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
Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

TensorFlow implementation of 3D Convolutional Neural Networks for Speaker Verification - Official Project Page - Pytorch Implementation This repositor

Amirsina Torfi 753 Dec 17, 2022
Learning Synthetic Environments and Reward Networks for Reinforcement Learning

Learning Synthetic Environments and Reward Networks for Reinforcement Learning We explore meta-learning agent-agnostic neural Synthetic Environments (

AutoML-Freiburg-Hannover 16 Sep 02, 2022
Waymo motion prediction challenge 2021: 3rd place solution

Waymo motion prediction challenge 2021: 3rd place solution 📜 Technical report 🗨️ Presentation 🎉 Announcement 🛆Motion Prediction Channel Website 🛆

158 Jan 08, 2023
Weight estimation in CT by multi atlas techniques

maweight A Python package for multi-atlas based weight estimation for CT images, including segmentation by registration, feature extraction and model

György Kovács 0 Dec 24, 2021
Main Results on ImageNet with Pretrained Models

This repository contains Pytorch evaluation code, training code and pretrained models for the following projects: SPACH (A Battle of Network Structure

Microsoft 151 Dec 14, 2022
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking

StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking Datasets You can download datasets that have been pre-pr

25 May 29, 2022
Official repo for AutoInt: Automatic Integration for Fast Neural Volume Rendering in CVPR 2021

AutoInt: Automatic Integration for Fast Neural Volume Rendering CVPR 2021 Project Page | Video | Paper PyTorch implementation of automatic integration

Stanford Computational Imaging Lab 149 Dec 22, 2022
Simple SN-GAN to generate CryptoPunks

CryptoPunks GAN Simple SN-GAN to generate CryptoPunks. Neural network architecture and training code has been modified from the PyTorch DCGAN example.

Teddy Koker 66 Dec 15, 2022
a Pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021"

A pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021" 1. Notes This is a pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in

91 Dec 26, 2022
code for CVPR paper Zero-shot Instance Segmentation

Code for CVPR2021 paper Zero-shot Instance Segmentation Code requirements python: python3.7 nvidia GPU pytorch1.1.0 GCC =5.4 NCCL 2 the other python

zhengye 86 Dec 13, 2022
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)

CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm

100 Dec 28, 2022
Code of PVTv2 is released! PVTv2 largely improves PVTv1 and works better than Swin Transformer with ImageNet-1K pre-training.

Updates (2020/06/21) Code of PVTv2 is released! PVTv2 largely improves PVTv1 and works better than Swin Transformer with ImageNet-1K pre-training. Pyr

1.3k Jan 04, 2023
Greedy Gaussian Segmentation

GGS Greedy Gaussian Segmentation (GGS) is a Python solver for efficiently segmenting multivariate time series data. For implementation details, please

Stanford University Convex Optimization Group 72 Dec 07, 2022
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

Expressive Body Capture: 3D Hands, Face, and Body from a Single Image [Project Page] [Paper] [Supp. Mat.] Table of Contents License Description Fittin

Vassilis Choutas 1.3k Jan 07, 2023
Object-Centric Learning with Slot Attention

Slot Attention This is a re-implementation of "Object-Centric Learning with Slot Attention" in PyTorch (https://arxiv.org/abs/2006.15055). Requirement

Untitled AI 72 Jan 02, 2023
Minimal But Practical Image Classifier Pipline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.

PyTorch Image Classifier Updates As for many users request, I released a new version of standared pytorch immage classification example at here: http:

JinTian 106 Nov 06, 2022
🗺 General purpose U-Network implemented in Keras for image segmentation

TF-Unet General purpose U-Network implemented in Keras for image segmentation Getting started • Training • Evaluation Getting started Looking for Jupy

Or Fleisher 2 Aug 31, 2022
这是一个利用facenet和retinaface实现人脸识别的库,可以进行在线的人脸识别。

Facenet+Retinaface:人脸识别模型在Keras当中的实现 目录 注意事项 Attention 所需环境 Environment 文件下载 Download 预测步骤 How2predict 参考资料 Reference 注意事项 该库中包含了两个网络,分别是retinaface和fa

Bubbliiiing 31 Nov 15, 2022
Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"

Memory Efficient Attention Pytorch Implementation of a memory efficient multi-head attention as proposed in the paper, Self-attention Does Not Need O(

Phil Wang 180 Jan 05, 2023
PINN Burgers - 1D Burgers equation simulated by PINN

PINN(s): Physics-Informed Neural Network(s) for Burgers equation This is an impl

ShotaDEGUCHI 1 Feb 12, 2022