OpenSfM
Structure from Motion library written in Python on top of OpenCV.
Dependencies
- OpenCV
- Ceres Solver
- Boost Python
- NumPy, SciPy, networkx, PyYAML, exifread
Installing dependencies on MacOSX
Use
brew tap homebrew/science
brew install opencv
brew install homebrew/science/ceres-solver
brew install boost
sudo pip install -r requirements.txt
Be sure to update your PYTHONPATH
to include /usr/local/lib/python2.7/site-packages
where OpenCV has been installed:
export PYTHONPATH=/usr/local/lib/python2.7/site-packages:$PYTHONPATH
Installing dependencies on Ubuntu
-
OpenCV - Install by following the steps in the Ubuntu OpenCV installation guide. An alternative instruction tested for Ubuntu 10.04 can be found at OpenCV Docs. OpenCV requires GCC and CMake among other things.
-
Ceres solver - Build Ceres according the documentation. Make sure to read the Linux note, follow the shared library instructions and compile Ceres with the -fPIC option. Install Ceres from the ceres-bin directory after
make
by:sudo make install
-
Boost Python - Install through apt-get:
sudo apt-get install libboost-python-dev
-
NumPy, networkx, PyYaml, exifread - Install pip and then run the following from the root of the project:
sudo pip install -r requirements.txt
-
SciPy - Install gfortran through apt-get and then install SciPy with:
sudo apt-get install gfortran sudo pip install scipy
Building inplace using setup.py
python setup.py build_clib
python setup.py build_ext --inplace
Building using CMake
mkdir build
cd build
cmake ../opensfm/src
make
Running
An example dataset is available at data/berlin
.
- Put some images in
data/DATASET_NAME/images/
- Put config.yaml in
data/DATASET_NAME/config.yaml
- Go to the root of the project and run
bin/run_all data/DATASET_NAME
- Start an http server from the root with
python -m SimpleHTTPServer
- Browse
http://localhost:8000/viewer/reconstruction.html#file=/data/DATASET_NAME/reconstruction.json
.