Implémentation en pyhton de l'article Depixelizing pixel art de Johannes Kopf et Dani Lischinski

Overview

Projet INF8801A - Session A21

Groupe A :

  • Mathis Lamidey, @Ithyx
  • Hugo Vandenbroucke-Menu, @thehyouz

Notre projet consistait à ré-implémenter en parti l'article "Depixelizing Pixel Art" de Johannes Kopf et Dani Lischinski. Disponible ici : https://johanneskopf.de/publications/pixelart/paper/pixel.pdf

Les images utilisées proviennent du dataset de l'article. Disponible ici : http://johanneskopf.de/publications/pixelart/supplementary/multi_comparison.html

Notre implémentation est en Python et il est possible de la lancer depuis le fichier main.ipynb.

Pipeline et exemple

pipeline

Pour un exemple détaillé de résultats, voir les images dans le dossier output.

Requis

L'ensemble des librairies nécessaire sont décrites dans le fichier requirements.txt. Pour les installer, utiliser pip install -r requirements.txt. Pour la bonne exécution du script, assurez-vous que le dossier output existe bien.

Utilisation

Notre code est présenté sous forme d'un jupyter notebook qui fait les appels de fonctions nécessaire pour traiter les images. Nous fournissons nos images de test dans le dossier data, et lors de l'exécution du notebook, en plus des images affichées directement dans le notebook, une copie sera sauvegardée dans le dossier output si vous changez le paramètre saveFig des signatures de fonction d'affichage (en utilisant les variables globales en haut du notebook). Nous vous encourageons à tester de changer l'image en entrée du programme, en modifiant le chemin dans la cellule "Step 1", mais notez que l'algorithme ne marche bien que sur des images de pixel art en basse résolution. Si vous essayez avec une image de trop grande taille, vous risquer un temps d'exécution très long.

Owner
TableauBits
TableauBits
The code for paper "Learning Implicit Fields for Generative Shape Modeling".

implicit-decoder The tensorflow code for paper "Learning Implicit Fields for Generative Shape Modeling", Zhiqin Chen, Hao (Richard) Zhang. Project pag

Zhiqin Chen 353 Dec 30, 2022
This is an official implementation of the High-Resolution Transformer for Dense Prediction.

High-Resolution Transformer for Dense Prediction Introduction This is the official implementation of High-Resolution Transformer (HRT). We present a H

HRNet 403 Dec 13, 2022
Official PyTorch code of DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization (ICCV 2021 Oral).

DeepPanoContext (DPC) [Project Page (with interactive results)][Paper] DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context G

Cheng Zhang 66 Nov 16, 2022
Platform-agnostic AI Framework 🔥

🇬🇧 TensorLayerX is a multi-backend AI framework, which can run on almost all operation systems and AI hardwares, and support hybrid-framework progra

TensorLayer Community 171 Jan 06, 2023
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks (MAPDN)

Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks (MAPDN) This is the implementation of the paper Multi-Age

Future Power Networks 83 Jan 06, 2023
Global Filter Networks for Image Classification

Global Filter Networks for Image Classification Created by Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou This repository contains PyTorch

Yongming Rao 273 Dec 26, 2022
subpixel: A subpixel convnet for super resolution with Tensorflow

subpixel: A subpixel convolutional neural network implementation with Tensorflow Left: input images / Right: output images with 4x super-resolution af

Atrium LTS 2.1k Dec 23, 2022
Unofficial PyTorch Implementation of "Augmenting Convolutional networks with attention-based aggregation"

Pytorch Implementation of Augmenting Convolutional networks with attention-based aggregation This is the unofficial PyTorch Implementation of "Augment

DK 20 Sep 09, 2022
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation

Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation Paper Multi-Target Adversarial Frameworks for Domain Adaptation in

Valeo.ai 20 Jun 21, 2022
Small little script to scrape, parse and check for active tor nodes. Can be used as proxies.

TorScrape TorScrape is a small but useful script made in python that scrapes a website for active tor nodes, parse the html and then save the nodes in

5 Dec 04, 2022
Repository for open research on optimizers.

Open Optimizers Repository for open research on optimizers. This is a test in sharing research/exploration as it happens. If you use anything from thi

Ariel Ekgren 6 Jun 24, 2022
This toolkit provides codes to download and pre-process the SLUE datasets, train the baseline models, and evaluate SLUE tasks.

slue-toolkit We introduce Spoken Language Understanding Evaluation (SLUE) benchmark. This toolkit provides codes to download and pre-process the SLUE

ASAPP Research 39 Sep 21, 2022
Introducing neural networks to predict stock prices

IntroNeuralNetworks in Python: A Template Project IntroNeuralNetworks is a project that introduces neural networks and illustrates an example of how o

Vivek Palaniappan 637 Jan 04, 2023
Code for the upcoming CVPR 2021 paper

The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel J. Brostow and Michael

Niantic Labs 496 Dec 30, 2022
Official implementation of "A Shared Representation for Photorealistic Driving Simulators" in PyTorch.

A Shared Representation for Photorealistic Driving Simulators The official code for the paper: "A Shared Representation for Photorealistic Driving Sim

VITA lab at EPFL 7 Oct 13, 2022
Code for our paper "Multi-scale Guided Attention for Medical Image Segmentation"

Medical Image Segmentation with Guided Attention This repository contains the code of our paper: "'Multi-scale self-guided attention for medical image

Ashish Sinha 394 Dec 28, 2022
Save-restricted-v-3 - Save restricted content Bot For telegram

Save restricted content Bot Contact: Telegram A stable telegram bot to get restr

DEVANSH 11 Dec 21, 2022
A Graph Neural Network Tool for Recovering Dense Sub-graphs in Random Dense Graphs.

PYGON A Graph Neural Network Tool for Recovering Dense Sub-graphs in Random Dense Graphs. Installation This code requires to install and run the graph

Yoram Louzoun's Lab 0 Jun 25, 2021
MLOps will help you to understand how to build a Continuous Integration and Continuous Delivery pipeline for an ML/AI project.

page_type languages products description sample python azure azure-machine-learning-service azure-devops Code which demonstrates how to set up and ope

1 Nov 01, 2021
MlTr: Multi-label Classification with Transformer

MlTr: Multi-label Classification with Transformer This is official implement of "MlTr: Multi-label Classification with Transformer". Abstract The task

程星 38 Nov 08, 2022