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A multi-purpose Video Labeling GUI in Python with integrated SOTA detector and tracker. Developed using PyQt5.

• SSH connection to a remote GPU server (see below to configure the server)
• YOLO and OpenPifPaf integrated object & pose detectors (single frame/video mode)
• Hungarian algorithm for track_id assignment
• SiamMask visual object tracking for missing or mislabeled boxes
• Zoom on video, resizable bounding boxes and skeletons
• Dark mode!

The integrated object detectors and trackers are based on the following codes:

• OpenPifPaf: for human pose estimation
• YOLO darknet: for object detection
• SiamMask: for visual object tracking
• Hungarian algorithm (scipy.optimize): for optimal instance ID assignment

Start by cloning the repository on your computer:

git clone https://github.com/alexandre01/UltimateLabeling.git
cd UltimateLabeling

We recommend installing the required packages in a virtual environment to avoid any library versions conflicts. The following will do this for you:

virtualenv --no-site-packages venv
source venv/bin/activate
pip install -r requirements.txt

Otherwise, just install the requirements on your main Python environment using pip as follows:

pip install -r requirements

Finally, open the GUI using:

python -m ultimatelabeling.main

To configure the remote GPU server (using the code in server files.), follow the steps below:

git clone https://github.com/alexandre01/UltimateLabeling_server.git
cd UltimateLabeling_server
pip install -r requirements.txt
bash siamMask/setup.sh
bash detection/setup.sh

The data images and videos should be placed in the folder data, similarly to the client code.

To extract video files, use the following script:

bash extract.sh data/video_file.mp4

To start labeling your videos, put these (folder of images or video file, the frames will be extracted automatically) inside the data folder.

• Import labels: To import existing .CSV labels, hit Cmd+I (or Ctrl+I). UltimateLabeling expects to read one .CSV file per frame, in the format: "class_id", "xc", "yc", "w", "h".

• Export labels: The annotations are internally saved in the output folder. To export them in a unique .CSV file, hit Cmd+E (or Ctrl+E) and choose the destination location.

If you need other file formats for your projects, please write a GitHub issue or submit a Pull request.

Keyboard:

• A (or Left key): next frame
• D (or Right key): previous frame
• W/S: class up/down
• T: start/stop tracking (last used tracker)
• Numberpad: assign given class_id
• Spacebar: play the video

Mouse:

• Click: select bounding box
• Click & hold: move in the image
• Cmd + click & hold: create new bounding box
• Right click: delete bounding box in current frame (+ in all previous / all following frames if the corresponding option is enabled)
• Scroll wheel (or swipe up/down): zoom in the image

Please write a GitHub issue if you experience any issue or wish an improvement. Or even better, submit a pull request!

Copyright (c) 2019 Alexandre Carlier, released under the MIT licence.