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Human Pose Annotation Tool
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src Added Custom RGB-only Dataset boilerplate code Aug 22, 2019
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Human Pose Annotation Tool

Example image_1

We aim to create a simple yet effective tool for create and modify annotation for Body Pose Estimation over depth images. The vast majority of available datasets are not precisely noted, usually annotations are obtained using the Kinect SDK, which is supposed to works in a limited environment, returning incorrect annotation for general case-of-use.

Example image_4

Our tool takes into account pre-existing annotations, plotting Body Joints over depth frames, for using such informations as a starting point. The RGB frame is also shown, to allow an easier reference. We noted down 3329 frames from watch-n-patch for our Body Pose Estimation CNN, annotations are available here.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. There's not much to do, just install prerequisites and download all the files.


Things you need to install to run the tool:

Python >= 3.6.7
pip install numpy
pip install opencv-python
pip install scipy

For running our test and to use our code without modifying a line, install PyTorch to use Dataset class.

pip install pytorch

Running the tests

To run the simple test, unzip the archive inside the test folder, then execute:

python src/ --data_dir ./test

This will open up one sequence of the Watch-N-Patch dataset, in which the 2 frames shown above came from. The sequence is stored in the test directory.


  • Functionality, shown in the tkinter menu.
    • Move Joints, click the joint you want to move, click the new position, press "y" to confirm.
    • Delete Joints, click the joint, press "esc" and confirm with "y".
    • Add not noted Joints. press "a", choose the joint you want to add, choose position and press "y" to confirm.
    • Reset action, after an action, press "esc" to reset.
    • Save frame, press "enter" to save and change frame.
    • Skip sequence, press "p" to skip the entire sequence.
  • Input, as default we used a PyTorch dataset wich return depth maps and keypoints value of the Watch-N-Patch Dataset. For custom use you need to redifine the Dataset class and load the correct RGB and Depth Images. With little code modification you can change the tool to work with only RGB or only Depth frames.
  • Output, JSON file containing image path as key and keypoints array as value.
  • OS, tested on Windows and Linux OS.


python --data_dir <path_to_dataset> --out <optional> --k <optional> --next <optional>  
                --scale <optional> --radius <optional> --split <optional>
  • --out, putput name file. Default: "good_annotations".
  • --k, choose if you want to resume annotations from the file specified in --k flag, enter "skip" for resume or "keep" for restarting from frame 1.
  • --next, choose how many frame you want to skip after everyone you note down.
  • --scale & ---radius, choose the dimension of images and keypoint visualization in the plot.
  • --split, additional parameter for custom datasets.



This project is licensed under the MIT License - see the LICENSE file for details

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