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
Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM)

Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM) Introduction The average lifetime of the $D^{0}$ me

Son Gyo Jung 1 Dec 17, 2021
Open source code for Paper "A Co-Interactive Transformer for Joint Slot Filling and Intent Detection"

A Co-Interactive Transformer for Joint Slot Filling and Intent Detection This repository contains the PyTorch implementation of the paper: A Co-Intera

67 Dec 05, 2022
(AAAI2022) Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation

SM-PPM This is a Pytorch implementation of our paper "Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Seman

W-zx-Y 10 Dec 07, 2022
Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation

Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation Our paper is accepted by ICCV2021. Picture: Overview of the proposed Plug-an

Yunfei Liu 32 Dec 10, 2022
Dataset and Source code of paper 'Enhancing Keyphrase Extraction from Academic Articles with their Reference Information'.

Enhancing Keyphrase Extraction from Academic Articles with their Reference Information Overview Dataset and code for paper "Enhancing Keyphrase Extrac

15 Nov 24, 2022
Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D

Hannes Stärk 95 Dec 30, 2022
Official implementation of the network presented in the paper "M4Depth: A motion-based approach for monocular depth estimation on video sequences"

M4Depth This is the reference TensorFlow implementation for training and testing depth estimation models using the method described in M4Depth: A moti

Michaël Fonder 76 Jan 03, 2023
Simultaneous Detection and Segmentation

Simultaneous Detection and Segmentation This is code for the ECCV Paper: Simultaneous Detection and Segmentation Bharath Hariharan, Pablo Arbelaez,

Bharath Hariharan 96 Jul 20, 2022
From a body shape, infer the anatomic skeleton.

OSSO: Obtaining Skeletal Shape from Outside (CVPR 2022) This repository contains the official implementation of the skeleton inference from: OSSO: Obt

Marilyn Keller 166 Dec 28, 2022
A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.

torchsynth The fastest synth in the universe. Introduction torchsynth is based upon traditional modular synthesis written in pytorch. It is GPU-option

torchsynth 229 Jan 02, 2023
Here I will explain the flow to deploy your custom deep learning models on Ultra96V2.

Xilinx_Vitis_AI This repo will help you to Deploy your Deep Learning Model on Ultra96v2 Board. Prerequisites Vitis Core Development Kit 2019.2 This co

Amin Mamandipoor 1 Feb 08, 2022
CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search

CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search This repository is the official implementation of CAPITAL: Optimal Subgrou

Hengrui Cai 0 Oct 19, 2021
This repo is customed for VisDrone.

Object Detection for VisDrone(无人机航拍图像目标检测) My environment 1、Windows10 (Linux available) 2、tensorflow = 1.12.0 3、python3.6 (anaconda) 4、cv2 5、ensemble

53 Jul 17, 2022
Group Fisher Pruning for Practical Network Compression(ICML2021)

Group Fisher Pruning for Practical Network Compression (ICML2021) By Liyang Liu*, Shilong Zhang*, Zhanghui Kuang, Jing-Hao Xue, Aojun Zhou, Xinjiang W

Shilong Zhang 129 Dec 13, 2022
A general python framework for visual object tracking and video object segmentation, based on PyTorch

PyTracking A general python framework for visual object tracking and video object segmentation, based on PyTorch. 📣 Two tracking/VOS papers accepted

2.6k Jan 04, 2023
Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition

Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition Official implementation of the Efficient Conforme

Maxime Burchi 145 Dec 30, 2022
Lipschitz-constrained Unsupervised Skill Discovery

Lipschitz-constrained Unsupervised Skill Discovery This repository is the official implementation of Seohong Park, Jongwook Choi*, Jaekyeom Kim*, Hong

Seohong Park 17 Dec 18, 2022
Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding (CVPR2022)

Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding by Qiaole Dong*, Chenjie Cao*, Yanwei Fu Paper and Supple

Qiaole Dong 190 Dec 27, 2022
🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (CVPR 2020) This is the official implementation of RandLA-Net (CVPR2020, Oral

Qingyong 1k Dec 30, 2022
190 Jan 03, 2023