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
Code for our paper at ECCV 2020: Post-Training Piecewise Linear Quantization for Deep Neural Networks

PWLQ Updates 2020/07/16 - We are working on getting permission from our institution to release our source code. We will release it once we are granted

54 Dec 15, 2022
[ICCV'21] PlaneTR: Structure-Guided Transformers for 3D Plane Recovery

PlaneTR: Structure-Guided Transformers for 3D Plane Recovery This is the official implementation of our ICCV 2021 paper News There maybe some bugs in

73 Nov 30, 2022
Melanoma Skin Cancer Detection using Convolutional Neural Networks and Transfer Learning🕵🏻‍♂️

This is a Kaggle competition in which we have to identify if the given lesion image is malignant or not for Melanoma which is a type of skin cancer.

Vipul Shinde 1 Jan 27, 2022
classify fashion-mnist dataset with pytorch

Fashion-Mnist Classifier with PyTorch Inference 1- clone this repository: git clone https://github.com/Jhamed7/Fashion-Mnist-Classifier.git 2- Instal

1 Jan 14, 2022
Python implementation of the multistate Bennett acceptance ratio (MBAR)

pymbar Python implementation of the multistate Bennett acceptance ratio (MBAR) method for estimating expectations and free energy differences from equ

Chodera lab // Memorial Sloan Kettering Cancer Center 169 Dec 02, 2022
The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting".

IGMTF The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting". Requirements The framework

Wentao Xu 24 Dec 05, 2022
Official implementation of "An Image is Worth 16x16 Words, What is a Video Worth?" (2021 paper)

An Image is Worth 16x16 Words, What is a Video Worth? paper Official PyTorch Implementation Gilad Sharir, Asaf Noy, Lihi Zelnik-Manor DAMO Academy, Al

213 Nov 12, 2022
Taichi Course Homework Template

太极图形课S1-标题部分 这个作业未来或将是你的开源项目,标题的内容可以来自作业中的核心关键词,让读者一眼看出你所完成的工作/做出的好玩demo 如果暂时未想好,起名时可以参考“太极图形课S1-xxx作业” 如下是作业(项目)展开说明的方法,可以帮大家理清思路,并且也对读者非常友好,请小伙伴们多多参

TaichiCourse 30 Nov 19, 2022
Neural-net-from-scratch - A simple Neural Network from scratch in Python using the Pymathrix library

A Simple Neural Network from scratch A Simple Neural Network from scratch in Pyt

Youssef Chafiqui 2 Jan 07, 2022
CKD - Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding

Collaborative Knowledge Distillation for Heterogeneous Information Network Embed

zhousheng 9 Dec 05, 2022
Look Who’s Talking: Active Speaker Detection in the Wild

Look Who's Talking: Active Speaker Detection in the Wild Dependencies pip install -r requirements.txt In addition to the Python dependencies, ffmpeg

Clova AI Research 60 Dec 08, 2022
A small library of 3D related utilities used in my research.

utils3D A small library of 3D related utilities used in my research. Installation Install via GitHub pip install git+https://github.com/Steve-Tod/util

Zhenyu Jiang 8 May 20, 2022
UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss

UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss This repository contains the TensorFlow implementation of the paper UnF

Simon Meister 270 Nov 06, 2022
Multi-agent reinforcement learning algorithm and environment

Multi-agent reinforcement learning algorithm and environment [en/cn] Pytorch implements multi-agent reinforcement learning algorithms including IQL, Q

万鲲鹏 7 Sep 20, 2022
LeViT a Vision Transformer in ConvNet's Clothing for Faster Inference

LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference This repository contains PyTorch evaluation code, training code and pretrained

Facebook Research 504 Jan 02, 2023
A sequence of Jupyter notebooks featuring the 12 Steps to Navier-Stokes

CFD Python Please cite as: Barba, Lorena A., and Forsyth, Gilbert F. (2018). CFD Python: the 12 steps to Navier-Stokes equations. Journal of Open Sour

Barba group 2.6k Dec 30, 2022
NER for Indian languages

CL-NERIL: A Cross-Lingual Model for NER in Indian Languages Code for the paper - https://arxiv.org/abs/2111.11815 Setup Setup a virtual environment Th

Akshara P 0 Nov 24, 2021
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum

CO-PILOT CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum, NeurIPS 2021, Shuang Ao, Tianyi Zhou, Guodong Long, Qingh

Shuang Ao 1 Feb 18, 2022
A customisable game where you have to quickly click on black tiles in order of appearance while avoiding clicking on white squares.

W.I.P-Aim-Memory-Game A customisable game where you have to quickly click on black tiles in order of appearance while avoiding clicking on white squar

dE_soot 1 Dec 08, 2021
[MedIA2021]MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning

MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning [MedIA or Arxiv] and [Demo] This repository pr

Healthcare Intelligence Laboratory 92 Dec 08, 2022