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

Related tags

Deep Learningvs-basicvsr
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

Jiezhang Cao 225 Nov 13, 2022
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

Yaping Zhao 6 Oct 20, 2021
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

Xinyi Ying 28 Dec 15, 2022
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

Jungtaek Kim 74 Dec 5, 2022
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

Enrico Fini 48 Sep 27, 2022
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

Qunjie Zhou 27 Nov 7, 2022
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

null 139 Jan 1, 2023
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

Hongsuk Choi 215 Jan 6, 2023
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

ZhengChang 20 Nov 25, 2022
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
GitHub Repository
Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

Noah Getz 3 Jun 22, 2022
Finding Donors for CharityML

Finding-Donors-for-CharityML - Investigated factors that affect the likelihood of charity donations being made based on real census data.

Moamen Abdelkawy 1 Dec 30, 2021
SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model

SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model Edresson Casanova, Christopher Shulby, Eren Gölge, Nicolas Michael Müller, Frede

Edresson Casanova 92 Dec 09, 2022
House_prices_kaggle - Predict sales prices and practice feature engineering, RFs, and gradient boosting

House Prices - Advanced Regression Techniques Predicting House Prices with Machine Learning This project is build to enhance my knowledge about machin

Gurpreet Singh 1 Jan 01, 2022
The implementation of the paper "A Deep Feature Aggregation Network for Accurate Indoor Camera Localization".

A Deep Feature Aggregation Network for Accurate Indoor Camera Localization This is the PyTorch implementation of our paper "A Deep Feature Aggregation

9 Dec 09, 2022
Code repository for the paper: Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild (ICCV 2021)

Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild Akash Sengupta, Ignas Budvytis, Robert

Akash Sengupta 149 Dec 14, 2022
Urban mobility simulations with Python3, RLlib (Deep Reinforcement Learning) and Mesa (Agent-based modeling)

Deep Reinforcement Learning for Smart Cities Documentation RLlib: https://docs.ray.io/en/master/rllib.html Mesa: https://mesa.readthedocs.io/en/stable

1 May 15, 2022
Learning Representational Invariances for Data-Efficient Action Recognition

Learning Representational Invariances for Data-Efficient Action Recognition Official PyTorch implementation for Learning Representational Invariances

Virginia Tech Vision and Learning Lab 27 Nov 22, 2022
Scalable Optical Flow-based Image Montaging and Alignment

SOFIMA SOFIMA (Scalable Optical Flow-based Image Montaging and Alignment) is a tool for stitching, aligning and warping large 2d, 3d and 4d microscopy

Google Research 16 Dec 21, 2022
PyTorch implementation of normalizing flow models

PyTorch implementation of normalizing flow models

Vincent Stimper 242 Jan 02, 2023
[v1 (ISBI'21) + v2] MedMNIST: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification

MedMNIST Project (Website) | Dataset (Zenodo) | Paper (arXiv) | MedMNIST v1 (ISBI'21) Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bili

683 Dec 28, 2022
Multilingual Image Captioning

Multilingual Image Captioning Authors: Bhavitvya Malik, Gunjan Chhablani Demo Link: https://huggingface.co/spaces/flax-community/multilingual-image-ca

Gunjan Chhablani 32 Nov 25, 2022
Implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

PRP Introduction This is the implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

yuanyao366 39 Dec 29, 2022
Weakly Supervised Posture Mining with Reverse Cross-entropy for Fine-grained Classification

Fine-grainedImageClassification Weakly Supervised Posture Mining with Reverse Cross-entropy for Fine-grained Classification We trained model here: lin

ZhenchaoTang 14 Oct 21, 2022
EfficientMPC - Efficient Model Predictive Control Implementation

efficientMPC Efficient Model Predictive Control Implementation The original algo

Vin 8 Dec 04, 2022
Official repo for SemanticGAN https://nv-tlabs.github.io/semanticGAN/

SemanticGAN This is the official code for: Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizat

151 Dec 28, 2022
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

Hongje Seong 72 Dec 14, 2022
Tensorflow implementation of "BEGAN: Boundary Equilibrium Generative Adversarial Networks"

BEGAN in Tensorflow Tensorflow implementation of BEGAN: Boundary Equilibrium Generative Adversarial Networks. Requirements Python 2.7 or 3.x Pillow tq

Taehoon Kim 922 Dec 21, 2022
PyTorch wrappers for using your model in audacity!

audacitorch This package contains utilities for prepping PyTorch audio models for use in Audacity. More specifically, it provides abstract classes for

Hugo Flores García 130 Dec 14, 2022
Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation

Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation This reposi

First Person Vision @ Image Processing Laboratory - University of Catania 1 Aug 21, 2022