Local Turk implements Amazon's Mechanical Turk API on your own machine.
It's handy if you want to:
- Develop a Mechanical Turk template
- Do some repetitive tasks on your own, without involving Turkers.
You could use it, for instance, to generate test and training data for a Machine Learning algorithm.
Install:
npm install -g localturk
Run:
cd localturk/sample
localturk --static_dir . transcribe.html tasks.csv outputs.csv
Then visit http://localhost:4321/ to start Turking.
Using Local Turk is just like using Amazon's Mechanical Turk. You create:
- An HTML template file with a <form>
- A CSV file of tasks
For example, say you wanted to record whether some images contained a red ball. You would make a CSV file containing the URLs for each image:
image_url
http://example.com/image_with_red_ball.png
http://example.com/image_without_red_ball.png
Then you'd make an HTML template for the task:
<img src="${image_url}" />
<input type=radio name=has_button value="yes" /> Has a red ball<br/>
<input type=radio name=has_button value="no" /> Does not have a red ball<br/>Finally, you'd start up the Local Turk server:
$ localturk path/to/template.html path/to/tasks.csv path/to/output.csv
Now you can visit http://localhost:4321/ to complete each task. When you're done, the output.csv file will contain
image_url,has_button
http://example.com/image_with_red_ball.png,yes
http://example.com/image_without_red_ball.png,no
The use case described above (classifying images) is an extremely common one.
To expedite this, localturk provides a separate script for doing image classification. The example above could be written as:
classify-images --labels 'Has a red ball,Does not have a red ball' *.png
This will bring up a web server with a UI for assigning one of those two labels
to each image on your local file system. The results will go in output.csv.
For more details, run classify-images --help.