StereoVision is a package for working with stereo cameras, especially with the intent of using them to produce 3D point clouds. The focus is on performance, ease of usability, and the ability to construct 3D imaging setups cheaply.
StereoVision relies heavily on OpenCV. If you're not sure about what a given variable does or what values would make sense for it and no explanation is provided in the StereoVision documentation, refer to OpenCV's documentation in order to better understand how they work.
It's available on PyPI, so you can install it like this:
pip install StereoVision
Tutorials are available on the Stackable blog:
If you find a bug or would like to request a feature, please report it with the issue tracker. If you'd like to contribute to StereoVision, feel free to fork it on GitHub.
StereoVision is released under the GNU General Public License, so feel free to use it any way you like. It would be nice to let me know if you do anything cool with it though.
Author: Daniel Lee
# Steps for calibrating camera 1. copy left/right images to a folder and then run the rename_left_right_images.py script to get the data ready. 2. run the calibrate camera script python calibrate_cameras --rows 9 --columns 6 --square-size 2.5 --show-chessboards /media/dcofer/Backup/weed_analysis/stereo_images/RGBIR_2016_10_25/calibration_images/ /media/dcofer/Backup/weed_analysis/stereo_images/RGBIR_2016_10_25/calibration/ 3. Run the block tuner to try and get good params. python block_tuner.py --bm_settings bm_settings_rgbir.json /media/dcofer/Backup/weed_analysis/stereo_images/RGBIR_2016_10_25/calibration/ /media/dcofer/Backup/weed_analysis/stereo_images/RGBIR_2016_10_25/test/