This repository contains the segmentation user interface from the OpenSurfaces project, extracted as a lightweight tool

Overview

OpenSurfaces Segmentation UI

This repository contains the segmentation user interface from the OpenSurfaces project, extracted as a lightweight tool. A dummy server backend is included to run the demo.

You can also view the demo online.

To run the demo, there are two versions: one with django, and one with no framework. The django version uses a dummy django server and compiles the website live as necessary. The non-django version is a flat html file extracted from the django version.

If you find this tool helpful, please cite our project:

@inproceedings{bell13opensurfaces,
	author = "Sean Bell and Paul Upchurch and Noah Snavely and Kavita Bala",
	title = "OpenSurfaces: A Richly Annotated Catalog of Surface Appearance",
	booktitle = "SIGGRAPH Conf. Proc.",
	volume = "32",
	number = "4",
	year = "2013",
}

and report any bugs using the GitHub issue tracker. Also, please "star" this project on GitHub; it's nice to see how many people are using our code.

Version 1: Run with Django (Ubuntu Linux)

  1. Install dependencies (coffee-script, django, django-compressor, ua-parser, BeautifulSoup):

    Note: this will change your django current installation if you are not somewhere between 1.4.* and 1.6.*. I suggest looking into the virtualenv package if this is a problem for you.

./django-setup-demo.sh
  1. Start the local webserver:
./django-run-demo.sh
  1. Visit localhost:8000 in a web browser

To get the demo to work on Mac and Windows, you will have to look at the above scripts and run the equivalent commands for your system.

After drawing 6 polygons, the submit button will show you the POST data that would have been sent to the server.

Version 2: Run without Django (Linux or Mac)

  1. Install npm and node.js. On Ubuntu, this is:
sudo apt-get install npm nodejs
  1. Install coffee-script:
sudo npm install -g coffee-script
  1. Build static files (js, css, img) and then start a local python-based webserver:
./python-run-demo.sh
  1. Visit localhost:8000 in a web browser

To get the demo to work on Windows, you will have to look at the above scripts and run the equivalent commands for your system.

Project Notes

POST data

When a user submits, the client will POST the data to the same URL. On success, the client expects the JSON response {"message": "success", "result": "success"}. The client will then notify the MTurk server that the task is completed. For more details, see example_project/segmentation/views.py.

When a user submits, the POST will contain these fields:

results: a dictionary mapping from the photo ID (which is just "1" in
	this example) to a list of polygons.  Example:
	{"1": [[x1,y1,x2,y2,x3,y3,...], [x1,y1,x2,y2,...]]}.
	Coordinates are scaled with respect to the source photo dimensions, so both
	x and y are in the range 0 to 1.

time_ms: amount of time the user spent (whether or not they were active)

time_active_ms: amount of time that the user was active in the current window

action_log: a JSON-encoded log of user actions

screen_width: user screen width

screen_height: user screen height

version: always "1.0"

feedback: omitted if there is no feedback; JSON encoded dictionary of the form:
{
	'thoughts': user's response to "What did you think of this task?",
	'understand': user's response to "What parts didn't you understand?",
	'other': user's response to "Any other feedback, improvements, or suggestions?"
}

Feedback survey

When the user finishes the task, a popup will ask for feedback. In the django version, disable this by setting ask_for_feedback to 'false' in the file example_project/segmentation/vies.py. In the non-django verfsion, update the window.ask_for_feedback variable in index.html.

I recommend asking for feedback after the 2nd or 3rd time a user has submitted, not the first time, and then not asking again (otherwise it gets annoying). Users usually don't have feedback until they have been working for a little while.

Compiling from coffeescript

The javascript for the tool is automatically compiled from coffeescript files by django-compressor and accessed by the client at a url of the form /static/cache/js/*.js. This is set up already if using django.

If not using django, the python-run-demo.sh does this for you by manually compiling coffeescript files and storing them in the /static/ folder.

Browser compatibility

This UI works in Chrome and Firefox only. The Django version includes a browser check that shows an error page if the user is not on Chrome or Firefox or is on a mobile device.

Local /static/ folder

After you run the demo setup, the directory /static/ will contain compiled css and javascript files.

If you are usikng django and change any part of the static files (js, css, images, coffeescript), you will need to repopulate the static folder with this command:

example_project/manage.py collectstatic --noinput

If you are building on top of this repository:

In example_project/settings.py:

  1. Change SECRET_KEY to some random string.
  2. Fill in the rest of the values (admin name, database, etc).

If you want to add this demo to your own (separate) Django project:

In your settings.py file, make the following changes:

  1. Make sure STATIC_ROOT is set to an absolute writable path.

  2. Add this to the STATICFILES_FINDERS tuple:

	'compressor.finders.CompressorFinder',
  1. Add this to the INSTALLED_APPS tuple:
	'django.contrib.humanize',
	'compressor',
	'segmentation',
  1. Add this to settings.py (e.g. at the end):
	# Django Compressor
	COMPRESS_ENABLED = True
	COMPRESS_OUTPUT_DIR = 'cache'
	COMPRESS_PRECOMPILERS = (
		('text/coffeescript', 'coffee --bare --compile --stdio'),
		('text/less', 'lessc -x {infile} {outfile}'),
	)
Owner
Sean Bell
CEO and Co-Founder, GrokStyle Inc. PhD, Cornell University
Sean Bell
Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training

ColossalAI An integrated large-scale model training system with efficient parallelization techniques. arXiv: Colossal-AI: A Unified Deep Learning Syst

HPC-AI Tech 7.9k Jan 08, 2023
Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning

SkFlow has been moved to Tensorflow. SkFlow has been moved to http://github.com/tensorflow/tensorflow into contrib folder specifically located here. T

3.2k Dec 29, 2022
Ankou: Guiding Grey-box Fuzzing towards Combinatorial Difference

Ankou Ankou is a source-based grey-box fuzzer. It intends to use a more rich fitness function by going beyond simple branch coverage and considering t

SoftSec Lab 54 Dec 24, 2022
Optimizes image files by converting them to webp while also updating all references.

About Optimizes images by (re-)saving them as webp. For every file it replaced it automatically updates all references. Works on single files as well

Watermelon Wolverine 18 Dec 23, 2022
Code for paper Novel View Synthesis via Depth-guided Skip Connections

Novel View Synthesis via Depth-guided Skip Connections Code for paper Novel View Synthesis via Depth-guided Skip Connections @InProceedings{Hou_2021_W

8 Mar 14, 2022
LAMDA: Label Matching Deep Domain Adaptation

LAMDA: Label Matching Deep Domain Adaptation This is the implementation of the paper LAMDA: Label Matching Deep Domain Adaptation which has been accep

Tuan Nguyen 9 Sep 06, 2022
Using NumPy to solve the equations of fluid mechanics together with Finite Differences, explicit time stepping and Chorin's Projection methods

Computational Fluid Dynamics in Python Using NumPy to solve the equations of fluid mechanics 🌊 🌊 🌊 together with Finite Differences, explicit time

Felix Köhler 4 Nov 12, 2022
A visualisation tool for Deep Reinforcement Learning

DRLVIS - Visualising Deep Reinforcement Learning Created by Marios Sirtmatsis with the support of Alex Bäuerle. DRLVis is an application used for visu

Marios Sirtmatsis 1 Nov 04, 2021
Anonymize BLM Protest Images

Anonymize BLM Protest Images This repository automates @BLMPrivacyBot, a Twitter bot that shows the anonymized images to help keep protesters safe. Us

Stanford Machine Learning Group 40 Oct 13, 2022
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification [NeurIPS 2021] Abstract Multiple instance learn

132 Dec 30, 2022
Code for the paper Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration

IMAGINE: Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration This repo contains the code base of the paper Language as a Cog

Flowers Team 26 Dec 22, 2022
Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

CLIP-GLaSS Repository for the paper Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search An in-browser demo is

Federico Galatolo 172 Dec 22, 2022
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

ENet in Caffe Execution times and hardware requirements Network 1024x512 1280x720 Parameters Model size (fp32) ENet 20.4 ms 32.9 ms 0.36 M 1.5 MB SegN

Timo Sämann 561 Jan 04, 2023
We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

Overview This repository provides the implementation for the paper "Improved Regularization and Robustness for Fine-tuning in Neural Networks", which

NEU-StatsML-Research 21 Sep 08, 2022
Fast and Easy Infinite Neural Networks in Python

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Google 1.9k Jan 09, 2023
Official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo'

IterMVS official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo' Introduction IterMVS is a novel lear

Fangjinhua Wang 127 Jan 04, 2023
Elastic weight consolidation technique for incremental learning.

Overcoming-Catastrophic-forgetting-in-Neural-Networks Elastic weight consolidation technique for incremental learning. About Use this API if you dont

Shivam Saboo 89 Dec 22, 2022
Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer)

Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer) Introduction By applying the

Son Gyo Jung 1 Jul 09, 2022
Easy way to add GoogleMaps to Flask applications. maintainer: @getcake

Flask Google Maps Easy to use Google Maps in your Flask application requires Jinja Flask A google api key get here Contribute To contribute with the p

Flask Extensions 611 Dec 05, 2022
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution (CVPR2021)

MASA-SR Official PyTorch implementation of our CVPR2021 paper MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Re

DV Lab 126 Dec 20, 2022