Segmentation UI from the OpenSurfaces Project
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gauenk and seanbell Update django-setup-demo.sh
This is an extra step I had to take when installing the program. Why not include it while you require sudo permissions anyway? Thread here: nodejs/node-v0.x-archive#3911
Latest commit 247abb6 May 24, 2017

README.md

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}'),
	)