Stereo vision made Simple.
SimpleStereo is a high level framework for stereo vision applications. It is written in Python 3, with C++ extensions. Documentation is available at https://decadenza.github.io/SimpleStereo/
- Python 3 (tested with 3.9.2)
- NumPy
- SciPy
- OpenCV
- matplotlib (for data visualisation)
Before starting, be sure to have the latest setuptools
package by running pip install --upgrade setuptools
. Then proceed with one of the two options below.
Install package from PyPI with:
pip3 install simplestereo
Clone or download the latest version and unzip. Then, from the folder containing setup.py
, run:
pip3 install .
I am aware of some issues while installing SimpleStereo. If you have errors during installation, please open an issue.
If during installation you get, together with other messages, the following error:
error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
Please install the Microsoft C++ Build Tools as indicated. These are required to build C++ extensions that are part of SimpleStereo. More information about compiling on Windows is available at https://wiki.python.org/moin/WindowsCompilers.
SimpleStereo helps you with common tasks. You can calibrate two cameras and initialise a stereoRig
with:
import simplestereo as ss
# Path to your images
images = [
("0_left.png", "0_right"),
("1_left.png", "1_right"),
("2_left.png", "2_right"),
...
]
# Calibrate and build StereoRig object
rig = ss.calibration.chessboardStereo( images, chessboardSize=(7,6), squareSize=60.5 )
# Save rig object to file
rig.save("myRig.json")
# Optionally print some info
print("Reprojection error:", rig.reprojectionError)
print("Centers:", rig.getCenters())
print("Baseline:", rig.getBaseline())
More examples available in the examples folder.
StereoRig
,RectifiedStereoRig
andStructuredLightRig
classes to easily manage your stereo rigs and their calibration- Basic stereo capture using OpenCV
cv2.videoCapture
- Export and import point cloud to PLY file
- Chessboard calibration (one and two cameras)
- Camera-projector calibration adapted (Moreno D. et al.), adapted from procam (
ss.calibration.chessboardProCam
) - Camera-projector calibration alternative version (
ss.calibration.chessboardProCamWhite
)
- Fusiello et al., A compact algorithm for rectification of stereo pairs, 2000 (
ss.rectification.fusielloRectify
) - Wrapper of OpenCV algorithm (
ss.rectification.stereoRectify
) - Loop and Zhang, Computing rectifying homographies for stereo vision, 1999 (
ss.rectification.loopRectify
) - Lafiosca and Ceccaroni, Rectifying homographies for stereo vision: analytical solution for minimal distortion, 2022, https://doi.org/10.1007/978-3-031-10464-0_33 (
ss.rectification.directRectify
, see also DirectStereoRectification)
- Adaptive Support Weight algorithm (K. Yoon et al., Adaptive support-weight approach for correspondence search, 2006)
- Geodesic Support Weight algorithm (simplified implementation, credits Asmaa Hosni et al.)
- Gray code
- StereoFTP (Lafiosca P. et al., Automated Aircraft Dent Inspection via a Modified Fourier Transform Profilometry Algorithm, Sensors, 2022)
- Infinite impulse response (Estrada et al., Noise robust linear dynamic system for phase unwrapping and smoothing, Optics Express, 2011)
Documentation follows numpydoc style guide.
Install documentation prerequisites with:
pip install Sphinx numpydoc
Build documentation with:
cd sphinx-documentation-generator
sh BUILD_SCRIPT.sh
cd ..
- Fix distortion coefficient issue (OpenCV related) when using 12 coefficients (currently 0, 4, 5, 8 and 14 are supported).
- Add support for fisheye cameras.
- Adapt structured light algorithms to work with two cameras.
- ArUco camera calibration algorithm.
Reporting issues and proposing integrations of other stereo vision algorithms is highly encouraged and it will be acknowledged. Please share your issues!