LoL Runes Recommender With Python

Overview

LoL-Runes-Recommender

Para ejecutar la aplicación se debe llamar a execute_app.py , acá hay dos modos:

  • El modo 'get_runes' es la app final para los usuarios" en donde se ingresa una imagen de un campeón de League of Legends como consulta y se retorna los set de runas adecuados para él
    • El comando de ejecución es: python execute_app.py get_runes [cropped_image_path]

-El modo 'full_execution' sirve para realizar debugging de la app y benchmarking de tiempos. n este modo se precomputan los descriptores sift, se generan crops aleatorios de imágenes se realiza match de vectores sift con un dataset extenso y se anotan los resultados en un archivo .txt - El comando de ejecución es

`python execute_app.py full_execution [original_images_folder_path] [sift_descriptors_folder_path] [cropped_images_folder_path] [results_text_file]`

Sobre los descriptores del proyecto

Para realizar la comparación entre el screenshot del usuario y el dataset se aplica la detección de puntos de interés mediante descriptores SIFT

Los descriptores SIFT calculados para las imágenes del proyecto utilizan el método de diferencias gaussianas (Difference of Gaussians). Además al momento de comparar las imágenes de los campeones con descriptores locales se restringen los matches entre keypoints, seleccionando solos los de menor distancia con respecto a un umbral Esto solo funciona con casos "fáciles", cuando la aparición del objeto es muy parecida a la consulta, el cual es el caso del proyecto.

Owner
Sebastián Salinas
Computer Science Engineering Student at University of Chile
Sebastián Salinas
Demo notebooks for Qiskit application modules demo sessions (Oct 8 & 15):

qiskit-application-modules-demo-sessions This repo hosts demo notebooks for the Qiskit application modules demo sessions hosted on Qiskit YouTube. Par

Qiskit Community 46 Nov 24, 2022
Score refinement for confidence-based 3D multi-object tracking

Score refinement for confidence-based 3D multi-object tracking Our video gives a brief explanation of our Method. This is the official code for the pa

Cognitive Systems Research Group 47 Dec 26, 2022
null

DeformingThings4D dataset Video | Paper DeformingThings4D is an synthetic dataset containing 1,972 animation sequences spanning 31 categories of human

208 Jan 03, 2023
The codes and models in 'Gaze Estimation using Transformer'.

GazeTR We provide the code of GazeTR-Hybrid in "Gaze Estimation using Transformer". We recommend you to use data processing codes provided in GazeHub.

65 Dec 27, 2022
This is an example implementation of the paper "Cross Domain Robot Imitation with Invariant Representation".

IR-GAIL This is an example implementation of the paper "Cross Domain Robot Imitation with Invariant Representation". Dependency The experiments are de

Zhao-Heng Yin 1 Jul 14, 2022
Complete* list of autonomous driving related datasets

AD Datasets Complete* and curated list of autonomous driving related datasets Contributing Contributions are very welcome! To add or update a dataset:

Daniel Bogdoll 13 Dec 19, 2022
Improving the robustness and performance of biomedical NLP models through adversarial training

RobustBioNLP Improving the robustness and performance of biomedical NLP models through adversarial training In this repository you can find suppliment

Milad Moradi 3 Sep 20, 2022
CVNets: A library for training computer vision networks

CVNets: A library for training computer vision networks This repository contains the source code for training computer vision models. Specifically, it

Apple 1.1k Jan 03, 2023
For holding anime-related object classification and detection models

Animesion An end-to-end framework for anime-related object classification, detection, segmentation, and other models. Update: 01/22/2020. Due to time-

Edwin Arkel Rios 72 Nov 30, 2022
Uni-Fold: Training your own deep protein-folding models.

Uni-Fold: Training your own deep protein-folding models. This package provides and implementation of a trainable, Transformer-based deep protein foldi

DeepModeling 88 Jan 03, 2023
(ICCV 2021 Oral) Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigation.

DARS Code release for the paper "Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigation", ICCV 2021

CVMI Lab 58 Jan 01, 2023
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning This repository is the official implementation of CARE.

ChongjianGE 89 Dec 02, 2022
Project Tugas Besar pertama Pengenalan Komputasi Institut Teknologi Bandung

Vending_Machine_(Mesin_Penjual_Minuman) Project Tugas Besar pertama Pengenalan Komputasi Institut Teknologi Bandung Raw Sketch untuk Essay Ringkasan P

QueenLy 1 Nov 08, 2021
ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers

ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers Official implementation of ViewFormer. ViewFormer is a NeRF-free neural rend

Jonáš Kulhánek 169 Dec 30, 2022
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC) Xin Lai*, Zhuotao Tian*, Li Jiang, Shu Liu, Hengshuang Zhao, Li

DV Lab 137 Dec 14, 2022
A light and fast one class detection framework for edge devices. We provide face detector, head detector, pedestrian detector, vehicle detector......

A Light and Fast Face Detector for Edge Devices Big News: LFD, which is a big update of LFFD, now is released (2021.03.09). It is strongly recommended

YonghaoHe 1.3k Dec 25, 2022
PyTorchVideo is a deeplearning library with a focus on video understanding work

PyTorchVideo is a deeplearning library with a focus on video understanding work. PytorchVideo provides resusable, modular and efficient components needed to accelerate the video understanding researc

Facebook Research 2.7k Jan 07, 2023
git《Investigating Loss Functions for Extreme Super-Resolution》(CVPR 2020) GitHub:

Investigating Loss Functions for Extreme Super-Resolution NTIRE 2020 Perceptual Extreme Super-Resolution Submission. Our method ranked first and secon

Sejong Yang 0 Oct 17, 2022
Datasets for new state-of-the-art challenge in disentanglement learning

High resolution disentanglement datasets This repository contains the Falcor3D and Isaac3D datasets, which present a state-of-the-art challenge for co

NVIDIA Research Projects 37 May 26, 2022
A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions

A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions Kapoutsis, A.C., Chatzichristofis,

Athanasios Ch. Kapoutsis 5 Oct 15, 2022