# scalabel
**Repository Path**: labeltools/scalabel
## Basic Information
- **Project Name**: scalabel
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-23
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
---
[](https://travis-ci.com/scalabel/scalabel)
[](https://lgtm.com/projects/g/scalabel/scalabel/context:javascript)
[](https://lgtm.com/projects/g/scalabel/scalabel/context:python)
[Scalabel](https://www.scalabel.ai) (pronounced "scalable") is a versatile and scalable annotation platform, supporting both 2D and 3D data labeling. [BDD100K](https://bair.berkeley.edu/blog/2018/05/30/bdd/) is labeled with this tool.
## Demos
- [Overview video](https://go.yf.io/scalabel-video-demo)
- [2D Bounding box](https://go.yf.io/scalabel-demo-box2d)
- [2D Segmentation](https://go.yf.io/scalabel-demo-seg2d)
- [2D Bounding box tracking](https://go.yf.io/scalabel-demo-box2d-tracking)
- [2D Segmentation tracking](https://go.yf.io/scalabel-demo-seg2d-tracking)
- [2D Lane marking](https://go.yf.io/scalabel-demo-lane)
- [3D Bounding box](https://go.yf.io/scalabel-demo-box3d)
- [3D Bounding box tracking](https://go.yf.io/scalabel-demo-box3d-tracking)
## Try It Yourself
More installation and usage details can be find in our [documentation](http://www.scalabel.ai/doc). It also includes Windows setup.
1. Check out the code
```
git clone git@github.com:scalabel/scalabel.git
cd scalabel
```
2. Compile the code
There are two alternative ways to get the compiled code
1. Usage docker (recommended if you only need to run the code)
Download from dockerhub
```
docker pull scalabel/www
```
or build the docker image yourself
```
docker build . -t scalabel/www
```
Depending on your system, you may also have to increase docker's memory limit (8 GB should be sufficient).
2. Compile the code yourself (recommended if you want to customize the source code)
Install [nodejs and npm](https://nodejs.org/en/download/) and [redis](https://redis.io/topics/quickstart).
On Mac
```
brew install redis node
```
On Ubuntu
```
apt-get install npm nodejs redis-server
```
Transpile or build Javascript code
```
npm install
node_modules/.bin/webpack --config webpack.config.js --mode=production
```
**Note** If you are debugging the code, it is helpful to build the javascript code in development mode, in which you can trace the javascript source code in your browser debugger. `--watch` tells webpack to monitor the code changes and recompile automatically.
```
node_modules/.bin/webpack --watch --config webpack.config.js --mode=development
```
Install python dependencies
```
python3.8 -m pip install -U -r scripts/requirements.txt
```
3. Prepare data directory
```
mkdir data
cp app/config/default_config.yml data/config.yml
```
4. Launch the server
If using docker,
```
docker run -it -v "`pwd`/data:/opt/scalabel/data" -p 8686:8686 -p 6379:6379 scalabel/www \
python3.8 scripts/launch_server.py --config /opt/scalabel/data/config.yml
```
Please note to map the correct ports for both http and redis servers.
Otherwise, without using docker,
```
python scripts/launch_server.py --config ./data/config.yml
```
Then, the server can be accessed at `http://localhost:8686`. You can now check out [example usage](#example-usage) to create your first annotation project. Please make sure secure your redis server following https://redis.io/topics/security/. By default redis will backup to local file storage, so ensure you have enough disk space or disable backups inside redis.conf.
5. Get labels
The collected labels can be directly downloaded from the project dashboard. The data can be follow [bdd data format](https://github.com/ucbdrive/bdd-data/blob/master/doc/format.md). After installing the requirements and setting up the paths of the [bdd data toolkit](https://github.com/ucbdrive/bdd-data), you can visualize the labels by
```
python3 -m bdd_data.show_labels.py -l
```
## Usage
### Create annotation projects
The entry point of creating an project is `http://localhost:8686/create`. The page looks like
`Project Name` is an arbitrary string. You can create multiple projects, but they cannot have duplicated names. You can choose `Item Type` and `Label Type` from the dropdown menus. An automatic page title will be provided based on the label settings. A project consists of multiple tasks. `Task Size` is the number of items (image or point cloud) in each task.
`Item List` is the list of images or point clouds to label. The format is either json or yaml with a list of frame objects in the [bdd data format](https://github.com/ucbdrive/bdd-data/blob/master/doc/format.md). The only required field for the item list is `url`. See [examples/image_list.yml](examples/image_list.yml) for an example of image list.
`Category` and `Attributes` are the list of tags giving to each label. Typical settings are shown in [examples/categories.yml](examples/categories.yml) and [examples/bbox_attributes.yml](examples/bbox_attributes.yml). We also support multi-level categories such as [two](examples/two_level_categories.yml) and [three](examples/three_level_categories.yml) levels. Scalabel also supports [image tagging](examples/image_tags.yml).
If you want to create an annotation project to label 2d bounding boxes, the setup will looks like
After `ENTER` is clicked, the project will be created and two dashboards will be generated. The links to the dashboards will appear in the project creation page.
`DASHBOARD` is the main dashboard for annotation progress and label downloading.
You can download the annotation results in BDD format from the `EXPORT RESULTS` button in the toolbar on the left.
`VENDOR DASHBOARD` is for the annotation vendor to check the list of tasks.
The task link will lead you to each task. In our example, the task is to label 2D bounding boxes with their categories and attributes.
### Semi-automatic Annotation
Because the image list is in [bdd data format](https://github.com/ucbdrive/bdd-data/blob/master/doc/format.md), it can also contain labels within each frame. For example, you can upload an image list like [examples/image_list_with_auto_labels.json](examples/image_list_with_auto_labels.json). The labels in this email list are generated by an object detector. The labels will be automatically loaded in the tasks and shown to the annotator for adjustment.
You can use an off-shelf object detector such as [Faster RCNN](https://github.com/facebookresearch/maskrcnn-benchmark). If the results generated by the detector is in COCO
format, you can use [our script](scripts/coco2bdd.py) to convert the results to BDD format.
Another use of this function is to provide further adjustment for existing labels generated by [Scalabel](https://www.scalabel.ai). You can directly upload the exported results from a previous annotation project and the labels will show up again in the new tasks.
### Collaborative Labeling
Use the synchronization config
```
cp app/config/sync_config.yml data/config.yml
```
Now you can open multiple sessions for the same project, and they will automatically synchronize the data.
### More Usage Info
Please go to [documentation](http://www.scalabel.ai/doc) for detailed annotation instructions and advanced usages.