In order to tag images, we use a piece of software called Sloth. To install it from the robosub debian repository, run the following command:
sudo aptitude install sloth
Now that you have sloth, you need to clone the
vision_dev repository. To do that, run this command wherever you want to clone it:
Using an SSH key (Do this if you have completed the Getting Started setup):
git clone email@example.com:PalouseRobosub/vision_dev.git
Without an SSH key:
git clone https://github.com/PalouseRobosub/vision_dev.git
To use sloth and start tagging images, you can run the following command.
sloth -c /path/to/vision_dev/sloth/robosub_config.py /path/to/annotation/file
*-c* Is a flag to give the path to a configuration file. This file is provided in the vision_dev/sloth directory. The path you provide should point to this file.
The last argument is a path to an annotation file. This is most likely named something like `labels.json`. You will need to provide this path in order to tag images.
Should you find this tedious, there is a script in the sloth directory of the vision_dev repository which performs some of this for you. It can be used as follows:
This removes the need to add the -c flag repeatedly. This script can also be symlinked to without issues.
You can set up an alias in your bashrc. To do so run (replace “~/vision_dev” with path to your vision_dev repo):
echo "alias sloth='~/vision_dev/sloth/./robosub_sloth.sh'" >> ~/.bashrc source ~/.bashrc
The full list of keybindings used in sloth can be found in the robosub_config.py file near the bottom. A shorthand list is provided below
Mark image as labeled/confirmed and go to next
Select next annotation
Select previous annotation
Fit current image/frame into window
Delete selected annotations
Exit insert mode
Mark current image as labeled
Mark all annotations in image as confirmed
Toggle the visibility of label names on annotation boxes
Delete all annotations from the current image and mark it as unlabeled
Copy all annotations from the previous image to this one
Mark image as labeled/confirmed and copy annotations to next image.
(Equivalent to Space then c)
Start Gate Post
While creating annotations, the following are useful mouse controls.
Resize an annotation. Resizing is based upon the quadrant of the annotation clicked on.
Select multiple annotations at once.
Getting, validating, and returning labeling data is handled through the
rslabel utility program. It currently only supports python 2.x versions. To install it, run
sudo pip install rslabel
To update rslabel for new features run
sudo pip install --upgrade rslabel
There are a number of commands to be used with
|Provides information about the number of datasets labeled, number of images properly validated, and counts any labeling sessions in progress.|
| Grabs an image dataset for labeling and places it in your current directory. The
|Returns a dataset to the server. If the data has not been completely labeled or validated, it will be returned for someone else to complete in the future.|
|Takes a ROS bag file and break the images out into datasets for labeling. It will then upload the files to the server for labeling.|
|Collects all of the labeled and validated images into a single dataset for use with object detection training.|
|A tool which is used for validating data, highlights the box which is there.|
|This command will show scoreboard table: how many images labeled, how many labels added, how many images validated and how many labels validated.|
People who want to label on Windows you can use this tool to check, download and upload labels. To use this just extract the folder in zip anywhere and run
WindowsSftp.exe, remember that
.dll file must be in the same folder as