Displaying 1 to 12 from 12 results

vpp - Video++, a C++14 high performance video and image processing library.

  •    C++

The generic container imageNd<V, N> represents a dense N-dimensional rectangle set of pixels with values of type V. For convenience, image1d, image2d, image3d are respectively aliases to imageNd<V, 1>, imageNd<V, 2>, and imageNd<V, 3>. These types provide accesses to the pixel buffer and to other piece of information useful to process the image. In contrast to std::vector, assigning an image to the other does not copy the data, but share them so no accidental expensive deep copy happen.

pyflow - Fast, accurate and easy to run dense optical flow with python wrapper

  •    C++

Python wrapper for Ce Liu's C++ implementation of Coarse2Fine Optical Flow. This is super fast and accurate optical flow method based on Coarse2Fine warping method from Thomas Brox. This python wrapper has minimal dependencies, and it also eliminates the need for C++ OpenCV library. For real time performance, one can additionally resize the images to a smaller size. This wrapper code was developed as part of our CVPR 2017 paper on Unsupervised Learning using unlabeled videos. Github repository for our CVPR 17 paper is here.

flow-io-opencv - Fork and OpenCV wrapper of the optical flow I/O and visualization code provided as part of the Sintel dataset [1]

  •    C++

lib/README-FlowIO: the original README shipped with the Sintel dataset.lib/imageLib/README: the original README of imageLib shipped with the Sintel dataset.

theano-flownet - FlowNetS and FlowNetC port to Theano

  •    Python

This is a port of the caffe implementation of the ICCV'15 paper "FlowNet: Learning Optical Flow with Convolutional Networks" by Dosovitskiy et al to Theano and Lasagne. It contains both FlowNetS and FlowNetC models and a port of the correlation layer. caffe_to_numpy.py script can be used to convert caffe models to the npz format. caffemodel and prototxt files should be placed in the model subdirectory. Alternatively you can download weights from Google Drive.

ADNS3080 - Example code for the ADNS-3080 optical flow sensor

  •    Python

The code is released under the GNU General Public License. This is an example code I wrote in order to test the ADNS-3080 optical flow sensor. It was inspired by this code provided by DIY Drones: https://github.com/diydrones/ardupilot/tree/5ddbcc296dd6dd9ac9ed6316ac3134c736ae8a78/libraries/AP_OpticalFlow written by Randy Mackay.

PWC-Net - PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018 (Oral)

  •    Cuda

Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). For Caffe users, please refer to Caffe/README.md.

tfvos - Semi-Supervised Video Object Segmentation (VOS) with Tensorflow

  •    Jupyter

The aim of this project is to implement and compare implementations of several video object segmentation (VOS) algorithms using Tensorflow. As part of the NIPS Paper Implementation Challenge, we chose MaskRNN: Instance Level Video Object Segmentation (NIPS 2017) [2017h] as our first implementation. See the MaskRNN Tensorflow Implementation Section for more info.

LiteFlowNet - LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018 (Spotlight)

  •    C++

This repository (https://github.com/twhui/LiteFlowNet) is the offical release of LiteFlowNet for my paper LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation in CVPR18 (Spotlight). The up-to-date version of the paper is available on arXiv. It comes as the modified Caffe from DispFlowNet and FlowNet2 with our new layers, scripts, and trained models.

tfoptflow - Optical Flow Prediction with TensorFlow

  •    Jupyter

This repo provides a TensorFlow-based implementation of the wonderful paper "PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume," by Deqing Sun et al. (CVPR 2018). There are already a few attempts at implementing PWC-Net using TensorFlow out there. However, they either use outdated architectures of the paper's CNN networks, only provide TF inference (no TF training), only work on Linux platforms, and do not support multi-GPU training.

ObjectFlow - Implemenation of the paper: "Video Segmentation via Object Flow", Y

  •    C++

Video Segmentation via Object Flow Yi-Hsuan Tsai, Ming-Hsuan Yang and Michael J. Black IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. This is the authors' MATLAB implementation described in the above paper. Please cite our paper if you use our code and model for your research.

pyoptflow - Optical Flow estimation in pure Python

  •    Python

Lucas-Kanade is also possible in the future, let me know if you're interested in Lucas Kanade. imageio loads a wide varity of images and video.