.. Py2vision documentation master file, created by sphinx-quickstart on Mon Jul 11 02:45:03 2022. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. ======================================= Welcome to Py2vision's documentation! ======================================= Here you can see every detail about py2vision package which is a tool to do object positioning with stereo vision and object detection, this package can be used in Robotics and Computer Vision. Installation ------------ Open a console and write this: :command:`pip install py2vision` .. important:: If you already have the opencv package installed, you have to uninstall it with :command:`pip uninstall opencv-python` and install opencv-contrib-python version 4.6.0.66. What could you archieve with py2vision? ----------------------------------------- #. It will allow you to obtain disparity maps using SGBM algorithm. #. Get distances in the 3D space. #. Measure objects dimensions and more. #. Train your own object detection model and infer with it. #. Combine detection and stereo vision to get homogeneous coordinates of objects im an scene. .. figure:: https://github.com/corvus96/PyTwoVision/blob/master/source/doc_static/disparity_map_example.jpg?raw=true :height: 250 :width: 250 :align: center Example of a disparity map .. figure:: https://github.com/corvus96/PyTwoVision/blob/master/source/doc_static/position_example.jpg?raw=true :height: 300 :width: 300 :align: center Example of positioning of objects .. toctree:: :maxdepth: 2 :caption: Modules :glob: module* Dependencies --------------- * numpy == 1.21.5 * tensorflow == 2.8.0 * opencv-contrib-python==4.6.0.66 * wget == 3.2 * pandas * pyyaml * h5py References ========== .. [1] C. Wheatstone, «Contributions to the Physiology of Vision.», Proceedings of the Royal Society of London, vol. 4, 0 1837. .. [2] M. M. Martín, Técnicas de visión estereoscópica para determinar la estruc- tura tridimensional de la escena Proyecto, Madrid, 2010. .. [3] J. Dembys, Y. Gao, A. Shafiekhani y G. Desouza, Object Detection and Pose Estimation Using CNN in Embedded Hardware for Assistive Technology, oct. de 2019. .. [4] H. Königshof, N. O. Salscheider y C. Stiller, «Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information», en 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, págs. 1405-1410. doi: 10.1109/ITSC.2019.8917330. .. [5] S. T. Barnard y M. A. Fischler, «Computational Stereo», ACM Computing Surveys (CSUR), vol. 14, 4 1982, issn: 15577341. doi: 10.1145/356893. 356896. .. [6] F. Torres, J. Pomares, P. Gil, S. T. Puente y R. Aracil, Robots y Sistemas Sensoriales. Madrid: Pearson Education, 2002, págs. 69-94. .. [7] Z. Zhang, «A flexible new technique for camera calibration», IEEE Transac- tions on Pattern Analysis and Machine Intelligence, vol. 22, 11 2000, issn: 01628828. doi: 10.1109/34.888718. .. [8] R. I. Hartley, «Theory and practice of projective rectification», International Journal of Computer Vision, vol. 35, 2 1999, issn: 09205691. doi: 10.1023/ A:1008115206617. .. [9] D. Scharstein y R. Szeliski, «A taxonomy and evaluation of dense two-frame stereo correspondence algorithms», International Journal of Computer Vi- sion, vol. 47, 1-3 2002, issn: 09205691. doi: 10.1023/A:1014573219977. .. [10] R. Szeliski, Computer Vision : Algorithms and Applications 2nd edition. 2020. .. [11] S. Dai y W. Huang, A-TVSNet: Aggregated Two-View Stereo Network for Multi-View Stereo Depth Estimation, 2020. arXiv: 2003.00711 [cs.CV]. .. [12] CS231n Convolutional Neural Networks for Visual Recognition, Accesado el 12, de febrero de 2021. dirección: https://cs231n.github.io/convolutional- networks/. .. [13] K. Kar, Mastering Computer Vision with TensorFlow 2.x. 2020. .. [14] K. He, X. Zhang, S. Ren y J. Sun, Identity Mappings in Deep Residual Networks, 2016. doi: 10 . 48550 / ARXIV . 1603 . 05027. dirección: https : //arxiv.org/abs/1603.05027. .. [15] R. Atienza, Advanced Deep Learning with Keras: Apply deep learning tech- niques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more. 2018. .. [16] J. Long, E. Shelhamer y T. Darrell, Fully Convolutional Networks for Se- mantic Segmentation, 2014. doi: 10.48550/ARXIV.1411.4038. dirección: https://arxiv.org/abs/1411.4038. .. [17] ——, StereoPi V2 quick start guide, 2014. dirección: https://wiki.stereopi. com/index.php?title=StereoPi_v2_Quick_Start_Guide. .. [18] G. B. Adrian Kaehler, Learning OpenCV 3: computer vision in C++ with the OpenCV library. California, USA: O’Reilly Media, Inc., 2016. .. [19] T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan y S. Belongie, Feature Pyramid Networks for Object Detection, 2016. doi: 10.48550/ARXIV.1612. 03144. dirección: https://arxiv.org/abs/1612.03144. .. [20] J. Redmon y A. Farhadi, «YOLOv3: An Incremental Improvement», arXiv, 2018. .. [21] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn y A. Zisserman, The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results, http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html.