Our tool supports both video and image annotations. We provide a script at
scripts/prepare_data.py to help you prepare data.
- For the video data, we will first split the video into frames at the specified frame rate (default is 5fps) and then generate a yaml file containing the paths to the frames.
python3 scripts/prepare_data.py -i <path_to_video> -t <output_directory> -f <fps>
--s3 <s3_bucket_name/folder>
Flag --s3 is optional. If you want to upload the output frames and the
frame list to your Amazon S3 bucket, you can specify the key to the bucket.
We assume the bucket is public readable so that the generated links
can be directly accessed by the tool.
Please refer to the doc
to configure your S3 bucket.
- For the image data, you need to specify the path to the folder that contains
the images. If
--s3is specified, the images will be uploaded to the S3 bucket together with the image list yaml file that contains the urls to the images.
python3 scripts/prepare_data.py -i <path_to_image_folder> --s3 <s3_bucket_name/folder>
Once obtaining the image_list.yml, we can proceed to create a new annotation project.
As provided in the README, you can either set up the tool with
the provided docker image or build the tool by yourself. Once you have the tool setup,
you can open http://localhost:8686 to create your annotation project.
The interface of creating a new project is show as follows. We use the video bounding box annotation as an example.
In this case, you need to upload image_list.yml which contains either
paths to the video frames or public readable urls to the frames in the Item List.
For Categories and Attributes, you can upload the yaml files that contains the
labels and attributes descriptions respectively. Please refer to examples/ folder
for more details.
At this point, you can click Go to Dashboard to monitor the project, download
results and try the annotation tool. The interface is shown as follows.
If you click task link, you can go to the annotation interface and
start your work.
For tasks like object detection, we need to draw bounding boxes for the objects of interest in the image. The interface of drawing 2D bounding boxes is shown as follows.
On the left side, we show all the label categories specified in categories.yml
and also provide Occluded and Truncated options to indicate whether or not
the object is occluded or truncated. For traffic lights, we also have the options
to indicate the color of the light on the left side.
When drawing the bounding boxes, it's better to zoom in the image by
clicking the + button on the top right if the object
of interest in too small. The zoom-in image is shown as follows. You can
use your mouse to move the canvas to annotate different regions. If you want
to remove one bounding box, simply click the bounding box to highlight it and
then click Remove button on the bottom left.
Once finish drawing all the bounding boxes, you can save the results by
clicking the Save button on the top right. Then the bounding boxes results
will be saved to your data folder specified in the configure.yml.
A short demo of drawing 2D bounding boxes is shown below. Please check out the video with higher resolution here.
The tool also supports the instance segmentation annotation. The interface is similar as the interface of 2D bounding boxes where on the left side, we provide label categories and other attributes of the labels.
To draw a mask of the instance, we draw multiple anchor points along the boundary
of the instance and end at the initial point to complete the drawing.
A short demo of drawing instance segmentation is shown below. Please check out the video with higher resolution here.
Besides, you can enter the quick draw mode by clicking the Quickdraw on the bottom left
or pressing s which allows the two instances to share the common border.
Check out the video demo with higher resolution here.
Our tool also allows you to bezier the curve to improve the annotation quality. Checkout the video demo here.
Lane marking is used in a similar way as the other image annotations, but you will need to select a different label type when you are creating your project. The operations are the same as the instance segmentation. In this case, you won’t see a whole chuck of color filled in your selected area, since lane labeling is just outlining different sections of a street.
Here is a short demo of lane marking below. Checkout the video with higher resolution here.
Similarly as other instance segmentation task, you can annotate the drivable area in the image. Details about the drivable area can be found in this blog.
An example annotation is shown below.
We also support video object tracking. Firstly, we draw a bounding box around the object of interest at the starting frame.
Then, continue playing the video until the ending frame. Move the bounding box to the ending position of the object and adjust the bounding box size.
At this point, if you replay the video, the bounding box will track the object and adjust its size across frames.
Here is a short demo to track a person in the video. Please check out the video with higher resolution here.
