Contributing to this project

• 1: Development environment
• 2: Setup additional components in development environment
• 3: JavaScript/Typescript coding style
• 4: Branching model
• 5: Using the issue tracker
• 6: Bug reports
• 7: Feature requests
• 8: Pull requests
• 9: How to add a new annotation format support
• 10: REST API design principles

1 - Development environment

Setup the dependencies:

• Install necessary dependencies:

  Ubuntu 18.04

  sudo apt-get update && sudo apt-get --no-install-recommends install -y build-essential curl git redis-server python3-dev python3-pip python3-venv python3-tk libldap2-dev libsasl2-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev
  

  # Install Node.js 16
  curl -sL https://deb.nodesource.com/setup_16.x | sudo -E bash -
  sudo apt-get install -y nodejs
  

  MacOS 10.15

  brew install git python pyenv redis curl openssl node sqlite3 geos
  

• Install Chrome

• Install FFmpeg libraries (libav*) version 4.0 or higher.

• Install VS Code.

• Install the following VScode extensions:

  • JavaScript Debugger
  • Python
  • ESLint
  • Stylelint
  • vscode-remark-lint
  • licenser
  • Trailing Spaces

• Make sure to use Python 3.9.0 or higher

  python3 --version
  

• Install CVAT on your local host:

  git clone https://github.com/openvinotoolkit/cvat
  cd cvat && mkdir logs keys
  python3 -m venv .env
  . .env/bin/activate
  pip install -U pip wheel setuptools
  pip install -r cvat/requirements/development.txt
  python manage.py migrate
  python manage.py collectstatic
  

  Note for Mac users

  If you have any problems with installing dependencies from cvat/requirements/*.txt, you may need to reinstall your system python In some cases after system update it can be configured incorrectly and cannot compile some native modules

  Make sure Homebrew lib path is in DYLD_LIBRARY_PATH. For Apple Silicon: export DYLD_LIBRARY_PATH=/opt/homebrew/lib:$DYLD_LIBRARY_PATH

  Homebrew will install FFMpeg 5.0 by default, which does not work, so you should install 4.X. You can install older 4.X FFMpeg using Homebrew like that:

   cd "$(brew --repo homebrew/core)"
   git checkout addd616edc9134f057e33694c420f4900be59db8
   brew unlink ffmpeg
   HOMEBREW_NO_AUTO_UPDATE=1 brew install ffmpeg
   git checkout master
  

  On Mac with Apple Silicon (M1) in order to install TensorFlow you will have to edit cvat/requirements/base.txt. Change tensorflow to tensorflow-macos

• Create a super user for CVAT:

  python manage.py createsuperuser
  

• Install npm packages for UI (run the following command from CVAT root directory):

  npm ci
  

  Note for Mac users

  If you faced with error

  Node Sass does not yet support your current environment: OS X 64-bit with Unsupported runtime (57)

  Read this article Node Sass does not yet support your current environment

• Install Docker Engine and Docker-Compose

• Pull OpenPolicyAgent Docker-image (run from CVAT root dir):

  sudo docker-compose -f docker-compose.yml -f docker-compose.dev.yml up cvat_opa
  

Run CVAT

• Start npm UI debug server (run the following command from CVAT root directory):

  • If you want to run CVAT in localhost:

    npm run start:cvat-ui
    

  • If you want to access CVAT from outside of your host:

    CVAT_UI_HOST='<YOUR_HOST_IP>' npm run start:cvat-ui
    

• Open a new terminal window.

• Run VScode from the virtual environment (run the following command from CVAT root directory):

  source .env/bin/activate && code
  

• Inside VScode, Open CVAT root dir

• Select server: debug configuration and run it (F5) to run REST server and its workers

• Make sure that Uncaught Exceptions option under breakpoints section is unchecked

• If you choose to run CVAT in localhost: Select server: chrome configuration and run it (F5) to open CVAT in Chrome

• Alternative: If you changed CVAT_UI_HOST just enter <YOUR_HOST_IP>:3000 in your browser.

You have done! Now it is possible to insert breakpoints and debug server and client of the tool.

Note for Windows users

You develop CVAT under WSL (Windows subsystem for Linux) following next steps.

• Install WSL using this guide.

• Following this guide install Ubuntu 18.04 Linux distribution for WSL.

• Run Ubuntu using start menu link or execute next command

  wsl -d Ubuntu-18.04
  

• Run all commands from this installation guide in WSL Ubuntu shell.

• You might have to manually start the redis server in wsl before you can start the configuration inside Visual Studio Code. You can do this with sudo service redis-server start. Alternatively you can also use a redis docker image instead of using the redis-server locally.

2 - Setup additional components in development environment

DL models as serverless functions

Follow this guide to install Nuclio:

• You have to install nuctl command line tool to build and deploy serverless functions.
• The simplest way to explore Nuclio is to run its graphical user interface (GUI) of the Nuclio dashboard. All you need in order to run the dashboard is Docker. See nuclio documentation for more details.
• Create cvat project inside nuclio dashboard where you will deploy new serverless functions and deploy a couple of DL models.

nuctl create project cvat

nuctl deploy --project-name cvat \
    --path serverless/openvino/dextr/nuclio \
    --volume `pwd`/serverless/common:/opt/nuclio/common \
    --platform local

20.07.17 12:02:23.247                     nuctl (I) Deploying function {"name": ""}
20.07.17 12:02:23.248                     nuctl (I) Building {"versionInfo": "Label: 1.4.8, Git commit: 238d4539ac7783896d6c414535d0462b5f4cbcf1, OS: darwin, Arch: amd64, Go version: go1.14.3", "name": ""}
20.07.17 12:02:23.447                     nuctl (I) Cleaning up before deployment
20.07.17 12:02:23.535                     nuctl (I) Function already exists, deleting
20.07.17 12:02:25.877                     nuctl (I) Staging files and preparing base images
20.07.17 12:02:25.891                     nuctl (I) Building processor image {"imageName": "cvat/openvino.dextr:latest"}
20.07.17 12:02:25.891     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.4.8-amd64"}
20.07.17 12:02:29.270     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/uhttpc:0.0.1-amd64"}
20.07.17 12:02:33.208            nuctl.platform (I) Building docker image {"image": "cvat/openvino.dextr:latest"}
20.07.17 12:02:34.464            nuctl.platform (I) Pushing docker image into registry {"image": "cvat/openvino.dextr:latest", "registry": ""}
20.07.17 12:02:34.464            nuctl.platform (I) Docker image was successfully built and pushed into docker registry {"image": "cvat/openvino.dextr:latest"}
20.07.17 12:02:34.464                     nuctl (I) Build complete {"result": {"Image":"cvat/openvino.dextr:latest","UpdatedFunctionConfig":{"metadata":{"name":"openvino.dextr","namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"openvino","spec":"","type":"interactor"}},"spec":{"description":"Deep Extreme Cut","handler":"main:handler","runtime":"python:3.6","env":[{"name":"NUCLIO_PYTHON_EXE_PATH","value":"/opt/nuclio/python3"}],"resources":{},"image":"cvat/openvino.dextr:latest","targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"","maxWorkers":2,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"/Users/nmanovic/Workspace/cvat/serverless/openvino/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"image":"cvat/openvino.dextr","baseImage":"openvino/ubuntu18_runtime:2020.2","directives":{"postCopy":[{"kind":"RUN","value":"curl -O https://download.01.org/openvinotoolkit/models_contrib/cvat/dextr_model_v1.zip"},{"kind":"RUN","value":"unzip dextr_model_v1.zip"},{"kind":"RUN","value":"pip3 install Pillow"},{"kind":"USER","value":"openvino"}],"preCopy":[{"kind":"USER","value":"root"},{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"ln -s /usr/bin/pip3 /usr/bin/pip"}]},"codeEntryType":"image"},"platform":{},"readinessTimeoutSeconds":60,"eventTimeout":"30s"}}}}
20.07.17 12:02:35.012            nuctl.platform (I) Waiting for function to be ready {"timeout": 60}
20.07.17 12:02:37.448                     nuctl (I) Function deploy complete {"httpPort": 55274}

nuctl deploy --project-name cvat \
    --path serverless/openvino/omz/public/yolo-v3-tf/nuclio \
    --volume `pwd`/serverless/openvino/common:/opt/nuclio/common \
    --platform local

20.07.17 12:05:23.377                     nuctl (I) Deploying function {"name": ""}
20.07.17 12:05:23.378                     nuctl (I) Building {"versionInfo": "Label: 1.4.8, Git commit: 238d4539ac7783896d6c414535d0462b5f4cbcf1, OS: darwin, Arch: amd64, Go version: go1.14.3", "name": ""}
20.07.17 12:05:23.590                     nuctl (I) Cleaning up before deployment
20.07.17 12:05:23.694                     nuctl (I) Function already exists, deleting
20.07.17 12:05:24.271                     nuctl (I) Staging files and preparing base images
20.07.17 12:05:24.274                     nuctl (I) Building processor image {"imageName": "cvat/openvino.omz.public.yolo-v3-tf:latest"}
20.07.17 12:05:24.274     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.4.8-amd64"}
20.07.17 12:05:27.432     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/uhttpc:0.0.1-amd64"}
20.07.17 12:05:31.462            nuctl.platform (I) Building docker image {"image": "cvat/openvino.omz.public.yolo-v3-tf:latest"}
20.07.17 12:05:32.798            nuctl.platform (I) Pushing docker image into registry {"image": "cvat/openvino.omz.public.yolo-v3-tf:latest", "registry": ""}
20.07.17 12:05:32.798            nuctl.platform (I) Docker image was successfully built and pushed into docker registry {"image": "cvat/openvino.omz.public.yolo-v3-tf:latest"}
20.07.17 12:05:32.798                     nuctl (I) Build complete {"result": {"Image":"cvat/openvino.omz.public.yolo-v3-tf:latest","UpdatedFunctionConfig":{"metadata":{"name":"openvino.omz.public.yolo-v3-tf","namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"openvino","name":"YOLO v3","spec":"[\n  { \"id\": 0, \"name\": \"person\" },\n  { \"id\": 1, \"name\": \"bicycle\" },\n  { \"id\": 2, \"name\": \"car\" },\n  { \"id\": 3, \"name\": \"motorbike\" },\n  { \"id\": 4, \"name\": \"aeroplane\" },\n  { \"id\": 5, \"name\": \"bus\" },\n  { \"id\": 6, \"name\": \"train\" },\n  { \"id\": 7, \"name\": \"truck\" },\n  { \"id\": 8, \"name\": \"boat\" },\n  { \"id\": 9, \"name\": \"traffic light\" },\n  { \"id\": 10, \"name\": \"fire hydrant\" },\n  { \"id\": 11, \"name\": \"stop sign\" },\n  { \"id\": 12, \"name\": \"parking meter\" },\n  { \"id\": 13, \"name\": \"bench\" },\n  { \"id\": 14, \"name\": \"bird\" },\n  { \"id\": 15, \"name\": \"cat\" },\n  { \"id\": 16, \"name\": \"dog\" },\n  { \"id\": 17, \"name\": \"horse\" },\n  { \"id\": 18, \"name\": \"sheep\" },\n  { \"id\": 19, \"name\": \"cow\" },\n  { \"id\": 20, \"name\": \"elephant\" },\n  { \"id\": 21, \"name\": \"bear\" },\n  { \"id\": 22, \"name\": \"zebra\" },\n  { \"id\": 23, \"name\": \"giraffe\" },\n  { \"id\": 24, \"name\": \"backpack\" },\n  { \"id\": 25, \"name\": \"umbrella\" },\n  { \"id\": 26, \"name\": \"handbag\" },\n  { \"id\": 27, \"name\": \"tie\" },\n  { \"id\": 28, \"name\": \"suitcase\" },\n  { \"id\": 29, \"name\": \"frisbee\" },\n  { \"id\": 30, \"name\": \"skis\" },\n  { \"id\": 31, \"name\": \"snowboard\" },\n  { \"id\": 32, \"name\": \"sports ball\" },\n  { \"id\": 33, \"name\": \"kite\" },\n  { \"id\": 34, \"name\": \"baseball bat\" },\n  { \"id\": 35, \"name\": \"baseball glove\" },\n  { \"id\": 36, \"name\": \"skateboard\" },\n  { \"id\": 37, \"name\": \"surfboard\" },\n  { \"id\": 38, \"name\": \"tennis racket\" },\n  { \"id\": 39, \"name\": \"bottle\" },\n  { \"id\": 40, \"name\": \"wine glass\" },\n  { \"id\": 41, \"name\": \"cup\" },\n  { \"id\": 42, \"name\": \"fork\" },\n  { \"id\": 43, \"name\": \"knife\" },\n  { \"id\": 44, \"name\": \"spoon\" },\n  { \"id\": 45, \"name\": \"bowl\" },\n  { \"id\": 46, \"name\": \"banana\" },\n  { \"id\": 47, \"name\": \"apple\" },\n  { \"id\": 48, \"name\": \"sandwich\" },\n  { \"id\": 49, \"name\": \"orange\" },\n  { \"id\": 50, \"name\": \"broccoli\" },\n  { \"id\": 51, \"name\": \"carrot\" },\n  { \"id\": 52, \"name\": \"hot dog\" },\n  { \"id\": 53, \"name\": \"pizza\" },\n  { \"id\": 54, \"name\": \"donut\" },\n  { \"id\": 55, \"name\": \"cake\" },\n  { \"id\": 56, \"name\": \"chair\" },\n  { \"id\": 57, \"name\": \"sofa\" },\n  { \"id\": 58, \"name\": \"pottedplant\" },\n  { \"id\": 59, \"name\": \"bed\" },\n  { \"id\": 60, \"name\": \"diningtable\" },\n  { \"id\": 61, \"name\": \"toilet\" },\n  { \"id\": 62, \"name\": \"tvmonitor\" },\n  { \"id\": 63, \"name\": \"laptop\" },\n  { \"id\": 64, \"name\": \"mouse\" },\n  { \"id\": 65, \"name\": \"remote\" },\n  { \"id\": 66, \"name\": \"keyboard\" },\n  { \"id\": 67, \"name\": \"cell phone\" },\n  { \"id\": 68, \"name\": \"microwave\" },\n  { \"id\": 69, \"name\": \"oven\" },\n  { \"id\": 70, \"name\": \"toaster\" },\n  { \"id\": 71, \"name\": \"sink\" },\n  { \"id\": 72, \"name\": \"refrigerator\" },\n  { \"id\": 73, \"name\": \"book\" },\n  { \"id\": 74, \"name\": \"clock\" },\n  { \"id\": 75, \"name\": \"vase\" },\n  { \"id\": 76, \"name\": \"scissors\" },\n  { \"id\": 77, \"name\": \"teddy bear\" },\n  { \"id\": 78, \"name\": \"hair drier\" },\n  { \"id\": 79, \"name\": \"toothbrush\" }\n]\n","type":"detector"}},"spec":{"description":"YOLO v3 via Intel OpenVINO","handler":"main:handler","runtime":"python:3.6","env":[{"name":"NUCLIO_PYTHON_EXE_PATH","value":"/opt/nuclio/common/python3"}],"resources":{},"image":"cvat/openvino.omz.public.yolo-v3-tf:latest","targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"","maxWorkers":2,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"/Users/nmanovic/Workspace/cvat/serverless/openvino/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"image":"cvat/openvino.omz.public.yolo-v3-tf","baseImage":"openvino/ubuntu18_dev:2020.2","directives":{"postCopy":[{"kind":"USER","value":"openvino"}],"preCopy":[{"kind":"USER","value":"root"},{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"ln -s /usr/bin/pip3 /usr/bin/pip"},{"kind":"RUN","value":"/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name yolo-v3-tf -o /opt/nuclio/open_model_zoo"},{"kind":"RUN","value":"/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/converter.py --name yolo-v3-tf --precisions FP32 -d /opt/nuclio/open_model_zoo -o /opt/nuclio/open_model_zoo"}]},"codeEntryType":"image"},"platform":{},"readinessTimeoutSeconds":60,"eventTimeout":"30s"}}}}
20.07.17 12:05:33.285            nuctl.platform (I) Waiting for function to be ready {"timeout": 60}
20.07.17 12:05:35.452                     nuctl (I) Function deploy complete {"httpPort": 57308}

• Display a list of running serverless functions using nuctl command or see them in nuclio dashboard:

nuctl get function

  NAMESPACE |                             NAME                              | PROJECT | STATE | NODE PORT | REPLICAS
  nuclio    | openvino.dextr                                                | cvat    | ready |     55274 | 1/1
  nuclio    | openvino.omz.public.yolo-v3-tf                                | cvat    | ready |     57308 | 1/1

• Test your deployed DL model as a serverless function. The command below should work on Linux and Mac OS.

image=$(curl https://upload.wikimedia.org/wikipedia/en/7/7d/Lenna_%28test_image%29.png --output - | base64 | tr -d '\n')
cat << EOF > /tmp/input.json
{"image": "$image"}
EOF
cat /tmp/input.json | nuctl invoke openvino.omz.public.yolo-v3-tf -c 'application/json'

20.07.17 12:07:44.519    nuctl.platform.invoker (I) Executing function {"method": "POST", "url": "http://:57308", "headers": {"Content-Type":["application/json"],"X-Nuclio-Log-Level":["info"],"X-Nuclio-Target":["openvino.omz.public.yolo-v3-tf"]}}
20.07.17 12:07:45.275    nuctl.platform.invoker (I) Got response {"status": "200 OK"}
20.07.17 12:07:45.275                     nuctl (I) >>> Start of function logs
20.07.17 12:07:45.275 ino.omz.public.yolo-v3-tf (I) Run yolo-v3-tf model {"worker_id": "0", "time": 1594976864570.9353}
20.07.17 12:07:45.275                     nuctl (I) <<< End of function logs

> Response headers:
Date = Fri, 17 Jul 2020 09:07:45 GMT
Content-Type = application/json
Content-Length = 100
Server = nuclio

> Response body:
[
    {
        "confidence": "0.9992254",
        "label": "person",
        "points": [
            39,
            124,
            408,
            512
        ],
        "type": "rectangle"
    }
]

Run Cypress tests

• Install Сypress as described in the documentation.
• Run cypress tests:

    cd <cvat_local_repository>/tests
    <cypress_installation_directory>/node_modules/.bin/cypress run --headless --browser chrome

For more information, see the documentation.

3 - JavaScript/Typescript coding style

We use the Airbnb JavaScript Style Guide for JavaScript code with a little exception - we prefer 4 spaces for indentation of nested blocks and statements.

4 - Branching model

The project uses a successful Git branching model. Thus it has a couple of branches. Some of them are described below:

• origin/master to be the main branch where the source code of HEAD always reflects a production-ready state

• origin/develop to be the main branch where the source code of HEAD always reflects a state with the latest delivered development changes for the next release. Some would call this the “integration branch”.

5 - Using the issue tracker

The issue tracker is the preferred channel for bug reports, features requests and submitting pull requests, but please respect the following restrictions:

• Please do not use the issue tracker for personal support requests (use Stack Overflow).

• Please do not derail or troll issues. Keep the discussion on topic and respect the opinions of others.

6 - Bug reports

A bug is a demonstrable problem that is caused by the code in the repository. Good bug reports are extremely helpful - thank you!

Guidelines for bug reports:

1. Use the GitHub issue search — check if the issue has already been reported.

2. Check if the issue has been fixed — try to reproduce it using the latest develop branch in the repository.

3. Isolate the problem — ideally create a reduced test case.

A good bug report shouldn’t leave others needing to chase you up for more information. Please try to be as detailed as possible in your report. What is your environment? What steps will reproduce the issue? What browser(s) and OS experience the problem? What would you expect to be the outcome? All these details will help people to fix any potential bugs.

Example:

Short and descriptive example bug report title

A summary of the issue and the browser/OS environment in which it occurs. If suitable, include the steps required to reproduce the bug.

1. This is the first step
2. This is the second step
3. Further steps, etc.

Any other information you want to share that is relevant to the issue being reported. This might include the lines of code that you have identified as causing the bug, and potential solutions (and your opinions on their merits).

7 - Feature requests

Feature requests are welcome. But take a moment to find out whether your idea fits with the scope and aims of the project. It’s up to you to make a strong case to convince the project’s developers of the merits of this feature. Please provide as much detail and context as possible.

8 - Pull requests

Good pull requests - patches, improvements, new features - are a fantastic help. They should remain focused in scope and avoid containing unrelated commits.

Please ask first before embarking on any significant pull request (e.g. implementing features, refactoring code, porting to a different language), otherwise you risk spending a lot of time working on something that the project’s developers might not want to merge into the project.

Please adhere to the coding conventions used throughout a project (indentation, accurate comments, etc.) and any other requirements (such as test coverage).

Follow this process if you’d like your work considered for inclusion in the project:

1. Fork the project, clone your fork, and configure the remotes:

   # Clone your fork of the repo into the current directory
   git clone https://github.com/<your-username>/<repo-name>
   # Navigate to the newly cloned directory
   cd <repo-name>
   # Assign the original repo to a remote called "upstream"
   git remote add upstream https://github.com/<upstream-owner>/<repo-name>
   

2. If you cloned a while ago, get the latest changes from upstream:

   git checkout <dev-branch>
   git pull upstream <dev-branch>
   

3. Create a new topic branch (off the main project development branch) to contain your feature, change, or fix:

   git checkout -b <topic-branch-name>
   

4. Commit your changes in logical chunks. Please adhere to these git commit message guidelines or your code is unlikely be merged into the main project. Use Git’s interactive rebase feature to tidy up your commits before making them public.

5. Locally merge (or rebase) the upstream development branch into your topic branch:

   git pull [--rebase] upstream <dev-branch>
   

6. Push your topic branch up to your fork:

   git push origin <topic-branch-name>
   

7. Open a Pull Request with a clear title and description.

IMPORTANT: By submitting a patch, you agree to allow the project owner to license your work under the same license as that used by the project.

9 - How to add a new annotation format support

1. Add a python script to dataset_manager/formats
2. Add an import statement to registry.py.
3. Implement some importers and exporters as the format requires.

Each format is supported by an importer and exporter.

It can be a function or a class decorated with importer or exporter from registry.py. Examples:

@importer(name="MyFormat", version="1.0", ext="ZIP")
def my_importer(file_object, task_data, **options):
  ...

@importer(name="MyFormat", version="2.0", ext="XML")
class my_importer(file_object, task_data, **options):
  def __call__(self, file_object, task_data, **options):
    ...

@exporter(name="MyFormat", version="1.0", ext="ZIP"):
def my_exporter(file_object, task_data, **options):
  ...

Each decorator defines format parameters such as:

• name

• version

• file extension. For the importer it can be a comma-separated list. These parameters are combined to produce a visible name. It can be set explicitly by the display_name argument.

Importer arguments:

• file_object - a file with annotations or dataset
• task_data - an instance of TaskData class.

Exporter arguments:

• file_object - a file for annotations or dataset

• task_data - an instance of TaskData class.

• options - format-specific options. save_images is the option to distinguish if dataset or just annotations are requested.

TaskData provides many task properties and interfaces to add and read task annotations.

Public members:

• TaskData. Attribute - class, namedtuple('Attribute', 'name, value')

• TaskData. LabeledShape - class, namedtuple('LabeledShape', 'type, frame, label, points, occluded, attributes, group, z_order')

• TrackedShape - namedtuple('TrackedShape', 'type, points, occluded, frame, attributes, outside, keyframe, z_order')

• Track - class, namedtuple('Track', 'label, group, shapes')

• Tag - class, namedtuple('Tag', 'frame, label, attributes, group')

• Frame - class, namedtuple('Frame', 'frame, name, width, height, labeled_shapes, tags')

• TaskData. shapes - property, an iterator over LabeledShape objects

• TaskData. tracks - property, an iterator over Track objects

• TaskData. tags - property, an iterator over Tag objects

• TaskData. meta - property, a dictionary with task information

• TaskData. group_by_frame() - method, returns an iterator over Frame objects, which groups annotation objects by frame. Note that TrackedShape s will be represented as LabeledShape s.

• TaskData. add_tag(tag) - method, tag should be an instance of the Tag class

• TaskData. add_shape(shape) - method, shape should be an instance of the Shape class

• TaskData. add_track(track) - method, track should be an instance of the Track class

Sample exporter code:

...
# dump meta info if necessary
...
# iterate over all frames
for frame_annotation in task_data.group_by_frame():
  # get frame info
  image_name = frame_annotation.name
  image_width = frame_annotation.width
  image_height = frame_annotation.height
  # iterate over all shapes on the frame
  for shape in frame_annotation.labeled_shapes:
    label = shape.label
    xtl = shape.points[0]
    ytl = shape.points[1]
    xbr = shape.points[2]
    ybr = shape.points[3]
    # iterate over shape attributes
    for attr in shape.attributes:
      attr_name = attr.name
      attr_value = attr.value
...
# dump annotation code
file_object.write(...)
...

Sample importer code:

...
#read file_object
...
for parsed_shape in parsed_shapes:
  shape = task_data.LabeledShape(
    type="rectangle",
    points=[0, 0, 100, 100],
    occluded=False,
    attributes=[],
    label="car",
    outside=False,
    frame=99,
  )
task_data.add_shape(shape)

Format specifications

• CVAT
• Datumaro
• LabelMe
• MOT
• MOTS
• COCO
• PASCAL VOC and mask
• YOLO
• TF detection API
• ImageNet
• CamVid
• WIDER Face
• VGGFace2
• Market-1501
• ICDAR13/15

10 - REST API design principles

REST API scheme

Common scheme for our REST API is <VERB> [namespace] <objects> <id> <action>.

• VERB can be POST, GET, PATCH, PUT, DELETE.
• namespace should scope some specific functionality like auth, lambda. It is optional in the scheme.
• Typical objects are tasks, projects, jobs.
• When you want to extract a specific object from a collection, just specify its id.
• An action can be used to simplify REST API or provide an endpoint for entities without objects endpoint like annotations, data, data/meta. Note: action should not duplicate other endpoints without a reason.

Design principles

• Use nouns instead of verbs in endpoint paths. For example, POST /api/tasks instead of POST /api/tasks/create.
• Accept and respond with JSON whenever it is possible
• Name collections with plural nouns (e.g. /tasks, /projects)
• Try to keep the API structure flat. Prefer two separate endpoints for /projects and /tasks instead of /projects/:id1/tasks/:id2. Use filters to extract necessary information like /tasks/:id2?project=:id1. In some cases it is useful to get all tasks. If the structure is hierarchical, it cannot be done easily. Also you have to know both :id1 and :id2 to get information about the task. Note: for now we accept GET /tasks/:id2/jobs but it should be replaced by /jobs?task=:id2 in the future.
• Handle errors gracefully and return standard error codes (e.g. 201, 400)
• Allow filtering, sorting, and pagination
• Maintain good security practices
• Cache data to improve performance
• Versioning our APIs. It should be done when you delete an endpoint or modify its behaviors. Versioning uses a schema with Accept header with vendor media type.

Links

• Best practices for REST API design
• Flat vs. nested resources
• REST API Design Best Practices for Sub and Nested Resources
• A specification for building APIs in JSON