion.depthCurrent);
staticFusion.intensityPrediction.swap(staticFusion.intensityCurrent);
staticFusion.depthBuffer[im_count % staticFusion.bufferLength] = staticFusion.depthPrediction.replicate(1,1);
staticFusion.intensityBuffer[im_count % staticFusion.bufferLength] = staticFusion.intensityPrediction.replicate(1,1);
staticFusion.odomBuffer[im_count % staticFusion.bufferLength] = Eigen::Matrix4f::Identity();
im_count += decimation;
stop = im_count >= filesDepth.size();
staticFusion.loadImageFromSequenceAssoc(filesDepth[im_count], filesColor[im_count], res_factor);
staticFusion.createImagePyramid(true); //pyramid for the old model
staticFusion.kb = 1.05f; //0.8
staticFusion.runSolver(true);
staticFusion.buildSegmImage();
staticFusion.depthBuffer[im_count % staticFusion.bufferLength] = staticFusion.depthCurrent.replicate(1,1);
staticFusion.intensityBuffer[im_count % staticFusion.bufferLength] = staticFusion.intensityCurrent.replicate(1,1);
staticFusion.odomBuffer[im_count % staticFusion.bufferLength] = staticFusion.T_odometry;
cv::eigen2cv(staticFusion.b_segm_perpixel, weightedImage);
staticFusion.reconstruction->fuseFrame((unsigned char *) staticFusion.color_full.data, (unsigned short *) staticFusion.depth_mm.data, (float *) weightedImage.data, im_count, &(staticFusion.T_odometry), 0, 1);
staticFusion.reconstruction->uploadWeightAndClustersForVisualization((float *) weightedImage.data, staticFusion.clusterAllocation[0], (unsigned short *) staticFusion.depth_mm.data);
}
while (!pangolin::ShouldQuit())
{
if (!( (im_count+decimation) >= filesDepth.size()) && !pangolin::ShouldQuit() && !staticFusion.gui->pause->Get() )
{
im_count += decimation;
staticFusion.loadImageFromSequenceAssoc(filesDepth[im_count], filesColor[im_count], res_factor);
denseModel = staticFusion.reconstruction->checkIfDenseEnough();
if (!denseModel && !modelInitialised) {
staticFusion.kb = 1.05f;
modelInitialised = true;
} else {
staticFusion.kb = 1.5f;
modelInitialised = true;
}
staticFusion.reconstruction->getPredictedImages(staticFusion.depthPrediction, staticFusion.intensityPrediction);
staticFusion.reconstruction->getFilteredDepth(staticFusion.depth_mm, staticFusion.depthCurrent);
staticFusion.createImagePyramid(true);
staticFusion.runSolver(true);
if (im_count - staticFusion.bufferLength >= 0) {
staticFusion.computeResidualsAgainstPreviousImage(im_count);
}
staticFusion.buildSegmImage();
staticFusion.depthBuffer[im_count % staticFusion.bufferLength] = staticFusion.depthCurrent.replicate(1,1);
staticFusion.intensityBuffer[im_count % staticFusion.bufferLength] = staticFusion.intensityCurrent.replicate(1,1);
staticFusion.odomBuffer[im_count % staticFusion.bufferLength] = staticFusion.T_odometry;
cv::eigen2cv(staticFusion.b_segm_perpixel, weightedImage);
staticFusion.reconstruction->fuseFrame((unsigned char *) staticFusion.color_full.data, (unsigned short *) staticFusion.depth_mm.data, (float *) weightedImage.data, im_count, &(staticFusion.T_odometry), 0, 1);
staticFusion.reconstruction->uploadWeightAndClustersForVisualization((float *) weightedImage.data, staticFusion.clusterAllocation[0], (unsigned short *) staticFusion.depth_mm.data);
staticFusion.updateGUI();
}
staticFusion.updateGUI();
}
return 0;
}