Our tool supports both video and image annotations. We provide a script
scripts/prepare_data.py to help you prepare data.
For the video data, we first split the video into frames at the specified frame rate (the 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>
--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
We assume that 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 as well as the 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
If you wish to serve your own data on the same domain as this server, you may do so. This means the item_list.yml you provide at project creation may contain URLs to this server. Any files or directories you place in “app/dist/” will be uploaded to the server with the prefix path removed. For example, say that you place a directory “images” in “app/dist/”. If you are testing the server locally, you can specify in your item list URLs such as “localhost:8686/images/image_1.jpg”.