SimpleStereo 1.0.3

Stereo vision made simple

SimpleStereo

Stereo Vision Made Simple.

SimpleStereo aims at being 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/

Dependencies

• Python 3 (tested with 3.9.2)
• NumPy
• SciPy
• OpenCV
• matplotlib (for data visualisation purposes)

Installation

Option 1

Install package from PyPI with:

pip3 install simplestereo

Option 2

Download the latest version and unzip. Then, from the folder containing setup.py, run:

pip3 install .

Basic example

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 advanced examples available in the examples folder.

Features and Future Work

General

• StereoRig, RectifiedStereoRig and StructuredLightRig 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

Calibration algorithms

• 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)

Stereo rectification algorithms

• 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)

Passive stereo matching algorithms

• 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.)

Active and Structured light algorithms

• Gray code
• De Brujin
• Adapt structured light algorithms to work with two cameras
• StereoFTP (Lafiosca P. et al., Automated Aircraft Dent Inspection via a Modified Fourier Transform Profilometry Algorithm, Sensors, 2022)

Unwrapping algorithms

• Infinite impulse response (Estrada et al., Noise robust linear dynamic system for phase unwrapping and smoothing, Optics Express, 2011)

Contributions

Reporting issues and proposing integrations of other stereo vision algorithms is highly encouraged and it will be acknowledged. Please share your thoughts!