A web based labeling tool for creating AI training data sets (2D and 3D). The tool has been developed in the context of autonomous driving research. It supports images (.jpg or .png) and point clouds (.pcd). It is a Meteor app developed with React, Paper.js and three.js.
Latest changes
:movie_camera: VIDEO: Bitmap labeling overview
:movie_camera: VIDEO: Point cloud labeling overview
sse-docker-stack.yml
)YOUR_IMAGES_PATH
) and run the tool using docker-composesse-docker-stack.yml
wget https://raw.githubusercontent.com/Hitachi-Automotive-And-Industry-Lab/semantic-segmentation-editor/master/sse-docker-stack.yml
wget https://raw.githubusercontent.com/Hitachi-Automotive-And-Industry-Lab/semantic-segmentation-editor/master/settings.json
METEOR_SETTINGS=$(cat ./settings.json) SSE_IMAGES=YOUR_IMAGES_PATH docker-compose -f stack.yml up
(Optional) You can modify settings.json
to customize classes data.
curl https://install.meteor.com/ | sh
or download Meteor Windows Installer
cd semantic-segmentation-editor-x.x.x
meteor npm install
meteor npm start
The editor will run by default on http://localhost:3000
(Optional) Edit settings.json
By default, images are served from your_home_dir/sse-images
and pointcloud binary segmentation data are stored in your_home_dir/sse-internal
.
You can configure these folders in settings.json by modifying images-folder
and internal-folder
properties.
On Windows, use '/' separators, example c:/Users/john/images
Check Meteor Environment Variables to configure your app
(MONGO_URL
, DISABLE_WEBSOCKETS
, etc...)
{
"configuration": {
"images-folder": "/mnt/images", // The root folder containing images and PCD files
"internal-folder": "/mnt/pointcloud_data" // Segmentation data (only 3D) will be stored in this folder
},
// The different sets of classes available in the tool
// For object classes, only the 'label' field is mandatory
// The icon field can be set with an icon from the mdi-material-ui package
"sets-of-classes": [
{
"name": "Cityscapes", "objects": [
{"label": "VOID", "color": "#CFCFCF"},
{"label": "Road", "color": "#804080", "icon": "Road"},
{"label": "Sidewalk", "color": "#F423E8", "icon": "NaturePeople"},
{"label": "Parking", "color": "#FAAAA0", "icon": "Parking"},
{"label": "Rail Track", "color": "#E6968C", "icon": "Train"},
{"label": "Person", "color": "#DC143C", "icon": "Walk"},
{"label": "Rider", "color": "#FF0000", "icon": "Motorbike"},
{"label": "Car", "color": "#0000E8", "icon": "Car"}
},
{ ... }
]
}
The editor is built around 3 different screens:
The file navigator let's you browse available files to select a bitmap images or a point cloud for labeling
The bitmap image editor is dedicated to the labeling of jpg and png files by drawing polygons
The point cloud editor is dedicated to the labeling of point clouds by creating objects made of subsets of 3D points
There are several tools to create labeling polygons:
x
, y
, z
, label
(optional integer), rgb
(optional integer)x
, y
, z
, label
, object
and rgb
(if available)/api/listing
: List all annotated images/api/json/[PATH_TO_FILE]
: (2D only) Get the polygons and other data for that file/api/pcdtext/[PATH_TO_FILE]
: (3D only) Get the labeling of a pcd file using 2 addditional
columns: label
and object
/api/pcdfile/[PATH_TO_FILE]
: (3D only) The same but returned as "plain/text" attachment file download