BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

Overview

BasicVSR

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

Ported from https://github.com/xinntao/BasicSR

Dependencies

  • NumPy
  • PyTorch, preferably with CUDA. Note that torchvision and torchaudio are not required and hence can be omitted from the command.
  • VapourSynth

Installation

pip install --upgrade vsbasicvsr

Usage

from vsbasicvsr import BasicVSR

ret = BasicVSR(clip)

See __init__.py for the description of the parameters.

You might also like...
VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).
VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).

VSR-Transformer By Jiezhang Cao, Yawei Li, Kai Zhang, Luc Van Gool This paper proposes a new Transformer for video super-resolution (called VSR-Transf

EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation

EFENet EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation Code is a bit messy now. I woud clean up soon. For training the EF

MoCoPnet - Deformable 3D Convolution for Video Super-Resolution
MoCoPnet - Deformable 3D Convolution for Video Super-Resolution

Deformable 3D Convolution for Video Super-Resolution Pytorch implementation of l

Simple, but essential Bayesian optimization package

BayesO: A Bayesian optimization framework in Python Simple, but essential Bayesian optimization package. http://bayeso.org Online documentation Instal

An essential implementation of BYOL in PyTorch + PyTorch Lightning
An essential implementation of BYOL in PyTorch + PyTorch Lightning

Essential BYOL A simple and complete implementation of Bootstrap your own latent: A new approach to self-supervised Learning in PyTorch + PyTorch Ligh

A 2D Visual Localization Framework based on Essential Matrices [ICRA2020]
A 2D Visual Localization Framework based on Essential Matrices [ICRA2020]

A 2D Visual Localization Framework based on Essential Matrices This repository provides implementation of our paper accepted at ICRA: To Learn or Not

Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.
Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.

Deep Image Search - AI-Based Image Search Engine Deep Image Search is an AI-based image search engine that includes deep transfer learning features Ex

Official Pytorch implementation of
Official Pytorch implementation of "Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video", CVPR 2021

TCMR: Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video Qualtitative result Paper teaser video Introduction This r

MAU: A Motion-Aware Unit for Video Prediction and Beyond, NeurIPS2021

MAU (NeurIPS2021) Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yan Ye, Xinguang Xiang, Wen GAo. Official PyTorch Code for "MAU: A Motion-Aware

Comments
  • Tile doesn't seem to work.

    Tile doesn't seem to work.

    Using:

    # Imports
    import vapoursynth as vs
    # getting Vapoursynth core
    core = vs.core
    # Loading Plugins
    core.std.LoadPlugin(path="I:/Hybrid/64bit/vsfilters/Support/fmtconv.dll")
    core.std.LoadPlugin(path="I:/Hybrid/64bit/vsfilters/SourceFilter/vsrawsource/vsrawsource.dll")
    # source: 'C:\Users\Selur\Desktop\stefan_sif.y4m'
    # current color space: YUV420P8, bit depth: 8, resolution: 352x240, fps: 29.97, color matrix: 470bg, yuv luminance scale: full, scanorder: progressive
    # Loading C:\Users\Selur\Desktop\stefan_sif.y4m using RawsSource
    clip = core.raws.Source("C:/Users/Selur/Desktop/stefan_sif.y4m")
    # making sure input color matrix is set as 470bg
    clip = core.resize.Bicubic(clip, matrix_in_s="470bg",range_s="full")
    # making sure frame rate is set to 29.970
    clip = core.std.AssumeFPS(clip=clip, fpsnum=30000, fpsden=1001)
    # Setting color range to PC (full) range.
    clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=0)
    # adjusting color space from YUV420P8 to RGBS for vsBasicVSR
    clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="470bg", range_s="full")
    # resizing using BasicVSR
    from vsbasicvsr import BasicVSR
    clip = BasicVSR(clip=clip, radius=15, device_type="cuda", tile=2)
    # adjusting resizing to hit target resolution 
    clip = core.fmtc.resample(clip=clip, w=1280, h=874, kernel="lanczos", interlaced=False, interlacedd=False)
    # adjusting output color from: RGB48 to YUV420P10 for x265Model
    clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P10, matrix_s="470bg", range_s="full")
    # set output frame rate to 29.970fps
    clip = core.std.AssumeFPS(clip=clip, fpsnum=30000, fpsden=1001)
    # Output
    clip.set_output()
    

    gives me

    Input and output sizes should be greater than 0, but got input (H: 0, W: 0) output (H: 0, W: 0)
    

    without the tile=2, it works.

    opened by Selur 5
Releases(v1.2.0)
Owner
Holy Wu
Holy Wu
Keyword-BERT: Keyword-Attentive Deep Semantic Matching

project discription An implementation of the Keyword-BERT model mentioned in my paper Keyword-Attentive Deep Semantic Matching (Plz cite this github r

1 Nov 14, 2021
Unsupervised clustering of high content screen samples

Microscopium Unsupervised clustering and dataset exploration for high content screens. See microscopium in action Public dataset BBBC021 from the Broa

60 Dec 05, 2022
This repository contains implementations and illustrative code to accompany DeepMind publications

DeepMind Research This repository contains implementations and illustrative code to accompany DeepMind publications. Along with publishing papers to a

DeepMind 11.3k Dec 31, 2022
[NeurIPS 2021] The PyTorch implementation of paper "Self-Supervised Learning Disentangled Group Representation as Feature"

IP-IRM [NeurIPS 2021] The PyTorch implementation of paper "Self-Supervised Learning Disentangled Group Representation as Feature". Codes will be relea

Wang Tan 67 Dec 24, 2022
This repository contains the official MATLAB implementation of the TDA method for reverse image filtering

ReverseFilter TDA This repository contains the official MATLAB implementation of the TDA method for reverse image filtering proposed in the paper: "Re

Fergaletto 2 Dec 13, 2021
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa

Zhao Jian 3.1k Jan 04, 2023
ConvMAE: Masked Convolution Meets Masked Autoencoders

ConvMAE ConvMAE: Masked Convolution Meets Masked Autoencoders Peng Gao1, Teli Ma1, Hongsheng Li2, Jifeng Dai3, Yu Qiao1, 1 Shanghai AI Laboratory, 2 M

Alpha VL Team of Shanghai AI Lab 345 Jan 08, 2023
Boundary IoU API (Beta version)

Boundary IoU API (Beta version) Bowen Cheng, Ross Girshick, Piotr Dollár, Alexander C. Berg, Alexander Kirillov [arXiv] [Project] [BibTeX] This API is

Bowen Cheng 177 Dec 29, 2022
Code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2021

The repo provides the code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2

Yuning Mao 18 May 24, 2022
ObjectDrawer-ToolBox: a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system

ObjectDrawer-ToolBox is a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system, Object Drawer.

77 Jan 05, 2023
A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).

SimGNN ⠀⠀⠀ A PyTorch implementation of SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (WSDM 2019). Abstract Graph similarity s

Benedek Rozemberczki 534 Dec 25, 2022
Canonical Appearance Transformations

CAT-Net: Learning Canonical Appearance Transformations Code to accompany our paper "How to Train a CAT: Learning Canonical Appearance Transformations

STARS Laboratory 54 Dec 24, 2022
Simulation of self-focusing of laser beams in condensed media

What is it? Program for scientific research, which allows to simulate the phenomenon of self-focusing of different laser beams (including Gaussian, ri

Evgeny Vasilyev 13 Dec 24, 2022
This is the code of "Multi-view Contrastive Graph Clustering" in NeurlPS 2021.

MCGC Description This is the code of "Multi-view Contrastive Graph Clustering" in NeurlPS 2021. Datasets Results ACM DBLP IMDB Amazon photos Amazon co

31 Nov 14, 2022
SCALoss: Side and Corner Aligned Loss for Bounding Box Regression (AAAI2022).

SCALoss PyTorch implementation of the paper "SCALoss: Side and Corner Aligned Loss for Bounding Box Regression" (AAAI 2022). Introduction IoU-based lo

TuZheng 20 Sep 07, 2022
E-RAFT: Dense Optical Flow from Event Cameras

E-RAFT: Dense Optical Flow from Event Cameras This is the code for the paper E-RAFT: Dense Optical Flow from Event Cameras by Mathias Gehrig, Mario Mi

Robotics and Perception Group 71 Dec 12, 2022
Code and data of the ACL 2021 paper: Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision

MetaAdaptRank This repository provides the implementation of meta-learning to reweight synthetic weak supervision data described in the paper Few-Shot

THUNLP 5 Jun 16, 2022
Realtime YOLO Monster Detection With Non Maximum Supression

Realtime-YOLO-Monster-Detection-With-Non-Maximum-Supression Table of Contents In

5 Oct 07, 2022
A Python package for performing pore network modeling of porous media

Overview of OpenPNM OpenPNM is a comprehensive framework for performing pore network simulations of porous materials. More Information For more detail

PMEAL 336 Dec 30, 2022
Algorithmic encoding of protected characteristics and its implications on disparities across subgroups

Algorithmic encoding of protected characteristics and its implications on disparities across subgroups This repository contains the code for the paper

Team MIRA - BioMedIA 15 Oct 24, 2022