Awesome

UltimateLabeling

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

Features

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!

Demo

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

Installation

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

Remote server configuration

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

Input / output

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.

Shortcuts / mouse controls

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

Improvements / issues

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