Bundle of scripts to create and use a SFM/photogrammetry workflow
Build requirements:
apt install cmake libceres-dev libboost-all-dev libfreeimage-dev libglew-dev qtbase5-dev libopencv-dev libcgal-dev
Runtime requirements:
apt install libgoogle-glog0v5 libfreeimage3 libceres1 libqt5opengl5 libqt5widgets5 libglew2.0 libboost-iostreams1.62 libboost-program-options1.62 libboost-system1.62 libboost-serialization1.62 libboost-regex1.62 libopencv-calib3d2.4v5 libopencv-contrib2.4v5 libopencv-core2.4v5 libopencv-features2d2.4v5 libopencv-flann2.4v5 libopencv-gpu2.4v5 libopencv-highgui2.4-deb0 libopencv-imgproc2.4v5 libopencv-legacy2.4v5 libopencv-ml2.4v5 libopencv-objdetect2.4v5 libopencv-ocl2.4v5 libopencv-photo2.4v5 libopencv-stitching2.4v5 libopencv-superres2.4v5 libopencv-ts2.4v5 libopencv-video2.4v5 libopencv-videostab2.4v5 libcgal12 libgmpxx4ldbl
There are two different modes, one is designed for a turntable (background should be removed), while the other is for "normal" photogrammetry.
First, we need to create a project:
python3 project-make.py --input path/*.jpg --project myproject
Photogrammetry:
cd myproject
make -f ../Makefile.ordered openmvs/model_dense_mesh_refine_texture.mvs
Or on a SLURM cluster:
sbatch <<options>> sfm-ordered.job myproject
First, we need to create a project:
python3 project-make.py --input path/*.jpg --project myproject --consecutivesigma 0
Photogrammetry:
cd myproject
make -f ../Makefile.unordered openmvs/model_dense_mesh_refine_texture.mvs
Or on a SLURM cluster:
sbatch <<options>> sfm-unordered.job myproject
The files are created in the project directory, however it may be practical to extract the 3D model:
python3 project-getoutput.py --project myproject --output basename
where basename is the output filename without extension (a .ply and a .png file will be created).
This project will not have been possible without two wonderful piece of software:
- COLMAP (https://colmap.github.io/)
- OpenMVS (http://cdcseacave.github.io/openMVS/)