Python package provinding tools for artistic interactive applications using AI

Last update: Sep 30, 2021

Overview

redrawing

Python package provinding tools for artistic interactive applications using AI

Created by ReDrawing Campinas team for the OpenCV AI 2021 Competition.

Installation

python -m pip install --upgrade pip
python -m pip install redrawing

Stages

Stages are the basic processing units of the redrawing package. They receive data objects from input channels, processes them and output data in the output channels.

Examples of stages:

Stage name	Use
OAK_Stage	Handles a OAK camera. Can be changed using OAK_Substages, like a Body detector or a Hand gesture detector
PCR_Viewer	Visualizer for image, depth and body pose data
CameraReceiver/IMUReceiver	Receives data from a smarthphone
UKF_IMU	UKF filter for orientation estimation using IMU data

Data and communication

All inter stage data communication occours using Data classes.

Examples of data classes:

Data class	Use
BodyPose	Stores body pose data (keypoints)
Image	Stores image data
Depth_Map	Stores depth data
IMU	Stores IMU data

For exchange data with client applications, the UDP_Stage can be used, using UDP protocol with JSON converted messages. We also have client libraries for receiving and decoding data:

redrawing_java: client library for Java
ReDrawing for Processing: client library for Processing, encapsulates the redrawing_java library

Authors

Legal

Submodules in ai_models/third_models submodules may be licensed under other licenses. A copy of the respective license can be found in the submodule folder.

Made with the support of "Conselho Nacional de Desenvolvimento Científico e Tecnológico" (CNPq) of Brazil

Python package provinding tools for artistic interactive applications using AI

Related tags

Overview

redrawing

Installation

Stages

Data and communication

Authors

Legal

Owner

ReDrawing Campinas

Implementation of MA-Trace - a general-purpose multi-agent RL algorithm for cooperative environments.

We have implemented shaDow-GNN as a general and powerful pipeline for graph representation learning. For more details, please find our paper titled Deep Graph Neural Networks with Shallow Subgraph Samplers, available on arXiv (https//arxiv.org/abs/2012.01380).

An executor that performs image segmentation on fashion items

Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.

VOLO: Vision Outlooker for Visual Recognition

Implementation of the algorithm shown in the article "Modelo de Predicción de Éxito de Canciones Basado en Descriptores de Audio"

Data Engineering ZoomCamp

Callable PyTrees and filtered JIT/grad transformations => neural networks in JAX.

Deep Text Search is an AI-powered multilingual text search and recommendation engine with state-of-the-art transformer-based multilingual text embedding (50+ languages).

BADet: Boundary-Aware 3D Object Detection from Point Clouds (Pattern Recognition 2022)

Pytorch implementation of COIN, a framework for compression with implicit neural representations 🌸

DeepFaceLive - Live Deep Fake in python, Real-time face swap for PC streaming or video calls

Python version of the amazing Reaction Mechanism Generator (RMG).

Pytorch Implementation of "Desigining Network Design Spaces", Radosavovic et al. CVPR 2020.

Code implementation of "Sparsity Probe: Analysis tool for Deep Learning Models"

DGN pymarl - Implementation of DGN on Pymarl, which could be trained by VDN or QMIX

Official PyTorch implementation of MAAD: A Model and Dataset for Attended Awareness

Train CPPNs as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images.

Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021

CSD: Consistency-based Semi-supervised learning for object Detection