freakthemighty/opensfm repository overview

⁠ OpenSfM

⁠Overview

OpenSfM is a Structure from Motion library written in Python on top of OpenCV⁠. The library serves as a processing pipeline for reconstructing camera poses and 3D scenes from multiple images. It consists of basic modules for Structure from Motion (feature detection/matching, minimal solvers) with a focus on building a robust and scalable reconstruction pipeline. It also integrates external sensor (e.g. GPS, accelerometer) measurements for geographical alignment and robustness. A JavaScript viewer is provided to preview the models and debug the pipeline.

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Checkout this blog post with more demos⁠

⁠Dependencies

• OpenCV⁠
• OpenGV⁠
• Ceres Solver⁠
• Boost Python⁠
• NumPy⁠, SciPy⁠, Networkx⁠, PyYAML, exifread

⁠Installing dependencies on MacOSX

Install OpenCV using

brew tap homebrew/science
brew install opencv
brew install homebrew/science/ceres-solver
brew install boost-python
sudo pip install -r requirements.txt

And install OpenGV using

git clone https://github.com/paulinus/opengv.git
cd opengv
mkdir build
cd build
cmake .. -DBUILD_TESTS=OFF -DBUILD_PYTHON=ON
make install

Be sure to update your PYTHONPATH to include /usr/local/lib/python2.7/site-packages where OpenCV and OpenGV have been installed. For example:

export PYTHONPATH=/usr/local/lib/python2.7/site-packages:$PYTHONPATH

⁠Installing dependencies on Ubuntu

See the Dockerfile⁠ for the commands to install all dependencies on Ubuntu 14.04. The steps are

1. Install OpenCV⁠, Boost Python⁠, NumPy⁠, SciPy⁠ using apt-get
2. Install python requirements using pip
3. Clone, build and install OpenGV⁠ following the receipt in the Dockerfile
4. Build and Install⁠ the Ceres solver⁠ from its source using the -fPIC compilation flag

⁠Install note

When running OpenSfM on top of OpenCV⁠ 3.0 the OpenCV Contrib⁠ modules are required for extracting SIFT or SURF features.

⁠Building

python setup.py build

⁠Running

An example dataset is available at data/berlin.

1. Put some images in data/DATASET_NAME/images/
2. Put config.yaml in data/DATASET_NAME/config.yaml
3. Go to the root of the project and run bin/run_all data/DATASET_NAME
4. Start an http server from the root with python -m SimpleHTTPServer
5. Browse http://localhost:8000/viewer/reconstruction.html#file=/data/DATASET_NAME/reconstruction.meshed.json.

Things you can do from there:

• Use datasets with more images
• Click twice on an image to see it. Then use arrows to move between images.
• Run bin/mesh data/berlin to build a reconstruction with sparse mesh that will produce smoother transitions from images

⁠Thanks to sponsors

• Thank you Jetbrains for supporting the project with free licenses for IntelliJ Ultimate⁠. Contact peter at mapillary dot com if you are contributor and need one. Apply your own project here⁠