A large-image collection explorer and fast classification tool

Related tags

Deep Learningimax
Overview

IMAX: Interactive Multi-image Analysis eXplorer

This is an interactive tool for visualize and classify multiple images at a time. It written in Python and Javascript. It is based on Leaflet and it reads the images from a single directory and there is no need for multiple resolutions folders as images are scaled dynamically when zooming in/out. It runs an asyncio server in the back end and supports up 10,000 images reasonable well. It can load more images but it will slower. It runs using multiple cores and has been tested with over 50K images.

You can move and label images all from the keyboard.

You can see a (not very good) gif demo ot the tool in action, a live demo or a better video is here

Demo

Deployment

Simple deployment

Clone this repository:

	git clone https://github.com/mgckind/imax.git
	cd imax/python_server

Create a config file template:

	cp config_template.yaml config.yaml

Edit the config.yaml file to have the correct parameters, see Configuration for more info.

Start the server:

   python3 server.py

Start the client and visit the url printed python_server:

   python3 client.py

If you are running locally you can go to http://localhost:8000/

Docker

  1. Create image from Dockerfile

     cd imax
     docker build -t imax .
    
  2. Create an internal network so server/client can talk through the internal network (is not need for now as we are exposing both services at the localhost)

     docker network create --driver bridge imaxnet
    
  3. Create local config file to be mounted inside the containers. Create config.yaml based on the template, and replace the image location.

  4. Start the server container and attach the volume with images, connect to network and expose port 8888 to localhost

        docker run -d --name server -p 8888:8888 -v {PATH TO CONFIG FILE}:/home/explorer/server/config.yaml -v {PATH TO LOCAL IMAGES}:{PATH TO CONTAINER IMAGES} --network imaxnet imax python server.py
    
  5. Start the client container, connect to network and expose the port 8000 to local host

        docker run -d --name client -p 8000:8000 -v {PATH TO CONFIG FILE}:/home/explorer/server/config.yaml  --network imaxnet imax python client.py
    

Now the containers can talk at the localhost. If you are running locally you can go to http://localhost:8000/

Usage

This is the Help window displayed


Help


-> Fullscreen
-> Invert colors
/ -> Toggle On/Off classified tiles.
First time it reads from DB.

-> Random. Show a new random subsample (if available data is larger)
-> Apply filter to the displayed data.
Use the checkboxes on the left bottom side. -1 means no classified.
-> Reset filters and view. Do not display deleted images.

Move around with mouse and keyboard , use the mouse wheel to zoom in/out and double click to focus on one image.

Keyboard

Use "w","a","s","d" to move the selected tile and the keyboard numbers to apply a class as defined in the configuration file
Use "+", "-" to zoom in/out
Use "c" to clear any class selection
Use "t" to toggle on/off the classes
Use "h" to toggle on/off the Help
Use "f" to toggle on/off Full screen
Defined classes will appear at the bottom right side of the map

Configuration

This is the template config file to use:

#### DISPLAY
display:
  dataname: '{FILL ME}' #Name for the sqlite DB and config file
  path: '{FILL ME}'
  nimages: 1200 #Number of objects to be displayed even if there are more in the folder
  xdim: 40 #X dimension for the display
  ydim: 30 #Y dimension for the display
  tileSize: 256 #Size of the tile for which images are resized at max zoom level
  minXrange: 0
  minYrange: 0
  deltaZoom: 3 #default == 3
#### SERVER
server:
  ssl: false #use ssl, need to have certificates
  sslName: test #prefix of .crt and .key files inside ssl/ folder e.g., ssl/{sslName.key}
  host: 'http://localhost' #if using ssl, change to https
  port: 8888
  rootUrl: '/cexp' #root url for server, e.g. request are made to /cexp/, if None use "/"
  #workers: None # None will default to the workers in the machine
#### CLIENT
client:
  host: 'http://localhost'
  port: 8000
#### OPERATIONS options
operation:
  updates: true #allows to update and/or remove classes to images, false and classes are fixed.
#### CLASSES
#### classes, use any classes from 0 to 9, class 0 is for hidden! class -1 is no class
classes:
    - Delete: 0
    - Spiral: 8
    - Elliptical: 9
    - Other: 7
Owner
Matias Carrasco Kind
Data Science Research Services @giesdsrs director at UIUC. Astrophysicist and former Senior Research Scientist at @ncsa
Matias Carrasco Kind
Pytorch Lightning Distributed Accelerators using Ray

Distributed PyTorch Lightning Training on Ray This library adds new PyTorch Lightning plugins for distributed training using the Ray distributed compu

167 Jan 02, 2023
Aligning Latent and Image Spaces to Connect the Unconnectable

About This repo contains the official implementation of the Aligning Latent and Image Spaces to Connect the Unconnectable paper. It is a GAN model whi

Ivan Skorokhodov 203 Jan 03, 2023
text_recognition_toolbox: The reimplementation of a series of classical scene text recognition papers with Pytorch in a uniform way.

text recognition toolbox 1. 项目介绍 该项目是基于pytorch深度学习框架,以统一的改写方式实现了以下6篇经典的文字识别论文,论文的详情如下。该项目会持续进行更新,欢迎大家提出问题以及对代码进行贡献。 模型 论文标题 发表年份 模型方法划分 CRNN 《An End-t

168 Dec 24, 2022
[BMVC2021] The official implementation of "DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations"

DomainMix [BMVC2021] The official implementation of "DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations" [paper] [de

Wenhao Wang 17 Dec 20, 2022
A large-scale face dataset for face parsing, recognition, generation and editing.

CelebAMask-HQ [Paper] [Demo] CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA da

switchnorm 1.7k Dec 26, 2022
A High-Level Fusion Scheme for Circular Quantities published at the 20th International Conference on Advanced Robotics

Monte Carlo Simulation to the Paper A High-Level Fusion Scheme for Circular Quantities published at the 20th International Conference on Advanced Robotics

Sören Kohnert 0 Dec 06, 2021
Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion"

DSPoint Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion" Coming soon, as soon as I finish a

Ziyao Zeng 14 Feb 26, 2022
Open source hardware and software platform to build a small scale self driving car.

Donkeycar is minimalist and modular self driving library for Python. It is developed for hobbyists and students with a focus on allowing fast experimentation and easy community contributions.

Autorope 2.4k Jan 04, 2023
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

MNE-Python MNE-Python software is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, E

MNE tools for MEG and EEG data analysis 2.1k Dec 28, 2022
For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

LongScientificFormer For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training. Some code

Athar Sefid 6 Nov 02, 2022
Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

PyTorch code to reproduce LyDROO algorithm [1], which is an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability an

Liang HUANG 87 Dec 28, 2022
Vision-and-Language Navigation in Continuous Environments using Habitat

Vision-and-Language Navigation in Continuous Environments (VLN-CE) Project Website — VLN-CE Challenge — RxR-Habitat Challenge Official implementations

Jacob Krantz 132 Jan 02, 2023
MANO hand model porting for the GraspIt simulator

Learning Joint Reconstruction of Hands and Manipulated Objects - ManoGrasp Porting the MANO hand model to GraspIt! simulator Yana Hasson, Gül Varol, D

Lucas Wohlhart 10 Feb 08, 2022
A JAX-based research framework for writing differentiable numerical simulators with arbitrary discretizations

jaxdf - JAX-based Discretization Framework Overview | Example | Installation | Documentation ⚠️ This library is still in development. Breaking changes

UCL Biomedical Ultrasound Group 65 Dec 23, 2022
xitorch: differentiable scientific computing library

xitorch is a PyTorch-based library of differentiable functions and functionals that can be widely used in scientific computing applications as well as deep learning.

24 Apr 15, 2021
Repositório criado para abrigar os notebooks com a listas de exercícios propostos pelo professor Gustavo Guanabara do canal Curso em Vídeo do YouTube durante o Curso de Python 3

Curso em Vídeo - Exercícios de Python 3 Sobre o repositório Este repositório contém os notebooks com a listas de exercícios propostos pelo professor G

João Pedro Pereira 9 Oct 15, 2022
A PyTorch implementation of the continual learning experiments with deep neural networks

Brain-Inspired Replay A PyTorch implementation of the continual learning experiments with deep neural networks described in the following paper: Brain

182 Dec 27, 2022
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs This is the code of paper ConE: Cone Embeddings for Multi-Hop Reasoning over Knowl

MIRA Lab 33 Dec 07, 2022
DC3: A Learning Method for Optimization with Hard Constraints

DC3: A learning method for optimization with hard constraints This repository is by Priya L. Donti, David Rolnick, and J. Zico Kolter and contains the

CMU Locus Lab 57 Dec 26, 2022
This repository contains all code and data for the Inside Out Visual Place Recognition task

Inside Out Visual Place Recognition This repository contains code and instructions to reproduce the results for the Inside Out Visual Place Recognitio

15 May 21, 2022